Identification of key genes and signalling pathways in clear cell renal cell carcinoma: An integrated bioinformatics approach.

IF 4.6 Q2 MATERIALS SCIENCE, BIOMATERIALS ACS Applied Bio Materials Pub Date : 2024-01-01 DOI:10.3233/CBM-230271

Vinoth S, Satheeswaran Balasubramanian, Ekambaram Perumal, Kirankumar Santhakumar

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Abstract

Background: Clear cell Renal Cell Carcinoma (ccRCC) is one of the most prevalent types of kidney cancer. Unravelling the genes responsible for driving cellular changes and the transformation of cells in ccRCC pathogenesis is a complex process.

Objective: In this study, twelve microarray ccRCC datasets were chosen from the gene expression omnibus (GEO) database and subjected to integrated analysis.

Methods: Through GEO2R analysis, 179 common differentially expressed genes (DEGs) were identified among the datasets. The common DEGs were subjected to functional enrichment analysis using ToppFun followed by construction of protein-protein interaction network (PPIN) using Cytoscape. Clusters within the DEGs PPIN were identified using the Molecular Complex Detection (MCODE) Cytoscape plugin. To identify the hub genes, the centrality parameters degree, betweenness, and closeness scores were calculated for each DEGs in the PPIN. Additionally, Gene Expression Profiling Interactive Analysis (GEPIA) was utilized to validate the relative expression levels of hub genes in the normal and ccRCC tissues.

Results: The common DEGs were highly enriched in Hypoxia-inducible factor (HIF) signalling and metabolic reprogramming pathways. VEGFA, CAV1, LOX, CCND1, PLG, EGF, SLC2A1, and ENO2 were identified as hub genes.

Conclusion: Among 8 hub genes, only the expression levels of VEGFA, LOX, CCND1, and EGF showed a unique expression pattern exclusively in ccRCC on compared to other type of cancers.

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鉴定透明细胞肾细胞癌的关键基因和信号通路：一种综合生物信息学方法。

背景：透明细胞肾细胞癌（ccRCC）是最常见的肾癌类型之一。揭示ccRCC发病机制中驱动细胞变化和细胞转化的基因是一个复杂的过程：本研究从基因表达总库（GEO）数据库中选取了 12 个微阵列 ccRCC 数据集进行综合分析：方法：通过 GEO2R 分析，在数据集中发现了 179 个常见的差异表达基因（DEGs）。利用 ToppFun 对这些常见的 DEGs 进行功能富集分析，然后利用 Cytoscape 构建蛋白质-蛋白质相互作用网络（PPIN）。使用分子复合体检测（MCODE）Cytoscape 插件识别 DEGs PPIN 中的聚类。为了确定枢纽基因，计算了 PPIN 中每个 DEGs 的中心性参数度、间隔度和接近度得分。此外，还利用基因表达谱交互分析（GEPIA）验证了枢纽基因在正常组织和ccRCC组织中的相对表达水平：结果：常见的DEGs高度富集于缺氧诱导因子（HIF）信号传导和代谢重编程通路。VEGFA、CAV1、LOX、CCND1、PLG、EGF、SLC2A1 和 ENO2 被确定为枢纽基因：结论：在 8 个中枢基因中，只有 VEGFA、LOX、CCND1 和 EGF 的表达水平与其他类型的癌症相比，在 ccRCC 中显示出独特的表达模式。

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