Integrative bioinformatics and machine learning identify iron metabolism genes MAP4, GPT, and HIRIP3 as diagnostic biomarkers and therapeutic targets in Alzheimer’s disease
BackgroundAlzheimer’s disease (AD) is a progressive neurodegenerative disorder characterized by cognitive decline, memory impairment, and the accumulation of pathological markers such as amyloid-beta plaques and neurofibrillary tangles. Recent evidence suggests a role for dysregulated iron metabolis...
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Frontiers Media S.A.
2025-06-01
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| Series: | Frontiers in Cellular Neuroscience |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fncel.2025.1610682/full |
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| author | Xiaoqiong An Xiangguang Zeng Zhenzhen Yi Manni Cao Yijia Wang Wenfeng Yu Wenfeng Yu Zhenkui Ren Zhenkui Ren |
| author_facet | Xiaoqiong An Xiangguang Zeng Zhenzhen Yi Manni Cao Yijia Wang Wenfeng Yu Wenfeng Yu Zhenkui Ren Zhenkui Ren |
| author_sort | Xiaoqiong An |
| collection | DOAJ |
| description | BackgroundAlzheimer’s disease (AD) is a progressive neurodegenerative disorder characterized by cognitive decline, memory impairment, and the accumulation of pathological markers such as amyloid-beta plaques and neurofibrillary tangles. Recent evidence suggests a role for dysregulated iron metabolism in the pathogenesis of AD, although the precise molecular mechanisms remain largely undefined.Materials and methodsTo address the role of iron metabolism in AD, we utilized an integrative bioinformatics approach that combines weighted gene co-expression network analysis (WGCNA) with machine learning techniques, including LASSO regression and Generalized Linear Models (GLM), to identify hub genes associated with AD. We used transcriptomic data derived from postmortem prefrontal cortex samples (GSE132903, comprising 97 AD cases and 98 controls). To assess changes in the immune microenvironment, single-sample gene set enrichment analysis (ssGSEA) was employed. Furthermore, pathway enrichment analysis and gene set variation analysis (GSVA) were performed to uncover the underlying biological mechanisms driving these alterations. Protein validation was carried out in APP/PS1 transgenic mice through Western blotting.ResultsThree genes related to iron metabolism—MAP4, GPT, and HIRIP3—are identified as strong biomarkers. The GLM classifier showed high diagnostic accuracy (AUC=0.879). AD samples had increased immune activity, with more M1 macrophages and neutrophils, indicating neuroinflammation. MAP4 and GPT were linked to Notch signaling and metabolic issues. In APP/PS1 mice, MAP4 decreased, while GPT and HIRIP3 increased.DiscussionThis analysis highlights these genes as diagnostic biomarkers and therapeutic targets, connecting iron balance, neuroinflammation, and metabolic problems in AD. The immune profile suggests potential for immunomodulatory treatments, enhancing understanding of AD and aiding precision diagnostics and therapies. |
| format | Article |
| id | doaj-art-dcd13601caa4443284ace8eeb6ab1af3 |
| institution | Kabale University |
| issn | 1662-5102 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Cellular Neuroscience |
| spelling | doaj-art-dcd13601caa4443284ace8eeb6ab1af32025-08-20T03:26:15ZengFrontiers Media S.A.Frontiers in Cellular Neuroscience1662-51022025-06-011910.3389/fncel.2025.16106821610682Integrative bioinformatics and machine learning identify iron metabolism genes MAP4, GPT, and HIRIP3 as diagnostic biomarkers and therapeutic targets in Alzheimer’s diseaseXiaoqiong An0Xiangguang Zeng1Zhenzhen Yi2Manni Cao3Yijia Wang4Wenfeng Yu5Wenfeng Yu6Zhenkui Ren7Zhenkui Ren8Department of Laboratory Medicine, The Second People’s Hospital of Guizhou Province, Guiyang, ChinaKey Laboratory of Molecular Biology, Guizhou Medical University, Guiyang, ChinaDepartment of Laboratory Medicine, Qianxinan People’s Hospital, Xingyi, ChinaCenter for Tissue Engineering and Stem Cell Research, Guizhou Medical University, Guiyang, ChinaKey Laboratory of Molecular Biology, Guizhou Medical University, Guiyang, ChinaKey Laboratory of Molecular Biology, Guizhou Medical University, Guiyang, ChinaKey Laboratory of Human Brain Bank for Functions and Diseases of Department of Education of Guizhou Province, Guizhou Medical University, Guiyang, ChinaDepartment of Laboratory Medicine, The Second People’s Hospital of Guizhou Province, Guiyang, ChinaDepartment of Laboratory Medicine, Qianxinan People’s Hospital, Xingyi, ChinaBackgroundAlzheimer’s disease (AD) is a progressive neurodegenerative disorder characterized by cognitive decline, memory impairment, and the accumulation of pathological markers such as amyloid-beta plaques and neurofibrillary tangles. Recent evidence suggests a role for dysregulated iron metabolism in the pathogenesis of AD, although the precise molecular mechanisms remain largely undefined.Materials and methodsTo address the role of iron metabolism in AD, we utilized an integrative bioinformatics approach that combines weighted gene co-expression network analysis (WGCNA) with machine learning techniques, including LASSO regression and Generalized Linear Models (GLM), to identify hub genes associated with AD. We used transcriptomic data derived from postmortem prefrontal cortex samples (GSE132903, comprising 97 AD cases and 98 controls). To assess changes in the immune microenvironment, single-sample gene set enrichment analysis (ssGSEA) was employed. Furthermore, pathway enrichment analysis and gene set variation analysis (GSVA) were performed to uncover the underlying biological mechanisms driving these alterations. Protein validation was carried out in APP/PS1 transgenic mice through Western blotting.ResultsThree genes related to iron metabolism—MAP4, GPT, and HIRIP3—are identified as strong biomarkers. The GLM classifier showed high diagnostic accuracy (AUC=0.879). AD samples had increased immune activity, with more M1 macrophages and neutrophils, indicating neuroinflammation. MAP4 and GPT were linked to Notch signaling and metabolic issues. In APP/PS1 mice, MAP4 decreased, while GPT and HIRIP3 increased.DiscussionThis analysis highlights these genes as diagnostic biomarkers and therapeutic targets, connecting iron balance, neuroinflammation, and metabolic problems in AD. The immune profile suggests potential for immunomodulatory treatments, enhancing understanding of AD and aiding precision diagnostics and therapies.https://www.frontiersin.org/articles/10.3389/fncel.2025.1610682/fullAlzheimer’s diseaseiron metabolismGPTMAP4HIRIP3machine learning |
| spellingShingle | Xiaoqiong An Xiangguang Zeng Zhenzhen Yi Manni Cao Yijia Wang Wenfeng Yu Wenfeng Yu Zhenkui Ren Zhenkui Ren Integrative bioinformatics and machine learning identify iron metabolism genes MAP4, GPT, and HIRIP3 as diagnostic biomarkers and therapeutic targets in Alzheimer’s disease Frontiers in Cellular Neuroscience Alzheimer’s disease iron metabolism GPT MAP4 HIRIP3 machine learning |
| title | Integrative bioinformatics and machine learning identify iron metabolism genes MAP4, GPT, and HIRIP3 as diagnostic biomarkers and therapeutic targets in Alzheimer’s disease |
| title_full | Integrative bioinformatics and machine learning identify iron metabolism genes MAP4, GPT, and HIRIP3 as diagnostic biomarkers and therapeutic targets in Alzheimer’s disease |
| title_fullStr | Integrative bioinformatics and machine learning identify iron metabolism genes MAP4, GPT, and HIRIP3 as diagnostic biomarkers and therapeutic targets in Alzheimer’s disease |
| title_full_unstemmed | Integrative bioinformatics and machine learning identify iron metabolism genes MAP4, GPT, and HIRIP3 as diagnostic biomarkers and therapeutic targets in Alzheimer’s disease |
| title_short | Integrative bioinformatics and machine learning identify iron metabolism genes MAP4, GPT, and HIRIP3 as diagnostic biomarkers and therapeutic targets in Alzheimer’s disease |
| title_sort | integrative bioinformatics and machine learning identify iron metabolism genes map4 gpt and hirip3 as diagnostic biomarkers and therapeutic targets in alzheimer s disease |
| topic | Alzheimer’s disease iron metabolism GPT MAP4 HIRIP3 machine learning |
| url | https://www.frontiersin.org/articles/10.3389/fncel.2025.1610682/full |
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