Significance of Bioinformatics in the Cancer Diagnosis Process

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Nikesh V. V.
Saifulla Khan M.
Rajkumar N.
Rajkumar N.


The improved genetic analysis increases the possibility of identifying mutations. Potentially useful prognostic or predictive biomarkers for patients with metastatic cancer to use in their fight against the disease and for enhancing their quality of life could also emerge from such an investigation. The advanced genomic analysis allows for detecting those at high risk of acquiring metastatic cancer and understanding the pathological process. Useful and making good use of the plethora of data made available by high-throughput experimental gene analysis, the methodologies for data analysis are a boon to the field. They are tasked with a wide range of classification and clustering activities, from diagnostic to mechanical, as well as survival analyses. The probable genes' relevance was hinted at using both mRNA expression analysis and existing CNA data.

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V. V., N., Khan M., S., N., R., & N., R. . (2023). Significance of Bioinformatics in the Cancer Diagnosis Process. Journal of Coastal Life Medicine, 11(1), 108–116. Retrieved from


Ding YG, Ren YL, Xu YS, Wei CS, Zhang Y bin, Zhang SK, et al. Identification of key candidate genes and pathways in anaplastic thyroid cancer by bioinformatics analysis. American Journal of Otolaryngology-Head and Neck Medicine and Surgery. 2020 May 1;41(3).

Wu JR, Zhao Y, Zhou XP, Qin X. Estrogen receptor 1 and progesterone receptor are distinct biomarkers and prognostic factors in estrogen receptor-positive breast cancer: Evidence from a bioinformatic analysis. Biomedicine and Pharmacotherapy. 2020 Jan 1;121.

Mehrgou A, Ebadollahi S, Jameie B, Teimourian S. Analysis of subtype-specific and common Gene/MiRNA expression profiles of four main breast cancer subtypes using bioinformatic approach; Characterization of four genes, and two MicroRNAs with possible diagnostic and prognostic values. Inform Med Unlocked. 2020 Jan 1;20.

Rahman F, Mahmud P, Karim R, Hossain T, Islam F. Determination of novel biomarkers and pathways shared by colorectal cancer and endometrial cancer via comprehensive bioinformatics analysis. Inform Med Unlocked. 2020 Jan 1;20.

Saheb Sharif-Askari N, Saheb Sharif-Askari F, Guraya SY, Bendardaf R, Hamoudi R. Integrative systematic review meta-analysis and bioinformatics identifies MicroRNA-21 and its target genes as biomarkers for colorectal adenocarcinoma. Vol. 73, International Journal of Surgery. Elsevier Ltd; 2020. p. 113–22.

Liu F, Wu Y, Mi Y, Gu L, Sang M, Geng C. Identification of core genes and potential molecular mechanisms in breast cancer using bioinformatics analysis. Pathol Res Pract. 2019 Jul 1;215(7).

Wang J, Wu A, Yang B, Zhu X, Teng Y, Ai Z. Profiling and bioinformatics analyses reveal differential circular RNA expression in ovarian cancer. Gene. 2020 Jan 15;724.

Behera A, Ashraf R, Srivastava AK, Kumar S. Bioinformatics analysis and verification of molecular targets in ovarian cancer stem-like cells. Heliyon. 2020 Sep 1;6(9).

Akhavan H, Ramezani S, Shams Z, Hosseini-Asl S. Revealing novel biomarkers involved in development and progression of gastric cancer by comprehensive bioinformatics analysis. Inform Med Unlocked. 2021 Jan 1;25.

Gendoo DMA. Bioinformatics and computational approaches for analyzing patient-derived disease models in cancer research. Vol. 18, Computational and Structural Biotechnology Journal. Elsevier BV; 2020. p. 375–80.

Hasan MR, Paul BK, Ahmed K, Bhuyian T. Design protein-protein interaction network and protein-drug interaction network for common cancer diseases: A bioinformatics approach. Inform Med Unlocked. 2020 Jan 1;18.

Bozgeyik E. Bioinformatic Analysis and in Vitro Validation of Let-7b and Let-7c in Breast Cancer. Comput Biol Chem. 2020 Feb 1;84.

Zaheed O, Samson J, Dean K. A bioinformatics approach to identify novel long, non-coding RNAs in breast cancer cell lines from an existing RNA-sequencing dataset. Non-coding RNA Res. 2020 Jun 1;5(2):48–59.

Hermawan A, Putri H, Ikawati M. Bioinformatic analysis reveals the molecular targets of tangeretin in overcoming the resistance of breast cancer to tamoxifen. Gene Rep. 2020 Dec 1;21.

Cun J, Yang Q. Bioinformatics-based interaction analysis of miR-92a-3p and key genes in tamoxifen-resistant breast cancer cells. Biomedicine and Pharmacotherapy. 2018 Nov 1;107:117–28.

Liang Y, Zhang C, Dai DQ. Identification of DNA methylation-regulated differentially-expressed genes and related pathways using Illumina 450K BeadChip and bioinformatic analysis in gastric cancer. Pathol Res Pract. 2019 Oct 1;215(10).

Shamsara E, Shamsara J. Bioinformatics analysis of the genes involved in the extension of prostate cancer to adjacent lymph nodes by supervised and unsupervised machine learning methods: The role of SPAG1 and PLEKHF2. Genomics. 2020 Nov 1;112(6):3871–82.

Norris JM, Simpson BS, Parry MA, Allen C, Ball R, Freeman A, et al. Genetic Landscape of Prostate Cancer Conspicuity on Multiparametric Magnetic Resonance Imaging: A Systematic Review and Bioinformatic Analysis. Vol. 20, European Urology Open Science. Elsevier BV; 2020. p. 37–47.

Puspo NA, Akter L, Siddique S, Paul BK, Ahmed K, Bhuiyan T, et al. Analyzing the protein-protein interaction network and the topological properties of prostate cancer and allied diseases: A computational bioinformatics approach. Gene Rep. 2020 Dec 1;21.

Wulandari F, Ikawati M, Meiyanto E, Kirihata M, Hermawan A. Bioinformatic analysis of CCA-1.1, a novel curcumin analog, uncovers furthermost noticeable target genes in colon cancer. Gene Rep. 2020 Dec 1;21.

You S, Gao L. Identification of NMU as a potential gene conferring alectinib resistance in non-small cell lung cancer based on bioinformatics analyses. Gene. 2018 Dec 15;678:137–42.

Cui Y, Hunt A, Li Z, Birkin E, Lane J, Ruge F, et al. Lead DEAD/H box helicase biomarkers with the therapeutic potential identified by integrated bioinformatic approaches in lung cancer. Comput Struct Biotechnol J. 2021 Jan 1;19:261–78.

Gong L, Zhang D, Dong Y, Lei Y, Qian Y, Tan X, et al. Integrated bioinformatical analysis for identificating the therapeutic targets of aspirin in small cell lung cancer. J Biomed Inform. 2018 Dec 1;88:20–8.

Shafana ARF, Uwanthika GAI, Kartheeswaran T. Exploring the molecular subclasses and stage-specific genes of oral cancer: A bioinformatics analysis. Cancer Treat Res Commun. 2021 Jan 1;27.

Wu Q, Zhang B, Wang Z, Hu X, Sun Y, Xu R, et al. Integrated bioinformatics analysis reveals novel key biomarkers and potential candidate small molecule drugs in gastric cancer. Pathol Res Pract. 2019 May 1;215(5):1038–48.

S S, Shukla V, Khan GN, Eswaran S, Adiga D, Kabekkodu SP. Integrated bioinformatic analysis of miR-15a/16-1 cluster network in cervical cancer. Reprod Biol. 2021 Mar 1;21(1).

Wang Y, Wang P, Liu M, Zhang X, Si Q, Yang T, et al. identification of tumor-associated antigens of lung cancer: SEREX combined with bioinformatics analysis. J Immunol Methods. 2021 May 1;492.