Please use this identifier to cite or link to this item: http://hdl.handle.net/10995/90397
Title: A co-expression network for differentially expressed genes in bladder cancer and a risk score model for predicting survival
Authors: Chen, Z.
Liu, G.
Hossain, A.
Danilova, I. G.
Bolkov, M. A.
Liu, G.
Tuzankina, I. A.
Tan, W.
Issue Date: 2019
Publisher: NLM (Medline)
Citation: A co-expression network for differentially expressed genes in bladder cancer and a risk score model for predicting survival / Z. Chen, G. Liu, A. Hossain, I. G. Danilova, et al. . — DOI 10.1186/s41065-019-0100-1 // Hereditas. — 2019. — Iss. 156. — P. 24-.
Abstract: Background: Urothelial bladder cancer (BLCA) is one of the most common internal malignancies worldwide with poor prognosis. This study aims to explore effective prognostic biomarkers and construct a prognostic risk score model for patients with BLCA. Methods: Weighted gene co-expression network analysis (WGCNA) was used for identifying the co-expression module related to the pathological stage of BLCA based on the RNA-Seq data retrieved from The Cancer Genome Atlas database. Prognostic biomarkers screened by Cox proportional hazard regression model and random forest were used to construct a risk score model that can predict the prognosis of patients with BLCA. The GSE13507 dataset was used as the independent testing dataset to test the performance of the risk score model in predicting the prognosis of patients with BLCA. Results: WGCNA identified seven co-expression modules, in which the brown module consisted of 77 genes was most significantly correlated with the pathological stage of BLCA. Cox proportional hazard regression model and random forest identified TPST1 and P3H4 as prognostic biomarkers. Elevated TPST1 and P3H4 expressions were associated with the high pathological stage and worse survival. The risk score model based on the expression level of TPST1 and P3H4 outperformed pathological stage indicators and previously proposed prognostic models. Conclusion: The gene co-expression network-based study could provide additional insight into the tumorigenesis and progression of BLCA, and our proposed risk score model may aid physicians in the assessment of the prognosis of patients with BLCA.
Keywords: BLADDER CANCER
P3H4
RISK SCORE MODEL
TPST1
WGCNA
ALGORITHM
BIOLOGY
BLADDER TUMOR
FEMALE
GENE EXPRESSION PROFILING
GENE EXPRESSION REGULATION
GENE ONTOLOGY
GENE REGULATORY NETWORK
GENETICS
HUMAN
MALE
METABOLISM
MORTALITY
PROCEDURES
PROGNOSIS
PROTEIN ANALYSIS
RECEIVER OPERATING CHARACTERISTIC
RISK ASSESSMENT
SURVIVAL ANALYSIS
THEORETICAL MODEL
ALGORITHMS
COMPUTATIONAL BIOLOGY
FEMALE
GENE EXPRESSION PROFILING
GENE EXPRESSION REGULATION, NEOPLASTIC
GENE ONTOLOGY
GENE REGULATORY NETWORKS
HUMANS
MALE
MODELS, THEORETICAL
PROGNOSIS
PROTEIN INTERACTION MAPPING
RISK ASSESSMENT
ROC CURVE
SURVIVAL ANALYSIS
URINARY BLADDER NEOPLASMS
URI: http://hdl.handle.net/10995/90397
https://elar.urfu.ru/handle/10995/90397
Access: info:eu-repo/semantics/openAccess
cc-by
SCOPUS ID: 85079018040
WOS ID: 000475591800001
PURE ID: 10304070
ISSN: 1601-5223
DOI: 10.1186/s41065-019-0100-1
Appears in Collections:Научные публикации, проиндексированные в SCOPUS и WoS CC

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