P1056 - Identification of Two Upregulated Genes in Tumor Tissues With Poor Prognosis in Pancreatic Ductal Adenocarcinoma via Bioinformatical Analysis

Award: Presidential Poster Award

Award: Presidential Poster Award

Chenyu Sun, MD, MSc¹, Yuting Huang, MD, PhD², John Pocholo W. Tuason, MD¹, Na Hyun Kim, MD¹, Chandur Bhan, MD¹, Yasmin A. Gerais, MD¹, Sudha Misra, MD¹, Mohamed R. Ismail, MBBCh¹, Shaodi Ma, ³, Yue Chen, ^4
1AMITA Health Saint Joseph Hospital, Chicago, IL; ²University of Maryland Medical Center Midtown Campus, Baltimore, MD; ³School of Public Health, Anhui Medical University, Hefei, Anhui, China; ⁴School of the First Clinical Medicine, Anhui Medical University, Hefei, Anhui, China

Introduction: The purpose of the present academic work was to identify genes with worse prognoses of pancreatic ductal adenocarcinoma (PDAC).

Methods: Gene expression profiles of GSE15471 and GSE46234 were extracted from GEO database. Differentially expressed genes (DEGs) between PDAC and normal pancreatic specimen were identified via GEO2R online tools with |logFC| > 2 and adjust P value < 0.05. Then, the raw data were checked in Venn software online to detect the commonly DEGs among the two datasets. The DEGs with log FC > 2 were considered as an up-regulated gene. Gene ontology (GO) analysis and KEGG pathway analysis were used to identify unique biological properties and pathways. DAVID online bioinformatics tool was used to visualize the DEGs enrichment of BP, MF, CC, and pathways (P < 0.05). PPI information was evaluated by STRING (Search Tool for the Retrieval of Interacting Genes). Then, cytoHubba app in Cytoscape was used to select hub genes of the PPI network. GEPIA website was used to analyze the data of RNA sequencing expression and to compute the log-rank P-value and hazard ratio (HR) with 95% confidence intervals.

Results: There are 43 PDAC & 43 normal tissue samples in the two profile datasets, and 232 and 717 up-regulated DEGs from GSE15471 and GSE46234 respectively were extracted via GEO2R. Then Venn diagram detected 90 commonly up-regulated DEGs in the PDAC tissues. GO analysis indicated that they were enriched in extracellular matrix organization, collagen catabolic process, and collagen fibril organization for biological processes (BP), and enriched in extracellular region, extracellular space, and extracellular exosome for GO cell component (CC), and enriched in extracellular matrix structural constituent, platelet-derived growth factor binding, and calcium ion binding for molecular function (MF). KEGG analysis found that they were enriched in ECM-receptor interaction, Focal adhesion, and Amoebiasis. Analysis of PPI network complex included 82 nodes and 194 edges, with top 10 genes of ITGA2, MMP1, COL1A1, COL1A2, COL11A1, FN1, COMP, COL5A1, COL3A1. Kaplan Meier plotter identified 2core genes (ITGA2, MMP1) associated with a worse prognosis (P < 0.05), and GEPIA found a higher expression level of them in PDAC tissues than normal tissue.

Discussion: We identified 2 up-regulated DEGs with poor prognosis in PDAC. They may be potential therapeutic targets for PDAC patients.




Disclosures:


Chenyu Sun, MD, MSc¹, Yuting Huang, MD, PhD², John Pocholo W. Tuason, MD¹, Na Hyun Kim, MD¹, Chandur Bhan, MD¹, Yasmin A. Gerais, MD¹, Sudha Misra, MD¹, Mohamed R. Ismail, MBBCh¹, Shaodi Ma, ³, Yue Chen, ⁴. P1056 - Identification of Two Upregulated Genes in Tumor Tissues With Poor Prognosis in Pancreatic Ductal Adenocarcinoma via Bioinformatical Analysis, ACG 2021 Annual Scientific Meeting Abstracts. Las Vegas, Nevada: American College of Gastroenterology.