Biomarkers in Disease Diagnosis and Monitoring: Insights into Clinical Applications and Mass Spectrometry-based Detection

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This review focuses on the classification of biomarkers based on three core categories: (i) their characteristics, (ii) clinical applications, and (iii) relevance in genetic and molecular biology. The importance of biomarkers across diseases is emphasized, along with recent advancements in their detection. A comprehensive discussion on the biomarker development pipeline, particularly mass spectrometry (MS)-based biomarker discovery, validation, and verification, is presented. The article also delves into MS-based techniques used for the detection of disease biomarkers such as Alzheimer’s, hepatocellular carcinoma, ovarian cancer, and tuberculosis, as well as highlighting recent research. Finally, the review explores future perspectives on biomarker discovery and detection, focusing on the evolving role of MS in advancing biomarker science and its application in clinical and research settings. Biomarker Classsification Biomarker discovery Validation Detection Mass spectrometry Cancer Figures Figure 1 Figure 2 Figure 3 1. Introduction Over the past fifty years, various definitions of biomarkers have been proposed and subsequently changed in response to advancements in science and medicine. In 1973, Rho et al. coined the word "biomarker" to refer to the presence or lack of particular biological material [ 1 ] . Nonetheless, the phrase is older; Mundkur used it to refer to "biochemical markers" in 1949 [ 2 ] , and Porter did the same in 1957 [ 3 ] . Hulka and colleagues (1990) defined biomarkers as "alterations in cellular, molecular, and biochemical processes that can be measured and evaluated in biological media, such as human cells, tissues, or fluids [ 4 ] . "Among the distinguishing characteristics that can be objectively measured and evaluated as a possible indicator of any normal or aberrant pathophysiological process or pharmacological response to a course of therapy are "biomarkers," or "biological markers". The definition given to biomarkers is "The substances, structures, or processes which can be quantified in the body or its products and influence or predict the incidences of outcomes or diseases" [ 5 ] . "A characteristic that is objectively measured and evaluated as an indicator of normal biological processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention" is the definition of a biomarker provided by the National Institutes of Health (NIH) [ 6 ] . In accordance with the World Health Organisation, "nearly any measurement reflecting an interaction between a biological system and a potential hazard, which may be chemical, physical, or biological, is considered a true biomarker. The measured response may be functional and physiological, biochemical at the cellular level, or a molecular interaction." [ 7 ] . Biomarkers have been increasing in significance in the pharmaceutical research in recent years. The cornerstone to personalised medicine and overall improved clinical outcomes is the identification of ideal biomarkers. An ideal biomarker, as illustrated in Fig. 1 , has specific characteristics that enable it to be used in the diagnosis of a given medical condition. Biomarkers are now used to determine the mechanism of action of drugs, examine efficacy and toxicity signals early in the research and development process, and to identify patients who are likely to react to therapy. Additionally, a number of potentially effective methods for interpreting such complexities are emerging in a variety of scientific domains, and the use of this information in personalised medicine has increased [ 8 ] . For biological, therapeutic, and the ranostic research endeavours, biomarkers play an essential and very crucial role in improving the entire drug development processes [ 9 ] . In addition to other factors like heart rate and blood pressure, biomarkers also involve far more complicated laboratory-based testing in biological fluids and other human tissues, as well as basic chemistry [ 10 ] . Clinical endpoints for many diseases, such death or disease recurrence, can take a long time to obtain, while biomarkers can give earlier data and enable more focused, smaller trials. In order to employ each biomarker in an appropriate situation, one must be aware of its features before using it in daily practice. The degree of sensitivity specificity, accuracy, and positive and negative predictive values are the main attributes [ 6 ] . Numerous biomarkers have been identified and quantified to monitor the treatment of nearly all diseases and health conditions, or as indicators of exposure to a diverse range of pharmaceuticals and environmental substances. A whole spectrum of bio-indicators, including nucleic acids, proteins, enzymes, antigens-antibodies, and other biological agents, are evaluated with the purpose of diagnosing or tracking pathophysiological disorders [ 11 ] . Some instances of biomarkers employed in identifying diseases include cardiac troponin for detecting cardiac muscle injury [ 12 ] , the presence of 3-hydroxy-fatty acids in planctomycetes [ 13 ] , and a panel of glycans utilized as cancer biomarkers [ 14 ] . The identification and examination of biomarkers have become feasible through diverse technologies and procedures, enabling the accumulation of extensive data for characterizing these biomolecular indicators within intricate mixtures of proteins, lipids, carbohydrates, and more [ 9 ] . Mass spectrometry has played a pivotal role in the exploration and assessment of biomarkers, thanks to several crucial characteristics such as its sensitive and specific detection capabilities, its capacity for analyzing multiple analytes simultaneously, and its ability to furnish structural insights. Due to these advantages, mass spectrometry has found extensive application in the quest for novel biomarkers, encompassing the study of both large molecules (proteomics) and small molecules (metabonomics). Moreover, mass spectrometry is increasingly being employed to facilitate quantitative assessments, aiding in the verification and validation of potential biomarker candidates [ 15 ] . Besides, unlike traditional immunoassays or molecular techniques, MS offers label-free detection, allowing for unbiased and hypothesis-free biomarker discovery. Additionally, MS-based approaches are amenable to automation, enabling high-throughput analysis and reproducibility. Metabolomics based on mass spectrometry provides precise quantitative evaluations with remarkable selectivity and sensitivity, along with the capability to discern metabolites. When coupled with a separation method, it diminishes the intricacy of mass spectra by segregating metabolites over time, ensuring distinction between isobars, and furnishing supplementary insights into the physicochemical attributes of metabolites [ 16 ] . Mass spectrometry has proven to be highly efficient not only in the analysis of protein biomarkers but also in the examination of other biomarker categories such as hormones and genetic markers [ 17 , 18 ] . In this review, we cover the different classifications of biomarkers and highlight their significance in the discovery and validation of various diseases. We also examine the diverse applications of mass spectrometry in identifying biomarkers, emphasizing its crucial role in advancing biomarker discovery across a range of biological contexts. 2. Classifications Biomarkers can be classified on the basis of various parameters, such as their characteristics, sources, genetic and molecular biology methods as well as clinical applications (Fig. 2 ). 2.1. Characteristics Based on their characteristics, biomarkers can be categorized into three primary types: cellular, molecular, and imaging biomarkers. 2.1.1. Molecular biomarkers Molecular biomarkers possess distinct biophysical properties and are detectable in various biological specimens including cerebrospinal fluid, plasma, serum, bronchoalveolar lavage fluid, and biopsies [ 19 ] . They encompass a diverse array of molecules, spanning from small compounds to larger entities such as peptides, proteins, lipids, metabolites, nucleic acids (DNA and RNA), and additional molecular species [ 20 – 22 ] . These biomarkers, detected using proteomic and genomic methods, play a vital role in disease diagnosis, prevention, prognosis, and treatment management. These biomarkers also find diverse applications in analytical epidemiology, randomized clinical trials [ 23 ] . 2.1.2. Imaging biomarkers Imaging biomarkers represent a unique category of biomarkers derived from in vivo medical imaging, offering an appealing option for clinical applications due to their real-time, non-invasive, and affordable nature. Although there is a wealth of research on biological biomarker development, there is still a lack of a well-defined roadmap for defining imaging biomarkers. However, various academic, clinical, industrial, and regulatory groups have addressed the standardization of imaging biomarkers procurement and analysis, as well as the harmonization of terminology, particularly in specific contexts [ 24 ] . Magnetic resonance imaging (MRI), computed tomography (CT), and positron emission tomography (PET) are the three categories of imaging biomarkers [ 25 ] . PET, which tracks and measures the body's cells' intake of glucose, can be used to evaluate the effectiveness of cancer therapy [ 26 ] . CT is a diagnostic technique that uses ionising radiation to create cross-sectional and three-dimensional (3D) images of the human body as well as to track the status of tumors [ 27 ] . MRI, which produces extremely high spatial resolution pictures, is the method most commonly employed to study cancer and neurodegenerative illnesses [ 28 ] . 2.1.3. Cellular biomarkers Clinical and laboratory testing can employ cellular biomarkers, which are quantifiable and biological indicators. Cellular biomarkers are frequently assessed for prognosis or likelihood of responding to a particular treatment in blood, bodily fluids, or soft tissues. These biomarkers enable the separation, classification, measurement, and description of cells based on their morphological and physiological characteristics [ 29 ] . 2.2. Clinical applications Biomarkers can be divided into three categories based on their clinical applicability in various phases of disease: therapeutic, prognostic, and diagnostic [ 19 ] . Proteins like exosomes and miRNAs are examples of therapeutic biomarkers that may be applied to targeted treatments [ 30 ] . Prognostic biomarkers provide information about the state of a disease by screening, tracking the progression of the condition, and assessing changes in the internal antecedents that a disease has a probability of attaining [ 19 ] . Diagnostic biomarkers are used to diagnose conditions such as cardiac troponin, which is used to diagnose heart muscle injury [ 12 ] , and the distributions of 3-hydroxy-fatty acids, which are used to diagnose planctomycetes [ 13 ] . 2.3. Genetic and molecular biology method Three categories of biomarkers are distinguished by the genetic and molecular biology method: categories 0, 1, and 2. In phase 0 clinical trials of a disease, type 0 is a natural history biomarker that can be assessed and is connected with clinical outcomes throughout time. A drug's mode of action, therapeutic effects, interactions, and toxicological effects are all indicated by the Type 1 drug activity biomarker. As a stand-in for clinical outcome evaluations of disease, type 2 biomarker aids in the prediction of how a therapeutic intervention would be received [ 29 , 31 ] . 3. Importance of biomarkers in different diseases Numerous biomarkers have been identified and quantified for the treatment of nearly all diseases and ailments, as well as serving as indicators of exposure to a broad range of pharmaceutical and environmental substances. A whole spectrum of bioindicators, including proteins, nucleic acids, enzymes, antigens, antibodies, and other biological agents, are evaluated with the purpose of diagnosing or tracking pathophysiological disorders [ 9 ] (Fig. 3 ). Deep learning technology and high-resolution computed tomography images were employed as potential diagnostic biomarkers in a study published by Pu et al. [ 32 ] , to look at parameters related to COVID-19. The results imply that distinct patterns or features in images might not be necessary to accurately differentiate all cases of COVID-19 from CAP. Nonetheless, their findings show that the imaging indicators have the ability to help identify a significant portion of instances that are not COVID-19. Machine learning (ML) sensors have been described by Velichko et al. [ 33 ] as a workable solution for the Internet of Things (IoT) in clinical decision support systems to establish the preliminary diagnosis of COVID-19 with an accuracy of up to 95% utilising RBV. The COVID-19 disease was effectively diagnosed using the histogram-based gradient boosting (HGB) model. The disease was identified with 100% accuracy in 6.39 seconds by the HGB model that was the fastest and most accurate in identifying the 11 most significant biomarkers, which included low-density lipoprotein, cholesterol, high-density lipoprotein cholesterol, mean corpuscular haemoglobin level, amylase, triglyceride, uric acid, lactate dehydrogenase, alkaline phosphatase, creatine kinase-myocardial band, as well as mean corpuscular haemoglobin. Cancer is the leading cause of mortality globally. It is a complex hereditary disease that spreads to major organs in the body [ 34 ] . A biopsy or surgical resection may be necessary for tissue-derived cancer biomarkers, which are found in the bloodstream (whole blood, plasma, or serum), secretions (urine, stools, sputum, or nipple discharge), or other human biological fluids. These biomarkers can be easily assessed serially and noninvasively [ 35 , 36 ] . Numerous cardiac and extracardiac pathophysiological pathways can induce heart failure (HF), which is a complex clinical illness with a wide range of phenotypes [ 37 ] . Biomarkers have a variety of roles in clinical care of cardiovascular disease, including risk stratification, prognostic assessment, therapeutic monitoring, and diagnosis. These functions allow for an integrated strategy. The BNP levels of 122 patients with acute decompensated heart failure and declining renal function were assessed in this study. Rehospitalization was positively correlated with a significant BNP value reduction of ≥ 40% during hospitalisation, or from baseline to release [ 38 ] . Additional diagnostic biomarkers for oxidative stress (growth differentiation factor-15), cardiac remodelling (gatein-3), and inflammation (soluble ST2 receptor) may be introduced to aid in the management of heart failure [ 39 ] . Biomarkers are also helpful in the diagnosis, prognosis, therapy, and prevention of neurological and neuropsychiatric diseases, including epilepsy, Parkinson's disease, Alzheimer's disease, stroke, and Huntington's disease [ 40 ] . Biomarkers for neurological disorders include 4-hydroxy-2,3-noenal, angiogenin, Cystatin-C, clusterin etc [ 41 , 42 , 42 , 43 ] . In blood and urine specimens for significant adverse renal events following heart surgery, hepcidin-25, an iron-binding protein linked to acute kidney injury, is employed as a new kidney biomarker [ 44 ] . For the purpose of identifying preclinical ASO-induced kidney pathology, the FDA has approved a panel of six urine creatinine-normalized biomarkers, which include clusterin, cystatin C, kidney injury molecule 1, N-acetyl-β-D-glucosaminidase, neutrophil gelatinase-associated lipocalin, and osteopontin [ 45 ] . When it comes to liver diseases, an extremely specific surrogate biomarker that can be found in the bloodstream is alanine aminotransferase [ 46 ] . Potential liver disease biomarkers include bilirubin, hyaluronic acid, laminin, cytokines, and fibroblast growth factor 21 [ 47 , 48 ] . 4. Biomarker development pipeline The biomarker pipeline is typically understood as a sequence of preclinical stages that include biomarker identification and validation prior to the ultimate clinical evaluation. The various stages of the biomarker pipeline employ different mass spectrometric techniques. For example, a typical proteomics discovery study employs shotgun proteomics, an untargeted method, to measure thousands of proteins relative to one sample. A list of dozens of proteins that are expressed differently in diseased and healthy samples is produced by the comparative analysis. After initial discovery, potential biomarker proteins undergo filtration through studies on more patients, additional time points, or higher-specificity mass spectrometry in a qualification step. Subsequently, verification occurs on 10–50 patient samples, followed by validation on 100–500 samples. Clinical validation of final biomarkers involves quantifying a small number of proteins on 500–1000 samples [ 49 , 50 ] . 4.1. MS based biomarker discovery Techniques for biomarker identification and quantification are part of MS-based biomarker discovery. Finding a shift in the relative abundance of peptides from a distinct protein in samples taken from disease patients in comparison to matched controls is the goal [ 51 ] . A peptide needs to be found and sequenced in order for it to be identified. Depletion of abundant proteins is hence frequently the first step in sample processing. Fractionation can be carried out at the protein, peptide, or both levels following depletion (and reduction/carboxymethylation), utilising techniques such size exclusion chromatography (SEC), ion exchange chromatography (SCX), and isoelectric focusing [ 50 ] . A variety of techniques can be used, from 1D or 2D gel electrophoresis combined with protein identification based on mass spectrometry to relative quantification techniques that are solely based on LC-MS. Known for their high scan speeds and resolution, instruments like Q-TOF or Q-Exactive MS systems are often utilised in this phase, allowing the capture of many peptide identifications and facilitating progressively accurate protein quantification [ 52 , 53 ] . Proteins are typically first digested in MS-based relative quantitation procedures, and the subsequent protein quantification is actually based on the quantitation of prototypic peptides that function as stand-ins for the protein of interest (a process known as the "bottom-up" method). For relative quantification, a range of isotopic labeling methods can be applied, such as TMT-tagging, isobaric tagging, or non-isobaric tagging. Label-free quantitation methods such spectrum counting are also used [ 51 , 52 , 54 , 55 ] . In order to identify proteins, a number of software programmes automate the examination of peptide tandem mass spectra and database searches. As said, these programmes are made to identify all interpreted spectra; hence, extra caution needs to be used to guarantee that only proteins that have been positively recognised are examined further. To fill preliminary lists of potential biomarkers, semiquantitative comparisons of protein abundance between patients and controls are utilized [ 56 , 57 ] . 4.2. MS-based biomarker validation/ verification There are multiple steps in the validation process that depend on the context of use of the candidate biomarker once it has been identified and a detection method has been established. The level of validation required intensifies as the biomarker progresses from research applications to clinical trials and, eventually, to clinical practice [ 58 ] . Analytical validation focuses on assessing the precision, dynamic range, and sensitivity of the detection method. In contrast, clinical validation evaluates the biomarker’s sensitivity and specificity in identifying, measuring, or predicting clinical outcomes. Sensitivity is the measure of true positive rate, while specificity relates to the true negative rate [ 59 ] . ELISAs are ideally suited for applications necessitating thorough sample validation or verification of a limited number of biomarkers. However, the unavailability of high-quality ELISA tests for targeted proteins is a common challenge, compounded by the requirement for specific antibodies against each protein or peptide [ 52 ] . Advanced mass spectrometry (MS) techniques have revolutionized the field of biomarker validation by providing highly sensitive and specific methods for analyzing complex biological samples. Methods such as Multiple Reaction Monitoring Mass Spectrometry (MRM-MS) allow for the precise quantification of protein biomarkers, which are crucial for distinguishing between healthy and diseased states. These techniques utilize isotope-labeled internal standards to enhance the accuracy and reproducibility of assays. Research by Percy et al., highlights the use of LC/MRM-MS for urinary protein quantitation [ 60 ] , while Mermelekas et al., focus on targeted proteomics for biomarker validation [ 61 ] . Additionally, Gallien et al., emphasize the role of LC-MS/MS in proteomics experiments [ 62 ] . These studies demonstrate the pivotal role of MS-based techniques in advancing biomarker validation and their potential to improve diagnostic as well as therapeutic strategies. 5. Mass Spectrometry-based detection of disease biomarkers In recent years, mass spectrometry (MS) has emerged as a powerful tool in the field of biomarker discovery and detection. With its ability to analyze complex biological samples with high sensitivity and specificity, MS has revolutionized our approach to identifying biomarkers for various diseases. By leveraging the unique mass-to-charge ratio of molecules, MS enables the precise measurement of proteins, peptides, metabolites, and other biomolecules present in biological specimens [ 63 ] . In this context, MS-based techniques offer unparalleled insights into disease pathogenesis, progression, and treatment response, paving the way for the development of novel diagnostic and therapeutic strategies [ 64 ] . In this article, we explore some of the recent applications of mass spectrometry in detecting biomarkers associated with different diseases. 5.1. Detection of biomarkers in Alzheimer's disease The most prevalent type of dementia, accounting for 60–70% of cases, is Alzheimer's disease (AD). In order to reliably estimate prognosis, intervene, and monitor AD, accurate diagnosis is necessary for early detection [ 65 ] (Table 1 ). Alzheimer's disease is distinguished by the accumulation of amyloid-β (Aβ). This pathological progression initiates several years to decades prior to the manifestation of clinical manifestations [ 66 ] . A recent study evaluated the diagnostic potential of plasma Aβ biomarkers for brain amyloidosis prediction using both the single-molecule array (SIMOA) immunoassay platform and LC/MS/MS measurement methods. The findings demonstrate that the amyloid PET state can be determined more accurately by LC-MS methods for assessing plasma amyloid-β1–42/amyloid-β1–40 and a plasma composite than by SIMOA amyloid-β42/amyloid-β40 and p-tau181 tests. The study suggests that plasma screening may be feasible in a preclinical cohort and adds to the increasing body of evidence that shows it can decrease the number of amyloid PET scans needed for detecting amyloid-β-positive individuals, for clinical trial recruitment, or eventually for anti-amyloid therapy administration [ 67 ] . Lim et al. conducted a study involving two distinct cohorts of cognitively normal elderly individuals to explore the relationship between a plasma Aβ composite biomarker and cognitive functioning. Employing immunoprecipitation and mass spectrometry techniques, they developed a highly effective plasma Aβ composite biomarker. In addition to enrichment using immunoprecipitation, they were able to precisely determine the mass of each Aβ peptide fragment using IP-MS. According to this research, Aβ + classified using IP MS is linked to a decline in cognitive functions that are essential to the early symptomatic manifestation of AD. This strongly suggests that the plasma Aβ composite marker will serve as a helpful prognostic marker for clinical outcomes, and formal investigations of these attributes are therefore necessary. Furthermore, it holds potential for identifying candidates at risk for AD prevention trials and provides a strong foundation for evaluating how effectively this plasma Aβ composite marker reflects changes in Aβ levels throughout the disease progression or in response to Aβ-lowering treatments. [ 68 ] . Tau is one of the cerebrospinal fluid (CSF) AD biomarkers that has been examined using mass spectrometry techniques in addition to immunoassays. The selectivity of the immunoassays has frequently been questioned because to the molecular variety of tau in CSF, which includes several isoforms, posttranslational modifications, and peptide fragments [ 69 ] . One group reported using Parallel reaction monitoring to develop a very sensitive and specific mass spectrometry approach for tau phosphorylation site identification. The quantity of tau protein phosphorylation sites in brain tissue and CSF from individuals with and without AD were compared using this technique. In brain tau that was fully grown, they found 29 different phosphorylation sites, and in CSF tau that was shortened, they found 12 sites. Additionally, they discovered phosphorylation at the threonine 153 and 175 sites, which were only present in AD CSF [ 70 ] . The research team also utilized their mass spectrometry technique to accurately identify various low-abundance variants of phosphorylated tau. They demonstrated that heightened levels of cerebrospinal fluid (CSF) p-tau217 offer a more sensitive detection method for both early and advanced stages of Alzheimer's disease (AD). Additionally, the team's findings indicated a similar performance between p-tau217 and elevated levels of p-tau181 in detecting AD at both its preclinical and advanced stages. However, despite these advancements, the precise identification of different forms of phosphorylated tau remains challenging, though it serves as a valuable tool in understanding the progression of tau phosphorylation in AD. Further evaluations are planned to compare the clinical efficacy of these and other forms of p-tau using established immunoassays and mass spectrometry techniques [ 71 , 72 ] . According to recent research, tau and amyloid β pathologies can be predicted as well as utilised to distinguish AD patients from other tauopathies by plasma p-tau181 testing [ 73 – 75 ] . Furthermore, p-tau231 can be used to identify AD patients from patients with non-AD neurological conditions with considerably higher accuracy than established MRI- and plasma-based biomarkers. P-tau217 increases during early stages of Alzheimer's disease and can be used to track the disease's progression. Unlike plasma p-tau181, the latter distinguishes people over the whole Braak stage spectrum [ 76 – 78 ] . Inoue, Makoto, et al. developed a blood test based on liquid chromatography-tandem mass spectrometry (LC-MS/MS) to find 8 plasma proteins {Albumin (Alb), apolipoprotein C1 (ApoC1), complement components 3 (C3), 4 gamma chain (C4G), alpha-2-antiplasmin (A2AP), alpha-2-macroglobulin (A2M), hemopexin (HPX), and alpha-1-glycoprotein (A1BG)} that can be utilised to diagnose Alzheimer's disease and moderate cognitive impairment (MCI). They measured plasma proteins with this method by labelling synthetic peptides with isotopes. The analysis included 192 patients in total: 63 with AD, 71 with MCI, and 58 non-demented controls (NDCs). They identified eight potential plasma protein biomarkers that are useful in differentiating between AD and MCI [ 79 ] . Table 1 Recent studies on detection of biomarkers in Alzheimer's disease using MS Technology Biomarker Sample Reference LC-MS/MS Amyloid β CSF [67] IC-MS Amyloid β CSF [68] MS Tau CSF [70] MS Tau CSF [71] LC-MS/MS Alb, ApoC1, C3, C4G, A2AP, A2M, HPX, A1BG Blood sample [79] 5.2. Detection of biomarkers in Hepatocellular carcinoma Hepatocellular carcinoma (HCC), the predominant type of liver cancer, is extremely aggressive and is continuing to develop worldwide. HCC ranks second in East Asia for cause of mortality, with a five-year survival rate of fewer than 15%. In order to minimise cancer-related mortality and preserve cancer patients' quality of life, early detection of HCC is essential [ 80 ] (Table 2 ). Metabolomics is a potential method for identifying tiny molecules related to disease and is commonly utilised in biomarker discovery. Thus, by examining Tumor expression, metabolomics—also known as metabolic phenotyping—is a method that may be used to identify possible biomarker candidates for HCC [ 81 ] . In a study, using a mixed-mode column and HDMS, a comprehensive and repeatable G-Met approach was used to identify novel biomarkers for HCC. The HCC biomarker candidates were found by comparing the values of the collision cross section and their fragment ions on the mass spectra acquired by HDMS, after they had been extracted using multivariate analysis. By locating the biomarkers in slices of tumor tissue using desorption electrospray ionisation (DESI) mass spectrometry (MSI), they were able to analyse the biomarkers. The combination study of DESI-MSI and UHPLC/QTOFMS demonstrated that the various molecular species of triglycerides were linked to the location of tumors and may be used to characterise the evolution of Tumor cells and identify potential biomarkers [ 82 ] . Oxoglutaric acid, citrulline, plasma N-formylglycine, and heptaethylene glycol together can be an effective new diagnostic biomarker for HCC, according to research by Liu, Zhiying, et al. [ 83 ] . In this investigation, gas chromatography-mass spectrometry was used to evaluate and validate plasma samples from 104 HCC, 76 cirrhosis, and 10 healthy people. Using multivariate logistic regression analysis, it was possible to differentiate HCC from cirrhosis using N-formylglycine, oxoglutaric acid, citrulline, and heptaethylene glycol among the candidate metabolites in the validation cohort. With area under curve, sensitivity, and specificity values of 0.940, 84.00%, and 97.56%, respectively, the combination of these four metabolites performed better than alpha fetoprotein (AFP), which is also frequently used in clinical practice as a diagnostic sign for HCC. However, when it comes to distinguishing between cirrhosis and early stage HCC, AFP is less useful than the panel consisting of N-formylglycine, heptaethylene glycol, and citrulline. A robust LC-MS/MS approach was developed in a study by Yue, Zhihong, et al. for the simultaneous detection of many serum lipids, such as 8,15-dihydroxy-5,9,11,13-eicosatetraenoic acid (8,15-DiHETE), hexadecanedioic acid (HAD), 15-keto-13,14-dihydroprostaglandin A2 (DHK-PGA2), ricinoleic acid (RCL), octadecanedioic acid (OA) and 16-hydroxy hexadecanoic acid (16OHHA). They discovered that serum 8,15-DiHETE, DHK-PGA2, HDA, and OA were considerably elevated in Type 2 diabetes mellitus (T2DM) positive HCC patients using LC-MS/MS technique. The potential for therapeutic application of a biomarker signature based on AFP, DHK-PGA2, and HDA was suggested by its excellent diagnostic efficacy in differentiating T2DM + ve HCC from T2DM and other T2DM + ve cancers, such as gastric cancer, pancreatic cancer, and colorectal cancer. The clinical significance of the biomarker signature in the diagnosis of T2DM + ve HCC is highlighted in this work, which has important ramifications for enhancing patient outcomes [ 84 ] . To explore the metabolic distinctions between hepatitis and hepatocellular carcinoma (HCC), In a research by Tao et al., serum AA levels were quantitatively analysed in 136 patients with hepatitis B (CHB) and 93 patients with hepatocellular carcinoma (HCC) associated with the hepatitis B virus (HBV) using targeted mmetabolomics based on ultraperformance liquid chromatography triple quadrupole mass spectrometry. Research shows that in patients with chronic hepatitis B (CHB), serum phenylalanine levels were higher, while levels of leucine, lysine, threonine, tryptophan, valine, serotonin, and taurine were lower, especially in those with hepatocellular carcinoma (HCC). Among HCC patients, those in Class C had lower valine and serotonin levels compared to Classes A and B. Additionally, higher phenylalanine levels were associated with higher Model for End-Stage Liver Disease (MELD) scores. In the decompensated stage, phenylalanine levels increased, while serotonin and leucine levels significantly decreased [ 85 ] . Table 2 Recent studies on detection of biomarkers in hepatocellular carcinoma using MS Technology Biomarkers Sample Reference DESI-MS & UHPLC/QTOFMS Triglycerides Tumor tissues [82] GC-MS N-formylglycine, oxoglutaric acid, citrulline, and heptaethylene glycol Plasma sample [83] LC-MS/MS 8,15-DiHETE, HAD, DHK-PGA2, RCL, OA, 16OHHA Peripheral blood sample [84] UPLC-Q3MS leucine, lysine, phenylalanine, threonine, tryptophan, valine, serotonin, and taurine Blood sample [85] 5.3. Detection of biomarker in ovarian cancer Ovarian cancer (OC) ranks ninth globally in terms of cancer-related death rates among women and is the most lethal type of gynaecological carcinoma [ 86 ] . Because the ovaries are located deep in the pelvis, it is challenging to diagnose epithelial ovarian cancer (EOC) at an early stage; 60% of patients receive a diagnosis at an advanced stage [ 87 ] . Standard treatments for EOC include platinum-based chemotherapy or radiation and surgical debulking [ 88 ] . Less than 30% of patients with advanced EOC survive for five years, despite the availability of better treatment methods [ 89 ] . Few treatment alternatives are available to those who have recurrent EOC beyond first therapy, which significantly reduces their life expectancy and quality of life. It is critically necessary to identify novel targets for therapy in EOC in order to enhance treatment outcomes. One method for discovering treatment targets in OC is proteomic profiling [ 90 ] . Table 3 presents some recent studies on the detection of biomarkers in ovarian cancer using MS. Ahn et al. [ 91 ] analyzed peripheral blood from HGSC patients, identifying nearly 408 metabolites using ectrospray ionization liquid chromatography–tandem mass spectrometry (ESI-LC–MS/MS) and flow injection analysis–tandem mass spectrometry (FIA–MS/MS) and 1289 proteins using Nano-LC-ESI–MS/MS. Out of 408 metabolites, 199 metabolites are quantified. These metabolites were categorized into three groups: small molecules (14%, including amino acids, biogenic amines, and a monosaccharide), neutral lipids (35%, including acylcarnitines, diglycerides, triglycerides, sphingomyelins, and cholesteryl esters), and polar lipids (51%, including phosphatidylcholines, lysophosphatidylcholines, and ceramides). Elevated levels of 34 metabolites were observed in healthy control samples. Furthermore, the ovarian cancer (OC) group exhibited differential expression of 197 proteins, with 89 downregulated and 108 upregulated compared to the healthy group. The extracellular matrix, homeostasis, immunological system, platelets, gluconeogenesis, responsiveness to stimuli, and signalling were all impacted by the OC-upregulated plasma proteins. These all show the growth of cancer, active energy metabolism, and the influence of the cancer environment on the development of OC. Using eleven paired biopsies, a proteomics study comparing ovarian tissue from OC to normal tissue revealed over 2000 charged proteins, many of which were important for protein translation and mitochondrial proteostasis. The study's findings produced an outline of the ovarian cancer proteome and provided insight into the role that HSP60 plays in the development of the disease. It was also shown that HSP60 was necessary for maintaining mitochondrial proteostasis. Adenine accumulated and the AMPK pathway was activated as a result of HSP60 knockdown, which disrupted the respiratory chain's integrity and downregulated translation-related proteins. This inhibited the mTOR pathway, which stopped protein synthesis and inhibited cell growth. According to these findings, HSP60 could potentially be a target for the therapy of ovarian cancer [ 90 ] . Similarly, investigations into OC plasma revealed elevated levels of secreted protein, acidic and rich in cysteine (SPARC) and thrombospondin 1 (THBS1) proteins compared to healthy donors. Utilizing nano-flow LC-MS for quantitative analysis of the depleted plasma proteome, researchers employed a bottom-up proteomics approach. Through comparative statistical analysis of four groups, potential plasma protein markers specific to BRCA1/2 mutation were identified. Among the 40 participants, 1505 protein candidates were isolated, and enzyme-linked immunosorbent assays were used to confirm the presence of THBS1 and SPARC. It was found that plasma concentrations of THBS1 and SPARC were lower in healthy BRCA1/2 carriers than in OC patients with BRCA1/2 variations [ 92 ] . Table 3 Recent studies on detection of biomarkers of ovarian cancer using MS Technology Biomarker Sample Reference ESI-LC-MS/MS, FIA-MS/MS, & Nano-LC-ESI-MS/MS Metabolites & Proteins Peripheral blood [91] LC-MS/MS HSP60 Ovarian tumor tissue sample [90] Nano-LC-ESI-MS/MS THBS1, SPARC Plasma sample [92] 5.4. Detection in biomarker in Tuberculosis Mycobacterium tuberculosis is the cause of tuberculosis (TB), which resulted in 1.6 million deaths globally in 2017 [ 93 ] . It is acknowledged that identifying active cases of tuberculosis is essential to starting treatment and halting further spread [ 94 ] . Even while PCR-based diagnostic instruments are very sensitive and capable of identifying numerous drug-resistant tuberculosis patients, their use for quick screening in populations with high tuberculosis burdens is restricted by the need to use sputum and their long turnaround times [ 95 ] . New screening techniques that can quickly and correctly identify active tuberculosis at a reasonable cost per test and that are operationally practicable in highly burdened settings are therefore desperately needed. The most common techniques for metabolite detection include gas chromatography-mass spectrometry, liquid chromatography-tandem mass spectrometry (LC-MS), and other metabolomics detection technologies with high flux and high sensitivity [ 96 ] . Mass spectrometry is finding increasing use in the realms of clinical diagnostics, environmental measures, and health care [ 97 – 99 ] . Typically, it takes only a few minutes to obtain high-resolution mass spectra, which offer extremely high mass accuracy and a low rate of false positives for molecular composition assignment [ 100 ] . As such, it presents a possibility for developing an advanced diagnostic tool with superior sensitivity, increased dynamic range, and unmatched mass accuracy [ 101 ] . Table 4 presents some recent studies on the detection of biomarkers in tuberculosis using mass spectrometry. Researchers utilized high-resolution mass spectrometry to identify specific lipids in peripheral lung fluid samples from TB patients and control subjects, using an innovative non-invasive sampling method. Exhaled respiratory particles were collected in liquid, concentrated, and infused into a mass spectrometer for analysis in dual ion mode, with chemical compositions determined via accurate mass measurement. The results indicated a general segregation between TB and non-TB samples, though some TB patients clustered with non-TB subjects in both modes. PCA of negative ion mode data revealed better segregation in the 900 to 2000 Da range, with distinguishable peaks primarily between 900 and 1000 Da. A combined approach using significance-analysis of microarray and a support vector machine algorithm identified the most discriminative features, primarily phospholipids, which were significantly elevated in TB patients [ 102 ] . To diagnose latent tuberculosis infection (LTBI) and monitor its progression to active TB, there is an urgent need for noninvasive simple markers. The purpose of a study by Li, Yan-Xia, et al. [ 103 ] , was to find biomarkers for LTBI diagnosis, track the infection's progression to active phase, and look into the underlying mechanisms. Whole blood supernatants were obtained from patients with LTBI, drug-resistant TB, drug-susceptible TB, and healthy controls in order to evaluate alterations in the metabolite composition linked to tuberculosis infection. Oscillation and deproteinization were used to extract metabolites from serum samples, and liquid chromatography-tandem mass spectrometry (LC-MS/MS) was used for detection. Pareto-scaling was used to normalise the data, and Metaboanalyst 4.0 software was used for differential analysis. One-way ANOVA (P < 0.05) was used to identify important metabolites. Theophylline, inosine, 16,16-dimethyl-6-keto prostaglandin E1, and cotinine were found to be viable serum biomarkers for the diagnosis of LTBI. Additionally, cotinine was found to be a sign of the disease's progression. By utilising small-molecule metabolic indicators, this approach improves the sensitivity and specificity of tuberculosis diagnosis and has intriguing therapeutic implications for disease evaluation. In an investigation to find molecular indicators for early tuberculosis (TB) diagnosis, 49 participants were subjected to metabolomics analysis and two cohorts of 29 and 34 individuals received proteome analysis. Three kinds of participants were identified: latent tuberculosis infection (LTBI) carriers, TB patients, and healthy controls. Blood serum protein and metabolite concentrations were examined using LC-MS/MS employing ROC, multivariate, and univariate analyses. Of the 149 proteins that were measured, 25 had differing abundances in TB patients and controls. Four of these proteins were included in a model whose ROC analysis produced an AUC of 0.96, 93% specificity, and 91% sensitivity. For the purpose of diagnosing tuberculosis, a signature consisting of five metabolites—trans-3-indoleacrylic acid, indole-3-lactic acid, hexanoylglycine, and N-acetyl-L-leucine—was discovered. This signature has a high level of sensitivity, specificity, and accuracy. Furthermore, comparable diagnostic performance was shown by a composite biomarker set that included four of the metabolites and the protein hemopexin. These indicators have interesting therapeutic implications for early tuberculosis detection since they are associated with heme catabolism, tryptophan metabolism, xenobiotic detoxification, and proteolytic degradation [ 104 ] . Table 4 Recent studies on detection of biomarkers of tuberculosis using MS Technology Biomarker Sample Reference Mass spectrometry Phospholipids Exhaled breath particle [102] LC-MS/MS Theophylline, inosine, 16,16-dimethyl-6-keto prostaglandin E1, and cotinine Blood sample [103] LC-MS/MS trans-3-indoleacrylic acid, indole-3-lactic acid, hexanoylglycine, and N-acetyl-L-leucine Peripheral blood sample [104] 6. Conclusion and future outlook In conclusion, biomarkers represent a crucial component in the advancement of personalized medicine, providing insights into disease mechanisms, early diagnosis, and targeted therapies. This review categorizes biomarkers based on their characteristics, clinical applications, and genetic/molecular profiles. Biomarkers play an important role in the diagnosis, monitoring, and treatment of a wide range of diseases. These include neurodegenerative disorders like Alzheimer's disease, liver cancers such as hepatocellular carcinoma, ovarian cancer, and infectious diseases like tuberculosis. The biomarker development pipeline, particularly using mass spectrometry, plays a pivotal role in both discovery and validation. MS-based techniques have revolutionized biomarker detection, offering precision in disease diagnosis and the potential for better clinical outcomes. This integrated approach underscores the importance of biomarker research in modern healthcare. The future of biomarker detection, especially utilizing mass spectrometry, holds significant promise for advancing personalized medicine, early disease diagnosis, and therapeutic monitoring. As technology evolves, the precision, sensitivity, and applicability of MS in clinical settings are expected to grow, thereby enhancing our ability to identify and utilize biomarkers effectively. Continuous improvements in MS technology, such as increased resolution, enhanced sensitivity, and faster data acquisition rates, will play a critical role in biomarker detection. Innovations like ion mobility spectrometry, improved ionization techniques, and the integration of MS with advanced bioinformatics tools are anticipated to provide deeper insights into complex biological systems and facilitate the discovery of novel biomarkers. The scope of MS-based biomarker detection is expected to expand beyond oncology to encompass a wide range of diseases, including cardiovascular, neurological, and infectious diseases. The ability of MS to detect and quantify a diverse array of biomarkers will enable comprehensive disease profiling and support the development of new diagnostic and prognostic tools​. The seamless integration of MS into clinical workflows is expected to revolutionize diagnostic and therapeutic practices. Automated sample preparation, coupled with robust data analysis software, will enable the routine use of MS for biomarker detection in clinical laboratories. This integration will support the rapid and accurate diagnosis of diseases, monitoring of disease progression, and assessment of treatment efficacy [ 64 , 105 ] . Collaborative efforts and standardization in biomarker research are crucial for the successful translation of MS-based discoveries into clinical practice. Consortia like the National Cancer Institute's Clinical Proteomic Tumor Analysis Consortium (CPTAC) and the Early Detection Research Network (EDRN) are instrumental in developing and validating biomarkers. These collaborations foster the sharing of data, resources, and methodologies, accelerating the pace of biomarker discovery and implementation. Mass spectrometry-based biomarker detection will be pivotal in the advancement of personalized medicine. By identifying specific biomarkers associated with individual patient profiles, clinicians can tailor therapeutic interventions to achieve optimal outcomes. MS can monitor patient responses to treatments in real-time, allowing for adjustments in therapy and minimizing adverse drug reactions. The integration of proteomic data with other omics data (genomics, transcriptomics, metabolomics) through advanced bioinformatics will enhance the understanding of disease mechanisms. This holistic approach will aid in the identification of robust biomarkers and facilitate the development of multi-omics diagnostic panels, offering a more detailed and accurate picture of health and disease​ [ 64 , 106 ] ​. 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Clin Proteom , 17 (1), 17. https://doi.org/10.1186/s12014-020-09283-w Cite Share Download PDF Status: Published Journal Publication published 03 Jan, 2026 Read the published version in Applied Biochemistry and Biotechnology → Version 1 posted Reviewers agreed at journal 16 May, 2025 Reviewers invited by journal 01 May, 2025 Editor invited by journal 15 Apr, 2025 First submitted to journal 13 Apr, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6333443","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":450551116,"identity":"11ce27f6-7f9b-48f5-b582-0e5fa95c4b42","order_by":0,"name":"Shibam Das","email":"","orcid":"","institution":"University of Turin: Universita degli Studi di Torino","correspondingAuthor":false,"prefix":"","firstName":"Shibam","middleName":"","lastName":"Das","suffix":""},{"id":450551117,"identity":"4a4ae3a5-810b-4309-8931-de124d40d642","order_by":1,"name":"Ankit Awasthi","email":"","orcid":"","institution":"Chitkara College of Pharmacy","correspondingAuthor":false,"prefix":"","firstName":"Ankit","middleName":"","lastName":"Awasthi","suffix":""},{"id":450551118,"identity":"286ae6ea-3eb3-472e-be63-9b561a299761","order_by":2,"name":"Ravindra Kumar Rawal","email":"","orcid":"","institution":"CSIR-NEIST: North East Institute of Science and Technology CSIR","correspondingAuthor":false,"prefix":"","firstName":"Ravindra","middleName":"Kumar","lastName":"Rawal","suffix":""},{"id":450551119,"identity":"f28e7d4b-3f44-4cac-9b17-a2a354709790","order_by":3,"name":"ROHIT BHATIA","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABE0lEQVRIiWNgGAWjYBACAwkgkQBiMDOwf/j4z0YOJHrgAX4tjA0QLcxsjDPY0ozBWhIIaQEzGJjZmDnYDieCefi0mEs3P3/woOaenDk7/7HHDDxp6fPDDj8E2mInp9uAXYvlnGOGDQnHio0tm5nZjQskbHI33k4zAGpJNjY7gMNhNxKAWtgSEjccZmaQnmGQlrtxdgJIy4HEbTi1pH9sSPgH1cKTcDjdcHb6BwJacgwbEtvAWtikeQ4cTpCXzsFvi+WMnMIZiX0JxgaHmY0NZzakGW6Qzik4kGCA2y/mEukbPv74liBncP7gwwcfG2zk5Wenb/7wocJODpcWLE4FqzQgVjkIyDeQonoUjIJRMApGAgAAQwJmpspaBNwAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0003-4194-8749","institution":"CCP: Chitkara College of Pharmacy","correspondingAuthor":true,"prefix":"","firstName":"ROHIT","middleName":"","lastName":"BHATIA","suffix":""}],"badges":[],"createdAt":"2025-03-29 10:16:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6333443/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6333443/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s12010-025-05549-x","type":"published","date":"2026-01-03T15:58:04+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":82058630,"identity":"0e6074ba-dc88-4ec3-b254-d68db03556c1","added_by":"auto","created_at":"2025-05-06 11:04:48","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":111832,"visible":true,"origin":"","legend":"\u003cp\u003eCharacteristics of an ideal biomarker\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6333443/v1/30ad71fe4ebff4a124de4f8e.png"},{"id":82058359,"identity":"9ed94164-ac08-43a7-b679-fc7c2c549b92","added_by":"auto","created_at":"2025-05-06 10:56:48","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":69301,"visible":true,"origin":"","legend":"\u003cp\u003eClassification of biomarkers\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6333443/v1/2dc155d4adca239e645373cb.png"},{"id":82058629,"identity":"2dd26532-87d7-4234-b0a1-44b5e98b3e39","added_by":"auto","created_at":"2025-05-06 11:04:48","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":240833,"visible":true,"origin":"","legend":"\u003cp\u003eIllustration of circulating biomarkers can be used in Disease diagnosis, prognosis, monitoring and treatment.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6333443/v1/b4609f69a38b96a5d2f82fe5.png"},{"id":99545318,"identity":"4d533e49-b421-4d7e-8bfe-ecd59d0fd7ad","added_by":"auto","created_at":"2026-01-05 16:05:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1484390,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6333443/v1/bc762b56-cf12-4535-ade1-46154b3e83fd.pdf"}],"financialInterests":"","formattedTitle":"Biomarkers in Disease Diagnosis and Monitoring: Insights into Clinical Applications and Mass Spectrometry-based Detection","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eOver the past fifty years, various definitions of biomarkers have been proposed and subsequently changed in response to advancements in science and medicine. In 1973, Rho et al. coined the word \"biomarker\" to refer to the presence or lack of particular biological material\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. Nonetheless, the phrase is older; Mundkur used it to refer to \"biochemical markers\" in 1949\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e, and Porter did the same in 1957\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. Hulka and colleagues (1990) defined biomarkers as \"alterations in cellular, molecular, and biochemical processes that can be measured and evaluated in biological media, such as human cells, tissues, or fluids\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. \"Among the distinguishing characteristics that can be objectively measured and evaluated as a possible indicator of any normal or aberrant pathophysiological process or pharmacological response to a course of therapy are \"biomarkers,\" or \"biological markers\". The definition given to biomarkers is \"The substances, structures, or processes which can be quantified in the body or its products and influence or predict the incidences of outcomes or diseases\"\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. \"A characteristic that is objectively measured and evaluated as an indicator of normal biological processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention\" is the definition of a biomarker provided by the National Institutes of Health (NIH)\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. In accordance with the World Health Organisation, \"nearly any measurement reflecting an interaction between a biological system and a potential hazard, which may be chemical, physical, or biological, is considered a true biomarker. The measured response may be functional and physiological, biochemical at the cellular level, or a molecular interaction.\"\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eBiomarkers have been increasing in significance in the pharmaceutical research in recent years. The cornerstone to personalised medicine and overall improved clinical outcomes is the identification of ideal biomarkers. An ideal biomarker, as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, has specific characteristics that enable it to be used in the diagnosis of a given medical condition. Biomarkers are now used to determine the mechanism of action of drugs, examine efficacy and toxicity signals early in the research and development process, and to identify patients who are likely to react to therapy. Additionally, a number of potentially effective methods for interpreting such complexities are emerging in a variety of scientific domains, and the use of this information in personalised medicine has increased\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. For biological, therapeutic, and the ranostic research endeavours, biomarkers play an essential and very crucial role in improving the entire drug development processes\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. In addition to other factors like heart rate and blood pressure, biomarkers also involve far more complicated laboratory-based testing in biological fluids and other human tissues, as well as basic chemistry\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. Clinical endpoints for many diseases, such death or disease recurrence, can take a long time to obtain, while biomarkers can give earlier data and enable more focused, smaller trials. In order to employ each biomarker in an appropriate situation, one must be aware of its features before using it in daily practice. The degree of sensitivity specificity, accuracy, and positive and negative predictive values are the main attributes\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. Numerous biomarkers have been identified and quantified to monitor the treatment of nearly all diseases and health conditions, or as indicators of exposure to a diverse range of pharmaceuticals and environmental substances. A whole spectrum of bio-indicators, including nucleic acids, proteins, enzymes, antigens-antibodies, and other biological agents, are evaluated with the purpose of diagnosing or tracking pathophysiological disorders\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. Some instances of biomarkers employed in identifying diseases include cardiac troponin for detecting cardiac muscle injury\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e, the presence of 3-hydroxy-fatty acids in planctomycetes\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e, and a panel of glycans utilized as cancer biomarkers\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe identification and examination of biomarkers have become feasible through diverse technologies and procedures, enabling the accumulation of extensive data for characterizing these biomolecular indicators within intricate mixtures of proteins, lipids, carbohydrates, and more\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. Mass spectrometry has played a pivotal role in the exploration and assessment of biomarkers, thanks to several crucial characteristics such as its sensitive and specific detection capabilities, its capacity for analyzing multiple analytes simultaneously, and its ability to furnish structural insights. Due to these advantages, mass spectrometry has found extensive application in the quest for novel biomarkers, encompassing the study of both large molecules (proteomics) and small molecules (metabonomics). Moreover, mass spectrometry is increasingly being employed to facilitate quantitative assessments, aiding in the verification and validation of potential biomarker candidates\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. Besides, unlike traditional immunoassays or molecular techniques, MS offers label-free detection, allowing for unbiased and hypothesis-free biomarker discovery. Additionally, MS-based approaches are amenable to automation, enabling high-throughput analysis and reproducibility. Metabolomics based on mass spectrometry provides precise quantitative evaluations with remarkable selectivity and sensitivity, along with the capability to discern metabolites. When coupled with a separation method, it diminishes the intricacy of mass spectra by segregating metabolites over time, ensuring distinction between isobars, and furnishing supplementary insights into the physicochemical attributes of metabolites\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. Mass spectrometry has proven to be highly efficient not only in the analysis of protein biomarkers but also in the examination of other biomarker categories such as hormones and genetic markers\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. In this review, we cover the different classifications of biomarkers and highlight their significance in the discovery and validation of various diseases. We also examine the diverse applications of mass spectrometry in identifying biomarkers, emphasizing its crucial role in advancing biomarker discovery across a range of biological contexts.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e"},{"header":"2. Classifications","content":"\u003cp\u003eBiomarkers can be classified on the basis of various parameters, such as their characteristics, sources, genetic and molecular biology methods as well as clinical applications (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Characteristics\u003c/h2\u003e \u003cp\u003eBased on their characteristics, biomarkers can be categorized into three primary types: cellular, molecular, and imaging biomarkers.\u003c/p\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003ch2\u003e2.1.1. Molecular biomarkers\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eMolecular biomarkers possess distinct biophysical properties and are detectable in various biological specimens including cerebrospinal fluid, plasma, serum, bronchoalveolar lavage fluid, and biopsies\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. They encompass a diverse array of molecules, spanning from small compounds to larger entities such as peptides, proteins, lipids, metabolites, nucleic acids (DNA and RNA), and additional molecular species\u003csup\u003e[\u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. These biomarkers, detected using proteomic and genomic methods, play a vital role in disease diagnosis, prevention, prognosis, and treatment management. These biomarkers also find diverse applications in analytical epidemiology, randomized clinical trials\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.1.2. Imaging biomarkers\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eImaging biomarkers represent a unique category of biomarkers derived from in vivo medical imaging, offering an appealing option for clinical applications due to their real-time, non-invasive, and affordable nature. Although there is a wealth of research on biological biomarker development, there is still a lack of a well-defined roadmap for defining imaging biomarkers. However, various academic, clinical, industrial, and regulatory groups have addressed the standardization of imaging biomarkers procurement and analysis, as well as the harmonization of terminology, particularly in specific contexts\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. Magnetic resonance imaging (MRI), computed tomography (CT), and positron emission tomography (PET) are the three categories of imaging biomarkers\u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e. PET, which tracks and measures the body's cells' intake of glucose, can be used to evaluate the effectiveness of cancer therapy\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. CT is a diagnostic technique that uses ionising radiation to create cross-sectional and three-dimensional (3D) images of the human body as well as to track the status of tumors\u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e. MRI, which produces extremely high spatial resolution pictures, is the method most commonly employed to study cancer and neurodegenerative illnesses\u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.1.3. Cellular biomarkers\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eClinical and laboratory testing can employ cellular biomarkers, which are quantifiable and biological indicators. Cellular biomarkers are frequently assessed for prognosis or likelihood of responding to a particular treatment in blood, bodily fluids, or soft tissues. These biomarkers enable the separation, classification, measurement, and description of cells based on their morphological and physiological characteristics\u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Clinical applications\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eBiomarkers can be divided into three categories based on their clinical applicability in various phases of disease: therapeutic, prognostic, and diagnostic\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. Proteins like exosomes and miRNAs are examples of therapeutic biomarkers that may be applied to targeted treatments\u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e. Prognostic biomarkers provide information about the state of a disease by screening, tracking the progression of the condition, and assessing changes in the internal antecedents that a disease has a probability of attaining\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. Diagnostic biomarkers are used to diagnose conditions such as cardiac troponin, which is used to diagnose heart muscle injury\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e, and the distributions of 3-hydroxy-fatty acids, which are used to diagnose planctomycetes\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Genetic and molecular biology method\u003c/h2\u003e \u003cp\u003eThree categories of biomarkers are distinguished by the genetic and molecular biology method: categories 0, 1, and 2. In phase 0 clinical trials of a disease, type 0 is a natural history biomarker that can be assessed and is connected with clinical outcomes throughout time. A drug's mode of action, therapeutic effects, interactions, and toxicological effects are all indicated by the Type 1 drug activity biomarker. As a stand-in for clinical outcome evaluations of disease, type 2 biomarker aids in the prediction of how a therapeutic intervention would be received\u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Importance of biomarkers in different diseases","content":"\u003cp\u003eNumerous biomarkers have been identified and quantified for the treatment of nearly all diseases and ailments, as well as serving as indicators of exposure to a broad range of pharmaceutical and environmental substances. A whole spectrum of bioindicators, including proteins, nucleic acids, enzymes, antigens, antibodies, and other biological agents, are evaluated with the purpose of diagnosing or tracking pathophysiological disorders\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eDeep learning technology and high-resolution computed tomography images were employed as potential diagnostic biomarkers in a study published by Pu et al.\u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e, to look at parameters related to COVID-19. The results imply that distinct patterns or features in images might not be necessary to accurately differentiate all cases of COVID-19 from CAP. Nonetheless, their findings show that the imaging indicators have the ability to help identify a significant portion of instances that are not COVID-19. Machine learning (ML) sensors have been described by Velichko et al.\u003csup\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e as a workable solution for the Internet of Things (IoT) in clinical decision support systems to establish the preliminary diagnosis of COVID-19 with an accuracy of up to 95% utilising RBV. The COVID-19 disease was effectively diagnosed using the histogram-based gradient boosting (HGB) model. The disease was identified with 100% accuracy in 6.39 seconds by the HGB model that was the fastest and most accurate in identifying the 11 most significant biomarkers, which included low-density lipoprotein, cholesterol, high-density lipoprotein cholesterol, mean corpuscular haemoglobin level, amylase, triglyceride, uric acid, lactate dehydrogenase, alkaline phosphatase, creatine kinase-myocardial band, as well as mean corpuscular haemoglobin. Cancer is the leading cause of mortality globally. It is a complex hereditary disease that spreads to major organs in the body\u003csup\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e. A biopsy or surgical resection may be necessary for tissue-derived cancer biomarkers, which are found in the bloodstream (whole blood, plasma, or serum), secretions (urine, stools, sputum, or nipple discharge), or other human biological fluids. These biomarkers can be easily assessed serially and noninvasively\u003csup\u003e[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e. Numerous cardiac and extracardiac pathophysiological pathways can induce heart failure (HF), which is a complex clinical illness with a wide range of phenotypes\u003csup\u003e[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003e. Biomarkers have a variety of roles in clinical care of cardiovascular disease, including risk stratification, prognostic assessment, therapeutic monitoring, and diagnosis. These functions allow for an integrated strategy. The BNP levels of 122 patients with acute decompensated heart failure and declining renal function were assessed in this study. Rehospitalization was positively correlated with a significant BNP value reduction of \u0026ge;\u0026thinsp;40% during hospitalisation, or from baseline to release\u003csup\u003e[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/sup\u003e. Additional diagnostic biomarkers for oxidative stress (growth differentiation factor-15), cardiac remodelling (gatein-3), and inflammation (soluble ST2 receptor) may be introduced to aid in the management of heart failure\u003csup\u003e[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/sup\u003e. Biomarkers are also helpful in the diagnosis, prognosis, therapy, and prevention of neurological and neuropsychiatric diseases, including epilepsy, Parkinson's disease, Alzheimer's disease, stroke, and Huntington's disease\u003csup\u003e[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]\u003c/sup\u003e. Biomarkers for neurological disorders include 4-hydroxy-2,3-noenal, angiogenin, Cystatin-C, clusterin etc\u003csup\u003e[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]\u003c/sup\u003e. In blood and urine specimens for significant adverse renal events following heart surgery, hepcidin-25, an iron-binding protein linked to acute kidney injury, is employed as a new kidney biomarker\u003csup\u003e[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]\u003c/sup\u003e. For the purpose of identifying preclinical ASO-induced kidney pathology, the FDA has approved a panel of six urine creatinine-normalized biomarkers, which include clusterin, cystatin C, kidney injury molecule 1, N-acetyl-β-D-glucosaminidase, neutrophil gelatinase-associated lipocalin, and osteopontin\u003csup\u003e[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]\u003c/sup\u003e. When it comes to liver diseases, an extremely specific surrogate biomarker that can be found in the bloodstream is alanine aminotransferase\u003csup\u003e[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]\u003c/sup\u003e. Potential liver disease biomarkers include bilirubin, hyaluronic acid, laminin, cytokines, and fibroblast growth factor 21\u003csup\u003e[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e"},{"header":"4. Biomarker development pipeline","content":"\u003cp\u003eThe biomarker pipeline is typically understood as a sequence of preclinical stages that include biomarker identification and validation prior to the ultimate clinical evaluation. The various stages of the biomarker pipeline employ different mass spectrometric techniques. For example, a typical proteomics discovery study employs shotgun proteomics, an untargeted method, to measure thousands of proteins relative to one sample. A list of dozens of proteins that are expressed differently in diseased and healthy samples is produced by the comparative analysis. After initial discovery, potential biomarker proteins undergo filtration through studies on more patients, additional time points, or higher-specificity mass spectrometry in a qualification step. Subsequently, verification occurs on 10\u0026ndash;50 patient samples, followed by validation on 100\u0026ndash;500 samples. Clinical validation of final biomarkers involves quantifying a small number of proteins on 500\u0026ndash;1000 samples\u003csup\u003e[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e4.1. MS based biomarker discovery\u003c/h2\u003e \u003cp\u003eTechniques for biomarker identification and quantification are part of MS-based biomarker discovery. Finding a shift in the relative abundance of peptides from a distinct protein in samples taken from disease patients in comparison to matched controls is the goal\u003csup\u003e[\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]\u003c/sup\u003e. A peptide needs to be found and sequenced in order for it to be identified. Depletion of abundant proteins is hence frequently the first step in sample processing. Fractionation can be carried out at the protein, peptide, or both levels following depletion (and reduction/carboxymethylation), utilising techniques such size exclusion chromatography (SEC), ion exchange chromatography (SCX), and isoelectric focusing\u003csup\u003e[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]\u003c/sup\u003e. A variety of techniques can be used, from 1D or 2D gel electrophoresis combined with protein identification based on mass spectrometry to relative quantification techniques that are solely based on LC-MS. Known for their high scan speeds and resolution, instruments like Q-TOF or Q-Exactive MS systems are often utilised in this phase, allowing the capture of many peptide identifications and facilitating progressively accurate protein quantification\u003csup\u003e[\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]\u003c/sup\u003e. Proteins are typically first digested in MS-based relative quantitation procedures, and the subsequent protein quantification is actually based on the quantitation of prototypic peptides that function as stand-ins for the protein of interest (a process known as the \"bottom-up\" method). For relative quantification, a range of isotopic labeling methods can be applied, such as TMT-tagging, isobaric tagging, or non-isobaric tagging. Label-free quantitation methods such spectrum counting are also used\u003csup\u003e[\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]\u003c/sup\u003e. In order to identify proteins, a number of software programmes automate the examination of peptide tandem mass spectra and database searches. As said, these programmes are made to identify all interpreted spectra; hence, extra caution needs to be used to guarantee that only proteins that have been positively recognised are examined further. To fill preliminary lists of potential biomarkers, semiquantitative comparisons of protein abundance between patients and controls are utilized\u003csup\u003e[\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e4.2. MS-based biomarker validation/ verification\u003c/h2\u003e \u003cp\u003eThere are multiple steps in the validation process that depend on the context of use of the candidate biomarker once it has been identified and a detection method has been established. The level of validation required intensifies as the biomarker progresses from research applications to clinical trials and, eventually, to clinical practice\u003csup\u003e[\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]\u003c/sup\u003e. Analytical validation focuses on assessing the precision, dynamic range, and sensitivity of the detection method. In contrast, clinical validation evaluates the biomarker\u0026rsquo;s sensitivity and specificity in identifying, measuring, or predicting clinical outcomes. Sensitivity is the measure of true positive rate, while specificity relates to the true negative rate\u003csup\u003e[\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]\u003c/sup\u003e. ELISAs are ideally suited for applications necessitating thorough sample validation or verification of a limited number of biomarkers. However, the unavailability of high-quality ELISA tests for targeted proteins is a common challenge, compounded by the requirement for specific antibodies against each protein or peptide\u003csup\u003e[\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]\u003c/sup\u003e. Advanced mass spectrometry (MS) techniques have revolutionized the field of biomarker validation by providing highly sensitive and specific methods for analyzing complex biological samples. Methods such as Multiple Reaction Monitoring Mass Spectrometry (MRM-MS) allow for the precise quantification of protein biomarkers, which are crucial for distinguishing between healthy and diseased states. These techniques utilize isotope-labeled internal standards to enhance the accuracy and reproducibility of assays. Research by Percy et al., highlights the use of LC/MRM-MS for urinary protein quantitation\u003csup\u003e[\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]\u003c/sup\u003e, while Mermelekas et al., focus on targeted proteomics for biomarker validation\u003csup\u003e[\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]\u003c/sup\u003e. Additionally, Gallien et al., emphasize the role of LC-MS/MS in proteomics experiments\u003csup\u003e[\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]\u003c/sup\u003e. These studies demonstrate the pivotal role of MS-based techniques in advancing biomarker validation and their potential to improve diagnostic as well as therapeutic strategies.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Mass Spectrometry-based detection of disease biomarkers","content":"\u003cp\u003eIn recent years, mass spectrometry (MS) has emerged as a powerful tool in the field of biomarker discovery and detection. With its ability to analyze complex biological samples with high sensitivity and specificity, MS has revolutionized our approach to identifying biomarkers for various diseases. By leveraging the unique mass-to-charge ratio of molecules, MS enables the precise measurement of proteins, peptides, metabolites, and other biomolecules present in biological specimens\u003csup\u003e[\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]\u003c/sup\u003e. In this context, MS-based techniques offer unparalleled insights into disease pathogenesis, progression, and treatment response, paving the way for the development of novel diagnostic and therapeutic strategies\u003csup\u003e[\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]\u003c/sup\u003e. In this article, we explore some of the recent applications of mass spectrometry in detecting biomarkers associated with different diseases.\u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e5.1. Detection of biomarkers in Alzheimer's disease\u003c/h2\u003e \u003cp\u003eThe most prevalent type of dementia, accounting for 60\u0026ndash;70% of cases, is Alzheimer's disease (AD). In order to reliably estimate prognosis, intervene, and monitor AD, accurate diagnosis is necessary for early detection\u003csup\u003e[\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]\u003c/sup\u003e (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAlzheimer's disease is distinguished by the accumulation of amyloid-β (Aβ). This pathological progression initiates several years to decades prior to the manifestation of clinical manifestations\u003csup\u003e[\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]\u003c/sup\u003e. A recent study evaluated the diagnostic potential of plasma Aβ biomarkers for brain amyloidosis prediction using both the single-molecule array (SIMOA) immunoassay platform and LC/MS/MS measurement methods. The findings demonstrate that the amyloid PET state can be determined more accurately by LC-MS methods for assessing plasma amyloid-β1\u0026ndash;42/amyloid-β1\u0026ndash;40 and a plasma composite than by SIMOA amyloid-β42/amyloid-β40 and p-tau181 tests. The study suggests that plasma screening may be feasible in a preclinical cohort and adds to the increasing body of evidence that shows it can decrease the number of amyloid PET scans needed for detecting amyloid-β-positive individuals, for clinical trial recruitment, or eventually for anti-amyloid therapy administration\u003csup\u003e[\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e]\u003c/sup\u003e. Lim et al. conducted a study involving two distinct cohorts of cognitively normal elderly individuals to explore the relationship between a plasma Aβ composite biomarker and cognitive functioning. Employing immunoprecipitation and mass spectrometry techniques, they developed a highly effective plasma Aβ composite biomarker. In addition to enrichment using immunoprecipitation, they were able to precisely determine the mass of each Aβ peptide fragment using IP-MS. According to this research, Aβ\u0026thinsp;+\u0026thinsp;classified using IP MS is linked to a decline in cognitive functions that are essential to the early symptomatic manifestation of AD. This strongly suggests that the plasma Aβ composite marker will serve as a helpful prognostic marker for clinical outcomes, and formal investigations of these attributes are therefore necessary. Furthermore, it holds potential for identifying candidates at risk for AD prevention trials and provides a strong foundation for evaluating how effectively this plasma Aβ composite marker reflects changes in Aβ levels throughout the disease progression or in response to Aβ-lowering treatments. \u003csup\u003e[\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTau is one of the cerebrospinal fluid (CSF) AD biomarkers that has been examined using mass spectrometry techniques in addition to immunoassays. The selectivity of the immunoassays has frequently been questioned because to the molecular variety of tau in CSF, which includes several isoforms, posttranslational modifications, and peptide fragments\u003csup\u003e[\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e]\u003c/sup\u003e. One group reported using Parallel reaction monitoring to develop a very sensitive and specific mass spectrometry approach for tau phosphorylation site identification. The quantity of tau protein phosphorylation sites in brain tissue and CSF from individuals with and without AD were compared using this technique. In brain tau that was fully grown, they found 29 different phosphorylation sites, and in CSF tau that was shortened, they found 12 sites. Additionally, they discovered phosphorylation at the threonine 153 and 175 sites, which were only present in AD CSF\u003csup\u003e[\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e]\u003c/sup\u003e. The research team also utilized their mass spectrometry technique to accurately identify various low-abundance variants of phosphorylated tau. They demonstrated that heightened levels of cerebrospinal fluid (CSF) p-tau217 offer a more sensitive detection method for both early and advanced stages of Alzheimer's disease (AD). Additionally, the team's findings indicated a similar performance between p-tau217 and elevated levels of p-tau181 in detecting AD at both its preclinical and advanced stages. However, despite these advancements, the precise identification of different forms of phosphorylated tau remains challenging, though it serves as a valuable tool in understanding the progression of tau phosphorylation in AD. Further evaluations are planned to compare the clinical efficacy of these and other forms of p-tau using established immunoassays and mass spectrometry techniques\u003csup\u003e[\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e, \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e]\u003c/sup\u003e. According to recent research, tau and amyloid β pathologies can be predicted as well as utilised to distinguish AD patients from other tauopathies by plasma p-tau181 testing\u003csup\u003e[\u003cspan additionalcitationids=\"CR74\" citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e]\u003c/sup\u003e. Furthermore, p-tau231 can be used to identify AD patients from patients with non-AD neurological conditions with considerably higher accuracy than established MRI- and plasma-based biomarkers. P-tau217 increases during early stages of Alzheimer's disease and can be used to track the disease's progression. Unlike plasma p-tau181, the latter distinguishes people over the whole Braak stage spectrum\u003csup\u003e[\u003cspan additionalcitationids=\"CR77\" citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eInoue, Makoto, et al. developed a blood test based on liquid chromatography-tandem mass spectrometry (LC-MS/MS) to find 8 plasma proteins {Albumin (Alb), apolipoprotein C1 (ApoC1), complement components 3 (C3), 4 gamma chain (C4G), alpha-2-antiplasmin (A2AP), alpha-2-macroglobulin (A2M), hemopexin (HPX), and alpha-1-glycoprotein (A1BG)} that can be utilised to diagnose Alzheimer's disease and moderate cognitive impairment (MCI). They measured plasma proteins with this method by labelling synthetic peptides with isotopes. The analysis included 192 patients in total: 63 with AD, 71 with MCI, and 58 non-demented controls (NDCs). They identified eight potential plasma protein biomarkers that are useful in differentiating between AD and MCI\u003csup\u003e[\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRecent studies on detection of biomarkers in Alzheimer's disease using MS\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTechnology\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBiomarker\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSample\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLC-MS/MS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAmyloid β\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCSF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[67]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIC-MS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAmyloid β\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCSF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[68]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTau\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCSF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[70]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTau\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCSF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[71]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLC-MS/MS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAlb, ApoC1, C3, C4G, A2AP, A2M, HPX, A1BG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBlood sample\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[79]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e5.2. Detection of biomarkers in Hepatocellular carcinoma\u003c/h2\u003e \u003cp\u003eHepatocellular carcinoma (HCC), the predominant type of liver cancer, is extremely aggressive and is continuing to develop worldwide. HCC ranks second in East Asia for cause of mortality, with a five-year survival rate of fewer than 15%. In order to minimise cancer-related mortality and preserve cancer patients' quality of life, early detection of HCC is essential\u003csup\u003e[\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e]\u003c/sup\u003e (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMetabolomics is a potential method for identifying tiny molecules related to disease and is commonly utilised in biomarker discovery. Thus, by examining Tumor expression, metabolomics\u0026mdash;also known as metabolic phenotyping\u0026mdash;is a method that may be used to identify possible biomarker candidates for HCC\u003csup\u003e[\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn a study, using a mixed-mode column and HDMS, a comprehensive and repeatable G-Met approach was used to identify novel biomarkers for HCC. The HCC biomarker candidates were found by comparing the values of the collision cross section and their fragment ions on the mass spectra acquired by HDMS, after they had been extracted using multivariate analysis. By locating the biomarkers in slices of tumor tissue using desorption electrospray ionisation (DESI) mass spectrometry (MSI), they were able to analyse the biomarkers. The combination study of DESI-MSI and UHPLC/QTOFMS demonstrated that the various molecular species of triglycerides were linked to the location of tumors and may be used to characterise the evolution of Tumor cells and identify potential biomarkers\u003csup\u003e[\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e]\u003c/sup\u003e. Oxoglutaric acid, citrulline, plasma N-formylglycine, and heptaethylene glycol together can be an effective new diagnostic biomarker for HCC, according to research by Liu, Zhiying, et al.\u003csup\u003e[\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e]\u003c/sup\u003e. In this investigation, gas chromatography-mass spectrometry was used to evaluate and validate plasma samples from 104 HCC, 76 cirrhosis, and 10 healthy people. Using multivariate logistic regression analysis, it was possible to differentiate HCC from cirrhosis using N-formylglycine, oxoglutaric acid, citrulline, and heptaethylene glycol among the candidate metabolites in the validation cohort. With area under curve, sensitivity, and specificity values of 0.940, 84.00%, and 97.56%, respectively, the combination of these four metabolites performed better than alpha fetoprotein (AFP), which is also frequently used in clinical practice as a diagnostic sign for HCC. However, when it comes to distinguishing between cirrhosis and early stage HCC, AFP is less useful than the panel consisting of N-formylglycine, heptaethylene glycol, and citrulline. A robust LC-MS/MS approach was developed in a study by Yue, Zhihong, et al. for the simultaneous detection of many serum lipids, such as 8,15-dihydroxy-5,9,11,13-eicosatetraenoic acid (8,15-DiHETE), hexadecanedioic acid (HAD), 15-keto-13,14-dihydroprostaglandin A2 (DHK-PGA2), ricinoleic acid (RCL), octadecanedioic acid (OA) and 16-hydroxy hexadecanoic acid (16OHHA). They discovered that serum 8,15-DiHETE, DHK-PGA2, HDA, and OA were considerably elevated in Type 2 diabetes mellitus (T2DM) positive HCC patients using LC-MS/MS technique. The potential for therapeutic application of a biomarker signature based on AFP, DHK-PGA2, and HDA was suggested by its excellent diagnostic efficacy in differentiating T2DM\u0026thinsp;+\u0026thinsp;ve HCC from T2DM and other T2DM\u0026thinsp;+\u0026thinsp;ve cancers, such as gastric cancer, pancreatic cancer, and colorectal cancer. The clinical significance of the biomarker signature in the diagnosis of T2DM\u0026thinsp;+\u0026thinsp;ve HCC is highlighted in this work, which has important ramifications for enhancing patient outcomes\u003csup\u003e[\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e]\u003c/sup\u003e. To explore the metabolic distinctions between hepatitis and hepatocellular carcinoma (HCC), In a research by Tao et al., serum AA levels were quantitatively analysed in 136 patients with hepatitis B (CHB) and 93 patients with hepatocellular carcinoma (HCC) associated with the hepatitis B virus (HBV) using targeted mmetabolomics based on ultraperformance liquid chromatography triple quadrupole mass spectrometry. Research shows that in patients with chronic hepatitis B (CHB), serum phenylalanine levels were higher, while levels of leucine, lysine, threonine, tryptophan, valine, serotonin, and taurine were lower, especially in those with hepatocellular carcinoma (HCC). Among HCC patients, those in Class C had lower valine and serotonin levels compared to Classes A and B. Additionally, higher phenylalanine levels were associated with higher Model for End-Stage Liver Disease (MELD) scores. In the decompensated stage, phenylalanine levels increased, while serotonin and leucine levels significantly decreased\u003csup\u003e[\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRecent studies on detection of biomarkers in hepatocellular carcinoma using MS\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTechnology\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBiomarkers\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSample\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDESI-MS \u0026amp; UHPLC/QTOFMS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTriglycerides\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTumor tissues\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[82]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGC-MS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN-formylglycine, oxoglutaric acid, citrulline, and heptaethylene glycol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePlasma sample\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[83]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLC-MS/MS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8,15-DiHETE, HAD, DHK-PGA2, RCL, OA, 16OHHA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePeripheral blood sample\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[84]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUPLC-Q3MS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eleucine, lysine, phenylalanine, threonine, tryptophan, valine, serotonin, and taurine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBlood sample\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[85]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e5.3. Detection of biomarker in ovarian cancer\u003c/h2\u003e \u003cp\u003eOvarian cancer (OC) ranks ninth globally in terms of cancer-related death rates among women and is the most lethal type of gynaecological carcinoma\u003csup\u003e[\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e]\u003c/sup\u003e. Because the ovaries are located deep in the pelvis, it is challenging to diagnose epithelial ovarian cancer (EOC) at an early stage; 60% of patients receive a diagnosis at an advanced stage\u003csup\u003e[\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e]\u003c/sup\u003e. Standard treatments for EOC include platinum-based chemotherapy or radiation and surgical debulking\u003csup\u003e[\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e]\u003c/sup\u003e. Less than 30% of patients with advanced EOC survive for five years, despite the availability of better treatment methods\u003csup\u003e[\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e]\u003c/sup\u003e. Few treatment alternatives are available to those who have recurrent EOC beyond first therapy, which significantly reduces their life expectancy and quality of life. It is critically necessary to identify novel targets for therapy in EOC in order to enhance treatment outcomes. One method for discovering treatment targets in OC is proteomic profiling\u003csup\u003e[\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e]\u003c/sup\u003e. Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents some recent studies on the detection of biomarkers in ovarian cancer using MS.\u003c/p\u003e \u003cp\u003eAhn et al.\u003csup\u003e[\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e]\u003c/sup\u003e analyzed peripheral blood from HGSC patients, identifying nearly 408 metabolites using ectrospray ionization liquid chromatography\u0026ndash;tandem mass spectrometry (ESI-LC\u0026ndash;MS/MS) and flow injection analysis\u0026ndash;tandem mass spectrometry (FIA\u0026ndash;MS/MS) and 1289 proteins using Nano-LC-ESI\u0026ndash;MS/MS. Out of 408 metabolites, 199 metabolites are quantified. These metabolites were categorized into three groups: small molecules (14%, including amino acids, biogenic amines, and a monosaccharide), neutral lipids (35%, including acylcarnitines, diglycerides, triglycerides, sphingomyelins, and cholesteryl esters), and polar lipids (51%, including phosphatidylcholines, lysophosphatidylcholines, and ceramides). Elevated levels of 34 metabolites were observed in healthy control samples. Furthermore, the ovarian cancer (OC) group exhibited differential expression of 197 proteins, with 89 downregulated and 108 upregulated compared to the healthy group. The extracellular matrix, homeostasis, immunological system, platelets, gluconeogenesis, responsiveness to stimuli, and signalling were all impacted by the OC-upregulated plasma proteins. These all show the growth of cancer, active energy metabolism, and the influence of the cancer environment on the development of OC.\u003c/p\u003e \u003cp\u003eUsing eleven paired biopsies, a proteomics study comparing ovarian tissue from OC to normal tissue revealed over 2000 charged proteins, many of which were important for protein translation and mitochondrial proteostasis. The study's findings produced an outline of the ovarian cancer proteome and provided insight into the role that HSP60 plays in the development of the disease. It was also shown that HSP60 was necessary for maintaining mitochondrial proteostasis. Adenine accumulated and the AMPK pathway was activated as a result of HSP60 knockdown, which disrupted the respiratory chain's integrity and downregulated translation-related proteins. This inhibited the mTOR pathway, which stopped protein synthesis and inhibited cell growth. According to these findings, HSP60 could potentially be a target for the therapy of ovarian cancer\u003csup\u003e[\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e]\u003c/sup\u003e. Similarly, investigations into OC plasma revealed elevated levels of secreted protein, acidic and rich in cysteine (SPARC) and thrombospondin 1 (THBS1) proteins compared to healthy donors. Utilizing nano-flow LC-MS for quantitative analysis of the depleted plasma proteome, researchers employed a bottom-up proteomics approach. Through comparative statistical analysis of four groups, potential plasma protein markers specific to BRCA1/2 mutation were identified. Among the 40 participants, 1505 protein candidates were isolated, and enzyme-linked immunosorbent assays were used to confirm the presence of THBS1 and SPARC. It was found that plasma concentrations of THBS1 and SPARC were lower in healthy BRCA1/2 carriers than in OC patients with BRCA1/2 variations\u003csup\u003e[\u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRecent studies on detection of biomarkers of ovarian cancer using MS\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTechnology\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBiomarker\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSample\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eESI-LC-MS/MS, FIA-MS/MS, \u0026amp; Nano-LC-ESI-MS/MS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMetabolites \u0026amp; Proteins\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePeripheral blood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[91]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLC-MS/MS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHSP60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOvarian tumor tissue sample\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[90]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNano-LC-ESI-MS/MS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTHBS1, SPARC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePlasma sample\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[92]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e5.4. Detection in biomarker in Tuberculosis\u003c/h2\u003e \u003cp\u003eMycobacterium tuberculosis is the cause of tuberculosis (TB), which resulted in 1.6\u0026nbsp;million deaths globally in 2017\u003csup\u003e[\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e]\u003c/sup\u003e. It is acknowledged that identifying active cases of tuberculosis is essential to starting treatment and halting further spread\u003csup\u003e[\u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e94\u003c/span\u003e]\u003c/sup\u003e. Even while PCR-based diagnostic instruments are very sensitive and capable of identifying numerous drug-resistant tuberculosis patients, their use for quick screening in populations with high tuberculosis burdens is restricted by the need to use sputum and their long turnaround times\u003csup\u003e[\u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e95\u003c/span\u003e]\u003c/sup\u003e. New screening techniques that can quickly and correctly identify active tuberculosis at a reasonable cost per test and that are operationally practicable in highly burdened settings are therefore desperately needed. The most common techniques for metabolite detection include gas chromatography-mass spectrometry, liquid chromatography-tandem mass spectrometry (LC-MS), and other metabolomics detection technologies with high flux and high sensitivity\u003csup\u003e[\u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e96\u003c/span\u003e]\u003c/sup\u003e. Mass spectrometry is finding increasing use in the realms of clinical diagnostics, environmental measures, and health care\u003csup\u003e[\u003cspan additionalcitationids=\"CR98\" citationid=\"CR97\" class=\"CitationRef\"\u003e97\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e99\u003c/span\u003e]\u003c/sup\u003e. Typically, it takes only a few minutes to obtain high-resolution mass spectra, which offer extremely high mass accuracy and a low rate of false positives for molecular composition assignment\u003csup\u003e[\u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e100\u003c/span\u003e]\u003c/sup\u003e. As such, it presents a possibility for developing an advanced diagnostic tool with superior sensitivity, increased dynamic range, and unmatched mass accuracy\u003csup\u003e[\u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e101\u003c/span\u003e]\u003c/sup\u003e. Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents some recent studies on the detection of biomarkers in tuberculosis using mass spectrometry.\u003c/p\u003e \u003cp\u003eResearchers utilized high-resolution mass spectrometry to identify specific lipids in peripheral lung fluid samples from TB patients and control subjects, using an innovative non-invasive sampling method. Exhaled respiratory particles were collected in liquid, concentrated, and infused into a mass spectrometer for analysis in dual ion mode, with chemical compositions determined via accurate mass measurement. The results indicated a general segregation between TB and non-TB samples, though some TB patients clustered with non-TB subjects in both modes. PCA of negative ion mode data revealed better segregation in the 900 to 2000 Da range, with distinguishable peaks primarily between 900 and 1000 Da. A combined approach using significance-analysis of microarray and a support vector machine algorithm identified the most discriminative features, primarily phospholipids, which were significantly elevated in TB patients\u003csup\u003e[\u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e102\u003c/span\u003e]\u003c/sup\u003e. To diagnose latent tuberculosis infection (LTBI) and monitor its progression to active TB, there is an urgent need for noninvasive simple markers. The purpose of a study by Li, Yan-Xia, et al.\u003csup\u003e[\u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e103\u003c/span\u003e]\u003c/sup\u003e, was to find biomarkers for LTBI diagnosis, track the infection's progression to active phase, and look into the underlying mechanisms. Whole blood supernatants were obtained from patients with LTBI, drug-resistant TB, drug-susceptible TB, and healthy controls in order to evaluate alterations in the metabolite composition linked to tuberculosis infection. Oscillation and deproteinization were used to extract metabolites from serum samples, and liquid chromatography-tandem mass spectrometry (LC-MS/MS) was used for detection. Pareto-scaling was used to normalise the data, and Metaboanalyst 4.0 software was used for differential analysis. One-way ANOVA (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) was used to identify important metabolites. Theophylline, inosine, 16,16-dimethyl-6-keto prostaglandin E1, and cotinine were found to be viable serum biomarkers for the diagnosis of LTBI. Additionally, cotinine was found to be a sign of the disease's progression. By utilising small-molecule metabolic indicators, this approach improves the sensitivity and specificity of tuberculosis diagnosis and has intriguing therapeutic implications for disease evaluation. In an investigation to find molecular indicators for early tuberculosis (TB) diagnosis, 49 participants were subjected to metabolomics analysis and two cohorts of 29 and 34 individuals received proteome analysis. Three kinds of participants were identified: latent tuberculosis infection (LTBI) carriers, TB patients, and healthy controls. Blood serum protein and metabolite concentrations were examined using LC-MS/MS employing ROC, multivariate, and univariate analyses. Of the 149 proteins that were measured, 25 had differing abundances in TB patients and controls. Four of these proteins were included in a model whose ROC analysis produced an AUC of 0.96, 93% specificity, and 91% sensitivity. For the purpose of diagnosing tuberculosis, a signature consisting of five metabolites\u0026mdash;trans-3-indoleacrylic acid, indole-3-lactic acid, hexanoylglycine, and N-acetyl-L-leucine\u0026mdash;was discovered. This signature has a high level of sensitivity, specificity, and accuracy. Furthermore, comparable diagnostic performance was shown by a composite biomarker set that included four of the metabolites and the protein hemopexin. These indicators have interesting therapeutic implications for early tuberculosis detection since they are associated with heme catabolism, tryptophan metabolism, xenobiotic detoxification, and proteolytic degradation\u003csup\u003e[\u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e104\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRecent studies on detection of biomarkers of tuberculosis using MS\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTechnology\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBiomarker\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSample\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMass spectrometry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePhospholipids\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExhaled breath particle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[102]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLC-MS/MS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTheophylline, inosine, 16,16-dimethyl-6-keto prostaglandin E1, and cotinine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBlood sample\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[103]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLC-MS/MS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003etrans-3-indoleacrylic acid, indole-3-lactic acid, hexanoylglycine, and N-acetyl-L-leucine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePeripheral blood sample\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[104]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"6. Conclusion and future outlook","content":"\u003cp\u003eIn conclusion, biomarkers represent a crucial component in the advancement of personalized medicine, providing insights into disease mechanisms, early diagnosis, and targeted therapies. This review categorizes biomarkers based on their characteristics, clinical applications, and genetic/molecular profiles. Biomarkers play an important role in the diagnosis, monitoring, and treatment of a wide range of diseases. These include neurodegenerative disorders like Alzheimer's disease, liver cancers such as hepatocellular carcinoma, ovarian cancer, and infectious diseases like tuberculosis. The biomarker development pipeline, particularly using mass spectrometry, plays a pivotal role in both discovery and validation. MS-based techniques have revolutionized biomarker detection, offering precision in disease diagnosis and the potential for better clinical outcomes. This integrated approach underscores the importance of biomarker research in modern healthcare. The future of biomarker detection, especially utilizing mass spectrometry, holds significant promise for advancing personalized medicine, early disease diagnosis, and therapeutic monitoring. As technology evolves, the precision, sensitivity, and applicability of MS in clinical settings are expected to grow, thereby enhancing our ability to identify and utilize biomarkers effectively. Continuous improvements in MS technology, such as increased resolution, enhanced sensitivity, and faster data acquisition rates, will play a critical role in biomarker detection. Innovations like ion mobility spectrometry, improved ionization techniques, and the integration of MS with advanced bioinformatics tools are anticipated to provide deeper insights into complex biological systems and facilitate the discovery of novel biomarkers. The scope of MS-based biomarker detection is expected to expand beyond oncology to encompass a wide range of diseases, including cardiovascular, neurological, and infectious diseases. The ability of MS to detect and quantify a diverse array of biomarkers will enable comprehensive disease profiling and support the development of new diagnostic and prognostic tools​. The seamless integration of MS into clinical workflows is expected to revolutionize diagnostic and therapeutic practices. Automated sample preparation, coupled with robust data analysis software, will enable the routine use of MS for biomarker detection in clinical laboratories. This integration will support the rapid and accurate diagnosis of diseases, monitoring of disease progression, and assessment of treatment efficacy\u003csup\u003e[\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e, \u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e105\u003c/span\u003e]\u003c/sup\u003e. Collaborative efforts and standardization in biomarker research are crucial for the successful translation of MS-based discoveries into clinical practice. Consortia like the National Cancer Institute's Clinical Proteomic Tumor Analysis Consortium (CPTAC) and the Early Detection Research Network (EDRN) are instrumental in developing and validating biomarkers. These collaborations foster the sharing of data, resources, and methodologies, accelerating the pace of biomarker discovery and implementation. Mass spectrometry-based biomarker detection will be pivotal in the advancement of personalized medicine. By identifying specific biomarkers associated with individual patient profiles, clinicians can tailor therapeutic interventions to achieve optimal outcomes. MS can monitor patient responses to treatments in real-time, allowing for adjustments in therapy and minimizing adverse drug reactions. The integration of proteomic data with other omics data (genomics, transcriptomics, metabolomics) through advanced bioinformatics will enhance the understanding of disease mechanisms. This holistic approach will aid in the identification of robust biomarkers and facilitate the development of multi-omics diagnostic panels, offering a more detailed and accurate picture of health and disease​\u003csup\u003e[\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e, \u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e106\u003c/span\u003e]\u003c/sup\u003e​.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical Approval: Not applicable\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate: Not applicable\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Publish: Not applicable\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding: Not applicable\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of Data and Materials: Not Applicable\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors Contributions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eShibam Das and ankit Awasthi has written the manuscript.\u003c/p\u003e\n\u003cp\u003eRohit Bhatia and Ravindra Kumar Rawal has conceptualized the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests: NIL\u003c/strong\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGarc\u0026iacute;a-Guti\u0026eacute;rrez, M. 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Recent Advances in Mass Spectrometry Based Clinical Proteomics: Applications to Cancer Research. \u003cem\u003eClin Proteom\u003c/em\u003e, \u003cem\u003e17\u003c/em\u003e(1), 17. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12014-020-09283-w\u003c/span\u003e\u003cspan address=\"10.1186/s12014-020-09283-w\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"applied-biochemistry-and-biotechnology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"abab","sideBox":"Learn more about [Applied Biochemistry and Biotechnology](https://www.springer.com/journal/12010)","snPcode":"12010","submissionUrl":"https://submission.nature.com/new-submission/12010/3","title":"Applied Biochemistry and Biotechnology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Biomarker, Classsification, Biomarker discovery, Validation, Detection, Mass spectrometry, Cancer","lastPublishedDoi":"10.21203/rs.3.rs-6333443/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6333443/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBiomarkers have become crucial tools in the diagnosis, prognosis, and therapeutic monitoring of various diseases. This review focuses on the classification of biomarkers based on three core categories: (i) their characteristics, (ii) clinical applications, and (iii) relevance in genetic and molecular biology. The importance of biomarkers across diseases is emphasized, along with recent advancements in their detection. A comprehensive discussion on the biomarker development pipeline, particularly mass spectrometry (MS)-based biomarker discovery, validation, and verification, is presented. The article also delves into MS-based techniques used for the detection of disease biomarkers such as Alzheimer\u0026rsquo;s, hepatocellular carcinoma, ovarian cancer, and tuberculosis, as well as highlighting recent research. Finally, the review explores future perspectives on biomarker discovery and detection, focusing on the evolving role of MS in advancing biomarker science and its application in clinical and research settings.\u003c/p\u003e","manuscriptTitle":"Biomarkers in Disease Diagnosis and Monitoring: Insights into Clinical Applications and Mass Spectrometry-based Detection","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-06 10:56:43","doi":"10.21203/rs.3.rs-6333443/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2025-05-16T19:48:00+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-05-01T08:03:12+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"Applied Biochemistry and Biotechnology","date":"2025-04-15T04:23:53+00:00","index":"","fulltext":""},{"type":"submitted","content":"Applied Biochemistry and Biotechnology","date":"2025-04-14T02:31:13+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"applied-biochemistry-and-biotechnology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"abab","sideBox":"Learn more about [Applied Biochemistry and Biotechnology](https://www.springer.com/journal/12010)","snPcode":"12010","submissionUrl":"https://submission.nature.com/new-submission/12010/3","title":"Applied Biochemistry and Biotechnology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"12f7ef09-7c30-4cf4-a00c-a622e4e3ed0a","owner":[],"postedDate":"May 6th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-01-05T16:01:23+00:00","versionOfRecord":{"articleIdentity":"rs-6333443","link":"https://doi.org/10.1007/s12010-025-05549-x","journal":{"identity":"applied-biochemistry-and-biotechnology","isVorOnly":false,"title":"Applied Biochemistry and Biotechnology"},"publishedOn":"2026-01-03 15:58:04","publishedOnDateReadable":"January 3rd, 2026"},"versionCreatedAt":"2025-05-06 10:56:43","video":"","vorDoi":"10.1007/s12010-025-05549-x","vorDoiUrl":"https://doi.org/10.1007/s12010-025-05549-x","workflowStages":[]},"version":"v1","identity":"rs-6333443","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6333443","identity":"rs-6333443","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}