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整合分析与结直肠癌相关的分子通路和关键候选生物标志物。

Integrative analyses of molecular pathways and key candidate biomarkers associated with colorectal cancer.

出版信息

Cancer Biomark. 2020;27(4):555-568. doi: 10.3233/CBM-191263.

Abstract

BACKGROUND

Colorectal cancer (CRC) is one of the leading causes of cancer-related deaths and mining the molecular factors underlying CRC pathogenesis is imperative for alleviating the disease burden.

OBJECTIVE

To highlight key molecular pathways, prioritize hub genes and their regulators related to CRC.

METHODS

Data sets of TCGA-COAD and GTEx were used to identify differentially expressed genes (DEGs) and their functional enrichments in pathways and biological processes were analyzed using bioinformatics tools. Protein-protein interaction network was constructed and hub genes were identified using Cytoscape. Ingenuity Pathway Analysis was used to analyze the relations of the hub genes with diseases and canonical pathways. Key regulators targeting the hub genes such as TFs, miRNAs and their interactions were identified using in silico tools.

RESULTS

AURKA, CDK1, MYC, CDH1, CCNB1, CDC20, UBE2C, PLK1, KIF11, and CCNA2 were prioritized as hub genes based on their topological properties. Enrichment analyses emphasized the roles of DEGs and hub genes in the cell cycle process. Interactions of the hub genes with TFs and miRNAs suggested TP53, EZH2 and KLF4 as being promising candidate biomarkers for CRC.

CONCLUSIONS

Our results provide in silico evidence for candidate biomolecules that may have strong biomarker potential for CRC-related translational strategies.

摘要

背景

结直肠癌(CRC)是癌症相关死亡的主要原因之一,挖掘 CRC 发病机制的分子因素对于减轻疾病负担至关重要。

目的

强调与 CRC 相关的关键分子途径、优先考虑枢纽基因及其调节剂。

方法

使用 TCGA-COAD 和 GTEx 数据集来识别差异表达基因(DEGs),并使用生物信息学工具分析其在途径和生物过程中的功能富集。使用 Cytoscape 构建蛋白质-蛋白质相互作用网络,并识别枢纽基因。使用 Ingenuity Pathway Analysis 分析枢纽基因与疾病和经典途径的关系。使用计算工具识别针对枢纽基因的关键调节剂,如 TF、miRNA 及其相互作用。

结果

基于拓扑特性,AURKA、CDK1、MYC、CDH1、CCNB1、CDC20、UBE2C、PLK1、KIF11 和 CCNA2 被优先选为枢纽基因。富集分析强调了 DEGs 和枢纽基因在细胞周期过程中的作用。枢纽基因与 TF 和 miRNA 的相互作用表明 TP53、EZH2 和 KLF4 可能是 CRC 的有前途的候选生物标志物。

结论

我们的结果为可能具有 CRC 相关转化策略强生物标志物潜力的候选生物分子提供了计算证据。

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