Comprehensive Analysis and Experimental Validation of Immune-Related Biomarkers and Immune Microenvironment in Diabetic Nephropathy.

IF 4.1 2区医学 Q2 IMMUNOLOGY

Journal of Inflammation Research Pub Date : 2025-10-03 eCollection Date: 2025-01-01 DOI:10.2147/JIR.S541886

Weini Zhou, Ziyang Zeng, Xunjia Li, Mei Yang

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Abstract

Background: The molecular mechanism of diabetic nephropathy (DN) is still not fully understood. There is ample evidence that the immune system plays a crucial role in the progression of DN. Further exploration of immune-related genes (IRGs) for DN diagnosis is therefore of significant clinical value.

Methods: Gene expression data from DN patients were obtained from the GEO database, and a weighted gene co-expression network analysis (WGCNA) was constructed. The overlapping IRGs derived by the least absolute shrinkage and selection operator (LASSO) and recursive feature elimination (RF) algorithms were identified as DN diagnostic biomarkers. A nomogram model was established to evaluate the diagnostic ability of feature biomarkers. The expression levels of the screened IRGs were validated in vitro using qRT-PCR. Type 2 diabetes mellitus (T2DM) mouse model with DN was also established to confirm the consistency with bioinformatic predictions.

Results: Three IRG-related DN characteristic diagnostic biomarkers (CCL9, EDN1 and HSPA1L) were identified. After verifying the DN diagnostic capability with nomogram model, pathway enrichment analysis, immunoinfiltration characteristics and correlation analysis were used to comprehensively analyze the potential effects of selected IRGs on DN. The differential expressions of screened IRGs were further confirmed by cell line and T2DM mouse model.

Conclusion: Our findings nominate CCL9, EDN1, and HSPA1L as key mediators of DN progression and unveil their potential as diagnostic biomarkers. Although prospective validation in human cohorts is a prerequisite for clinical translation, these IRGs represent a compelling foundation for a precision medicine tool. This tool could transform patient management by facilitating pre-symptomatic diagnosis and informing tailored interventions to halt DN development.

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糖尿病肾病免疫相关生物标志物和免疫微环境的综合分析与实验验证。

背景：糖尿病肾病（DN）的分子机制尚不完全清楚。有充分的证据表明，免疫系统在DN的进展中起着至关重要的作用。因此，进一步探索免疫相关基因（IRGs）对DN的诊断具有重要的临床价值。方法：从GEO数据库中获取DN患者基因表达数据，构建加权基因共表达网络分析（WGCNA）。通过最小绝对收缩和选择算子（LASSO）和递归特征消除（RF）算法得到的重叠IRGs被确定为DN诊断生物标志物。建立nomogram模型来评价特征生物标志物的诊断能力。筛选的IRGs在体外用qRT-PCR验证表达水平。建立2型糖尿病（T2DM）伴DN小鼠模型，以证实与生物信息学预测的一致性。结果：鉴定出3个与irg相关的DN特征诊断生物标志物（CCL9、EDN1和HSPA1L）。在用nomogram模型验证DN的诊断能力后，利用pathway富集分析、免疫浸润特性分析和相关性分析综合分析所选IRGs对DN的潜在影响。通过细胞系和T2DM小鼠模型进一步证实了筛选的IRGs的差异表达。结论：我们的研究结果表明CCL9、EDN1和HSPA1L是DN进展的关键介质，并揭示了它们作为诊断生物标志物的潜力。虽然在人类队列中进行前瞻性验证是临床翻译的先决条件，但这些IRGs代表了精确医学工具的令人信服的基础。该工具可以通过促进症状前诊断和提供量身定制的干预措施来阻止DN的发展，从而改变患者管理。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Inflammation Research Immunology and Microbiology-Immunology

CiteScore

6.10

自引率

2.20%

发文量

658

审稿时长

16 weeks

期刊介绍： An international, peer-reviewed, open access, online journal that welcomes laboratory and clinical findings on the molecular basis, cell biology and pharmacology of inflammation.