Identification of the shared gene signatures and molecular mechanisms between multiple sclerosis and non-small cell lung cancer.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37251402", "kind": "science-record", "revision": 2, "content_hash": "sha256:8a60b5e5f86d0b07185e461aad9e91a8287ab3a3fff6b111b86f74bc95a3fa12", "created_at": "2026-11-01T19:01:58Z", "updated_at": "2026-11-01T21:30:53Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of the shared gene signatures and molecular mechanisms between multiple sclerosis and non-small cell lung cancer.", "doi": "10.3389/fimmu.2023.1180449", "pmid": "37251402", "authors": ["Jingyun Yang", "Xiaofei Hu", "Yu Wang", "Wenying Liu", "Mengjie Zhang", "Anmei Zhang", "Bing Ni"], "journal": "Frontiers in immunology", "year": 2023, "abstract": "Introduction: The association between multiple sclerosis (MS) and non-small cell lung cancer (NSCLC) has been the subject of investigation in clinical cohorts, yet the molecular mechanisms underpinning this relationship remain incompletely understood. To address this, our study aimed to identify shared genetic signatures, shared local immune microenvironment, and molecular mechanisms between MS and NSCLC.\nMethods: We selected multiple Gene Expression Omnibus (GEO) datasets, including GSE19188, GSE214334, GSE199460, and GSE148071, to obtain gene expression levels and clinical information from patients or mice with MS and NSCLC. We employed Weighted Gene Co-expression Network Analysis (WGCNA) to investigate co-expression networks linked to MS and NSCLC and used single-cell RNA sequencing (scRNA-seq) analysis to explore the local immune microenvironment of MS and NSCLC and identify possible shared components.\nResults: Our analysis identified the most significant shared gene in MS and NSCLC, phosphodiesterase 4A (PDE4A), and we analyzed its expression in NSCLC patients and its impact on patient prognosis, as well as its molecular mechanism. Our results demonstrated that high expression of PDE4A was associated with poor prognoses in NSCLC patients, and Gene Set Enrichment Analysis (GSEA) revealed that PDE4A is involved in immune-related pathways and has a significant regulatory effect on human immune responses. We further observed that PDE4A was closely linked to the sensitivity of several chemotherapy drugs.\nConclusion: Given the limitation of studies investigating the molecular mechanisms underlying the correlation between MS and NSCLC, our findings suggest that there are shared pathogenic processes and molecular mechanisms between these two diseases and that PDE4A represents a potential therapeutic target and immune-related biomarker for patients with both MS and NSCLC.", "claims": [{"id": "claim-method", "statement": "We selected multiple Gene Expression Omnibus (GEO) datasets, including GSE19188, GSE214334, GSE199460, and GSE148071, to obtain gene expression levels and clinical information from patients or mice with MS and NSCLC. We employed Weighted Gene Co-expression Network Analysis (WGCNA) to investigate co-expression networks linked to MS and NSCLC and used single-cell RNA sequencing (scRNA-seq) analysis to explore the local immune microenvironment of MS and NSCLC and identify possible shared components.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37251402", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "We selected multiple Gene Expression Omnibus (GEO) datasets, including GSE19188, GSE214334, GSE199460, and GSE148071, to obtain gene expression levels and clinical information from patients or mice with MS and NSCLC. We employed Weighted Gene Co-expression Network Analysis (WGCNA) to investigate co-expression networks linked to MS and NSCLC and used single-cell RNA sequencing (scRNA-seq) analysis to explore the local immune microenvironment of MS and NSCLC and identify possible shared components."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The original contributions presented in the study are included in the article/ Supplementary Material . Further inquiries can be directed to the corresponding authors.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37251402", "locator": "sections[23] \"Data availability statement\" paragraph 2", "quote": "The original contributions presented in the study are included in the article/ Supplementary Material . Further inquiries can be directed to the corresponding authors."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Finally, we obtained one shared gene, PDE4A, which might be the most important shared gene during the progression of these two diseases ( Figure 5F ).", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:37251402", "locator": "sections[17] \"Further analysis of shared molecular mechanisms between MS and NSCLC\" paragraph 4", "quote": "Finally, we obtained one shared gene, PDE4A, which might be the most important shared gene during the progression of these two diseases ( Figure 5F )."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Finally, we obtained one shared gene, PDE4A, which might be the most important shared gene during the progression of these two diseases ( Figure 5F ).", "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:37251402"], "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:GSE148071"], "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:GSE148071"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:37251402", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fimmu.2023.1180449", "sha256": "sha256:64bbd5d1a5e073aa023f3c5c49b351c62a13cb45241a7417b166a4eacc573392", "bytes": 40775, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fimmu.2023.1180449", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC10213509", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE148071", "role": "raw", "format": "geo-series", "media_type": "application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE148071", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE148071", "version": null, "note": "The original contributions presented in the study are included in the article/ Supplementary Material . 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We employed Weighted Gene Co-expression Network Analysis (WGCNA) to investigate co-expression networks linked to MS and NSCLC and used single-cell RNA sequencing (scRNA-seq) analysis to explore the local immune microenvironment of MS and NSCLC and identify possible shared components."}}], "targets": [], "gaps": ["method.reader_named_unmapped"]}, "capability": {"state": "complete", "requirements": [{"capability_id": "capability:computational_analysis", "label": "Computational analysis platform", "assay_families": ["meta_analysis", "machine_learning", "survival_analysis", "statistical_regression", "pca", "molecular_docking", "network_analysis", "differential_expression", "gwas"], "availability_state": "owned", "tier_unlocked": "R1", "candidate": "Automated Assays Laravel application and simulated adapters"}], "gaps": []}, "readiness": {"state": "complete", "tier": "R1", "blocking_axis": "none", "blocked_on": "nothing:replicable_now", "axes": {"method": {"tier": "R1", "expressible": true, "required_operations": ["pca_fit"], "missing_operations": [], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: pca."}, "capability": {"tier": "R1", "capability_id": "capability:computational_analysis", "availability_state": "owned", "basis": "The computational analysis is owned. 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