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基于机器学习算法的糖尿病肾病关键免疫相关基因及潜在治疗药物的鉴定。

Identification of key immune-related genes and potential therapeutic drugs in diabetic nephropathy based on machine learning algorithms.

机构信息

The Second Affiliated Hospital of Harbin Medical University, 246 Xuefu Road, Nangang District, Harbin, 10086, China.

出版信息

BMC Med Genomics. 2024 Aug 26;17(1):220. doi: 10.1186/s12920-024-01995-4.

DOI:10.1186/s12920-024-01995-4
PMID:39187837
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11348758/
Abstract

BACKGROUND

Diabetic nephropathy (DN) is a major contributor to chronic kidney disease. This study aims to identify immune biomarkers and potential therapeutic drugs in DN.

METHODS

We analyzed two DN microarray datasets (GSE96804 and GSE30528) for differentially expressed genes (DEGs) using the Limma package, overlapping them with immune-related genes from ImmPort and InnateDB. LASSO regression, SVM-RFE, and random forest analysis identified four hub genes (EGF, PLTP, RGS2, PTGDS) as proficient predictors of DN. The model achieved an AUC of 0.995 and was validated on GSE142025. Single-cell RNA data (GSE183276) revealed increased hub gene expression in epithelial cells. CIBERSORT analysis showed differences in immune cell proportions between DN patients and controls, with the hub genes correlating positively with neutrophil infiltration. Molecular docking identified potential drugs: cysteamine, eltrombopag, and DMSO. And qPCR and western blot assays were used to confirm the expressions of the four hub genes.

RESULTS

Analysis found 95 and 88 distinctively expressed immune genes in the two DN datasets, with 14 consistently differentially expressed immune-related genes. After machine learning algorithms, EGF, PLTP, RGS2, PTGDS were identified as the immune-related hub genes associated with DN. In addition, the mRNA and protein levels of them were obviously elevated in HK-2 cells treated with glucose for 24 h, as well as their mRNA expressions in kidney tissues of mice with DN.

CONCLUSION

This study identified 4 hub immune-related genes (EGF, PLTP, RGS2, PTGDS), as well as their expression profiles and the correlation with immune cell infiltration in DN.

摘要

背景

糖尿病肾病（DN）是慢性肾脏病的主要病因。本研究旨在鉴定 DN 中的免疫生物标志物和潜在治疗药物。

方法

我们使用 Limma 包分析了两个 DN 微阵列数据集（GSE96804 和 GSE30528）中的差异表达基因（DEGs），并将其与 ImmPort 和 InnateDB 中的免疫相关基因重叠。LASSO 回归、SVM-RFE 和随机森林分析确定了四个枢纽基因（EGF、PLTP、RGS2、PTGDS）作为 DN 的优秀预测因子。该模型在 GSE142025 上的 AUC 为 0.995，并进行了验证。单细胞 RNA 数据（GSE183276）显示上皮细胞中枢纽基因表达增加。CIBERSORT 分析显示 DN 患者和对照组之间免疫细胞比例存在差异，枢纽基因与中性粒细胞浸润呈正相关。分子对接鉴定出潜在药物：半胱胺、依曲泊帕和 DMSO。并使用 qPCR 和 Western blot 测定来验证四个枢纽基因的表达。

结果

分析发现两个 DN 数据集中有 95 个和 88 个独特表达的免疫基因，有 14 个一致差异表达的免疫相关基因。经过机器学习算法，EGF、PLTP、RGS2、PTGDS 被鉴定为与 DN 相关的免疫相关枢纽基因。此外，在葡萄糖处理 24 小时的 HK-2 细胞中，以及在 DN 小鼠肾脏组织中，它们的 mRNA 和蛋白水平明显升高。

结论

本研究鉴定了 4 个枢纽免疫相关基因（EGF、PLTP、RGS2、PTGDS），以及它们在 DN 中的表达谱及其与免疫细胞浸润的相关性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2584/11348758/0f46fbf5c274/12920_2024_1995_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2584/11348758/1a153541c7e3/12920_2024_1995_Fig1_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2584/11348758/ef60cb38f79e/12920_2024_1995_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2584/11348758/feaea2770d26/12920_2024_1995_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2584/11348758/0f46fbf5c274/12920_2024_1995_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2584/11348758/1a153541c7e3/12920_2024_1995_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2584/11348758/cfd6fbfd767d/12920_2024_1995_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2584/11348758/e2a6c1106802/12920_2024_1995_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2584/11348758/10567d668a78/12920_2024_1995_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2584/11348758/ef60cb38f79e/12920_2024_1995_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2584/11348758/feaea2770d26/12920_2024_1995_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2584/11348758/0f46fbf5c274/12920_2024_1995_Fig7_HTML.jpg

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