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Published on: July 22, 2020
Screening of potential pain genes in pancreatic ductal adenocarcinoma (PDAC) based on bioinformatics methods
Qian Xu1, Wei Wang1, Hongyu Hu1
1Department of Anesthesiology, Affiliated Hospital of Nanjing University of Chinese Medicine, Jiangsu Province Hospital of Chinese Medicine, Nanjing, China.
Background:
We aimed to identify cancer pain genes in pancreatic ductal adenocarcinoma (PDAC) using bioinformatic tools to provide evidence for pain treatment in PDAC patients.
Methods:
The GSE50570 data were obtained from the high-throughput Gene Expression Omnibus (GEO) database and subsequently analyzed. A volcano map, principal component analysis (PCA) map, box plot, and heat map were drawn, and a Venn diagram was constructed by comparison with human secreted histone genes. The differentially expressed secreted histone genes in PDAC were obtained. Then, Gene Ontology (GO) functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed, followed by protein-protein interaction (PPI) network analysis and key genetic screening.
Results:
In comparison to normal samples, the expression of 81 secreted protein-related genes was downregulated, and the expression of 12 secreted protein-related genes was upregulated in PDAC. According to the GO and KEGG enrichment analysis results, these differentially expressed genes are mainly involved in the PI3K-Akt signaling pathway, protein digestion and absorption, extracellular matrix (ECM) receptor interaction, AGE-RAGE (advanced glycation endproducts-the Receptor of Advanced Glycation Endproducts) signaling pathway, relaxin signaling pathway, interleukin-17 (IL-17) signaling pathway, and transforming growth factor-β (TGF-β) signaling pathway, affecting the different manifestations of PDAC cancer pain. We used Cytoscape software to construct a protein interaction network of common differentially expressed genes and obtained three clusters with high scores. Our literature review found that several genes, including PTGS2, VCAN, and CCL2, were directly related to cancer pain occurrence.
Conclusions:
By data mining the PDAC tumor expression, dozens of differentially expressed genes were identified in this study, several of which have been associated with the frequency and severity of cancer pain. This study provides an important foundation for the pain treatment of PDAC tumor patients.
