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构建肺鳞癌预后模型和 miRNA-mRNA 调控网络。

Construction of Prognostic Model and miRNA-mRNA Regulatory Network for Lung Squamous Cell Carcinoma.

出版信息

Altern Ther Health Med. 2024 Feb;30(2):183-187.

Abstract

The purpose of this paper was to construct a prognostic model, miRNA-mRNA regulatory network and protein-protein interaction (PPI) network for lung squamous cell carcinoma (LUSC) used data from the cancer genome atlas (TCGA) database. In this study, we first downloaded and sorted out the expression matrix containing 19962 mRNA transcripts (including 502 LUSC and 51 normal control (NC) samples) and the expression matrix containing 2205 miRNA transcripts (including 478 LUSC and 45 NC samples) from the TCGA database. We obtained 389 differentially expressed miRNAs (DE-miRNAs), of which 305 were upregulated and 84 down-regulated DE-miRNAs. Next, a total of 7 prognosis-related DE-miRNAs (PDE-miRNAs) were identified by Cox regression analysis, and the prognosis model consisting of three PDE-miRNAs (hsa-miR-4746-5p, hsa-miR-556-3p and hsa-miR-489-3p) was optimized. Then, we drew the survival curves and found that the survival rates of the three PDE-miRNA high and low expression groups and the survival rates of the high-risk and low-risk patients in the prognosis model had significant statistical differences. In addition, the receiver operating characteristics (ROC) curve analysis and independent prognostic analysis confirmed that the prognostic model we built has a relatively accurate ability to predict the grouping and prognosis of LUSC patients. Finally, Cox regression analysis were used to construct the miRNA-mRNA regulatory network, which showed the regulatory relationship between PDE-miRNAs and targeted mRNAs. Moreover, we constructed the PPI network composed of 145 targeted mRNAs and the subnetwork composed of 10 hub-targeted mRNAs (FCGR3A, IL13, CCR2, PPARGC1A, FCGR3B, ACSL1, PLXNA4, LPL, KAT2B and AOC3), which showed the interaction between targeted mRNAs. The above results indicated that the prognosis model we built can predict LUSC patients relatively accurately. The miRNA-mRNA regulatory network and the PPI network of targeted mRNAs illustrated the regulatory mechanisms and interactions between RNAs, which were of certain reference significance for us to further understand the molecular pathogenesis of LUSC and for clinical early diagnosis and treatment.

摘要

本文旨在利用癌症基因组图谱(TCGA)数据库的数据构建肺鳞癌(LUSC)的预后模型、miRNA-mRNA 调控网络和蛋白质-蛋白质相互作用(PPI)网络。在本研究中,我们首先从 TCGA 数据库中下载并整理出包含 19962 个 mRNA 转录本(包括 502 个 LUSC 和 51 个正常对照(NC)样本)的表达矩阵,以及包含 2205 个 miRNA 转录本(包括 478 个 LUSC 和 45 个 NC 样本)的表达矩阵。我们获得了 389 个差异表达的 miRNA(DE-miRNA),其中 305 个上调,84 个下调。接下来,通过 Cox 回归分析共鉴定出 7 个预后相关的 DE-miRNA(PDE-miRNA),并优化了由 3 个 PDE-miRNA(hsa-miR-4746-5p、hsa-miR-556-3p 和 hsa-miR-489-3p)组成的预后模型。然后,我们绘制了生存曲线,发现这三个 PDE-miRNA 高、低表达组的生存率以及预后模型中高、低危患者的生存率均有显著的统计学差异。此外,受试者工作特征(ROC)曲线分析和独立预后分析证实,我们构建的预后模型具有相对准确的预测 LUSC 患者分组和预后的能力。最后,通过 Cox 回归分析构建 miRNA-mRNA 调控网络,显示了 PDE-miRNA 与靶向 mRNAs 之间的调控关系。此外,我们构建了由 145 个靶向 mRNAs 组成的 PPI 网络和由 10 个关键靶向 mRNAs(FCGR3A、IL13、CCR2、PPARGC1A、FCGR3B、ACSL1、PLXNA4、LPL、KAT2B 和 AOC3)组成的子网,显示了靶向 mRNAs 之间的相互作用。上述结果表明,我们构建的预后模型可以相对准确地预测 LUSC 患者。靶向 mRNAs 的 miRNA-mRNA 调控网络和 PPI 网络说明了 RNA 之间的调控机制和相互作用,这对我们进一步了解 LUSC 的分子发病机制以及临床早期诊断和治疗具有一定的参考意义。

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