ArticleTranslational cancer research2024
Identification and verification of anoikis-related gene markers to predict the prognosis of patients with bladder cancer and assist in the diagnosis and treatment of bladder cancer.
Article in Translational cancer research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Integrative Analysis of Biomarkers for Cancer Stem Cells in Bladder Cancer and Their Therapeutic Potential.Genes · 2025Article
- Invasion and metastasis in cancer: molecular insights and therapeutic targets.Signal transduction and targeted therapy · 2025Review
- Identification of hub programmed cell death-related genes and immune infiltration in Crohn's disease using bioinformatics.Frontiers in genetics · 2024Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: The recurrence and mortality rates of bladder cancer are extremely high, and its diagnosis and treatment are global concerns. The mechanism of anoikis is closely related to tumor metastasis. Methods: First, we obtained all the data needed for this study from a public database through a formal operational process. The data were then analyzed by bioinformatics technology. Through the limma package, we screened and obtained 313 anoikis-related genes [false discovery rate (FDR) <0.05, |log fold change (FC) | >0.585]. Then, through univariate independent prognostic analysis, we further screened 146 genes (P<0.05) related to the prognosis of bladder cancer from 313 differential genes. These 146 prognostically relevant differential genes were used for least absolute shrinkage and selection operator (LASSO) regression for further screening to obtain model-related genes and output model formulas. Through the nomogram, we can calculate the survival rate of patients more accurately. The accuracy of the nomogram was also confirmed by calibration curves, independent prognostic analysis, receiver operating characteristic (ROC) curves, decision curve analysis (DCA) curves. We then analysed the sensitivity of immunotherapy in bladder cancer patients with different risk scores via Tumor Immune Dysfunction and Exclusion (TIDE). Results: Through bioinformatics technology and public databases, a prognostic model including 9 anoikis-related genes ( Conclusions: In this study, a prognostic model that can accurately predict the prognosis of patients with bladder cancer was constructed.
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