An improved clear cell renal cell carcinoma stage prediction model based on gene sets

• Published in: BMC bioinformatics Vol. 21; no. 1; pp. 232 - 15
• Main Authors: Li, Fangjun, Yang, Mu, Li, Yunhe, Zhang, Mingqiang, Wang, Wenjuan, Yuan, Dongfeng, Tang, Dongqi
• Format: Journal Article
• Language: English
• Published: England BioMed Central 08.06.2020; BMC
• Subjects: Algorithms; Area Under Curve; Cancer; Cancer stage; Carcinoma, Renal Cell - genetics; Carcinoma, Renal Cell - pathology; Classification; Clear cell renal cell carcinoma; Clear cell-type renal cell carcinoma; Correlation coefficient; Correlation coefficients; Feature extraction; Feature selection; Female; Function analysis; Gene expression; Genomes; Humans; Information processing; Kidney cancer; Kidney Neoplasms - genetics; Kidney Neoplasms - pathology; Learning algorithms; Logistic Models; Machine Learning; Metastasis; Model accuracy; Molecular modelling; Neoplasm Staging; Prediction models; Regression analysis; Ribonucleic acid; RNA; RNA - metabolism; ROC Curve; Survival; Variables; Cancer stage; Feature selection; Clear cell renal cell carcinoma; Machine learning

Clear cell renal cell carcinoma (ccRCC) is the most common subtype of renal cell carcinoma and accounts for cancer-related deaths. Survival rates are very low when the tumor is discovered in the late-stage. Thus, developing an efficient strategy to stratify patients by the stage of the cancer and in...