Pattern Recognition Receptor based Prognostic Biomarkers for predicting Survival of Uterine Corpus Endometrial Cancer Patients

Abstract

In this study, we attempted to identify prognostic biomarkers for predicting survival risk of uterine corpus endometrial cancer (UCEC) patients from the gene expression profile of pattern recognition receptors (PRRs). A wide range of feature selection techniques have been tried, including network-based methods to identify a small number of genes from 331 PRR genes. Firstly, a risk stratification model has been developed using biomarker genes selected using a network-based approach and achieved HR=1.37 with p=0.294. Secondly, we developed a risk stratification model using biomarker of seven genes obtained from clustering and achieved HR=9.14 and p= 1.49×10-12. Finally, we developed various combinatorial models using biomarker of 15 PRR genes that were significantly associated with UCEC survival. We found that a multiple genes-based risk stratification model using nine genes (CLEC1B, CLEC3A, IRF7, CTSB, FCN1, RIPK2, NLRP10, NLRP9 and SARM1) gave the best result (HR=10.70, p=1.1×10-12, C=0.76, log-rank-p=8.15×10-14). The performance of this model improved significantly when we used the clinical stage of patients in combination with the expression of nine genes and achieved HR=15.23 (p=2.21×10-7, C=0.78, log-rank-p=2.76×10-17). We also developed classification models that can classify high-risk patients (survive ≤ 4.3 years) and low-risk patients (survive > 4.3 years) and achieved AUROC of 0.86. It was observed that specific genes are positively correlated with overall survival of UCEC patients. Based on these observations, we identified potential immunotherapeutic agents for treating UCEC patients.