ArticlePeerJ2018
Candidate genes in gastric cancer identified by constructing a weighted gene co-expression network.
Article in PeerJ, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers.
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Who cites it
39 citing papers in PubMed, 78 citations in OpenAlex.
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- Bioinformatics Identification of Therapeutic Gene Targets for Gastric Cancer.Advances in therapy · 2023Article
- Long non-coding RNAs as potential biomarkers or therapeutic targets in gastric cancer.Gastroenterology and hepatology from bed to bench · 2023Article
- Using machine learning to identify gene interaction networks associated with breast cancer.BMC cancer · 2022Article
- Association between germline variants and somatic mutations in colorectal cancer.Scientific reports · 2022Article
- Proteomic Identification of a Gastric Tumor ECM Signature Associated With Cancer Progression.Frontiers in molecular biosciences · 2022Article
- PDGFRB is a potential prognostic biomarker and correlated with immune infiltrates in gastric cancer.Cancer biomarkers : section A of Disease markers · 2022Article
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- Identification of Prognostic Genes for Colon Cancer through Gene Co-expression Network Analysis.Current medical science · 2021Article
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- Assessing immune infiltration and the tumor microenvironment for the diagnosis and prognosis of sarcoma.Cancer cell international · 2020Article
- Identification and validation of potential novel prognostic biomarkers for patients with glioma based on a gene co-expression network.Translational cancer research · 2020Article
- Upregulation of COL8A1 indicates poor prognosis across human cancer types and promotes the proliferation of gastric cancer cells.Oncology letters · 2020Article
- Multi-omics sequencing provides insight into floral transition in Catalpa bungei. C.A. Mey.BMC genomics · 2020Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundGastric cancer (GC) is one of the most common cancers with high mortality globally. However, the molecular mechanisms of GC are unclear, and the prognosis of GC is poor. Therefore, it is important to explore the underlying mechanisms and screen for novel prognostic markers and treatment targets.
methodsThe genetic and clinical data of GC patients in The Cancer Genome Atlas (TCGA) was analyzed by weighted gene co-expression network analysis (WGCNA). Modules with clinical significance and preservation were distinguished, and gene ontology and pathway enrichment analysis were performed. Hub genes of these modules were validated in the TCGA dataset and another independent dataset from the Gene Expression Omnibus (GEO) database by
resultsWe found a preserved module consisting of 506 genes was associated with clinical traits including pathologic T stage and histologic grade. PDGFRB, COL8A1, EFEMP2, FBN1, EMILIN1, FSTL1 and KIRREL were identified as candidate genes in the module. Their expression levels were correlated with pathologic T stage and histologic grade, also affected overall survival of GC patients.
conclusionThese candidate genes may be involved in proliferation and differentiation of GC cells. They may serve as novel prognostic markers and treatment targets. Moreover, most of them were first reported in GC and deserved further research.
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