Gene prioritization of colorectal cancer using computational approach

[{“box”:0,”content”:”[if 992 equals=”Open Access”]n

n

n

n

Open Access

nn

n

n[/if 992]n

n

Year : May 24, 2024 at 5:47 pm | [if 1553 equals=””] Volume : [else] Volume :[/if 1553] | [if 424 equals=”Regular Issue”]Issue[/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424] : | Page : –

n

n

n

n

n

n

By

n

[foreach 286]n

n

n

Pankaj Kumar Tripathi, Chakresh Kumar Jain

n

    n t

  • n

n

n[/foreach]

n

n[if 2099 not_equal=”Yes”]n

    [foreach 286] [if 1175 not_equal=””]n t

  1. Research Scholar Department of Biotechnology, Jaypee Institute of Information Technology, Department of Biotechnology, Jaypee Institute of Information Technology Uttar Pradesh, Uttar Pradesh India, India
  2. n[/if 1175][/foreach]

n[/if 2099][if 2099 equals=”Yes”][/if 2099]n

n

Abstract

nColorectal cancer (CRC) continues to be a major worldwide health issue, underscoring the need to pinpoint crucial genetic elements influencing its initiation and advancement. In this investigation, we utilize sophisticated computational methods to prioritize potential genes linked to CRC development. Through the utilization of various bioinformatics tools and comprehensive methodologies, we systematically examine extensive microarray datasets to identify potential genetic contributors. Initially, a vast amount of CRC microarray data (approximately more than 175 GSE datasets) was collected for gene expression analysis and applied filters such as unavailability of sample vs control, non-significant genes, untreated, non-published data, etc. After applying filters, we selected 25 datasets for further analysis. From these datasets, we find the common upregulated and downregulated genes and perform the network analysis to prioritize the genes based on their topological analysis, functional relevance, and biological interactions in CRC tissues. By integrating information from various sources, and applying different filters, we aim to identify key molecular players who may serve as biomarkers for CRC treatments. After applying our gene prioritizing method, we filter out the top ten genes (CDK1, CCNA2, BUB1B, CCNB1, KIF20A, BUB1, KIF11, CENPF, TOP2A, PLK1) have been identified, which are crucial for the development and proliferation of tumour cells, either directly or indirectly. Although CENPF gene role in CRC is unclear, this study shows it’s significantly involved in cell division, spindle assembly checkpoint, chromosome segregation, microtubule binding, chromatin binding, and protein binding. Therefore, our finding proposed CENPF as a novel biomarker for CRC progression. This extensive computational gene prioritization study enhances the current comprehension of the molecular landscape of CRC, offering valuable insights for subsequent experimental validation and potential clinical applications.

n

n

n

Keywords: Colorectal Cancer, Microarray data, Bioinformatics, Gene Identification, Pathway analysis, Network analysis

n[if 424 equals=”Regular Issue”][This article belongs to International Journal of Bioinformatics and Computational Biology(ijbcb)]

n

[/if 424][if 424 equals=”Special Issue”][This article belongs to Special Issue under section in International Journal of Bioinformatics and Computational Biology(ijbcb)][/if 424][if 424 equals=”Conference”]This article belongs to Conference [/if 424]

n

n

n

How to cite this article: Pankaj Kumar Tripathi, Chakresh Kumar Jain. Gene prioritization of colorectal cancer using computational approach. International Journal of Bioinformatics and Computational Biology. May 24, 2024; ():-.

n

How to cite this URL: Pankaj Kumar Tripathi, Chakresh Kumar Jain. Gene prioritization of colorectal cancer using computational approach. International Journal of Bioinformatics and Computational Biology. May 24, 2024; ():-. Available from: https://journals.stmjournals.com/ijbcb/article=May 24, 2024/view=0

nn


nn[if 992 equals=”Open Access”] Full Text PDF Download[/if 992] n[if 992 not_equal=”Open Access”]

[/if 992]n[if 992 not_equal=”Open Access”]


n


n

n[/if 992]nn[if 379 not_equal=””]n

Browse Figures

n

n

[foreach 379]n

n[/foreach]n

nn

n

n[/if 379]n

n

References

n[if 1104 equals=””]n

[1]       Jayarathna DK, Rentería ME, Kho PF, Batra J, Gandhi NS. Dehydroepiandrosterone Sulfate and Colorectal Cancer Risk: A Mendelian Randomization Analysis. Twin Research and Human Genetics 2022;25:180–6. https://doi.org/10.1017/thg.2022.31.

[2]      Dunn J, Lynch B, Rinaldis M, Pakenham K, McPherson L, Owen N, et al. Dimensions of quality of life and psychosocial variables most salient to colorectal cancer patients. Psychooncology 2006;15:20–30. https://doi.org/10.1002/pon.919.

[3]      Han W, Xing W, Wang K, Wang B, Bai K. Alisol A attenuates malignant phenotypes of colorectal cancer cells by inactivating PI3K/Akt signaling. Oncol Lett 2022;24:249. https://doi.org/10.3892/ol.2022.13369.

[4]      Woolcott CG, Wilkens LR, Nomura AMY, Horst RL, Goodman MT, Murphy SP, et al. Plasma 25-Hydroxyvitamin D Levels and the Risk of Colorectal Cancer: The Multiethnic Cohort Study. Cancer Epidemiology, Biomarkers & Prevention 2010;19:130–4. https://doi.org/10.1158/1055-9965.EPI-09-0475.

[5]      Guertin KA, Li XS, Graubard BI, Albanes D, Weinstein SJ, Goedert JJ, et al. Serum Trimethylamine N-oxide, Carnitine, Choline, and Betaine in Relation to Colorectal Cancer Risk in the Alpha Tocopherol, Beta Carotene Cancer Prevention Study. Cancer Epidemiology, Biomarkers & Prevention 2017;26:945–52. https://doi.org/10.1158/1055-9965.EPI-16-0948.

[6]      Zhou JJ, Zheng S. Colorectal Cancer: Basic and Translational Research. Gastrointest Tumors 2014;1:18–24. https://doi.org/10.1159/000354994.

[7]      Liang X, Hendryx M, Qi L, Lane D, Luo J. Association between prediagnosis depression and mortality among postmenopausal women with colorectal cancer. PLoS One 2020;15:e0244728. https://doi.org/10.1371/journal.pone.0244728.

[8]      Corbo C, Cevenini A, Salvatore F. Biomarker discovery by proteomics‐based approaches for early detection and personalized medicine in colorectal cancer. Proteomics Clin Appl 2017;11. https://doi.org/10.1002/prca.201600072.

[9]      Janani B, Vijayakumar M, Priya K, Kim JH, Geddawy A, Shahid M, et al. A network-based pharmacological investigation to identify the mechanistic regulatory pathway of andrographolide against colorectal cancer. Front Pharmacol 2022;13. https://doi.org/10.3389/fphar.2022.967262.

[10]    Qiang R, Zhao Z, Tang L, Wang Q, Wang Y, Huang Q. Identification of 5 Hub Genes Related to the Early Diagnosis, Tumour Stage, and Poor Outcomes of Hepatitis B Virus-Related Hepatocellular Carcinoma by Bioinformatics Analysis. Comput Math Methods Med 2021;2021:1–20. https://doi.org/10.1155/2021/9991255.

[11]    Barrett T, Edgar R. Mining Microarray Data at NCBI’s Gene Expression Omnibus (GEO)* Gene Mapping, Discovery, and Expression, New Jersey: Humana Press; n.d., p. 175–90. https://doi.org/10.1385/1-59745-097-9:175.

[12]    Kolur V, Vastrad B, Vastrad C, Kotturshetti S, Tengli A. Identification of candidate biomarkers and therapeutic agents for heart failure by bioinformatics analysis. BMC Cardiovasc Disord 2021;21:329. https://doi.org/10.1186/s12872-021-02146-8.

[13]    Doncheva NT, Morris JH, Gorodkin J, Jensen LJ. Cytoscape StringApp: Network Analysis and Visualization of Proteomics Data. J Proteome Res 2019;18:623–32. https://doi.org/10.1021/acs.jproteome.8b00702.

[14]    Assenov Y, Ramírez F, Schelhorn S-E, Lengauer T, Albrecht M. Computing topological parameters of biological networks. Bioinformatics 2008;24:282–4. https://doi.org/10.1093/bioinformatics/btm554.

[15]    Chin C-H, Chen S-H, Wu H-H, Ho C-W, Ko M-T, Lin C-Y. cytoHubba: identifying hub objects and sub-networks from complex interactome. BMC Syst Biol 2014;8:S11. https://doi.org/10.1186/1752-0509-8-S4-S11.

[16]    Yan X, Liu X-P, Guo Z-X, Liu T-Z, Li S. Identification of Hub Genes Associated With Progression and Prognosis in Patients With Bladder Cancer. Front Genet 2019;10. https://doi.org/10.3389/fgene.2019.00408.

[17]    Bader GD, Hogue CW. An automated method for finding molecular complexes in large protein interaction networks. BMC Bioinformatics 2003;4:2. https://doi.org/10.1186/1471-2105-4-2.

[18]    Dennis G, Sherman BT, Hosack DA, Yang J, Gao W, Lane HC, et al. DAVID: Database for Annotation, Visualization, and Integrated Discovery. Genome Biol 2003;4:R60. https://doi.org/10.1186/gb-2003-4-9-r60.

[19]    Kuleshov M V., Jones MR, Rouillard AD, Fernandez NF, Duan Q, Wang Z, et al. Enrichr: a comprehensive gene set enrichment analysis web server 2016 update. Nucleic Acids Res 2016;44:W90–7. https://doi.org/10.1093/nar/gkw377.

[20]    Tang Z, Kang B, Li C, Chen T, Zhang Z. GEPIA2: an enhanced web server for large-scale expression profiling and interactive analysis. Nucleic Acids Res 2019;47:W556–60. https://doi.org/10.1093/nar/gkz430.

[21]    Greene AN, Nguyen ET, Paranjpe A, Lane A, Privette Vinnedge LM, Solomon MB. In silico gene expression and pathway analysis of DEK in the human brain across the lifespan. European Journal of Neuroscience 2022;56:4720–43. https://doi.org/10.1111/ejn.15791.

[22]    Lind GE, Danielsen SA, Ahlquist T, Merok MA, Andresen K, Skotheim RI, et al. Identification of an epigenetic biomarker panel with high sensitivity and specificity for colorectal cancer and adenomas. Mol Cancer 2011;10:85. https://doi.org/10.1186/1476-4598-10-85.

[23]    Zhang P, Kawakami H, Liu W, Zeng X, Strebhardt K, Tao K, et al. Targeting CDK1 and MEK/ERK Overcomes Apoptotic Resistance in BRAF-Mutant Human Colorectal Cancer. Molecular Cancer Research 2018;16:378–89. https://doi.org/10.1158/1541-7786.MCR-17-0404.

[24]    Liu P, Kao TP, Huang H. CDK1 promotes cell proliferation and survival via phosphorylation and inhibition of FOXO1 transcription factor. Oncogene 2008;27:4733–44. https://doi.org/10.1038/onc.2008.104.

[25]    Ding X, Duan H, Luo H. Identification of Core Gene Expression Signature and Key Pathways in Colorectal Cancer. Front Genet 2020;11. https://doi.org/10.3389/fgene.2020.00045.

[26]    Li Z, Zhang Y, Zhou Y, Wang F, Yin C, Ding L, et al. Tanshinone IIA suppresses the progression of lung adenocarcinoma through regulating CCNA2-CDK2 complex and AURKA/PLK1 pathway. Sci Rep 2021;11:23681. https://doi.org/10.1038/s41598-021-03166-2.

[27]    Burum‐Auensen E, DeAngelis PM, Schjølberg AR, Røislien J, Mjåland O, Clausen OPF. Reduced level of the spindle checkpoint protein BUB1B is associated with aneuploidy in colorectal cancers. Cell Prolif 2008;41:645–59. https://doi.org/10.1111/j.1365-2184.2008.00539.x.

[28]    Zhuang L, Yang Z, Meng Z. Upregulation of BUB1B, CCNB1, CDC7, CDC20, and MCM3 in Tumor Tissues Predicted Worse Overall Survival and Disease-Free Survival in Hepatocellular Carcinoma Patients. Biomed Res Int 2018;2018:1–8. https://doi.org/10.1155/2018/7897346.

[29]    Fang Y, Yu H, Liang X, Xu J, Cai X. Chk1-induced CCNB1 overexpression promotes cell proliferation and tumor growth in human colorectal cancer. Cancer Biol Ther 2014;15:1268–79. https://doi.org/10.4161/cbt.29691.

[30]    Xiong M, Zhuang K, Luo Y, Lai Q, Luo X, Fang Y, et al. KIF20A promotes cellular malignant behavior and enhances resistance to chemotherapy in colorectal cancer through regulation of the JAK/STAT3 signaling pathway. Aging 2019;11:11905–21. https://doi.org/10.18632/aging.102505.

[31]    Zhang W, He W, Shi Y, Gu H, Li M, Liu Z, et al. High Expression of KIF20A Is Associated with Poor Overall Survival and Tumor Progression in Early-Stage Cervical Squamous Cell Carcinoma. PLoS One 2016;11:e0167449. https://doi.org/10.1371/journal.pone.0167449.

[32]    Li H, Zhang W, Sun X, Chen J, Li Y, Niu C, et al. Overexpression of kinesin family member 20A is associated with unfavorable clinical outcome and tumor progression in epithelial ovarian cancer. Cancer Manag Res 2018;Volume 10:3433–50. https://doi.org/10.2147/CMAR.S169214.

[33]    Jiang N, Liao Y, Wang M, Wang Y, Wang K, Guo J, et al. BUB1 drives the occurrence and development of bladder cancer by mediating the STAT3 signaling pathway. Journal of Experimental & Clinical Cancer Research 2021;40:378. https://doi.org/10.1186/s13046-021-02179-z.

[34]    Jiang W, Yu Y, Bhandari A, Hirachan S, Dong X, Huang X, et al. Budding uninhibited by benzimidazoles 1 might be a poor prognosis biomarker promoting the progression of papillary thyroid cancer. Environ Toxicol 2023;38:2047–56. https://doi.org/10.1002/tox.23812.

[35]    Zhou J, Chen W-R, Yang L-C, Wang J, Sun J-Y, Zhang W-W, et al. KIF11 Functions as an Oncogene and Is Associated with Poor Outcomes from Breast Cancer. Cancer Res Treat 2019;51:1207–21. https://doi.org/10.4143/crt.2018.460.

[36]    Pei Y, Li G, Ran J, Wan X, Wei F, Wang L. Kinesin Family Member 11 Enhances the Self-Renewal Ability of Breast Cancer Cells by Participating in the Wnt/β-Catenin Pathway. J Breast Cancer 2019;22:522. https://doi.org/10.4048/jbc.2019.22.e51.

[37]    Neska-Długosz I, Buchholz K, Durślewicz J, Gagat M, Grzanka D, Tojek K, et al. Prognostic Impact and Functional Annotations of KIF11 and KIF14 Expression in Patients with Colorectal Cancer. Int J Mol Sci 2021;22:9732. https://doi.org/10.3390/ijms22189732.

[38]    Brase JC, Schmidt M, Fischbach T, Sültmann H, Bojar H, Koelbl H, et al. ERBB2 and TOP2A in Breast Cancer: A Comprehensive Analysis of Gene Amplification, RNA Levels, and Protein Expression and Their Influence on Prognosis and Prediction. Clinical Cancer Research 2010;16:2391–401. https://doi.org/10.1158/1078-0432.CCR-09-2471.

[39]    Heestand GM, Schwaederle M, Gatalica Z, Arguello D, Kurzrock R. Topoisomerase expression and amplification in solid tumours: Analysis of 24,262 patients. Eur J Cancer 2017;83:80–7. https://doi.org/10.1016/j.ejca.2017.06.019.

[40]    Ding X, Duan H, Luo H. Identification of Core Gene Expression Signature and Key Pathways in Colorectal Cancer. Front Genet 2020;11. https://doi.org/10.3389/fgene.2020.00045.

[41]    Weng Ng WT, Shin J-S, Roberts TL, Wang B, Lee CS. Molecular interactions of polo-like kinase 1 in human cancers. J Clin Pathol 2016;69:557–62. https://doi.org/10.1136/jclinpath-2016-203656.

[42]    Tan J, Li Z, Lee PL, Guan P, Aau MY, Lee ST, et al. PDK1 Signaling Toward PLK1–MYC Activation Confers Oncogenic Transformation, Tumor-Initiating Cell Activation, and Resistance to mTOR-Targeted Therapy. Cancer Discov 2013;3:1156–71. https://doi.org/10.1158/2159-8290.CD-12-0595.

[43]    Yu C, Luo D, Yu J, Zhang M, Zheng X, Xu G, et al. Genome-wide CRISPR-cas9 knockout screening identifies GRB7 as a driver for MEK inhibitor resistance in KRAS mutant colon cancer. Oncogene 2022;41:191–203. https://doi.org/10.1038/s41388-021-02077-w.

nn[/if 1104][if 1104 not_equal=””]n

    [foreach 1102]n t

  1. [if 1106 equals=””], [/if 1106][if 1106 not_equal=””],[/if 1106]
  2. n[/foreach]

n[/if 1104]

nn


nn[if 1114 equals=”Yes”]n

n[/if 1114]

n

n

[if 424 not_equal=””][else]Ahead of Print[/if 424] Open Access Original Research

n

n

[if 2146 equals=”Yes”][/if 2146][if 2146 not_equal=”Yes”][/if 2146]n

n

n

n

n

n

n

n

n

n

n

n

n

n

n

n

n

n

n

n

n[if 1748 not_equal=””]

[else]

[/if 1748]n

n

n

Volume
[if 424 equals=”Regular Issue”]Issue[/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424]
Received April 30, 2024
Accepted May 13, 2024
Published May 24, 2024

n

n

n

n

n

n function myFunction2() {n var x = document.getElementById(“browsefigure”);n if (x.style.display === “block”) {n x.style.display = “none”;n }n else { x.style.display = “Block”; }n }n document.querySelector(“.prevBtn”).addEventListener(“click”, () => {n changeSlides(-1);n });n document.querySelector(“.nextBtn”).addEventListener(“click”, () => {n changeSlides(1);n });n var slideIndex = 1;n showSlides(slideIndex);n function changeSlides(n) {n showSlides((slideIndex += n));n }n function currentSlide(n) {n showSlides((slideIndex = n));n }n function showSlides(n) {n var i;n var slides = document.getElementsByClassName(“Slide”);n var dots = document.getElementsByClassName(“Navdot”);n if (n > slides.length) { slideIndex = 1; }n if (n (item.style.display = “none”));n Array.from(dots).forEach(n item => (item.className = item.className.replace(” selected”, “”))n );n slides[slideIndex – 1].style.display = “block”;n dots[slideIndex – 1].className += ” selected”;n }n”}]