Identification of ferroptosis related biomarkers and immune infiltration in Parkinson's disease by integrated bioinformatic analysis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36918862", "kind": "science-record", "revision": 3, "content_hash": "sha256:25c9634001d3f3c2517afb0fd0dcf68c44779c91372b1dfd3f53fc62a8e9e23e", "created_at": "2026-11-01T19:01:33Z", "updated_at": "2026-11-01T22:09:03Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of ferroptosis related biomarkers and immune infiltration in Parkinson's disease by integrated bioinformatic analysis.", "doi": "10.1186/s12920-023-01481-3", "pmid": "36918862", "authors": ["Na Xing", "Ziye Dong", "Qiaoli Wu", "Yufeng Zhang", "Pengcheng Kan", "Yuan Han", "Xiuli Cheng", "Yaru Wang", "Biao Zhang"], "journal": "BMC medical genomics", "year": 2023, "abstract": "BACKGROUND: Increasing evidence has indicated that ferroptosis engages in the progression of Parkinson's disease (PD). This study aimed to explore the role of ferroptosis-related genes (FRGs), immune infiltration and immune checkpoint genes (ICGs) in the pathogenesis and development of PD.\nMETHODS: The microarray data of PD patients and healthy controls (HC) from the Gene Expression Omnibus (GEO) database was downloaded. Weighted gene co-expression network analysis (WGCNA) was processed to identify the significant modules related to PD in the GSE18838 dataset. Machine learning algorithms were used to screen the candidate biomarkers based on the intersect between WGCNA, FRGs and differentially expressed genes. Enrichment analysis of GSVA, GSEA, GO, KEGG, and immune infiltration, group comparison of ICGs were also performed. Next, candidate biomarkers were validated in clinical samples by ELISA and receiver operating characteristic curve (ROC) was used to assess diagnose ability.\nRESULTS: In this study, FRGs had correlations with ICGs, immune infiltration. Then, plasma levels of LPIN1 in PD was significantly lower than that in healthy controls, while the expression of TNFAIP3 was higher in PD in comparison with HC. ROC curves showed that the area under curve (AUC) of the LPIN1 and TNFAIP3 combination was 0.833 (95% CI: 0.750-0.916). Moreover, each biomarker alone could discriminate the PD from HC (LPIN1: AUC = 0.754, 95% CI: 0.659-0.849; TNFAIP3: AUC = 0.754, 95% CI: 0.660-0.849). For detection of early PD from HC, the model of combination maintained diagnostic accuracy with an AUC of 0.831 (95% CI: 0.734-0.927), LPIN1 also performed well in distinguishing the early PD from HC (AUC = 0.817, 95% CI: 0.717-0.917). However, the diagnostic efficacy was relatively poor in distinguishing the early from middle-advanced PD patients.\nCONCLUSION: The combination model composed of LPIN1 and TNFAIP3, and each biomarker may serve as an efficient tool for distinguishing PD from HC.", "claims": [{"id": "claim-method", "statement": "Receiver operating characteristic (ROC) curves were generated to evaluate their sensitivities and specificities in distinguishing PD from the healthy controls.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36918862", "locator": "sections[11] \"Statistical analysis\" paragraph 2", "quote": "Receiver operating characteristic (ROC) curves were generated to evaluate their sensitivities and specificities in distinguishing PD from the healthy controls."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets generated and/or analyzed (GSE18838 and GSE72267) during this study are publicly available in the GEO database (https://www.ncbi.nlm.nih.gov/geo/), the original contributions presented in this study are included in the article/Supplementary material, further inquiries can be directed to the corresponding author.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36918862", "locator": "sections[29] \"Data Availability\" paragraph 2", "quote": "The datasets generated and/or analyzed (GSE18838 and GSE72267) during this study are publicly available in the GEO database (https://www.ncbi.nlm.nih.gov/geo/), the original contributions presented in this study are included in the article/Supplementary material, further inquiries can be directed to the corresponding author."}], "state": "complete"}, {"id": "claim-target-result", "statement": "ROC curves showed that the area under curve (AUC) of the LPIN1 and TNFAIP3 combination was 0.833 (95% CI: 0.750–0.916).", "kind": "quantitative", "target": {"value": 95.0, "unit": "per cent", "tolerance": null, "source": "ROC curves showed that the area under curve (AUC) of the LPIN1 and TNFAIP3 combination was 0.833 (95% CI: 0.750–0.916)."}, "evidence": [{"asset": "source:pmid:36918862", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "ROC curves showed that the area under curve (AUC) of the LPIN1 and TNFAIP3 combination was 0.833 (95% CI: 0.750–0.916)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "ROC curves showed that the area under curve (AUC) of the LPIN1 and TNFAIP3 combination was 0.833 (95% CI: 0.750–0.916).", "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:36918862"], "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. 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