狼疮性肾炎的血红素代谢相关基因诊断和免疫学特征的开发和验证。

IF 2.9 3区医学 Q2 MEDICAL LABORATORY TECHNOLOGY

Clinica Chimica Acta Pub Date : 2025-09-19 DOI:10.1016/j.cca.2025.120611

Fang Wu , Beiyuan Chi , Jie Chang

{"title":"狼疮性肾炎的血红素代谢相关基因诊断和免疫学特征的开发和验证。","authors":"Fang Wu , Beiyuan Chi , Jie Chang","doi":"10.1016/j.cca.2025.120611","DOIUrl":null,"url":null,"abstract":"<div><h3>Background</h3><div>Lupus nephritis (LN) is one of the most common and severe complications of systemic lupus erythematosus (SLE). Heme metabolism, a critical component of energy metabolism and redox homeostasis, has been strongly implicated in the pathogenesis and progression of various tissue diseases. This study aims to develop accurate and effective diagnostic biomarkers for LN based on heme metabolism-related genes (HMGs) to enhance early diagnosis and precision treatment of LN.</div></div><div><h3>Methods</h3><div>This study is based on transcriptomic data and clinical information from LN patients (human kidney tissue samples) in the GEO database, diagnostic genes for LN were identified through differential analysis, weighted gene co-expression network analysis (WGCNA), and least absolute shrinkage and selection operator (LASSO) regression analysis. Further, single-gene gene set enrichment analysis (GSEA) investigated the potential biological functions and signaling pathways associated with diagnostic genes in LN. Immunocellular infiltration levels in LN and Control groups were assessed utilizing CIBERSORT and ssGSEA. To elucidate the regulatory mechanisms of diagnostic genes, corresponding transcription factor and microRNA regulatory networks were constructed. Additionally, based upon the expression profiles of diagnostic genes, LN patients were molecularly subtyped utilizing consensus clustering analysis.</div></div><div><h3>Results</h3><div>This study identified four diagnostic genes for LN (BTG2, CD163, UCP2, and LMO2) and constructed a diagnostic model with robust predictive performance for LN. ssGSEA immune infiltration analysis indicated that most immune related functions and immune cell infiltration levels were significantly elevated in the LN group. KEGG enrichment analysis further revealed that diagnostic genes were enriched in the Toll-like receptor signaling pathway. Through consensus clustering analysis, LN samples were divided into two molecular subtypes with significant differences.</div></div><div><h3>Conclusion</h3><div>The diagnostic model constructed drawing on HMGs can effectively distinguish LN patients and their immune characteristics, thereby providing a new perspective on the relationship between HMGs and LN.</div></div>","PeriodicalId":10205,"journal":{"name":"Clinica Chimica Acta","volume":"579 ","pages":"Article 120611"},"PeriodicalIF":2.9000,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Development and validation of a heme metabolism-related genes signature for diagnosis and immunological characterization of lupus nephritis\",\"authors\":\"Fang Wu , Beiyuan Chi , Jie Chang\",\"doi\":\"10.1016/j.cca.2025.120611\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Background</h3><div>Lupus nephritis (LN) is one of the most common and severe complications of systemic lupus erythematosus (SLE). Heme metabolism, a critical component of energy metabolism and redox homeostasis, has been strongly implicated in the pathogenesis and progression of various tissue diseases. This study aims to develop accurate and effective diagnostic biomarkers for LN based on heme metabolism-related genes (HMGs) to enhance early diagnosis and precision treatment of LN.</div></div><div><h3>Methods</h3><div>This study is based on transcriptomic data and clinical information from LN patients (human kidney tissue samples) in the GEO database, diagnostic genes for LN were identified through differential analysis, weighted gene co-expression network analysis (WGCNA), and least absolute shrinkage and selection operator (LASSO) regression analysis. Further, single-gene gene set enrichment analysis (GSEA) investigated the potential biological functions and signaling pathways associated with diagnostic genes in LN. Immunocellular infiltration levels in LN and Control groups were assessed utilizing CIBERSORT and ssGSEA. To elucidate the regulatory mechanisms of diagnostic genes, corresponding transcription factor and microRNA regulatory networks were constructed. Additionally, based upon the expression profiles of diagnostic genes, LN patients were molecularly subtyped utilizing consensus clustering analysis.</div></div><div><h3>Results</h3><div>This study identified four diagnostic genes for LN (BTG2, CD163, UCP2, and LMO2) and constructed a diagnostic model with robust predictive performance for LN. ssGSEA immune infiltration analysis indicated that most immune related functions and immune cell infiltration levels were significantly elevated in the LN group. KEGG enrichment analysis further revealed that diagnostic genes were enriched in the Toll-like receptor signaling pathway. Through consensus clustering analysis, LN samples were divided into two molecular subtypes with significant differences.</div></div><div><h3>Conclusion</h3><div>The diagnostic model constructed drawing on HMGs can effectively distinguish LN patients and their immune characteristics, thereby providing a new perspective on the relationship between HMGs and LN.</div></div>\",\"PeriodicalId\":10205,\"journal\":{\"name\":\"Clinica Chimica Acta\",\"volume\":\"579 \",\"pages\":\"Article 120611\"},\"PeriodicalIF\":2.9000,\"publicationDate\":\"2025-09-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Clinica Chimica Acta\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0009898125004905\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"MEDICAL LABORATORY TECHNOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Clinica Chimica Acta","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0009898125004905","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MEDICAL LABORATORY TECHNOLOGY","Score":null,"Total":0}

引用次数: 0

摘要

背景：狼疮肾炎（LN）是系统性红斑狼疮（SLE）最常见、最严重的并发症之一。血红素代谢是能量代谢和氧化还原稳态的重要组成部分，与各种组织疾病的发病和进展密切相关。本研究旨在基于血红素代谢相关基因（hmg）开发准确有效的LN诊断生物标志物，以提高LN的早期诊断和精准治疗。方法：本研究基于GEO数据库中LN患者（人肾组织样本）的转录组学数据和临床信息，通过差异分析、加权基因共表达网络分析（WGCNA）和最小绝对收缩和选择算子（LASSO）回归分析鉴定LN的诊断基因。此外，单基因基因集富集分析（GSEA）研究了LN中与诊断基因相关的潜在生物学功能和信号通路。利用CIBERSORT和ssGSEA评估LN组和对照组的免疫细胞浸润水平。为了阐明诊断基因的调控机制，构建了相应的转录因子和microRNA调控网络。此外，根据诊断基因的表达谱，利用共识聚类分析对LN患者进行分子分型。结果：本研究确定了LN的4个诊断基因（BTG2、CD163、UCP2和LMO2），并构建了具有可靠预测性能的LN诊断模型。ssGSEA免疫浸润分析显示LN组大部分免疫相关功能和免疫细胞浸润水平显著升高。KEGG富集分析进一步表明，诊断基因富集于toll样受体信号通路。通过一致聚类分析，LN样本分为两种差异显著的分子亚型。结论：利用hmg构建的诊断模型能有效区分LN患者及其免疫特征，为研究hmg与LN的关系提供了新的视角。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Development and validation of a heme metabolism-related genes signature for diagnosis and immunological characterization of lupus nephritis

Background

Lupus nephritis (LN) is one of the most common and severe complications of systemic lupus erythematosus (SLE). Heme metabolism, a critical component of energy metabolism and redox homeostasis, has been strongly implicated in the pathogenesis and progression of various tissue diseases. This study aims to develop accurate and effective diagnostic biomarkers for LN based on heme metabolism-related genes (HMGs) to enhance early diagnosis and precision treatment of LN.

Methods

This study is based on transcriptomic data and clinical information from LN patients (human kidney tissue samples) in the GEO database, diagnostic genes for LN were identified through differential analysis, weighted gene co-expression network analysis (WGCNA), and least absolute shrinkage and selection operator (LASSO) regression analysis. Further, single-gene gene set enrichment analysis (GSEA) investigated the potential biological functions and signaling pathways associated with diagnostic genes in LN. Immunocellular infiltration levels in LN and Control groups were assessed utilizing CIBERSORT and ssGSEA. To elucidate the regulatory mechanisms of diagnostic genes, corresponding transcription factor and microRNA regulatory networks were constructed. Additionally, based upon the expression profiles of diagnostic genes, LN patients were molecularly subtyped utilizing consensus clustering analysis.

Results

This study identified four diagnostic genes for LN (BTG2, CD163, UCP2, and LMO2) and constructed a diagnostic model with robust predictive performance for LN. ssGSEA immune infiltration analysis indicated that most immune related functions and immune cell infiltration levels were significantly elevated in the LN group. KEGG enrichment analysis further revealed that diagnostic genes were enriched in the Toll-like receptor signaling pathway. Through consensus clustering analysis, LN samples were divided into two molecular subtypes with significant differences.

Conclusion

The diagnostic model constructed drawing on HMGs can effectively distinguish LN patients and their immune characteristics, thereby providing a new perspective on the relationship between HMGs and LN.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

Clinica Chimica Acta 医学-医学实验技术

CiteScore

10.10

自引率

2.00%

发文量

1268

审稿时长

23 days

期刊介绍： The Official Journal of the International Federation of Clinical Chemistry and Laboratory Medicine (IFCC) Clinica Chimica Acta is a high-quality journal which publishes original Research Communications in the field of clinical chemistry and laboratory medicine, defined as the diagnostic application of chemistry, biochemistry, immunochemistry, biochemical aspects of hematology, toxicology, and molecular biology to the study of human disease in body fluids and cells. The objective of the journal is to publish novel information leading to a better understanding of biological mechanisms of human diseases, their prevention, diagnosis, and patient management. Reports of an applied clinical character are also welcome. Papers concerned with normal metabolic processes or with constituents of normal cells or body fluids, such as reports of experimental or clinical studies in animals, are only considered when they are clearly and directly relevant to human disease. Evaluation of commercial products have a low priority for publication, unless they are novel or represent a technological breakthrough. Studies dealing with effects of drugs and natural products and studies dealing with the redox status in various diseases are not within the journal''s scope. Development and evaluation of novel analytical methodologies where applicable to diagnostic clinical chemistry and laboratory medicine, including point-of-care testing, and topics on laboratory management and informatics will also be considered. Studies focused on emerging diagnostic technologies and (big) data analysis procedures including digitalization, mobile Health, and artificial Intelligence applied to Laboratory Medicine are also of interest.