Relationships between systemic sclerosis and atherosclerosis: screening for mitochondria-related biomarkers.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39050259", "kind": "science-record", "revision": 4, "content_hash": "sha256:0fd69fb6306b66ddf237d1f89f1ffe29ef32e6364b48489c69b5f1ced7cc2ed5", "created_at": "2026-11-01T20:29:07Z", "updated_at": "2026-11-01T22:54:45Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Relationships between systemic sclerosis and atherosclerosis: screening for mitochondria-related biomarkers.", "doi": "10.3389/fgene.2024.1375331", "pmid": "39050259", "authors": ["Fei Wang", "Xiao Yan Lyu", "Yi Ming Qin", "Mei Juan Xie"], "journal": "Frontiers in genetics", "year": 2024, "abstract": "Background: Patients with systemic sclerosis (SSc) are known to have higher incidence of atherosclerosis (AS). Mitochondrial injuries in SSc can cause endothelial dysfunction, leading to AS; thus, mitochondria appear to be hubs linking SSc to AS. This study aimed to identify the mitochondria-related biomarkers of SSc and AS.\nMethods: We identified common differentially expressed genes (DEGs) in the SSc (GSE58095) and AS (GSE100927) datasets of the Gene Expression Omnibus (GEO) database. Considering the intersection between genes with identical expression trends and mitochondrial genes, we used the least absolute shrinkage and selection operator (LASSO) as well as random forest (RF) algorithms to identify four mitochondria-related hub genes. Diagnostic nomograms were then constructed to predict the likelihood of SSc and AS. Next, we used the CIBERSORT algorithm to evaluate immune infiltration in both disorders, predicted the transcription factors for the hub genes, and validated these genes for the two datasets.\nResults: A total of 112 genes and 13 mitochondria-related genes were identified; these genes were then significantly enriched for macrophage differentiation, collagen-containing extracellular matrix, collagen binding, antigen processing and presentation, leukocyte transendothelial migration, and apoptosis. Four mitochondria-related hub DEGs (IFI6, FSCN1, GAL, and SGCA) were also identified. The nomograms showed good diagnostic values for GSE58095 (area under the curve (AUC) = 0.903) and GSE100927 (AUC = 0.904). Further, memory B cells, γδT cells, M0 macrophages, and activated mast cells were significantly higher in AS, while the resting memory CD4+ T cells were lower and M1 macrophages were higher in SSc; all of these were closely linked to multiple immune cells. Gene set enrichment analysis (GSEA) showed that IFI6 and FSCN1 were involved in immune-related pathways in both AS and SSc; GAL and SGCA are related to mitochondrial metabolism pathways in both SSc and AS. Twenty transcription factors (TFs) were predicted, where two TFs, namely BRCA1 and PPARγ, were highly expressed in both SSc and AS.\nConclusion: Four mitochondria-related biomarkers were identified in both SSc and AS, which have high diagnostic value and are associated with immune cell infiltration in both disorders. Hence, this study provides new insights into the pathological mechanisms underlying SSc and AS. The specific roles and action mechanisms of these genes require further clinical validation in SSc patients with AS.", "claims": [{"id": "claim-method", "statement": "The DEGs of the GSE58095 and GSE100927 datasets were analyzed using the “limma” package in R software.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39050259", "locator": "sections[4] \"2.2 Differential gene expression analysis\" paragraph 2", "quote": "The DEGs of the GSE58095 and GSE100927 datasets were analyzed using the “limma” package in R software."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The original contributions presented in the study are included in the article/Supplementary Material, and any further inquiries may be directed to the corresponding author.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39050259", "locator": "sections[22] \"Data availability statement\" paragraph 2", "quote": "The original contributions presented in the study are included in the article/Supplementary Material, and any further inquiries may be directed to the corresponding author."}], "state": "complete"}, {"id": "claim-target-result", "statement": "We used the intersection of LASSO and random forest results to identify four mitochondria-related hub genes (IFI6, FSCN1, GAL, and SGCA).", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:39050259", "locator": "sections[14] \"3.3 Screening mitochondria-related hub genes and their diagnostic values\" paragraph 2", "quote": "We used the intersection of the results of these two algorithms to identify four mitochondria-related hub genes (IFI6, FSCN1, GAL, and SGCA) (Figure 4E)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "We used the intersection of LASSO and random forest results to identify four mitochondria-related hub genes (IFI6, FSCN1, GAL, and SGCA).", "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:39050259"], "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": "blocked", "blocked_on": "data:accession_absent", "description": "The GEO series accession resolves and its sample accessions are listed. 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