Submitted Successfully!
To reward your contribution, here is a gift for you: A free trial for our video production service.
Thank you for your contribution! You can also upload a video entry or images related to this topic.
Version Summary Created by Modification Content Size Created at Operation
1
Manas Gartia
 -- 7331 2024-01-17 16:46:00 |
2 Reference format revised.
Lindsay Dong
 + 4 word(s) 7335 2024-01-19 03:21:35 |

Video Upload Options

Do you have a full video?
Send video materials Upload full video

Confirm

Are you sure to Delete?
Yes No
Cite
If you have any further questions, please contact Encyclopedia Editorial Office.
Das, S.; Dey, M.K.; Devireddy, R.; Gartia, M.R. Biomarkers in Cancer Detection, Diagnosis, and Prognosis. Encyclopedia. Available online: https://encyclopedia.pub/entry/53982 (accessed on 06 July 2024).
Das S, Dey MK, Devireddy R, Gartia MR. Biomarkers in Cancer Detection, Diagnosis, and Prognosis. Encyclopedia. Available at: https://encyclopedia.pub/entry/53982. Accessed July 06, 2024.
Das, Sreyashi, Mohan Kumar Dey, Ram Devireddy, Manas Ranjan Gartia. "Biomarkers in Cancer Detection, Diagnosis, and Prognosis" Encyclopedia, https://encyclopedia.pub/entry/53982 (accessed July 06, 2024).
Das, S., Dey, M.K., Devireddy, R., & Gartia, M.R. (2024, January 17). Biomarkers in Cancer Detection, Diagnosis, and Prognosis. In Encyclopedia. https://encyclopedia.pub/entry/53982
Das, Sreyashi, et al. "Biomarkers in Cancer Detection, Diagnosis, and Prognosis." Encyclopedia. Web. 17 January, 2024.
Biomarkers in Cancer Detection, Diagnosis, and Prognosis
Edit
This entry is adapted from the peer-reviewed paper 10.3390/s24010037

Biomarkers are vital in healthcare as they provide valuable insights into disease diagnosis, prognosis, treatment response, and personalized medicine. They serve as objective indicators, enabling early detection and intervention, leading to improved patient outcomes and reduced costs. Biomarkers also guide treatment decisions by predicting disease outcomes and facilitating individualized treatment plans. They play a role in monitoring disease progression, adjusting treatments, and detecting early signs of recurrence. 

biomarkers DNA RNA cancer

1. Introduction

A biomarker is a biological phenomenon that can be difficult to find, yet indicates a clinically significant outcome or interim consequence. Biomarker applications include identifying, characterizing, and monitoring diseases. Additionally, biomarkers can act as prognostic indicators, inform individualized treatment plans, and anticipate and manage negative medication reactions. Understanding the fundamental link between a biomarker and its clinical result is crucial for adequately appreciating its significance [1].
Cancer is an intricate condition marked by genetic and epigenetic changes that throw off the balance between cellular development and cell death. It is a major global health issue that kills many people every year worldwide [2]. Significant molecular and tissue alterations are required for cancer growth. Invaluable clinical data in the form of biomarkers can be generated by analyzing biomolecules such as nucleic acids, carbohydrates, proteins, lipids, and metabolites linked to cancer development [3]. Early detection plays a crucial role in minimizing the morbidity and mortality associated with cancer. Therefore, there is an urgent need for genuine and reliable cancer indicators. Prostate-Specific Antigen (PSA), Carcinoembryonic Antigen (CEA), and CA-125/MUC16 are frequently used cancer indicators, while exosomes, microRNA, and circulating tumor cells are emerging as a new source of biomarkers [4].

2. Challenges Associated with Detecting Early-Stage Tumors

Successful cancer treatment depends on early detection [5]. Yet, physiological and mass transport barriers restrict the amount of biological indicators that can be released from early lesions [6][7].
Finding intrinsic biomarkers through blood and biofluid examination is the primary objective of ongoing research. To improve specificity, bioengineered sensors and synthetic markers are being developed. Imaging systems also aid in detecting and localizing tumors [8][9][10]. The typical spatial resolution of a positron emission tomography (PET) scanner is about 1 cm3, and hence, very small tumors (diameter < 5 mm) will be missed by the PET imagers. The typical blood draw is 5–10 mL, which is three orders of magnitude (1/1000th) smaller than the body’s total blood volume (~5 L). This means that the biomarkers shed by the tumor will be diluted > 1000 times when it is detected (Figure 1). Further, there are challenges in detecting genomic materials. For example, circulating tumor DNA (ctDNA) has a half-life of ~1.5 h. So, in a 24 h time period, it will undergo 16 half-lives. This means that by the time it is detected, only 0.0015% of the original materials will remain [9][10][11][12]. A potential ten-year window for early cancer detection is suggested by multicompartment models and studies on the genomic timeline. However, current screening techniques can find cancers that have been present for ten years or longer and are indolent. Cancers that spread quickly and aggressively and have poor clinical outcomes include triple-negative breast cancer and high-grade serous ovarian carcinoma. These problems are intended to be solved by activity-based or genetically encoded mechanisms for early detection in synthetic biomarker research.
Sensors 24 00037 g001
Figure 1. Difficulties related to the identification of tumors in their early stages. Due to their tiny size and the difficulties in transferring biomarkers from the tumor microenvironment to the bloodstream, early-stage cancers are challenging to detect. This is brought on by difficulties with biomarker transfer, dilution, and the kidneys’ quick degradation and filtration processes. Only a few tumor-associated biomarkers can be found in a typical blood sample of 5–10 mL, which is a small part of the overall blood volume.

3. Biomarkers in Cancer Detection, Diagnosis, and Prognosis

3.1. Biofluid Biomarkers

Biofluids provide a way to quickly evaluate and track diseases [12]. Urine, saliva, blood, and sweat are examples of biofluids that contain important data regarding the disease under investigation. These biofluid specimens can be easily collected non-invasively and are ideal for clinical studies [13]. Each biofluid has unique advantages and challenges [14]. Saliva is readily available and includes electrolytes like sodium, potassium, calcium, magnesium, bicarbonate, and phosphates, whereas urine contains urea, chloride, sodium, and potassium salts. Sweat mainly contains sodium, chloride, minerals, lactic acid, and urea [15].
Cancer detection and tracking uses various biofluids, such as urine, saliva, blood, and cerebrospinal fluid (CSF) [16]. Studies have identified KRAS, MBD3L2, ACRV1, and DPM1 as biomarkers in salivary mRNA to detect pancreatic cancer with high specificity [17][18][19][20][21]. Salivary proteins with high specificity and sensitivity to identify lung cancer include calprotectin, AZGP1, and HP. Salivary DNA can also detect mutations in the genes PI3K, CDKN2A, FBXW7, HRAS, and KRAS in mouth and throat tumors [22][23][24][25][26].
There are various techniques to detect genomics (qPCR, RNA, and DNA sequencing), proteomics (mass spectrometry, ELISA, and Western blotting), and lipidomics (mass spectrometry) to find cancer biomarkers in biofluids. For protein extraction and separation, several methods are used, including surface-enhanced laser desorption/ionization (SELDI), 2-dimensional gel electrophoresis (2-DE), difference gel electrophoresis (2D-DIGE), and Liquid Chip. Mass spectrometry and bioinformatics are used to identify proteins, with Western blot and ELISA used to confirm the results. Sample variability, inter-laboratory analytical variability, and sample type selection are difficulties in biomarker discovery. Many studies have looked into finding cancer-associated hypermethylated DNA fragments in cancer patients’ circulating free DNA (cfDNA), especially in cases of gastric cancer (GC). A number of hypermethylated genes, including RPRM, XAF1, and a KCNA4 and CYP26B1 combination, have demonstrated high diagnostic value for GC detection. Before these assays can be used in clinical settings, a few technical issues must be resolved. The majority of studies employ sodium bisulfite treatment followed by methylation-specific PCR (MSP) or DNA sequencing, but these methods could produce false-positive results because unmethylated cytosine residues are not completely converted.

3.2. Imaging Biomarkers

Tumor, node, metastasis (TNM) staging, objective response, and left ventricular ejection fraction are just a few of the imaging biomarkers (IBs) that are critical for clinical oncology [27]. Cancer research frequently uses imaging techniques like computed tomography (CT), magnetic resonance imaging (MRI), positron emission tomography (PET), and ultrasonography. New IBs need to be validated and qualified in order to fill in the translational gaps [28]. A total of 14 important recommendations have been made by Cancer Research UK (CRUK) and the European Organization for Research and Treatment of Cancer (EORTC) to hasten the clinical translation of IBs [29][30][31]. These suggestions for achieving IB qualification emphasize parallel validation procedures, cost-effectiveness analysis, standardization, accreditation systems, precision evaluation, alternative validation frameworks, and multicenter studies [32][33][34][35][36][37].
IBs are derived from medical images. They offer non-invasive, cost-effective screening, tumor detection, patient progress, and therapy response monitoring tools [38]. Staging systems document the existence, dimensions, and quantity of abnormalities in tumor, lymph node, and additional metastatic locations to establish a structured categorical indicator of the patient’s disease severity. IBs have the ability to map tumor heterogeneity, monitor changes in tumors over time, and assess a person’s multiple lesions [39][40][41].
The evaluation of lesions at tumor, nodal, and metastatic sites using staging systems is crucial for the diagnosis and prognosis of cancer. The American Joint Committee on Cancer (AJCC) offers recommendations for precise and consistent reporting in radiology. TNM staging is frequently used and has prognostic value for a variety of cancer types. It is based on imaging modalities like CT, MRI, SPECT, and PET. TNM staging can occasionally be used to forecast treatment outcomes.

3.3. Needle Biopsy

Imaging tests are essential in identifying and tracking cancer [42]. These examinations use various forms of energy, such as X-rays, sound waves, radioactive particles, or magnetic fields, to produce finely detailed images that reveal important details about the structure and location of the tumor [43][44][45][46]. It is crucial to remember that imaging tests do have their limitations. They cannot identify specific cancer cells, and their results are inconclusive. Imaging tests are typically validated by biopsy [47].
A cancer biopsy is a test for diagnosis employed to identify the kind and properties of the tumor cells and confirm or rule out the existence of cancer. Findings are crucial for making additional medical choices (grading of tumor; chemotherapy vs. radiation vs. immunotherapy) [48][49][50][51]. In accordance with the precise spot and accessibility of the suspicious region, biopsies can be carried out using a variety of approaches (Figure 2), including surgical biopsies, endoscopic biopsies, and needle biopsies [52]. Needle biopsy may employ a larger needle for collecting large tissue specimens or a fine needle aspiration for gathering a small sample from cells and fluid. A special needle with a suction mechanism is used in vacuum-assisted biopsy for acquiring tissue specimens. These methods provide versatility in gathering appropriate samples for analysis [53][54].
Sensors 24 00037 g004
Figure 2. Commonly used non-invasive techniques for examining biomarkers in solid tumors.

3.4. Tissue Imaging

Immunohistochemistry (IHC), an approach used for tissue image processing, allows researchers to analyze particular proteins or antigens in tissue samples. Antibodies designed to bind to specific protein targets in tissue sections are used in this procedure. A secondary antibody is coupled to a recognition molecule after the primary antibody is bound to its target. The results are visualized under a microscope. The results are typically in the form of an alteration in color or fluorescence, indicating the target protein’s existence and location. IHC is a widely used method in pathology studies and diagnostics that can reveal important details about the arrangement, expression levels, and localization of particular proteins in tissue specimens.

4. Types of Cancer Biomarkers

4.1. Genetic Biomarkers

4.1.1. Mutations and Gene Alterations

Mutations and gene alterations are important cancer biomarkers that can provide valuable information about the underlying genetic changes driving the development and progression of cancer. Here are some examples of mutation- and gene alteration-based cancer biomarkers. The BRAF V600E mutation activates cell growth, aiding targeted therapy selection in melanoma patients [55]. EGFR mutations (e.g., exon 19 deletions, L858R point mutation) increase sensitivity to EGFR inhibitors in non-small-cell lung cancer (NSCLC) [56]. In colorectal, KRAS mutations (30–40% cases) activate signaling pathways, affecting treatment response [57]. BRCA1/BRCA2 mutations increase cancer risk in breast/ovarian cancer, and guide therapy selection [58]. HER2 amplification/overexpression indicates aggressive behavior in breast/gastric cancer, and is generally treated with anti-HER2 antibodies [59]. IDH mutations affect cellular metabolism and serve as diagnostic and prognostic markers in glioma patients [60].

4.1.2. Gene Expression Profiles

Gene-expression-profile-based cancer biomarkers involve analyzing the patterns of gene expression in cancer cells to provide insights into tumor behavior, prognosis, and treatment response. Here are some examples of gene-expression-profile-based cancer biomarkers:
Oncotype DX in Breast Cancer: Oncotype DX is a genomic test that assesses the expression of a panel of about 16 genes involved in breast cancer. It provides a recurrence score (RS) that predicts the likelihood of disease recurrence and guides treatment decisions, particularly in early-stage hormone receptor-positive breast cancer. The genes in question are ERBB2 (also known as HER2), ESR1 (estrogen receptor 1), PGR (progesterone receptor), BIRC5 (survivin), SCUBE2 (signal peptide, CUB domain, EGF-like 2), STK15 (Aurora kinase A), BCL2 (B-cell lymphoma 2), MKI67 (Ki-67), GSTM1 (glutathione S-transferase mu 1), CD68 (cluster of differentiation 68), BAG1 (BCL2-associated athanogene 1), MMP11 (matrix metallopeptidase 11), CTSL2 (cathepsin L2), GRB7 (growth factor receptor-bound protein 7), GSTM1 (glutathione S-transferase mu 1), and CDKN1B (cyclin-dependent kinase inhibitor 1B) [61].
MammaPrint in Breast Cancer: MammaPrint is a gene-expression-based assay used to analyze the activity of a set of genes (~18 genes) in breast cancer. It provides a genomic risk score (RS) that helps determine the risk of distant metastasis and assists in treatment decision making, particularly in early-stage breast cancer. The list of genes includes AURKA (Aurora kinase A), BIRC5 (survivin), CCNB1 (cyclin B1), CDC2 (cell division cycle 2), CKS1B (CDC28 protein kinase regulatory subunit 1B), DLG7 (discs large homolog 7), ERBB2 (also known as HER2), ESR1 (estrogen receptor 1), FOXM1 (forkhead box M1), MMP11 (matrix metallopeptidase 11), MYBL2 (myb-related protein B), NDC80 (kinetochore protein NDC80 homolog), NEK2 (NIMA-related kinase 2), RACGAP1 (Rac GTPase-activating protein 1), RRM2 (ribonucleotide reductase M2 subunit), STK15 (Aurora kinase A), TYMS (thymidylate synthase), and UBE2C (ubiquitin-conjugating enzyme E2C) [62].

4.1.3. DNA as a Cancer Biomarker

The initial markers tested for tumor staging were circulating DNA, as shown in Figure 3. Elevated concentrations of serum DNA have been linked to cancer (most particularly, metastatic cancer). Oncogene alterations, mismatch-repair gene mutations, and mutations in tumor suppressor genes can all be used as DNA biomarkers. In over 50% of sporadic malignancies, mutations in the p53 tumor suppressor gene are found, and mutations in the KRAS oncogene indicate metastatic spread [55][56][57]. A TP53 mutation passed down through the generations (Li–Fraumeni syndrome) raises the likelihood of acquiring several of the same malignancies. Several genes have single nucleotide polymorphisms, including RAD1, CYP1A1, and BRCA1/2 (breast cancer), PGS2 (lung cancer), and XRCC1, p53, and ATM (lung, head, and neck cancers). Diagnosis has been associated with mutations in DNA nucleotides in tumor promoters such as APC, RAS, and tumor suppressor genes. Tissue, sputum, serum, saliva, cerebrospinal fluid (CSF), bronchial tear, tumor cells circulating in the bone marrow, and blood are all potential sources of DNA [63][64][65]. Mutations in mitochondrial DNA have been postulated as diagnostics biomarkers for various malignancies [63][66][67]. Haplotype analysis was used to investigate the mitochondrial inheritance pattern in cancer patients. Researchers used polymerase chain reaction to look for critical polymorphic locations in the mitochondrial DNA in specimens from cancer patients and healthy subjects to see if there is a link connecting mitochondrial genotype and cancer. Nine mitochondrial genomic haplogroups have been described, namely, H, I, J, K, T, U, V, W, and X. U is linked with a high chance of developing renal and prostate cancer among these haplogroups [63].
Sensors 24 00037 g005
Figure 3. DNA from free cells and malignant cells in circulation. Circulating tumor cells (CTC) spread throughout the blood vessels after escaping from original locations and forming metastases in the distal organs. Dead cancer cells or expanding tumor cells release cell-free DNAs (cf-DNAs) into the bloodstream. RBC = red blood cell; WBC = white blood cell [68].

4.1.4. RNA as a Cancer Biomarker

Differential display, RT-qPCR, bead-based approaches, and micro-array analysis are among the techniques applied to diagnose potential biomarkers at the RNA expression level [69]. MicroRNAs (miRNAs) are short non-coding RNAs linked to clinical features in several cancers. The expression of particular miRNA populations is related to clinical features in a time- and tissue-dependent pattern [70][71][72]. To promote tumorigenesis, metastasis, immune evasion, and angiogenesis, microRNAs regulate the transcription of their target mRNAs [73][74]. Tumor microRNA profiles can be used to identify important subgroups, survival rates, and responsiveness to therapy.
Furthermore, cancer-associated microRNA markers may be detectable in bodily fluid, enabling individuals with cancer microRNAs to be monitored with less invasive approaches [75]. In 2002, the first report on microRNA dysregulation in cancer was published. In chronic lymphocytic leukemia, groups of two microRNAs (miR-16 and miR 15) were discovered [76].
Circular RNAs, which are non-coding RNAs with a closed loop structure, are generated by the splicing of a precursor RNA (pre-mRNA) and covalent binding of 3′ poly(A) tails and 5′capping [77] CircRNAs play a significant role in gene regulation [78][79]. According to Zhu et al. [80], Hsa circ 0013958 was higher in all lung adenocarcinomas, with 20 circRNAs down-regulated and 39 up-regulated. The study reported that Hsa circ 0013958 might be applied as a potent non-invasive marker for the early diagnosis of lung adenocarcinoma. To find Hsa circ 0013958, researchers used real-time PCR to look for its levels in lung adenocarcinoma (LAC). 

4.1.5. Epigenetics as a Cancer Biomarker

Epigenetic alterations are potent biomarkers for cancer as they are frequent for specific genes, are stable, and can be detected in a minimally invasive mode. Numerous studies have discovered that DNA methyltransferases that insert methyl groups into cytosine groups of DNA are changed in cancer cells [81]. The hypermethylation of local CpG island promoter silences the tumor suppressor genes, stimulating their gene mutations. NKX2-6, SPAG6, PER1, and ITIH5 gene methylation was detected in breast cancer patients’ serum [82]. The hypermethylation of promoter p16 in serum DNA, for instance, is linked to recurring colorectal cancer. The methylation of the RASSF1A and p16Ink4 genes has been related to a 15-fold elevation in the comparative risk of lung cancer. The methylation status of multiple genes in clinical specimens might be a viable non-invasive technique for detecting smokers at risk of developing lung cancer [83].

4.2. Protein Biomarkers

Proteins as Cancer Biomarkers

The proteome is a complex system made up of several proteins which interact with one another in dynamic intermolecular interactions and posttranslational alterations. Because they modulate molecular processes and pathways in normal and cancerous cells, proteomic markers are relevant to tumorigenesis and progression [84][85]. Proteins from pancreatic cancer can be found in a number of bodily fluids, including bile, pancreatic juice, urine, and fluid from pancreatic cysts, as shown in Figure 4. These proteins have a great deal of potential as useful biomarkers with a range of therapeutic applications, including early identification, illness staging, treatment prognosis, and in-flight patient monitoring. The majority of the FDA-approved cancer biomarkers in clinical usage are single proteins obtained from serum. HCG, AFP, and LDH are utilized to stage testicular cancer.
Sensors 24 00037 g007
Figure 4. Identification of possible protein biomarkers for pancreatic cancer using bodily fluids. Bodily fluids that include cancer-derived proteins include bile, blood, pancreatic juice, urine, and pancreatic cyst fluid. For the management of pancreatic cancer patients, these proteins have a high potential as tumor biomarkers and a variety of clinical applications, including screening in high-risk populations for pancreatic cancer, early diagnosis, disease staging, the evaluation of tumor resection and prognosis, the prediction of therapy response to inform treatment decisions, and real-time patient monitoring [86].
The biomarkers CD171, CD151, and tetraspanin 8 were the most significant indicators between lung cancer patients of all subgroups and healthy individuals [87]. Recent research reveals novel plasma biomarker proteins that may aid in the early diagnosis of bladder cancer. The amount of haptoglobin was found to be significantly higher in patients with low-grade bladder cancer, suggesting that this protein may have a role in the initial stages of bladder tumorigenesis. With reasonable specificity and sensitivity (AUC > 0.87), haptoglobin could differentiate between patients with low-grade bladder cancer and controls [88]. 
Overexpressed or mutated proteins: Cancer biomarkers include mutated or overexpressed proteins with varying concentration levels depending on cancer type, stage, and individual characteristics. Common examples are HER2 in breast and gastric cancers, EGFR in lung, colorectal, and head and neck cancers, KRAS in colorectal, pancreatic, and lung adenocarcinoma, BRAF in melanoma and colorectal cancer, ALK in some NSCLC cases, and PSA as a prostate cancer biomarker. Detection methods such as IHC, FISH, PCR, and NGS are utilized for assessment. These biomarkers play a crucial role in cancer diagnosis, classification, and treatment decision making.
Signaling pathways and protein interactions: Signaling pathways and protein interactions play a critical role in cancer development and progression. The dystregulation of these pathways and interactions can lead to uncontrolled cell growth, invasion, and metastasis. Several key signaling pathways have been implicated in cancer, including the PI3K/AKT/mTOR pathway, Wnt/β-catenin pathway, Ras/Raf/MEK/ERK pathway, Notch signaling pathway, and TGF-β signaling pathway. The activation of the PI3K/AKT/mTOR pathway promotes cell survival, proliferation, and resistance to apoptosis. The Wnt/β-catenin pathway, when aberrantly activated, leads to altered gene expression, promoting cell proliferation and tumor progression. Mutations in Ras genes and dysregulation of downstream components in the Ras/Raf/MEK/ERK pathway commonly occur in cancers, resulting in uncontrolled cell growth, survival, and metastasis. The dysregulation of the Notch signaling pathway can drive tumor cell proliferation, survival, and angiogenesis. The TGF-β signaling pathway, with its diverse roles in normal development and cancer, when dysregulated, contributes to cancer progression, including increased cell proliferation, epithelial-to-mesenchymal transition (EMT), and immune evasion.

4.3. Metabolic Biomarkers

4.3.1. Metabolites and Metabolic Pathways

Metabolites and metabolic pathways are essential in cancer cells as they undergo alterations to support their growth and survival [89]. Metabolic biomarkers derived from these pathways and metabolites can provide valuable information about cancer metabolism and aid in diagnosis, prognosis, and treatment. Here are some examples of metabolites and metabolic pathways used as metabolic biomarkers in cancer:
Glycolysis: Increased glucose consumption and aerobic glycolysis (the Warburg effect) are characteristic metabolic changes in cancer cells. Biomarkers associated with glycolysis include the following: (1) Lactate: elevated lactate levels in tumor tissues or serum indicate increased glycolytic activity. (2) Glucose transporters (e.g., GLUT1): the overexpression of glucose transporters facilitates glucose uptake in cancer cells [90].
TCA Cycle (Citric Acid Cycle): The tricarboxylic acid (TCA) cycle plays a vital role in energy production and biosynthesis [91]. The dysregulation of the TCA cycle intermediates can serve as metabolic biomarkers: (1) Fumarate and succinate: the accumulation of fumarate and succinate is associated with specific genetic mutations, such as in fumarate hydratase (FH) and succinate dehydrogenase (SDH), respectively. (2) α-Ketoglutarate: altered α-ketoglutarate levels are observed in certain cancer types, such as renal cell carcinoma [92].
Lipid Metabolism: Altered lipid metabolism is common in cancer cells, and several metabolites and pathways are associated with lipid metabolism biomarkers: (1) Choline: increased choline levels, measured using magnetic resonance spectroscopy (MRS), are found in several cancers, including breast and prostate cancers. (2) Fatty acid synthase (FASN): the overexpression of FASN, an enzyme involved in fatty acid synthesis, is observed in various cancers [93][94].
Amino Acid Metabolism: Cancer cells exhibit altered amino acid metabolism, resulting in the production and consumption of specific metabolites: (1) Glutamine: increased glutamine uptake and utilization are common in cancer cells. Glutamine metabolism is associated with pathways such as the TCA cycle and nucleotide synthesis. (2) Serine and glycine: dysregulated serine and glycine metabolism is observed in several cancers, including breast and colorectal cancers [95].
Nucleotide Metabolism: Rapidly dividing cancer cells require nucleotides for DNA and RNA synthesis. Biomarkers related to nucleotide metabolism include deoxythymidine (dThd). Elevated levels of dThd have been associated with certain cancer types and can be detected in urine or plasma [96].

4.3.2. Metabolic Imaging Techniques

Metabolic imaging techniques are used to visualize and assess the metabolic activity of cancer cells. These techniques provide valuable information about tumor metabolism and can aid in cancer diagnosis, staging, treatment planning, and monitoring. Here are some commonly used metabolic imaging techniques in cancer:
Positron Emission Tomography (PET): PET imaging utilizes radiolabeled tracers that are taken up by cells based on their metabolic activity. The most commonly used tracer in PET imaging is fluorodeoxyglucose (FDG), a glucose analog. FDG-PET measures glucose metabolism and is particularly useful in detecting and staging various cancers, including lung, colorectal, and breast cancers [97].
Magnetic Resonance Spectroscopy (MRS): MRS allows the non-invasive assessment of metabolite concentrations in tissues. It provides information on metabolites such as choline, creatine, and lactate, which are associated with cellular metabolism. MRS is used in brain tumor imaging to assess tumor grade, identify tumor margins, and monitor treatment response [98].
Magnetic Resonance Imaging with Hyperpolarized Substrates (HP-MRI): HP-MRI is an emerging technique that utilizes hyperpolarized substrates, such as pyruvate or fumarate, which are metabolized in real time to visualize metabolic pathways. This technique provides dynamic information on metabolic fluxes, such as glycolysis or TCA cycle activity, and holds promise for assessing tumor metabolism and treatment response [99].
Single-Photon Emission Computed Tomography (SPECT): SPECT imaging uses radiotracers that emit gamma rays to detect specific metabolic processes. SPECT can be used to assess various metabolic functions, such as blood flow, metabolism, and receptor binding. Examples include technetium-99m sestamibi for imaging myocardial perfusion and iodine-123 ioflupane for imaging dopamine transporter function in neuroendocrine tumors [100].
Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI): DCE-MRI involves the administration of a contrast agent to evaluate the tumor’s vascularity and blood flow. By measuring the kinetics of contrast agent uptake and washout, DCE-MRI provides information on tumor perfusion, angiogenesis, and vascular permeability. It is used in various cancers, including breast, prostate, and brain tumors [101].
Optical Imaging: Optical imaging techniques, such as fluorescence imaging and bioluminescence imaging, can be used to assess metabolic processes at a cellular level. Fluorescent probes and reporter genes are utilized to visualize specific metabolic activities, such as pH, reactive oxygen species, or enzyme activity. Optical imaging is commonly employed in preclinical research and experimental studies. These metabolic imaging techniques offer complementary information about tumor metabolism and aid in understanding the biological characteristics of cancer cells.

4.3.3. Molecular Probes and Contrast Agents

Molecular probes and contrast agents are invaluable tools in cancer research and clinical imaging. They are designed to specifically target and highlight certain molecular features or physiological processes associated with cancer. Here are some examples of molecular probes and contrast agents used in cancer:
Fluorescent Probes: Fluorescent probes emit light at specific wavelengths when excited by the light of a different wavelength. They can be conjugated to antibodies or other targeting molecules to visualize specific cancer-related targets or processes. For example, fluorescently labeled antibodies can be used to target and detect specific proteins or receptors overexpressed in cancer cells [102].
Magnetic Resonance Imaging (MRI) Contrast Agents: MRI contrast agents enhance the contrast between normal and cancerous tissues in MRI scans. These agents often contain gadolinium, manganese, or iron oxide nanoparticles. They can help visualize tumor morphology, angiogenesis, and tissue perfusion. Examples include gadolinium-based contrast agents and superparamagnetic iron oxide nanoparticles (SPIONs) [103].
Positron Emission Tomography (PET) Tracers: PET tracers are radiolabeled molecules that are administered to patients and emit positrons, which can be detected by PET scanners. They are designed to target specific molecular pathways or processes associated with cancer. For example, fluorodeoxyglucose (FDG) is a radiolabeled glucose analog used to detect increased glucose metabolism in cancer cells, and 18F-fluorothymidine (FLT) is used to assess cell proliferation by targeting DNA synthesis [104].
Ultrasound Contrast Agents: Ultrasound contrast agents are microbubbles filled with gas that enhance the contrast during ultrasound imaging. These agents can help visualize blood flow, angiogenesis, and tumor vascularity. Microbubbles can be conjugated with targeting ligands to selectively bind to specific markers on cancer cells or blood vessels [105].
Near-Infrared (NIR) Imaging Probes: NIR imaging probes emit light in the near-infrared spectrum, which can penetrate deeper into tissues. They are used for the non-invasive imaging of tumors, lymph nodes, and other structures. NIR probes can target specific cancer markers or processes, allowing for real-time imaging during surgery or molecular imaging studies [106].

4.4. Cells as Cancer Biomarkers

4.4.1. Circulating Tumor Cells as Cancer Biomarkers

In the realm of cancer, circulating tumor cells (CTCs) are basic yet effective biomarkers. The existence of CTCs has been demonstrated to determine patient survival with invasive breast cancer at various periods during treatment [107]. Cancer treatment targets (CTTs) are better predictors of prognosis than traditional tumor markers (e.g., CA27-29). The prevalence of therapeutic targets on CTCs can also influence the choice of an effective treatment regime, and the impact of treatment can be assessed after the initial cycle of medication [108]. The prevalence of CTCs has been reported to predict patient survival with metastatic breast cancer at various periods throughout treatment [107]. For patients undergoing systemic therapy for metastatic breast cancer, CTC gives an early and accurate indication of the progression of the disease and survival. CTC counts have been confirmed to be a consistent indicator for prognosis and therapy response in patients with metastatic prostate cancer.

4.4.2. Immune Cells as Cancer Biomarkers

The immune system can differentiate between self-antigen and foreign antigens, promoting the maintenance of immune tolerance and inducing defensive immunity towards foreign antigens. Across several tumor entities, such as colorectal cancer and liver metastases, immune cell count in scanned tissue has already been employed to identify reliable and clinically useful biomarkers [109][110][111]. Macrophages and T lymphocytes are the tumor site’s most prevalent immune cells linked to clinical effects [112][113][114][115]. The histopathological examination of tumor-infiltrating lymphoid cells has been confirmed to be a credible and prognostically useful biomarker [116][117]. T cells aid in thwarting immune pathologies by sustaining self-tolerance [118][119]. Studies reported that upregulated regulatory T-cells (T-regs) expression had been linked to poor immunological responses to tumor antigens in cancer patients, indicating that it may promote immune dysregulation and tumor progression [120][121]. T-regs have already been detected in large numbers in patients with lung, breast, pancreatic, skin, and liver cancers, either in the bloodstream or in the tumor [120][122]. The prevalence of T-regs, which impair tumor-specific T-cell immunity, was negatively related to survival in ovarian cancer patients [123]. T-regs are essential for the emergence of metastasis to lungs in breast cancer, according to Olkhanud et al. [124]. The infiltration of T-regs in primary tumor sites has also been correlated with the prevalence of circulating tumor cell cells in breast cancer patients, implying involvement in cancer cell dissemination [125].

4.4.3. Cancer Stem Cells as Cancer Biomarkers

Within tumors, subpopulations of cancerous cells have long been identified that imitate the hierarchical developmental system of the healthy tissue from which cancer arises. The tumors are propelled and sustained by a small population of cells that can self-renew and produce the more differentiated cells that constitute the mass of the tumor [126]. Various researchers have termed the former subpopulation cancer stem cells (CSCs) to signify that exclusively these cells can produce new tumors when transplanted to animals with immune deficiency [127]. The cancer stem cell model has received a lot of attention recently. CSCs were first detected via research on acute myelogenous leukemia patients (AML). Numerous solid cancers, notably prostate cancer, glioblastoma, breast cancer, medulloblastoma, and melanoma, have been shown to contain CSCs [128]. Because CSC (cancer stem cell) destruction is expected to be a crucial factor in achieving cure, their prevalence has enormous consequences on both cancer biology and treatment. Self-renewal, tumor-originating capacity, asymmetric cell division, and differentiation capacity are all features that identify potential CSCs [129][130]. CD24, CD133, CD166 (ALCAM), CD44, EpCAM, CD29, Lgr5, ALDH1B1, and ALDH1A1 are some of the cytoplasmic and surface markers which have been utilized to detect putative cancer CSCs. Metastatic colon malignancies from patient populations were associated with an elevated expression of ALDH1B1 (p = 0.001) compared with healthy colon tissue [131]. Other investigations have correlated the degree of CD24 expression in colorectal tumors to lymphovascular invasion and decreased survival rates [132][133][134]. The expression of CD44v9 is associated with initial stage lung adenocarcinoma and epidermal growth factor receptor mutations in lung malignancies [135]. CD44 variants are also found in gastric malignancies, where they stimulate tumor initiation [136]. Thus, a CSC biomarker has been suggested as a marker for diagnosis, interventional, and prognostic purposes.

4.5. Lamins as Cancer Biomarkers

The nuclei of animal cells are identifiable by their well-defined chromatin compartmentalization and nuclear structure. In higher vertebrates, the intricate nuclear architecture has been associated with the surge in genomic intricacy and the demand for spatiotemporal control of gene expression. The nucleoplasm, nuclear pore complex, and lamina are the three main constituents of a standard multicellular nucleus. The lamina is a protein meshwork located on the inner nuclear membrane’s nucleoplasmic side. The main element of this lamina is a group of class V intermediate filaments proteins termed lamins which are abnormally expressed in tumors. Lamins control differentiation, apoptosis, gene expression, and DNA repair in a direct or indirect way. By analyzing abnormalities in the expression profile of lamins in different forms of malignancies, several researchers and cancer biologists were able to pinpoint the link between abnormal lamin expression and cancer subtype. The medication betulinic acid has anti-cancer properties in pancreatic cancer by limiting lamin B1 production, and it might be used as a biomarker for cancer.
Scientists have looked into alterations in lamin patterns of expression in a variety of malignancy types in order to better understand the association between lamin transcription and cancer subgroups. Lamins, especially A-type lamins, communicate with transcription elements to control the growth and differentiation of cells [137]. In mature stem cells, the overexpression of the lamin A mutant inhibits the maturation and repair of tissue. The proliferation of cells is linked with decreased differentiation and zero or impaired gene expression for A-type lamins [138][139]. Lamins may function as indicators for cancer risk, forecasting the course and outcome of tumor growth. Nuclear lobulations and morphological alterations may result from lamin A depletion [140]. Colorectal malignancy, which has aberrant or misinterpreted lamin expression, is among the three most common malignancies worldwide. There is a strong correlation amongst lamin A/C expression, prognosis for patients, and the advancement of colorectal cancer, according to recent research. Death rates were almost twice as high in patients whose tumors tested positive for A-type lamin overexpression. Lamin A/C expression may serve as a risk signal for colorectal cancer-dependent mortality since it elevated T-plastin, reduced E-cadherin, and enhanced cell migration in colorectal cancer cells when GFP-lamin A was expressed ectopically. 

4.6. Galectins as Cancer Biomarkers

Galectins are a class of beta-galactoside-binding lectins widely found in all species. The genesis, progression, and pathological aggressiveness of tumors are linked to aberrant tumor-associated galectin expression. Rather than being a carcinoma diagnostic biomarker, galectin-3 is more of a malignancy function-related biomarker that can be applied in conjunction with certain other metabolic biomarkers. It is released into the tumor stroma and promotes tumor growth and angiogenesis [141]. Galectin-3 protein expression was much higher in breast tumor tissues relative to precancerous tissue, and triple-negative breast tumors have significantly higher levels of galectin-3 expression than other subtypes of breast cancer [142][143].
Evidence from a variety of cancer types suggests that the expression of galectin-1 is frequently higher in tumor tissues in contrast with healthy or benign tissues. Malignancies of the reproductive organs, gastrointestinal tract, lymphatic malignancies, myeloproliferative tumors, respiratory and urinary system, thyroid, and skin tumors all exhibit this pattern [144][145][146][147][148][149][150][151][152][153][154][155]. Although three studies found that galectin-1 expression was decreased in head and neck squamous cell carcinoma, cancers of the uterus, and prostate cancer, these results do not agree with those of the majority of studies, which may indicate that patient demographics, tumor subtypes, or methodologies may differ [156][157][158].
The expression of galectin-7 varies between cancer types; it is expressed less in malignancies of the skin, cervix, and stomach and more in cancers of the gastrointestinal tract, breast, thyroid, larynx, and indolent lymphoproliferative diseases. The expression of galectin-7 is also dependent on the subtype of cancer and the location of the disease inside the cell; it is absent in carcinomas of basal cells and present in squamous cell tumors, which are head and neck malignancies [159][160][161][162][163].
Malignant tissues release circulating galectins, which can be utilized as a biomarker for diagnosis. There have been reports of elevated amounts of galectin-1 and -3 in thyroid, pulmonary, skin, bladder, colon, and breast cancers. However, they are not very useful in diagnosing thyroid cancer. Glycoproteins that bind to lectin may potentially function as diagnostic markers [164][165][166][167][168].

4.7. Carbohydrate Antigens as Cancer Biomarkers

Carbohydrate antigen (CA) biomarkers are cancer indicators that have been identified because of efforts to construct antibodies targeting extracts or cell lines derived from tumors. CA indicators are glycoproteins of high molecular weight. The most invariably utilized serum tumor biomarker for detecting malignancies of the digestive organs is CA19-9. The validated marker for detecting ovarian cancer recurrence and evaluating therapy response is CA-125 [169]. CA-125’s diagnosis sensitivity is limited, and it has been demonstrated that this glycoprotein is widely dispersed on the surface of cells in a variety of malignant or benign conditions other than ovarian cancer, leaving its efficacy in the diagnosis in jeopardy [170]. Carcinoembryonic antigen (CEA) is a glycoprotein found on the surface of cells that offers an important function in adhesion. CEA is produced by healthy mucosal cells, and its level in normal adults is as minimal as 2.5 ng/mL and as high as 5.0 ng/mL in people who smoke; but, in the existence of a tumor, it can reach 100 ng/mL.

4.8. Viruses as Cancer Biomarkers

Hepatocellular carcinoma (HCC) is among the most widespread viral-induced tumors [171]. Over 80% of HCC cases are reported in underdeveloped nations. The risk factors are chronic hepatitis viral infections, caused primarily by the prevalent hepatitis B virus (HBV), and hepatitis C virus (HCV) infection in a small percentage of HCC patients (12–17%) [171]. HBV can induce tumorigenesis by genomic instability mediated by its frequent incorporation in host DNA [172]. Cervical is the second most prevalent cancer in women, accounting for most cancer-related fatalities worldwide, and chronic infection with particular strains of HPV is the most prevalent trigger for cervical cancer. HPV has been detected in a substantial amount in anal, oral, penile, esophageal, vulvar, and vaginal cancers, as well as a tiny portion in laryngeal, lung, and stomach cancers in some regions of the world [173]. Cervical carcinoma samples were utilized to diagnose, clone, and sequence papillomaviruses for the first time. Antibodies to HPV (E6 and E7) produced by participants act as biomarkers of an HPV-related carcinoma [174]. Due to the sheer rise in HPV-related disease, especially HPV16 infection, oropharyngeal squamous cell carcinoma (OPSCC) is presumed to be the third most prevalent malignancy in middle-aged, non-Hispanic, white men by 2045.

4.9. Exosomes as a Cancer Biomarker

Exosomes, which are the smallest (diameter of 30–150 nm) extracellular vesicles, are secreted by endothelial cells, erythrocytes, epithelial cells, dendritic cells, oligodendroglial cells, mesenchymal stem cells (MSCs), neural cells, and tumor cells [175][176]. Exosomes can serve as “cellular postmen” for carrying genomic material for inter- and intracellular communication because they are loaded with physiologically active components such as RNA, cytoplasmic proteins, cellular metabolites, and lipids [177]. Exosomes can be found in blood, breast milk, synovial fluid, amniotic fluid, urine, bronchoalveolar lavage fluid, pleural fluid, serum, and saliva [178][179]. Due to their widespread prevalence in physiological fluids and their resemblance to the contents of original cells, exosomes are potentially useful as circulating biomarkers for numerous kinds of cancers. By constructing or modulating the tumor microenvironment and encouraging angiogenesis and tumor invasion, tumor-derived exosomes (TEXs) serve a crucial role in tumorigenesis and progression [180][181]. TEXs contain a multitude of endogenous cargos which partly imitate the components and resemble the pathophysiological condition or signaling abnormalities of parent cells, rendering them potential biomarkers for early cancer detection. Exosomal proteins are emerging diagnosis and monitoring markers for cancers because there are plentiful cancer-related proteins in exosomes. In exosomes secreted from pancreatic cancer, overexpressed proteoglycan Glypican-1 (GPC-1) is found. Patients with pancreatic ductal adenocarcinoma (PDAC) were reported as having higher levels of exosomal protein 4 (CKAP 4) than healthy people. Exosomes containing CKAP 4 in the serum can be utilized as a potential biomarker for PDAC [182]. Trp5 (Transient Receptor Potential Channel 5) is overexpressed in exosomes from breast cancer, has a major function in drug resistance, and can be utilized to anticipate chemotherapy resistance in patients with breast cancer [183]. Exosomes have a double-layer lipid barrier that protects internal nucleic acids from being damaged. Consequently, exosomal nucleic acids can be potential indicators in cancer diagnostics. Hepatocellular carcinoma and other malignancies may benefit from exosomal miRNAs as potential serological markers [184]. Elevated exosomal miR-375 and miR-1290 levels in the plasma of prostate cancer (castration-resistant) patients were linked to a poor overall survival rate (OS) [185].

4.10. Lipids as Cancer Biomarkers

Jiang and colleagues reported the lipid species that could be utilized as markers for early detection of breast cancer. In comparison to healthy controls, researchers noticed higher amounts of Phytosterol Diosgenin (DG), and Phosphatidylcholines (PC) in breast cancer samples. The level of Phosphatidylethanolamine (PE) was shown to be lower in breast cancer samples [27]. Prostate cancer patients have a 2.7-fold elevation in Lysophosphatidylcholine (LPC) relative to healthy subjects, according to Zhou and colleagues [28].

5. Emerging Technologies and Techniques

5.1. Liquid Biopsy

Liquid biopsy is a minimally invasive diagnostic procedure that involves the examination of numerous elements found in physiological fluids like blood or urine, including exosomes, circulating tumor cells, cell-free DNA (cfDNA), and proteins. With the help of this method, early cancer detection, treatment monitoring, and the discovery of potential therapeutic targets are made possible through insights into a patient’s molecular profile. Figure 5 provides an illustration of the methods used in liquid biopsy analysis. In this method, a single blood sample’s cfDNA/ctDNA profile is made up of both wild-type and genetically and epigenetically changed DNA fragments released by various tissues and organs through various pathways [186].
Sensors 24 00037 g012
Figure 5. Liquid biopsy analysis [186]. (A) Liquid biopsy analysis involves the examination of circulating cancer cells, circulating tumor DNA (ctDNA), and extracellular vesicles containing proteins, RNA, ctDNA, and cell-free DNA (cfDNA) from both primary and secondary tumor sites. This approach is considered a potential cancer biomarker, enabling the quantification of ctDNA levels and the detection of (epi)genetic alterations. (B) Methods employed for ctDNA analysis encompass real-time PCR, BEAMing (beads, emulsion, amplification, and magnetics), coamplification at lower denaturation temperature PCR (COLD-PCR), digital PCR, and next-generation sequencing.

5.2. Single-Cell Analysis

Single-cell analysis has revolutionized our understanding of tumor heterogeneity by enabling the characterization of individual cells within a tumor. Here is how single-cell analysis helps to characterize tumor heterogeneity:
Identifying Subpopulations: Single-cell analysis allows the identification of distinct subpopulations of cells within a tumor. By analyzing the transcriptomic or genomic profiles of individual cells, researchers can identify and classify different cell types or states within the tumor. This reveals the heterogeneity in gene expression patterns, signaling pathways, and functional characteristics among tumor cells.
Uncovering Clonal Diversity: Tumors are composed of clonal cell populations with genetic alterations acquired during tumor evolution. Single-cell genomic sequencing techniques, such as single-nucleus sequencing or single-cell whole-genome sequencing, can identify and characterize somatic mutations, copy number variations, and chromosomal rearrangements in individual cells. This helps to reveal clonal diversity and understand the evolutionary trajectory of the tumor.
Profiling Transcriptomic Variability: Single-cell RNA sequencing (scRNA-seq) enables the profiling of gene expression patterns in individual cells. This allows the identification of different transcriptional states, gene regulatory networks, and functional states within the tumor. By analyzing the transcriptomic variability, researchers gain insights into the cellular heterogeneity, cell lineage relationships, and potential cell subpopulations with distinct biological properties.
Assessing Protein Expression and Signaling: Techniques such as immunohistochemistry (IHC) and mass cytometry (CyTOF) at the single-cell level enable the characterization of protein expression profiles and signaling pathways in individual cells. This helps to understand the heterogeneity in protein expression, cellular phenotypes, and the activation of key signaling molecules within the tumor microenvironment.
Mapping Spatial Heterogeneity: Spatial transcriptomics and imaging-based single-cell analysis techniques allow the assessment of cellular heterogeneity in the context of the tumor microenvironment. By characterizing the spatial distribution of different cell types, gene expression patterns, or immune cell infiltrates, researchers can unravel the spatial organization and heterogeneity of tumor cells within the tissue architecture. By combining these single-cell analysis approaches, researchers can comprehensively characterize the complexity and heterogeneity of tumors at the cellular level. This deeper understanding of tumor heterogeneity has implications for predicting treatment response, identifying therapy-resistant cell populations, and developing personalized treatment strategies in cancer patients.

5.3. Artificial Intelligence and Machine Learning

Artificial intelligence (AI) and machine learning (ML) techniques have made significant contributions to various aspects of cancer research, diagnosis, treatment, and patient care. Here are some key ways AI and ML are used in cancer:
Image Analysis and Medical Imaging: AI and ML algorithms are used to analyze medical images, such as mammograms, CT scans, and histopathology slides. These algorithms can assist in early cancer detection, tumor segmentation, identifying suspicious lesions, and predicting treatment response. Deep learning models have demonstrated remarkable accuracy in image-based cancer diagnosis.
Genomic Analysis: AI and ML techniques are applied to genomic data analysis, including DNA sequencing and gene expression profiling. These algorithms can identify genomic alterations, mutations, and biomarkers associated with specific cancer types, helping in diagnosis, prognosis, and personalized treatment selection.
Clinical Decision Support: AI and ML models can aid clinicians in making more informed decisions regarding cancer treatment plans. These models leverage patient data, such as medical records, imaging results, and genetic profiles, to provide personalized treatment recommendations, predict treatment outcomes, and optimize treatment strategies.
Drug Discovery and Development: AI and ML are utilized in the early stages of drug discovery to identify potential drug targets, predict drug interactions, and design novel compounds. These techniques can also assist in drug repurposing by analyzing large-scale datasets and identifying existing drugs that may be effective against specific cancer types.
Precision Medicine: AI and ML algorithms enable precision medicine approaches by integrating patient-specific data, including clinical, genomic, and imaging information. These models help identify patient subgroups that are more likely to respond to specific treatments, thus guiding personalized treatment selection and improving patient outcomes.
Data Integration and Knowledge Extraction: AI and ML techniques can integrate and analyze large-scale, heterogeneous datasets from various sources, including electronic health records, medical literature, and public databases. By extracting knowledge and patterns from these data, AI models can identify associations, predict disease outcomes, and generate new insights for cancer research.
Prognosis and Risk Assessment: AI and ML models can predict cancer prognosis, recurrence risk, and patient survival outcomes based on clinical and molecular features. These predictions assist in treatment planning, patient counseling, and monitoring long-term outcomes.

References

  1. Allegra, C.J.; Jessup, J.M.; Somerfield, M.R.; Hamilton, S.R.; Hammond, E.H.; Hayes, D.F.; McAllister, P.K.; Morton, R.F.; Schilsky, R.L. American Society of Clinical Oncology provisional clinical opinion: Testing for KRAS gene mutations in patients with metastatic colorectal carcinoma to predict response to anti–epidermal growth factor receptor monoclonal antibody therapy. J. Clin. Oncol. 2009, 27, 2091–2096.
  2. Burstein, H.J.; Mangu, P.B.; Somerfield, M.R.; Schrag, D.; Samson, D.; Holt, L.; Zelman, D.; Ajani, J.A. American Society of Clinical Oncology clinical practice guideline update on the use of chemotherapy sensitivity and resistance assays. J. Clin. Oncol. 2011, 29, 3328–3330.
  3. Carlson, R.W.; Allred, D.C.; Anderson, B.O.; Burstein, H.J.; Carter, W.B.; Edge, S.B.; Erban, J.K.; Farrar, W.B.; Forero, A.; Giordano, S.H. Invasive breast cancer. J. Natl. Compr. Cancer Netw. 2011, 9, 136–222.
  4. Cristofanilli, M.; Budd, G.T.; Ellis, M.J.; Stopeck, A.; Matera, J.; Miller, M.C.; Reuben, J.M.; Doyle, G.V.; Allard, W.J.; Terstappen, L.W. Circulating tumor cells, disease progression, and survival in metastatic breast cancer. N. Engl. J. Med. 2004, 351, 781–791.
  5. Freidlin, B.; McShane, L.M.; Korn, E.L. Randomized clinical trials with biomarkers: Design issues. J. Natl. Cancer Inst. 2010, 102, 152–160.
  6. Hammarström, S. The carcinoembryonic antigen (CEA) family: Structures, suggested functions and expression in normal and malignant tissues. Semin. Cancer Biol. 1999, 9, 67–81.
  7. Imperiale, T.F.; Ransohoff, D.F.; Itzkowitz, S.H.; Levin, T.R.; Lavin, P.; Lidgard, G.P.; Ahlquist, D.A.; Berger, B.M. Multitarget stool DNA testing for colorectal-cancer screening. N. Engl. J. Med. 2014, 370, 1287–1297.
  8. Serganova, I.; Blasberg, R.G. Molecular imaging with reporter genes: Has its promise been delivered? J. Nucl. Med. 2019, 60, 1665–1681.
  9. Gilad, A.A.; Shapiro, M.G. Molecular imaging in synthetic biology, and synthetic biology in molecular imaging. Mol. Imaging Biol. 2017, 19, 373–378.
  10. Condeelis, J.; Weissleder, R. In vivo imaging in cancer. Cold Spring Harb. Perspect. Biol. 2010, 2, a003848.
  11. Cho, W.C. Contribution of oncoproteomics to cancer biomarker discovery. Mol. Cancer 2007, 6, 25.
  12. Siravegna, G.; Marsoni, S.; Siena, S.; Bardelli, A. Integrating liquid biopsies into the management of cancer. Nat. Rev. Clin. Oncol. 2017, 14, 531–548.
  13. Pantel, K.; Alix-Panabières, C. Circulating tumour cells in cancer patients: Challenges and perspectives. Trends Mol. Med. 2010, 16, 398–406.
  14. Bardelli, A.; Pantel, K. Liquid biopsies, what we do not know (yet). Cancer Cell 2017, 31, 172–179.
  15. Tan, G.H.; Nason, G.; Ajib, K.; Woon, D.T.S.; Herrera-Caceres, J.; Alhunaidi, O.; Perlis, N. Smarter screening for prostate cancer. World J. Urol. 2019, 37, 991–999.
  16. Gahan, P.; Stroun, M. The biology of circulating nucleic acids in plasma and serum (CNAPS). In Extracellular Nucleic Acids; Springer: Berlin/Heidelberg, Germany, 2010; pp. 167–189.
  17. Porto-Mascarenhas, E.C.; Assad, D.X.; Chardin, H.; Gozal, D.; Canto, G.D.L.; Acevedo, A.C.; Guerra, E.N.S. Salivary biomarkers in the diagnosis of breast cancer: A review. Crit. Rev. Oncol./Hematol. 2017, 110, 62–73.
  18. Lawrence, H.P. Salivary markers of systemic disease: Noninvasive diagnosis of disease and monitoring of general health. J.-Can. Dent. Assoc. 2002, 68, 170–175.
  19. Zhang, L.; Farrell, J.J.; Zhou, H.; Elashoff, D.; Akin, D.; Park, N.H.; Chia, D.; Wong, D.T. Salivary transcriptomic biomarkers for detection of resectable pancreatic cancer. Gastroenterology 2010, 138, 949–957.e7.
  20. Gao, S.; Chen, L.-Y.; Wang, P.; Liu, L.-M.; Chen, Z. MicroRNA expression in salivary supernatant of patients with pancreatic cancer and its relationship with ZHENG. BioMed Res. Int. 2014, 2014, 756347.
  21. Humeau, M.; Vignolle-Vidoni, A.; Sicard, F.; Martins, F.; Bournet, B.; Buscail, L.; Torrisani, J.; Cordelier, P. Salivary microRNA in pancreatic cancer patients. PLoS ONE 2015, 10, e0130996.
  22. Lau, C.; Kim, Y.; Chia, D.; Spielmann, N.; Eibl, G.; Elashoff, D.; Wei, F.; Lin, Y.-L.; Moro, A.; Grogan, T. Role of pancreatic cancer-derived exosomes in salivary biomarker development. J. Biol. Chem. 2013, 288, 26888–26897.
  23. Zhang, L.; Xiao, H.; Zhou, H.; Santiago, S.; Lee, J.M.; Garon, E.B.; Yang, J.; Brinkmann, O.; Yan, X.; Akin, D. Development of transcriptomic biomarker signature in human saliva to detect lung cancer. Cell. Mol. Life Sci. 2012, 69, 3341–3350.
  24. Wu, Z.-Z.; Wang, J.-G.; Zhang, X.-L. Diagnostic model of saliva protein finger print analysis of patients with gastric cancer. World J. Gastroenterol. WJG 2009, 15, 865.
  25. Wang, X.; Kaczor-Urbanowicz, K.E.; Wong, D.T. Salivary biomarkers in cancer detection. Med. Oncol. 2017, 34, 7.
  26. Landgren, O.; Porwit MacDonald, A.; Tani, E.; Czader, M.; Grimfors, G.; Skoog, L.; Ost, A.; Wedelin, C.; Axdorph, U.; Svedmyr, E. A prospective comparison of fine-needle aspiration cytology and histopathology in the diagnosis and classification of lymphomas. Hematol. J. 2004, 5, 69–76.
  27. Barker, A.; Kettle, J.G.; Nowak, T.; Pease, J.E. Expanding medicinal chemistry space. Drug Discov. Today 2013, 18, 298–304.
  28. Blagg, J.; Workman, P. Chemical biology approaches to target validation in cancer. Curr. Opin. Pharmacol. 2014, 17, 87–100.
  29. Atlas, A.B.-R. Breast Imaging Reporting and Data System; American College of Radiology: Reston, VA, USA, 2013.
  30. Edge, S.B.; Cancer, A.J.C.o. AJCC Cancer Staging Manual; Springer: Berlin/Heidelberg, Germany, 2010; Volume 7.
  31. Therasse, P.; Arbuck, S.G.; Eisenhauer, E.A.; Wanders, J.; Kaplan, R.S.; Rubinstein, L.; Verweij, J.; Van Glabbeke, M.; van Oosterom, A.T.; Christian, M.C. New guidelines to evaluate the response to treatment in solid tumors. J. Natl. Cancer Inst. 2000, 92, 205–216.
  32. Eisenhauer, E.A.; Therasse, P.; Bogaerts, J.; Schwartz, L.H.; Sargent, D.; Ford, R.; Dancey, J.; Arbuck, S.; Gwyther, S.; Mooney, M. New response evaluation criteria in solid tumours: Revised RECIST guideline (version 1.1). Eur. J. Cancer 2009, 45, 228–247.
  33. Reid, D.M.; Doughty, J.; Eastell, R.; Heys, S.D.; Howell, A.; McCloskey, E.V.; Powles, T.; Selby, P.; Coleman, R.E. Guidance for the management of breast cancer treatment-induced bone loss: A consensus position statement from a UK Expert Group. Cancer Treat. Rev. 2008, 34, S3–S18.
  34. Plana, J.C.; Galderisi, M.; Barac, A.; Ewer, M.S.; Ky, B.; Scherrer-Crosbie, M.; Ganame, J.; Sebag, I.A.; Agler, D.A.; Badano, L.P. Expert consensus for multimodality imaging evaluation of adult patients during and after cancer therapy: A report from the American Society of Echocardiography and the European Association of Cardiovascular Imaging. Eur. Heart J.–Cardiovasc. Imaging 2014, 15, 1063–1093.
  35. Tzogani, K.; Skibeli, V.; Westgaard, I.; Dalhus, M.; Thoresen, H.; Slot, K.B.; Damkier, P.; Hofland, K.; Borregaard, J.; Ersbøll, J. The European Medicines Agency approval of axitinib (Inlyta) for the treatment of advanced renal cell carcinoma after failure of prior treatment with sunitinib or a cytokine: Summary of the scientific assessment of the committee for medicinal products for human use. Oncologist 2015, 20, 196–201.
  36. U.S. Food & Drug Administration. Novel Drug Approvals for 2018. Available online: https://www.fda.gov/drugs/new-drugs-fda-cders-new-molecular-entities-and-new-therapeutic-biological-products/novel-drug-approvals-2018 (accessed on 18 December 2023).
  37. Bergström, M.; Hargreaves, R.J.; Burns, H.D.; Goldberg, M.R.; Sciberras, D.; Reines, S.A.; Petty, K.J.; Ögren, M.; Antoni, G.; Långström, B. Human positron emission tomography studies of brain neurokinin 1 receptor occupancy by aprepitant. Biol. Psychiatry 2004, 55, 1007–1012.
  38. Willett, C.G.; Boucher, Y.; Di Tomaso, E.; Duda, D.G.; Munn, L.L.; Tong, R.T.; Chung, D.C.; Sahani, D.V.; Kalva, S.P.; Kozin, S.V. Erratum: Direct evidence that the VEGF-specific antibody bevacizumab has antivascular effects in human rectal cancer (Nature Medicine (2004) 10 (145-147)). Nat. Med. 2004, 10, 649.
  39. Avril, N.; Propper, D. Functional PET imaging in cancer drug development. Future Med. 2007, 3, 215–228.
  40. O’connor, J.P.; Jackson, A.; Parker, G.J.; Roberts, C.; Jayson, G.C. Dynamic contrast-enhanced MRI in clinical trials of antivascular therapies. Nat. Rev. Clin. Oncol. 2012, 9, 167–177.
  41. Lassau, N.; Chapotot, L.; Benatsou, B.; Vilgrain, V.; Kind, M.; Lacroix, J.; Cuinet, M.; Taieb, S.; Aziza, R.; Sarran, A. Standardization of dynamic contrast-enhanced ultrasound for the evaluation of antiangiogenic therapies: The French multicenter Support for Innovative and Expensive Techniques Study. Investig. Radiol. 2012, 47, 711–716.
  42. Gupta, R.; Naran, S.; Lallu, S.; Fauck, R. The diagnostic value of fine needle aspiration cytology (FNAC) in the assessment of palpable supraclavicular lymph nodes: A study of 218 cases. Cytopathology 2003, 14, 201–207.
  43. Ben-Yehuda, D.; Polliack, A.; Okon, E.; Sherman, Y.; Fields, S.; Lebenshart, P.; Lotan, H.; Libson, E. Image-guided core-needle biopsy in malignant lymphoma: Experience with 100 patients that suggests the technique is reliable. J. Clin. Oncol. 1996, 14, 2431–2434.
  44. Pappa, V.; Hussain, H.; Reznek, R.; Whelan, J.; Norton, A.; Wilson, A.; Love, S.; Lister, T.; Rohatiner, A. Role of image-guided core-needle biopsy in the management of patients with lymphoma. J. Clin. Oncol. 1996, 14, 2427–2430.
  45. Campo, E.; Swerdlow, S.H.; Harris, N.L.; Pileri, S.; Stein, H.; Jaffe, E.S. The 2008 WHO classification of lymphoid neoplasms and beyond: Evolving concepts and practical applications. Blood J. Am. Soc. Hematol. 2011, 117, 5019–5032.
  46. Tatli, S.; Gerbaudo, V.H.; Mamede, M.; Tuncali, K.; Shyn, P.B.; Silverman, S.G. Abdominal masses sampled at PET/CT-guided percutaneous biopsy: Initial experience with registration of prior PET/CT images. Radiology 2010, 256, 305–311.
  47. Lawson, M.H.; Rassl, D.M.; Cummings, N.M.; Russell, R.; Morjaria, J.B.; Brenton, J.D.; Murphy, G.; Rintoul, R.C. Tissue banking of diagnostic lung cancer biopsies for extraction of high quality RNA. J. Thorac. Oncol. 2010, 5, 956–963.
  48. de Kerviler, E.; Guermazi, A.; Zagdanski, A.M.; Meignin, V.; Gossot, D.; Oksenhendler, E.; Mariette, X.; Brice, P.; Frija, J. Image-guided core-needle biopsy in patients with suspected or recurrent lymphomas. Cancer 2000, 89, 647–652.
  49. Demharter, J.; Müller, P.; Wagner, T.; Schlimok, G.; Haude, K.; Bohndorf, K. Percutaneous core-needle biopsy of enlarged lymph nodes in the diagnosis and subclassification of malignant lymphomas. Eur. Radiol. 2001, 11, 276–283.
  50. Loubeyre, P.; McKee, T.; Copercini, M.; Rosset, A.; Dietrich, P. Diagnostic precision of image-guided multisampling core needle biopsy of suspected lymphomas in a primary care hospital. Br. J. Cancer 2009, 100, 1771–1776.
  51. Amador-Ortiz, C.; Chen, L.; Hassan, A.; Frater, J.L.; Burack, R.; Nguyen, T.T.; Kreisel, F. Combined core needle biopsy and fine-needle aspiration with ancillary studies correlate highly with traditional techniques in the diagnosis of nodal-based lymphoma. Am. J. Clin. Pathol. 2011, 135, 516–524.
  52. Shaw, J.; Rumball, E. Complications and local recurrence following lymphadenectomy. Br. J. Surg. 1990, 77, 760–764.
  53. de Kerviler, E.; de Bazelaire, C.; Mounier, N.; Mathieu, O.; Brethon, B.; Brière, J.; Marolleau, J.-P.; Brice, P.; Gisselbrecht, C.; Frija, J. Image-guided core-needle biopsy of peripheral lymph nodes allows the diagnosis of lymphomas. Eur. Radiol. 2007, 17, 843–849.
  54. Sklair-Levy, M.; Amir, G.; Spectre, G.; Lebensart, P.; Applbaum, Y.; Agid, R.; Lieberman, S.; Ben-Yehuda, D.; Sherman, Y.; Libson, E. Image-guided cutting-edge-needle biopsy of peripheral lymph nodes and superficial masses for the diagnosis of lymphoma. J. Comput. Assist. Tomogr. 2005, 29, 369–372.
  55. Tangella, L.P.; Clark, M.E.; Gray, E.S. Resistance mechanisms to targeted therapy in BRAF-mutant melanoma-A mini review. Biochim. Biophys. Acta (BBA)-Gen. Subj. 2021, 1865, 129736.
  56. Passaro, A.; Mok, T.; Peters, S.; Popat, S.; Ahn, M.-J.; De Marinis, F. Recent advances on the role of EGFR tyrosine kinase inhibitors in the management of NSCLC with uncommon, non exon 20 insertions, EGFR mutations. J. Thorac. Oncol. 2021, 16, 764–773.
  57. Zenonos, K.; Kyprianou, K. RAS signaling pathways, mutations and their role in colorectal cancer. World J. Gastrointest. Oncol. 2013, 5, 97.
  58. Pujol, P.; Barberis, M.; Beer, P.; Friedman, E.; Piulats, J.M.; Capoluongo, E.D.; Foncillas, J.G.; Ray-Coquard, I.; Penault-Llorca, F.; Foulkes, W.D. Clinical practice guidelines for BRCA1 and BRCA2 genetic testing. Eur. J. Cancer 2021, 146, 30–47.
  59. Amisha, F.; Malik, P.; Saluja, P.; Gautam, N.; Patel, T.H.; Roy, A.M.; Singh, S.R.; Malapati, S.J. A Comprehensive Review on the Role of Human Epidermal Growth Factor Receptor 2 (HER2) as a Biomarker in Extra-Mammary and Extra-Gastric Cancers. Onco 2023, 3, 96–124.
  60. Ma, R.; de Pennington, N.; Hofer, M.; Blesing, C.; Stacey, R. Diagnostic and prognostic markers in gliomas—An update. Br. J. Neurosurg. 2013, 27, 311–315.
  61. Bernhardt, S.M.; Dasari, P.; Wrin, J.; Raymond, W.; Edwards, S.; Walsh, D.; Townsend, A.R.; Price, T.J.; Ingman, W.V. Discordance in 21-gene recurrence scores between paired breast cancer samples is inversely associated with patient age. Breast Cancer Res. 2020, 22, 90.
  62. Haan, J.C.; Bhaskaran, R.; Ellappalayam, A.; Bijl, Y.; Griffioen, C.J.; Lujinovic, E.; Audeh, W.M.; Penault-Llorca, F.; Mittempergher, L.; Glas, A.M. MammaPrint and BluePrint comprehensively capture the cancer hallmarks in early-stage breast cancer patients. Genes Chromosomes Cancer 2022, 61, 148–160.
  63. Verma, M.; Kumar, D. Application of mitochondrial genome information in cancer epidemiology. Clin. Chim. Acta 2007, 383, 41–50.
  64. Sidransky, D. Emerging molecular markers of cancer. Nat. Rev. Cancer 2002, 2, 210–219.
  65. Leon, S.; Shapiro, B.; Sklaroff, D.; Yaros, M. Free DNA in the serum of cancer patients and the effect of therapy. Cancer Res. 1977, 37, 646–650.
  66. Verma, M.; Srivastava, S. Epigenetics in cancer: Implications for early detection and prevention. Lancet Oncol. 2002, 3, 755–763.
  67. Verma, M.; Kagan, J.; Sidransky, D.; Srivastava, S. Proteomic analysis of cancer-cell mitochondria. Nat. Rev. Cancer 2003, 3, 789–795.
  68. Jin, H.; Ma, Y.; Shen, Q.; Wang, X. Circulating methylated DNA as biomarkers for cancer detection. In Methylation: From DNA, RNA and Histones to Diseases and Treatment; InTech: London, UK, 2012; p. 137.
  69. Velculescu, V.E.; Zhang, L.; Vogelstein, B.; Kinzler, K.W. Serial analysis of gene expression. Science 1995, 270, 484–487.
  70. Bartels, C.L.; Tsongalis, G.J. MicroRNAs: Novel biomarkers for human cancer. Clin. Chem. 2009, 55, 623–631.
  71. Calin, G.A.; Croce, C.M. MicroRNA signatures in human cancers. Nat. Rev. Cancer 2006, 6, 857–866.
  72. Lu, J.; Getz, G.; Miska, E.A.; Alvarez-Saavedra, E.; Lamb, J.; Peck, D.; Sweet-Cordero, A.; Ebert, B.L.; Mak, R.H.; Ferrando, A.A. MicroRNA expression profiles classify human cancers. Nature 2005, 435, 834–838.
  73. Kasinski, A.L.; Slack, F.J. MicroRNAs en route to the clinic: Progress in validating and targeting microRNAs for cancer therapy. Nat. Rev. Cancer 2011, 11, 849–864.
  74. Stahlhut, C.; Slack, F.J. MicroRNAs and the cancer phenotype: Profiling, signatures and clinical implications. Genome Med. 2013, 5, 111.
  75. Manterola, L.; Guruceaga, E.; Pérez-Larraya, J.G.; González-Huarriz, M.; Jauregui, P.; Tejada, S.; Diez-Valle, R.; Segura, V.; Samprón, N.; Barrena, C. A small noncoding RNA signature found in exosomes of GBM patient serum as a diagnostic tool. Neuro-Oncol. 2014, 16, 520–527.
  76. Calin, G.A.; Dumitru, C.D.; Shimizu, M.; Bichi, R.; Zupo, S.; Noch, E.; Aldler, H.; Rattan, S.; Keating, M.; Rai, K. Frequent deletions and down-regulation of micro-RNA genes miR15 and miR16 at 13q14 in chronic lymphocytic leukemia. Proc. Natl. Acad. Sci. USA 2002, 99, 15524–15529.
  77. Sanger, H.L.; Klotz, G.; Riesner, D.; Gross, H.J.; Kleinschmidt, A.K. Viroids are single-stranded covalently closed circular RNA molecules existing as highly base-paired rod-like structures. Proc. Natl. Acad. Sci. USA 1976, 73, 3852–3856.
  78. Memczak, S.; Jens, M.; Elefsinioti, A.; Torti, F.; Krueger, J.; Rybak, A.; Maier, L.; Mackowiak, S.D.; Gregersen, L.H.; Munschauer, M. Circular RNAs are a large class of animal RNAs with regulatory potency. Nature 2013, 495, 333–338.
  79. Kristensen, L.S.; Andersen, M.S.; Stagsted, L.V.; Ebbesen, K.K.; Hansen, T.B.; Kjems, J. The biogenesis, biology and characterization of circular RNAs. Nat. Rev. Genet. 2019, 20, 675–691.
  80. Zhu, X.; Wang, X.; Wei, S.; Chen, Y.; Chen, Y.; Fan, X.; Han, S.; Wu, G. hsa_circ_0013958: A Circular RNA and Potential Novel Biomarker for Lung Adenocarcinoma; Wiley Online Library: Hoboken, NJ, USA, 2017; Volume 284, pp. 2170–2182.
  81. Herman, J.G.; Baylin, S.B. Gene silencing in cancer in association with promoter hypermethylation. N. Engl. J. Med. 2003, 349, 2042–2054.
  82. Egger, G.; Liang, G.; Aparicio, A.; Jones, P.A. Epigenetics in human disease and prospects for epigenetic therapy. Nature 2004, 429, 457–463.
  83. Belinsky, S.A. Gene-promoter hypermethylation as a biomarker in lung cancer. Nat. Rev. Cancer 2004, 4, 707–717.
  84. Anderson, N.L.; Anderson, N.G. The human plasma proteome: History, character, and diagnostic prospects. Mol. Cell. Proteom. 2002, 1, 845–867.
  85. Colantonio, D.A.; Chan, D.W. The clinical application of proteomics. Clin. Chim. Acta 2005, 357, 151–158.
  86. Jimenez-Luna, C.; Torres, C.; Ortiz, R.; Dieguez, C.; Martinez-Galan, J.; Melguizo, C.; Prados, J.C.; Caba, O. Proteomic biomarkers in body fluids associated with pancreatic cancer. Oncotarget 2018, 9, 16573.
  87. Sandfeld-Paulsen, B.; Jakobsen, K.R.; Bæk, R.; Folkersen, B.H.; Rasmussen, T.R.; Meldgaard, P.; Varming, K.; Jørgensen, M.M.; Sorensen, B.S. Exosomal proteins as diagnostic biomarkers in lung cancer. J. Thorac. Oncol. 2016, 11, 1701–1710.
  88. Nedjadi, T.; Benabdelkamal, H.; Albarakati, N.; Masood, A.; Al-Sayyad, A.; Alfadda, A.A.; Alanazi, I.O.; Al-Ammari, A.; Al-Maghrabi, J. Circulating proteomic signature for detection of biomarkers in bladder cancer patients. Sci. Rep. 2020, 10, 10999.
  89. Pavlova, N.N.; Thompson, C.B. The emerging hallmarks of cancer metabolism. Cell Metab. 2016, 23, 27–47.
  90. Nava, G.M.; Madrigal Perez, L.A. Metabolic profile of the Warburg effect as a tool for molecular prognosis and diagnosis of cancer. Expert Rev. Mol. Diagn. 2022, 22, 439–447.
  91. Cardaci, S.; Ciriolo, M.R. TCA cycle defects and cancer: When metabolism tunes redox state. Int. J. Cell Biol. 2012, 2012, 161837.
  92. Minton, D.R.; Fu, L.; Chen, Q.; Robinson, B.D.; Gross, S.S.; Nanus, D.M.; Gudas, L.J. Analyses of the transcriptome and metabolome demonstrate that HIF1α mediates altered tumor metabolism in clear cell renal cell carcinoma. PLoS ONE 2015, 10, e0120649.
  93. Mori, N.; Wildes, F.; Takagi, T.; Glunde, K.; Bhujwalla, Z.M. The tumor microenvironment modulates choline and lipid metabolism. Front. Oncol. 2016, 6, 262.
  94. Wu, X.; Qin, L.; Fako, V.; Zhang, J.-T. Molecular mechanisms of fatty acid synthase (FASN)-mediated resistance to anti-cancer treatments. Adv. Biol. Regul. 2014, 54, 214–221.
  95. Lieu, E.L.; Nguyen, T.; Rhyne, S.; Kim, J. Amino acids in cancer. Exp. Mol. Med. 2020, 52, 15–30.
  96. Aird, K.M.; Zhang, R. Nucleotide metabolism, oncogene-induced senescence and cancer. Cancer Lett. 2015, 356, 204–210.
  97. Mankoff, D.A.; Eary, J.F.; Link, J.M.; Muzi, M.; Rajendran, J.G.; Spence, A.M.; Krohn, K.A. Tumor-specific positron emission tomography imaging in patients: fluorodeoxyglucose and beyond. Clin. Cancer Res. 2007, 13, 3460–3469.
  98. Glunde, K.; Artemov, D.; Penet, M.-F.; Jacobs, M.A.; Bhujwalla, Z.M. Magnetic resonance spectroscopy in metabolic and molecular imaging and diagnosis of cancer. Chem. Rev. 2010, 110, 3043–3059.
  99. Kurhanewicz, J.; Vigneron, D.B.; Ardenkjaer-Larsen, J.H.; Bankson, J.A.; Brindle, K.; Cunningham, C.H.; Gallagher, F.A.; Keshari, K.R.; Kjaer, A.; Laustsen, C. Hyperpolarized 13C MRI: Path to clinical translation in oncology. Neoplasia 2019, 21, 1–16.
  100. Vallabhajosula, S.; Killeen, R.P.; Osborne, J.R. Altered biodistribution of radiopharmaceuticals: Role of radiochemical/pharmaceutical purity, physiological, and pharmacologic factors. Semin. Nucl. Med. 2010, 40, 220–241.
  101. Barrett, T.; Brechbiel, M.; Bernardo, M.; Choyke, P.L. MRI of tumor angiogenesis. J. Magn. Reson. Imaging Off. J. Int. Soc. Magn. Reson. Med. 2007, 26, 235–249.
  102. Koyama, Y.; Barrett, T.; Hama, Y.; Ravizzini, G.; Choyke, P.L.; Kobayashi, H. In vivo molecular imaging to diagnose and subtype tumors through receptor-targeted optically labeled monoclonal antibodies. Neoplasia 2007, 9, 1021–1029.
  103. Xiao, Y.-D.; Paudel, R.; Liu, J.; Ma, C.; Zhang, Z.-S.; Zhou, S.-K. MRI contrast agents: Classification and application. Int. J. Mol. Med. 2016, 38, 1319–1326.
  104. Barwick, T.; Bencherif, B.; Mountz, J.M.; Avril, N. Molecular PET and PET/CT imaging of tumour cell proliferation using F-18 fluoro-L-thymidine: A comprehensive evaluation. Nucl. Med. Commun. 2009, 30, 908–917.
  105. Klibanov, A.L. Ligand-carrying gas-filled microbubbles: Ultrasound contrast agents for targeted molecular imaging. Bioconjugate Chem. 2005, 16, 9–17.
  106. Bu, L.; Shen, B.; Cheng, Z. Fluorescent imaging of cancerous tissues for targeted surgery. Adv. Drug Deliv. Rev. 2014, 76, 21–38.
  107. Ring, A.; Smith, I.E.; Dowsett, M. Circulating tumour cells in breast cancer. Lancet Oncol. 2004, 5, 79–88.
  108. Cristofanilli, M.; Budd, G.; Ellis, M.; Stopeck, A.; Matera, J.; Miller, M.; Doyle, G.; Allard, W.; Terstappen, L.; Hayes, D. Presence of circulating tumor cells (CTC) in metastatic breast cancer (MBC) predicts rapid progression and poor prognosis. J. Clin. Oncol. 2005, 23, 524.
  109. Galon, J.; Costes, A.; Sanchez-Cabo, F.; Kirilovsky, A.; Mlecnik, B.; Lagorce-Pagès, C.; Tosolini, M.; Camus, M.; Berger, A.; Wind, P. Type, density, and location of immune cells within human colorectal tumors predict clinical outcome. Science 2006, 313, 1960–1964.
  110. Halama, N.; Braun, M.; Kahlert, C.; Spille, A.; Quack, C.; Rahbari, N.; Koch, M.; Weitz, J.; Kloor, M.; Zoernig, I. Natural killer cells are scarce in colorectal carcinoma tissue despite high levels of chemokines and cytokines. Clin. Cancer Res. 2011, 17, 678–689.
  111. Mlecnik, B.; Bindea, G.; Angell, H.K.; Maby, P.; Angelova, M.; Tougeron, D.; Church, S.E.; Lafontaine, L.; Fischer, M.; Fredriksen, T. Integrative analyses of colorectal cancer show immunoscore is a stronger predictor of patient survival than microsatellite instability. Immunity 2016, 44, 698–711.
  112. Fridman, W.H.; Zitvogel, L.; Sautès–Fridman, C.; Kroemer, G. The immune contexture in cancer prognosis and treatment. Nat. Rev. Clin. Oncol. 2017, 14, 717–734.
  113. Kather, J.N.; Poleszczuk, J.; Suarez-Carmona, M.; Krisam, J.; Charoentong, P.; Valous, N.A.; Weis, C.-A.; Tavernar, L.; Leiss, F.; Herpel, E. In silico modeling of immunotherapy and stroma-targeting therapies in human colorectal cancer. Cancer Res. 2017, 77, 6442–6452.
  114. Fridman, W.H.; Pagès, F.; Sautès-Fridman, C.; Galon, J. The immune contexture in human tumours: Impact on clinical outcome. Nat. Rev. Cancer 2012, 12, 298–306.
  115. Kather, J.N.; Suarez-Carmona, M.; Charoentong, P.; Weis, C.-A.; Hirsch, D.; Bankhead, P.; Horning, M.; Ferber, D.; Kel, I.; Herpel, E. Topography of cancer-associated immune cells in human solid tumors. Elife 2018, 7, e36967.
  116. Galon, J.; Mlecnik, B.; Bindea, G.; Angell, H.K.; Berger, A.; Lagorce, C.; Lugli, A.; Zlobec, I.; Hartmann, A.; Bifulco, C. Towards the introduction of the ‘Immunoscore’in the classification of malignant tumours. J. Pathol. 2014, 232, 199–209.
  117. Savas, P.; Salgado, R.; Denkert, C.; Sotiriou, C.; Darcy, P.K.; Smyth, M.J.; Loi, S. Clinical relevance of host immunity in breast cancer: From TILs to the clinic. Nat. Rev. Clin. Oncol. 2016, 13, 228–241.
  118. Lee, S.; de Boer, W.B.; Fermoyle, S.; Platten, M.; Kumarasinghe, M.P. Human epidermal growth factor receptor 2 testing in gastric carcinoma: Issues related to heterogeneity in biopsies and resections. Histopathology 2011, 59, 832–840.
  119. Haikal, A.; Borba, E.; Khaja, T.; Doolittle, G.; Schmidt, P. Nivolumab-induced new-onset seronegative rheumatoid arthritis in a patient with advanced metastatic melanoma: A case report and literature review. Avicenna J. Med. 2018, 8, 34–36.
  120. Liyanage, U.K.; Moore, T.T.; Joo, H.-G.; Tanaka, Y.; Herrmann, V.; Doherty, G.; Drebin, J.A.; Strasberg, S.M.; Eberlein, T.J.; Goedegebuure, P.S. Prevalence of regulatory T cells is increased in peripheral blood and tumor microenvironment of patients with pancreas or breast adenocarcinoma. J. Immunol. 2002, 169, 2756–2761.
  121. Siddiqui, S.A.; Frigola, X.; Bonne-Annee, S.; Mercader, M.; Kuntz, S.M.; Krambeck, A.E.; Sengupta, S.; Dong, H.; Cheville, J.C.; Lohse, C.M. Tumor-infiltrating Foxp3− CD4+ CD25+ T cells predict poor survival in renal cell carcinoma. Clin. Cancer Res. 2007, 13, 2075–2081.
  122. Woo, E.Y.; Yeh, H.; Chu, C.S.; Schlienger, K.; Carroll, R.G.; Riley, J.L.; Kaiser, L.R.; June, C.H. Cutting edge: Regulatory T cells from lung cancer patients directly inhibit autologous T cell proliferation. J. Immunol. 2002, 168, 4272–4276.
  123. Curiel, T.J.; Coukos, G.; Zou, L.; Alvarez, X.; Cheng, P.; Mottram, P.; Evdemon-Hogan, M.; Conejo-Garcia, J.R.; Zhang, L.; Burow, M. Specific recruitment of regulatory T cells in ovarian carcinoma fosters immune privilege and predicts reduced survival. Nat. Med. 2004, 10, 942–949.
  124. Olkhanud, P.B.; Baatar, D.; Bodogai, M.; Hakim, F.; Gress, R.; Anderson, R.L.; Deng, J.; Xu, M.; Briest, S.; Biragyn, A. Breast cancer lung metastasis requires expression of chemokine receptor CCR4 and regulatory T cells. Cancer Res. 2009, 69, 5996–6004.
  125. Xue, D.; Xia, T.; Wang, J.; Chong, M.; Wang, S.; Zhang, C. Role of regulatory T cells and CD8+ T lymphocytes in the dissemination of circulating tumor cells in primary invasive breast cancer. Oncol. Lett. 2018, 16, 3045–3053.
  126. Clarke, M.F.; Dick, J.E.; Dirks, P.B.; Eaves, C.J.; Jamieson, C.H.; Jones, D.L.; Visvader, J.; Weissman, I.L.; Wahl, G.M. Cancer stem cells—Perspectives on current status and future directions: AACR Workshop on cancer stem cells. Cancer Res. 2006, 66, 9339–9344.
  127. Clarke, M.F.; Fuller, M. Stem cells and cancer: Two faces of eve. Cell 2006, 124, 1111–1115.
  128. Takaishi, S.; Okumura, T.; Wang, T.C. Gastric cancer stem cells. J. Clin. Oncol. Off. J. Am. Soc. Clin. Oncol. 2008, 26, 2876.
  129. Xin, H.-W.; Hari, D.M.; Mullinax, J.E.; Ambe, C.M.; Koizumi, T.; Ray, S.; Anderson, A.J.; Wiegand, G.W.; Garfield, S.H.; Thorgeirsson, S.S. Tumor-initiating label-retaining cancer cells in human gastrointestinal cancers undergo asymmetric cell division. Stem Cells 2012, 30, 591–598.
  130. Marquardt, J.U.; Thorgeirsson, S.S. Stem cells in hepatocarcinogenesis: Evidence from genomic data. Semin. Liver Dis. 2010, 30, 26–34.
  131. Langan, R.C.; Mullinax, J.E.; Ray, S.; Raiji, M.T.; Schaub, N.; Xin, H.-W.; Koizumi, T.; Steinberg, S.M.; Anderson, A.; Wiegand, G. A pilot study assessing the potential role of non-CD133 colorectal cancer stem cells as biomarkers. J. Cancer 2012, 3, 231.
  132. Papailiou, J.; Bramis, K.J.; Gazouli, M.; Theodoropoulos, G. Stem cells in colon cancer. A new era in cancer theory begins. Int. J. Color. Dis. 2011, 26, 1–11.
  133. Lim, S.-C.; Oh, S.-H. The role of CD24 in various human epithelial neoplasias. Pathol.-Res. Pract. 2005, 201, 479–486.
  134. Weichert, W.; Denkert, C.; Burkhardt, M.; Gansukh, T.; Bellach, J.; Altevogt, P.; Dietel, M.; Kristiansen, G. Cytoplasmic CD24 expression in colorectal cancer independently correlates with shortened patient survival. Clin. Cancer Res. 2005, 11, 6574–6581.
  135. Akamine, T.; Tagawa, T.; Ijichi, K.; Toyokawa, G.; Takamori, S.; Hirai, F.; Okamoto, T.; Oda, Y.; Maehara, Y. The significance of CD44 variant 9 in resected lung adenocarcinoma: Correlation with pathological early-stage and EGFR mutation. Ann. Surg. Oncol. 2019, 26, 1544–1551.
  136. Lau, W.M.; Teng, E.; Chong, H.S.; Lopez, K.A.P.; Tay, A.Y.L.; Salto-Tellez, M.; Shabbir, A.; So, J.B.Y.; Chan, S.L. CD44v8-10 is a cancer-specific marker for gastric cancer stem cells. Cancer Res. 2014, 74, 2630–2641.
  137. Shimi, T.; Pfleghaar, K.; Kojima, S.-i.; Pack, C.-G.; Solovei, I.; Goldman, A.E.; Adam, S.A.; Shumaker, D.K.; Kinjo, M.; Cremer, T. The A-and B-type nuclear lamin networks: Microdomains involved in chromatin organization and transcription. Genes Dev. 2008, 22, 3409–3421.
  138. Burke, B.; Stewart, C.L. The nuclear lamins: Flexibility in function. Nat. Rev. Mol. Cell Biol. 2013, 14, 13–24.
  139. Meng, E.; Mitra, A.; Tripathi, K.; Finan, M.A.; Scalici, J.; McClellan, S.; da Silva, L.M.; Reed, E.; Shevde, L.A.; Palle, K. ALDH1A1 maintains ovarian cancer stem cell-like properties by altered regulation of cell cycle checkpoint and DNA repair network signaling. PLoS ONE 2014, 9, e107142.
  140. Tripathi, K.; Mani, C.; Barnett, R.; Nalluri, S.; Bachaboina, L.; Rocconi, R.P.; Athar, M.; Owen, L.B.; Palle, K. Gli1 protein regulates the S-phase checkpoint in tumor cells via Bid protein, and its inhibition sensitizes to DNA topoisomerase 1 inhibitors. J. Biol. Chem. 2014, 289, 31513–31525.
  141. Dings, R.P.; Miller, M.C.; Griffin, R.J.; Mayo, K.H. Galectins as molecular targets for therapeutic intervention. Int. J. Mol. Sci. 2018, 19, 905.
  142. Zhang, H.; Liang, X.; Duan, C.; Liu, C.; Zhao, Z. Galectin-3 as a marker and potential therapeutic target in breast cancer. PLoS ONE 2014, 9, e103482.
  143. Koo, J.S.; Jung, W. Clinicopathlogic and immunohistochemical characteristics of triple negative invasive lobular carcinoma. Yonsei Med. J. 2011, 52, 89–97.
  144. Cindolo, L.; Benvenuto, G.; Salvatore, P.; Pero, R.; Salvatore, G.; Mirone, V.; Prezioso, D.; Altieri, V.; Bruni, C.B.; Chiariotti, L. Galectin-1 and galectin-3 expression in human bladder transitional-cell carcinomas. Int. J. Cancer 1999, 84, 39–43.
  145. Gillenwater, A.; Xu, X.C.; El-Naggar, A.K.; Clayman, G.L.; Lotan, R. Expression of galectins in head and neck squamous cell carcinoma. Head Neck J. Sci. Spec. Head Neck 1996, 18, 422–432.
  146. Kuo, P.-L.; Hung, J.-Y.; Huang, S.-K.; Chou, S.-H.; Cheng, D.-E.; Jong, Y.-J.; Hung, C.-H.; Yang, C.-J.; Tsai, Y.-M.; Hsu, Y.-L. Lung cancer-derived galectin-1 mediates dendritic cell anergy through inhibitor of DNA binding 3/IL-10 signaling pathway. J. Immunol. 2011, 186, 1521–1530.
  147. Langbein, S.; Brade, J.; Badawi, J.K.; Hatzinger, M.; Kaltner, H.; Lensch, M.; Specht, K.; André, S.; Brinck, U.; Alken, P. Gene-expression signature of adhesion/growth-regulatory tissue lectins (galectins) in transitional cell cancer and its prognostic relevance. Histopathology 2007, 51, 681–690.
  148. Szöke, T.; Kayser, K.; Baumhäkel, J.-D.; Trojan, I.; Furak, J.; Tiszlavicz, L.; Horvath, A.; Szluha, K.; Gabius, H.-J.; Andre, S. Prognostic significance of endogenous adhesion/growth-regulatory lectins in lung cancer. Oncology 2005, 69, 167–174.
  149. Tang, C.-E.; Tan, T.; Li, C.; Chen, Z.-C.; Ruan, L.; Wang, H.-H.; Su, T.; Zhang, P.-F.; Xiao, Z.-Q. Identification of Galectin-1 as a novel biomarker in nasopharyngeal carcinoma by proteomic analysis. Oncol. Rep. 2010, 24, 495–500.
  150. von Klot, C.-A.; Kramer, M.W.; Peters, I.; Hennenlotter, J.; Abbas, M.; Scherer, R.; Herrmann, T.R.; Stenzl, A.; Kuczyk, M.A.; Serth, J. Galectin-1 and Galectin-3 mRNA expression in renal cell carcinoma. BMC Clin. Pathol. 2014, 14, 15.
  151. Chiariotti, L.; Berlingieri, M.T.; Battaglia, C.; Benvenuto, G.; Martelli, M.L.; Salvatore, P.; Chiappettaxy, G.; Bruni, C.B.; Fusco, A. Expression of galectin-1 in normal human thyroid gland and in differentiated and poorly differentiated thyroid tumors. Int. J. Cancer 1995, 64, 171–175.
  152. Torres-Cabala, C.; Bibbo, M.; Panizo-Santos, A.; Barazi, H.; Krutzsch, H.; Roberts, D.D.; Merino, M.J. Proteomic identification of new biomarkers and application in thyroid cytology. Acta Cytol. 2006, 50, 518–528.
  153. Xu, X.-C.; El-Naggar, A.K.; Lotan, R. Differential expression of galectin-1 and galectin-3 in thyroid tumors. Potential diagnostic implications. Am. J. Pathol. 1995, 147, 815.
  154. Čada, Z.; Smetana Jr, K.; Lacina, L.; Plzáková, Z.; Štork, J.; Kaltner, H.; Russwurm, R.; Lensch, M.; André, S.; Gabius, H. Immunohistochemical Fingerprinting of the Network of Seven Adhe sion/Growth-Regulatory Lectins in Human Skin and De-tection of Distinct Tumour-Associated Alterations. Folia Biol. 2009, 55, 145–152.
  155. Mathieu, V.; De Lassalle, E.M.; Toelen, J.; Mohr, T.; Bellahcene, A.; Van Goietsenoven, G.; Verschuere, T.; Bouzin, C.; Debyser, Z.; De Vleeschouwer, S. Galectin-1 in melanoma biology and related neo-angiogenesis processes. J. Investig. Dermatol. 2012, 132, 2245–2254.
  156. Choufani, G.; Nagy, N.; Saussez, S.; Marchant, H.; Bisschop, P.; Burchert, M.; Danguy, A.; Louryan, S.; Salmon, I.; Gabius, H.J. The levels of expression of galectin-1, galectin-3, and the Thomsen–Friedenreich antigen and their binding sites decrease as clinical aggressiveness increases in head and neck cancers. Cancer Interdiscip. Int. J. Am. Cancer Soc. 1999, 86, 2353–2363.
  157. Makino, K.; Kawamura, K.; Sato, W.; Kawamura, N.; Fujimoto, T.; Terada, Y. Inhibition of uterine sarcoma cell growth through suppression of endogenous tyrosine kinase B signaling. PLoS ONE 2012, 7, e41049.
  158. Chung, H.; Kim, B.; Jung, S.-H.; Won, K.-J.; Jiang, X.; Lee, C.-K.; Lim, S.D.; Yang, S.-K.; Song, K.H.; Kim, H.S. Does phosphorylation of cofilin affect the progression of human bladder cancer? BMC Cancer 2013, 13, 45.
  159. Kim, S.-J.; Hwang, J.-A.; Ro, J.Y.; Lee, Y.-S.; Chun, K.-H. Galectin-7 is epigenetically-regulated tumor suppressor in gastric cancer. Oncotarget 2013, 4, 1461.
  160. Zhu, X.; Ding, M.; Yu, M.-L.; Feng, M.-X.; Tan, L.-J.; Zhao, F.-K. Identification of galectin-7 as a potential biomarker for esophageal squamous cell carcinoma by proteomic analysis. BMC Cancer 2010, 10, 290.
  161. Demers, M.; Rose, A.A.; Grosset, A.-A.; Biron-Pain, K.; Gaboury, L.; Siegel, P.M.; St-Pierre, Y. Overexpression of galectin-7, a myoepithelial cell marker, enhances spontaneous metastasis of breast cancer cells. Am. J. Pathol. 2010, 176, 3023–3031.
  162. Rorive, S.; Eddafali, B.; Fernandez, S.; Decaestecker, C.; André, S.; Kaltner, H.; Kuwabara, I.; Liu, F.-T.; Gabius, H.-J.; Kiss, R. Changes in galectin-7 and cytokeratin-19 expression during the progression of malignancy in thyroid tumors: Diagnostic and biological implications. Mod. Pathol. 2002, 15, 1294–1301.
  163. Cada, Z.; Chovanec, M.; Smetana Jr, K.; Betka, J.; Lacina, L.; Plzák, J.; Kodet, R.; Stork, J.; Lensch, M.; Kaltner, H. Galectin-7, will the lectinrsquos activity establish clinical correlations in head and neck squamous cell and basal cell carcinomas? Histol. Histopathol. 2009, 24, 41–48.
  164. Sakaki, M.; Oka, N.; Nakanishi, R.; Yamaguchi, K.; Fukumori, T.; Kanayama, H.-o. Serum level of galectin-3 in human bladder cancer. J. Med. Investig. 2008, 55, 127–132.
  165. Barrow, H.; Guo, X.; Wandall, H.H.; Pedersen, J.W.; Fu, B.; Zhao, Q.; Chen, C.; Rhodes, J.M.; Yu, L.-G. Serum galectin-2,-4, and-8 are greatly increased in colon and breast cancer patients and promote cancer cell adhesion to blood vascular endothelium. Clin. Cancer Res. 2011, 17, 7035–7046.
  166. Chen, C.; Duckworth, C.A.; Zhao, Q.; Pritchard, D.M.; Rhodes, J.M.; Yu, L.-G. Increased circulation of galectin-3 in cancer induces secretion of metastasis-promoting cytokines from blood vascular endothelium. Clin. Cancer Res. 2013, 19, 1693–1704.
  167. Iacovazzi, P.A.; Notarnicola, M.; Caruso, M.G.; Guerra, V.; Frisullo, S.; Altomare, D.F.; Correale, M. Serum levels of galectin-3 and its ligand 90k/mac-2bp in colorectal cancer patients. Immunopharmacol. Immunotoxicol. 2010, 32, 160–164.
  168. Watanabe, M.; Takemasa, I.; Kaneko, N.; Yokoyama, Y.; Matsuo, E.-I.; Iwasa, S.; Mori, M.; Matsuura, N.; Monden, M.; Nishimura, O. Clinical significance of circulating galectins as colorectal cancer markers. Oncol. Rep. 2011, 25, 1217–1226.
  169. Köbel, M.; Kalloger, S.E.; Boyd, N.; McKinney, S.; Mehl, E.; Palmer, C.; Leung, S.; Bowen, N.J.; Ionescu, D.N.; Rajput, A. Ovarian carcinoma subtypes are different diseases: Implications for biomarker studies. PLoS Med. 2008, 5, e232.
  170. De Caceres, I.I.; Battagli, C.; Esteller, M.; Herman, J.G.; Dulaimi, E.; Edelson, M.I.; Bergman, C.; Ehya, H.; Eisenberg, B.L.; Cairns, P. Tumor cell-specific BRCA1 and RASSF1A hypermethylation in serum, plasma, and peritoneal fluid from ovarian cancer patients. Cancer Res. 2004, 64, 6476–6481.
  171. Chen, C.J.; Wang, L.Y.; Yu, M.W. Epidemiology of hepatitis B virus infection in the Asia–Pacific region. J. Gastroenterol. Hepatol. 2000, 15, E3–E6.
  172. Kirk, G.D.; Bah, E.; Montesano, R. Molecular epidemiology of human liver cancer: Insights into etiology, pathogenesis and prevention from The Gambia, West Africa. Carcinogenesis 2006, 27, 2070–2082.
  173. Shukla, S.; Bharti, A.C.; Mahata, S.; Hussain, S.; Kumar, R.; Hedau, S.; Das, B.C. Infection of human papillomaviruses in cancers of different human organ sites. Indian J. Med. Res. 2009, 130, 222–233.
  174. Kreimer, A.R.; Clifford, G.M.; Snijders, P.J.; Castellsagué, X.; Meijer, C.J.; Pawlita, M.; Viscidi, R.; Herrero, R.; Franceschi, S. HPV16 semiquantitative viral load and serologic biomarkers in oral and oropharyngeal squamous cell carcinomas. Int. J. Cancer 2005, 115, 329–332.
  175. Farooqi, A.A.; Desai, N.N.; Qureshi, M.Z.; Librelotto, D.R.N.; Gasparri, M.L.; Bishayee, A.; Nabavi, S.M.; Curti, V.; Daglia, M. Exosome biogenesis, bioactivities and functions as new delivery systems of natural compounds. Biotechnol. Adv. 2018, 36, 328–334.
  176. Kowal, J.; Tkach, M.; Théry, C. Biogenesis and secretion of exosomes. Curr. Opin. Cell Biol. 2014, 29, 116–125.
  177. Raposo, G.; Stoorvogel, W. Extracellular vesicles: Exosomes, microvesicles, and friends. J. Cell Biol. 2013, 200, 373–383.
  178. Ha, D.; Yang, N.; Nadithe, V. Exosomes as therapeutic drug carriers and delivery vehicles across biological membranes: Current perspectives and future challenges. Acta Pharm. Sin. B 2016, 6, 287–296.
  179. Masaoutis, C.; Mihailidou, C.; Tsourouflis, G.; Theocharis, S. Exosomes in lung cancer diagnosis and treatment. From the translating research into future clinical practice. Biochimie 2018, 151, 27–36.
  180. Whiteside, T.L. Tumor-derived exosomes and their role in cancer progression. Adv. Clin. Chem. 2016, 74, 103–141.
  181. Suchorska, W.M.; Lach, M.S. The role of exosomes in tumor progression and metastasis. Oncol. Rep. 2016, 35, 1237–1244.
  182. Kimura, H.; Yamamoto, H.; Harada, T.; Fumoto, K.; Osugi, Y.; Sada, R.; Maehara, N.; Hikita, H.; Mori, S.; Eguchi, H. CKAP4, a DKK1 receptor, is a biomarker in exosomes derived from pancreatic cancer and a molecular target for therapy. Clin. Cancer Res. 2019, 25, 1936–1947.
  183. Schwarzenbach, H.; Gahan, P.B. Predictive value of exosomes and their cargo in drug response/resistance of breast cancer patients. Cancer Drug Resist. 2020, 3, 63.
  184. Sohn, W.; Kim, J.; Kang, S.H.; Yang, S.R.; Cho, J.-Y.; Cho, H.C.; Shim, S.G.; Paik, Y.-H. Serum exosomal microRNAs as novel biomarkers for hepatocellular carcinoma. Exp. Mol. Med. 2015, 47, e184.
  185. Huang, X.; Yuan, T.; Liang, M.; Du, M.; Xia, S.; Dittmar, R.; Wang, D.; See, W.; Costello, B.A.; Quevedo, F. Exosomal miR-1290 and miR-375 as prognostic markers in castration-resistant prostate cancer. Eur. Urol. 2015, 67, 33–41.
  186. Pessoa, L.S.; Heringer, M.; Ferrer, V.P. ctDNA as a cancer biomarker: A broad overview. Crit. Rev. Oncol./Hematol. 2020, 155, 103109.
More
© Text is available under the terms and conditions of the Creative Commons Attribution (CC BY) license; additional terms may apply. By using this site, you agree to the Terms and Conditions and Privacy Policy.
Upload a video for this entry
Information
Subjects: Biochemistry & Molecular Biology
Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register :
Sreyashi Das
,
Mohan Kumar Dey
,
Ram Devireddy
,
Manas Ranjan Gartia
View Times: 174
Revisions: 2 times (View History)
Update Date: 19 Jan 2024
Table of Contents
    1000/1000
    Hot Most Recent
    Video Production Service
    Feedback