Department of Pharmacy, The People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, Guangxi, China.
Medicine (Baltimore). 2022 Sep 30;101(39):e30681. doi: 10.1097/MD.0000000000030681.
Colon adenocarcinoma (COAD) is one of the most common types of colon cancer, represents a major public health issue due to its high incidence and mortality. Competing endogenous RNAs (ceRNAs) hypothesis has generated a great interest in the study of molecular biological mechanisms of cancer progression. The aim of this study was to identify potential prediction prognostic biomarker associated with progression of COAD and illuminate regulatory mechanisms. Two RNA sequencing datasets downloaded from the Genotype-Tissue Expression and TCGA. The differentially expressed RNAs were analyzed. Weighted correlation network analysis was used to analyze the similarity of genes model with a trait in the network. Interactions between lncRNAs, miRNAs, and target mRNAs were predicted by MiRcode, starBase, miRTarBase, miRDB, and TargetScan, and the risk score of mRNAs was established. Based on the identified prognostic signature and independent clinical factors, then the nomogram survival model was built. Totally, we identified 3537 differentially expressed mRNAs, 2379 lncRNAs, and 449 microRNAs. Based on the 8 prognosis-associated mRNAs (CCNA2 + CEBPA + NEBL + SOX9 + DLG4 + RIMKLB + TCF7L1 + TUB), the risk score was proposed. After the independent clinical prognostic factors were identified, the nomogram survival model was built. LncRNA-miRNA-mRNA ceRNA network was built by 68 lncRNAs, 4 miRNAs, and 6 mRNAs, which might serve as prognostic biomarkers of COAD. These findings suggest several genes in ceRNA network might be novel important prognostic biomarkers and potential targets for COAD. CeRNA networks could provide further insight into the mRNA-related regulatory mechanism and COAD prognosis.
结直肠腺癌（COAD）是最常见的结肠癌类型之一，由于其发病率和死亡率高，是一个主要的公共卫生问题。竞争内源性 RNA（ceRNA）假说激发了人们对癌症进展分子生物学机制的研究兴趣。本研究旨在鉴定与 COAD 进展相关的潜在预测预后生物标志物，并阐明其调控机制。从 Genotype-Tissue Expression 和 TCGA 下载了两个 RNA 测序数据集。分析了差异表达的 RNA。使用加权相关网络分析来分析网络中基因模型与性状之间的相似性。通过 MiRcode、starBase、miRTarBase、miRDB 和 TargetScan 预测 lncRNA、miRNA 和靶 mRNA 之间的相互作用，并建立 mRNA 的风险评分。基于确定的预后特征和独立的临床因素，构建了列线图生存模型。总共鉴定出 3537 个差异表达的 mRNAs、2379 个 lncRNAs 和 449 个 microRNAs。基于 8 个与预后相关的 mRNAs（CCNA2+CEBPA+NEBL+SOX9+DLG4+RIMKLB+TCF7L1+TUB），提出了风险评分。确定独立的临床预后因素后，构建了列线图生存模型。通过 68 个 lncRNA、4 个 miRNA 和 6 个 mRNA 构建了 lncRNA-miRNA-mRNA ceRNA 网络，该网络可能作为 COAD 的预后生物标志物。这些发现表明，ceRNA 网络中的几个基因可能是新的重要的预后生物标志物和 COAD 的潜在治疗靶点。ceRNA 网络可以为进一步了解与 mRNA 相关的调控机制和 COAD 的预后提供帮助。
