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image of Disease Biomarkers and their Utility in LC-MS and NMR Studies: An Overview

Abstract

Introduction

To evaluate early disease diagnosis, disease progression, medication response, disease prevention, and therapeutic target selection, biomarker discovery is a crucial tool. It is of paramount clinical importance to identify biomarkers using various detection techniques and to characterize these biomarkers. The combination of proteomics, metabolomics, LC-MS, and NMR holds great promise for the easy identification of biomarkers by mapping the early biochemical alterations in illnesses. Analyzing a complex biological system calls for a robust and intelligent method. As a result of its adaptability, clarity, accuracy, speed, and increased productivity, LC-MS has become the gold standard approach for biomarker research. Proteins and nucleic acids are examples of big molecules that have been studied using the same approach. NMR spectroscopy enables the nondestructive detection and measurement of a vast array of novel metabolite biomarkers in biological fluids and tissues. Thus, NMR & LC-MS-based metabolomics are a huge help in illness diagnosis and biomarker identification.

Objectives

This article discusses the present function of LC-MS and NMR in developing biomarkers for disease diagnosis and strategies for identifying biomarkers in various diseases.

Methods

The methodology employed is based on the extraction of data (2002-2024) from various databases such as PubMed, Google Scholar, Web of Science, and Google with strict inclusion and exclusion criteria.

Results

Drug discovery, early disease diagnosis, and the identification of impaired metabolic reactions have all been made more by merging mass spectrometry and 1H NMR spectroscopic studies with comprehensive statistical data analysis.

Conclusion

Emerging high-throughput technologies for biomarker detection in disease diagnostics are the subject of this review. To improve therapy and illness prevention, personalization will be essential.

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2025-07-18
2025-09-05
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References

  1. Singh S. Gupta S.K. Seth P.K. Biomarkers for detection, prognosis and therapeutic assessment of neurological disorders. Rev. Neurosci. 2018 29 7 771 789 10.1515/revneuro‑2017‑0097 29466244
    [Google Scholar]
  2. The Biomarkers Consortium. United States Foundation for the National Institutes of Health 2018
    [Google Scholar]
  3. Iweala E.E. Amuji D.N. Nnaji F.C. Protein biomarkers for diagnosis of breast cancer. Scient. Afr. 2024 25 e02308 10.1016/j.sciaf.2024.e02308
    [Google Scholar]
  4. Kumar S. Mohan A. Guleria R. Biomarkers in cancer screening, research and detection: Present and future: A review. Biomarkers 2006 11 5 385 405 10.1080/13547500600775011 16966157
    [Google Scholar]
  5. Mayeux R. Biomarkers: Potential uses and limitations. NeuroRx 2004 1 2 182 188 10.1602/neurorx.1.2.182 15717018
    [Google Scholar]
  6. Patel S. Role of proteomics in biomarker discovery: Prognosis and diagnosis of neuropsychiatric disorders. Adv. Protein Chem. Struct. Biol. 2014 94 39 75 10.1016/B978‑0‑12‑800168‑4.00003‑2
    [Google Scholar]
  7. Alharbi R.A. Proteomics approach and techniques in identification of reliable biomarkers for diseases. Saudi J. Biol. Sci. 2020 27 3 968 974 10.1016/j.sjbs.2020.01.020 32127776
    [Google Scholar]
  8. Tolstikov V. Moser A.J. Sarangarajan R. Narain N.R. Kiebish M.A. Current status of metabolomic biomarker discovery: Impact of study design and demographic characteristics. Metabolites 2020 10 6 224 10.3390/metabo10060224 32485899
    [Google Scholar]
  9. El-Ansary A. Al-Afaleg N. Al-Yafaee Y. Biomarker discovery in neurological diseases: A metabolomic approach. J. Clin. Trials 2009 1 27 41 10.2147/OAJCT.S7746
    [Google Scholar]
  10. Tabago M.K.A.G. Calingacion M.N. Garcia J. Recent advances in NMR-based metabolomics of alcoholic beverages. Food Chemistry: Mol. Sci. 2021 2 100009 10.1016/j.fochms.2020.100009 35415632
    [Google Scholar]
  11. Yusa T. Tateda K. Ohara A. Miyazaki S. New possible biomarkers for diagnosis of infections and diagnostic distinction between bacterial and viral infections in children. J. Infect. Chemother. 2017 23 2 96 100 10.1016/j.jiac.2016.11.002 27894819
    [Google Scholar]
  12. Dasilva N. Díez P. Matarraz S. González-González M. Paradinas S. Orfao A. Fuentes M. Biomarker discovery by novel sensors based on nanoproteomics approaches. Sensors 2012 12 2 2284 2308 10.3390/s120202284 22438764
    [Google Scholar]
  13. Xu K. Liu Q. Wu K. Liu L. Zhao M. Yang H. Wang X. Wang W. Extracellular vesicles as potential biomarkers and therapeutic approaches in autoimmune diseases. J. Transl. Med. 2020 18 1 432 10.1186/s12967‑020‑02609‑0 33183315
    [Google Scholar]
  14. Wu H. Liao J. Li Q. Yang M. Zhao M. Lu Q. Epigenetics as biomarkers in autoimmune diseases. Clin. Immunol. 2018 196 34 39 10.1016/j.clim.2018.03.011 29574040
    [Google Scholar]
  15. Hueber W. Robinson W.H. Proteomic biomarkers for autoimmune disease. Proteomics 2006 6 14 4100 4105 10.1002/pmic.200600017 16786488
    [Google Scholar]
  16. Gibson D.S. Banha J. Penque D. Costa L. Conrads T.P. Cahill D.J. O’Brien J.K. Rooney M.E. Diagnostic and prognostic biomarker discovery strategies for autoimmune disorders. J. Proteomics 2010 73 6 1045 1060 10.1016/j.jprot.2009.11.013 19995622
    [Google Scholar]
  17. Karachaliou C.E. Livaniou E. Immunosensors for autoimmune-disease-related biomarkers: A literature review. Sensors 2023 23 15 6770 10.3390/s23156770 37571553
    [Google Scholar]
  18. Liu C.H. Abrams N.D. Carrick D.M. Chander P. Dwyer J. Hamlet M.R.J. Macchiarini F. PrabhuDas, M.; Shen, G.L.; Tandon, P.; Vedamony, M.M. Biomarkers of chronic inflammation in disease development and prevention: Challenges and opportunities. Nat. Immunol. 2017 18 11 1175 1180 10.1038/ni.3828 29044245
    [Google Scholar]
  19. Pierce B.L. Ballard-Barbash R. Bernstein L. Baumgartner R.N. Neuhouser M.L. Wener M.H. Baumgartner K.B. Gilliland F.D. Sorensen B.E. McTiernan A. Ulrich C.M. Elevated biomarkers of inflammation are associated with reduced survival among breast cancer patients. J. Clin. Oncol. 2009 27 21 3437 3444 10.1200/JCO.2008.18.9068 19470939
    [Google Scholar]
  20. Bozkurt B. Mann D.L. Deswal A. Biomarkers of inflammation in heart failure. Heart Fail. Rev. 2010 15 4 331 341 10.1007/s10741‑009‑9140‑3 19363700
    [Google Scholar]
  21. Srikanthan K. Feyh A. Visweshwar H. Shapiro J.I. Sodhi K. Systematic review of metabolic syndrome biomarkers: A panel for early detection, management, and risk stratification in the West Virginian population. Int. J. Med. Sci. 2016 13 1 25 38 10.7150/ijms.13800 26816492
    [Google Scholar]
  22. Zheng X. Chen T. Zhao A. Ning Z. Kuang J. Wang S. You Y. Bao Y. Ma X. Yu H. Zhou J. Jiang M. Li M. Wang J. Ma X. Zhou S. Li Y. Ge K. Rajani C. Xie G. Hu C. Guo Y. Lu A. Jia W. Jia W. Hyocholic acid species as novel biomarkers for metabolic disorders. Nat. Commun. 2021 12 1 1487 10.1038/s41467‑021‑21744‑w 33674561
    [Google Scholar]
  23. Müller G. Microvesicles/exosomes as potential novel biomarkers of metabolic diseases. Diabetes Metab. Syndr. Obes. 2012 5 247 282 10.2147/DMSO.S32923 22924003
    [Google Scholar]
  24. Alawieh A. Zaraket F.A. Li J.L. Mondello S. Nokkari A. Razafsha M. Fadlallah B. Boustany R.M. Kobeissy F.H. Systems biology, bioinformatics, and biomarkers in neuropsychiatry. Front. Neurosci. 2012 6 187 10.3389/fnins.2012.00187 23269912
    [Google Scholar]
  25. Cacabelos R. Pharmacogenomic biomarkers in neuropsychiatry: The path to personalized medicine in mental disorders. InThe handbook of neuropsychiatric biomarkers, endophenotypes and genes. Cham Springer 2009 3 63
    [Google Scholar]
  26. Pratt J. Hall J. Biomarkers in neuropsychiatry: A prospect for the twenty-first century?InBiomarkers in psychiatry. Cham Springer 2018 3 10 10.1007/7854_2018_58
    [Google Scholar]
  27. Goossens N. Nakagawa S. Sun X. Hoshida Y. Cancer biomarker discovery and validation. Transl. Cancer Res. 2015 4 3 256 269 26213686
    [Google Scholar]
  28. Dumbrava I.E. Meric-Bernstam F. Yap T.A. Challenges with biomarkers in cancer drug discovery and development. Expert Opin. Drug Discov. 2018 13 8 685 690 10.1080/17460441.2018.1479740 29792354
    [Google Scholar]
  29. Ludwig J.A. Weinstein J.N. Biomarkers in cancer staging, prognosis and treatment selection. Nat. Rev. Cancer 2005 5 11 845 856 10.1038/nrc1739 16239904
    [Google Scholar]
  30. Hirsch F.R. Merrick D.T. Franklin W.A. Role of biomarkers for early detection of lung cancer and chemoprevention. Eur. Respir. J. 2002 19 6 1151 1158 10.1183/09031936.02.00294102 12108871
    [Google Scholar]
  31. Henri C. Heinonen T. Tardif J.C. The role of biomarkers in decreasing risk of cardiac toxicity after cancer therapy. Biomark. Cancer 2016 8 Suppl. 2 39 42 10.4137/BIC.S31798
    [Google Scholar]
  32. Ackermann B.L. Hale J.E. Duffin K.L. The role of mass spectrometry in biomarker discovery and measurement. Curr. Drug Metab. 2006 7 5 525 539 10.2174/138920006777697918 16787160
    [Google Scholar]
  33. Vandenbogaert M. Li-Thiao-Té S. Kaltenbach H.M. Zhang R. Aittokallio T. Schwikowski B. Alignment of LC‐MS images, with applications to biomarker discovery and protein identification. Proteomics 2008 8 4 650 672 10.1002/pmic.200700791 18297649
    [Google Scholar]
  34. Heo S.H. Lee S.J. Ryoo H.M. Park J.Y. Cho J.Y. Identification of putative serum glycoprotein biomarkers for human lung adenocarcinoma by multilectin affinity chromatography and LC‐MS/MS. Proteomics 2007 7 23 4292 4302 10.1002/pmic.200700433 17963278
    [Google Scholar]
  35. Rüger A.M. Schneeweiss A. Seiler S. Tesch H. Mackelenbergh V.M. Marmé F. Lübbe K. Sinn B. Karn T. Stickeler E. Müller V. Schem C. Denkert C. Fasching P.A. Nekljudova V. Garfias-Macedo T. Hasenfuß G. Haverkamp W. Loibl S. Haehling V.S. Cardiotoxicity and cardiovascular biomarkers in patients with breast cancer: Data from the GeparOcto‐GBG 84 trial. J. Am. Heart Assoc. 2020 9 23 e018143 10.1161/JAHA.120.018143 33191846
    [Google Scholar]
  36. https://en.wikipedia.org/wiki/Isobaric_tag_for_relative_and_absolute_quantitation
  37. Son A. Kim W. Park J. Park Y. Lee W. Lee S. Kim H. Mass spectrometry advancements and applications for biomarker discovery, diagnostic innovations, and personalized medicine. Int. J. Mol. Sci. 2024 25 18 9880 10.3390/ijms25189880 39337367
    [Google Scholar]
  38. Wang H. Shi T. Qian W.J. Liu T. Kagan J. Srivastava S. Smith R.D. Rodland K.D. Camp D.G. II The clinical impact of recent advances in LC-MS for cancer biomarker discovery and verification. Expert Rev. Proteomics 2016 13 1 99 114 10.1586/14789450.2016.1122529 26581546
    [Google Scholar]
  39. Markley J.L. Brüschweiler R. Edison A.S. Eghbalnia H.R. Powers R. Raftery D. Wishart D.S. The future of NMR-based metabolomics. Curr. Opin. Biotechnol. 2017 43 34 40 10.1016/j.copbio.2016.08.001 27580257
    [Google Scholar]
  40. Neubert H. Shuford C.M. Olah T.V. Garofolo F. Schultz G.A. Jones B.R. Amaravadi L. Laterza O.F. Xu K. Ackermann B.L. Protein biomarker quantification by immunoaffinity liquid chromatography–tandem mass spectrometry: Current state and future vision. Clin. Chem. 2020 66 2 282 301 10.1093/clinchem/hvz022 32040572
    [Google Scholar]
  41. Isotope-coded affinity tag Available from: https://en.wikipedia. org/wiki/Isotope-coded_affinity_tag
  42. Ho C.S. Lam C.W. Chan M.H. Cheung R.C. Law L.K. Lit L.C. Ng K.F. Suen M.W. Tai H.L. Electrospray ionisation mass spectrometry: Principles and clinical applications. Clin. Biochem. Rev. 2003 24 1 3 12 18568044
    [Google Scholar]
  43. Iravani S. Conrad T.O. An Interpretable Deep Learning Approach for Biomarker Detection in LC-MS Proteomics Data. bioRxiv 2021 1 9
    [Google Scholar]
  44. The scientist Available from: https://www.the-scientist.com/how-it-works/q-tof-mass-spectrometer-48021
  45. Raj D.A.A. Fiume I. Capasso G. Pocsfalvi G. A multiplex quantitative proteomics strategy for protein biomarker studies in urinary exosomes. Kidney Int. 2012 81 12 1263 1272 10.1038/ki.2012.25 22418980
    [Google Scholar]
  46. Graves P.R. Haystead T.A.J. Molecular biologist’s guide to proteomics. Microbiol. Mol. Biol. Rev. 2002 66 1 39 63 10.1128/MMBR.66.1.39‑63.2002 11875127
    [Google Scholar]
  47. https://en.wikipedia.org/wiki/Surface-enhanced_laser_desorption/ionization#SELDI-TOF-MS
  48. Gebregiworgis T. Powers R. Application of NMR metabolomics to search for human disease biomarkers. Comb. Chem. High Throug. Screen. 2012 15 8 595 610 10.2174/138620712802650522 22480238
    [Google Scholar]
  49. Shanaiah N. Zhang S. Desilva M.A. Raftery D. NMR-based metabolomics for biomarker discovery. Biomarker Methods in Drug Discovery and Development. Totowa, New Jersey Humana Press 2008 341 368 10.1007/978‑1‑59745‑463‑6_16
    [Google Scholar]
  50. Nilsen M.M. Uleberg K.E. Janssen E.A.M. Baak J.P.A. Andersen O.K. Hjelle A. From SELDI-TOF MS to protein identification by on-chip elution. J. Proteomics 2011 74 12 2995 2998 10.1016/j.jprot.2011.06.027 21798383
    [Google Scholar]
  51. Smolinska A. Blanchet L. Buydens L.M.C. Wijmenga S.S. NMR and pattern recognition methods in metabolomics: From data acquisition to biomarker discovery: A review. Anal. Chim. Acta 2012 750 82 97 10.1016/j.aca.2012.05.049 23062430
    [Google Scholar]
  52. Liu Y.Y. Yang Z.X. Ma L.M. Wen X.Q. Ji H.L. Li K. 1 H-NMR spectroscopy identifies potential biomarkers in serum metabolomic signatures for early stage esophageal squamous cell carcinoma. PeerJ 2019 7 e8151 10.7717/peerj.8151 31803539
    [Google Scholar]
  53. Letertre M.P.M. Giraudeau P. Tullio D.P. Nuclear magnetic resonance spectroscopy in clinical metabolomics and personalized medicine: Current challenges and perspectives. Front. Mol. Biosci. 2021 8 698337 10.3389/fmolb.2021.698337 34616770
    [Google Scholar]
  54. Zhang A. Sun H. Qiu S. Wang X. NMR‐based metabolomics coupled with pattern recognition methods in biomarker discovery and disease diagnosis. Magn. Reson. Chem. 2013 51 9 549 556 10.1002/mrc.3985 23828598
    [Google Scholar]
  55. Emwas A.H. Roy R. McKay R.T. Tenori L. Saccenti E. Gowda G.A.N. Raftery D. Alahmari F. Jaremko L. Jaremko M. Wishart D.S. NMR spectroscopy for metabolomics research. Metabolites 2019 9 7 123 10.3390/metabo9070123 31252628
    [Google Scholar]
  56. Koskela H. Heikkilä O. Kilpeläinen I. Heikkinen S. Quantitative two-dimensional HSQC experiment for high magnetic field NMR spectrometers. J. Magn. Reson. 2010 202 1 24 33 10.1016/j.jmr.2009.09.021 19853484
    [Google Scholar]
  57. Wishart D.S. Emerging applications of metabolomics in drug discovery and precision medicine. Nat. Rev. Drug Discov. 2016 15 7 473 484 10.1038/nrd.2016.32 26965202
    [Google Scholar]
  58. Pinu F.R. Beale D.J. Paten A.M. Kouremenos K. Swarup S. Schirra H.J. Wishart D. Systems biology and multi-omics integration: Viewpoints from the metabolomics research community. Metabolites 2019 9 4 76 10.3390/metabo9040076 31003499
    [Google Scholar]
  59. Silva R.A. Pereira T.C.S. Souza A.R. Ribeiro P.R. 1H NMR-based metabolite profiling for biomarker identification. Clin. Chim. Acta 2020 502 269 279 10.1016/j.cca.2019.11.015 31778675
    [Google Scholar]
  60. Marshall D.D. Powers R. Beyond the paradigm: Combining mass spectrometry and nuclear magnetic resonance for metabolomics. Prog. Nucl. Magn. Reson. Spectrosc. 2017 100 1 16 10.1016/j.pnmrs.2017.01.001 28552170
    [Google Scholar]
  61. Elipe S.M.V. Advantages and disadvantages of nuclear magnetic resonance spectroscopy as a hyphenated technique. Anal. Chim. Acta 2003 497 1-2 1 25 10.1016/j.aca.2003.08.048
    [Google Scholar]
  62. Letertre M.P.M. Dervilly G. Giraudeau P. Combined nuclear magnetic resonance spectroscopy and mass spectrometry approaches for metabolomics. Anal. Chem. 2021 93 1 500 518 10.1021/acs.analchem.0c04371 33155816
    [Google Scholar]
  63. Van Q.N. Issaq H.J. Jiang Q. Li Q. Muschik G.M. Waybright T.J. Lou H. Dean M. Uitto J. Veenstra T.D. Comparison of 1D and 2D NMR spectroscopy for metabolic profiling. J. Proteome Res. 2008 7 2 630 639 10.1021/pr700594s 18081246
    [Google Scholar]
  64. Robinette S.L. Ajredini R. Rasheed H. Zeinomar A. Schroeder F.C. Dossey A.T. Edison A.S. Hierarchical alignment and full resolution pattern recognition of 2D NMR spectra: Application to nematode chemical ecology. Anal. Chem. 2011 83 5 1649 1657 10.1021/ac102724x 21314130
    [Google Scholar]
  65. Marchand J. Martineau E. Guitton Y. Bizec L.B. Dervilly-Pinel G. Giraudeau P. A multidimensional 1H NMR lipidomics workflow to address chemical food safety issues. Metabolomics 2018 14 5 60 10.1007/s11306‑018‑1360‑x 30830413
    [Google Scholar]
  66. Marchand J. Martineau E. Guitton Y. Dervilly-Pinel G. Giraudeau P. Multidimensional NMR approaches towards highly resolved, sensitive and high-throughput quantitative metabolomics. Curr. Opin. Biotechnol. 2017 43 49 55 10.1016/j.copbio.2016.08.004 27639136
    [Google Scholar]
  67. Plainchont B. Berruyer P. Dumez J.N. Jannin S. Giraudeau P. Dynamic nuclear polarization opens new perspectives for NMR spectroscopy in analytical chemistry. Anal. Chem. 2018 90 6 3639 3650 10.1021/acs.analchem.7b05236
    [Google Scholar]
  68. Lerche M.H. Yigit D. Frahm A.B. Ardenkjær-Larsen J.H. Malinowski R.M. Jensen P.R. Stable isotope-resolved analysis with quantitative dissolution dynamic nuclear polarization. Anal. Chem. 2018 90 1 674 678 10.1021/acs.analchem.7b02779 29200272
    [Google Scholar]
  69. Hernández-Mesa M. Monteau F. Bizec L.B. Dervilly-Pinel G. Potential of ion mobility-mass spectrometry for both targeted and non-targeted analysis of phase II steroid metabolites in urine. Anal. Chim. Acta. X. 2019 1 100006 10.1016/j.acax.2019.100006
    [Google Scholar]
  70. Zhou J. Yin Y. Strategies for large-scale targeted metabolomics quantification by liquid chromatography-mass spectrometry. Analyst 2016 141 23 6362 6373 10.1039/C6AN01753C 27722450
    [Google Scholar]
  71. Lu W. Bennett B.D. Rabinowitz J.D. Analytical strategies for LC–MS-based targeted metabolomics. J. Chromatogr. B Analyt. Technol. Biomed. Life Sci. 2008 871 2 236 242 10.1016/j.jchromb.2008.04.031 18502704
    [Google Scholar]
  72. Hu S. Yu T. Xie Y. Yang Y. Li Y. Zhou X. Tsung S. Loo R.R. Loo J.R. Wong D.T. Discovery of oral fluid biomarkers for human oral cancer by mass spectrometry. Cancer Genom. Proteom. 2007 4 2 55 64 17804867
    [Google Scholar]
  73. Kang X. Sun L. Guo K. Shu H. Yao J. Qin X. Liu Y. Serum protein biomarkers screening in HCC patients with liver cirrhosis by ICAT-LC-MS/MS. J. Cancer Res. Clin. Oncol. 2010 136 8 1151 1159 10.1007/s00432‑010‑0762‑6 20130913
    [Google Scholar]
  74. Lin L. Huang Z. Gao Y. Yan X. Xing J. Hang W. LC-MS based serum metabonomic analysis for renal cell carcinoma diagnosis, staging, and biomarker discovery. J. Proteome Res. 2011 10 3 1396 1405 10.1021/pr101161u 21186845
    [Google Scholar]
  75. Feng X. Zhang J. Chen W.N. Ching, CB Proteome profiling of Epstein–Barr virus infected nasopharyngeal carcinoma cell line: Identification of potential biomarkers by comparative iTRAQ-coupled 2D LC/MS-MS analysis. J. Proteomics 2011 4 4 567 576 10.1016/j.jprot.2011.01.017
    [Google Scholar]
  76. Yang N. Feng S. Shedden K. Xie X. Liu Y. Rosser C.J. Lubman D.M. Goodison S. Urinary glycoprotein biomarker discovery for bladder cancer detection using LC/MS-MS and label-free quantification. Clin. Cancer Res. 2011 17 10 3349 3359 10.1158/1078‑0432.CCR‑10‑3121 21459797
    [Google Scholar]
  77. Moriarty M. Lee A. O’Connell B. Kelleher A. Keeley H. Furey A. Development of an LC-MS/MS method for the analysis of serotonin and related compounds in urine and the identification of a potential biomarker for attention deficit hyperactivity/hyperkinetic disorder. Anal. Bioanal. Chem. 2011 401 8 2481 2493 10.1007/s00216‑011‑5322‑7 21866401
    [Google Scholar]
  78. Su L. Cao L. Zhou R. Jiang Z. Xiao K. Kong W. Wang H. Deng J. Wen B. Tan F. Zhang Y. Xie L. Identification of novel biomarkers for sepsis prognosis via urinary proteomic analysis using iTRAQ labeling and 2D-LC-MS/MS. PLoS One 2013 8 1 e54237 10.1371/journal.pone.0054237 23372690
    [Google Scholar]
  79. Xu D.D. Deng D.F. Li X. Wei L.L. Li Y.Y. Yang X.Y. Yu W. Wang C. Jiang T.T. Li Z.J. Chen Z.L. Zhang X. Liu J.Y. Ping Z.P. Qiu Y.Q. Li J.C. Discovery and identification of serum potential biomarkers for pulmonary tuberculosis using iTRAQ‐coupled two‐dimensional LC‐MS/MS. Proteomics 2014 14 2-3 322 331 10.1002/pmic.201300383 24339194
    [Google Scholar]
  80. Liu Y. Hong Z. Tan G. Dong X. Yang G. Zhao L. Chen X. Zhu Z. Lou Z. Qian B. Zhang G. Chai Y. NMR and LC/MS-based global metabolomics to identify serum biomarkers differentiating hepatocellular carcinoma from liver cirrhosis. Int. J. Cancer 2014 135 3 658 668 10.1002/ijc.28706 24382646
    [Google Scholar]
  81. Zhang P. Zhu S. Li Y. Zhao M. Liu M. Gao J. Ding S. Li J. Quantitative proteomics analysis to identify diffuse axonal injury biomarkers in rats using iTRAQ coupled LC–MS/MS. J. Proteom. 2016 133 93 99 10.1016/j.jprot.2015.12.014 26710722
    [Google Scholar]
  82. Beretov J. Wasinger V.C. Millar E.K.A. Schwartz P. Graham P.H. Li Y. Proteomic analysis of urine to identify breast cancer biomarker candidates using a label-free LC-MS/MS approach. PLoS One 2015 10 11 e0141876 10.1371/journal.pone.0141876 26544852
    [Google Scholar]
  83. Yoneyama T. Ohtsuki S. Honda K. Kobayashi M. Iwasaki M. Uchida Y. Okusaka T. Nakamori S. Shimahara M. Ueno T. Tsuchida A. Sata N. Ioka T. Yasunami Y. Kosuge T. Kaneda T. Kato T. Yagihara K. Fujita S. Huang W. Yamada T. Tachikawa M. Terasaki T. Identification of IGFBP2 and IGFBP3 as compensatory biomarkers for CA19-9 in early-stage pancreatic cancer using a combination of antibody-based and LC-MS/MS-based proteomics. PLoS One 2016 11 8 e0161009 10.1371/journal.pone.0161009 27579675
    [Google Scholar]
  84. Ren J. Zhao G. Sun X. Liu H. Jiang P. Chen J. Wu Z. Peng D. Fang Y. Zhang C. Identification of plasma biomarkers for distinguishing bipolar depression from major depressive disorder by iTRAQ-coupled LC–MS/MS and bioinformatics analysis. Psychoneuroendocrinology 2017 86 17 24 10.1016/j.psyneuen.2017.09.005 28910601
    [Google Scholar]
  85. Chang L. Ni J. Beretov J. Wasinger V.C. Hao J. Bucci J. Malouf D. Gillatt D. Graham P.H. Li Y. Identification of protein biomarkers and signaling pathways associated with prostate cancer radioresistance using label-free LC-MS/MS proteomic approach. Sci. Rep. 2017 7 1 41834 10.1038/srep41834 28225015
    [Google Scholar]
  86. Yang J. Zhou M. Zhao R. Peng S. Luo Z. Li X. Cao L. Tang K. Ma J. Xiong W. Fan S. Schmitt D.C. Tan M. Li X. Li G. Identification of candidate biomarkers for the early detection of nasopharyngeal carcinoma by quantitative proteomic analysis. J. Proteomics 2014 109 109 162 175 10.1016/j.jprot.2014.06.025 24998431
    [Google Scholar]
  87. Karagiannis G.S. Pavlou M.P. Saraon P. Musrap N. Xie A. Batruch I. Prassas I. Dimitromanolakis A. Petraki C. Diamandis E.P. In-depth proteomic delineation of the colorectal cancer exoproteome: Mechanistic insight and identification of potential biomarkers. J. Proteom. 2014 103 103 121 136 10.1016/j.jprot.2014.03.018 24681409
    [Google Scholar]
  88. Cordeiro A.P. Pereira R.A.S. Chapeaurouge A. Coimbra C.S. Perales J. Oliveira G. Candiani T.M.S. Coimbra R.S. Comparative proteomics of cerebrospinal fluid reveals a predictive model for differential diagnosis of pneumococcal, meningococcal, and enteroviral meningitis, and novel putative therapeutic targets. BMC Genomics 2015 16 S5 S11 10.1186/1471‑2164‑16‑S5‑S11 26040285
    [Google Scholar]
  89. Njunge J.M. Oyaro I.N. Kibinge N.K. Rono M.K. Kariuki S.M. Newton C.R. Berkley J.A. Gitau E.N. Cerebrospinal fluid markers to distinguish bacterial meningitis from cerebral malaria in children. Wellcome Open Res. 2017 2 2 47 10.12688/wellcomeopenres.11958.1 29181450
    [Google Scholar]
  90. Bonnet J. Garcia C. Leger T. Couquet M.P. Vignoles P. Vatunga G. Ndung’u J. Boudot C. Bisser S. Courtioux B. Proteome characterization in various biological fluids of Trypanosoma brucei gambiense-infected subjects. J. Proteomics 2019 196 196 150 161 10.1016/j.jprot.2018.11.005 30414516
    [Google Scholar]
  91. Bharucha T. Gangadharan B. Kumar A. Lamballerie D.X. Newton P.N. Winterberg M. Dubot-Pérès A. Zitzmann N. Mass spectrometry-based proteomic techniques to identify cerebrospinal fluid biomarkers for diagnosing suspected central nervous system infections. A systematic review. J. Infect. 2019 79 5 407 418 10.1016/j.jinf.2019.08.005 31404562
    [Google Scholar]
  92. Kumari S. Kumaran S.S. Goyal V. Sharma R.K. Sinha N. Dwivedi S.N. Srivastava A.K. Jagannathan N.R. Identification of potential urine biomarkers in idiopathic parkinson’s disease using NMR. Clin. Chim. Acta 2020 510 442 449 10.1016/j.cca.2020.08.005 32791135
    [Google Scholar]
  93. Zhong W. Yang H. Wang Y. Yang Y. Guo C. Wang C. Ji Q. Proteomic profiles of patients with atrial fibrillation provide candidate biomarkers for diagnosis. Int. J. Cardiol. 2021 344 205 212 10.1016/j.ijcard.2021.09.047 34592249
    [Google Scholar]
  94. Gautam S.S. Singh R.P. Karsauliya K. Sonker A.K. Reddy P.J. Mehrotra D. Gupta S. Singh S. Kumar R. Singh S.P. Label-free plasma proteomics for the identification of the putative biomarkers of oral squamous cell carcinoma. J. Proteomics 2022 259 104541 10.1016/j.jprot.2022.104541 35231661
    [Google Scholar]
  95. Khan I.M.Z. Tam S.Y. Azam Z. Law H.K.W. Proteomic profiling of metabolic proteins as potential biomarkers of radioresponsiveness for colorectal cancer. J. Proteomics 2022 262 104600 10.1016/j.jprot.2022.104600 35526805
    [Google Scholar]
  96. Lu H. Pan Y. Ruan Y. Zhu C. Hassan H.M. Gao J. Gao J. Fan L. Liang X. Wang H. Ying S. Chen Q. Biomarker Discovery for early diagnosis of papillary thyroid carcinoma using high-throughput enhanced quantitative plasma proteomics. J. Proteome Res. 2023 22 10 3200 3212 10.1021/acs.jproteome.3c00187 37624590
    [Google Scholar]
  97. Zhang Y. Shui J. Wang L. Wang F. Serum proteomics identifies S100A11 and MMP9 as novel biomarkers for predicting the early efficacy of sublingual immunotherapy in allergic rhinitis. Int. Immunopharmacol. 2023 124 (Pt A) 110857 10.1016/j.intimp.2023.110857 37647677
    [Google Scholar]
  98. Shin D. Kim Y. Park J. Kim Y. High-throughput proteomics-guided biomarker discovery of hepatocellular carcinoma. Biomed. J. 2025 48 1 100752 10.1016/j.bj.2024.100752 38901798
    [Google Scholar]
  99. Li C. Zang X. Liu H. Yin S. Cheng X. Zhang W. Meng X. Chen L. Lu S. Wu J. Olink proteomics for the identification of biomarkers for early diagnosis of postmenopausal osteoporosis. J. Proteome Res. 2024 23 10 4567 4578 10.1021/acs.jproteome.4c00470 39226440
    [Google Scholar]
  100. Niebla-Cárdenas A. Bueno-Hernández N. Hernández A.P. Fuentes M. Méndez-Sánchez R. Arroyo-Anlló E.M. Orera I. Lattanzio G. Juanes-Velasco P. Arias-Hidalgo C. Puente-González A.S. Potential protein biomarkers in saliva for detection of frailty syndrome by targeted proteomics. Mech. Ageing Dev. 2024 221 111974 10.1016/j.mad.2024.111974 39038666
    [Google Scholar]
  101. Zhang H.Y. Liu Q. Wang F.S. Mu W. Zhu Y. Zhang Q.Y. Feng S.G. Yao J. Yan B. Targeted proteomics profiling for biomarker discovery in glaucoma using the olink proteomics platform. J. Proteome Res. 2024 23 10 4674 4683 10.1021/acs.jproteome.4c00593 39319515
    [Google Scholar]
  102. Rejas-González R. Montero-Calle A. Salvador P.N. Carballés C.M.J. Ausín-González E. Sánchez-Naves J. Calderón P.S. Barderas R. Guzman-Aranguez A. Unraveling the nexus of oxidative stress, ocular diseases, and small extracellular vesicles to identify novel glaucoma biomarkers through in-depth proteomics. Redox Biol. 2024 77 103368 10.1016/j.redox.2024.103368 39326071
    [Google Scholar]
  103. Zhang Y. Zhao Y. Zhang J. Gao Y. Gao X. Li S. Chang C. Yang G. Proteomics of plasma-derived extracellular vesicles reveals S100A8 as a novel biomarker for Alzheimer’s disease: A preliminary study. J. Proteomics 2024 308 105279 10.1016/j.jprot.2024.105279 39159863
    [Google Scholar]
  104. Huang Q. Hao S. Yao X. You J. Li X. Lai D. Han C. Schilling J. Hwa K.Y. Thyparambil S. Whitin J. Cohen H.J. Chubb H. Ceresnak S.R. McElhinney D.B. Wong R.J. Shaw G.M. Stevenson D.K. Sylvester K.G. Ling X.B. High-throughput quantitation of serological ceramides/dihydroceramides by LC/MS/MS: Pregnancy baseline biomarkers and potential metabolic messengers. J. Pharm. Biomed. Anal. 2021 192 113639 10.1016/j.jpba.2020.113639 33017796
    [Google Scholar]
  105. Rahbari R. Niewaal V.J. Bleavins M.R. Biomarkers in Drug Discovery and Development: A Handbook of Practice, Application, and Strategy. Hoboken, New Jersey John Wiley & Sons 2020 10.1002/9781119187547
    [Google Scholar]
  106. Song Z. Wang H. Yin X. Deng P. Jiang W. Application of NMR metabolomics to search for human disease biomarkers in blood. Clin. Chem. Laborat Med. 2019 57 4 417 441 10.1515/cclm‑2018‑0380 30169327
    [Google Scholar]
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