BIOINFORMATICAL ANALYSIS OF MICROARRAY GENE CHIP DATA FOR THE SCREENING OF KEY GENES INVOLVED IN PANCREATIC DUCTAL ADENOCARCINOMA

Mubeen Hussein Arawker, Li Zhen Nan*, Shantanu Baral, Mohammad Said Jalloh and Kashif Ali

ABSTRACT

Objective- The goal of this study is to use bioinformatics to investigate important genes and pathways linked to pancreatic ductal adenocarcinoma (PDAC) in order to better understand the underlying processes. Our discovery sheds fresh light on the pathophysiology of PDAC. Methods: GSE28735, GSE15471, and GSE101448 gene expression profiles were acquired from the Gene Expression Omnibus collection, which included 108 pancreatic ductal adenocarcinoma samples and 97 precancerous tissues. DEGs were evaluated with the help of the R programmes limma and impute. The gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were carried out using the online analytic tools DAVID. STRING was used to create a protein-protein interaction network, which was then displayed using Cytoscape software. The Molecular Complex Detection (MCODE) plugin was used to identify the hub genes. Results: A total of 161 DEGs with overlap were identified, including 54 up-regulated genes and 107 down-regulated genes (| log2 fold-change (FC) | > 2, P 0. 05). Extracellular exosome, extracellular space, and extracellular matrix structure were all substantially enriched in DEGs. In addition, three KEGG pathways, including pancreatic secretion, protein digestion and absorption, and ECM - receptor interaction, were substantially enriched. Conclusion: The hub genes ALB, COL1 1 A1, COL3 A1, FN1, EGF, COL1 A1, MMP9, COL5A2, ITGA2, and COL6A3 might be used as biomarkers or therapeutic targets for PDAC. Furthermore, protein digestion and absorption, as well as ECM-receptor interaction pathways, play important roles in PDAC development. Our discovery sheds fresh light on the pathophysiology of PDAC. Summary: To analyze pancreatic ductal adenocarcinoma utilizing bioinformatics approaches (PDAC) and screen important genes. Gene Expression Database from a Public Database was used as the approach (GEO) PDAC Gene Expression Profiling Chip (PDAC) may be downloaded here. GSE28735, GSE15471, GSE101448. 108 example PDAC Sample and 97 sample of tissue adjacent to cancer. R Code Language limma Package was used in screening of differentially expressed genes. DAVID Database and online analysis tools were used to carry out differential genes separately GO Functional enrichment analysis and KEGG Pathway enrichment analysis. We created a differential protein interaction network and further screened important genes using the STRING Database and Cytoscape tools. the outcome: 3 All gene expression profiling chips have this feature. 54 Up-regulated genes and 107 Down-regulated genes are among the 164 differentially expressed genes (| log2 fold - change (FC) | > 2,P 0.05). GO Functional enrichment analysis showed that the differential gene and extracellular exosome, extracellular space, extracellular matrix organization closely related. KEGG Pathway analysis shows that differential genes are mainly enriched in protein digestion and absorption, ECM － receptor interaction with focal adhesion. The protein interaction network diagram shows the most nodes 10 The pivot genes are ALB, COL11A1, COL3A1, FN1, EGF, COL1A1, MMP9, COL5A2, ITGA2, COL6A3. Conclusion: These 10 Key genes may be Play an important role in the occurrence and development, and is expected to become PDAC Biological targets for diagnosis and treatment, for further research PDAC The molecular mechanism of development provides a theoretical basis.

Keywords: PDAC, Bioinformatics, Microarray, Gene, Gene Chip, Cancer, Prognosis.