School of Life Science, Bengbu Medical College, Bengbu, 233030, Anhui, People's Republic of China.
Department of Gastroenterology, The First Affiliated Hospital of Bengbu Medical College, Bengbu, 233004, Anhui, People's Republic of China.
J Ovarian Res. 2021 Feb 7;14(1):27. doi: 10.1186/s13048-021-00777-1.
Ovarian cancer (OC) is one of the most common gynecological malignant tumors worldwide, with high mortality and a poor prognosis. As the early symptoms of malignant ovarian tumors are not obvious, the cause of the disease is still unclear, and the patients' postoperative quality of life of decreases. Therefore, early diagnosis is a problem requiring an urgent solution.
We obtained the gene expression profiles of ovarian cancer and normal samples from TCGA and GTEx databases for differential expression analysis. From existing literature reports, we acquired the RNA-binding protein (RBP) list for the human species. Utilizing the online tool Starbase, we analyzed the interaction relationship between RBPs and their target genes and selected the modules of RBP target genes through Cytoscape. Finally, univariate and multivariate Cox regression analyses were used to determine the prognostic RBP signature.
We obtained 527 differentially expressed RBPs, which were involved in many important cellular events, such as RNA splicing, the cell cycle, and so on. We predicted several target genes of RBPs, constructed the interaction network of RBPs and their target genes, and obtained many modules from the Cytoscape analysis. Functional enrichment of RBP target genes also includes these important biological processes. Through Cox regression analysis, OC prognostic RBPs were identified and a 10-RBP model constructed. Further analysis showed that the model has high accuracy and sensitivity in predicting the 3/5-year survival rate.
Our study identified differentially expressed RBPs and their target genes in OC, and the results promote our understanding of the molecular mechanism of ovarian cancer. The current study could develop novel biomarkers for the diagnosis, treatment, and prognosis of OC and provide new ideas and prospects for future clinical research.
卵巢癌（OC）是全球最常见的妇科恶性肿瘤之一，死亡率高，预后差。由于恶性卵巢肿瘤的早期症状不明显，病因尚不清楚，患者术后生活质量下降。因此，早期诊断是一个亟待解决的问题。
我们从 TCGA 和 GTEx 数据库中获取卵巢癌和正常样本的基因表达谱进行差异表达分析。从现有的文献报告中，我们获得了人类的 RNA 结合蛋白（RBP）列表。利用在线工具 Starbase，我们分析了 RBPs 与其靶基因之间的相互作用关系，并通过 Cytoscape 选择 RBP 靶基因模块。最后，使用单变量和多变量 Cox 回归分析确定预后 RBP 特征。
我们获得了 527 个差异表达的 RBPs，它们参与了许多重要的细胞事件，如 RNA 剪接、细胞周期等。我们预测了一些 RBP 的靶基因，构建了 RBPs 及其靶基因的相互作用网络，并从 Cytoscape 分析中获得了许多模块。RBP 靶基因的功能富集也包括这些重要的生物学过程。通过 Cox 回归分析，确定了 OC 的预后 RBPs，并构建了 10-RBP 模型。进一步分析表明，该模型在预测 3/5 年生存率方面具有较高的准确性和敏感性。
本研究鉴定了 OC 中差异表达的 RBPs 及其靶基因，结果促进了我们对卵巢癌分子机制的理解。目前的研究可以为 OC 的诊断、治疗和预后提供新的生物标志物，并为未来的临床研究提供新的思路和前景。
