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PMID: 42245289 已发表 · epublish 英语

Shared molecular features and candidate pathways underlying gastric cancer-depression comorbidity: a systems biology analysis.

Frontiers in bioinformatics ·第 6 卷

Liu B, Hou B, Zhao Y, Niu J, Hou J, He J, Liu F

摘要

The bidirectional association between gastric cancer (GC) and depression remains incompletely elucidated. This investigation examines the genetic and molecular correlations between GC and depression through bioinformatics and experimental approaches. Utilizing Gene Expression Omnibus (RRID: SCR_005012), DisGeNET (RRID: SCR_006178), and GeneCards (RRID: SCR_002773) databases, 130 GC-associated and 534 depression-associated genes were identified. Overlapping genes underwent further analysis via Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and protein-protein interaction network methodologies. Six pivotal hub genes were identified: SERPINE1, COL4A1, PDGFRB, BMP1, NOTCH3, and EDNRA, with SERPINE1 emerging as a particularly significant hub gene. These genes demonstrated upregulation in GC tissues and exhibited correlation with diminished survival outcomes. Furthermore, single-sample Gene Set Enrichment Analysis revealed their association with immune cell infiltration patterns. Additionally, miRNA-mRNA network analysis demonstrated that specific microRNAs, including miR-21-5p, miR-145-5p, miR-16-5p, miR-34a-5p, and miR-491-5p, were predicted to target these genes. Real-time quantitative polymerase chain reaction and Western blot validation subsequently verified the distinct expression patterns of these mRNAs in GC tissues. Critical pathways, encompassing the PI3K-Akt, AGE-RAGE, and proteoglycans pathways, may contribute to the interconnection between GC and depression. These findings illuminate potential molecular linkages between GC and depression, though additional investigation is required to elucidate the underlying mechanisms.

关键词
bioinformatics comorbidity depression gastric cancer miRNA-mRNA network
文献信息
期刊
Frontiers in bioinformatics
期刊简称
Front Bioinform
ISSN
2673-7647
语言
英语
国家/地区
Switzerland
NLM ID
9918227263306676
分析服务
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