Identification of Hedyotis diffusa Willd-specific mRNA-miRNA-lncRNA network in rheumatoid arthritis based on network pharmacology, bioinformatics analysis, and experimental verification.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:38491124", "kind": "science-record", "revision": 5, "content_hash": "sha256:b94f45dbe83e95345cb435ef9f8f28cce53a154d479b5ac617c57fe8529a65ff", "created_at": "2026-11-01T19:20:26Z", "updated_at": "2026-11-01T22:53:53Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of Hedyotis diffusa Willd-specific mRNA-miRNA-lncRNA network in rheumatoid arthritis based on network pharmacology, bioinformatics analysis, and experimental verification.", "doi": "10.1038/s41598-024-56880-y", "pmid": "38491124", "authors": ["Jing Jiang", "Meng Huang", "Si-Si Zhang", "Yong-Gang Wu", "Xiao-Long Li", "Hui Deng", "Xin-Yu Qili", "Jian-Lin Chen", "Yao Meng", "Wen-Kui Sun"], "journal": "Scientific reports", "year": 2024, "abstract": "Hedyotis diffusa Willd (HDW) possesses heat-clearing, detoxification, anti-cancer, and anti-inflammatory properties. However, its effects on rheumatoid arthritis (RA) remain under-researched. In this study, we identified potential targets of HDW and collected differentially expressed genes of RA from the GEO dataset GSE77298, leading to the construction of a drug-component-target-disease regulatory network. The intersecting genes underwent GO and KEGG analysis. A PPI protein interaction network was established in the STRING database. Through LASSO, RF, and SVM-RFE algorithms, we identified the core gene MMP9. Subsequent analyses, including ROC, GSEA enrichment, and immune cell infiltration, correlated core genes with RA. mRNA-miRNA-lncRNA regulatory networks were predicted using databases like TargetScan, miRTarBase, miRWalk, starBase, lncBase, and the GEO dataset GSE122616. Experimental verification in RA-FLS cells confirmed HDW's regulatory impact on core genes and their ceRNA expression. We obtained 11 main active ingredients of HDW and 180 corresponding targets, 2150 RA-related genes, and 36 drug-disease intersection targets. The PPI network diagram and three machine learning methods screened to obtain MMP9, and further analysis showed that MMP9 had high diagnostic significance and was significantly correlated with the main infiltrated immune cells, and the molecular docking verification also showed that MMP9 and the main active components of HDW were well combined. Next, we predicted 6 miRNAs and 314 lncRNAs acting on MMP9, and two ceRNA regulatory axes were obtained according to the screening. Cellular assays indicated HDW inhibits RA-FLS cell proliferation and MMP9 protein expression dose-dependently, suggesting HDW might influence RA's progression by regulating the MMP9/miR-204-5p/MIAT axis. This innovative analytical thinking provides guidance and reference for the future research on the ceRNA mechanism of traditional Chinese medicine in the treatment of RA.", "claims": [{"id": "claim-method", "statement": "We screen meaningful key genes in the training dataset through three machine learning algorithms (LASSO, SVM-RFE, and RF) to distinguish RA samples. The LASSO algorithm selected 6 genes from 16 key genes (Fig. 6A). At the same time, in the SVM-RFE algorithm, we identified a key gene (maximum accuracy = 0.833, minimum RMSE = 0.167) (Fig. 6B). The RF algorithm was used to determine the importance of key genes, and we identified 3 genes with an importance greater than 1.0 bits (Fig. 6C). The concrete results of the three algorithms are in Supplementary Table S4. Finally, we combined the results of three machine learning algorithms to identify a core gene (MMP9, Fig. 6D).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38491124", "locator": "sections[7] \"Screening of HDW anti-RA hub targets\" paragraph 3", "quote": "We screen meaningful key genes in the training dataset through three machine learning algorithms (LASSO, SVM-RFE, and RF) to distinguish RA samples. The LASSO algorithm selected 6 genes from 16 key genes (Fig. 6A). At the same time, in the SVM-RFE algorithm, we identified a key gene (maximum accuracy = 0.833, minimum RMSE = 0.167) (Fig. 6B). The RF algorithm was used to determine the importance of key genes, and we identified 3 genes with an importance greater than 1.0 bits (Fig. 6C). The concrete results of the three algorithms are in Supplementary Table S4. Finally, we combined the results of three machine learning algorithms to identify a core gene (MMP9, Fig. 6D)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "All data generated or analysed during this study are included in its supplementary information files.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38491124", "locator": "sections[36] \"notes\" paragraph 1", "quote": "All data generated or analysed during this study are included in its supplementary information files."}], "state": "complete"}, {"id": "claim-target-result", "statement": "MMP9 was identified as the core gene of RA through three computational learning algorithms.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:38491124", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "The PPI network diagram and three machine learning methods screened to obtain MMP9, and further analysis showed that MMP9 had high diagnostic significance and was significantly correlated with the main infiltrated immune cells, and the molecular docking verification also showed that MMP9 and the main active components of HDW were well combined."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "MMP9 was identified as the core gene of RA through three computational learning algorithms.", "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:38491124"], "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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CCK-8 experiments showed that HDW inhibited the pr", "pharmacology and bioinformatic analysis. (A) CCK8 assays of different HDW concentrations (0, 0"], "order_basis": "paper_text_order:2410", "id": "assay:1"}, {"role": "data_generation", "definition_id": "western_blot", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.93, "evidence": ["expression levels of MMP9 was measured using western blotting. (C) The expression levels of MMP9, miR-204-"], "order_basis": "paper_text_order:4037", "id": "assay:2"}, {"role": "analysis", "definition_id": "machine_learning", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["We screen meaningful key genes in the training dataset through three machine learning algorithms (LASSO, SVM-RFE, and RF) to distinguish RA samples. The LASSO algorithm selected 6 genes from 16 key genes (Fig. 6A). At the same time, in the SVM-RFE algorithm, we identified a key gene (maximum accuracy = 0.833, minimum RMSE = 0.167) (Fig. 6B). The RF algorithm was used to determine the importance of key genes, and we identified 3 genes with an importance greater than 1.0 bits (Fig. 6C). The concrete results of the three algorithms are in Supplementary Table S4. Finally, we combined the results of three machine learning algorithms to identify a core gene (MMP9, Fig. 6D)."], "order_basis": "consumes_earlier_output", "id": "assay:3"}]}