Immune Microenvironment Alterations and Identification of Key Diagnostic Biomarkers in Chronic Kidney Disease - Using Integrated Bioinformatics and Machine Learning - Research article summary

Aug 5, 2026

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Chronic kidney disease involves immune dysregulation and widespread changes in gene expression, but the diagnostic biomarkers underlying these alterations remain unclear. This study evaluates immune cell infiltration and immune microenvironment scores in CKD using xCell, identifies differentially expressed genes with limma, and performs GSEA/GSVA to define pathway enrichment. LASSO and Random Forest narrow diagnostic candidates, and a nomogram model is validated in GSE96804 and clinical samples. Four genes—RGS1, IL4I1, NR4A3, and SOCS3—show diagnostic potential and correlate with immune cell recruitment.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages14

Reading Time35 min

AccessFree to download

Background

Methods

Results

Conclusion

Pharmacogenomics and Personalized Medicine downloaded from [https://www.dovepress.com/](https://www.dovepress.com/) For personal use only. Pharmacogenomics and Personalized Medicine Dovepress Open Access Full Text Article ORIGINAL RESEARCH Immune Microenvironment Alterations and Identification of Key Diagnostic Biomarkers in Chronic Kidney Disease Using Integrated Bioinformatics and Machine Learning Jinbao Shi, Aliang Xu, Liuying Huang, Shaojie Liu, Binxuan Wu, Zuhong Zhang Department of Nephrology, Ningde Hospital of Traditional Chinese Medicine, Ningde, Fujian, People’s Republic of China Correspondence: Jinbao Shi, Department of Nephrology, Ningde Hospital of Traditional Chinese Medicine, No. 16 Donghu Road, Ningde, Fujian, People’s Republic of China, Email [shijinbao00721@aliyun.com](shijinbao00721@aliyun.com) | Background: Chronic kidney disease (CKD) involves complex immune dysregulation and altered gene expression profiles. This study investigates immune cell infiltration, differential gene expression, and pathway enrichment in CKD patients to identify key diagnostic biomarkers through machine learning methods.
Methods: We assessed immune cell infiltration and immune microenvironment scores using the xCell algorithm. Differentially expressed genes (DEGs) were identified using the limma package. Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA) were performed to evaluate pathway enrichment. Machine learning techniques (LASSO and Random Forest) pinpointed diagnostic genes. A nomogram model was constructed and validated for diagnostic prediction. Spearman correlation explored associations between key genes and immune cell recruitment.
Results: The CKD group exhibited significantly altered immune cell infiltration and increased immune microenvironment scores compared to the normal group. We identified 2335 DEGs, including 124 differentially expressed immune-related genes. GSEA highlighted significant enrichment of inflammatory and immune pathways in the high immune score (HIS) subgroup, while GSVA indicated upregulation of immune responses and metabolic processes in HIS. Machine learning identified four key diagnostic genes: RGS1, IL4I1, NR4A3, and SOCS3 . Validation in an independent dataset (GSE96804) and clinical samples confirmed their diagnostic potential. The nomogram model integrating these genes demonstrated high predictive accuracy. Spearman correlation revealed positive associations between the key genes and various immune cells, indicating their roles in immune modulation and CKD pathogenesis. Conclusion: This study provides a comprehensive analysis of immune alterations and gene expression profiles in CKD. The identified diagnostic genes and the constructed nomogram model offer potent tools for CKD diagnosis. The immunomodulatory roles of RGS1, IL4I1, NR4A3, and SOCS3 warrant further investigation as potential therapeutic targets in CKD.
Keywords: chronic kidney disease, diagnostic biomarkers, immune microenvironment, GSEA, GSVA, machine learning | | --- | | Introduction
Chronic kidney disease (CKD) is a progressive condition that poses a significant public health challenge worldwide, affecting millions of people and leading to high morbidity and mortality rates.1 CKD is characterized by the gradual loss of kidney function over time, which can eventually lead to end-stage renal disease, necessitating dialysis or kidney transplantation.2 The pathogenesis of CKD involves a complex interplay of genetic, environmental, and immunological factors.3 Recent studies have highlighted the crucial role of immune dysregulation and altered gene expression profiles in the progression of CKD.4 However, the specific mechanisms underlying these changes and their impact on disease progression remain poorly understood.
Numerous studies have explored the role of immune cells in CKD, demonstrating that immune cell infiltration and activation are key features of CKD pathology.5,

FAQs

How were immune microenvironment changes in chronic kidney disease evaluated?

Immune cell infiltration and immune microenvironment scores were assessed using the xCell algorithm, comparing CKD and normal groups.

Which bioinformatics and enrichment analyses were used to identify relevant pathways?

Differentially expressed genes were identified with limma, and GSEA plus GSVA were applied to evaluate pathway enrichment in CKD subgroups.

What diagnostic biomarkers were identified for chronic kidney disease?

Machine learning highlighted four key diagnostic genes: RGS1, IL4I1, NR4A3, and SOCS3, with validation in an independent dataset and clinical samples.

Aug 5, 2026

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