Identification of potential therapeutic targets for systemic lupus erythematosus based on GEO database analysis and Mendelian randomization analysis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39445158", "kind": "science-record", "revision": 2, "content_hash": "sha256:50e701da1b658cc8c579c655529869b3aa838c6ba11caa4b7e9b130f0b31f7ee", "created_at": "2026-11-01T20:29:42Z", "updated_at": "2026-11-01T21:34:13Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of potential therapeutic targets for systemic lupus erythematosus based on GEO database analysis and Mendelian randomization analysis.", "doi": "10.3389/fgene.2024.1454486", "pmid": "39445158", "authors": ["Aishanjiang Apaer", "Yanyan Shi", "Alimijiang Aobulitalifu", "Fujie Wen", "Adalaiti Muhetaer", "Nuermaimaiti Ajimu", "Maierhaba Sulitan", "Lei Cheng"], "journal": "Frontiers in genetics", "year": 2024, "abstract": "Background: Systemic lupus erythematosus (SLE) is a complex autoimmune disease. Current treatments mainly rely on immunosuppressants, which lack specificity and pose challenges during treatment. This study aims to deeply explore the molecular pathogenic mechanism of SLE through gene expression databases (GEO) and bioinformatics analysis methods, combined with Mendelian randomization analysis, to provide key clues for new therapeutic targets.\nMethods: In this study, the SLE-related gene chip dataset GSE65391 was selected from the GEO database, and the data were preprocessed and statistically analyzed using R language and bioinformatics tools. Differential expression analysis, weighted gene co-expression network analysis (WGCNA), GO, and KEGG enrichment analysis were used to screen differentially expressed genes (DEGs) for functional annotation and pathway localization. Furthermore, Mendelian randomization analysis was conducted to identify core genes closely related to SLE risk, and immune cell infiltration analysis and compound molecular docking studies were performed on the core gene ISG15.\nResults: The study successfully screened 3,456 DEGs and identified core gene modules highly related to SLE through WGCNA analysis, including key genes closely related to the pathogenesis of SLE, such as STAT1, DDX58, ISG15, IRF7, and IFIH1. In particular, this study found a significant positive correlation between the ISG15 gene and SLE, suggesting that it may be a potential risk factor for SLE. Additionally, through molecular docking technology, it was discovered that the ISG15 gene can effectively bind to two compounds, genistein, and flavopiridol, which have anti-inflammatory and immunosuppressive effects, respectively. This provides new potential drug targets for SLE treatment.\nDiscussion: As an immunomodulatory cytokine, ISG15 plays a crucial role in the pathogenesis of SLE. This study found that variations in the ISG15 gene may increase the risk of SLE and exacerbate inflammatory responses and tissue damage through multiple mechanisms. Furthermore, molecular docking revealed that genistein and flavopiridol can effectively bind to ISG15, offering a new approach for SLE treatment. These two compounds, with their anti-inflammatory and immunosuppressive properties, have the potential to slow the progression of SLE by influencing the expression and function of ISG15.\nConclusion: Through comprehensive bioinformatics analysis and Mendelian randomization analysis, this study deeply explored the molecular pathogenic mechanism of SLE and successfully identified ISG15 as a potential therapeutic target for SLE. Simultaneously, molecular docking technology revealed that two compounds, genistein and flavopiridol, have potential therapeutic effects with ISG15, providing new potential drugs for SLE treatment. These discoveries not only enhance our understanding of the pathogenesis of SLE but also provide important clues for developing new treatment strategies.", "claims": [{"id": "claim-method", "statement": "We employed a two-sample Mendelian randomization (MR) analysis to investigate the causal relationship between characteristic genes and the risk of systemic lupus erythematosus (SLE), using single nucleotide polymorphisms (SNPs) as instrumental variables.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39445158", "locator": "sections[10] \"MR analysis between signature genes and SLE\" paragraph 2", "quote": "We employed a two-sample Mendelian randomization (MR) analysis to investigate the causal relationship between characteristic genes and the risk of systemic lupus erythematosus (SLE), using single nucleotide polymorphisms (SNPs) as instrumental variables."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets generated and analyzed in this study are available in the GEO data repository (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE65391): GSE65391. Additionally, the datasets used for Mendelian randomization in this study can be found in the IEU Open GWAS Project repository, with the exposure ID being prot-a-1573 (https://gwas.mrcieu.ac.uk/datasets/prot-a-1573/) and the outcome ID for systemic lupus erythematosus (SLE) being finn-b-M13_SLE (https://gwas.mrcieu.ac.uk/datasets/finn-b-M13_SLE/).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39445158", "locator": "sections[25] \"Data availability statement\" paragraph 2", "quote": "The datasets generated and analyzed in this study are available in the GEO data repository (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE65391): GSE65391. Additionally, the datasets used for Mendelian randomization in this study can be found in the IEU Open GWAS Project repository, with the exposure ID being prot-a-1573 (https://gwas.mrcieu.ac.uk/datasets/prot-a-1573/) and the outcome ID for systemic lupus erythematosus (SLE) being finn-b-M13_SLE (https://gwas.mrcieu.ac.uk/datasets/finn-b-M13_SLE/)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "", "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:39445158"], "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:GSE65391"], "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:GSE65391"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:39445158", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fgene.2024.1454486", "sha256": "sha256:71ad840e51c0fbe2209ce9bfc9ff137cc73001a12c76ccb8b23bbfe65e9cd677", "bytes": 32952, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fgene.2024.1454486", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC11496559", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE65391", "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=GSE65391", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE65391", "version": null, "note": "The datasets generated and analyzed in this study are available in the GEO data repository (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE65391): GSE65391. Additionally, the datasets used for Mendelian randomization in this study can be found in the IEU Open GWAS Project repository, with the exposure ID being prot-a-1573 (https://gwas.mrcieu.ac.uk/datasets/prot-a-1573/) and the outcome ID for systemic lupus erythematosus (SLE) being finn-b-M13_SLE (https://gwas.mrcieu.ac.uk/datasets/finn-b-M13_SLE/).", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The datasets generated and analyzed in this study are available in the GEO data repository (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE65391): GSE65391. Additionally, the datasets used for Mendelian randomization in this study can be found in the IEU Open GWAS Project repository, with the exposure ID being prot-a-1573 (https://gwas.mrcieu.ac.uk/datasets/prot-a-1573/) and the outcome ID for systemic lupus erythematosus (SLE) being finn-b-M13_SLE (https://gwas.mrcieu.ac.uk/datasets/finn-b-M13_SLE/).", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM1594219", "GSM1594220", "GSM1594221", "GSM1594222", "GSM1594223", "GSM1594224", "GSM1594225", "GSM1594226", "GSM1594227", "GSM1594228", "GSM1594229", "GSM1594230", "GSM1594231", "GSM1594232", "GSM1594233", "GSM1594234", "GSM1594235", "GSM1594236", "GSM1594237", "GSM1594238", "GSM1594239", "GSM1594240", "GSM1594241", "GSM1594244", "GSM1594247", "GSM1594250", "GSM1594253", "GSM1594254", "GSM1594255", "GSM1594256", "GSM1594257", "GSM1594258", "GSM1594259", "GSM1594260", "GSM1594261", "GSM1594262", "GSM1594263", "GSM1594264", "GSM1594265", "GSM1594266", 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