Identification of prognostic subtypes and the role of FXYD6 in ovarian cancer through multi-omics clustering

作者：Boyi Ma, Chenlu Ren, Yun Gong, Jia Xi, Yuan Shi, Shuhua Zhao, Yadong Yin, Hong Yang · 发表于：Frontiers in Immunology · 年份：2025 · DOI：10.3389/fimmu.2025.1556715 · 研究领域：Ferroptosis and cancer prognosis、Cancer-related molecular mechanisms research、Cancer Mechanisms and Therapy

Background: Ovarian cancer (OC), as a malignant tumor that seriously endangers the lives and health of women, is renowned for its complex tumor heterogeneity. Multi-omics analysis, as an effective method for distinguishing tumor heterogeneity, can more accurately differentiate the prognostic subtypes with differences among patients with OC. The aim of this study is to explore the prognostic subtypes of OC and analyze the molecular characteristics among the different subtypes. Methods: We utilized 10 clustering algorithms to analyze the multi-omics data of OC patients from The Cancer Genome Atlas (TCGA). After that, we integrated them with ten different machine-learning methods in order to determine high-resolution molecular subgroups and generate machine-learning-driven characteristics that are both resilient and consensus-based. Following the application of multi-omics clustering, we were able to identify two cancer subtypes (CSs) that were associated with the prognosis. Among these, CS2 demonstrated the most positive predictive outcome. Subsequently, five genes that constitute the machine learning (ML)-driven features were screened out by ML algorithms, and these genes possess a powerful predictive ability for prognosis. Subsequently, the function of FXYD Domain-Containing Ion Transport Regulator 6 (FXYD6) in OC was analyzed through gene knockdown and overexpression, and the mechanism by which it affects the functions of OC was explored. Results: Through multi-omics analysi...