Immune Microenvironment Alterations and Identification of Key Diagnostic Biomarkers in Chronic Kidney Disease Using Integrated Bioinformatics and Machine Learning.

IF 1.8 4区 医学 Q3 PHARMACOLOGY & PHARMACY
Pharmacogenomics & Personalized Medicine Pub Date : 2024-11-19 eCollection Date: 2024-01-01 DOI:10.2147/PGPM.S488143
Jinbao Shi, Aliang Xu, Liuying Huang, Shaojie Liu, Binxuan Wu, Zuhong Zhang
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引用次数: 0

Abstract

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.

利用综合生物信息学和机器学习技术改变免疫微环境并识别慢性肾脏病的关键诊断生物标志物。
背景:慢性肾脏病(CKD)涉及复杂的免疫失调和基因表达谱改变。本研究通过机器学习方法研究 CKD 患者的免疫细胞浸润、差异基因表达和通路富集,以确定关键的诊断生物标志物:我们使用 xCell 算法评估了免疫细胞浸润和免疫微环境得分。使用limma软件包确定差异表达基因(DEGs)。基因组富集分析(Gene Set Enrichment Analysis,GSEA)和基因组变异分析(Gene Set Variation Analysis,GSVA)用于评估通路富集。机器学习技术(LASSO 和随机森林)确定了诊断基因。构建并验证了诊断预测的提名图模型。斯皮尔曼相关性探讨了关键基因与免疫细胞招募之间的关联:结果:与正常组相比,CKD 组的免疫细胞浸润明显改变,免疫微环境评分增加。我们发现了 2335 个 DEGs,包括 124 个差异表达的免疫相关基因。GSEA强调了高免疫评分(HIS)亚组中炎症和免疫通路的显着富集,而GSVA表明了HIS中免疫反应和代谢过程的上调。机器学习确定了四个关键诊断基因:RGS1、IL4I1、NR4A3 和 SOCS3。独立数据集(GSE96804)和临床样本的验证证实了这些基因的诊断潜力。整合了这些基因的提名图模型显示了很高的预测准确性。斯皮尔曼相关性揭示了关键基因与各种免疫细胞之间的正相关,表明它们在免疫调节和 CKD 发病机制中的作用:本研究对 CKD 的免疫改变和基因表达谱进行了全面分析。结论:本研究全面分析了 CKD 的免疫改变和基因表达谱,确定的诊断基因和构建的提名图模型为 CKD 诊断提供了有效工具。RGS1、IL4I1、NR4A3 和 SOCS3 作为 CKD 的潜在治疗靶点,其免疫调节作用值得进一步研究。
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来源期刊
Pharmacogenomics & Personalized Medicine
Pharmacogenomics & Personalized Medicine Biochemistry, Genetics and Molecular Biology-Molecular Medicine
CiteScore
3.30
自引率
5.30%
发文量
110
审稿时长
16 weeks
期刊介绍: Pharmacogenomics and Personalized Medicine is an international, peer-reviewed, open-access journal characterizing the influence of genotype on pharmacology leading to the development of personalized treatment programs and individualized drug selection for improved safety, efficacy and sustainability. In particular, emphasis will be given to: Genomic and proteomic profiling Genetics and drug metabolism Targeted drug identification and discovery Optimizing drug selection & dosage based on patient''s genetic profile Drug related morbidity & mortality intervention Advanced disease screening and targeted therapeutic intervention Genetic based vaccine development Patient satisfaction and preference Health economic evaluations Practical and organizational issues in the development and implementation of personalized medicine programs.
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