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pmid:40148430

Construction and evaluation of a diagnostic model for Alzheimer's disease based on mitophagy-related genes.

Jiarui Liu · Mengyu Yan · Lihua Chen · Weihua Yu · Yang Lü

Scientific reports · 2025 doi:10.1038/s41598-025-89980-4

Paper

complete

Experiment

partial

Data

partial

Analysis

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Execution

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Record details sci_version · id · kind · revision · content_hash · schema · created_at · updated_at · generator
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pmid:40148430
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science-record
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2026-11-01T20:30:20Z
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2026-11-01T22:13:49Z
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title
Construction and evaluation of a diagnostic model for Alzheimer's disease based on mitophagy-related genes.
doi
10.1038/s41598-025-89980-4
pmid
40148430
authors array · 5 items
[0]
Jiarui Liu
[1]
Mengyu Yan
[2]
Lihua Chen
[3]
Weihua Yu
[4]
Yang Lü
journal
Scientific reports
year
2025
abstract
Alzheimer's disease (AD) is the most common cause of dementia. Mitophagy fulfills crucial functions in neurodegenerative disorders and neuronal survival but the relationship between mitophagy and AD is unclear. Mitophagy correlation scores between AD samples and control samples were calculated using single-sample GSEA (ssGSEA) based on two datasets from gene expression omnibus (GEO) database. Mitophagy-related genes (MRGs) and differentially expressed genes (DEGs) in AD screened by WGCNA and "limma" package were intersected to take common genes. These overlapping genes were further compressed and used for diagnostic modeling by adopting the recursive feature elimination (RFE) and LASSO analysis. The reliability of the diagnostic model was verified based on the receiver operating characteristic (ROC) curve. Then, a transcription factor (TF)-mRNA regulatory network of these key genes was established. Lastly, ssGSEA was employed to examine the relationship between the identified genes and cellular pathways and immune cell infiltration. AD samples had notably lower mitophagy correlation scores than control samples. A total of 12 MRGs in the module with the greatest mitophagy connection with AD patients were identified. Functional enrichment analysis revealed that the DEGs were significantly enriched in synaptic function-related pathways. Based on GSE122063, a diagnostic prediction model was created and validated using two mitophagy-related genes (YWHAZ and NDE1), showing an area under ROC curve (AUC) greater than 0.7. This confirmed that the diagnostic model had a high predictive value. The TF-mRNA network showed that four TFs, namely, FOXC1, FOXL1, HOXA5 and GATA2, were regulated by both YWHAZ and NDE1 genes. Immune infiltration analysis revealed that NDE1 promoted the infiltration of most immune cells, while YWHAZ mainly inhibited the infiltration of most immune cells. The current findings improved our understanding of mitophagy in AD, contributing to future research and treatment development in AD.
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Then, the R package “timeROC”32 was employed to compute the AUC and show time-dependent receiver operating characteristic (ROC) curves to evaluate the efficacy of the biomarkers in differentiating AD from control samples in the training set GSE132903 and the test set GSE122063.
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sections[6] "Screening MRGs-related feature genes to develop a diagnostic model and validation" paragraph 2
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Then, the R package “timeROC”32 was employed to compute the AUC and show time-dependent receiver operating characteristic (ROC) curves to evaluate the efficacy of the biomarkers in differentiating AD from control samples in the training set GSE132903 and the test set GSE122063.
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The datasets generated and/or analyzed during the current study are available in the [GSE122063] repository, [https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc = GSE122063] and [GSE132903] repository, [https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc = GSE132903].
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sections[26] "Data availability" paragraph 2
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The datasets generated and/or analyzed during the current study are available in the [GSE122063] repository, [https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc = GSE122063] and [GSE132903] repository, [https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc = GSE132903].
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Based on GSE122063, a diagnostic prediction model was created and validated using two mitophagy-related genes (YWHAZ and NDE1), showing an area under ROC curve (AUC) greater than 0.7.
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Based on GSE122063, a diagnostic prediction model was created and validated using two mitophagy-related genes (YWHAZ and NDE1), showing an area under ROC curve (AUC) greater than 0.7.
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Based on GSE122063, a diagnostic prediction model was created and validated using two mitophagy-related genes (YWHAZ and NDE1), showing an area under ROC curve (AUC) greater than 0.7.
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Receiver operating characteristic (ROC) curve analysis using the R package "timeROC" to compute AUC
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Then, the R package “timeROC”32 was employed to compute the AUC and show time-dependent receiver operating characteristic (ROC) curves to evaluate the efficacy of the biomarkers in differentiating AD from control samples in the training set GSE132903 and the test set GSE122063.
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Automated Assays Laravel application and simulated adapters
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The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: wilcoxon.
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The datasets generated and/or analyzed during the current study are available in the [GSE122063] repository, [https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc = GSE122063] and [GSE132903] repository, [https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc = GSE132903].
basis
The paper names GSE132903, GSE122063 in GEO gene expression omnibus and the accession resolves in the repository. No dataset was downloaded and no licence was read.
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Complete file
{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:40148430", "kind": "science-record", "revision": 3, "content_hash": "sha256:87a48937331ab06bb913dae780f2a0cd7b04c1938a6763dde1604ec73e9765c0", "created_at": "2026-11-01T20:30:20Z", "updated_at": "2026-11-01T22:13:49Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Construction and evaluation of a diagnostic model for Alzheimer's disease based on mitophagy-related genes.", "doi": "10.1038/s41598-025-89980-4", "pmid": "40148430", "authors": ["Jiarui Liu", "Mengyu Yan", "Lihua Chen", "Weihua Yu", "Yang Lü"], "journal": "Scientific reports", "year": 2025, "abstract": "Alzheimer's disease (AD) is the most common cause of dementia. Mitophagy fulfills crucial functions in neurodegenerative disorders and neuronal survival but the relationship between mitophagy and AD is unclear. Mitophagy correlation scores between AD samples and control samples were calculated using single-sample GSEA (ssGSEA) based on two datasets from gene expression omnibus (GEO) database. Mitophagy-related genes (MRGs) and differentially expressed genes (DEGs) in AD screened by WGCNA and \"limma\" package were intersected to take common genes. These overlapping genes were further compressed and used for diagnostic modeling by adopting the recursive feature elimination (RFE) and LASSO analysis. The reliability of the diagnostic model was verified based on the receiver operating characteristic (ROC) curve. Then, a transcription factor (TF)-mRNA regulatory network of these key genes was established. Lastly, ssGSEA was employed to examine the relationship between the identified genes and cellular pathways and immune cell infiltration. AD samples had notably lower mitophagy correlation scores than control samples. A total of 12 MRGs in the module with the greatest mitophagy connection with AD patients were identified. Functional enrichment analysis revealed that the DEGs were significantly enriched in synaptic function-related pathways. Based on GSE122063, a diagnostic prediction model was created and validated using two mitophagy-related genes (YWHAZ and NDE1), showing an area under ROC curve (AUC) greater than 0.7. This confirmed that the diagnostic model had a high predictive value. The TF-mRNA network showed that four TFs, namely, FOXC1, FOXL1, HOXA5 and GATA2, were regulated by both YWHAZ and NDE1 genes. Immune infiltration analysis revealed that NDE1 promoted the infiltration of most immune cells, while YWHAZ mainly inhibited the infiltration of most immune cells. 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