Identification of drug targets and prognosis projection for uterine carcinosarcoma based on alternative splicing events

Abstract

BackgroundUterine carcinosarcoma (UCS) is an invasive variant of endometrial cancer. The complicated heterogeneity and low frequency of UCS suggest the relevant research is lack. There is an urgent need to further explore the pathogenic mechanism and identify new biomarkers of UCS from different angels to improve its diagnosis and prognosis. ObjectiveThis study is to explore the importance of alternative splicing (AS) events in UCS, construct AS-based prognosis model and excavate key splicing factors (SFs). MethodUCS related gene transcriptome data and AS events data were collected from The Cancer Genome Atlas (TCGA) and TCGA SpliceSeq database. The AS events related to survival were determined by Cox regression analysis, Least absolute shrinkage and selection operator (Lasso) regression analysis and optimal subset analysis. The corresponding risk score was calculated and its efficiency on prognosis was evaluated by Kaplan-Meier (K-M) survival estimate and validated by the receiver operating characteristic (ROC) curve. The prognosis model was constructed with risk score and clinic characters as independent variables to predict patients’ survival. On the other hand, Kendall test was applied to inspect the correlation between the SFs and the prognosis-related AS events and a AS-SF network was constructed. Finally, the key SFs were screened through network nodes analysis and survival analysis. ResultSeven AS events the most related to survival were detected and the risk score was obtained. K-M survival estimate and ROC curve validation suggested the risk score was effective. Then Cox model was constructed based on the risk score and a nomogram model was obtained which provided the highest prediction accuracy of 95%. Through the AS-SF network analysis, 16 SFs were screened, among which four survival-related SFs were eventually obtained. ConclusionThe prognosis model could predict the survival rate of UCS patients by their clinical characters and AS-based risk score. And four newly discovered SFs could reveal the molecular mechanism of UCS and act as the potential drug targets and prognosis biomarkers.