Inference of Molecular Subtypes of Uterine Corpus Endometrioid Carcinoma with Different Survival Based on Cancer Hallmarks-associated Long Non-Coding RNAs and Gene Modules

Abstract

Background: Uterine corpus endometrioid carcinoma (UCEC), a common gynecological malignancy with high incidence, affects the mental and physical health of women. It is increasingly evident that long non-coding RNAs (lncRNAs) have causative roles in cancers including UCEC. However, very little is known about the cancer hallmark-related risks for UCEC based on lncRNAs and genes. Methods: In this study, a computational integrated pipeline was development to evaluate cancer hallmark-related risk for each UCEC patient based on gene and lncRNA expression. Results: Some UCEC-specific cancer hallmark-related genes and lncRNAs were identified. Core modules were extracted from co-expressed UCEC-specific lncRNAs and genes networks for each cancer hallmark. Some core modules showed specific features and functions. Follow a multi-dimensional rank approach and core modules, each UCEC sample was given an integrated cancer hallmark-related risk score. We divided all the UCEC patients to diverse hallmark risk groups including multi-hallmark, media-hallmark and single-hallmark groups and they showed different prognosis. We also identified some key lncRNAs which participated in multiple kinds of cancer hallmarks. These key lncRNAs were associated with some essential pathways such as estrogen receptor signaling. Conclusions: In conclusion, the present study provide novel insights to the classification and treatment of UCEC.