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Abdullah Ali Alfaifi, PhD candidate
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Lindsay Dong
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Alfaifi, A.; Refai, M.Y.; Alsaadi, M.; Bahashwan, S.; Malhan, H.; Al-Kahiry, W.; Dammag, E.; Ageel, A.; Mahzary, A.; Albiheyri, R.; et al. Role of Metabolomics in B-Cell Non-Hodgkin’s Lymphoma Diagnosis. Encyclopedia. Available online: https://encyclopedia.pub/entry/43687 (accessed on 19 May 2024).
Alfaifi A, Refai MY, Alsaadi M, Bahashwan S, Malhan H, Al-Kahiry W, et al. Role of Metabolomics in B-Cell Non-Hodgkin’s Lymphoma Diagnosis. Encyclopedia. Available at: https://encyclopedia.pub/entry/43687. Accessed May 19, 2024.
Alfaifi, Abdullah, Mohammed Y. Refai, Mohammed Alsaadi, Salem Bahashwan, Hafiz Malhan, Waiel Al-Kahiry, Enas Dammag, Ageel Ageel, Amjed Mahzary, Raed Albiheyri, et al. "Role of Metabolomics in B-Cell Non-Hodgkin’s Lymphoma Diagnosis" Encyclopedia, https://encyclopedia.pub/entry/43687 (accessed May 19, 2024).
Alfaifi, A., Refai, M.Y., Alsaadi, M., Bahashwan, S., Malhan, H., Al-Kahiry, W., Dammag, E., Ageel, A., Mahzary, A., Albiheyri, R., Almehdar, H., & Qadri, I. (2023, May 02). Role of Metabolomics in B-Cell Non-Hodgkin’s Lymphoma Diagnosis. In Encyclopedia. https://encyclopedia.pub/entry/43687
Alfaifi, Abdullah, et al. "Role of Metabolomics in B-Cell Non-Hodgkin’s Lymphoma Diagnosis." Encyclopedia. Web. 02 May, 2023.
Role of Metabolomics in B-Cell Non-Hodgkin’s Lymphoma Diagnosis
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This entry is adapted from the peer-reviewed paper 10.3390/diagnostics13050861

A wide range of histological as well as clinical properties are exhibited by B-cell non-Hodgkin’s lymphomas. These properties could make the diagnostics process complicated. The diagnosis of lymphomas at an initial stage is essential because early remedial actions taken against destructive subtypes are commonly deliberated as successful and restorative. New possibilities are now open for diagnosing cancer with the help of metabolomics. The study of all the metabolites synthesised in the human body is called “metabolomics.” A patient’s phenotype is directly linked with metabolomics, which can help in providing some clinically beneficial biomarkers and is applied in the diagnostics of B-cell non-Hodgkin’s lymphoma. In cancer research, it can analyse the cancerous metabolome to identify the metabolic biomarkers. 

metabolomics B-cell non-Hodgkin’s lymphoma biomarkers metabolites early diagnosis

1. Introduction

B-cell non-Hodgkin’s lymphomas (B-NHLs) are a genetically, metabolically, and clinically heterogeneous group of neoplasms, with most emerging from B lymphocytes in the germinal centre (GC). B-NHLs account for approximately 90% of all non-Hodgkin’s lymphomas [1]. Diffuse large B-cell lymphomas (DLBCLs), follicular lymphoma (FL), Burkitt lymphoma (BL), and B-cell chronic lymphocytic leukaemia/small lymphocytic lymphoma (CLL/SLL) are typical B-NHL subtypes [2]. Myc amplification [3] and metabolic heterogeneity in B-NHL are important biologically because they influence therapy responses and can predict clinical outcomes [4][5].
As cells are driven to grow, proliferate, or die, their metabolic needs fluctuate, and it is essential that cellular metabolism correspond to these needs [6]. B-cell lymphoma and cancer cells have dysregulated metabolisms that promote uncontrolled proliferation [7][8]. This altered metabolism leads to metabolic phenotypes that can be utilised for earlier cancer detection and/or therapy response biomarkers [9].
Metabolomics is a comprehensive evaluation of both qualitative and quantitative parameters of all the metabolites present in cells, tissues, and bodily fluids, which can reveal crucial information about the cancer state that would not be obvious otherwise. Metabolomics-based diagnosis investigates the metabolites present in the human body and how they react under stress conditions, like various diseases and disorders [10][11]. Metabolomics is a powerful tool that can identify cancer biomarkers and drivers of tumorigenesis. An example includes the de novo synthesis of phospholipid compounds in malignant tissues, which increases at the time of the progression of the tumour [12][13]. Worthy, LDH-A was the first metabolic target demonstrated to be directly regulated by an oncogene (MYC), and genetic or pharmacologic inhibition of LDH-A diminishes MYC-dependent tumours [14]. Even now, it is a challenging task to detect and treat the lymphoma at an initial stage.

2. Metabolism in B-Cell Non-Hodgkin’s Lymphoma (B-NHL)

Cell metabolism is a well-defined set of metabolic activities that generate and store energy equivalents, maintain redox homeostasis, synthesise biologically active macromolecules, and eliminate organic waste [15]. Catabolism breaks down carbon sources into simpler intermediates, which are then employed as building blocks in the production of lipids, amino acids, carbohydrates, and nucleotides (anabolism) [16]. Tumour cells are able to survive, grow, and divide because of their metabolic versatility and plasticity, which allow them to produce ATP as an energy source while maintaining the reduction–oxidation (redox) balance and devoting resources to biosynthesis [17].
Metabolic alterations in B-NHL are characterised by the production of enough energy and maintenance of anabolism for survival, growth, and division in the face of low levels of nutrients and oxygen (such as HIF1 and MYC), deregulation of metabolic regulators (like mTORC1), and rewiring of metabolic pathways (e.g., BCR signalling) [18][19].
The Warburg effect promotes aerobic glycolysis over aerobic oxidation [20], and this is supported by HIF1-alpha and MYC. This leads to the production of lactate and poor producing ATP, but helps create biomass. As a result, the body’s reaction to hypoxia-induced metabolic abnormalities may promote anabolism in GC-derived B-cell lymphoma [18].
MYC oncogene aberrations, including translocations or overexpression, are characteristics of B-cell lymphoma aetiology [21]. B-cell lymphomas require higher MYC levels to maintain their rapid proliferation rate. MYC upregulates nucleoside metabolism, which is essential for cell development. Glutamine metabolism is similarly regulated by MYC expression [21][22]. Glucose uptake, glycolysis, and lipid biosynthesis are all controlled by MYC as well [23]. On the other hand, alpha-ketoglutarate (αKG) synthesis can be inhibited by hypoxia and mitochondrial dysfunction, which in turn reduces the activity of αKG-dependent enzymes, leading to increased DNA and histone hypermethylation and stabilisation of HIF1α. HIF1α is the primary transcriptional regulator of the adaptive response to hypoxia and is constitutively stabilised in a significant proportion of DLBCLs and FLs [18]. HIF1α and MYC promote anaerobic glycolysis by activating genes for glucose transporter (GLUT), hexokinase (HK), monocarboxylate transporter (MTC), pyruvate dehydrogenase (PDK), phosphofructokinase (PFK), phosphoglycerate kinase (PGK), pyruvate kinase (PK), and lactate dehydrogenase (LDHA) [23].
mTORC1 is essential for generating metabolic precursors via the tricarboxylic acid cycle (TCA) and stimulating cellular proliferation. Activation of mTORC1 thereby enhances the survival of B-cell lymphoma. T-cell-selected GC B cells in the light zone necessitate mTORC1 activation in order to proliferate and mutate in the dark zone. mTORC1 may be aberrantly activated in GCB-DLBCL through activating mutations of PI3K/Akt/mTOR pathway genes [18].
A further marker of B-cell lymphoma is altered B-cell receptor (BCR) signalling, which is essential for the maintenance and creation of both healthy and malignant B cells [24]. PI3K/AKT/mTORC1 is one of the BCR signalling pathway’s downstream branches. PI3K regulates glycolysis and energy generation, and consequent AKT signalling influences the cellular metabolome. AKT promotes glucose uptake and glycolysis by increasing the expression and translocation of GLUT1 and glycolytic enzymes, including hexokinase (HK) expression and activation [24].
In a subset of DLBCL and MCL, PTEN mutations lead to AKT/mTORC1 pathway gene expression [25]. RagC mutations in FL enhance mTORC1 signalling by eliminating amino acid dependence [26]. Numerous anabolic and energy-generating processes, including protein synthesis, pyrimidine synthesis, HIF1α expression, glycolysis, the oxidative portion of the pentose phosphate pathway (PPP), lipid and mitochondrial metabolism, and glutaminolysis, are stimulated by mTORC1 expression [19].
There is an urgent need for biomarkers based on non-invasive sampling procedures (e.g., blood, urine, etc.) that can help in the diagnosis of lymphoma, such as metabolite profiling. The perfect test should be easy, reliable, and accurate. “What simple, non-invasive, painless, and convenient tests can be used to detect cancer early?” ranked as the most important research priority for the early detection of cancer in the UK-focused research gap survey performed by the James Lind Alliance, which includes patients and doctors [27] (Figure 1).
Diagnostics 13 00861 g001 550
Figure 1. Altered gene expression and mutations associated with key metabolic pathways found in B-NHL subtypes. The figure illustrates: (A) The major B-cell non-Hodgkin’s lymphoma subtypes that emerge from different cells that originate within the lymph node; (B) mutated genes that influence metabolic reprogramming; and (C) critical metabolic pathways observed in B-NHL subtypes. The references used for this figure are CLL/SLL [28][29][30], MCL [31], BL [32], FL [33], and DLBCL [28][34].

3. Metabolomics and B-NHL Biomarker Discovery

There are a variety of steps to metabolomics analysis (Figure 2), each with their own set of benefits and drawbacks [35][36].
Diagnostics 13 00861 g002 550
Figure 2. B-NHL Metabolomics Workflow Steps: (1) study design; (2) pre-analytical process, including sample collection and processing; (3) analytical process, which is platform choice (either LC–MS, GC–MS, or NMR); and (4) post-analytical process, including data processing, results interpretation, and biomarker identification.

3.1. Metabolomics Study Design

Metabolomics studies can be divided into two classes: targeted and non-targeted. Targeted analysis is used for the identification and quantification of pre-defined metabolites and can be used for quantitative as well as qualitative analysis [37]. Non-targeted analysis consists of analysing all accessible metabolites in a given sample and is the first choice for cancer biomarker discovery studies [38].

3.2. Sample Collection and Preparation

The collection of the sample, its preparation, and storage are the second step in the metabolomics study plan. The most common samples for conducting clinical metabolomics research are blood and urine [39]. It is important to design the research based on metabolomics to reduce the influence of certain constituents such as age, gender, state of fasting, diet, physical activity, exercise, and the day and time of sample collection. Before starting the actual research, it is important to conduct a pilot study of healthy individuals and report it as part of the research to validate the results’ reproducibility. The samples (particularly plasma, serum, and urine) must be kept in various aliquots soon after collection to avoid the production of compounds from the many freeze–thaw cycles used for different metabolomics studies [40]. The factors used for processing the sample, such as pH buffering and extraction, should also be uniform and follow standard operating procedures (SOPs) [35][36][41][42]. The samples that are non-invasive in nature, such as blood or urine, are the best for regular clinical analysis [43].

3.3. Analytical Techniques

3.3.1. LC–MS

The MS technique has the ability to isolate the intricate mixture of compounds for their detection and quantification with elevated sensitivity and specificity, and can also demonstrate information regarding molecular structures [44]. MS separation techniques are essential for reducing sample complexity and minimising ionisation suppression effects [45]. A preceding separation stage, such as high-performance liquid chromatography (HPLC), or ultra-performance liquid chromatography (UPLC), and capillary electrophoresis (CE), is frequently required.

3.3.2. GC–MS

GC–MS is a technique that combines great separation efficacy with sensitive, selective, and versatile mass evaluation and is suitable for comprehensive analysis. It is a combination of MS and GS that is used for the detection and quantification of a wide range of chemical compounds, such as natural products, blood, and urine. GS–MS is used in many fields of study, such as detecting drugs, amino acid evaluation, doping control, and the detection of natural materials like food products [46]. EI, or electron ionization, is used for combining MS with GC in almost all the metabolomics applications that are based on GC. The EI–MS method works well for chemical compounds that do not change when heated and that are volatile and are separated by chromatography at high temperatures [47].

3.3.3. NMR

NMR spectroscopy is a universal metabolite detection method that allows for direct analysis of samples with little sample preparation and simultaneous measurement of numerous types of tiny metabolites [48][49]. However, it has limitations, such as high equipment costs, high maintenance costs, and decreased sensitivity [50][51]. Mass spectrometry is better than NMR in several ways, although NMR has its own advantages.

3.4. Data Acquisition and Processing

When the metabolomics data are produced, it is important to ensure that they are reproducible [40][52]. Quality standardisation and quality control are considered for the optimisation of the reproducibility of results. Data analysis and bioinformatics are used to process the data, which are then subjected to statistical analysis. There are two classical approaches to the statistical analysis of multivariate data: unsupervised learning and supervised learning. A popular unsupervised learning method is principal component analysis (PCA). The second main approach is supervised learning, such as with artificial neural networks (ANN), partial least squares discriminate analysis (PLS-DA), etc., which can be used for excavating the data further to obtain the biomarkers [53][54]. The discovery process of biomarkers can be driven through supervised models that can be linked with clinical results, histopathological scores, and various other omics data. It is important to test the supervised models with precise internal cross-validation processes or external tests to obtain trusted biomarkers and models and to decrease the chances of data overfitting [55].

3.5. Metabolites Identification: Biomarker Discovery and Validation

Profiling the metabolites in each biological entity is incomplete without accurate data measurement and precise interpretation. To identify the features of potent spectral biomarkers, attempts are made to recognise the unidentified spectral biomarkers. The peaks can be identified with the help of public metabolomics databases and in-house spectral databases such as the Golm database, LIPID MAPS, human metabolome database (HMDB), METLIN database, etc. Following the identification of metabolomics biomarkers, additional experiments are required to validate or test the biomarkers [36][54][56].

4. Applications of Metabolomics in B-NHL

4.1. Discovering Targeted Therapies Based on Metabolomics

Metabolism in B-NHL plays a crucial role in established therapeutic approaches. Antimetabolites were the name given to the chemical compounds that were first used to treat cancer. The reason for choosing this name was that these compounds were found to resemble endogenous metabolites in their chemical structure and disrupt the process of normal metabolism. In comparison to other omics, metabolomics is best for evaluating the potential of these cancer treatment regimens. The study was carried out to discover whether the therapies could cause alterations in the metabolic pathways and detect the pharmacokinetics of drugs simultaneously or not. In the coming time, it will become crucial to combine the study of pharmacometabolomics with other biological systems knowledge, such as mRNA, genetics, miRNA, and imaging. This will help in determining the correlation of the metabolomics response with the cancer stage, undesirable incidents, and the growth or recession of the tumour. The study of pharmacometabolomics is capable of monitoring a patient’s metabolic response to a drug; thus, it is very interesting to use metabolomics in detecting cancerous growth, prognosis, and therapy management [36].

4.2. Determining B-NHL Diagnostic and Prognostic Biomarkers

A recent metabolomics study suggested a methodology for discovering novel biomarkers that can be used for the diagnosis and characterisation of various lymphoma subtypes. The GC–MS method was used for the investigation and evaluation of plasma samples taken from individuals with different subtypes of lymphomas. The results showed a significant prevalence of elaidic acid and hypoxanthine (HX) in patients suffering from Hodgkin’s lymphoma, MM, CLL, and DLBCL compared with healthy control individuals in all the study groups [57].

4.3. Determining the Lymphomagenesis Risk Factors

Genetic mutations accumulate sequentially during tumour development, eventually resulting in malignant tumours. However, it has also been shown that metabolic processes and inflammatory factors indirectly contribute to the development of the tumour [58]. In their study, Pettersena et al. proved that the cell line of B-cell lymphoma surrounds numerous amplified genomic uracil concentrations in comparison with non-lymphoma cell lines or normal lymphocytes. They utilised a method based on liquid chromatography combined with mass spectrometry (LC/MS) for quantifying the genomic sequence of 2-deoxyuridine and proving their study. In harmony with uracil generated by activation-induced cytidine deaminase (AID), they discovered a distinctive mutational signature of an AID hotspot in the lymphoma area where there was clustered mutation. They also presented an important revelation about the expression of SMUG1 and uracil-DNA glycosylases UNG along with the excision capacity of uracil by stating its negative correlation with the concentration of genomic uracil, which somewhat decreased the AID effect [59]. Another study was also conducted on the metabolomic pattern of Burkitt lymphoma that was induced by MYC glucose deprivation, as well as hypoxic and aerobic conditions. They used a [U-13C, 15N]-glutamine tracer to detect glutamine import and metabolism via the TCA cycle under hypoxia conditions and discovered that glutamine is significantly precipitated to citrate carbons. The deficiency of glucose leads to the significant augmentation of citrate, fumarate, and glutamine-derived malate. Their arrangements showed a different pathway for the generation of energy called glutaminolysis, which is associated with the glucose-independent TCA cycle. Under the conditions of hypoxia and scarcity of glucose, the critical role of glutamine in the proliferation of cells makes them susceptible to BPTES (glutaminase inhibitors), which in turn can be used for treating tumours [60].

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Subjects: Medical Laboratory Technology; 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 :
Abdullah Alfaifi
,
Mohammed Y. Refai
,
Mohammed Alsaadi
,
Salem Bahashwan
,
Hafiz Malhan
,
Waiel Al-Kahiry
,
Enas Dammag
,
Ageel Ageel
,
Amjed Mahzary
,
Raed Albiheyri
,
Hussein Almehdar
,
Ishtiaq Qadri
View Times: 384
Revisions: 2 times (View History)
Update Date: 03 May 2023
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