Machine learning screening for Parkinson's disease-related cuproptosis-related typing development and validation and exploration of personalized drugs for cuproptosis genes.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36760248", "kind": "science-record", "revision": 4, "content_hash": "sha256:47c60412197fcde565837c676148d3f2c3324b9034f6911e32af6323d91d9cf1", "created_at": "2026-11-01T19:01:20Z", "updated_at": "2026-11-01T22:51:13Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Machine learning screening for Parkinson's disease-related cuproptosis-related typing development and validation and exploration of personalized drugs for cuproptosis genes.", "doi": "10.21037/atm-22-5756", "pmid": "36760248", "authors": ["Ji Wu", "Chengjian Qin", "Yuankun Cai", "Jiabin Zhou", "Dongyuan Xu", "Yu Lei", "Guoxing Fang", "Songshan Chai", "Nanxiang Xiong"], "journal": "Annals of translational medicine", "year": 2023, "abstract": "Background: Parkinson's disease (PD) is a common, degenerative disease of the nervous system that is characterized by the death of dopaminergic neurons in the substantia nigra densa (SNpc). There is growing evidence that copper (Cu) is involved in myelin formation and is involved in cell death through modulation of synaptic activity as well as neurotrophic factor-induced excitotoxicity.\nMethods: This study aimed to explore potential cuproptosis-related genes (CRGs) and immune infiltration patterns in PD and the development of Cu chelators relevant for PD treatment. The PD datasets GSE7621, GSE20141, and GSE49036 were downloaded from the Gene Expression Omnibus (GEO) database. The consensus clustering method was used to classify the specimens of PD. Using weighted gene co-expression network analysis (WGCNA) and random forest (RF) tree model, support vector machine (SVM) learning model, extreme gradient boosting (XGBoost) model, and general linear model (GLM) algorithms to screen disease progression-related models, the column charts were created to verify the accuracy of these CRGs in predicting PD progression. Single sample genomic enrichment analysis (ssGSEA) was used to estimate the correlation between genes associated with copper poisoning and genes associated with immune cells and immune function. Molecular docking was used to verify interactions with copper chelating agents associated with cuproptosis for PD treatment.\nResults: Through ssGSEA, we identified three copper poisoning related genes ATP7A, NFE2L2 and MTF1, which are related to immune cells in PD. We also verified that LAGASCATRIOL can bind to NFE2L2 through molecular docking. Consistent cluster analysis identified two subtypes, among which C2 subtype was just enriched in PD. And to more accurately diagnose PD progression, patients can benefit from a feature map based on these genes.\nConclusions: CRGs such as NFE2L2, MTF1, and ATP7B were identified to be associated with the pathogenesis of PD and provide a possible new direction for the treatment of PD, which needs further in-depth study.", "claims": [{"id": "claim-method", "statement": "To find differences in the expression of cuproptosis genes in the substantia nigra of PD patients compared to normal controls, CRGs were screened from the GSE49036 dataset using the limma package of R software (The R Foundation for Statistical Computing, Vienna, Austria) and CR-DEGs screening was performed on 20 PD samples and 8 control brain tissues.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36760248", "locator": "sections[5] \"Data processing and CR-DEGs screening\" paragraph 2", "quote": "To find differences in the expression of cuproptosis genes in the substantia nigra of PD patients compared to normal controls, CRGs were screened from the GSE49036 dataset using the limma package of R software (The R Foundation for Statistical Computing, Vienna, Austria) and CR-DEGs screening was performed on 20 PD samples and 8 control brain tissues."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Three tissue sequencing datasets of RNA microarray data on PD nigrostriatal samples were downloaded from the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/): GSE7621 (9 control brain samples and 16 PD brain samples), GSE20141 (8 control brain samples and 10 PD brain samples), and GSE49036 (8 control brain samples and 20 PD brain samples).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36760248", "locator": "sections[4] \"Data download\" paragraph 2", "quote": "Three tissue sequencing datasets of RNA microarray data on PD nigrostriatal samples were downloaded from the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/): GSE7621 (9 control brain samples and 16 PD brain samples), GSE20141 (8 control brain samples and 10 PD brain samples), and GSE49036 (8 control brain samples and 20 PD brain samples)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Using limma differential-expression analysis of GSE49036 nigrostriatal microarray data, the study identified three cuproptosis-related differentially expressed genes in Parkinson’s disease: ATP7B, NFE2L2, and MTF1.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:36760248", "locator": "sections[16] \"Identification of CR-DEGs in PD and correlation analysis between genes\" paragraph 2", "quote": "The GSE49036 data were normalized and CRGs were extracted using the limma package of R software and screened for DEG analysis."}, {"asset": "source:pmid:36760248", "locator": "sections[16] \"Identification of CR-DEGs in PD and correlation analysis between genes\" paragraph 3", "quote": "CR-DEGs in PD. (A) Heat maps of the 3 CR-DEGs in PD; the color of the columns represents the gene expression of the tissue samples, with red indicating high expression and green indicating low expression."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Using limma differential-expression analysis of GSE49036 nigrostriatal microarray data, the study identified three cuproptosis-related differentially expressed genes in Parkinson’s disease: ATP7B, NFE2L2, and MTF1.", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:36760248"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "complete", "blocked_on": null, "description": "The GEO series accession resolves and its sample accessions are listed. This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE20141"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "complete", "blocked_on": null, "description": "Apply the paper's statistical method to the resolved series and compare the result with the published target.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE20141"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:36760248", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.21037/atm-22-5756", "sha256": 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