A prognostic prediction model based on immune-related miRNA-mRNA network for breast cancer

HUANG Ping; CHANG Kai; CHEN Ming

doi:10.13431/j.cnki.immunol.j.20240127

Abstract：Objective To investigate the potential in a risk regression model based on an immune-related microRNA-mRNA network to evaluate immune cell infiltration and prognosis in breast cancer(BC).Methods Transcriptomic and clinical data were obtained from The Cancer Genome Atlas(TCGA)and Gene Expression Omnibus DataSets(GEO)databases.Differential expressions of immune-related miRNAs and mRNAs were elaborated using differential and co-expression analysis and LASSO(Least Absolute Shrinkage and Selection Operator)regression analysis.COX regression analysis was used to select prognostic factors and construct risk regression.Kaplan-Meier survival analysis,ROC curve,univariate/multivariate independent prognostic analysis,and correlation analysis were used to evaluate the predictive value of this risk model.Results Three differential immune-related miRNAs and five differential immune mRNAs were considered as prognostic factors for BC.Finally,two miRNAs(miR-21-5p and miR-141-3p)and one mRNA(INHBA)were utilized to construct the risk regression model.Kaplan-Meier survival analysis showed that the high-risk group had a poor prognosis compared to the low-risk group(P＜0.001).The receiver operating characteristic(ROC)curve showed that area value under curve(AUC)was 0.893.The univariate and multivariate independent prognostic analysis showed that risk score was an independent prognostic factor for BC(HR:1.234,P＜0.001).Moreover,there was a positive correlation between the risk score and macrophage immune score.Conclusion This study identified characteristic differential immune-related miRNAs and mRNAs associated with BC prognosis.This risk regression model based on IRM-MN has predictive value in prognostic and tumor immune infiltration evaluation.Therefore,this model can satisfactorily evaluate the prognosis of BC patients and provide a reliable reference for individualized therapy.