ArticleOncotarget2017
Risk score based on three mRNA expression predicts the survival of bladder cancer.
Article in Oncotarget, 2017. 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.
- Oncogenic mechanisms of COL10A1 in cancer and clinical challenges (Review).Oncology reports · 2024Review
- Plasma COL10A1 Level, a Potential Diagnostic and Prognostic Biomarker for Pancreatic Ductal Adenocarcinoma.OncoTargets and therapy · 2024Article
- Identification and validation of a five-gene prognostic signature based on bioinformatics analyses in breast cancer.Heliyon · 2023Article
- Identification of a Seven-Differentially Expressed Gene-Based Recurrence-Free Survival Model for Melanoma Patients.Disease markers · 2022Article
- Role of FRG1 in predicting the overall survivability in cancers using multivariate based optimal model.Scientific reports · 2021Article
- A radiogenomics signature for predicting the clinical outcome of bladder urothelial carcinoma.European radiology · 2020Article
- 31 gene expression-based signatures serve as indicators of prognosis for patients with glioma.Oncology letters · 2019Article
- Transcriptomic model-based lncRNAs and mRNAs serve as independent prognostic indicators in head and neck squamous cell carcinoma.Oncology letters · 2019Article
- An eight-mRNA signature predicts the prognosis of patients with bladder urothelial carcinoma.PeerJ · 2019Article
- A co-expression network for differentially expressed genes in bladder cancer and a risk score model for predicting survival.Hereditas · 2019Article
- Deep Learning data integration for better risk stratification models of bladder cancer.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2018Article
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5 authors.
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Abstract
Bladder cancer (BLCA) is one of the most malignant cancers worldwide, and its prognosis varies. 1214 BLCA samples in five different datasets and 2 platforms were enrolled in this study. By utilizing the gene expression in The Cancer Genome Atlas (TCGA) dataset, and another two datasets, in GSE13507 and GSE31684, we constructed a risk score staging system with Cox multivariate regression to evaluate predict the outcome of BLCA patients. Three genes consist of RCOR1, ST3GAL5, and COL10A1 were used to predict the survival of BLCA patients. The patients with low risk score have a better survival rate than those with high risk score, significantly. The survival profiles of another two datasets (GSE13507 and GSE31684), which were used for candidate gene selection, were similar as the training dataset (TCGA). Furthermore, survival prediction effect of risk score staging system in another 2 independent datasets, GSE40875 and E-TABM-4321, were also validated. Compared with other clinical observations, and the risk score performs better in evaluating the survival of BLCA patients. Moreover, the correlation between radiation were also evaluated, and we found that patients have a poor survival in high risk group, regardless of radiation. Gene Set Enrichment Analysis was also implemented to find the difference between high-risk and low-risk groups on biological pathways, and focal adhesion and JAK signaling pathway were significantly enriched. In summary, we developed a risk staging model for BLCA patients with three gene expression. The model is independent from and performs better than other clinical information.
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