Identification Of Predictors Of Recurrence-free Survival In Patients With Prostate Cancer Or Renal Cancer

Background and Objective: Prostate cancer(PCa)is one of the leading causes for cancer induced death among males.Here,we applied an integrated bioinformatics way aiming to identify key prognostic factors for PCa patients.Materials and Methods: The gene expression data were obtained from the UCSC Xena website.We calculated the differentially expressed genes(DEGs)between PCa tissues and normal controls.Kyoto Encyclopedia of Genes and Genomes(KEGG)and Gene Set Enrichment Analysis(GSEA)analyses found cell cycle related pathways might play crucial roles during the PCa tumorigenesis.Weighted gene co-expression network analysis(WGCNA)was constructed and genes were divided into 22 modules.Results: We found that the purple and red modules were significantly associated with the Gleason score,pathological N,pathological T,recurrence and recurrence-free survival(RFS).In addition,Kaplan-Meier curve analysis found eight modules were significantly associated with RFS,which could be served as prognostic markers for PCa patients.Followed,the hub-genes in the most two important modules(purple and red)were visualized by Cytoscape software,and the pathway enrichment analyses confirmed the above findings that cell-cycle related pathways might play critical roles during PCa initiation and progression.Lastly,we randomly chose the PILR?(also termed PILRB)in the red module for clinical validation.The Immunohistochemistry(IHC)results found that the PILR? was significantly increased in the high-risk PCa population compared with low/middle-risk patients.Conclusions: To sum up,we applied the integrate bioinformatics approaches and identified hub genes could serve as prognosis markers and potential treatment targets for PCa patients.Purposes: Prognostic models are needed that reflect contemporary clinical practice for men with renal cancer.We hunted and identified the prognostic variables associated with recurrence-free survival(RFS)for renal cancer patients.Patients and methods: A total of 187 renal cancer patients,who had received the curative radical/partial nephrectomy between November 2011 and January 2017 were enrolled in current study.These patients were randomly split into the training(n = 95)and validation sets(n = 92)by the ratio of 5:5.Univariate and the multivariable Cox regression analysis were used to establish the nomogram,which was then evaluated by the receiver operating characteristic(ROC)and Kaplan-Meier(K-M)analyses.Results: Patient characteristics and outcomes were well balanced between training and validation sets;median RFS was 54.1 and 58.9 months for the training and validation cohorts,respectively.The final nomogram included six independent prognostic variables [prothrombin time(%),prothrombin time(second),albumin/globulin ratio,platelet,sex and fibrinogen].The mean value of RFS for low-and high-risk groups defined by a prognostic formula was 56.22 ± 18.50 months,and 49.54 ± 23.57 months in the training cohort,and were 59.00 ± 19.50,and 53.32 ± 19.95 months in the validation cohort.The significance and stability of the model were tested by the timedependent K-M and ROC curves,respectively.Conclusions: Our validated prognostic model incorporates variables routinely collected from renal cancer patients,identifying subsets of patients with different survival outcomes,which provides useful information for patient care and clinical trial design.