Identification of a novel disulfidptosis-related gene signature in osteoarthritis using bioinformatics analysis and experimental validation.

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Disulfidptosis, a novel form of programmed cell death, has recently been identified; however, the effects and roles of disulfidptosis-related genes (DR-DEGs) in OA remain unclear. We obtained six OA datasets from the GEO database, using four as training sets and two as validation sets. Differential expression analysis was employed to identify DR-DEGs, and unique molecular subtypes of OA were constructed based on these DR-DEGs. Subsequently, the immune microenvironment of OA patients was comprehensively analyzed using the \"CIBERSORT\" algorithm for immune infiltration. Various machine learning algorithms were utilized to screen characteristic DR-DEGs, and nomogram models and ROC curves were built based on these genes. The scRNA dataset (GSE169454) was used to classify chondrocytes in OA samples into distinct cell types, further exploring the gene distribution and correlation of characteristic DR-DEGs with specific cell subpopulations. Moreover, the expression levels of four characteristic DR-DEGs were validated through OA cell models and rat models. In our study, we identified 10 DR-DEGs with significant differences in expression within OA samples. Based on these DR-DEGs, two distinct molecular subtypes were recognized (cluster 1 and 2). ZNF484 and NDUFS1 were found to be significantly overexpressed in subtype 1, while the infiltration abundance of activated mast cells was markedly elevated in subtype 2. Moreover, significant differences were observed in the infiltration proportions of 11 immune cell types between OA and control samples, with 9 DR-DEGs demonstrating substantial correlations with immune cell infiltration levels. Further analysis of the scRNA dataset revealed that SLC3A2 and NDUFC1 were predominantly expressed in the preHTC subpopulation. All 10 DR-DEGs exhibited notably higher expression in the EC subpopulation across various cell types. The proportion of EC subgroups with high SLC3A2 expression increased, mainly enriching pathways related to inflammation, such as the IL-17 signaling pathway and TGF-beta signaling pathway. Using machine learning, we identified four characteristic DR-DEGs, which, in combination with the nomogram and ROC models, demonstrated promising performance in the diagnosis of OA. Additionally, in vivo validation confirmed a significant elevation of PPM1F expression in OA models. This study identified DR-DEGs as potential biomarkers for the diagnosis and classification of OA and provided a preliminary understanding of their role in the immune microenvironment. However, further experimental and clinical studies are required to validate their diagnostic value and therapeutic potential.", "claims": [{"id": "claim-method", "statement": "To extract DR-DEGs in OA patients, we utilized the “limma” package17 to perform differential expression analysis of genes between the OA and control groups, which included upregulated and downregulated genes.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39779817", "locator": "sections[4] \"Identification of disulfidptosis-related differentially expressed genes (DR-DEGs)\" paragraph 2", "quote": "To extract DR-DEGs in OA patients, we utilized the “limma” package17 to perform differential expression analysis of genes between the OA and control groups, which included upregulated and downregulated genes."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The microarray data used to support the findings of this study can be downloaded from the GSE51588, GSE55235, GSE55457, GSE117999, GSE167077 and GSE169454 datasets (https://www.ncbi.nlm.nih.gov/geo). The processed data are available from the corresponding author upon request.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39779817", "locator": "sections[36] \"Data availability\" paragraph 2", "quote": "The microarray data used to support the findings of this study can be downloaded from the GSE51588, GSE55235, GSE55457, GSE117999, GSE167077 and GSE169454 datasets (https://www.ncbi.nlm.nih.gov/geo). The processed data are available from the corresponding author upon request."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Four characteristic DR-DEGs were identified by integrating multiple machine-learning algorithms, and the resulting logistic-regression model achieved an AUC of 0.873 in the validation set.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:39779817", "locator": "sections[28] \"Diagnostic value of characteristic DR-DEGs in OA\" paragraph 2", "quote": "In the validation set ROC curves, the AUC values for SLC3A2 and LRPPRC were both greater than 0.700, and the AUC value for the logistics regression model was 0.873 (Fig. 10F and G)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Four characteristic DR-DEGs were identified by integrating multiple machine-learning algorithms, and the resulting logistic-regression model achieved an AUC of 0.873 in the validation set.", "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:39779817"], "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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Additionally, in vivo validation confirmed a significant elevation", "Gs were validated through OA cell models and rat models. In our study, we identified 10 DR-DE", "ate the expression of characteristic DR-DEGs in vivo, we established an OA rat model. A total of", "utured layer by layer. Four weeks later, the rats were euthanized using cervical dislocation, and t"], "order_basis": "paper_text_order:1327", "id": "assay:1"}, {"role": "data_generation", "definition_id": "qpcr", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["characteristic DR-DEGs in OA tissues through RT-qPCR revealed that except for GYS1, whose mRNA le", "firming the bioinformatics analysis results. RT-qPCR validation of key DR-DEGs. (A) The expressio", "n the two groups, it was consistent with the RT-qPCR results. These results suggest that the tren", "racteristic DR-DEGs in OA tissues through RT-qPCR revealed that except for GYS1, whose mRNA le"], "order_basis": "paper_text_order:5974", "id": "assay:2"}, {"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": ["oup were compared (Fig. 11B). The results of Western Blot analysis indicated that the protein level of"], "order_basis": "paper_text_order:6723", "id": "assay:3"}, {"role": "analysis", "definition_id": "differential_expression", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["To extract DR-DEGs in OA patients, we utilized the “limma” package17 to perform differential expression analysis of genes between the OA and control groups, which included upregulated and downregulated genes."], "order_basis": "consumes_earlier_output", "id": "assay:4"}]}