南美超级胎牛血清999尝鲜特惠
转染试剂低至5折
基质胶阶梯特惠
积分商城,好礼送不停
南美超级胎牛血清999尝鲜特惠
转染试剂低至5折
基质胶阶梯特惠
积分商城,好礼送不停
分子生物学
IVD分子诊断
细胞培养与分析
蛋白研究
细胞因子
重组蛋白
抗体
高通量测序建库
病原检测UCF系列
生物医药
工具酶
抑制剂激活剂与常用试剂
仪器
耗材

转录组分析揭示了达格列净治疗2型糖尿病患者过程中相关的分子变化及候选转录组特征。

Transcriptomic analysis reveals molecular alterations and candidate transcriptomic features associated with dapagliflozin treatment in patients with type 2 diabetes mellitus

Zhong Di, Jiang Zhaohui, Ren Tingting

Journal:Scientific Reports

IF:4.9

DOI:10.1038/s41598-026-65879-6

PMID:

Published:2026-08-07

research field:农学植物生理学分子生物学土壤微生物学

Abstract

Type 2 diabetes mellitus (T2DM) is characterized by insulin resistance, chronic inflammation, and metabolic imbalance. Although dapagliflozin, a sodium-glucose cotransporter 2 (SGLT2) inhibitor, enhances glycemic control and provides cardio-renal benefits, the transcriptional alterations associated with its treatment remain insufficiently understood. Paired transcriptomic profiles from patients with T2DM before and after dapagliflozin treatment were analyzed to identify differentially expressed genes (DEGs). Candidate genes were screened using three machine-learning approaches: least absolute shrinkage and selection operator (LASSO) regression, random forest, and Boruta, followed by expression validation. Subsequent analysis included functional enrichment, immune-cell infiltration assessment, transcription factor (TF)-mRNA-miRNA network construction, molecular docking, and reverse transcription quantitative real-time polymerase chain reaction (RT-qPCR). A total of 137 DEGs associated with dapagliflozin treatment were identified. Integrated machine-learning analysis pinpointed TAS2R60, GPLD1, and GPR42 as three candidate transcriptomic feature genes, all upregulated post-treatment. In the predicted regulatory network, GPR42 exhibited the highest connectivity. Estimations of immune-cell abundance via single-sample gene set enrichment analysis (ssGSEA) revealed differences in scores for regulatory T cells, memory B cells, natural killer T cells, and central memory CD4+ T cells, with GPR42 expression positively correlating with abundance scores of central memory CD4+ T cells and memory B cells. Molecular docking predicted binding conformations between dapagliflozin and the three candidate proteins; however, these results do not constitute evidence of direct interactions. Preliminary RT-qPCR results supported increased expression of GPLD1 and GPR42, whereas TAS2R60 demonstrated only a nonsignificant upward trend. TAS2R60, GPLD1, and GPR42 emerge as potential transcripto

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