整合批量和单细胞RNA测序以鉴定卵巢癌中与RNA修饰相关的预后特征

Integration of Bulk and Single-Cell RNA Sequencing to Identify RNA Modifications-Related Prognostic Signature in Ovarian Cancer.

作者信息

Wang Shaoyu, Zheng Qiaomei, Chen Lihong

机构信息

Department of Obstetrics and Gynecology, The First Affiliated Hospital, Fujian Medical University, Fuzhou, 350005, People's Republic of China.

Department 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.

出版信息

Int J Gen Med. 2025 May 20;18:2629-2647. doi: 10.2147/IJGM.S523878. eCollection 2025.

DOI:10.2147/IJGM.S523878

PMID:40417417

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12103173/

Abstract

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

, and 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 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 , and were upregulated in OC, while , and were downregulated.

CONCLUSION

We constructed an RNA modifications-related prognostic signature that can effectively predict clinical outcomes and therapeutic responses in patients with OC.

摘要

背景

卵巢癌（OC）是女性常见的致命恶性肿瘤，预后较差。RNA修饰与OC的发生发展相关。在本研究中，我们旨在通过整合批量和单细胞RNA测序（scRNA-seq）数据来鉴定和验证OC中与RNA修饰相关的预后基因。

方法

转录组数据来自公共数据库，与RNA修饰相关的基因（RMRGs）从文献中获取。通过将RMRGs与OC患者中的差异表达基因（DEGs）相交来鉴定候选基因。通过机器学习技术，特别是LASSO回归获得预后基因。构建风险模型以预测预后。根据风险评分将OC患者分为高风险和低风险组。后续分析包括富集分析、免疫微环境、突变分析和化疗药物敏感性。此外，评估scRNA-seq数据中的关键细胞及其基因表达。最后，应用RT-qPCR鉴定预后基因的表达。

结果

选择了[具体基因名称1]、[具体基因名称2]和[具体基因名称3]作为预后基因。风险模型表现出优异的预测能力。17条通路如钙信号通路被富集，7种差异免疫细胞如调节性T细胞和浆细胞样树突状细胞被鉴定，[具体基因名称4]具有最高的突变率。47种药物如紫杉醇的半数最大抑制浓度（IC50）值在两个风险组之间存在差异。预后基因主要分布在成纤维细胞、上皮细胞和内皮细胞中。在成纤维细胞分化过程中，预后基因的表达有不同程度的波动。RT-qPCR表明，[具体基因名称1]、[具体基因名称2]和[具体基因名称3]在OC中表达上调，而[具体基因名称5]、[具体基因名称6]和[具体基因名称7]表达下调。