{"title":"揭示阿尔茨海默病中锰代谢相关生物标志物：对诊断和治疗靶点的见解","authors":"Qianqian Mou, Li Zhao, Huiling Niu, Hongyan Li, Haiqing Jin, Wenjing Wang, Wenjing Tian, Nana Feng, Bing Wu","doi":"10.1016/j.cca.2025.120676","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Alzheimer's disease (AD), a neurodegenerative disorder with multifactorial etiologies, has been closely associated with disturbances in manganese metabolism. However, its specific biomarkers remain insufficiently characterized. This study aimed to identify manganese metabolism-related biomarkers implicated in AD.</p><p><strong>Methods: </strong>Differentially expressed genes (DEGs) in AD were extracted from the GSE63060 dataset. A total of 1399 manganese metabolism-related genes were curated from the literature. Weighted gene co-expression network analysis was applied to isolate AD-related module genes. The intersection of these three datasets produced manganese metabolism-related DEGs (MMR-DEGs). Candidate biomarkers were subsequently screened through machine learning approaches and validated by expression analyses. Bioinformatics investigations, including nomogram modeling, immune infiltration analysis, gene set enrichment analysis (GSEA), gene-gene interaction (GGI) network construction, molecular regulatory network mapping, and drug prediction, were conducted to delineate potential functions. Finally, quantitative reverse transcription-PCR (qRT-PCR) was performed to verify mRNA expression levels of the biomarkers.</p><p><strong>Results: </strong>Nine MMR-DEGs were identified, among which four genes (OPTN, HSP90AA1, NDUFS4, and HSPE1) demonstrated favorable predictive performance as biomarkers for AD. Immune infiltration analysis indicated a consistent negative correlation between these biomarkers and M0 macrophages. GSEA revealed predominant enrichment in translation-associated pathways. Within the molecular regulatory network, 24 transcription factors and 72 microRNAs were predicted to target these biomarkers. Additionally, 107 candidate drugs were identified as potential therapeutic agents, and 16 genes exhibited functional interactions with these biomarkers in the GGI network. Moreover, qRT-PCR confirmed that the expression of OPTN, HSP90AA1, and NDUFS4 was significantly down-regulated in AD samples, in agreement with computational predictions.</p><p><strong>Conclusions: </strong>OPTN, HSP90AA1, NDUFS4, and HSPE1 were identified as manganese metabolism-related potential biomarkers in AD. These findings may advance understanding of AD pathophysiology and may provide potential molecular targets for diagnosis and therapeutic intervention.</p>","PeriodicalId":10205,"journal":{"name":"Clinica Chimica Acta","volume":" ","pages":"120676"},"PeriodicalIF":2.9000,"publicationDate":"2025-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Unveiling manganese metabolism-related biomarkers in Alzheimer's disease: Insights into diagnosis and therapeutic targets.\",\"authors\":\"Qianqian Mou, Li Zhao, Huiling Niu, Hongyan Li, Haiqing Jin, Wenjing Wang, Wenjing Tian, Nana Feng, Bing Wu\",\"doi\":\"10.1016/j.cca.2025.120676\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Alzheimer's disease (AD), a neurodegenerative disorder with multifactorial etiologies, has been closely associated with disturbances in manganese metabolism. However, its specific biomarkers remain insufficiently characterized. This study aimed to identify manganese metabolism-related biomarkers implicated in AD.</p><p><strong>Methods: </strong>Differentially expressed genes (DEGs) in AD were extracted from the GSE63060 dataset. A total of 1399 manganese metabolism-related genes were curated from the literature. Weighted gene co-expression network analysis was applied to isolate AD-related module genes. The intersection of these three datasets produced manganese metabolism-related DEGs (MMR-DEGs). Candidate biomarkers were subsequently screened through machine learning approaches and validated by expression analyses. Bioinformatics investigations, including nomogram modeling, immune infiltration analysis, gene set enrichment analysis (GSEA), gene-gene interaction (GGI) network construction, molecular regulatory network mapping, and drug prediction, were conducted to delineate potential functions. Finally, quantitative reverse transcription-PCR (qRT-PCR) was performed to verify mRNA expression levels of the biomarkers.</p><p><strong>Results: </strong>Nine MMR-DEGs were identified, among which four genes (OPTN, HSP90AA1, NDUFS4, and HSPE1) demonstrated favorable predictive performance as biomarkers for AD. Immune infiltration analysis indicated a consistent negative correlation between these biomarkers and M0 macrophages. GSEA revealed predominant enrichment in translation-associated pathways. Within the molecular regulatory network, 24 transcription factors and 72 microRNAs were predicted to target these biomarkers. Additionally, 107 candidate drugs were identified as potential therapeutic agents, and 16 genes exhibited functional interactions with these biomarkers in the GGI network. Moreover, qRT-PCR confirmed that the expression of OPTN, HSP90AA1, and NDUFS4 was significantly down-regulated in AD samples, in agreement with computational predictions.</p><p><strong>Conclusions: </strong>OPTN, HSP90AA1, NDUFS4, and HSPE1 were identified as manganese metabolism-related potential biomarkers in AD. These findings may advance understanding of AD pathophysiology and may provide potential molecular targets for diagnosis and therapeutic intervention.</p>\",\"PeriodicalId\":10205,\"journal\":{\"name\":\"Clinica Chimica Acta\",\"volume\":\" \",\"pages\":\"120676\"},\"PeriodicalIF\":2.9000,\"publicationDate\":\"2025-10-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Clinica Chimica Acta\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1016/j.cca.2025.120676\",\"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://doi.org/10.1016/j.cca.2025.120676","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MEDICAL LABORATORY TECHNOLOGY","Score":null,"Total":0}
Unveiling manganese metabolism-related biomarkers in Alzheimer's disease: Insights into diagnosis and therapeutic targets.
Background: Alzheimer's disease (AD), a neurodegenerative disorder with multifactorial etiologies, has been closely associated with disturbances in manganese metabolism. However, its specific biomarkers remain insufficiently characterized. This study aimed to identify manganese metabolism-related biomarkers implicated in AD.
Methods: Differentially expressed genes (DEGs) in AD were extracted from the GSE63060 dataset. A total of 1399 manganese metabolism-related genes were curated from the literature. Weighted gene co-expression network analysis was applied to isolate AD-related module genes. The intersection of these three datasets produced manganese metabolism-related DEGs (MMR-DEGs). Candidate biomarkers were subsequently screened through machine learning approaches and validated by expression analyses. Bioinformatics investigations, including nomogram modeling, immune infiltration analysis, gene set enrichment analysis (GSEA), gene-gene interaction (GGI) network construction, molecular regulatory network mapping, and drug prediction, were conducted to delineate potential functions. Finally, quantitative reverse transcription-PCR (qRT-PCR) was performed to verify mRNA expression levels of the biomarkers.
Results: Nine MMR-DEGs were identified, among which four genes (OPTN, HSP90AA1, NDUFS4, and HSPE1) demonstrated favorable predictive performance as biomarkers for AD. Immune infiltration analysis indicated a consistent negative correlation between these biomarkers and M0 macrophages. GSEA revealed predominant enrichment in translation-associated pathways. Within the molecular regulatory network, 24 transcription factors and 72 microRNAs were predicted to target these biomarkers. Additionally, 107 candidate drugs were identified as potential therapeutic agents, and 16 genes exhibited functional interactions with these biomarkers in the GGI network. Moreover, qRT-PCR confirmed that the expression of OPTN, HSP90AA1, and NDUFS4 was significantly down-regulated in AD samples, in agreement with computational predictions.
Conclusions: OPTN, HSP90AA1, NDUFS4, and HSPE1 were identified as manganese metabolism-related potential biomarkers in AD. These findings may advance understanding of AD pathophysiology and may provide potential molecular targets for diagnosis and therapeutic intervention.
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.
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