Unraveling the copper-death connection: Decoding COVID-19‘s immune landscape through advanced bioinformatics and machine learning approaches
ABSTRACTThis study aims to analyze Coronavirus Disease 2019 (COVID-19)-associated copper-death genes using the Gene Expression Omnibus (GEO) dataset and machine learning, exploring their immune microenvironment correlation and underlying mechanisms. Utilizing GEO, we analyzed the GSE217948 dataset w...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2024-12-01
|
| Series: | Human Vaccines & Immunotherapeutics |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/21645515.2024.2310359 |
| _version_ | 1797265471208161280 |
|---|---|
| author | Qi Wang Zhenzhong Su Jing Zhang He Yan Jie Zhang |
| author_facet | Qi Wang Zhenzhong Su Jing Zhang He Yan Jie Zhang |
| author_sort | Qi Wang |
| collection | DOAJ |
| description | ABSTRACTThis study aims to analyze Coronavirus Disease 2019 (COVID-19)-associated copper-death genes using the Gene Expression Omnibus (GEO) dataset and machine learning, exploring their immune microenvironment correlation and underlying mechanisms. Utilizing GEO, we analyzed the GSE217948 dataset with control samples. Differential expression analysis identified 16 differentially expressed copper-death genes, and Cell type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT) quantified immune cell infiltration. Gene classification yielded two copper-death clusters, with Weighted Gene Co-expression Network Analysis (WGCNA) identifying key module genes. Machine learning models (random forest, Support Vector Machine (SVM), Generalized Linear Model (GLM), eXtreme Gradient Boosting (XGBoost)) selected 6 feature genes validated by the GSE213313 dataset. Ferredoxin 1 (FDX1) emerged as the top gene, corroborated by Area Under the Curve (AUC) analysis. Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA) revealed enriched pathways in T cell receptor, natural killer cytotoxicity, and Peroxisome Proliferator-Activated Receptor (PPAR). We uncovered differentially expressed copper-death genes and immune infiltration differences, notably CD8 T cells and M0 macrophages. Clustering identified modules with potential implications for COVID-19. Machine learning models effectively predicted COVID-19 risk, with FDX1‘s pivotal role validated. FDX1‘s high expression was associated with immune pathways, suggesting its role in COVID-19 pathogenesis. This comprehensive approach elucidated COVID-19-related copper-death genes, their immune context, and risk prediction potential. FDX1‘s connection to immune pathways offers insights into COVID-19 mechanisms and therapy. |
| first_indexed | 2024-04-25T00:45:19Z |
| format | Article |
| id | doaj.art-9cd3a3d78d04422e97ca984380d84bea |
| institution | Directory Open Access Journal |
| issn | 2164-5515 2164-554X |
| language | English |
| last_indexed | 2024-04-25T00:45:19Z |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Human Vaccines & Immunotherapeutics |
| spelling | doaj.art-9cd3a3d78d04422e97ca984380d84bea2024-03-12T04:59:26ZengTaylor & Francis GroupHuman Vaccines & Immunotherapeutics2164-55152164-554X2024-12-0120110.1080/21645515.2024.2310359Unraveling the copper-death connection: Decoding COVID-19‘s immune landscape through advanced bioinformatics and machine learning approachesQi Wang0Zhenzhong Su1Jing Zhang2He Yan3Jie Zhang4Department of Respiratory and Critical Care Medicine, The Second Hospital of Jilin University, Changchun, Jilin, ChinaDepartment of Respiratory and Critical Care Medicine, The Second Hospital of Jilin University, Changchun, Jilin, ChinaDepartment of General Gynecology, The First Hospital of Jilin University, Changchun, Jilin, ChinaDepartment of Respiratory and Critical Care Medicine, The Second Hospital of Jilin University, Changchun, Jilin, ChinaDepartment of Respiratory and Critical Care Medicine, The Second Hospital of Jilin University, Changchun, Jilin, ChinaABSTRACTThis study aims to analyze Coronavirus Disease 2019 (COVID-19)-associated copper-death genes using the Gene Expression Omnibus (GEO) dataset and machine learning, exploring their immune microenvironment correlation and underlying mechanisms. Utilizing GEO, we analyzed the GSE217948 dataset with control samples. Differential expression analysis identified 16 differentially expressed copper-death genes, and Cell type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT) quantified immune cell infiltration. Gene classification yielded two copper-death clusters, with Weighted Gene Co-expression Network Analysis (WGCNA) identifying key module genes. Machine learning models (random forest, Support Vector Machine (SVM), Generalized Linear Model (GLM), eXtreme Gradient Boosting (XGBoost)) selected 6 feature genes validated by the GSE213313 dataset. Ferredoxin 1 (FDX1) emerged as the top gene, corroborated by Area Under the Curve (AUC) analysis. Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA) revealed enriched pathways in T cell receptor, natural killer cytotoxicity, and Peroxisome Proliferator-Activated Receptor (PPAR). We uncovered differentially expressed copper-death genes and immune infiltration differences, notably CD8 T cells and M0 macrophages. Clustering identified modules with potential implications for COVID-19. Machine learning models effectively predicted COVID-19 risk, with FDX1‘s pivotal role validated. FDX1‘s high expression was associated with immune pathways, suggesting its role in COVID-19 pathogenesis. This comprehensive approach elucidated COVID-19-related copper-death genes, their immune context, and risk prediction potential. FDX1‘s connection to immune pathways offers insights into COVID-19 mechanisms and therapy.https://www.tandfonline.com/doi/10.1080/21645515.2024.2310359COVID-19differential expression genesbioinformaticsimmune infiltrationWGCNAmachine learning models |
| spellingShingle | Qi Wang Zhenzhong Su Jing Zhang He Yan Jie Zhang Unraveling the copper-death connection: Decoding COVID-19‘s immune landscape through advanced bioinformatics and machine learning approaches Human Vaccines & Immunotherapeutics COVID-19 differential expression genes bioinformatics immune infiltration WGCNA machine learning models |
| title | Unraveling the copper-death connection: Decoding COVID-19‘s immune landscape through advanced bioinformatics and machine learning approaches |
| title_full | Unraveling the copper-death connection: Decoding COVID-19‘s immune landscape through advanced bioinformatics and machine learning approaches |
| title_fullStr | Unraveling the copper-death connection: Decoding COVID-19‘s immune landscape through advanced bioinformatics and machine learning approaches |
| title_full_unstemmed | Unraveling the copper-death connection: Decoding COVID-19‘s immune landscape through advanced bioinformatics and machine learning approaches |
| title_short | Unraveling the copper-death connection: Decoding COVID-19‘s immune landscape through advanced bioinformatics and machine learning approaches |
| title_sort | unraveling the copper death connection decoding covid 19 s immune landscape through advanced bioinformatics and machine learning approaches |
| topic | COVID-19 differential expression genes bioinformatics immune infiltration WGCNA machine learning models |
| url | https://www.tandfonline.com/doi/10.1080/21645515.2024.2310359 |
| work_keys_str_mv | AT qiwang unravelingthecopperdeathconnectiondecodingcovid19simmunelandscapethroughadvancedbioinformaticsandmachinelearningapproaches AT zhenzhongsu unravelingthecopperdeathconnectiondecodingcovid19simmunelandscapethroughadvancedbioinformaticsandmachinelearningapproaches AT jingzhang unravelingthecopperdeathconnectiondecodingcovid19simmunelandscapethroughadvancedbioinformaticsandmachinelearningapproaches AT heyan unravelingthecopperdeathconnectiondecodingcovid19simmunelandscapethroughadvancedbioinformaticsandmachinelearningapproaches AT jiezhang unravelingthecopperdeathconnectiondecodingcovid19simmunelandscapethroughadvancedbioinformaticsandmachinelearningapproaches |