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Identification of core genes associated with type 2 diabetes mellitus and gastric cancer by bioinformatics analysis

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),...

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Autores principales: Liu, Shiwei, Zhao, Yuxiang, Duan, Ruixue, Wu, Yaru, Chen, Xiaoqin, Li, Naishi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: AME Publishing Company 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8987879/
https://www.ncbi.nlm.nih.gov/pubmed/35402578
http://dx.doi.org/10.21037/atm-21-3635
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author Liu, Shiwei
Zhao, Yuxiang
Duan, Ruixue
Wu, Yaru
Chen, Xiaoqin
Li, Naishi
author_facet Liu, Shiwei
Zhao, Yuxiang
Duan, Ruixue
Wu, Yaru
Chen, Xiaoqin
Li, Naishi
author_sort Liu, Shiwei
collection PubMed
description 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 (CASP3), and tumor protein P53 (TP53) 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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spelling pubmed-89878792022-04-08 Identification of core genes associated with type 2 diabetes mellitus and gastric cancer by bioinformatics analysis Liu, Shiwei Zhao, Yuxiang Duan, Ruixue Wu, Yaru Chen, Xiaoqin Li, Naishi Ann Transl Med Original Article 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 (CASP3), and tumor protein P53 (TP53) 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. AME Publishing Company 2022-03 /pmc/articles/PMC8987879/ /pubmed/35402578 http://dx.doi.org/10.21037/atm-21-3635 Text en 2022 Annals of Translational Medicine. All rights reserved. https://creativecommons.org/licenses/by-nc-nd/4.0/Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0 (https://creativecommons.org/licenses/by-nc-nd/4.0/) .
spellingShingle Original Article
Liu, Shiwei
Zhao, Yuxiang
Duan, Ruixue
Wu, Yaru
Chen, Xiaoqin
Li, Naishi
Identification of core genes associated with type 2 diabetes mellitus and gastric cancer by bioinformatics analysis
title Identification of core genes associated with type 2 diabetes mellitus and gastric cancer by bioinformatics analysis
title_full Identification of core genes associated with type 2 diabetes mellitus and gastric cancer by bioinformatics analysis
title_fullStr Identification of core genes associated with type 2 diabetes mellitus and gastric cancer by bioinformatics analysis
title_full_unstemmed Identification of core genes associated with type 2 diabetes mellitus and gastric cancer by bioinformatics analysis
title_short Identification of core genes associated with type 2 diabetes mellitus and gastric cancer by bioinformatics analysis
title_sort identification of core genes associated with type 2 diabetes mellitus and gastric cancer by bioinformatics analysis
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8987879/
https://www.ncbi.nlm.nih.gov/pubmed/35402578
http://dx.doi.org/10.21037/atm-21-3635
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