ArticleBioscience reports2021
Identification of pivotal genes associated with the prognosis of gastric carcinoma through integrated analysis.
Article in Bioscience reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 18 citations in OpenAlex.
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- Development of Novel PANoptosis-Related Gene Signatures to Predict the Prognosis of Patients With Stomach Adenocarcinoma.Human mutation · 2025Article
- Molecular mechanisms underlying Tao-Hong-Si-Wu decoction treating hyperpigmentation based on network pharmacology, Mendelian randomization analysis, and experimental verification.Pharmaceutical biology · 2024Article
- Identification of Biomarkers and Molecular Pathways Implicated in Smoking and COVID-19 Associated Lung Cancer Using Bioinformatics and Machine Learning Approaches.International journal of environmental research and public health · 2024Article
- Review
- Identification of key genes and therapeutic drugs for cocaine addiction using integrated bioinformatics analysis.Frontiers in neuroscience · 2023Article
- The predictive effect of immune therapy and chemotherapy under T cell-related gene prognostic index for Gastric cancer.Frontiers in cell and developmental biology · 2023Article
- Cancer-Associated Fibroblasts: Mechanisms of Tumor Progression and Novel Therapeutic Targets.Cancers · 2022Review
- Article
- Patterns of Immune Infiltration and the Key Immune-Related Genes in Acute Type A Aortic Dissection in Bioinformatics Analyses.International journal of general medicine · 2021Article
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeDetecting and diagnosing gastric cancer (GC) during its early period remains greatly difficult. Our analysis was performed to detect core genes correlated with GC and explore their prognostic values.
methodsMicroarray datasets from the Gene Expression Omnibus (GEO) (GSE54129) and The Cancer Genome Atlas (TCGA)-stomach adenocarcinoma (STAD) datasets were applied for common differentially co-expressed genes using differential gene expression analysis and Weighted Gene Co-expression Network Analysis (WGCNA). Functional enrichment analysis and protein-protein interaction (PPI) network analysis of differentially co-expressed genes were performed. We identified hub genes via the CytoHubba plugin. Prognostic values of hub genes were explored. Afterward, Gene Set Enrichment Analysis (GSEA) was used to analyze survival-related hub genes. Finally, the tumor-infiltrating immune cell (TIC) abundance profiles were estimated.
resultsSixty common differentially co-expressed genes were found. Functional enrichment analysis implied that cell-cell junction organization and cell adhesion molecules were primarily enriched. Hub genes were identified using the degree, edge percolated component (EPC), maximal clique centrality (MCC), and maximum neighborhood component (MNC) algorithms, and serpin family E member 1 (SERPINE1) was highly associated with the prognosis of GC patients. Moreover, GSEA demonstrated that extracellular matrix (ECM) receptor interactions and pathways in cancers were correlated with SERPINE1 expression. CIBERSORT analysis of the proportion of TICs suggested that CD8+ T cell and T-cell regulation were negatively associated with SERPINE1 expression, showing that SERPINE1 may inhibit the immune-dominant status of the tumor microenvironment (TME) in GC.
conclusionsOur analysis shows that SERPINE1 is closely correlated with the tumorigenesis and progression of GC. Furthermore, SERPINE1 acts as a candidate therapeutic target and prognostic biomarker of GC.
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