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pmid:40417081
Therapeutic gene targets and epigenetic modifications in rheumatoid arthritis: Insights from MTX, JAK inhibitors, and LLDT-8.
Jianan Zhao · Yunshen Li · Runrun Zhang · Yu Shan · Chenyang Song · Yaxin Cheng · Yiming Shi · Yixin Zheng · Fuyu Zhao · Junyu Fan · Cen Chang · Yuejuan Zheng · Dongyi He
Biochemistry and biophysics reports · 2025 doi:10.1016/j.bbrep.2025.102038
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Experiment
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Complete file
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RNA sequencing identified differentially expressed genes (DEGs) under each treatment. The intersection of differentially expressed genes with trend analysis identified as potential drug target genes. Functional enrichment analyses were performed to explore associated pathways. Methylation changes in key genes were examined using the GEO database (GSE46364), and clinical relevance was evaluated using RA patient data from the PEAC database.\nResults: Compared to the osteoarthritis FLS group, the RA FLS group showed significant differences in 5095 genes (3300 upregulated and 1795 downregulated). After JAK inhibitor treatment, 3795 significant differential genes were identified in RA FLS (1733 upregulated and 2062 downregulated). MTX treatment resulted in 3195 significant differential genes (2171 upregulated and 1023 downregulated), while LLDT-8 treatment revealed 12,993 significant differential genes (7801 upregulated and 5192 downregulated). Following trend analysis and de-duplication, we identified 45 important drug target genes that exhibited significant correlations with common clinical indices in RA patients. Among these, GALNT9 and CCNF showed notable alterations in both methylation and expression levels, suggesting potential roles in RA pathogenesis and drug response. Functional enrichment highlighted critical pathways, including cell cycle, FoxO, and p53 signaling, which are implicated in FLS regulation and RA progression.\nConclusion: This study highlights therapeutic gene targets and their epigenetic modifications as potential mechanisms underlying the effects of MTX, JAK inhibitors, and LLDT-8 in RA treatment. GALNT9 and CCNF emerge as promising candidates for further exploration in targeted therapeutic strategies.", "claims": [{"id": "claim-method", "statement": "To identify potential target genes of the drugs, trend analysis was performed on all genes using the Mfuzz package [23].", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40417081", "locator": "sections[6] \"Trend analysis\" paragraph 2", "quote": "To identify potential target genes of the drugs, trend analysis was performed on all genes using the Mfuzz package [23]."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The data that has been used is confidential.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40417081", "locator": "sections[24] \"Data availability\" paragraph 2", "quote": "The data that has been used is confidential."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Following trend analysis and de-duplication, 45 important drug target genes exhibiting significant correlations with common clinical indices in RA patients were identified.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:40417081", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Following trend analysis and de-duplication, we identified 45 important drug target genes that exhibited significant correlations with common clinical indices in RA patients."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Following trend analysis and de-duplication, 45 important drug target genes exhibiting significant correlations with common clinical indices in RA patients were identified.", "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:40417081"], "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:GSE46364"], "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:GSE46364"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:40417081", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1016/j.bbrep.2025.102038", "sha256": "sha256:dcef2921de4db4112dee0e7e49b37e459049575c55a9e25c1e747b677223e328", "bytes": 36385, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1016/j.bbrep.2025.102038", "version": null, "note": "Full article fetched from PMC; 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