Suppr超能文献

一个由与生存相关的长链非编码 RNA、miRNA 和信使 RNA 组成的 ceRNA 网络在肾透明细胞癌中的作用。

A ceRNA Network Composed of Survival-Related lncRNAs, miRNAs, and mRNAs in Clear Cell Renal Carcinoma.

机构信息

Department of Pediatric Surgery, The Second Hospital Affiliated to Harbin Medical University, Harbin, Heilongjiang Province, China.

Department of Basic Medicine College, Harbin Medical University, Harbin, Heilongjiang Province, China.

出版信息

Comput Math Methods Med. 2022 Apr 28;2022:8504441. doi: 10.1155/2022/8504441. eCollection 2022.

Abstract

Clear cell renal carcinoma (ccRCC) is one of the most common renal carcinomas worldwide, which has worse prognosis compared with other subtypes of tumors. We propose a potential RNA regulatory mechanism associated with ccRCC progression. Accordingly, we screened out clinical factors and the expression of RNAs and miRNAs of ccRCC from the TCGA database. 9 lncRNAs (FGF12-AS2, WT1-AS, TRIM36-IT1, AC009093.1, LINC00443, TCL6, COL18A1-AS1, AC110619.1, HOTTIP), 2 miRNAs (mir-155 and mir-21), and 3 mRNAs (COL4A4, ERMP1, PRELID2) were selected from differential expression RNAs and built predictive survival models. The survival models performed very well in predicting prognosis and were found to be highly correlated with tumor stage. In addition, the survival-related lncRNA-miRNA-mRNA (ceRNA) network was constructed by 18 RNAs including 12 mRNAs, 2 miRNAs, and 4 lncRNAs. It is found that the "ECM-receptor interaction," "Pathways in cancer," and "Chemokine signaling pathway" as the main pathways in KEGG pathway analysis. Overall, we established predictive survival model and ceRNA network based on multivariate Cox regression analysis. It may open a new approach and potential biomarkers for clinical prognosis and treatment of ccRCC patients.

摘要

透明细胞肾细胞癌(ccRCC)是全球最常见的肾癌之一,其预后比其他肿瘤亚型差。我们提出了一个与 ccRCC 进展相关的潜在 RNA 调控机制。因此,我们从 TCGA 数据库中筛选出了 ccRCC 的临床因素和 RNA 及 miRNA 的表达。从差异表达的 RNA 中筛选出 9 个长链非编码 RNA(FGF12-AS2、WT1-AS、TRIM36-IT1、AC009093.1、LINC00443、TCL6、COL18A1-AS1、AC110619.1、HOTTIP)、2 个 miRNA(mir-155 和 mir-21)和 3 个 mRNA(COL4A4、ERMP1、PRELID2),构建了预测生存模型。生存模型在预测预后方面表现非常出色,并且与肿瘤分期高度相关。此外,通过 18 个 RNA(包括 12 个 mRNA、2 个 miRNA 和 4 个 lncRNA)构建了 lncRNA-miRNA-mRNA(ceRNA)相关的生存网络。结果发现,KEGG 通路分析中的主要通路为“ECM-受体相互作用”、“癌症通路”和“趋化因子信号通路”。总的来说,我们基于多变量 Cox 回归分析建立了预测生存模型和 ceRNA 网络。它可能为 ccRCC 患者的临床预后和治疗开辟新的途径和潜在的生物标志物。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d4ed/9071875/37718154583e/CMMM2022-8504441.001.jpg

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验