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Identification of Core Genes Associated with Type 2 Diabetes Mellitus and Gastric Cancer by Bioinformatics Analysis

Overview
Journal Ann Transl Med
Date 2022 Apr 11
PMID 35402578
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Abstract

Background: Gastric cancer (GC) is the most common type of malignant neoplasm of the digestive system. Diabetes mellitus (DM) or hyperglycemia may increase the incidence or mortality of GC. We aimed to investigate the possible genetic relationship between GC, DM, and type 2 diabetes mellitus (T2DM), and to identify core genes that are associated with T2DM and GC.

Methods: The GeneCards database was used to screen DM-, T2DM-, and GC-related genes, and a protein-protein interaction (PPI) network of the genes/proteins associated with overlapping genes between DM, T2DM, and GC was constructed. Molecular Complex Detection (MCODE) was used to identify the significant module. CytoHubba (U.S. National Institute of General Medical Sciences) was utilized to detect hub genes in the PPI. The Database for Annotation, Visualization, and Integrated Discovery (DAVID) resources were used to analyze selected module genes, as well as Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Ontology (GO) enrichment of PPI networks. The Kaplan-Meier plotter database, Gene Expression Profiling Interactive Analysis (GEPIA), UALCAN and western blot were used to identify the prognostic value of hub genes and their expression in GC and normal tissue.

Results: One thousand one hundred and fifty-two DM-related genes, 466 GC-related genes, and 531 T2DM-related genes were obtained. Subsequently, 401 genes/proteins associated with 59 overlapping genes were screened. Two significant modules, which had higher scores, and 10 hub genes were chosen. Finally, caspase 3 (), and tumor protein P53 () were identified as core genes.

Conclusions: We identified two genes that may play key roles in T2DM and GC: CASP3, TP53. Our study will contribute to further understanding the possible mechanism of diabetes progression to GC and provide useful information to identify new biomarkers for GC, and provided theoretical basis for the prevention of the occurrence and development of GC.

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References
1.
Chin C, Chen S, Wu H, Ho C, Ko M, Lin C . cytoHubba: identifying hub objects and sub-networks from complex interactome. BMC Syst Biol. 2014; 8 Suppl 4:S11. PMC: 4290687. DOI: 10.1186/1752-0509-8-S4-S11. View

2.
Oronsky B, Oronsky N, Fanger G, Parker C, Caroen S, Lybeck M . Follow the ATP: tumor energy production: a perspective. Anticancer Agents Med Chem. 2014; 14(9):1187-98. DOI: 10.2174/1871520614666140804224637. View

3.
Szklarczyk D, Franceschini A, Wyder S, Forslund K, Heller D, Huerta-Cepas J . STRING v10: protein-protein interaction networks, integrated over the tree of life. Nucleic Acids Res. 2014; 43(Database issue):D447-52. PMC: 4383874. DOI: 10.1093/nar/gku1003. View

4.
Lanczky A, Nagy A, Bottai G, Munkacsy G, Szabo A, Santarpia L . miRpower: a web-tool to validate survival-associated miRNAs utilizing expression data from 2178 breast cancer patients. Breast Cancer Res Treat. 2016; 160(3):439-446. DOI: 10.1007/s10549-016-4013-7. View

5.
Hinault C, Kawamori D, Liew C, Maier B, Hu J, Keller S . Δ40 Isoform of p53 controls β-cell proliferation and glucose homeostasis in mice. Diabetes. 2011; 60(4):1210-22. PMC: 3064094. DOI: 10.2337/db09-1379. View