ArticleBMC cancer2022
A novel focal adhesion-related risk model predicts prognosis of bladder cancer -- a bioinformatic study based on TCGA and GEO database.
Article in BMC cancer, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 15 citations in OpenAlex.
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- Establishment of a m6 A-associated lncRNAs-derived risk model for enhanced patient prognosis stratification and personalized therapy approaches in bladder cancer.Discover oncology · 2025Article
- Mechanistic insights into SIRT7 and EZH2 regulation of cisplatin resistance in bladder cancer cells.Cell death & disease · 2024Article
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- Exploring the impact of PDGFD in osteosarcoma metastasis through single-cell sequencing analysis.Cellular oncology (Dordrecht, Netherlands) · 2024Article
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- Nomogram for customized recurrence prediction in primary non-muscle-invasive bladder cancer based on routine blood and urine parameters.BMC urology · 2024Article
- Identification of DNA repair gene signature and potential molecular subtypes in hepatocellular carcinoma.Frontiers in oncology · 2023Article
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundBladder cancer (BLCA) is the ninth most common cancer globally, as well as the fourth most common cancer in men, with an incidence of 7%. However, few effective prognostic biomarkers or models of BLCA are available at present.
methodsThe prognostic genes of BLCA were screened from one cohort of The Cancer Genome Atlas (TCGA) database through univariate Cox regression analysis and functionally annotated by Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. The intersecting genes of the BLCA gene set and focal adhesion-related gene were obtained and subjected to the least absolute shrinkage and selection operator regression (LASSO) to construct a prognostic model. Gene set enrichment analysis (GSEA) of high- and low-risk patients was performed to explore further the biological process related to focal adhesion genes. Univariate and multivariate Cox analysis, receiver operating characteristic (ROC) curve analysis, and Kaplan-Meier survival analysis (KM) were used to evaluate the prognostic model. DNA methylation analysis was presented to explore the relationship between prognosis and gene methylation. Furthermore, immune cell infiltration was assessed by CIBERSORT, ESTIMATE, and TIMER. The model was verified in an external GSE32894 cohort of the Gene Expression Omnibus (GEO) database, and the Prognoscan database presented further validation of genes. The HPA database validated the related protein level, and functional experiments verified significant risk factors in the model.
resultsVCL, COL6A1, RAC3, PDGFD, JUN, LAMA2, and ITGB6 were used to construct a prognostic model in the TCGA-BLCA cohort and validated in the GSE32894 cohort. The 7-gene model successfully stratified the patients into both cohorts' high- and low-risk groups. The higher risk score was associated with a worse prognosis.
conclusionsThe 7-gene prognostic model can classify BLCA patients into high- and low-risk groups based on the risk score and predict the overall survival, which may aid clinical decision-making.
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