The correlation of miRNA expression and tumor mutational burden in uterine corpus endometrial carcinoma

Background:The relationship between microRNA(miRNA)expression patterns and tumor mutation burden(TMB)in uterine corpus endometrial carcinoma(UCEC)was investigated in this study.Methods:The UCEC dataset from The Cancer Genome Atlas(TCGA)database was used to identify the miRNAs that differ in expression between high TMB and low TMB sample sets.The total sample sets were divided into a training set and a test set.TMB levels were predicted using miRNA-based signature classifiers developed by Lasso Cox regression.Test sets were used to validate the classifier.This study investigated the relationship between a miRNA-based signature classifier and three immune checkpoint molecules(programmed cell death protein 1[PD-1],programmed cell death ligand 1[PD-L1],cytotoxic T lymphocyte-associated antigen 4[CTLA-4]).For the miRNA-based signature classifier,functional enrichment analysis was performed on the miRNAs.An analysis of the relationship between PD-1,PD-L1,and CTLA-4 immune checkpoint genes was carried out using the miRNA-based signature classifier.Results:We identified 27 differentially expressed miRNAs in miRNA-base signature.For predicting the TMB level,27-miRNA-based signature classifiers had accuracies of 0.8689 in the training cohort,0.8276 in the test cohort,and 0.8524 in the total cohort.The correlation between the miRNA-based signature classifier and PD-1 was negative,while the correlation between PD-L1 and CTLA4 was positive.Based on the miRNA profiling described above,we validated the expression levels of 9 miRNAs in clinical samples by quantitative reverse transcription PCR(qRT-PCR).Four of them were highly expressed and many cancer-related and immune-associated biological processes were linked to these 27 miRNAs.Thus,the developed miRNA-based signature classifier was correlated with TMB levels that could also predict TMB levels in UCEC samples.Conclusion:In this study,we investigated the relationship between a miRNAbased signature classifier and TMB levels in Uterine Corpus Endometrial Carcinoma.Further,this is the first study to confirm their relationship in clinical samples,which may provide more evidence support for immunotherapy of endometrial cancer.

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.