Construction and validation of a prognostic risk model for uterine corpus endometrial carcinoma based on alternative splicing events

Objective To establish a prognostic risk model for uterine corpus endometrial carcinoma(UCEC)based on alternative splicing(AS)event data from The Cancer Genome Atlas(TCGA)and assess the accuracy of the model.Methods TCGA and SpliceSeq databases were used to acquire a summary of AS events and clinical data related to UCEC.Bioinformatic analysis was performed to identify differentially expressed AS events in UCEC.Least absolute shrinkage and selection operator(LASSO)regression and multivariate Cox regression analyses were used for constructing a prognostic risk model.Next,using the receiver operating characteristic(ROC)curve,Kaplan-Meier survival analysis,and independent prognostic analysis,we assessed the accuracy of the model.In addition,a splicing network was established based on the association between potential splicing factors and AS events.Results We downloaded clinical data and AS events of 527 UCEC cases from TCGA and SpliceSeq databases,respectively.We obtained 18,779 survival-associated AS events in UCEC using univariate Cox regression analysis and 487 AS events using LASSO regression analysis.Multivariate Cox regression analysis established a prognostic risk model for UCEC based on the percentage splicing value of 13 AS events.Independent prognostic effect on UCEC risk was then assessed using multivariate and univariate Cox regression analyses(P<0.001).The area under the curve was 0.827.The pathological stage and risk score were independent prognostic factors for UCEC.Herein,we established a regulatory network between alternative endometrial cancer-related splicing events and splicing factors.Conclusion We constructed a prognostic model of UCEC based on 13 AS events by analyzing datasets from TCGA and SpliceSeq databases with medium accuracy.The pathological stage and risk score were independent prognostic factors in the prognostic risk model.

Characterization of progression-related alternative splicing events in testicular germ cell tumors

Accumulating evidence supports the significance of aberrant alternative splicing(AS)events in cancer;however,genome-wide profiling of progression-free survival(PFS)-related AS events in testicular germ cell tumors(TGCT)has not been reported.Here,we analyzed high-throughput RNA-sequencing data and percent-spliced-in values for 150 patients with TGCT.Using univariate and multivariate Cox regression analysis and a least absolute shrinkage and selection operator method,we identified the top 15 AS events most closely associated with disease progression.A risk-associated AS score(ASS)for the 15 AS events was calculated for each patient.ASS,pathological stage,and T stage were significantly associated with disease progression by univariate analysis,but only ASS and pathological stage remained significant by multivariate analysis.The ability of these variables to predict 5-year progression was assessed using receiver operating characteristic curve analysis.ASS had stronger predictive value than a combination of age,pathological stage,and T stage(area under the curve=0.899 and 0.715,respectively).Furthermore,Kaplan—Meier analysis of patients with low and high ASS demonstrated that high ASS was associated with significantly worse PFS than low ASS(P=1.46 × 10^(-7)).We also analyzed the biological functions of the PFS-related AS-related genes and found enrichment in pathways associated with DNA repair and modification.Finally,we identified a regulatory network of splicing factors with expression levels that correlated significantly with AS events in TGCT.Collectively,this study identifies a novel method for risk stratification of patients and provides insight into the molecular events underlying TGCT.