Proteomics techniques in protein biomarker discovery

Abstract

Protein biomarkers represent specific biological activities and processes, so they have had a critical role in cancer diagnosis and medical care for more than 50 years. With the recent improvement in proteomics technologies, thousands of protein biomarker candidates have been developed for diverse disease states. Studies have used different types of samples for proteomics diagnosis. Samples were pretreated with appropriate techniques to increase the selectivity and sensitivity of the downstream analysis and purified to remove the contaminants. The purified samples were analyzed by several principal proteomics techniques to identify the specific protein. In this study, recent improvements in protein biomarker discovery, verification, and validation are investigated. Furthermore, the advantages, and disadvantages of conventional techniques, are discussed. Studies have used mass spectroscopy (MS) as a critical technique in the identification and quantification of candidate biomarkers. Nevertheless, after protein biomarker discovery, verification and validation have been required to reduce the false-positive rate where there have been higher number of samples. Multiple reaction monitoring (MRM), parallel reaction monitoring (PRM), and selected reaction monitoring (SRM), in combination with stable isotope-labeled internal standards, have been examined as options for biomarker verification, and enzyme-linked immunosorbent assay (ELISA) for validation.

Keywords

biomarker discovery / cancer biomarker / gel-based methods / gel-free methods / mass spectroscopy / proteomics

References

[1] You C-P, Leung M-H, Tsang W-C, Khoo U-S, Tsoi H. Androgen receptor as an emerging feasible biomarker for breast cancer. Biomolecules. 2022;12(1):72.

[2] Wishart DS, Bartok B, Oler E, Liang KYH, Budinski Z, Berjanskii M, et al. Markerdb: an online database of molecular biomarkers. Nucleic Acids Res. 2020;49(D1):D1259–67.

[3] Mishra A, Verma M. Cancer biomarkers: are we ready for the prime time? Cancers. 2010;2(1):190–208.

[4] Brattås MK, Lilleeng K, Hovland R, Lægreid IJ, Vorland M, Leh F, et al. Philadelphia chromosome positive aml arising from jak2-positive myelofibrosis. Biomark Res. 2018;6(1):33.

[5] Soverini S, Bassan R, Lion T. Treatment and monitoring of Philadelphia chromosome-positive leukemia patients: recent advances and remaining challenges. J Hematol Oncol. 2019;12(1):39.

[6] Ballman KV. Biomarker: predictive or prognostic? J Clin Oncol. 2015;33:3968–71.

[7] Jin TY, Park KS, Nam SE, Yoo YB, Park WS, Yun IJ. Brca1/2 serves as a biomarker for poor prognosis in breast carcinoma. Int J Mol Sci. 2022;23(7):3754.

[8] Weigel MT, Dowsett M. Current and emerging biomarkers in breast cancer: prognosis and prediction. Endocr Relat Cancer. 2010;17(4):R245–62.

[9] El Bairi K, Atanasov AG, Amrani M, Afqir S. The arrival of predictive biomarkers for monitoring therapy response to natural compounds in cancer drug discovery. Biomed Pharmacother. 2019;109:2492–8.

[10] Ulaner GA, Riedl CC, Dickler MN, Jhaveri K, Pandit-Taskar N, Weber W. Molecular imaging of biomarkers in breast cancer. J Nucl Med. 2016;57(Suppl 1):53S–9S.

[11] FDA-NIH Biomarker Working Group. Best (biomarkers, endpoints, and other tools) resource. Silver Spring (MD): Food and Drug Administration (US); 2016.

[12] Gao Y, Liu Y, Sun L, Ouyang X, Zhu C, Qin X. Mad2l1 functions as a novel diagnostic and predictive biomarker in cholangiocarcinoma. Genet Test Mol Biomarkers. 2021;25(11):685–95.

[13] Gracia KC, Husi H. Computational approaches in proteomics. In: Computational biology. Exon Publications; 2019.

[14] Graves PR, Haystead TA. Molecular biologist’s guide to proteomics. Microbiol Mol Biol Rev. 2002;66(1):39–63.

[15] Alharbi RA. Proteomics approach and techniques in identification of reliable biomarkers for diseases. Saudi J Biol Sci. 2020;27(3):968–74.

[16] Stastna M, Van Eyk JE. Secreted proteins as a fundamental source for biomarker discovery. Proteomics. 2012;12(4-5):722–35.

[17] Komor MA, Bosch LJ, Coupé VM, Rausch C, Pham TV, Piersma SR, et al. Proteins in stool as biomarkers for noninvasive detection of colorectal adenomas with high risk of progression. J Pathol. 2020;250(3):288–98.

[18] Hajeb P, Zhu L, Bossi R, Vorkamp K. Sample preparation techniques for suspect and non-target screening of emerging contaminants. Chemosphere. 2022;287:132306.

[19] Lee CH. A simple outline of methods for protein isolation and purification. Endocrinology and metabolism (Seoul, Korea). 2017;32(1):18–22.

[20] Lucas N, Robinson AB, Marcker Espersen M, Mahboob S, Xavier D, Xue J, et al. Accelerated barocycler lysis and extraction sample preparation for clinical proteomics by mass spectrometry. J Proteome Res. 2019;18:399–405.

[21] Feist P, Hummon AB. Proteomic challenges: sample preparation techniques for microgram-quantity protein analysis from biological samples. Int J Mol Sci. 2015;16(2):3537–63.

[22] Camerini S, Mauri P. The role of protein and peptide separation before mass spectrometry analysis in clinical proteomics. J Chromatogr A. 2015;1381:1–12.

[23] Aslam B, Basit M, Nisar MA, Khurshid M, Rasool MH. Proteomics: technologies and their applications. J Chromatogr Sci. 2017;55(2):182–96.

[24] Magdeldin S, Enany S, Yoshida Y, Xu B, Zhang Y, Zureena Z, et al. Basics and recent advances of two dimensionalpolyacrylamide gel electrophoresis. Clin Proteonomics. 2014;11(1):16.

[25] Issaq HJ, Veenstra TD. Two-dimensional polyacrylamide gel electrophoresis (2D-PAGE): advances and perspectives. Biotechniques. 2008;44(5):697–700.

[26] Timms JF, Cramer R. Difference gel electrophoresis. Proteomics. 2008;8(23-24):4886–97.

[27] Chandramouli, K, Qian, PY. Proteomics: challenges, techniques and possibilities to overcome biological sample complexity. Hum Genom Proteonomics: HGP. 2009;1(1):239204.

[28] N’Cho JS, Fofana I, Hadjadj Y, Beroual A. Review of physicochemical-based diagnostic techniques for assessing insulation condition in aged transformers. Energies. 2016;9(5):367.

[29] Timchenko Y. Advantages and disadvantages of highperformance liquid chromatography (HPCL). J Environ Anal Chem. 2021;8:335.

[30] Deb PK, Kokaz SF, Abed SN, Paradkar A, Tekade RK. Pharmaceutical and biomedical applications of polymers. In: Basic fundamentals of drug delivery. Elsevier; 2019. p. 203–67.

[31] Held D, Kilz P. Size-exclusion chromatography as a useful tool for the assessment of polymer quality and determination of macromolecular properties. Chemistry Teacher International. 2021;3(2):77–103.

[32] Holbrook RD, Galyean AA, Gorham JM, Herzing A, Pettibone J. Chapter 2 — overview of nanomaterial characterization and metrology. In: Baalousha M, Lead JR, editors. Frontiers of nanoscience. Elsevier; 2015. p. 47–87.

[33] Mukherjee S. Chapter 2 — isolation and purification of industrial enzymes: advances in enzyme technology. In: Singh RS, Singhania RR, Pandey A, Larroche C, editors. Advances in enzyme technology. Elsevier; 2019. p. 41–70.

[34] Özlem Bahadir A. Ion-exchange chromatography and its applications. In: Dean FM, Barbara BM, editors. Column chromatography. Rijeka: IntechOpen; 2013. Ch. 2.

[35] Anderson C. Ion chromatography: a new technique for clinical chemistry. Clin Chem. 1976;22(9):1424–6.

[36] Garfin DE. One-dimensional gel electrophoresis. In: Methods in enzymology. Elsevier; 1990. p. 425–41.

[37] Ramos Y, Almeida A, Carpio J, Rodríguez-Ulloa A, Perera Y, González LJ, et al. Gel electrophoresis/electroelution sorting fractionator combined with filter aided sample preparation for deep proteomic analysis. J Separ Sci. 2022;45(10):1784–96.

[38] O’Farrell PH. High resolution two-dimensional electrophoresis of proteins. J Biol Chem. 1975;250(10):4007–21.

[39] Wittmann-Liebold B, Graack HR, Pohl T. Two-dimensional gel electrophoresis as tool for proteomics studies in combination with protein identification by mass spectrometry. Proteomics. 2006;6(17):4688–703.

[40] Ünlü M, Morgan ME, Minden JS. Difference gel electrophoresis. A single gel method for detecting changes in protein extracts. Electrophoresis. 1997;18(11):2071–7.

[41] Kirana C, Peng L, Miller R, Keating JP, Glenn C, Shi H, et al. Combination of laser microdissection, 2D-DIGE and maldi-tof MS to identify protein biomarkers to predict colorectal cancer spread. Clin Proteonomics. 2019;16(1):3.

[42] Jafari M, Primo V, Smejkal GB, Moskovets EV, Kuo WP, Ivanov AR. Comparison of in-gel protein separation techniques commonly used for fractionation in mass spectrometrybased proteomic profiling. Electrophoresis. 2012;33(16): 2516–26.

[43] Moreno-Arribas MV, Polo MC. Chromatography | high-performance liquid chromatography. In: Caballero B, editor. Encyclopedia of food sciences and nutrition. 2nd ed. Oxford: Academic Press; 2003. p. 1274–80.

[44] Madadlou A, O’Sullivan S, Sheehan D. Fast protein liquid chromatography. Protein Chromatography: Methods and Protocols. 2011:439–47.

[45] Gellerstedt G Gel permeation chromatography. In: Methods in lignin chemistry. Springer; 1992. p. 487–97.

[46] Shiny Matilda C, Madhusudan I, Gaurav Isola R, Shanthi C. Potential of proteomics to probe microbes. J Basic Microbiol. 2020;60(6):471–83.

[47] Hasegawa S, Chen C-S, Yoshimoto N, Yamamoto S. Optimization of flow-through chromatography of proteins. J Chem Eng Jpn. 2020;53(5):214–21.

[48] Acikara ÖB. Ion exchange chromatography and its applications. Column Chromato. 2013:31–58.

[49] Xiao GG, Recker RR, Deng H-W. Recent advances in proteomics and cancer biomarker discovery. Clin Med Oncol. 2008;2:63–72.

[50] Hribar, JA. Capillary gradient chromatofocusing-mass spectrometry; a sensitive approach for protein analysis. ETD Archive. 2011;137.

[51] Hudler, P, Kocevar, N, Komel, R. Proteomic approaches in biomarker discovery: new perspectives in cancer diagnostics. Sci World J. 2014,260348.

[52] Wang Y, Li Z, Yu H. Aptamer-based western blot for selective protein recognition. Front Chem. 2020;8.

[53] Doerr A. Single-cell proteomics. Nat Methods. 2019;16(1):20.

[54] Melby JA, Roberts DS, Larson EJ, Brown KA, Bayne EF, Jin S, et al. Novel strategies to address the challenges in top-down proteomics. J Am Soc Mass Spectrom. 2021;32(6):1278–94.

[55] Rajawat J, Jhingan G. Mass spectroscopy. In: Data processing handbook for complex biological data sources. Elsevier; 2019. p. 1–20.

[56] Rozanova S, Barkovits K, Nikolov M, Schmidt C, Urlaub H, Marcus K. Quantitative mass spectrometry-based proteomics: an overview. In: Marcus K, Eisenacher M, Sitek B, editors. Quantitative methods in proteomics. New York: Springer US; 2021. p. 85–116.

[57] Neagu A-N, Jayathirtha M, Baxter E, Donnelly M, Petre BA, Darie CC. Applications of tandem mass spectrometry (MS/ MS) in protein analysis for biomedical research. Molecules. 2022;27(8):2411.

[58] Guilhaus M. Mass spectrometry | time-of-flight. In: Worsfold P, Townshend A, Poole C, editors. Encyclopedia of analytical science. 2nd ed. Oxford: Elsevier; 2005. p. 412–23.

[59] Reiding KR, Bondt A, Franc V, Heck AJR. The benefits of hybrid fragmentation methods for glycoproteomics. TrAC, Trends Anal Chem. 2018;108:260–8.

[60] Liu Y, Cao Q, Li L. Chapter 8 — isolation and characterization of glycosylated neuropeptides. In: Garcia BA, editor. Methods in enzymology. Academic Press; 2019. p. 147–202.

[61] Dass C. 9.12 - mass spectrometry: structure determination of proteins and peptides. In: Liu H.-W, Mander L, editors. Comprehensive natural products ii. Oxford: Elsevier; 2010. p. 457–96.

[62] Brewis IA, Brennan P. Chapter 1 — proteomics technologies for the global identification and quantification of proteins. In: Donev R, editor. Advances in protein chemistry and structural biology. Academic Press; 2010. p. 1–44.

[63] Sobott F, Watt SJ, Smith J, Edelmann MJ, Kramer HB, Kessler BM. Comparison of CID versus etd based MS/MS fragmentation for the analysis of protein ubiquitination. J Am Soc Mass Spectrom. 2009;20(9):1652–9.

[64] Kolbowski L, Mendes ML, Rappsilber J. Optimizing the parameters governing the fragmentation of cross-linked peptides in a tribrid mass spectrometer. Anal Chem. 2017;89(10): 5311–8.

[65] Jedrychowski MP, Huttlin EL, Haas W, Sowa ME, Rad R, Gygi SP. Evaluation ofHCD- and CID-type fragmentation within their respective detection platforms for murine phosphoproteomics. Mol Cell Proteomics: MCP. 2011;10(12):M111.009910.

[66] Daniels, CM, Ong, S-E, Leung, A. Adp-ribosylated peptide enrichment and site identification: the phosphodiesterasebased method. 2017:79–93.

[67] Merkley ED, Kaiser BL, Kreuzer H. A proteomics tutorial. In: Applications in forensic proteomics: protein identification and profiling. ACS Publications; 2019. p. 9–28.

[68] Schessner JP, Voytik E, Bludau I. A practical guide to interpreting and generating bottom-up proteomics data visualizations. Proteomics. 2022;22(8):2100103.

[69] Gao Y, Yates JR, III. Protein analysis by shotgun proteomics. Mass Spectrometry-Based Chemical Proteomics. 2019:1–38.

[70] Catherman AD, Skinner OS, Kelleher NL. Top down proteomics: facts and perspectives. Biochem Biophys Res Commun. 2014;445(4):683–93.

[71] Banach P, Suchy W, Dereziński P, Matysiak J, Kokot ZJ, Nowak-Markwitz E. Mass spectrometry as a tool for biomarkers searching in gynecological oncology. Biomed Pharmacother. 2017;92:836–42.

[72] Gillet LC, Leitner A, Aebersold R. Mass spectrometry applied to bottom-up proteomics: entering the high-throughput era for hypothesis testing. Annu Rev Anal Chem. 2016;9(1):449–72.

[73] Hu Y, Zhou Y, Zhao N, Liu F, Xu F-J. Multifunctional PDNA-conjugated polycationic au nanorod-coated fe3o4 hierarchical nanocomposites for trimodal imaging and combined photothermal/gene therapy. Small. 2016;12(18):2459–68.

[74] Wang Z, Mülleder M, Batruch I, Chelur A, Textoris-Taube K, Schwecke T, et al. High-throughput proteomics of nanogramscale samples with zeno swath ms. Elife. 2022;11.

[75] Tsou CC, Tsai CF, Teo GC, Chen YJ, Nesvizhskii AI. Untargeted, spectral library-free analysis of data-independent acquisition proteomics data generated using orbitrap mass spectrometers. Proteomics. 2016;16(15-16):2257–71.

[76] Fu L, An Q, Zhang K, Liu Y, Tong Y, Xu J, et al. Quantitative proteomic characterization of human sperm cryopreservation: using data-independent acquisition mass spectrometry. BMC Urol. 2019;19(1):133.

[77] Gillet LC, Navarro P, Tate S, Röst H, Selevsek N, Reiter L, et al. Targeted data extraction of the MS/MS spectra generated by data-independent acquisition: a new concept for consistent and accurate proteome analysis. Mol Cell Proteomics. 2012;11(6):O111.016717.

[78] Govaert E, Van Steendam K, Willems S, Vossaert L, Dhaenens M, Deforce D. Comparison of fractionation proteomics for local swath library building. Proteomics. 2017;17(15-16):1700052.

[79] Chen M-X, Zhang Y, Fernie AR, Liu Y-G, Zhu F-Y. Swath-MS-based proteomics: strategies and applications in plants. Trends Biotechnol. 2021;39(5):433–7.

[80] Ludwig C, Gillet L, Rosenberger G, Amon S, Collins BC, Aebersold R. Data-independent acquisition-based swath-MS for quantitative proteomics: a tutorial. Mol Syst Biol. 2018;14(8).

[81] Min L, Zhu S, Wei R, Zhao Y, Liu S, Li P, et al. Integrating swath-MS proteomics and transcriptome analysis identifies chi3l1 as a plasma biomarker for early gastric cancer. Molecular Therapy - Oncolytics. 2020;17:257–66.

[82] Singh AN, Sharma N. Quantitative swath-based proteomic profiling for identification of mechanism-driven diagnostic biomarkers conferring in the progression of metastatic prostate cancer. Front Oncol. 2020;10.

[83] Anand S, Samuel M, Ang C-S, Keerthikumar S, Mathivanan S. Label-based and label-free strategies for protein quantitation. In: Methods in molecular biology; 2017. p. 31–43.

[84] Zargar S, Gupta N, Mir D, Rai V. Shift from gel based to gel free proteomics to unlock unknown regulatory network in plants: a comprehensive review. J Adv Res. 2016;1(2):1–19.

[85] DeSouza LV, Siu KWM. Mass spectrometry-based quantification. Clin Biochem. 2013;46(6):421–31.

[86] Wang Y, Xiao K, Tian Z. Quantitative n-glycoproteomics using stable isotopic diethyl labeling. Talanta. 2020;219:121359.

[87] Zhou B, Zhou Z, Chen Y, Deng H, Cai Y, Rao X, et al. Plasma proteomics-based identification of novel biomarkers in early gastric cancer. Clin Biochem. 2020;76:5–10.

[88] Wang L, McShane AJ, Castillo MJ, Yao X. Quantitative proteomics in development of disease protein biomarkers. In: Proteomic and metabolomic approaches to biomarker discovery. Elsevier; 2020. p. 261–88.

[89] Pappireddi N, Martin L, Wühr M. A review on quantitative multiplexed proteomics. Chembiochem. 2019;20(10):1210–24.

[90] Sandow JJ, Rainczuk A, Infusini G, Makanji M, Bilandzic M, Wilson AL, et al. Discovery and validation of novel protein biomarkers in ovarian cancer patient urine. Proteonomics Clin Appl. 2018;12(3):e1700135.

[91] de Oliveira TM, de Lacerda JTJG, Leite GGF, Dias M, Mendes MA, Kassab P, et al. Label-free peptide quantification coupled with in silico mapping of proteases for identification of potential serum biomarkers in gastric adenocarcinoma patients. Clin Biochem. 2020;79:61–9.

[92] Chen C, Hou J, Tanner JJ, Cheng J. Bioinformatics methods for mass spectrometry-based proteomics data analysis. Int J Mol Sci. 2020;21(8):2873.

[93] Cox J, Mann M. Maxquant enables high peptide identification rates, individualized ppb-range mass accuracies and proteome-wide protein quantification. Nat Biotechnol. 2008;26(12):1367–72.

[94] Brodbelt JS, Russell DH. Focus on the 20-year anniversary of sequest. J Am Soc Mass Spectrom. 2015;26(11):1797–8.

[95] Fu Y, Yang Q, Sun R, Li D, Zeng R, Ling CX, et al. Exploiting the kernel trick to correlate fragment ions for peptide identification via tandem mass spectrometry. Bioinformatics. 2004;20(12):1948–54.

[96] Kou Q, Xun L, Liu X. Toppic: a software tool for top-down mass spectrometry-based proteoform identification and characterization. Bioinformatics. 2016;32(22):3495–7.

[97] Ma B, Zhang K, Hendrie C, Liang C, Li M, Doherty-Kirby A, et al. Peaks: powerful software for peptide de novo sequencing by tandem mass spectrometry. Rapid Commun Mass Spectrom: RCM (Rapid Commun Mass Spectrom). 2003;17(20):2337–42.

[98] Frank A, Pevzner P. Pepnovo: de novo peptide sequencing via probabilistic network modeling. Anal Chem. 2005;77(4): 964–73.

[99] Tran NH, Zhang X, Xin L, Shan B, Li M. De novo peptide sequencing by deep learning. Proc Natl Acad Sci USA. 2017;114(31):8247–52.

[100] Yang H, Chi H, Zeng W-F, Zhou W-J, He S-M. Pnovo 3: precise de novo peptide sequencing using a learning-to-rank framework. Bioinformatics. 2019;35(14):i183–90.

[101] Jeong K, Kim S, Pevzner PA. Uninovo: a universal tool for de novo peptide sequencing. Bioinformatics. 2013;29(16): 1953–62.

[102] Bime C, Camp S, Casanova N, Oita R, Ndukum J, Garcia J, et al. The acute respiratory distress syndrome biomarker pipeline: crippling gaps between discovery and clinical utility. Transl Res. 2020;226:105–15.

[103] Issaq HJ, Veenstra TD. Biomarker discovery: study design and execution. In: Proteomic and metabolomic approaches to biomarker discovery. Elsevier; 2020. p. 1–16.

[104] Norman G, Monteiro S, Salama S. Sample size calculations: should the emperor’s clothes be off the peg or made to measure? BMJ (Clinical research ed). 2012;345(aug23 1):e5278.

[105] Suppers A, Gool AJv, Wessels HJCT. Integrated chemometrics and statistics to drive successful proteomics biomarker discovery. Proteomes. 2018;6(2):20.

[106] de Valpine P, Bitter H-M, Brown MP, Heller J. A simulation–approximation approach to sample size planning for high-dimensional classification studies. Biostatistics. 2009;10(3): 424–35.

[107] Skates SJ, Gillette MA, LaBaer J, Carr SA, Anderson L, Liebler DC, et al. Statistical design for biospecimen cohort size in proteomics-based biomarker discovery and verification studies. J Proteome Res. 2013;12:5383–94.

[108] Brzhozovskiy A, Kononikhin A, Bugrova AE, Kovalev GI, Schmit P-O, Kruppa G, et al. The parallel reaction monitoringparallel accumulation–serial fragmentation (prm-pasef) approach for multiplexed absolute quantitation of proteins in human plasma. Anal Chem. 2022;94(4):2016–22.

[109] Rifai N, Gillette MA, Carr SA. Protein biomarker discovery and validation: the long and uncertain path to clinical utility. Nat Biotechnol. 2006;24(8):971–83.

[110] Chan PP, Wasinger VC, Leong RW. Current application of proteomics in biomarker discovery for inflammatory bowel disease. World J Gastrointest Pathophysiol. 2016;7(1):27–37.

[111] Califf RM. Biomarker definitions and their applications. Exp Biol Med. 2018;243(3):213–21.

[112] Ang C, Baker M, Nice E. Mass spectrometry-based analysis for the discovery and validation of potential colorectal cancer stool biomarkers. In: Methods in enzymology. Elsevier; 2017. p. 247–74.

[113] Parker CE, Borchers CH. Mass spectrometry based biomarker discovery, verification, and validation—quality assurance and control of protein biomarker assays. Mol Oncol. 2014;8(4): 840–58.

[114] Surinova S, Schiess R, Hüttenhain R, Cerciello F, Wollscheid B, Aebersold R. On the development of plasma protein biomarkers. J Proteome Res. 2011;10(1):5–16.

[115] Meng Z, Veenstra TD. Targeted mass spectrometry approaches for protein biomarker verification. J Proteonomics. 2011;74(12):2650–9.

[116] Veenstra TD. Mass spectrometry-based approach for protein biomarker verification. In: Proteomic and metabolomic approaches to biomarker discovery. Elsevier; 2020. p. 353–68.

[117] Arora A, Patil V, Kundu P, Kondaiah P, Hegde AS, Arivazhagan A, et al. Serum biomarkers identification by itraq and verification by mrm: S100a8/s100a9 levels predict tumorstroma involvement and prognosis in glioblastoma. Sci Rep. 2019;9(1):2749.

[118] Zhou Q, Andersson R, Hu D, Bauden M, Kristl T, Sasor A, et al. Quantitative proteomics identifies brain acid soluble protein 1 (basp1) as a prognostic biomarker candidate in pancreatic cancer tissue. EBioMedicine. 2019;43:282–94.

[119] Hu R, Sou K, Takeoka S. A rapid and highly sensitive biomarker detection platform based on a temperatureresponsive liposome-linked immunosorbent assay. Sci Rep. 2020;10(1):18086.

[120] Alhajj M, Zubair M, Farhana A. Enzyme linked immunosorbent assay. StatPearls [Internet]; 2023.

[121] Ourradi K, Xu Y, de Seny D, Kirwan J, Blom A, Sharif M. Development and validation of novel biomarker assays for osteoarthritis. PLoS One. 2017;12(7):e0181334.

[122] Beretov J, Wasinger VC, Graham PH, Millar EK, Kearsley JH, Li Y. Chapter 4 — proteomics for breast cancer urine biomarkers. In: Makowski GS, editor. Advances in clinical chemistry. Elsevier; 2014. p. 123–67.

[123] Chi L-M, Hsiao Y-C, Chien K-Y, Chen S-F, Chuang Y-N, Lin S-Y, et al. Assessment of candidate biomarkers in paired saliva and plasma samples from oral cancer patients by targeted mass spectrometry. J Proteonomics. 2020;211:103571.

[124] Sjödin S, Hansson O, Öhrfelt A, Brinkmalm G, Zetterberg H, Brinkmalm A, et al. Mass spectrometric analysis of cerebrospinal fluid ubiquitin in alzheimer’s disease and parkinsonian disorders. Proteonomics Clin Appl. 2017;11(11-12):1700100.

[125] Rauniyar N. Parallel reaction monitoring: a targeted experiment performed using high resolution and high mass accuracy mass spectrometry. Int J Mol Sci. 2015;16(12): 28566–81.

[126] Park J, Oh HJ, Han D, Wang JI, Park IA, Ryu HS, et al. Parallel reaction monitoring-mass spectrometry (prm-MS)- based targeted proteomic surrogates for intrinsic subtypes in breast cancer: comparative analysis with immunohistochemical phenotypes. J Proteome Res. 2019;19(7):2643–53.

[127] Bao H, Li X, Cao Z, Huang Z, Chen L, Wang M, et al. Identification of copa as a potential prognostic biomarker and pharmacological intervention target of cervical cancer by quantitative proteomics and experimental verification. J Transl Med. 2022;20(1):18.

[128] Gallien S, Duriez E, Crone C, Kellmann M, Moehring T, Domon B. Targeted proteomic quantification on quadrupoleorbitrap mass spectrometer. Mol Cell Proteomics: MCP. 2012;11(12):1709–23.

[129] Ronsein GE, Pamir N, von Haller PD, Kim DS, Oda MN, Jarvik GP, et al. Parallel reaction monitoring (prm) and selected reaction monitoring (srm) exhibit comparable linearity, dynamic range and precision for targeted quantitative hdl proteomics. J Proteonomics. 2015;113:388–99.

[130] Kraus VB. Biomarkers as drug development tools: discovery, validation, qualification and use. Nat Rev Rheumatol. 2018;14(6):354–62.

[131] Goossens N, Nakagawa S, Sun X, Hoshida Y. Cancer biomarker discovery and validation. Transl Cancer Res. 2015;4:256.

[132] Park J, Lee E, Park KJ, Park HD, Kim JW, Woo HI, et al. Large-scale clinical validation of biomarkers for pancreatic cancer using a mass spectrometry-based proteomics approach. Oncotarget. 2017;8(26):42761–71.

[133] Hurley LC, Levin NK, Chatterjee M, Coles J, Muszkat S, Howarth Z, et al. Evaluation of paraneoplastic antigens reveals trim21 autoantibodies as biomarker for early detection of ovarian cancer in combination with autoantibodies to ny-eso-1 and tp53. Cancer Biomarkers. 2020;27(3):407–21.

[134] Zheng X, Xu K, Zhou B, Chen T, Huang Y, Li Q, et al. A circulating extracellular vesicles-based novel screening tool for colorectal cancer revealed by shotgun and dataindependent acquisition mass spectrometry. J Extracell Vesicles. 2020;9(1):1750202.

[135] Burton C, Ma Y. Current trends in cancer biomarker discovery using urinary metabolomics: achievements and new challenges. Curr Med Chem. 2019;26(1):5–28.

[136] Ma D, Li L. Searching for reliable premortem protein biomarkers for prion diseases: progress and challenges to date. Expet Rev Proteonomics. 2012;9(3):267–80.

[137] Zhang W, Segers K, Mangelings D, Van Eeckhaut A, Hankemeier T, Vander Heyden Y, et al. Assessing the suitability of capillary electrophoresis-mass spectrometry for biomarker discovery in plasma-based metabolomics. Electrophoresis. 2019;40(18-19):2309–20.

[138] Zhao Y, Li Y, Liu W, Xing S, Wang D, Chen J, et al. Identification of noninvasive diagnostic biomarkers for hepatocellular carcinoma by urinary proteomics. J Proteonomics. 2020;225:103780.

[139] del Campo M, Jongbloed W, Twaalfhoven HAM, Veerhuis R, Blankenstein MA, Teunissen CE. Facilitating the validation of novel protein biomarkers for dementia: an optimal workflow for the development of sandwich immunoassays. Front Neurol. 2015;6.

[140] Srivastava, S, editor. Biomarker discovery in the developing world: dissecting the pipeline for meeting the challenges. Springer Nature; 2016.

[141] Ku EJ, Cho K-C, Lim C, Kang JW, Oh JW, Choi YR, et al. Discovery of plasma biomarkers for predicting the severity of coronary artery atherosclerosis by quantitative proteomics. BMJ Open Diabetes Res Care. 2020;8(1):e001152.

[142] van Steenoven I, Koel-Simmelink MJA, Vergouw LJM, Tijms BM, Piersma SR, Pham TV, et al. Identification of novel cerebrospinal fluid biomarker candidates for dementia with lewy bodies: a proteomic approach. Mol Neurodegener. 2020;15(1):36.

[143] Millán O, Brunet M. Flow cytometry as platform for biomarker discovery and clinical validation. In: General methods in biomarker research and their applications, 17; 2015.

[144] Ou F-S, Michiels S, Shyr Y, Adjei AA, Oberg AL. Biomarker discovery and validation: statistical considerations. J Thorac Oncol. 2021;16(4):537–45.

[145] Duffy MJ, Sturgeon CM, Sölétormos G, Barak V, Molina R, Hayes DF. Validation of new cancer biomarkers: a position statement from the european group on tumor markers. Clin Chem. 2015;61(6):809–20.

[146] Cui M, Cheng C, Zhang L. High-throughput proteomics: a methodological mini-review. Lab Invest. 2022;102(11): 1170–81.

[147] Ding Z, Wang N, Ji N, Chen Z-S. Proteomics technologies for cancer liquid biopsies. Mol Cancer. 2022;21(1):53.

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