Identification of Transcriptome Biomarkers for Severe COVID-19 with Machine Learning Methods
The rapid spread of COVID-19 has become a major concern for people’s lives and health all around the world. COVID-19 patients in various phases and severity require individualized treatment given that different patients may develop different symptoms. We employed machine learning methods to discover...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-11-01
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| Series: | Biomolecules |
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| Online Access: | https://www.mdpi.com/2218-273X/12/12/1735 |
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| author | Xiaohong Li Xianchao Zhou Shijian Ding Lei Chen Kaiyan Feng Hao Li Tao Huang Yu-Dong Cai |
| author_facet | Xiaohong Li Xianchao Zhou Shijian Ding Lei Chen Kaiyan Feng Hao Li Tao Huang Yu-Dong Cai |
| author_sort | Xiaohong Li |
| collection | DOAJ |
| description | The rapid spread of COVID-19 has become a major concern for people’s lives and health all around the world. COVID-19 patients in various phases and severity require individualized treatment given that different patients may develop different symptoms. We employed machine learning methods to discover biomarkers that may accurately classify COVID-19 in various disease states and severities in this study. The blood gene expression profiles from 50 COVID-19 patients without intensive care, 50 COVID-19 patients with intensive care, 10 non-COVID-19 individuals without intensive care, and 16 non-COVID-19 individuals with intensive care were analyzed. Boruta was first used to remove irrelevant gene features in the expression profiles, and then, the minimum redundancy maximum relevance was applied to sort the remaining features. The generated feature-ranked list was fed into the incremental feature selection method to discover the essential genes and build powerful classifiers. The molecular mechanism of some biomarker genes was addressed using recent studies, and biological functions enriched by essential genes were examined. Our findings imply that genes including UBE2C, PCLAF, CDK1, CCNB1, MND1, APOBEC3G, TRAF3IP3, CD48, and GZMA play key roles in defining the different states and severity of COVID-19. Thus, a new point of reference is provided for understanding the disease’s etiology and facilitating a precise therapy. |
| first_indexed | 2024-03-09T17:17:28Z |
| format | Article |
| id | doaj.art-9be773a5ff1344049e3c99d1109b2e6e |
| institution | Directory Open Access Journal |
| issn | 2218-273X |
| language | English |
| last_indexed | 2024-03-09T17:17:28Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biomolecules |
| spelling | doaj.art-9be773a5ff1344049e3c99d1109b2e6e2023-11-24T13:32:26ZengMDPI AGBiomolecules2218-273X2022-11-011212173510.3390/biom12121735Identification of Transcriptome Biomarkers for Severe COVID-19 with Machine Learning MethodsXiaohong Li0Xianchao Zhou1Shijian Ding2Lei Chen3Kaiyan Feng4Hao Li5Tao Huang6Yu-Dong Cai7School of Biological and Food Engineering, Jilin Engineering Normal University, Changchun 130052, ChinaCenter for Single-Cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, ChinaSchool of Life Sciences, Shanghai University, Shanghai 200444, ChinaCollege of Information Engineering, Shanghai Maritime University, Shanghai 201306, ChinaDepartment of Computer Science, Guangdong AIB Polytechnic College, Guangzhou 510507, ChinaSchool of Biological and Food Engineering, Jilin Engineering Normal University, Changchun 130052, ChinaBio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Shanghai 200031, ChinaSchool of Life Sciences, Shanghai University, Shanghai 200444, ChinaThe rapid spread of COVID-19 has become a major concern for people’s lives and health all around the world. COVID-19 patients in various phases and severity require individualized treatment given that different patients may develop different symptoms. We employed machine learning methods to discover biomarkers that may accurately classify COVID-19 in various disease states and severities in this study. The blood gene expression profiles from 50 COVID-19 patients without intensive care, 50 COVID-19 patients with intensive care, 10 non-COVID-19 individuals without intensive care, and 16 non-COVID-19 individuals with intensive care were analyzed. Boruta was first used to remove irrelevant gene features in the expression profiles, and then, the minimum redundancy maximum relevance was applied to sort the remaining features. The generated feature-ranked list was fed into the incremental feature selection method to discover the essential genes and build powerful classifiers. The molecular mechanism of some biomarker genes was addressed using recent studies, and biological functions enriched by essential genes were examined. Our findings imply that genes including UBE2C, PCLAF, CDK1, CCNB1, MND1, APOBEC3G, TRAF3IP3, CD48, and GZMA play key roles in defining the different states and severity of COVID-19. Thus, a new point of reference is provided for understanding the disease’s etiology and facilitating a precise therapy.https://www.mdpi.com/2218-273X/12/12/1735COVID-19transcriptomicmachine learningbiomarkerenrichment analysis |
| spellingShingle | Xiaohong Li Xianchao Zhou Shijian Ding Lei Chen Kaiyan Feng Hao Li Tao Huang Yu-Dong Cai Identification of Transcriptome Biomarkers for Severe COVID-19 with Machine Learning Methods Biomolecules COVID-19 transcriptomic machine learning biomarker enrichment analysis |
| title | Identification of Transcriptome Biomarkers for Severe COVID-19 with Machine Learning Methods |
| title_full | Identification of Transcriptome Biomarkers for Severe COVID-19 with Machine Learning Methods |
| title_fullStr | Identification of Transcriptome Biomarkers for Severe COVID-19 with Machine Learning Methods |
| title_full_unstemmed | Identification of Transcriptome Biomarkers for Severe COVID-19 with Machine Learning Methods |
| title_short | Identification of Transcriptome Biomarkers for Severe COVID-19 with Machine Learning Methods |
| title_sort | identification of transcriptome biomarkers for severe covid 19 with machine learning methods |
| topic | COVID-19 transcriptomic machine learning biomarker enrichment analysis |
| url | https://www.mdpi.com/2218-273X/12/12/1735 |
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