Comprehensive Bioinformatics Method to Explore Immune-Related Genes in the Pathogenesis of Myocardial Infarction
Objective: This study was aimed at exploring immune-related genes and their expression changes in myocardial infarction (MI) through comprehensive bioinformatics methods and validating these genes as potential diagnostic and therapeutic targets. Methods: Gene expression data were analyzed from thr...
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| Format: | Article |
| Language: | English |
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Compuscript Ltd
2025-01-01
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| Series: | Cardiovascular Innovations and Applications |
| Online Access: | https://www.scienceopen.com/hosted-document?doi=10.15212/CVIA.2024.0067 |
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| _version_ | 1825201886207148032 |
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| author | Xin Zhang Yiren Yao Ying Ding Wenting Yan Yang Gu Xiwen Zhang Xiaojin Xu |
| author_facet | Xin Zhang Yiren Yao Ying Ding Wenting Yan Yang Gu Xiwen Zhang Xiaojin Xu |
| author_sort | Xin Zhang |
| collection | DOAJ |
| description | Objective: This study was aimed at exploring immune-related genes and their expression changes in myocardial infarction (MI) through comprehensive bioinformatics methods and validating these genes as potential diagnostic and therapeutic targets. Methods: Gene expression data were analyzed from three datasets: GSE29111 and GSE66360, which were combined as a training set, and GSE48060, which served as the validation set. We performed differential gene expression analysis, GO/KEGG enrichment analysis, protein-protein interaction (PPI) network analysis, weighted gene co-expression network analysis (WGCNA), gene set enrichment analysis, and immune infiltration studies to identify core immune-related genes associated with MI. The diagnostic capabilities of these genes were assessed with receiver operating characteristic curves, and RT-PCR was used to verify their expression levels between patients with MI and controls. The relationships of BCL6 with the inflammatory response and oxidative stress were explored through detection of the inflammatory factors TNF-α, IL-1, and IL-6; NADPH oxidase subunits p67 and gp91; SOD activity; and MDA content. Results: Ninety-one differentially expressed genes were identified. Enrichment analyses highlighted their involvement in the response to lipopolysaccharide and the IL-17 signaling pathway. From the PPI network of these genes, four core genes were initially recognized, and WGCNA further identified 13 genes. Intersection analysis finalized the identification of S100A12 and BCL6 as key biomarkers. Both genes showed significant differential expression between the MI and control groups (P < 0.01), with diagnostic AUCs of 0.809 and 0.837, respectively. These findings were corroborated in the validation set by similarly favorable AUCs. Furthermore, immune infiltration analysis revealed a positive correlation between these genes and immune cell markers. After BCL6 knockdown, an exacerbated inflammatory response and oxidative stress were observed, as indicated by higher expression of inflammatory factors and NADPH oxidase subunits, and lower SOD activity, in the MI group than the control group (P < 0.01). Conclusion: S100A12 and BCL6 might serve as candidate biomarkers for early detection of MI and have promise as new therapeutic targets. |
| format | Article |
| id | doaj-art-2627898f5c51497682fe4373d53babd2 |
| institution | Kabale University |
| issn | 2009-8618 2009-8782 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Compuscript Ltd |
| record_format | Article |
| series | Cardiovascular Innovations and Applications |
| spelling | doaj-art-2627898f5c51497682fe4373d53babd22025-02-07T17:00:10ZengCompuscript LtdCardiovascular Innovations and Applications2009-86182009-87822025-01-0110199010.15212/CVIA.2024.0067Comprehensive Bioinformatics Method to Explore Immune-Related Genes in the Pathogenesis of Myocardial InfarctionXin ZhangYiren YaoYing DingWenting YanYang GuXiwen ZhangXiaojin XuObjective: This study was aimed at exploring immune-related genes and their expression changes in myocardial infarction (MI) through comprehensive bioinformatics methods and validating these genes as potential diagnostic and therapeutic targets. Methods: Gene expression data were analyzed from three datasets: GSE29111 and GSE66360, which were combined as a training set, and GSE48060, which served as the validation set. We performed differential gene expression analysis, GO/KEGG enrichment analysis, protein-protein interaction (PPI) network analysis, weighted gene co-expression network analysis (WGCNA), gene set enrichment analysis, and immune infiltration studies to identify core immune-related genes associated with MI. The diagnostic capabilities of these genes were assessed with receiver operating characteristic curves, and RT-PCR was used to verify their expression levels between patients with MI and controls. The relationships of BCL6 with the inflammatory response and oxidative stress were explored through detection of the inflammatory factors TNF-α, IL-1, and IL-6; NADPH oxidase subunits p67 and gp91; SOD activity; and MDA content. Results: Ninety-one differentially expressed genes were identified. Enrichment analyses highlighted their involvement in the response to lipopolysaccharide and the IL-17 signaling pathway. From the PPI network of these genes, four core genes were initially recognized, and WGCNA further identified 13 genes. Intersection analysis finalized the identification of S100A12 and BCL6 as key biomarkers. Both genes showed significant differential expression between the MI and control groups (P < 0.01), with diagnostic AUCs of 0.809 and 0.837, respectively. These findings were corroborated in the validation set by similarly favorable AUCs. Furthermore, immune infiltration analysis revealed a positive correlation between these genes and immune cell markers. After BCL6 knockdown, an exacerbated inflammatory response and oxidative stress were observed, as indicated by higher expression of inflammatory factors and NADPH oxidase subunits, and lower SOD activity, in the MI group than the control group (P < 0.01). Conclusion: S100A12 and BCL6 might serve as candidate biomarkers for early detection of MI and have promise as new therapeutic targets.https://www.scienceopen.com/hosted-document?doi=10.15212/CVIA.2024.0067 |
| spellingShingle | Xin Zhang Yiren Yao Ying Ding Wenting Yan Yang Gu Xiwen Zhang Xiaojin Xu Comprehensive Bioinformatics Method to Explore Immune-Related Genes in the Pathogenesis of Myocardial Infarction Cardiovascular Innovations and Applications |
| title | Comprehensive Bioinformatics Method to Explore Immune-Related Genes in the Pathogenesis of Myocardial Infarction |
| title_full | Comprehensive Bioinformatics Method to Explore Immune-Related Genes in the Pathogenesis of Myocardial Infarction |
| title_fullStr | Comprehensive Bioinformatics Method to Explore Immune-Related Genes in the Pathogenesis of Myocardial Infarction |
| title_full_unstemmed | Comprehensive Bioinformatics Method to Explore Immune-Related Genes in the Pathogenesis of Myocardial Infarction |
| title_short | Comprehensive Bioinformatics Method to Explore Immune-Related Genes in the Pathogenesis of Myocardial Infarction |
| title_sort | comprehensive bioinformatics method to explore immune related genes in the pathogenesis of myocardial infarction |
| url | https://www.scienceopen.com/hosted-document?doi=10.15212/CVIA.2024.0067 |
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