Identification of novel immune-related biomarker and therapeutic drugs in Parkinson disease via integrated bioinformatics analysis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37543820", "kind": "science-record", "revision": 4, "content_hash": "sha256:a0b12a2d06c374751f733f1d3a6e60bee9ae9e3e9fac6756c562768273788162", "created_at": "2026-11-01T19:02:19Z", "updated_at": "2026-11-01T22:52:27Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of novel immune-related biomarker and therapeutic drugs in Parkinson disease via integrated bioinformatics analysis.", "doi": "10.1097/MD.0000000000034456", "pmid": "37543820", "authors": ["Xiaoxia Yang", "Zhiyun Wang"], "journal": "Medicine", "year": 2023, "abstract": "BACKGROUND: The present study was designed to identify immune-related biomarker and candidate drugs for Parkinson disease (PD) by weighted gene co-expression network analysis.\nMETHODS: Differentially expressed genes were identified in PD and healthy samples in the Gene Expression Omnibus (GEO) database. Besides, immune-related genes were obtained from the immunology database. Then, a co-expression network was constructed by the weighted gene co-expression network analysis package. Diagnostic model for PD was constructed by Lasso and multivariate Cox regression. Furthermore, differentially expressed genes (DEGs) were used to establish PPI and competing endogenous RNA (ceRNA) networks. Functional enrichment and pathway analysis were performed. Drug-hub gene interaction analysis was performed via DGIdb database.\nRESULTS: PD samples and normal samples were found to have 220 upregulated genes and 216 downregulated genes in the GSE6613 dataset. The differentially expressed genes contained 50 immune-related genes, with 40 upregulated genes and 10 downregulated genes. We obtained 7 hub genes by intersecting the DEGs and candidate hub genes. As potential diagnostic markers, 2 immune-related DEGs were identified among the 7 hub genes. According to functional enrichment analysis, these DEGs were mainly enriched in immune response, inflammatory response, and cytokine-cytokine receptor interactions. Totally, we obtained 182 drug-gene interaction pairs in Drug-Gene Interaction database (DGIdb).\nCONCLUSION: Our results revealed crucial genes and candidate drugs for PD patients and deepen our understanding of the molecular mechanisms involved in PD.", "claims": [{"id": "claim-method", "statement": "In the experimental group GSE6613 data, to reduce the number of genes in the risk model, the Lasso (Least absolute shrinkage and selection operator, Tibshirani [1996]) method was used. Finally, the immune-related diagnostic model was constructed using a multivariate Cox regression model and feature genes were used to construct the diagnostic model. Receiver operating characteristic (ROC) analysis was performed using the R package “pROC (version 1.15.3, https://github.com/xrobin/pROC)” to calculate the area under the curve (AUC).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37543820", "locator": "sections[6] \"2.4. Construction and validation of diagnostic models\" paragraph 2", "quote": "In the experimental group GSE6613 data, to reduce the number of genes in the risk model, the Lasso (Least absolute shrinkage and selection operator, Tibshirani [1996]) method was used. Finally, the immune-related diagnostic model was constructed using a multivariate Cox regression model and feature genes were used to construct the diagnostic model. Receiver operating characteristic (ROC) analysis was performed using the R package “pROC (version 1.15.3, https://github.com/xrobin/pROC)” to calculate the area under the curve (AUC)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets generated during and/or analyzed during the current study are publicly available.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37543820", "locator": "sections[22] \"fn-group\" paragraph 1", "quote": "The datasets generated during and/or analyzed during the current study are publicly available."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The LILRB3- and CSF3R-based diagnostic model achieved a diagnostic efficiency of 0.75 in GSE6613 and an AUC of 0.792 in the GSE72267 validation dataset.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:37543820", "locator": "sections[13] \"3.3. Construction and validation of Parkinson diagnostic models\" paragraph 2", "quote": "Risk Score = (0.07*LILRB3) + (0.15*CSF3R)."}, {"asset": "source:pmid:37543820", "locator": "sections[13] \"3.3. Construction and validation of Parkinson diagnostic models\" paragraph 2", "quote": "We also used the ROC curve to evaluate the accuracy and specificity of the risk model, and it showed a diagnostic efficiency of 0.75, and the expression of both characteristic genes was upregulated, as shown in Figure 4D–F."}, {"asset": "source:pmid:37543820", "locator": "sections[13] \"3.3. Construction and validation of Parkinson diagnostic models\" paragraph 2", "quote": "And in the validation group GSE72267, the AUC area of this model reached 0.792, and the expression of 2 characteristic genes were also upregulated, as shown in Figure 5."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The LILRB3- and CSF3R-based diagnostic model achieved a diagnostic efficiency of 0.75 in GSE6613 and an AUC of 0.792 in the GSE72267 validation dataset.", "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:37543820"], "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:GSE6613"], "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:GSE6613"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:37543820", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1097/MD.0000000000034456", "sha256": 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