鉴定糖尿病肾病中与细胞焦亡相关的基因和潜在药物。

Identification of pyroptosis-related genes and potential drugs in diabetic nephropathy.

机构信息

Jiangsu Key Laboratory of New Drug Research and Clinical Pharmacy, Xuzhou Medical University, Xuzhou, China.

Department of Clinical Pharmacology, School of Pharmacy, Xuzhou Medical University, No. 209 Tongshan Road, Xuzhou, 221004, Jiangsu, China.

出版信息

J Transl Med. 2023 Jul 21;21(1):490. doi: 10.1186/s12967-023-04350-w.

DOI:10.1186/s12967-023-04350-w

PMID:37480090

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10360355/

Abstract

BACKGROUND

Diabetic nephropathy (DN) is one of the serious microvascular complications of diabetes mellitus (DM). A growing body of research has demonstrated that the inflammatory state plays a critical role in the incidence and development of DN. Pyroptosis is a new way of programmed cell death, which has the particularity of natural immune inflammation. The inhibition of inflammatory cytokine expression and regulation of pathways related to pyroptosis may be a novel strategy for DN treatment. The aim of this study is to identify pyroptosis-related genes and potential drugs for DN.

METHODS

DN differentially expressed pyroptosis-related genes were identified via bioinformatic analysis Gene Expression Omnibus (GEO) dataset GSE96804. Dataset GSE30528 and GSE142025 were downloaded to verify pyroptosis-related differentially expressed genes (DEGs). Least absolute shrinkage and selection operator (LASSO) regression analysis was used to construct a pyroptosis-related gene predictive model. A consensus clustering analysis was performed to identify pyroptosis-related DN subtypes. Subsequently, Gene Set Variation Analysis (GSVA), Gene Ontology (GO) function enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were conducted to explore the differences between DN clusters. A protein-protein interaction (PPI) network was used to select hub genes and DGIdb database was utilized to screen potential therapeutic drugs/compounds targeting hub genes.

RESULTS

A total of 24 differentially expressed pyroptosis-related genes were identified in DN. A 16 gene predictive model was conducted via LASSO regression analysis. According to the expression level of these 16 genes, DN cases were divided into two subtypes, and the subtypes are mainly associated with inflammation, activation of immune response and cell metabolism. In addition, we identified 10 hub genes among these subtypes, and predicted 65 potential DN therapeutics that target key genes.

CONCLUSION

We identified two pyroptosis-related DN clusters and 65 potential therapeutical agents/compounds for DN, which might shed a light on the treatment of DN.

摘要

背景

糖尿病肾病（DN）是糖尿病（DM）的严重微血管并发症之一。越来越多的研究表明，炎症状态在 DN 的发生和发展中起着关键作用。细胞焦亡是一种新的程序性细胞死亡方式，具有天然免疫炎症的特殊性。抑制炎症细胞因子的表达和调节与细胞焦亡相关的途径可能是治疗 DN 的一种新策略。本研究旨在鉴定与 DN 相关的细胞焦亡相关基因和潜在药物。

方法

通过生物信息学分析 GEO 数据集 GSE96804 鉴定 DN 差异表达的细胞焦亡相关基因。下载数据集 GSE30528 和 GSE142025 以验证与细胞焦亡相关的差异表达基因（DEGs）。采用最小绝对收缩和选择算子（LASSO）回归分析构建细胞焦亡相关基因预测模型。进行共识聚类分析以识别与细胞焦亡相关的 DN 亚型。随后，进行基因集变异分析（GSVA）、基因本体论（GO）功能富集分析和京都基因与基因组百科全书（KEGG）通路分析，以探讨 DN 聚类之间的差异。构建蛋白质-蛋白质相互作用（PPI）网络，筛选关键基因，并利用 DGIdb 数据库筛选针对关键基因的潜在治疗药物/化合物。

结果

共鉴定出 24 个与 DN 相关的差异表达细胞焦亡相关基因。通过 LASSO 回归分析构建了一个 16 基因预测模型。根据这 16 个基因的表达水平，将 DN 病例分为两种亚型，这些亚型主要与炎症、免疫反应激活和细胞代谢有关。此外，在这些亚型中我们鉴定出 10 个关键基因，并预测了 65 种针对关键基因的潜在 DN 治疗药物/化合物。