Identification of Candidate Genes Involved in Causing Pancreatic Cancer Through an Integrated Bioinformatics Approach

Atiqa  Nawab; Maliha  Ghaffar; Saman  Zahra; Ayesha  Zulifqar; Humaira  Amin; Samavia  Mustafa; Anam  Abbas

doi:10.54112/bcsrj.v6i8.1687

Authors

Atiqa Nawab Faculty of Life Sciences, Department of Biology, University of Okara, Pakistan
Maliha Ghaffar Faculty of Life Sciences, Department of Biology, University of Okara, Pakistan
Saman Zahra Faculty of Life Sciences, Department of Biology, University of Okara, Pakistan
Ayesha Zulifqar Faculty of Life Sciences, Department of Biology, University of Okara, Pakistan
Humaira Amin Cholistan University of veterinary and animal sciences, Bahawalpur, Punjab, Pakistan 63100
Samavia Mustafa Faculty of Life Sciences, Department of Biology, University of Okara, Pakistan
Anam Abbas Faculty of Life Sciences, Department of Biology, University of Okara, Pakistan

DOI:

https://doi.org/10.54112/bcsrj.v6i8.1687

Keywords:

Pancreatic Cancer, DEG, GEO2R, KEGG, STRING, Hub genes

Abstract

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive malignancy with poor survival, underscoring the need for robust molecular markers that can refine diagnosis and guide therapy. Objective: The goal of this study is to enhance diagnostic and treatment approaches for Pancreatic Ductal Adenocarcinoma (PDAC), a highly aggressive malignancy. We utilized RNA sequencing (RNA-Seq) data to identify differentially expressed genes (DEGs), examine gene networks, and uncover critical molecular links that are involved in the development and progression of PDAC. Methods: Four microarray datasets (GSE15471, GSE16515, GSE62165, and GSE62452) were obtained from the Gene Expression Omnibus (GEO). Data normalization and differential expression analysis were conducted using GEO2R, with a p-value cut-off of < 0.001 to identify DGEs. Functional annotation of these genes was carried out using DAVID Bioinformatics Resources, focusing on KEGG pathways and Gene Ontology (GO) terms. Gene interaction networks were constructed using STRING, and hub genes were identified with the cytoHubba plugin in Cytoscape. Results: A total of 614 up-regulated and 314 down-regulated genes were identified. The functional annotation of these genes revealed notable enrichment in several KEGG pathways and GO categories associated with molecular functions, biological processes, and cellular constituents. Key biological pathways implicated in PDAC were identified by the gene interaction networks derived from the DEGs. The top ten hub genes were identified. Conclusion: This comprehensive analysis of RNA-Seq data from multiple PDAC datasets successfully identified hub genes. These findings provide a molecular foundation for developing novel therapeutic strategies and improved diagnostic markers for the treatment of PDAC.

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