Shaoyu Wang,1– 3 Qiaomei Zheng,1– 3 Lihong Chen1– 3 

1Department of Obstetrics and Gynecology, The First Affiliated Hospital, Fujian Medical University, Fuzhou, 350005, People’s Republic of China; 2Department of Obstetrics and Gynecology, National Regional Medical Center, Binhai Campus of The First Affiliated Hospital, Fujian Medical University, Fuzhou, 350212, People’s Republic of China; 3Fujian Key Laboratory of Precision Medicine for Cancer, The First Affiliated Hospital, Fujian Medical University, Fuzhou, 350005, People’s Republic of China

Correspondence: Lihong Chen, Email wscclh@163.com

Background: Ovarian cancer (OC), a common fatal malignancy in women, has a poor prognosis. RNA modifications are associated with the development of OC. In this study, we aimed to identify and verify RNA modifications-related prognostic genes in OC by integrating bulk and single-cell RNA sequencing (scRNA-seq) data.
Methods: Transcriptome data came from public databases and RNA modifications-related genes (RMRGs) were obtained from literature. Candidate genes were identified by intersecting RMRGs with differentially expressed genes (DEGs) in OC patients. Prognostic genes were gained via machine learning techniques, particularly LASSO regression. A risk model was built to predict the prognosis. OC patients were divided into high-risk and low-risk groups according to risk score. Subsequent analyses covered enrichment analysis, immune microenvironment, mutation analysis, and chemotherapeutic drug sensitivity. In addition, scRNA-seq data was assessed for key cells and gene expression in them. Finally, RT-qPCR was applied to identify the expression of prognostic genes.
Results: LSM4, SNRPC, ZC3H13, LSM2, WTAP, DCP2, PUS7, and TUT1 were selected as prognostic genes. The risk model exhibited excellent predictive abilities. Seventeen pathways were enriched like calcium signaling pathway, 7 differential immune cells were identified like regulatory T cells and plasmacytoid dendritic cells, and TP53 had highest mutation rate. Half-maximal inhibitory concentrations (IC50) values of 47 drugs like paclitaxel differed between two risk groups. The prognostic genes were distributed mainly in fibroblast cells, epithelial cells and endothelial cells. During fibroblast cells differentiation, expression of prognostic genes fluctuated to varying degrees. The RT-qPCR demonstrated that the expression of LSM2, LSM4, PUS7, SNRPC, and TUT1 were upregulated in OC, while DCP2, WTAP, and ZC3H13 were downregulated.
Conclusion: We constructed an RNA modifications-related prognostic signature that can effectively predict clinical outcomes and therapeutic responses in patients with OC.

Keywords: ovarian cancer, RNA modifications, single-cell RNA sequencing, immune microenvironment, prognostic genes