Diabetes is a spectrum of metabolic diseases affecting millions of people worldwide. The loss of pancreatic β-cell mass by either autoimmune destruction or apoptosis, in type 1-diabetes (T1D) and type 2-diabetes (T2D), respectively, represents a pathophysiological process leading to insulin deficiency. Therefore, therapeutic strategies focusing on restoring β-cell mass and β-cell insulin secretory capacity may impact disease management. This study took advantage of powerful integrative bioinformatic tools to scrutinize publicly available diabetes-associated gene expression data to unveil novel potential molecular targets associated with β-cell dysfunction. A comprehensive literature search for human studies on gene expression alterations in the pancreas associated with T1D and T2D was performed. A total of 6 studies were selected for data extraction and for bioinformatic analysis. Pathway enrichment analyses of differentially expressed genes (DEGs) were conducted, together with protein–protein interaction networks and the identification of potential transcription factors (TFs). For noncoding differentially expressed RNAs, microRNAs (miRNAs) and long noncoding RNAs (lncRNAs), which exert regulatory activities associated with diabetes, identifying target genes and pathways regulated by these RNAs is fundamental for establishing a robust regulatory network. Comparisons of DEGs among the 6 studies showed 59 genes in common among 4 or more studies. Besides alterations in mRNA, it was possible to identify differentially expressed miRNA and lncRNA. Among the top transcription factors (TFs), HIPK2, KLF5, STAT1 and STAT3 emerged as potential regulators of the altered gene expression. Integrated analysis of protein-coding genes, miRNAs, and lncRNAs pointed out several pathways involved in metabolism, cell signaling, the immune system, cell adhesion, and interactions. Interestingly, the GABAergic synapse pathway emerged as the only common pathway to all datasets. This study demonstrated the power of bioinformatics tools in scrutinizing publicly available gene expression data, thereby revealing potential therapeutic targets like the GABAergic synapse pathway, which holds promise in modulating α-cells transdifferentiation into β-cells.

中文翻译：

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基因表达分析揭示糖尿病相关基因特征

糖尿病是影响全世界数百万人的一系列代谢疾病。在 1 型糖尿病 (T1D) 和 2 型糖尿病 (T2D) 中，胰腺 β 细胞质量分别因自身免疫破坏或细胞凋亡而丧失，代表了导致胰岛素缺乏的病理生理过程。因此，专注于恢复 β 细胞质量和 β 细胞胰岛素分泌能力的治疗策略可能会影响疾病管理。这项研究利用强大的综合生物信息学工具来仔细审查公开的糖尿病相关基因表达数据，以揭示与 β 细胞功能障碍相关的新的潜在分子靶点。对与 T1D 和 T2D 相关的胰腺基因表达改变的人类研究进行了全面的文献检索。总共选择了6项研究进行数据提取和生物信息学分析。对差异表达基因（DEG）进行通路富集分析，同时进行蛋白质-蛋白质相互作用网络和潜在转录因子（TF）的鉴定。对于发挥与糖尿病相关的调节活性的非编码差异表达RNA、微小RNA（miRNA）和长非编码RNA（lncRNA）来说，识别这些RNA调节的靶基因和通路对于建立强大的调节网络至关重要。 6 项研究之间的 DEG 比较显示 4 项或更多研究中有 59 个共同基因。除了 mRNA 的改变之外，还可以识别差异表达的 miRNA 和 lncRNA。在顶级转录因子 (TF) 中，HIPK2、KLF5、STAT1 和 STAT3 成为改变基因表达的潜在调节因子。对蛋白质编码基因、miRNA 和 lncRNA 的综合分析指出了涉及代谢、细胞信号传导、免疫系统、细胞粘附和相互作用的几个途径。有趣的是，GABA 突触通路成为所有数据集的唯一通用通路。这项研究证明了生物信息学工具在审查公开基因表达数据方面的强大功能，从而揭示了潜在的治疗靶点，例如 GABA 能突触通路，该通路有望调节 α 细胞向 β 细胞的转分化。