ArticleCancer biomarkers : section A of Disease markers2024
A risk model based on lncRNA-miRNA-mRNA gene signature for predicting prognosis of patients with bladder cancer.
Article in Cancer biomarkers : section A of Disease markers, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed.
- Discovery of a Ferroptosis-Related lncRNA-miRNA-mRNA Gene Signature in Endometrial Cancer Through a Comprehensive Co-Expression Network Analysis.Current oncology (Toronto, Ont.) · 2026Article
- A novel ferroptosis-related microRNAs signature for predicting prognosis in endometrial cancer: An observational study.Medicine · 2025Observational
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4 authors.
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Abstract
objectivesWe aimed to analyze lncRNAs, miRNAs, and mRNA expression profiles of bladder cancer (BC) patients, thereby establishing a gene signature-based risk model for predicting prognosis of patients with BC.
methodsWe downloaded the expression data of lncRNAs, miRNAs and mRNA from The Cancer Genome Atlas (TCGA) as training cohort including 19 healthy control samples and 401 BC samples. The differentially expressed RNAs (DERs) were screened using limma package, and the competing endogenous RNAs (ceRNA) regulatory network was constructed and visualized by the cytoscape. Candidate DERs were screened to construct the risk score model and nomogram for predicting the overall survival (OS) time and prognosis of BC patients. The prognostic value was verified using a validation cohort in GSE13507.
resultsBased on 13 selected. lncRNAs, miRNAs and mRNA screened using L1-penalized algorithm, BC patients were classified into two groups: high-risk group (including 201 patients ) and low risk group (including 200 patients). The high-risk group's OS time ( hazard ratio [HR], 2.160; 95% CI, 1.586 to 2.942; P= 5.678e-07) was poorer than that of low-risk groups' (HR, 1.675; 95% CI, 1.037 to 2.713; P= 3.393 e-02) in the training cohort. The area under curve (AUC) for training and validation datasets were 0.852. Younger patients (age ⩽ 60 years) had an improved OS than the patients with advanced age (age > 60 years) (HR 1.033, 95% CI 1.017 to 1.049; p= 2.544E-05). We built a predictive model based on the TCGA cohort by using nomograms, including clinicopathological factors such as age, recurrence rate, and prognostic score.
conclusionsThe risk model based on 13 DERs patterns could well predict the prognosis for patients with BC.
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