Establishment and Verification of a Gene Signature for Diagnosing Type 2 Diabetics by WGCNA, LASSO Analysis, and In Vitro Experiments.
Establishment and Verification of a Gene Signature for Diagnosing Type 2 Diabetics by WGCNA, LASSO Analysis, and In Vitro Experiments.
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DOI：
10.1155/2022/4446342
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发表时间：
2022
影响因子：
--
通讯作者：
Jia, Changxin
中科院分区：
文献类型：
--
作者：
Shao, Huaming;Zhang, Yong;Liu, Yishuai;Yang, Yan;Tang, Xiaozhu;Li, Jiajia;Jia, Changxin
2型糖尿病的发病率和患病率随着年龄的增长而增加。然而，缺乏敏感的诊断工具和有效的治疗方案。我们的目标是建立和验证一个实用和有效的诊断工具，为这种疾病。WGCNA在GSE25724和GSE38642联合数据集中对2型糖尿病和正常胰岛的表达谱进行了报道。通过LASSO Cox回归分析，构建了基于糖尿病相关模块基因的基因签名。绘制ROC曲线评估诊断效果。分析了基因与免疫细胞浸润及通路的相关性。BST2和BTBD1在糖毒性诱导和正常胰岛β细胞中表达。转染si-BST2后，研究BST2对β细胞功能障碍的影响。共构建了14个共表达模块，其中红色和青色模块与糖尿病相关。LASSO基因标记（BST2、BTBD1、IFIT1、IFIT3和RTP4）被开发出来。合并数据集和GSE20966数据集的auc分别为0.914和0.910，证实了诊断2型糖尿病的优异性能。模型中各基因与免疫细胞浸润及关键信号通路（TGF-β、P53等）有明显相关性。糖中毒诱导的β细胞中BST2和BTBD1表达异常。BST2敲低可改善β细胞功能障碍，改变TGF-β和P53通路的激活。我们的研究结果提出了一种高效诊断2型糖尿病的基因标记，可以帮助和改善早期诊断和治疗。
The incidence and prevalence of type 2 diabetes are increasing with age. Nevertheless, there is lack of sensitive diagnostic tools and effective therapeutic regimens. We aimed to establish and verify a practical and valid diagnostic tool for this disease. WGCNA was presented on the expression profiling of type 2 diabetic and normal islets in combined GSE25724 and GSE38642 datasets. By LASSO Cox regression analyses, a gene signature was constructed based on the genes in diabetes-related modules. ROC curves were plotted for assessing the diagnostic efficacy. Correlations between the genes and immune cell infiltration and pathways were analyzed. BST2 and BTBD1 expression was verified in glucotoxicity-induced and normal islet β cells. The influence of BST2 on β cell dysfunction was investigated under si-BST2 transfection. Totally, 14 coexpression modules were constructed, and red and cyan modules displayed the correlations to diabetes. The LASSO gene signature (BST2, BTBD1, IFIT1, IFIT3, and RTP4) was developed. The AUCs in the combined datasets and GSE20966 dataset were separately 0.914 and 0.910, confirming the excellent performance in diagnosing type 2 diabetes. Each gene in the model was distinctly correlated to immune cell infiltration and key signaling pathways (TGF-β and P53, etc.). The abnormal expression of BST2 and BTBD1 was confirmed in glucotoxicity-induced β cells. BST2 knockdown ameliorated β cell dysfunction and altered the activation of TGF-β and P53 pathways. Our findings propose a gene signature with high efficacy to diagnose type 2 diabetes, which could assist and improve early diagnosis and therapy.
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