THE
PANCREATIC TIMES SINCE 1848
AUGUST 2020
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By Ananya Chinni Krishnan, Stella Maris College & Dr. Payel Ghosh, Bioinformatics Centre, SPPU Rationale: The current treatment procedures do not aid in improving survival rates of patients and there are many biomarkers described in literature lacking clinical approval.
Background: Pancreatic cancer causes 4,30,000 deaths globally and contributes to 4.5% of cancer-related mortalities.
Strategy: To identify the biomarkers, I performed differential expression analysis of RNA-Seq and miRNA-Seq data in TNM stage categories. The DE-miRNAs and the target genes, which are also differentially expressed were used to generate a mRNA-miRNA network.
EXCLUSIVE: IDENTIFYING KEY REGULATORS OF PANCREATIC DUCTAL ADENOCARCINOMA USING mRNA-miRNA NETWORK
Results: The objective of this study is to identify the biomarkers of Pancreatic ductal adenocarcinoma, to facilitate detection of the cancer at an earlier stage. Biomarkers are genes, that are differentially expressed between tumour and normal tissue samples and are regulated by miRNAs.
The Differentially expressed genes were found to be associated with cell adhesion, cell migration processes. The regulatory miRNAs were related to cell migration, cell proliferation, chemoresistance. The interactions between the genes and the miRNAs were understood by the generation of the mRNA-miRNA network. The 436 DE-miRNAs with logFC <20 values and 1346 genes were used to generate the network.
Target genes
DEGs (2845) P.value<0.05
14536
1346
1499
Cluster 2 Mir-6797-3p
Footnotes: CYCS
Cluster 1
GATA6
Mir-519a-3p
Mir-891b
Mir-514b-3p
KLHL15 ZNF264
TXNIP Mir-499a-3p
Mir-6868-5p
Mir-301b-5p
Mir-190b SOD2 LRRC58 Mir-3690
Cluster 3
Mir-3157-3p
CRCP
Conclusion: As evidenced by the results, I narrowed down the biomarkers to tens from a list of hundreds available in the literature, which needs clinical validation. This can help in early detection of pancreatic ductal adenocarcinoma. TCGA, GEO are public databases which regularly deposits data from Microarray and RNA-Seq experiments. It is commonly used for omics analysis. • https://portal.gdc.cancer.gov/ • Assenov, Y., Ramírez, F., Schelhorn, S. E. S. E., Lengauer, T., & Albrecht, M. (2008). Computing topological parameters of biological networks. Bioinformatics, 24(2), 282–284. https://doi.org/10.1093/bioinformatic s/btm554 • Shannon P., Markiel A., Ozier O., Baliga N.S., Wang J. T., Ramage D., Amin N., Ideker T. & Schwikowski B., I. T. (2003). Cytoscape: A Software Environment for Integrated Models. Genome Research, 13(22), 426. https://doi.org/10.1101/gr.1239303.me tabolite •