Dataset Information

Identification of Biomarkers Correlated with the TNM Staging and Overall Survival of Patients with Bladder Cancer.

ABSTRACT: Objective: To identify candidate biomarkers correlated with clinical prognosis of patients with bladder cancer (BC). Methods: Weighted gene co-expression network analysis was applied to build a co-expression network to identify hub genes correlated with tumor node metastasis (TNM) staging of BC patients. Functional enrichment analysis was conducted to functionally annotate the hub genes. Protein-protein interaction network analysis of hub genes was performed to identify the interactions among the hub genes. Survival analyses were conducted to characterize the role of hub genes on the survival of BC patients. Gene set enrichment analyses were conducted to find the potential mechanisms involved in the tumor proliferation promoted by hub genes. Results: Based on the results of topological overlap measure based clustering and the inclusion criteria, top 50 hub genes were identified. Hub genes were enriched in cell proliferation associated gene ontology terms (mitotic sister chromatid segregation, mitotic cell cycle and, cell cycle, etc.) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways (cell cycle, Oocyte meiosis, etc.). 17 hub genes were found to interact with ?5 of the hub genes. Survival analysis of hub genes suggested that lower expression of MMP11, COL5A2, CDC25B, TOP2A, CENPF, CDCA3, TK1, TPX2, CDCA8, AEBP1, and FOXM1were associated with better overall survival of BC patients. BC samples with higher expression of hub genes were enriched in gene sets associated with P53 pathway, apical junction, mitotic spindle, G2M checkpoint, and myogenesis, etc. Conclusions: We identified several candidate biomarkers correlated with the TNM staging and overall survival of BC patients. Accordingly, they might be used as potential diagnostic biomarkers and therapeutic targets with clinical utility.

SUBMITTER: Li S

PROVIDER: S-EPMC5712410 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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Identification of Biomarkers Correlated with the TNM Staging and Overall Survival of Patients with Bladder Cancer.

Li Sheng S Liu Xiaoping X Liu Tongzu T Meng Xiangyu X Yin Xiaohong X Fang Cheng C Huang Di D Cao Yue Y Weng Hong H Zeng Xiantao X Wang Xinghuan X

Frontiers in physiology 20171128

<b>Objective:</b> To identify candidate biomarkers correlated with clinical prognosis of patients with bladder cancer (BC). <b>Methods:</b> Weighted gene co-expression network analysis was applied to build a co-expression network to identify hub genes correlated with tumor node metastasis (TNM) staging of BC patients. Functional enrichment analysis was conducted to functionally annotate the hub genes. Protein-protein interaction network analysis of hub genes was performed to identify the interac ...[more]

PMID: 29234286

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Project description:BackgroundBoth lncRNAs and glycolysis are considered to be key influencing factors in the progression of bladder cancer (BCa). Studies have shown that glycolysis-related lncRNAs are an important factor affecting the overall survival and prognosis of patients with bladder cancer. In this study, a prognostic model of BCa patients was constructed based on glycolysis-related lncRNAs to provide a point of reference for clinical diagnosis and treatment decisions.MethodsThe transcriptome, clinical data, and glycolysis-related pathway gene sets of BCa patients were obtained from The Cancer Genome Atlas (TCGA) database and the Gene Set Enrichment Analysis (GSEA) official website. Next, differentially expressed glycolysis-related lncRNAs were screened out, glycolysis-related lncRNAs with prognostic significance were identified through LASSO regression analysis, and a risk scoring model was constructed through multivariate Cox regression analysis. Then, based on the median of the risk scores, all BCa patients were divided into either a high-risk or low-risk group. Kaplan-Meier (KM) survival analysis and the receiver operating characteristic (ROC) curve were used to evaluate the predictive power of the model. A nomogram prognostic model was then constructed based on clinical indicators and risk scores. A calibration chart, clinical decision curve, and ROC curve analysis were used to evaluate the predictive performance of the model, and the risk score of the prognostic model was verified using the TCGA data set. Finally, Gene Set Enrichment Analysis (GSEA) was performed on glycolysis-related lncRNAs.ResultsA total of 59 differentially expressed glycolysis-related lncRNAs were obtained from 411 bladder tumor tissues and 19 pericarcinomatous tissues, and 9 of those glycolysis-related lncRNAs (AC099850.3, AL589843.1, MAFG-DT, AC011503.2, NR2F1-AS1, AC078778.1, ZNF667-AS1, MNX1-AS1, and AC105942.1) were found to have prognostic significance. A signature was then constructed for predicting survival in BCa based on those 9 glycolysis-related lncRNAs. ROC curve analysis and a nomogram verified the accuracy of the signature.ConclusionThrough this study, a novel prognostic prediction model for BCa was established based on 9 glycolysis-related lncRNAs that could effectively distinguish high-risk and low-risk BCa patients, and also provide a new point of reference for clinicians to make individualized treatment and review plans for patients with different levels of risk.

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Dataset Information

Identification of Biomarkers Correlated with the TNM Staging and Overall Survival of Patients with Bladder Cancer.

Publications

Identification of Biomarkers Correlated with the TNM Staging and Overall Survival of Patients with Bladder Cancer.

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