Bioinformatics approaches for cancer biomarker discovery

Abstract

Cancer biomarker discovery is essential for early diagnosis, prognosis, and personalized treatment strategies. Recent advancements in bioinformatics have significantly enhanced the identification of novel biomarkers, providing deeper insights into the molecular underpinnings of cancer. This review explores various bioinformatics approaches used in cancer biomarker discovery, including genomics, transcriptomics, proteomics, and metabolomics. We discuss how high-throughput sequencing, data integration, and machine learning tools enable the identification of diagnostic, prognostic, and predictive biomarkers across cancer types. Additionally, the review highlights the challenges associated with cancer biomarker discovery, such as data heterogeneity, validation issues, and ethical concerns. Furthermore, emerging technologies like single-cell RNA sequencing, CRISPR-based screening, and 3D tumor modeling are shaping the future of cancer biomarker research, enabling a more personalized approach to cancer treatment. Bioinformatics is pivotal in driving precision medicine, facilitating the development of targeted therapies based on tumor-specific biomarkers. As these technologies continue to evolve, bioinformatics will play a crucial role in advancing cancer diagnosis, improving treatment outcomes, and ultimately enhancing patient survival.

References

1. Ferlay J, Ervik M, Lam F, Colombet M, Mery L, Piñeros M, et al. Global cancer observatory: cancer today. Lyon Int agency Res cancer. 2020;20182020.
2. Sarhadi VK, Armengol G. Molecular biomarkers in cancer. Biomolecules. 2022;12(8):1021.
3. Nicolini A, Ferrari P, Duffy MJ. Prognostic and predictive biomarkers in breast cancer: Past, present and future. In: Seminars in cancer biology. Elsevier; 2018. p. 56–73.
4. Lynch TJ, Bell DW, Sordella R, Gurubhagavatula S, Okimoto RA, Brannigan BW, et al. Activating mutations in the epidermal growth factor receptor underlying responsiveness of non–small-cell lung cancer to gefitinib. N Engl J Med. 2004;350(21):2129–39.
5. Patel JN, Fong MK, Jagosky M. Colorectal cancer biomarkers in the era of personalized medicine. J Pers Med. 2019;9(1):3.
6. Hanahan D, Weinberg RA. Hallmarks of cancer: the next generation. Cell. 2011;144(5):646–74.
7. Vogelstein B, Papadopoulos N, Velculescu VE, Zhou S, Diaz Jr LA, Kinzler KW. Cancer genome landscapes. Science (80- ). 2013;339(6127):1546–58.
8. Ritchie ME, Phipson B, Wu DI, Hu Y, Law CW, Shi W, et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015;43(7):e47–e47.
9. Hayes JH, Barry MJ. Screening for Prostate Cancer With the Prostate-Specific Antigen Test: A Review of Current Evidence. JAMA [Internet]. 2014 Mar 19;311(11):1143–9. Available from: https://doi.org/10.1001/jama.2014.2085
10. Razmi N, Hasanzadeh M. Current advancement on diagnosis of ovarian cancer using biosensing of CA 125 biomarker: Analytical approaches. TrAC Trends Anal Chem [Internet]. 2018;108:1–12. Available from: https://www.sciencedirect.com/science/article/pii/S0165993618303650
11. Slamon DJ, Clark GM, Wong SG, Levin WJ, Ullrich A, McGuire WL. Human Breast Cancer: Correlation of Relapse and Survival with Amplification of the HER-2/neu Oncogene. Science (80- ) [Internet]. 1987 Jan 9;235(4785):177–82. Available from: https://doi.org/10.1126/science.3798106
12. Arrington AK, Heinrich EL, Lee W, Duldulao M, Patel S, Sanchez J, et al. Prognostic and Predictive Roles of KRAS Mutation in Colorectal Cancer. Vol. 13, International Journal of Molecular Sciences. 2012. p. 12153–68.
13. Ravnan MC, Matalka MS. Vemurafenib in Patients With BRAF V600E Mutation–Positive Advanced Melanoma. Clin Ther [Internet]. 2012;34(7):1474–86. Available from: https://www.sciencedirect.com/science/article/pii/S0149291812003724
14. Bettegowda C, Sausen M, Leary RJ, Kinde I, Wang Y, Agrawal N, et al. Detection of Circulating Tumor DNA in Early- and Late-Stage Human Malignancies. Sci Transl Med [Internet]. 2014 Feb 19;6(224):224ra24-224ra24. Available from: https://doi.org/10.1126/scitranslmed.3007094
15. Hu X, Lin Z, Wang Z, Zhou Q. Emerging role of PD-L1 modification in cancer immunotherapy. Am J Cancer Res. 2021;11(8):3832–40.
16. Ansorge WJ. Next-generation DNA sequencing techniques. N Biotechnol [Internet]. 2009;25(4):195–203. Available from: https://www.sciencedirect.com/science/article/pii/S1871678409000089
17. Shendure J, Ji H. Next-generation DNA sequencing. Nat Biotechnol [Internet]. 2008;26(10):1135–45. Available from: https://doi.org/10.1038/nbt1486
18. Bartha Á, Győrffy B. Comprehensive Outline of Whole Exome Sequencing Data Analysis Tools Available in Clinical Oncology. Vol. 11, Cancers. 2019.
19. McKenna A, Hanna M, Banks E, Sivachenko A, Cibulskis K, Kernytsky A, et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 2010 Sep;20(9):1297–303.
20. Wang Z, Gerstein M, Snyder M. RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet [Internet]. 2009;10(1):57–63. Available from: https://doi.org/10.1038/nrg2484
21. Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol [Internet]. 2014;15(12):550. Available from: https://doi.org/10.1186/s13059-014-0550-8
22. Robinson MD, McCarthy DJ, Smyth GK. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics [Internet]. 2010 Jan 1;26(1):139–40. Available from: https://doi.org/10.1093/bioinformatics/btp616
23. Schmitt AM, Chang HY. Long Noncoding RNAs in Cancer Pathways. Cancer Cell [Internet]. 2016 Apr 11;29(4):452–63. Available from: https://doi.org/10.1016/j.ccell.2016.03.010
24. Calin GA, Croce CM. MicroRNA signatures in human cancers. Nat Rev Cancer [Internet]. 2006;6(11):857–66. Available from: https://doi.org/10.1038/nrc1997
25. Cox J, Mann M. MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nat Biotechnol [Internet]. 2008;26(12):1367–72. Available from: https://doi.org/10.1038/nbt.1511
26. Tyanova S, Temu T, Sinitcyn P, Carlson A, Hein MY, Geiger T, et al. The Perseus computational platform for comprehensive analysis of (prote)omics data. Nat Methods [Internet]. 2016;13(9):731–40. Available from: https://doi.org/10.1038/nmeth.3901
27. Szklarczyk D, Franceschini A, Wyder S, Forslund K, Heller D, Huerta-Cepas J, et al. STRING v10: protein–protein interaction networks, integrated over the tree of life. Nucleic Acids Res [Internet]. 2015 Jan 28;43(D1):D447–52. Available from: https://doi.org/10.1093/nar/gku1003
28. Slamon DJ, Leyland-Jones B, Shak S, Fuchs H, Paton V, Bajamonde A, et al. Use of chemotherapy plus a monoclonal antibody against HER2 for metastatic breast cancer that overexpresses HER2. N Engl J Med. 2001 Mar;344(11):783–92.
29. Vander Heiden MG, Cantley LC, Thompson CB. Understanding the Warburg Effect: The Metabolic Requirements of Cell Proliferation. Science (80- ) [Internet]. 2009 May 22;324(5930):1029–33. Available from: https://doi.org/10.1126/science.1160809
30. Smith CA, Want EJ, O’Maille G, Abagyan R, Siuzdak G. XCMS: Processing Mass Spectrometry Data for Metabolite Profiling Using Nonlinear Peak Alignment, Matching, and Identification. Anal Chem [Internet]. 2006 Feb 1;78(3):779–87. Available from: https://doi.org/10.1021/ac051437y
31. Chong J, Soufan O, Li C, Caraus I, Li S, Bourque G, et al. MetaboAnalyst 4.0: towards more transparent and integrative metabolomics analysis. Nucleic Acids Res [Internet]. 2018 Jul 2;46(W1):W486–94. Available from: https://doi.org/10.1093/nar/gky310
32. Sourbier C, Ricketts CJ, Matsumoto S, Crooks DR, Liao P-J, Mannes PZ, et al. Targeting ABL1-Mediated Oxidative Stress Adaptation in Fumarate Hydratase-Deficient Cancer. Cancer Cell [Internet]. 2014 Dec 8;26(6):840–50. Available from: https://doi.org/10.1016/j.ccell.2014.10.005
33. Ježek P. 2-Hydroxyglutarate in Cancer Cells. Antioxid Redox Signal [Internet]. 2019 Dec 18;33(13):903–26. Available from: https://doi.org/10.1089/ars.2019.7902
34. Shen R, Mo Q, Schultz N, Seshan VE, Olshen AB, Huse J, et al. Integrative Subtype Discovery in Glioblastoma Using iCluster. PLoS One [Internet]. 2012 Apr 23;7(4):e35236. Available from: https://doi.org/10.1371/journal.pone.0035236
35. Argelaguet R, Arnol D, Bredikhin D, Deloro Y, Velten B, Marioni JC, et al. MOFA+: a statistical framework for comprehensive integration of multi-modal single-cell data. Genome Biol [Internet]. 2020;21(1):111. Available from: https://doi.org/10.1186/s13059-020-02015-1
36. Cutler A, Cutler DR, Stevens JR. Random Forests BT - Ensemble Machine Learning: Methods and Applications. In: Zhang C, Ma Y, editors. New York, NY: Springer New York; 2012. p. 157–75. Available from: https://doi.org/10.1007/978-1-4419-9326-7_5
37. Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature [Internet]. 2017;542(7639):115–8. Available from: https://doi.org/10.1038/nature21056
38. Tran KA, Kondrashova O, Bradley A, Williams ED, Pearson J V, Waddell N. Deep learning in cancer diagnosis, prognosis and treatment selection. Genome Med [Internet]. 2021;13(1):152. Available from: https://doi.org/10.1186/s13073-021-00968-x
39. Jolliffe IT, editor. Principal Component Analysis and Factor Analysis BT - Principal Component Analysis. In New York, NY: Springer New York; 2002. p. 150–66. Available from: https://doi.org/10.1007/0-387-22440-8_7
40. Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003 Nov;13(11):2498–504.
41. Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, et al. Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci [Internet]. 2005 Oct 25;102(43):15545–50. Available from: https://doi.org/10.1073/pnas.0506580102
42. Chang K, Creighton CJ, Davis C, Donehower L, Drummond J, Wheeler D, et al. The Cancer Genome Atlas Pan-Cancer analysis project. Nat Genet [Internet]. 2013;45(10):1113–20. Available from: https://doi.org/10.1038/ng.2764
43. Kent WJ, Sugnet CW, Furey TS, Roskin KM, Pringle TH, Zahler AM, et al. The human genome browser at UCSC. Genome Res. 2002 Jun;12(6):996–1006.
44. Yates AD, Achuthan P, Akanni W, Allen J, Allen J, Alvarez-Jarreta J, et al. Ensembl 2020. Nucleic Acids Res. 2020 Jan;48(D1):D682–8.
45. Perou CM, Sørlie T, Eisen MB, van de Rijn M, Jeffrey SS, Rees CA, et al. Molecular portraits of human breast tumours. Nature [Internet]. 2000;406(6797):747–52. Available from: https://doi.org/10.1038/35021093
46. Jia Q, Chu H, Jin Z, Long H, Zhu B. High-throughput single-сell sequencing in cancer research. Signal Transduct Target Ther [Internet]. 2022;7(1):145. Available from: https://doi.org/10.1038/s41392-022-00990-4
47. Brooks JD. Translational genomics: the challenge of developing cancer biomarkers. Genome Res. 2012 Feb;22(2):183–7.
48. Bensalah K, Montorsi F, Shariat SF. Challenges of Cancer Biomarker Profiling. Eur Urol [Internet]. 2007;52(6):1601–9. Available from: https://www.sciencedirect.com/science/article/pii/S0302283807012109
49. Angell M. The ethics of clinical research in the Third World. Vol. 337, The New England journal of medicine. United States; 1997. p. 847–9.
50. Oliver JM, Slashinski MJ, Wang T, Kelly PA, Hilsenbeck SG, McGuire AL. Balancing the Risks and Benefits of Genomic Data Sharing: Genome Research Participants’ Perspectives. Public Health Genomics [Internet]. 2011 Dec 30;15(2):106–14. Available from: https://doi.org/10.1159/000334718
51. Navin N, Kendall J, Troge J, Andrews P, Rodgers L, McIndoo J, et al. Tumour evolution inferred by single-cell sequencing. Nature [Internet]. 2011;472(7341):90–4. Available from: https://doi.org/10.1038/nature09807
52. Shalem O, Sanjana NE, Zhang F. High-throughput functional genomics using CRISPR–Cas9. Nat Rev Genet [Internet]. 2015;16(5):299–311. Available from: https://doi.org/10.1038/nrg3899
53. Hannoun Z, Steichen C, Dianat N, Weber A, Dubart-Kupperschmitt A. The potential of induced pluripotent stem cell derived hepatocytes. J Hepatol [Internet]. 2016;65(1):182–99. Available from: https://www.sciencedirect.com/science/article/pii/S0168827816001586
54. Jänne PA, Engelman JA, Johnson BE. Epidermal Growth Factor Receptor Mutations in Non–Small-Cell Lung Cancer: Implications for Treatment and Tumor Biology. J Clin Oncol [Internet]. 2025 Feb 2;23(14):3227–34.
55. Mishra G, Rahi S. Need of toxicity studies for cosmetic products and their approaches. biolsciences [Internet]. 2022 Jan. 17 [cited 2025 Mar. 3];2(1):105-9.
56. Gaurav Mishra, Amisha Thakur, Sukhbir Kaur. Process Validation of Metformin Hydrochloride Tablet (500 mg) according to USFDA. biolsciences [Internet]. 2021 Apr. 7 [cited 2025 Mar. 3];1(1):7-18.