Identification and Analysis of a Four-Gene Set for Diagnosing SFTS Virus Infection Based on Machine Learning Methods and Its Association with Immune Cell Infiltration
Severe Fever with thrombocytopenia syndrome (SFTS) is a highly fatal viral infectious disease that poses a significant threat to public health. Currently, the phase and pathogenesis of SFTS are not well understood, and there are no specific vaccines or effective treatment available. Therefore, it is...
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| Format: | Article |
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MDPI AG
2023-10-01
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| Series: | Viruses |
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| Online Access: | https://www.mdpi.com/1999-4915/15/10/2126 |
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| author | Tao Huang Xueqi Wang Yuqian Mi Tiezhu Liu Yang Li Ruixue Zhang Zhen Qian Yanhan Wen Boyang Li Lina Sun Wei Wu Jiandong Li Shiwen Wang Mifang Liang |
| author_facet | Tao Huang Xueqi Wang Yuqian Mi Tiezhu Liu Yang Li Ruixue Zhang Zhen Qian Yanhan Wen Boyang Li Lina Sun Wei Wu Jiandong Li Shiwen Wang Mifang Liang |
| author_sort | Tao Huang |
| collection | DOAJ |
| description | Severe Fever with thrombocytopenia syndrome (SFTS) is a highly fatal viral infectious disease that poses a significant threat to public health. Currently, the phase and pathogenesis of SFTS are not well understood, and there are no specific vaccines or effective treatment available. Therefore, it is crucial to identify biomarkers for diagnosing acute SFTS, which has a high mortality rate. In this study, we conducted differentially expressed genes (DEGs) analysis and WGCNA module analysis on the GSE144358 dataset, comparing the acute phase of SFTSV-infected patients with healthy individuals. Through the LASSO–Cox and random forest algorithms, a total of 2128 genes were analyzed, leading to the identification of four genes: ADIPOR1, CENPO, E2F2, and H2AC17. The GSEA analysis of these four genes demonstrated a significant correlation with immune cell function and cell cycle, aligning with the functional enrichment findings of DEGs. Furthermore, we also utilized CIBERSORT to analyze the immune cell infiltration and its correlation with characteristic genes. The results indicate that the combination of ADIPOR1, CENPO, E2F2, and H2AC17 genes has the potential as characteristic genes for diagnosing and studying the acute phase of SFTS virus (SFTSV) infection. |
| first_indexed | 2024-03-10T20:48:48Z |
| format | Article |
| id | doaj.art-5cb4f87661954df49acc0d3f125a91d0 |
| institution | Directory Open Access Journal |
| issn | 1999-4915 |
| language | English |
| last_indexed | 2024-03-10T20:48:48Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Viruses |
| spelling | doaj.art-5cb4f87661954df49acc0d3f125a91d02023-11-19T18:28:42ZengMDPI AGViruses1999-49152023-10-011510212610.3390/v15102126Identification and Analysis of a Four-Gene Set for Diagnosing SFTS Virus Infection Based on Machine Learning Methods and Its Association with Immune Cell InfiltrationTao Huang0Xueqi Wang1Yuqian Mi2Tiezhu Liu3Yang Li4Ruixue Zhang5Zhen Qian6Yanhan Wen7Boyang Li8Lina Sun9Wei Wu10Jiandong Li11Shiwen Wang12Mifang Liang13National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases (NITFID), Institute for Viral Disease Control and Prevention, China CDC, Beijing 102206, ChinaCapital Institute of Pediatrics, Beijing 100020, ChinaShanxi Academy of Advanced Research and Innovation, Taiyuan 030032, ChinaNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases (NITFID), Institute for Viral Disease Control and Prevention, China CDC, Beijing 102206, ChinaChongqing Research Institute of Big Data, Peking University, Chongqing 400039, ChinaNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases (NITFID), Institute for Viral Disease Control and Prevention, China CDC, Beijing 102206, ChinaNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases (NITFID), Institute for Viral Disease Control and Prevention, China CDC, Beijing 102206, ChinaNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases (NITFID), Institute for Viral Disease Control and Prevention, China CDC, Beijing 102206, ChinaNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases (NITFID), Institute for Viral Disease Control and Prevention, China CDC, Beijing 102206, ChinaNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases (NITFID), Institute for Viral Disease Control and Prevention, China CDC, Beijing 102206, ChinaNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases (NITFID), Institute for Viral Disease Control and Prevention, China CDC, Beijing 102206, ChinaNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases (NITFID), Institute for Viral Disease Control and Prevention, China CDC, Beijing 102206, ChinaNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases (NITFID), Institute for Viral Disease Control and Prevention, China CDC, Beijing 102206, ChinaNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases (NITFID), Institute for Viral Disease Control and Prevention, China CDC, Beijing 102206, ChinaSevere Fever with thrombocytopenia syndrome (SFTS) is a highly fatal viral infectious disease that poses a significant threat to public health. Currently, the phase and pathogenesis of SFTS are not well understood, and there are no specific vaccines or effective treatment available. Therefore, it is crucial to identify biomarkers for diagnosing acute SFTS, which has a high mortality rate. In this study, we conducted differentially expressed genes (DEGs) analysis and WGCNA module analysis on the GSE144358 dataset, comparing the acute phase of SFTSV-infected patients with healthy individuals. Through the LASSO–Cox and random forest algorithms, a total of 2128 genes were analyzed, leading to the identification of four genes: ADIPOR1, CENPO, E2F2, and H2AC17. The GSEA analysis of these four genes demonstrated a significant correlation with immune cell function and cell cycle, aligning with the functional enrichment findings of DEGs. Furthermore, we also utilized CIBERSORT to analyze the immune cell infiltration and its correlation with characteristic genes. The results indicate that the combination of ADIPOR1, CENPO, E2F2, and H2AC17 genes has the potential as characteristic genes for diagnosing and studying the acute phase of SFTS virus (SFTSV) infection.https://www.mdpi.com/1999-4915/15/10/2126SFTSSFTS acute phasemachine learningLASSO–Coximmune cells infiltration |
| spellingShingle | Tao Huang Xueqi Wang Yuqian Mi Tiezhu Liu Yang Li Ruixue Zhang Zhen Qian Yanhan Wen Boyang Li Lina Sun Wei Wu Jiandong Li Shiwen Wang Mifang Liang Identification and Analysis of a Four-Gene Set for Diagnosing SFTS Virus Infection Based on Machine Learning Methods and Its Association with Immune Cell Infiltration Viruses SFTS SFTS acute phase machine learning LASSO–Cox immune cells infiltration |
| title | Identification and Analysis of a Four-Gene Set for Diagnosing SFTS Virus Infection Based on Machine Learning Methods and Its Association with Immune Cell Infiltration |
| title_full | Identification and Analysis of a Four-Gene Set for Diagnosing SFTS Virus Infection Based on Machine Learning Methods and Its Association with Immune Cell Infiltration |
| title_fullStr | Identification and Analysis of a Four-Gene Set for Diagnosing SFTS Virus Infection Based on Machine Learning Methods and Its Association with Immune Cell Infiltration |
| title_full_unstemmed | Identification and Analysis of a Four-Gene Set for Diagnosing SFTS Virus Infection Based on Machine Learning Methods and Its Association with Immune Cell Infiltration |
| title_short | Identification and Analysis of a Four-Gene Set for Diagnosing SFTS Virus Infection Based on Machine Learning Methods and Its Association with Immune Cell Infiltration |
| title_sort | identification and analysis of a four gene set for diagnosing sfts virus infection based on machine learning methods and its association with immune cell infiltration |
| topic | SFTS SFTS acute phase machine learning LASSO–Cox immune cells infiltration |
| url | https://www.mdpi.com/1999-4915/15/10/2126 |
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