Exploration of potential biomarkers and immune cell infiltration characteristics for peripheral atherosclerosis in sjögren’s syndrome based on comprehensive bioinformatics analysis and machine learning
BackgroundSjögren’s syndrome (SS) is an autoimmune disorder impacting exocrine glands, while peripheral atherosclerosis (PA) demonstrates a close link to inflammation. Despite a notable rise in atherosclerosis risk among SS patients in prior investigations, the precise mechanisms remain elusive.Meth...
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Frontiers Media S.A.
2025-07-01
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| Series: | Frontiers in Genetics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fgene.2025.1546315/full |
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| author | Chunjiang Liu Yuan Wang Lina Zhou Feifei Cai Xiaoqi Tang Liying Wang Xiang Zhang |
| author_facet | Chunjiang Liu Yuan Wang Lina Zhou Feifei Cai Xiaoqi Tang Liying Wang Xiang Zhang |
| author_sort | Chunjiang Liu |
| collection | DOAJ |
| description | BackgroundSjögren’s syndrome (SS) is an autoimmune disorder impacting exocrine glands, while peripheral atherosclerosis (PA) demonstrates a close link to inflammation. Despite a notable rise in atherosclerosis risk among SS patients in prior investigations, the precise mechanisms remain elusive.MethodsA comprehensive analysis was conducted on seven microarray datasets (GSE7451, GSE23117, GSE143153, GSE28829, GSE100927, GSE159677, and GSE40611). The LIMMA package, in conjunction with weighted gene co-expression network analysis (WGCNA), provides a robust method for identifying differentially expressed genes (DEGs) associated with peripheral atherosclerosis (PA) in Sjögren’s syndrome (SS). Subsequently, machine learning algorithms and protein-protein interaction (PPI) network analysis were employed to further investigate potential predictive genes. These findings were utilized to construct a nomogram and a receiver operating characteristic (ROC) curve, which assessed the predictive accuracy of these genes in PA patients with SS. Additionally, extensive analyses of immune cell infiltration and single-sample gene set enrichment analysis (ssGSEA) were conducted to elucidate the underlying biological mechanisms.ResultsUsing the LIMMA package and WGCNA, 135 DEGs associated with PA in SS were identified. PPI network analysis revealed 17 candidate hub genes. The intersection of gene sets identified by three distinct machine learning algorithms highlighted CCL4, CSF1R, and MX1 as key DEGs. ROC analysis and nomogram construction demonstrated their high predictive accuracy (AUC: 0.971, 95% CI: 0.941–1.000). Analysis of immune cell infiltration showed a significant positive correlation between these hub genes and dysregulated immune cells. Additionally, ssGSEA provided critical biological insights into the progression of PA in SS.ConclusionThis study systematically identified three promising hub genes (CCL4, CSF1R, and MX1) and developed a nomogram for predicting PA in SS. Analysis of immune cell infiltration demonstrated that dysregulated immune cells significantly contribute to the progression of PA. Additionally, ssGSEA analysis offered important insights into the mechanisms by which SS leads to PA. |
| format | Article |
| id | doaj-art-733814000dfd48229504516021499793 |
| institution | DOAJ |
| issn | 1664-8021 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Genetics |
| spelling | doaj-art-733814000dfd482295045160214997932025-08-20T03:09:38ZengFrontiers Media S.A.Frontiers in Genetics1664-80212025-07-011610.3389/fgene.2025.15463151546315Exploration of potential biomarkers and immune cell infiltration characteristics for peripheral atherosclerosis in sjögren’s syndrome based on comprehensive bioinformatics analysis and machine learningChunjiang Liu0Yuan Wang1Lina Zhou2Feifei Cai3Xiaoqi Tang4Liying Wang5Xiang Zhang6Department of General Surgery, Division of Vascular Surgery, Shaoxing People’s Hospital (The First Affiliated Hospital, Shaoxing University), Shaoxing, ChinaDepartment of Intervention Vascular, Hefei Hospital Affiliated to Anhui Medical University, Hefei, ChinaDepartment of Anesthesiology, Shaoxing People’s Hospital (The First Affiliated Hospital, Shaoxing University), Shaoxing, ChinaDepartment of Radiology, Shaoxing People’s Hospital (The First Affiliated Hospital, Shaoxing University), Shaoxing, ChinaDepartment of General Surgery, Division of Vascular Surgery, Shaoxing People’s Hospital (The First Affiliated Hospital, Shaoxing University), Shaoxing, ChinaDepartment of General Surgery, Wuxi No.2 People’s Hospital (Jiangnan University Medical Center), Wuxi, ChinaDepartment of General Surgery, Wuxi No.2 People’s Hospital (Jiangnan University Medical Center), Wuxi, ChinaBackgroundSjögren’s syndrome (SS) is an autoimmune disorder impacting exocrine glands, while peripheral atherosclerosis (PA) demonstrates a close link to inflammation. Despite a notable rise in atherosclerosis risk among SS patients in prior investigations, the precise mechanisms remain elusive.MethodsA comprehensive analysis was conducted on seven microarray datasets (GSE7451, GSE23117, GSE143153, GSE28829, GSE100927, GSE159677, and GSE40611). The LIMMA package, in conjunction with weighted gene co-expression network analysis (WGCNA), provides a robust method for identifying differentially expressed genes (DEGs) associated with peripheral atherosclerosis (PA) in Sjögren’s syndrome (SS). Subsequently, machine learning algorithms and protein-protein interaction (PPI) network analysis were employed to further investigate potential predictive genes. These findings were utilized to construct a nomogram and a receiver operating characteristic (ROC) curve, which assessed the predictive accuracy of these genes in PA patients with SS. Additionally, extensive analyses of immune cell infiltration and single-sample gene set enrichment analysis (ssGSEA) were conducted to elucidate the underlying biological mechanisms.ResultsUsing the LIMMA package and WGCNA, 135 DEGs associated with PA in SS were identified. PPI network analysis revealed 17 candidate hub genes. The intersection of gene sets identified by three distinct machine learning algorithms highlighted CCL4, CSF1R, and MX1 as key DEGs. ROC analysis and nomogram construction demonstrated their high predictive accuracy (AUC: 0.971, 95% CI: 0.941–1.000). Analysis of immune cell infiltration showed a significant positive correlation between these hub genes and dysregulated immune cells. Additionally, ssGSEA provided critical biological insights into the progression of PA in SS.ConclusionThis study systematically identified three promising hub genes (CCL4, CSF1R, and MX1) and developed a nomogram for predicting PA in SS. Analysis of immune cell infiltration demonstrated that dysregulated immune cells significantly contribute to the progression of PA. Additionally, ssGSEA analysis offered important insights into the mechanisms by which SS leads to PA.https://www.frontiersin.org/articles/10.3389/fgene.2025.1546315/fullperipheral atherosclerosisSjögren’s syndromebiomarkersimmune infiltrationbioinformatics analysismachine learning |
| spellingShingle | Chunjiang Liu Yuan Wang Lina Zhou Feifei Cai Xiaoqi Tang Liying Wang Xiang Zhang Exploration of potential biomarkers and immune cell infiltration characteristics for peripheral atherosclerosis in sjögren’s syndrome based on comprehensive bioinformatics analysis and machine learning Frontiers in Genetics peripheral atherosclerosis Sjögren’s syndrome biomarkers immune infiltration bioinformatics analysis machine learning |
| title | Exploration of potential biomarkers and immune cell infiltration characteristics for peripheral atherosclerosis in sjögren’s syndrome based on comprehensive bioinformatics analysis and machine learning |
| title_full | Exploration of potential biomarkers and immune cell infiltration characteristics for peripheral atherosclerosis in sjögren’s syndrome based on comprehensive bioinformatics analysis and machine learning |
| title_fullStr | Exploration of potential biomarkers and immune cell infiltration characteristics for peripheral atherosclerosis in sjögren’s syndrome based on comprehensive bioinformatics analysis and machine learning |
| title_full_unstemmed | Exploration of potential biomarkers and immune cell infiltration characteristics for peripheral atherosclerosis in sjögren’s syndrome based on comprehensive bioinformatics analysis and machine learning |
| title_short | Exploration of potential biomarkers and immune cell infiltration characteristics for peripheral atherosclerosis in sjögren’s syndrome based on comprehensive bioinformatics analysis and machine learning |
| title_sort | exploration of potential biomarkers and immune cell infiltration characteristics for peripheral atherosclerosis in sjogren s syndrome based on comprehensive bioinformatics analysis and machine learning |
| topic | peripheral atherosclerosis Sjögren’s syndrome biomarkers immune infiltration bioinformatics analysis machine learning |
| url | https://www.frontiersin.org/articles/10.3389/fgene.2025.1546315/full |
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