Serous ovarian carcinoma (SOC) is a gynecological malignancy with high mortality rates. Currently, there is a lack of reliable biomarkers for accurate SOC patient prognosis. Here, we analyzed SOC RNA-Seq data from The Cancer Genome Atlas (TCGA) to identify prognostic biomarkers. Through the pearson correlation analysis, univariate Cox regression analysis, and LASSO-penalized Cox regression analysis, we identified nine lncRNAs significantly associated with four types of RNA modification writers (m6A, m1A, APA, and A-I) and with the prognosis of SOC patients (P <0.05). Six writer-related lncRNAs were ultimately selected following multivariate Cox analysis. We established a risk prediction model based on these six lncRNAs and evaluated its prognostic value in multiple groups (training set, testing set, and entire set). Our risk prediction model could effectively predict the prognosis of SOC patients with different clinical characteristics and their responses to immunotherapy. Lastly, we validated the predictive reliability and sensitivity of the lncRNA-based model via a nomogram. This study explored the association between RNA modification writer-related lncRNAs and SOC prognosis, providing a potential complement for the clinical management of SOC patients.
Keywords: RNA modification writers; immune microenvironment; lncRNA; prognosis; serous ovarian carcinoma.
Copyright © 2022 Ye, Pan, Fang, Wu, Chen, Tang, Wang, Zhang, Tong, Shi, Feng, Xiang, Zou, Hu, Xue and Guo.