Genomic and algorithm-based predictive risk assessment models for benzene exposure
AimIn this research, we leveraged bioinformatics and machine learning to pinpoint key risk genes associated with occupational benzene exposure and to construct genomic and algorithm-based predictive risk assessment models.Subject and methodsWe sourced GSE9569 and GSE21862 microarray data from the Ge...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Public Health |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpubh.2024.1419361/full |
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| author | Minyun Jiang Minyun Jiang Na Cai Na Cai Juan Hu Lei Han Lei Han Fanwei Xu Baoli Zhu Baoli Zhu Baoli Zhu Baoli Zhu Boshen Wang |
| author_facet | Minyun Jiang Minyun Jiang Na Cai Na Cai Juan Hu Lei Han Lei Han Fanwei Xu Baoli Zhu Baoli Zhu Baoli Zhu Baoli Zhu Boshen Wang |
| author_sort | Minyun Jiang |
| collection | DOAJ |
| description | AimIn this research, we leveraged bioinformatics and machine learning to pinpoint key risk genes associated with occupational benzene exposure and to construct genomic and algorithm-based predictive risk assessment models.Subject and methodsWe sourced GSE9569 and GSE21862 microarray data from the Gene Expression Omnibus. Utilizing R software, we performed an initial screen for differentially expressed genes (DEGs), which was followed by the enrichment analyses to elucidate the affected functions and pathways. Subsequent steps included the application of three machine learning algorithms for key gene identification, and the validation of these genes within both a cohort exposed to benzene and a benzene-exposed mice model. We then conducted a functional prediction analysis on these genes using four machine learning models, complemented by GSVA enrichment analysis.ResultsOut of the data, 40 DEGs were identified, primarily linked to cytokine signaling, lipopolysaccharide response, and chemokine pathways. NFKB1, PHACTR1, PTGS2, and PTX3 were pinpointed as significant through machine learning. Validation confirmed substantial changes in NFKB1 and PTX3 following exposure, with PTX3 emerging as paramount, suggesting its utility as a diagnostic biomarker for benzene damage.ConclusionRisk assessment models, informed by oxidative stress markers, successfully discriminated between benzene-injured patients and controls. |
| format | Article |
| id | doaj-art-6ce110cbaf3245608b773d4c459999b7 |
| institution | Kabale University |
| issn | 2296-2565 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Public Health |
| spelling | doaj-art-6ce110cbaf3245608b773d4c459999b72025-01-22T13:45:16ZengFrontiers Media S.A.Frontiers in Public Health2296-25652025-01-011210.3389/fpubh.2024.14193611419361Genomic and algorithm-based predictive risk assessment models for benzene exposureMinyun Jiang0Minyun Jiang1Na Cai2Na Cai3Juan Hu4Lei Han5Lei Han6Fanwei Xu7Baoli Zhu8Baoli Zhu9Baoli Zhu10Baoli Zhu11Boshen Wang12School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, ChinaJiangsu Province Center for Disease Prevention and Control, Institute of Occupational Disease Prevention, Nanjing, Jiangsu, ChinaSchool of Public Health, Nanjing Medical University, Nanjing, Jiangsu, ChinaJiangsu Province Center for Disease Prevention and Control, Institute of Occupational Disease Prevention, Nanjing, Jiangsu, ChinaSchool of Public Health, Southeast University, Nanjing, Jiangsu, ChinaJiangsu Province Center for Disease Prevention and Control, Institute of Occupational Disease Prevention, Nanjing, Jiangsu, ChinaJiangsu Preventive Medical Association, Nanjing, Jiangsu, ChinaSchool of Public Health, Southeast University, Nanjing, Jiangsu, ChinaSchool of Public Health, Nanjing Medical University, Nanjing, Jiangsu, ChinaJiangsu Province Center for Disease Prevention and Control, Institute of Occupational Disease Prevention, Nanjing, Jiangsu, ChinaJiangsu Preventive Medical Association, Nanjing, Jiangsu, ChinaCenter for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, ChinaJiangsu Province Center for Disease Prevention and Control, Institute of Occupational Disease Prevention, Nanjing, Jiangsu, ChinaAimIn this research, we leveraged bioinformatics and machine learning to pinpoint key risk genes associated with occupational benzene exposure and to construct genomic and algorithm-based predictive risk assessment models.Subject and methodsWe sourced GSE9569 and GSE21862 microarray data from the Gene Expression Omnibus. Utilizing R software, we performed an initial screen for differentially expressed genes (DEGs), which was followed by the enrichment analyses to elucidate the affected functions and pathways. Subsequent steps included the application of three machine learning algorithms for key gene identification, and the validation of these genes within both a cohort exposed to benzene and a benzene-exposed mice model. We then conducted a functional prediction analysis on these genes using four machine learning models, complemented by GSVA enrichment analysis.ResultsOut of the data, 40 DEGs were identified, primarily linked to cytokine signaling, lipopolysaccharide response, and chemokine pathways. NFKB1, PHACTR1, PTGS2, and PTX3 were pinpointed as significant through machine learning. Validation confirmed substantial changes in NFKB1 and PTX3 following exposure, with PTX3 emerging as paramount, suggesting its utility as a diagnostic biomarker for benzene damage.ConclusionRisk assessment models, informed by oxidative stress markers, successfully discriminated between benzene-injured patients and controls.https://www.frontiersin.org/articles/10.3389/fpubh.2024.1419361/fullbenzene-induced damagebenzene exposuremachine learningbioinformaticsrisk assessmentoccupational health |
| spellingShingle | Minyun Jiang Minyun Jiang Na Cai Na Cai Juan Hu Lei Han Lei Han Fanwei Xu Baoli Zhu Baoli Zhu Baoli Zhu Baoli Zhu Boshen Wang Genomic and algorithm-based predictive risk assessment models for benzene exposure Frontiers in Public Health benzene-induced damage benzene exposure machine learning bioinformatics risk assessment occupational health |
| title | Genomic and algorithm-based predictive risk assessment models for benzene exposure |
| title_full | Genomic and algorithm-based predictive risk assessment models for benzene exposure |
| title_fullStr | Genomic and algorithm-based predictive risk assessment models for benzene exposure |
| title_full_unstemmed | Genomic and algorithm-based predictive risk assessment models for benzene exposure |
| title_short | Genomic and algorithm-based predictive risk assessment models for benzene exposure |
| title_sort | genomic and algorithm based predictive risk assessment models for benzene exposure |
| topic | benzene-induced damage benzene exposure machine learning bioinformatics risk assessment occupational health |
| url | https://www.frontiersin.org/articles/10.3389/fpubh.2024.1419361/full |
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