Department of Biotechnology & Bioinformatics, Yogi Vemana University, Kadapa 516003, Andhra Pradesh, India.
Comput Biol Chem. 2019 Apr;79:24-35. doi: 10.1016/j.compbiolchem.2019.01.010. Epub 2019 Jan 24.
Diabetes mellitus is clinically characterized by hyperglycemia. Though many studies have been done to understand the mechanism of Type 2 Diabetes (T2D), however, the complete network of diabetes and its associated disorders through polygenic involvement is still under debate. The present study designed to re-analyze publicly available T2D related microarray raw datasets present in GEO database and T2D genes information present in GWAS catalog for screening out differentially expressed genes (DEGs) and identify key hub genes associated with T2D. T2D related microarray data downloaded from Gene Expression Omnibus (GEO) database and re-analysis performed with in house R packages scripts for background correction, normalization and identification of DEGs in T2D. Also retrieved T2D related DEGs information from GWAS catalog. Both DEGs lists were grouped after removal of overlapping genes. These screened DEGs were utilized further for identification and characterization of key hub genes in T2D and its associated diseases using STRING, WebGestalt and Panther databases. Computational analysis reveal that out of 99 identified key hub gene candidates from 348 DEGs, only four genes (CCL2, ELMO1, VEGFA and TCF7L2) along with FOS playing key role in causing T2D and its associated disorders, like nephropathy, neuropathy, rheumatoid arthritis and cancer via p53 or Wnt signaling pathways. MIR-29, and MAZ_Q6 are identified potential target microRNA and TF along with probable drugs alprostadil, collagenase and dinoprostone for the key hub gene candidates. The results suggest that identified key DEGs may play promising roles in prevention of diabetes.
糖尿病的临床特征是高血糖。尽管已经有许多研究致力于了解 2 型糖尿病（T2D）的发病机制，但涉及多基因参与的糖尿病及其相关疾病的完整网络仍存在争议。本研究旨在重新分析 GEO 数据库中公开的 T2D 相关微阵列原始数据集和 GWAS 目录中 T2D 基因信息，以筛选差异表达基因（DEGs）并确定与 T2D 相关的关键枢纽基因。从基因表达综合数据库（GEO）数据库中下载 T2D 相关微阵列数据，并使用内部 R 包脚本进行重新分析，以进行背景校正、归一化和识别 T2D 中的 DEGs。还从 GWAS 目录中检索了 T2D 相关 DEGs 信息。在去除重叠基因后，将这两个 DEGs 列表进行分组。利用这些筛选出的 DEGs，进一步使用 STRING、WebGestalt 和 Panther 数据库，鉴定和表征 T2D 及其相关疾病中的关键枢纽基因。计算分析显示，在从 348 个 DEGs 中确定的 99 个关键枢纽基因候选物中，只有 4 个基因（CCL2、ELMO1、VEGFA 和 TCF7L2）以及 FOS 通过 p53 或 Wnt 信号通路在导致 T2D 及其相关疾病中发挥关键作用，如肾病、神经病变、类风湿关节炎和癌症。鉴定出 MIR-29 和 MAZ_Q6 是潜在的靶 microRNA 和 TF，以及可能的药物前列地尔、胶原蛋白酶和地诺前列酮，用于关键枢纽基因候选物。结果表明，鉴定出的关键 DEGs 可能在预防糖尿病方面发挥有希望的作用。
