Early Diagnosis of Lung Cancer: Gaps in Biomarkers Discovery

Version	Summary	Created by	Modification	Content Size	Created at	Operation
1		Angela Sabalic	--	2582	2023-12-11 10:39:19	\|
2	The description and content: was modifyed as suggested by Editorial Office	Angela Sabalic	-25 word(s)	2557	2023-12-11 11:38:36	\| \|
3	format correct	Catherine Yang	Meta information modification	2557	2023-12-12 02:42:21	\|

This entry is adapted from the peer-reviewed paper 10.3390/jcm12237244

Lung cancer is the leading cause of global cancer-related deaths. The main issue is lacking an effective screening test in clinical practice. Noninvasive biomarkers are urgently needed. Although low-dose computed tomography (LD-CT) shows a 20% reduction in lung cancer mortality, its cost, radiation, and false-positive rate limit its clinical suitability. Much research has focused on biological fluid biomarkers, but none have transitioned from lab to practice. Future research will be needed to introduce biomarkers into clinical practice.

early diagnosis lung cancer biomarkers body fluids

1. Introduction

Lung cancer is an aggressive neoplasm and is the leading cause of cancer-related deaths worldwide, with an estimated 1.8 million deaths ^[1]. The five-year survival rate is associated with the stage of the disease—67% for stage I and 23% for stage III—and the mortality is also strongly associated with late diagnosis ^[2]. This scenario is aggravated by the absence of a noninvasive screening test, for example, mammography and the fecal occult blood test currently in use for other aggressive neoplasms such as breast cancer and colorectal cancer (survival rate 60–80% respectively). Although low-dose computed tomography (LDCT) has shown a 20% reduction in mortality ^[3], its application remains limited to the high-risk population (heavy smokers aged 50–80 years), excluding the growing number of young individuals (<50 years) diagnosed with advanced-stage lung cancer ^[4]^[5]. Furthermore, the prevalence of false positives leading to unnecessary invasive diagnostic procedures, coupled with the high costs of the methodology, renders it unsuitable for integration into screening initiatives in low-income developing countries ^[6]. Concerning clinical practice, there is a pressing need for an alternative solution to address the key questions such as noninvasiveness and test reliability while favoring easily obtainable biological samples that can be analyzed with cost-effective tools and reagents, thus making it feasible for adoption even in less industrialized countries. According to the National Institute of Health (NIH), a biomarker is defined as “a characteristic used to measure and evaluate objectively normal biological processes, pathogenic processes, or pharmacological responses to a therapeutic intervention” ^[7]. In this regard, during the last decade, a considerable number of research studies have focused on the investigation of new technologies for the identification of biomarkers that should be suitable for mass screening, tackling the complexity of the biological and histological heterogeneity of lung cancer. Several biological molecules such as proteins, microRNAs (miRNAs), circulants tumor cells (CTCs), tumor DNA (ctDNA), and volatile organic compounds (VOCs) have been investigated to understand their predictive value. Another key point of early detection is the issue of sample choice. Body fluids such as blood (serum and plasma), urine, stools, exhaled breath, sputum, and saliva meet clinical needs because of their simplicity of collection and noninvasiveness ^[8]^[9]. Was carried out a comprehensive analysis of the reviews available in the literature , published in the last year using the keywords “ Lung Cancer” AND “early diagnosis” AND “biomarkers”. Following the removal of duplicate and further screening six reviews were selected for the final text. The ultimate goal was to identify which studies are currently in the validation phase and which biomarkers hold future potential as predictive elements for lung cancer.

2. Investigated biomarkers

2.1. Circulating Blood Proteins and Autoantibodies

Circulating proteins can stem from various sources, including the overexpression of cancer cells, increased secretion from diseased tissue, or inflammation linked to malignancy. The proteome has been widely studied in the oncological field to identify serum proteins as potential biomarkers for the early diagnosis of lung cancer. Among the most interesting studies conducted in the last 10 years, CancerSEEK reported a panel of eight proteins (CA-125, CEA, HGF, Myeloperoxidase, OPN, Prolactin, and TIMP-1) effective in distinguishing lung cancer patients from healthy controls ^[10]. Moreover, the combination with cfDNA increases the sensitivity of this protein panel ^[10]^[11]. In another study, a panel of three proteins and one autoantibody (NY-ESO-1) were assessed, and a sensitivity of 71% and specificity of 88% were observed ^[12]. Mazzone et al. performed a separate clinical trial with the same test (PAULA), demonstrating a sensitivity and specificity of 49% and 96%, respectively ^[13]. A prospective proteomic study based on two proteins (LG3BP and C163A) integrated with clinical and imaging features showed a sensitivity of 97% and a specificity of 44% ^[14]. A more recent project involves the development of a 36-protein multiplex assay for the risk assessment of lung cancer. However, more studies should be conducted to demonstrate that these approaches are suitable to implement in clinical practice ^[9]. Cancer cells stimulate the immune system through the release of protein inducing the production of circulating autoantibodies against tumor-associated antigens (TAAs). EarlyCDT, a panel of seven autoantibodies (p53, NY-ESO-1, CAGE, GBU4-5, HuD, MAGEA4, and SOX2), is commercially available as a blood test to assess the risk of malignancy in people with solid pulmonary nodules ^[15]. A clinical trial with EarlyCDT on a symptomatic lung cancer patient showed a sensitivity of 41% and a specificity of 91%, and a follow-up study on a high-risk cohort revealed a sensitivity and a specificity of 37% and 91%, respectively ^[16]. Moreover, Qiang Du et al. tested p53, PGP9.5, SOX2, GAGE7, GBU4-5, MAGEA1, and CAGE and found no statistically significant difference between stages I/II and III/IV, concluding that the test is capable of detecting both early and advanced stages. This phenomenon could be related to the amplification of the immune system. Further studies will be needed to understand the potential prognostic power of proteins and TAAs ^[17]^[18]. Their stability in the serum allows them to be detected via immunoenzymatic assays (ELISAs) and makes TAAs possible biomarkers for the early diagnosis of lung cancer ^[19].

2.2. microRNA (miRNAs)

MiRNAs are small noncoding RNAs that are involved as regulators of gene expression at the post-transcriptional level. They can be aberrantly expressed in many pathological processes as well as in cancer. MiRNAs can be detected in different body fluids such as urine, sputum, and blood (serum and plasma) ^[20]. In 2002, Calin et al. reported the involvement of microRNAs in lung cancer pathogenesis ^[21]. They preserve their stability from initial development to metastasis formation, making them appealing biomarkers for the diagnosis and prognosis of lung cancer ^[8]^[9]^[20]. An early study conducted on lung tissue detected 12 miRNAs expressed differently between lung cancer tissue and benign lung tissue ^[22]. In addition, studies on miRNAs in sputum have shown that the combination of multiple miRNAs can differentiate lung cancer patients from healthy individuals with a sensitivity of 73% to 80% and a specificity of 91% to 96% ^[23]. Two further studies have compared different miRNA panels in lung cancer patients before and after lung cancer resection and in healthy controls. Le HB et al. showed an increased expression in the serum of miR-21, miR-205, miR-30d, and miR-24 before lung cancer surgery. The same miRNA was upregulated in the serum of early-stage lung cancer patients in comparison to healthy subjects, suggesting their role as a screening biomarker as well as for postoperative disease relapse ^[24]. Moreover, an 18-month postsurgery follow-up conducted by Leidinger et al. demonstrated a significant reduction in the expression levels of miRNA over time after the surgery ^[25]. Currently, the miR-Test ^[26] and MSC (microRNA signature classifier) ^[27] are undergoing validation. The serum signature of miRNA identified in high-risk subjects enrolling in a screening program with LDCT showed a sensitivity and specificity of 77.8% and 74.8%, respectively ^[26]. Sozzi et.al. based on 24 miRNA expression ratios stratified the population into low, inter-mediate, or high risk of lung cancer ^[28]. Their study revealed 87% sensitivity and 81% specificity. Both studies exhibited a reduction in the LDCT false-positive rate ^[26]^[27]^[28].

2.3. Circulating Tumor Cells (CTCs) and Circulating Tumor DNA (ctDNA)

CTCs are derived from the primary tumor mass. During this process, the cells detached from the tumor mass enter the circulatory stream. CTCs were evaluated in a group of 168 patients with chronic obstructive pulmonary disease (COPD) followed with annual CT scans for 4 years. It was found that COPD patients who tested positive for CTCs in the annual CT screening developed lung nodules 1–4 years later. These studies suggest that CTCs could be used for early diagnosis ^[29]^[30]. Another study showed that the sensitivity and specificity of CTCs for diagnosing lung cancer were 73.2% and 84.1%, respectively ^[31], while Wang et al. obtained a sensitivity of 77.7% and a specificity of 89.5%. The comparison between the sensitivity of stage I and stage II revealed that the two values almost overlapped (69.8% and 72.2%) ^[32]. A study of a larger lung cancer patient cohort demonstrated sensitivity and specificity values similar to other studies, but with the combination of CEA and additional biomolecules, these values could be increased to 84.21% and 88.78%, respectively ^[31]. Emerging research with negative enrichment fluorescence in situ hybridization methods or the FISH approach demonstrated that the sensitivity and specificity were increased (89–100%) ^[30]^[32]^[33].

ctDNA is a part of cell-free DNA derived from tumor cells. The concentration of ctDNA in plasma varies from 0.01% to 90% ^[34]. Newman et al. observed a 100% rate of ctDNA in patients with stage II-IV lung cancer, while a 50% rate was observed in early-stage patients ^[35]. The combination with protein showed a specificity of 99% and a sensitivity of 59% ^[10]^[11]. Using deep sequencing (CAPP-seq), Chabon et al. investigated cancer profiling to analyze the ctDNA. This approach demonstrated that ctDNA levels were low in early-stage lung cancer. The same research group developed and validated a machine learning method (Lung-CLiP) using the findings described above in conjunction with other molecular features, and a specificity of 96% was achieved ^[36]. Phomaryova demonstrated that in lung cancer patients, the concentration of ctDNA is eight times higher than that in healthy individuals ^[37]. Furthermore, studies report that high concentrations of circulating ctDNA are correlated with a worse clinical outcome ^[33]. However, ctDNA has demonstrated poor sensitivity, and most patients have levels of less than 0.1%, which is challenging to detect in the blood ^[9].

2.4. Future Directions and Challenges: Volatile Organic Compounds (VOCs)

Since the 1970s, volatile organic compounds have been used in the field of medicine ^[38]. Lung cancer studies emphasize the presence of VOCs in exhaled breath ^[39]. The most widely used approach for the analysis of respiratory VOCs is gas chromatography combined with mass spectrometry (GC/MS) ^[40]. This method has shown a discriminatory power to detect the specific volatile compounds of lung cancer patients. In one study, GC/MS combined with artificial neural networks showed a sensitivity of 80% and specificity of 91% ^[41]. In a prospective pilot study, Peled et al. demonstrated the potential of breath analysis to distinguish malignant nodules from benign nodules in high-risk subjects ^[42]. Another promising measurement device in the field of early diagnosis is the electronic nose (e-nose). This emergent technology is based on the binding of VOCs to different sensors or sensor arrays within handheld devices. The investigators analyzed 214 breath samples using an e-nose with 11 gas sensors. The experimental results revealed an accuracy of 95.75%, a sensitivity of 94.78%, and a specificity of 96.96% ^[43]. Shlomi D et al. compared patients with benign lung nodules and patients with lung cancer. Moreover, the lung cancer group was divided into two subgroups: patients who harbored the EGFR mutation and lung cancer patients with wild-type EGFR. This study showed the discriminatory power to distinguish the early LC from benign nodules and had 87% accuracy ^[44]. Two other studies used an e-nose to detect a specific lung cancer signature (in lung cancer patients vs. high-risk healthy controls) with a sensitivity of 81% and specificity of 91% ^[45]. Moreover, Gasparri et al. demonstrated that an e-nose with 12 sensors has a greater sensitivity to lung cancer at stage I with respect to stage II/III/IV (92% and 58%, respectively) ^[46]. Additionally, a recent multicentric case–control study yielded a sensitivity of 95% and a specificity of 49% ^[47].

So far, more than 100 volatile urinary biomarkers have been suggested as being related to cancer. Urinary VOC patterns in cancer patients are often different from those found in the urine samples of control subjects, and these differences also depend on cancer type and stage ^[48]. In 2023, investigators isolated for the first time five specific VOCs of early-stage lung cancer (I/II) with a specificity and sensitivity of 85% and 90%, respectively ^[49]. Results with greater robustness are warranted before these may be fully integrated into workflows or incorporated into clinical guidelines.

All suitable biomarkers are shown in Table 1.

Table 1. Selected lung cancer biomarkers.

Study	Population	Method	Biomarkers	Main Results
Xu BJ ^[10]	40 LC 8 HR	MALDI-MS	Proteins	75% accuracy
Doseeva V ^[12]	75 LC 75 HR	IMMUNOASSAY xMAP	Proteins and autoantibody	77% sensitivity 80% specificity
Mazzone PJ ^[13]	155 LC 245 HR	IMMUNOASSAY MAGPIX	Proteins and autoantibody	74% sensitivity 80% specificity
Silvestri GA ^[14]	29 LC 149 HR	MS	Proteins	97% sensitivity 44% specificity
Chapman CJ ^[16]	235 LC 266 HR	ELISA	Autoantibodies	92 % accuracy
Du Q ^[17]	305 LC 74 HR	ELISA	Autoantibodies	56.53% sensitivity 91.60% specificity
Yu L ^[23]	64 LC 58 HR	qRT-PCR	miRNA	80.6% sensitivity 91.7% specificity
Montani F ^[26]	74 LC 115 HR	NA	miRNA	77.8% sensitivity 74.8% specificity
Sozzi G ^[28]	69 LC 870 HR	PCR	miRNA	87% sensitivity 81% specificity
Yu Y ^[31]	153 LC 93 H	RT-PCR + FISH	CTCs	67.2% sensitivity for stage I 84.1% specificity
Katz RL ^[32]	107 LC 100 H	FISH	CTCs	89% sensitivity 100% specificity
Newman AM ^[35]	13LC 13 H	CAPP-Seq	ctDNA	96% specificity
Ponomaryova AA ^[37]	60 LC 32 H	TaqMan PCR (MSP)	cirDNA	87% sensitivity 75% specificity
Rudnicka J ^[41]	86 LC 41 H	GC/MS	VOCs	80% sensitivity 91.23% specificity
Shlomi D ^[44]	89 LC 30 H	eNOSE	VOCS	83% accuracy 79% sensitivity 85% specificity
McWilliams A ^[45]	25 LC 166 H	eNOSE	VOCs	80% accuracy
Gasparri R ^[46]	70 LC 76 H	eNOSE	VOCs	81% sensitivity 91% specificity
Hanai Y ^[48]	20 LC 20 H	GC/MS	VOCs	95% sensitivity 70–100% specificity
Gasparri R ^[49]	46 LC 81 H	GC/MS	VOCs	85% sensitivity 90% specificity

LC = lung cancer patients; H = healthy subjects.

3. Discussion and Future Perspective

The early diagnosis of lung cancer ranks among the most crucial health issues. The five-year survival is strongly correlated with stage (90% stage I vs. 10% stage IV) ^[50]. Considerable advances have been made in metastatic lung cancer diagnosis and treatment by finding numerous disease subtypes defined by specific oncogenic driver mutations (EGFR, ALK, ROS1, BRAF, HER2, MET, RET or KRASG12C, and PD-L1). This has led to the development of a range of molecularly targeted therapies, which have exerted a significant impact on patient survival rates ^[51]. By contrast, although numerous studies have been conducted to search for useful biomarkers for early diagnosis, none of the investigated molecules have been incorporated into clinical practice. Currently, in a clinical setting, serum tumor markers are increasingly being used as a supplement to radiological examinations (CT and PET) for therapy monitoring and disease recurrence.

All biomarkers examined in the results section exhibited the capability to differentiate between individuals with lung cancer and healthy subjects or those at high risk. However, for a biomarker to be integrated into clinical practice, it must possess high reliability, specificity, and sensitivity. Unfortunately, achieving a simultaneous presence of these three characteristics is challenging, affecting the clinical performance of these biomarkers. On the contrary, other biomarkers are specific, sensitive, and reliable but necessitate highly expensive and complex analytical techniques^[34]^[35]^[36].

In the past decade, there has been a growing interest in researching Volatile Organic Compounds (VOCs).^[39]The emergence of novel technologies utilizing sensors facilitates the swift and straightforward detection of specific lung cancer signature. Moreover, when combined with traditional analytical methods like GC/MS, it allows for a qualitative analysis of VOCs.^[43]^[44]^[45]^[46]^[47]^[48] Nonetheless, further studies are imperative to standardize the analytical methodology and identify which VOCs genuinely classify early-stage lung cancer, thereby minimizing biases related to inter- and intra-subjective diversity.

Future research endeavors should be directed at overcoming significant existing limitations, such as small sample sizes, population stratification, absence of follow-up, and the need for standardized methodologies. Furthermore, the development and deployment of artificial intelligence and machine learning tools can be used to process, overlay, and integrate molecular biomarkers with clinical and epidemiological data.

In summary, despite the evident potential of the biomarkers in distinguishing lung cancer patients from healthy individuals, no biomarkers are currently in clinical practice for the early diagnosis of lung cancer. Future research should prioritize population stratification, the utilization of standardized and reproducible methodologies, and the implementation of long-term follow-ups particularly for the high-risk population. Collaborative efforts among multiple researchers could prove pivotal in advancing this field.

References

Newman, A.M.; Bratman, S.V.; To, J.; Wynne, J.F.; Eclov, N.C.W.; Modlin, L.A.; Liu, C.L.; Neal, J.W.; Wakelee, H.A.; Merritt, R.E.; et al. An ultrasensitive method for quantitating circulating tumor DNA with broad patient coverage. Nat. Med. 2014, 20, 548–554.
Chabon, J.J.; Hamilton, E.G.; Kurtz, D.M.; Esfahani, M.S.; Moding, E.J.; Stehr, H.; Schroers-Martin, J.; Nabet, B.Y.; Chen, B.; Chaudhuri, A.A.; et al. Integrating genomic features for non-invasive early lung cancer detection. Nature 2020, 580, 245–251.
Ponomaryova, A.A.; Rykova, E.Y.; Cherdyntseva, N.V.; Skvortsova, T.E.; Dobrodeev, A.Y.; Zav’yalov, A.A.; Bryzgalov, L.O.; Tuzikov, S.A.; Vlassov, V.V.; Laktionov, P.P. Potentialities of aberrantly methylated circulating DNA for diagnostics and post-treatment follow-up of lung cancer patients. Lung Cancer 2013, 81, 397–403.
Chen, X.; Muhammad, K.G.; Madeeha, C.; Fu, W.; Xu, L.; Hu, Y.; Liu, J.; Ying, K.; Chen, L.; Yurievna, G.O. Calculated indices of volatile organic compounds (VOCs) in exhalation for lung cancer screening and early detection. Lung Cancer 2021, 154, 197–205.
Shlomi, D.; Abud, M.; Liran, O.; Bar, J.; Gai-Mor, N.; Ilouze, M.; Onn, A.; Ben-Nun, A.; Haick, H.; Peled, N. Detection of Lung Cancer and EGFR Mutation by Electronic Nose System. J. Thorac. Oncol. 2017, 12, 1544–1551.
McWilliams, A.; Beigi, P.; Srinidhi, A.; Lam, S.; MacAulay, C.E. Sex and Smoking Status Effects on the Early Detection of Early Lung Cancer in High-Risk Smokers Using an Electronic Nose. IEEE Trans. Biomed. Eng. 2015, 62, 2044–2054.
Gasparri, R.; Santonico, M.; Valentini, C.; Sedda, G.; Borri, A.; Petrella, F.; Maisonneuve, P.; Pennazza, G.; D’amico, A.; Di Natale, C.; et al. Volatile signature for the early diagnosis of lung cancer. J. Breath. Res. 2016, 10, 016007.
Kort, S.; Brusse-Keizer, M.; Schouwink, H.; Citgez, E.; de Jongh, F.H.; van Putten, J.W.; Borne, B.v.D.; Kastelijn, E.A.; Stolz, D.; Schuurbiers, M.; et al. Diagnosing Non-Small Cell Lung Cancer by Exhaled Breath Profiling Using an Electronic Nose. Chest 2022, 163, 697–706.
Gasparri, R.; Capuano, R.; Guaglio, A.; Caminiti, V.; Canini, F.; Catini, A.; Sedda, G.; Paolesse, R.; Di Natale, C.; Spaggiari, L. Volatolomic urinary profile analysis for diagnosis of the early stage of lung cancer. J. Breath. Res. 2022, 16, 046008.
Hanai, Y.; Shimono, K.; Matsumura, K.; Vachani, A.; Albelda, S.; Yamazaki, K.; Beauchamp, G.K.; Oka, H. Urinary volatile compounds as biomarkers for lung cancer. Biosci. Biotechnol. Biochem. 2012, 76, 679–684.
Silvestri, G.A.; Tanner, N.T.; Kearney, P.; Vachani, A.; Massion, P.P.; Porter, A.; Springmeyer, S.C.; Fang, K.C.; Midthun, D.; Mazzone, P.J.; et al. Assessment of Plasma Proteomics Biomarker’s Ability to Distinguish Benign From Malignant Lung Nodules: Results of the PANOPTIC (Pulmonary Nodule Plasma Proteomic Classifier) Trial. Chest 2018, 154, 491–500.
Jett, J.R.; Dyer, D.; Kern, J.; Rollins, D.; Phillips, M. Screening for lung cancer with the EarlyCDT-Lung and computed tomography. J. Thorac. Oncol. 2015, 10, S306.
Chapman, C.J.; Healey, G.F.; Murray, A.; Boyle, P.; Robertson, C.; Peek, L.J.; Allen, J.; Thorpe, A.J.; Hamilton-Fairley, G.; Parsy-Kowalska, C.B.; et al. EarlyCDT®-Lung test: Improved clinical utility through additional autoantibody assays. Tumor Biol. 2012, 33, 1319–1326.
Du, Q.; Yu, R.; Wang, H.; Yan, D.; Yuan, Q.; Ma, Y.; Slamon, D.; Hou, D.; Wang, H.; Wang, Q. Significance of tumor-associated autoantibodies in the early diagnosis of lung cancer. Clin. Respir. J. 2018, 12, 2020–2028.
Paez, R.; Kammer, M.N.; Tanner, N.T.; Shojaee, S.; Heideman, B.E.; Peikert, T.; Balbach, M.L.; Iams, W.T.; Ning, B.; Lenburg, M.E.; et al. Update on Biomarkers for the Stratification of Indeterminate Pulmonary Nodules. Chest 2023, 164, 1028–1041.
Solassol, J.; Maudelonde, T.; Mange, A.; Pujol, J.-L. Clinical relevance of autoantibody detection in lung cancer. J. Thorac. Oncol. 2011, 6, 955–962.
Marmor, H.N.; Zorn, J.T.; Deppen, S.A.; Massion, P.P.; Grogan, E.L. Biomarkers in Lung Cancer Screening: A Narrative Review. Curr. Chall. Thorac. Surg. 2023, 5, 5.
Calin, G.A.; Dumitru, C.D.; Shimizu, M.; Bichi, R.; Zupo, S.; Noch, E.; Aldler, H.; Rattan, S.; Keating, M.; Rai, K.; et al. 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.
Yanaihara, N.; Caplen, N.J.; Bowman, E.; Seike, M.; Kumamoto, K.; Yi, M.; Stephens, R.M.; Okamoto, A.; Yokota, J.; Tanaka, T.; et al. Unique microRNA molecular profiles in lung cancer diagnosis and prognosis. Cancer Cell 2006, 9, 189–198.
Yu, L.; Todd, N.W.; Xing, L.; Xie, Y.; Zhang, H.; Liu, Z.; Fang, H.; Zhang, J.; Katz, R.L.; Jiang, F. Early detection of lung adenocarcinoma in sputum by a panel of microRNA markers. Int. J. Cancer 2010, 127, 2870–2878.
Le, H.-B.; Zhu, W.-Y.; Chen, D.-D.; He, J.-Y.; Huang, Y.-Y.; Liu, X.-G.; Zhang, Y.-K. Evaluation of dynamic change of serum miR-21 and miR-24 in pre- and post-operative lung carcinoma patients. Med. Oncol. 2012, 29, 3190–3197.
Leidinger, P.; Keller, A.; Backes, C.; Huwer, H.; Meese, E. MicroRNA expression changes after lung cancer resection: A follow-up study. RNA Biol. 2012, 9, 900–910.
Montani, F.; Marzi, M.J.; Dezi, F.; Dama, E.; Carletti, R.M.; Bonizzi, G.; Bertolotti, R.; Bellomi, M.; Rampinelli, C.; Maisonneuve, P.; et al. miR-Test: A blood test for lung cancer early detection. JNCI J. Natl. Cancer Inst. 2015, 107, djv063.
Pastorino, U.; Boeri, M.; Sestini, S.; Sabia, F.; Milanese, G.; Silva, M.; Suatoni, P.; Verri, C.; Cantarutti, A.; Sverzellati, N.; et al. Baseline computed tomography screening and blood microRNA predict lung cancer risk and define adequate intervals in the BioMILD trial. Ann. Oncol. 2022, 33, 395–405.
Sozzi, G.; Boeri, M.; Rossi, M.; Verri, C.; Suatoni, P.; Bravi, F.; Roz, L.; Conte, D.; Grassi, M.; Sverzellati, N.; et al. Clinical utility of a plasma-based miRNA signature classifier within computed tomography lung cancer screening: A correlative MILD trial study. J. Clin. Oncol. 2014, 32, 768–773.
Ilie, M.; Hofman, V.; Long, E.; Selva, E.; Vignaud, J.-M.; Padovani, B.; Mouroux, J.; Marquette, C.H.; Hofman, P. “Sentinel” circulating tumor cells allow early diagnosis of lung cancer in patients with chronic obstructive pulmonary disease. PLoS ONE 2014, 9, e111597.
Chang, L.; Li, J.; Zhang, R. Liquid biopsy for early diagnosis of non-small cell lung carcinoma: Recent research and detection technologies. Biochim. Biophys. Acta Rev. Cancer 2022, 1877, 188729.
Yu, Y.; Chen, Z.; Dong, J.; Wei, P.; Hu, R.; Zhou, C.; Sun, N.; Luo, M.; Yang, W.; Yao, R.; et al. Folate receptor-positive circulating tumor cells as a novel diagnostic biomarker in non-small cell lung cancer. Transl. Oncol. 2013, 6, 697–702.
Katz, R.L.; Zaidi, T.M.; Pujara, D.; Shanbhag, N.D.; Truong, D.; Patil, S.; Mehran, R.J.; El-Zein, R.A.; Shete, S.S.; Kuban, J.D. Identification of circulating tumor cells using 4-color fluorescence in situ hybridization: Validation of a noninvasive aid for ruling out lung cancer in patients with low-dose computed tomography–detected lung nodules. Cancer Cytopathol. 2020, 128, 553–562.
Lei, Y.; Sun, N.; Zhang, G.; Liu, C.; Lu, Z.; Huang, J.; Zhang, C.; Zang, R.; Che, Y.; Mao, S.; et al. Combined detection of aneuploid circulating tumor-derived endothelial cells and circulating tumor cells may improve diagnosis of early stage non-small-cell lung cancer. Clin. Transl. Med. 2020, 10, e128.
Herath, S.; Rad, H.S.; Radfar, P.; Ladwa, R.; Warkiani, M.; O’byrne, K.; Kulasinghe, A. The Role of Circulating Biomarkers in Lung Cancer. Front. Oncol. 2022, 11, 801269.
Newman, A.M.; Bratman, S.V.; To, J.; Wynne, J.F.; Eclov, N.C.W.; Modlin, L.A.; Liu, C.L.; Neal, J.W.; Wakelee, H.A.; Merritt, R.E.; et al. An ultrasensitive method for quantitating circulating tumor DNA with broad patient coverage. Nat. Med. 2014, 20, 548–554.
Chabon, J.J.; Hamilton, E.G.; Kurtz, D.M.; Esfahani, M.S.; Moding, E.J.; Stehr, H.; Schroers-Martin, J.; Nabet, B.Y.; Chen, B.; Chaudhuri, A.A.; et al. Integrating genomic features for non-invasive early lung cancer detection. Nature 2020, 580, 245–251.
Ponomaryova, A.A.; Rykova, E.Y.; Cherdyntseva, N.V.; Skvortsova, T.E.; Dobrodeev, A.Y.; Zav’yalov, A.A.; Bryzgalov, L.O.; Tuzikov, S.A.; Vlassov, V.V.; Laktionov, P.P. Potentialities of aberrantly methylated circulating DNA for diagnostics and post-treatment follow-up of lung cancer patients. Lung Cancer 2013, 81, 397–403.
Sinues, P.M.-L.; Zenobi, R.; Kohler, M. Analysis of the exhalome: A diagnostic tool of the future. Chest 2013, 144, 746–749.
Schmidt, F.; Kohlbrenner, D.; Malesevic, S.; Huang, A.; Klein, S.D.; Puhan, M.A.; Kohler, M. Mapping the landscape of lung cancer breath analysis: A scoping review (ELCABA). Lung Cancer 2022, 175, 131–140.
Chen, X.; Muhammad, K.G.; Madeeha, C.; Fu, W.; Xu, L.; Hu, Y.; Liu, J.; Ying, K.; Chen, L.; Yurievna, G.O. Calculated indices of volatile organic compounds (VOCs) in exhalation for lung cancer screening and early detection. Lung Cancer 2021, 154, 197–205.
Rudnicka, J.; Kowalkowski, T.; Buszewski, B. Searching for selected VOCs in human breath samples as potential markers of lung cancer. Lung Cancer 2019, 135, 123–129.
Peled, N.; Hakim, M.; Bunn, P.A.; Miller, Y.E.; Kennedy, T.C.; Mattei, J.; Mitchell, J.D.; Hirsch, F.R.; Haick, H. Non-invasive breath analysis of pulmonary nodules. J. Thorac. Oncol. 2012, 7, 1528–1533.
Liu, L.; Li, W.; He, Z.; Chen, W.; Liu, H.; Chen, K.; Pi, X. Detection of lung cancer with electronic nose using a novel ensemble learning framework. J. Breath. Res. 2021, 15, 026014.
Shlomi, D.; Abud, M.; Liran, O.; Bar, J.; Gai-Mor, N.; Ilouze, M.; Onn, A.; Ben-Nun, A.; Haick, H.; Peled, N. Detection of Lung Cancer and EGFR Mutation by Electronic Nose System. J. Thorac. Oncol. 2017, 12, 1544–1551.
McWilliams, A.; Beigi, P.; Srinidhi, A.; Lam, S.; MacAulay, C.E. Sex and Smoking Status Effects on the Early Detection of Early Lung Cancer in High-Risk Smokers Using an Electronic Nose. IEEE Trans. Biomed. Eng. 2015, 62, 2044–2054.
Gasparri, R.; Santonico, M.; Valentini, C.; Sedda, G.; Borri, A.; Petrella, F.; Maisonneuve, P.; Pennazza, G.; D’amico, A.; Di Natale, C.; et al. Volatile signature for the early diagnosis of lung cancer. J. Breath. Res. 2016, 10, 016007.
Kort, S.; Brusse-Keizer, M.; Schouwink, H.; Citgez, E.; de Jongh, F.H.; van Putten, J.W.; Borne, B.v.D.; Kastelijn, E.A.; Stolz, D.; Schuurbiers, M.; et al. Diagnosing Non-Small Cell Lung Cancer by Exhaled Breath Profiling Using an Electronic Nose. Chest 2022, 163, 697–706.
Hanai, Y.; Shimono, K.; Matsumura, K.; Vachani, A.; Albelda, S.; Yamazaki, K.; Beauchamp, G.K.; Oka, H. Urinary volatile compounds as biomarkers for lung cancer. Biosci. Biotechnol. Biochem. 2012, 76, 679–684.
Ning, J.; Ge, T.; Jiang, M.; Jia, K.; Wang, L.; Li, W.; Chen, B.; Liu, Y.; Wang, H.; Zhao, S.; et al. Early diagnosis of lung cancer: Which is the optimal choice? Aging 2021, 13, 6214–6227. [Google Scholar] [CrossRef]
Otano, I.; Ucero, A.C.; Zugazagoitia, J.; Paz-Ares, L. At the crossroads of immunotherapy for oncogene-addicted subsets of NSCLC. Nat. Rev. Clin. Oncol. 2023, 20, 143–159
Hanai, Y.; Shimono, K.; Matsumura, K.; Vachani, A.; Albelda, S.; Yamazaki, K.; Beauchamp, G.K.; Oka, H. Urinary volatile compounds as biomarkers for lung cancer. Biosci. Biotechnol. Biochem. 2012, 76, 679–684.
Gasparri, R.; Capuano, R.; Guaglio, A.; Caminiti, V.; Canini, F.; Catini, A.; Sedda, G.; Paolesse, R.; Di Natale, C.; Spaggiari, L. Volatolomic urinary profile analysis for diagnosis of the early stage of lung cancer. J. Breath. Res. 2022, 16, 046008.
Ning, J.; Ge, T.; Jiang, M.; Jia, K.; Wang, L.; Li, W.; Chen, B.; Liu, Y.; Wang, H.; Zhao, S.; et al. Early diagnosis of lung cancer: Which is the optimal choice? Aging 2021, 13, 6214–6227.
Otano, I.; Ucero, A.C.; Zugazagoitia, J.; Paz-Ares, L. At the crossroads of immunotherapy for oncogene-addicted subsets of NSCLC. Nat. Rev. Clin. Oncol. 2023, 20, 143–159.

© 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: Oncology

Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register :

Roberto Gasparri

Angela Sabalic

Lorenzo Spaggiari

View Times: 122

Update Date: 12 Dec 2023

Table of Contents

Video Upload Options

Confirm