Identification of Drug-Induced Liver Injury Biomarkers from Multiple Microarrays Based on Machine Learning and Bioinformatics Analysis
Drug-induced liver injury (DILI) is the most common adverse effect of numerous drugs and a leading cause of drug withdrawal from the market. In recent years, the incidence of DILI has increased. However, diagnosing DILI remains challenging because of the lack of specific biomarkers. Hence, we used m...
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MDPI AG
2022-10-01
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| Series: | International Journal of Molecular Sciences |
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| Online Access: | https://www.mdpi.com/1422-0067/23/19/11945 |
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| author | Kaiyue Wang Lin Zhang Lixia Li Yi Wang Xinqin Zhong Chunyu Hou Yuqi Zhang Congying Sun Qian Zhou Xiaoying Wang |
| author_facet | Kaiyue Wang Lin Zhang Lixia Li Yi Wang Xinqin Zhong Chunyu Hou Yuqi Zhang Congying Sun Qian Zhou Xiaoying Wang |
| author_sort | Kaiyue Wang |
| collection | DOAJ |
| description | Drug-induced liver injury (DILI) is the most common adverse effect of numerous drugs and a leading cause of drug withdrawal from the market. In recent years, the incidence of DILI has increased. However, diagnosing DILI remains challenging because of the lack of specific biomarkers. Hence, we used machine learning (ML) to mine multiple microarrays and identify useful genes that could contribute to diagnosing DILI. In this prospective study, we screened six eligible microarrays from the Gene Expression Omnibus (GEO) database. First, 21 differentially expressed genes (DEGs) were identified in the training set. Subsequently, a functional enrichment analysis of the DEGs was performed. We then used six ML algorithms to identify potentially useful genes. Based on receiver operating characteristic (ROC), four genes, DDIT3, GADD45A, SLC3A2, and RBM24, were identified. The average values of the area under the curve (AUC) for these four genes were higher than 0.8 in both the training and testing sets. In addition, the results of immune cell correlation analysis showed that these four genes were highly significantly correlated with multiple immune cells. Our study revealed that DDIT3, GADD45A, SLC3A2, and RBM24 could be biomarkers contributing to the identification of patients with DILI. |
| first_indexed | 2024-03-09T21:36:02Z |
| format | Article |
| id | doaj.art-266a457c434c49ac928c5973436f64e8 |
| institution | Directory Open Access Journal |
| issn | 1661-6596 1422-0067 |
| language | English |
| last_indexed | 2024-03-09T21:36:02Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
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| series | International Journal of Molecular Sciences |
| spelling | doaj.art-266a457c434c49ac928c5973436f64e82023-11-23T20:41:51ZengMDPI AGInternational Journal of Molecular Sciences1661-65961422-00672022-10-0123191194510.3390/ijms231911945Identification of Drug-Induced Liver Injury Biomarkers from Multiple Microarrays Based on Machine Learning and Bioinformatics AnalysisKaiyue Wang0Lin Zhang1Lixia Li2Yi Wang3Xinqin Zhong4Chunyu Hou5Yuqi Zhang6Congying Sun7Qian Zhou8Xiaoying Wang9Key Laboratory of Pharmacology of Traditional Chinese Medical Formulae, Ministry of Education, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, ChinaKey Laboratory of Component-Based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, ChinaKey Laboratory of Pharmacology of Traditional Chinese Medical Formulae, Ministry of Education, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, ChinaKey Laboratory of Pharmacology of Traditional Chinese Medical Formulae, Ministry of Education, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, ChinaKey Laboratory of Pharmacology of Traditional Chinese Medical Formulae, Ministry of Education, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, ChinaKey Laboratory of Pharmacology of Traditional Chinese Medical Formulae, Ministry of Education, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, ChinaKey Laboratory of Pharmacology of Traditional Chinese Medical Formulae, Ministry of Education, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, ChinaKey Laboratory of Pharmacology of Traditional Chinese Medical Formulae, Ministry of Education, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, ChinaCollege of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, ChinaKey Laboratory of Pharmacology of Traditional Chinese Medical Formulae, Ministry of Education, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, ChinaDrug-induced liver injury (DILI) is the most common adverse effect of numerous drugs and a leading cause of drug withdrawal from the market. In recent years, the incidence of DILI has increased. However, diagnosing DILI remains challenging because of the lack of specific biomarkers. Hence, we used machine learning (ML) to mine multiple microarrays and identify useful genes that could contribute to diagnosing DILI. In this prospective study, we screened six eligible microarrays from the Gene Expression Omnibus (GEO) database. First, 21 differentially expressed genes (DEGs) were identified in the training set. Subsequently, a functional enrichment analysis of the DEGs was performed. We then used six ML algorithms to identify potentially useful genes. Based on receiver operating characteristic (ROC), four genes, DDIT3, GADD45A, SLC3A2, and RBM24, were identified. The average values of the area under the curve (AUC) for these four genes were higher than 0.8 in both the training and testing sets. In addition, the results of immune cell correlation analysis showed that these four genes were highly significantly correlated with multiple immune cells. Our study revealed that DDIT3, GADD45A, SLC3A2, and RBM24 could be biomarkers contributing to the identification of patients with DILI.https://www.mdpi.com/1422-0067/23/19/11945drug-induced liver injurymachine learningdiagnosisbiomarkermultiple microarrays |
| spellingShingle | Kaiyue Wang Lin Zhang Lixia Li Yi Wang Xinqin Zhong Chunyu Hou Yuqi Zhang Congying Sun Qian Zhou Xiaoying Wang Identification of Drug-Induced Liver Injury Biomarkers from Multiple Microarrays Based on Machine Learning and Bioinformatics Analysis International Journal of Molecular Sciences drug-induced liver injury machine learning diagnosis biomarker multiple microarrays |
| title | Identification of Drug-Induced Liver Injury Biomarkers from Multiple Microarrays Based on Machine Learning and Bioinformatics Analysis |
| title_full | Identification of Drug-Induced Liver Injury Biomarkers from Multiple Microarrays Based on Machine Learning and Bioinformatics Analysis |
| title_fullStr | Identification of Drug-Induced Liver Injury Biomarkers from Multiple Microarrays Based on Machine Learning and Bioinformatics Analysis |
| title_full_unstemmed | Identification of Drug-Induced Liver Injury Biomarkers from Multiple Microarrays Based on Machine Learning and Bioinformatics Analysis |
| title_short | Identification of Drug-Induced Liver Injury Biomarkers from Multiple Microarrays Based on Machine Learning and Bioinformatics Analysis |
| title_sort | identification of drug induced liver injury biomarkers from multiple microarrays based on machine learning and bioinformatics analysis |
| topic | drug-induced liver injury machine learning diagnosis biomarker multiple microarrays |
| url | https://www.mdpi.com/1422-0067/23/19/11945 |
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