Development and Validation of a Five-immune Gene Pair Signature in Endometrial Carcinoma

Abstract

Background: Endometrial cancer (EC) is a common gynecological malignancy worldwide. Immunity is closely related to the occurrence and prognosis of EC. At the same time, immune-related genes have great potential as prognostic markers in many types of cancer.

Objective: Therefore, we attempt to develop immune-related gene markers to enhance prognosis prediction of EC.

Methods: 542 samples of EC gene expression data and clinical follow-up information were downloaded from The Cancer Genome Atlas (TCGA). The samples were randomly divided into two groups, one group as a training set (N=271), and one set as a validation set. (N=271). In the training set, the gene pairs were established based on the relative expression levels of 271 immune genes, and the prognosis-related gene pairs were screened. The lasso was used to select the features, and finally, the robust biomarkers were screened. Finally, the prognostic model of the immune gene pair was established and verified by the validation data set.

Results: 10030 immune gene pair (IRGPs) were obtained, and univariate survival analysis was used to identify 1809 prognostic-related IRGPs (p<0.05). 5-IRGPs were obtained by lasso regression feature selection, and multivariate regression was used to establish 5-IRGPs signature, 5-IRGPs signature is an independent prognostic factor for EC patients, and could be risk stratified in patients with TCGA datasets, age, ethnicity, stage, and histological classification (p<0.05). The mean AUC of survival in both the training set and the validation set was greater than 0.7, indicating that 5-IRGPs signature has superior classification performance in patients with EC. In addition, 5-IRGPs have the highest average C index (0.795) compared to the prognostic characteristics of the three endometrial cancers reported in the past and Stage and Age.

Conclusion: This study constructed a 5-IRGPs signature as a novel prognostic marker for predicting survival in patients with EC.

Keywords: Bioinformatics; TCGA; endometrial cancer; immune-related gene pairs; malignancy; prognostic markers.