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pmid:39430588
Exploring the therapeutic potential of "Tianyu" medicine pair in rheumatoid arthritis: an integrated study combining LC-MS/MS, bioinformatics, network pharmacology, and experimental validation.
Lu Tang · Dingyuan Guo · Dongye Jia · Songlan Piao · Chunqiu Fang · Yueya Zhu · Yinghang Wang · Zhi Pan
Frontiers in medicine · 2024 doi:10.3389/fmed.2024.1475239
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Complete file
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In advanced stages, RA can result in joint deformities and loss of function, severely impacting patients' quality of life. The \"Tianyu\" pair (TYP) is a traditional Chinese medicine formulation developed from clinical experience and has shown some effectiveness in treating RA. However, its role in the complex biological mechanisms underlying RA remains unclear and warrants further investigation.\nMethods: We obtained gene sequencing data of synovial tissues from both RA patients and healthy individuals using two gene microarrays, GSE77298 and GSE55235, from the GEO database. Through an integrated approach involving bioinformatics, machine learning, and network pharmacology, we identified the core molecular targets of the \"Tianyu\" medicine pair (TYP) for RA treatment. Liquid chromatography-mass spectrometry was then employed to analyze the chemical components of TYP. To validate our findings, we conducted animal experiments with Wistar rats, comparing histopathological and key gene expression changes before and after TYP treatment.\nResults: Our data analysis suggests that the onset of RA may be associated with inflammation-related immune cells involved in both adaptive and innate immune responses. Potential key targets for TYP treatment in RA include AKR1B10, MMP13, FABP4, NCF1, SPP1, COL1A1, and RASGRP1. Among the components of TYP, Kaempferol, Quercetin, and Salidroside were identified as key, with MMP13 and NCF1 showing the strongest binding affinity to these compounds. Animal experiments confirmed the findings from bioinformatics and network pharmacology, validating the key targets and therapeutic effects of TYP in treating RA.\nConclusion: Our study reveals that TYP has potential clinical value in the treatment of rheumatoid arthritis. This research enhances our understanding of RA's pathogenesis and provides insight into potential therapeutic mechanisms.", "claims": [{"id": "claim-method", "statement": "We applied Least Absolute Shrinkage and Selection Operator (LASSO) analysis and Random Forest (RF) analysis to the key genes identified in Section 2.3, using the R packages “Glmnet” and “RandomForest,” respectively.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39430588", "locator": "sections[8] \"Machine learning\" paragraph 2", "quote": "We applied Least Absolute Shrinkage and Selection Operator (LASSO) analysis and Random Forest (RF) analysis to the key genes identified in Section 2.3, using the R packages “Glmnet” and “RandomForest,” respectively."}], "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:39430588", "locator": "sections[35] \"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": "LASSO and random forest analyses identified AKR1B10, FABP4, MMP13, SPP1, and NCF1 as five key genes for rheumatoid arthritis.", "kind": "quantitative", "target": {"value": 95.0, "unit": "per cent", "tolerance": null, "source": "The Area Under the Curve (AUC) and the 95% confidence interval (CI) for these genes were calculated in the LASSO regression analysis, with the resulting Receiver Operating Characteristic (ROC) curve showing an AUC of 0.995 (95% CI: 0.985–1.000) (Figures 4D,E)."}, "evidence": [{"asset": "source:pmid:39430588", "locator": "sections[23] \"Identification of candidate hub genes via machine learning\" paragraph 4", "quote": "By intersecting the results from both the LASSO and RF analyses, we identified five key genes: AKR1B10, FABP4, MMP13, SPP1, and NCF1 (Figure 4G)."}, {"asset": "source:pmid:39430588", "locator": "sections[23] \"Identification of candidate hub genes via machine learning\" paragraph 2", "quote": "The Area Under the Curve (AUC) and the 95% confidence interval (CI) for these genes were calculated in the LASSO regression analysis, with the resulting Receiver Operating Characteristic (ROC) curve showing an AUC of 0.995 (95% CI: 0.985–1.000) (Figures 4D,E)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "LASSO and random forest analyses identified AKR1B10, FABP4, MMP13, SPP1, and NCF1 as five key genes for rheumatoid arthritis.", "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:39430588"], "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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