Department of Genetic Engineering, Zebrafish Genetics Laboratory, SRM Institute of Science and Technology, Kattankulathur, Chengalpattu, Tamil Nadu, India.
Department of Biotechnology, Molecular Toxicology Laboratory, Bharathiar University, Coimbatore, Tamil Nadu, India.
Cancer Biomark. 2024;40(1):111-123. doi: 10.3233/CBM-230271.
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.
In this study, twelve microarray ccRCC datasets were chosen from the gene expression omnibus (GEO) database and subjected to integrated analysis.
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.
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.
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.
透明细胞肾细胞癌（ccRCC）是最常见的肾癌类型之一。阐明导致细胞变化和 ccRCC 发病机制中细胞转化的基因是一个复杂的过程。
本研究从基因表达综合数据库（GEO）中选择了 12 个微阵列 ccRCC 数据集，并进行了综合分析。
通过 GEO2R 分析，在数据集之间鉴定出 179 个常见差异表达基因（DEG）。使用 ToppFun 对常见 DEG 进行功能富集分析，然后使用 Cytoscape 构建蛋白质-蛋白质相互作用网络（PPIN）。使用 Cytoscape 的 Molecular Complex Detection（MCODE）插件识别 DEG 的 PPIN 中的聚类。为了鉴定枢纽基因，计算了 PPIN 中每个 DEG 的中心性参数度、介数和接近度得分。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和 ccRCC 组织中的相对表达水平。
常见的 DEG 在缺氧诱导因子（HIF）信号和代谢重编程途径中高度富集。鉴定出 VEGFA、CAV1、LOX、CCND1、PLG、EGF、SLC2A1 和 ENO2 为枢纽基因。
在 8 个枢纽基因中，只有 VEGFA、LOX、CCND1 和 EGF 的表达水平在与其他类型癌症相比时在 ccRCC 中表现出独特的表达模式。
