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Development and Validation of a Prognostic Gene Signature in Clear Cell Renal Cell Carcinoma.
Zhan, Chuanchuan; Wang, Zichu; Xu, Chao; Huang, Xiao; Su, Junzhou; Chen, Bisheng; Wang, Mingshan; Qi, Zhihong; Bai, Peiming.
Afiliación
  • Zhan C; Shaoxing people's Hospital, Shaoxing, China.
  • Wang Z; Zhongshan Hospital, Xiamen University, Xiamen, China.
  • Xu C; Shaoxing people's Hospital, Shaoxing, China.
  • Huang X; Nanchang Five Elements Bio-Technology Co., Ltd, Nanchang, China.
  • Su J; Zhongshan Hospital, Xiamen University, Xiamen, China.
  • Chen B; Zhongshan Hospital, Xiamen University, Xiamen, China.
  • Wang M; Zhongshan Hospital, Xiamen University, Xiamen, China.
  • Qi Z; Zhongshan Hospital, Xiamen University, Xiamen, China.
  • Bai P; Zhongshan Hospital, Xiamen University, Xiamen, China.
Front Mol Biosci ; 8: 609865, 2021.
Article en En | MEDLINE | ID: mdl-33968978
Clear cell renal cell carcinoma (ccRCC), one of the most common urologic cancer types, has a relatively good prognosis. However, clinical diagnoses are mostly done during the medium or late stages, when mortality and recurrence rates are quite high. Therefore, it is important to perform real-time information tracking and dynamic prognosis analysis for these patients. We downloaded the RNA-seq data and corresponding clinical information of ccRCC from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. A total of 3,238 differentially expressed genes were identified between normal and ccRCC tissues. Through a series of Weighted Gene Co-expression Network, overall survival, immunohistochemical and the least absolute shrinkage selection operator (LASSO) analyses, seven prognosis-associated genes (AURKB, FOXM1, PTTG1, TOP2A, TACC3, CCNA2, and MELK) were screened. Their risk score signature was then constructed. Survival analysis showed that high-risk scores exhibited significantly worse overall survival outcomes than low-risk patients. Accuracy of this prognostic signature was confirmed by the receiver operating characteristic curve and was further validated using another cohort. Gene set enrichment analysis showed that some cancer-associated phenotypes were significantly prevalent in the high-risk group. Overall, these findings prove that this risk model can potentially improve individualized diagnostic and therapeutic strategies.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: Front Mol Biosci Año: 2021 Tipo del documento: Article País de afiliación: China Pais de publicación: Suiza

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: Front Mol Biosci Año: 2021 Tipo del documento: Article País de afiliación: China Pais de publicación: Suiza