Identification of biomarkers associated with coronary artery disease and non-alcoholic fatty liver disease by bioinformatics analysis and machine learning
Abstract The constantly emerging evidence indicates a close association between coronary artery disease (CAD) and non-alcoholic fatty liver disease (NAFLD). However, the exact mechanisms underlying their mutual relationship remain undefined. This study aims to explore the common signature genes, pot...
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Nature Portfolio
2025-01-01
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| author | Chuan Lu Mei Han Qiqi Ma Li Ying Yue Zhang |
| author_facet | Chuan Lu Mei Han Qiqi Ma Li Ying Yue Zhang |
| author_sort | Chuan Lu |
| collection | DOAJ |
| description | Abstract The constantly emerging evidence indicates a close association between coronary artery disease (CAD) and non-alcoholic fatty liver disease (NAFLD). However, the exact mechanisms underlying their mutual relationship remain undefined. This study aims to explore the common signature genes, potential mechanisms, diagnostic markers, and therapeutic targets for CAD and NAFLD. We downloaded CAD and NAFLD datasets from the Gene Expression Omnibus (GEO) database and analyzed the differentially expressed genes (DEGs) by limma. Protein–protein interaction (PPI) network was constructed with common DEGs (co-DEGs), and hub genes were screened by Maximal Clique Centrality (MCC) algorithm. Candidate biomarkers were selected from intersection of three machine learning algorithms. Expression levels, nomogram, the areas under the receiver operating characteristic curve (AUC) of candidate biomarkers were performed. CIBERSORT algorithm was used to assess the immune cell infiltration, and Spearman’s correlations tests were used for calculating the correlation of biomarker genes. A total of 554 overlapping DEGs associated with CAD and NAFLD were obtained by analysis of GSE113079 and GSE89632 datasets. Gene Ontology, Kyoto Encyclopedia of Genes and Genomes enrichment analysis showed that the co-DEGs were significantly enriched in immune effector process, inflammation response and lipid metabolism. The PPI network generated a 1245-edge network, and top 50 genes were selected using the MCC algorithm. The candidate biomarkers were screened from intersection of machine learning in GSE89632, including CEBPA, CXCL2, JUN and FOXO1. The ROC results showed that these four biomarker genes have good diagnostic value for patients with both CAD and NAFLD. Then we explored the immune landscape, immune infiltration and the correlation between biomarker gene expression in CAD and NAFLD samples. In this study, we predict that CEBPA, CXCL2, JUN and FOXO1 can be used to diagnose CAD and NAFLD. Our study provided new insights for potential biomarkers, molecular mechanism and therapeutic targets for both diseases. |
| format | Article |
| id | doaj-art-6001f1922f1a40899c38bf49b65f9514 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-6001f1922f1a40899c38bf49b65f95142025-02-02T12:24:37ZengNature PortfolioScientific Reports2045-23222025-01-0115111310.1038/s41598-025-87923-7Identification of biomarkers associated with coronary artery disease and non-alcoholic fatty liver disease by bioinformatics analysis and machine learningChuan Lu0Mei Han1Qiqi Ma2Li Ying3Yue Zhang4Department of Cardiology, the Second Hospital of Dalian Medical UniversityDepartment of Gastroenterology, the Second Hospital of Dalian Medical UniversityDepartment of Gastroenterology, the Second Hospital of Dalian Medical UniversityDepartment of Gastroenterology, the Second Hospital of Dalian Medical UniversityDepartment of Gastroenterology, the Second Hospital of Dalian Medical UniversityAbstract The constantly emerging evidence indicates a close association between coronary artery disease (CAD) and non-alcoholic fatty liver disease (NAFLD). However, the exact mechanisms underlying their mutual relationship remain undefined. This study aims to explore the common signature genes, potential mechanisms, diagnostic markers, and therapeutic targets for CAD and NAFLD. We downloaded CAD and NAFLD datasets from the Gene Expression Omnibus (GEO) database and analyzed the differentially expressed genes (DEGs) by limma. Protein–protein interaction (PPI) network was constructed with common DEGs (co-DEGs), and hub genes were screened by Maximal Clique Centrality (MCC) algorithm. Candidate biomarkers were selected from intersection of three machine learning algorithms. Expression levels, nomogram, the areas under the receiver operating characteristic curve (AUC) of candidate biomarkers were performed. CIBERSORT algorithm was used to assess the immune cell infiltration, and Spearman’s correlations tests were used for calculating the correlation of biomarker genes. A total of 554 overlapping DEGs associated with CAD and NAFLD were obtained by analysis of GSE113079 and GSE89632 datasets. Gene Ontology, Kyoto Encyclopedia of Genes and Genomes enrichment analysis showed that the co-DEGs were significantly enriched in immune effector process, inflammation response and lipid metabolism. The PPI network generated a 1245-edge network, and top 50 genes were selected using the MCC algorithm. The candidate biomarkers were screened from intersection of machine learning in GSE89632, including CEBPA, CXCL2, JUN and FOXO1. The ROC results showed that these four biomarker genes have good diagnostic value for patients with both CAD and NAFLD. Then we explored the immune landscape, immune infiltration and the correlation between biomarker gene expression in CAD and NAFLD samples. In this study, we predict that CEBPA, CXCL2, JUN and FOXO1 can be used to diagnose CAD and NAFLD. Our study provided new insights for potential biomarkers, molecular mechanism and therapeutic targets for both diseases.https://doi.org/10.1038/s41598-025-87923-7Coronary atherosclerotic diseaseNon-alcoholic fatty liver diseaseTranscriptomic analysisBiomarker genesLipid metabolismImmune |
| spellingShingle | Chuan Lu Mei Han Qiqi Ma Li Ying Yue Zhang Identification of biomarkers associated with coronary artery disease and non-alcoholic fatty liver disease by bioinformatics analysis and machine learning Scientific Reports Coronary atherosclerotic disease Non-alcoholic fatty liver disease Transcriptomic analysis Biomarker genes Lipid metabolism Immune |
| title | Identification of biomarkers associated with coronary artery disease and non-alcoholic fatty liver disease by bioinformatics analysis and machine learning |
| title_full | Identification of biomarkers associated with coronary artery disease and non-alcoholic fatty liver disease by bioinformatics analysis and machine learning |
| title_fullStr | Identification of biomarkers associated with coronary artery disease and non-alcoholic fatty liver disease by bioinformatics analysis and machine learning |
| title_full_unstemmed | Identification of biomarkers associated with coronary artery disease and non-alcoholic fatty liver disease by bioinformatics analysis and machine learning |
| title_short | Identification of biomarkers associated with coronary artery disease and non-alcoholic fatty liver disease by bioinformatics analysis and machine learning |
| title_sort | identification of biomarkers associated with coronary artery disease and non alcoholic fatty liver disease by bioinformatics analysis and machine learning |
| topic | Coronary atherosclerotic disease Non-alcoholic fatty liver disease Transcriptomic analysis Biomarker genes Lipid metabolism Immune |
| url | https://doi.org/10.1038/s41598-025-87923-7 |
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