Unlocking potential biomarkers bridging coronary atherosclerosis and pyrimidine metabolism-associated genes through an integrated bioinformatics and machine learning approach
Abstract Background This study delves into the intricate landscape of atherosclerosis (AS), a chronic inflammatory disorder with significant implications for cardiovascular health. AS poses a considerable burden on global healthcare systems, elevating both mortality and morbidity rates. The patholog...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2024-03-01
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| Series: | BMC Cardiovascular Disorders |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12872-024-03819-w |
| _version_ | 1797266918163349504 |
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| author | Fanli Bu Xiao Qin Tiantian Wang Na Li Man Zheng Zixuan Wu Kai Ma |
| author_facet | Fanli Bu Xiao Qin Tiantian Wang Na Li Man Zheng Zixuan Wu Kai Ma |
| author_sort | Fanli Bu |
| collection | DOAJ |
| description | Abstract Background This study delves into the intricate landscape of atherosclerosis (AS), a chronic inflammatory disorder with significant implications for cardiovascular health. AS poses a considerable burden on global healthcare systems, elevating both mortality and morbidity rates. The pathological underpinnings of AS involve a marked metabolic disequilibrium, particularly within pyrimidine metabolism (PyM), a crucial enzymatic network central to nucleotide synthesis and degradation. While the therapeutic relevance of pyrimidine metabolism in diverse diseases is acknowledged, the explicit role of pyrimidine metabolism genes (PyMGs) in the context of AS remains elusive. Utilizing bioinformatics methodologies, this investigation aims to reveal and substantiate PyMGs intricately linked with AS. Methods A set of 41 candidate PyMGs was scrutinized through differential expression analysis. GSEA and GSVA were employed to illuminate potential biological pathways and functions associated with the identified PyMGs. Simultaneously, Lasso regression and SVM-RFE were utilized to distill core genes and assess the diagnostic potential of four quintessential PyMGs (CMPK1, CMPK2, NT5C2, RRM1) in discriminating AS. The relationship between key PyMGs and clinical presentations was also explored. Validation of the expression levels of the four PyMGs was performed using the GSE43292 and GSE9820 datasets. Results This investigation identified four PyMGs, with NT5C2 and RRM1 emerging as key players, intricately linked to AS pathogenesis. Functional analysis underscored their critical involvement in metabolic processes, including pyrimidine-containing compound metabolism and nucleotide biosynthesis. Diagnostic evaluation of these PyMGs in distinguishing AS showcased promising results. Conclusion In conclusion, this exploration has illuminated a constellation of four PyMGs with a potential nexus to AS pathogenesis. These findings unveil emerging biomarkers, paving the way for novel approaches to disease monitoring and progression, and providing new avenues for therapeutic intervention in the realm of atherosclerosis. |
| first_indexed | 2024-04-25T01:08:19Z |
| format | Article |
| id | doaj.art-8ddad78e74ed4fe8a632eb1cc69c5793 |
| institution | Directory Open Access Journal |
| issn | 1471-2261 |
| language | English |
| last_indexed | 2024-04-25T01:08:19Z |
| publishDate | 2024-03-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Cardiovascular Disorders |
| spelling | doaj.art-8ddad78e74ed4fe8a632eb1cc69c57932024-03-10T12:05:18ZengBMCBMC Cardiovascular Disorders1471-22612024-03-0124111610.1186/s12872-024-03819-wUnlocking potential biomarkers bridging coronary atherosclerosis and pyrimidine metabolism-associated genes through an integrated bioinformatics and machine learning approachFanli Bu0Xiao Qin1Tiantian Wang2Na Li3Man Zheng4Zixuan Wu5Kai Ma6Dongying People’s Hospital (Dongying Hospital of Shandong Provincial Hospital Group)Dongying People’s Hospital (Dongying Hospital of Shandong Provincial Hospital Group)Dongying People’s Hospital (Dongying Hospital of Shandong Provincial Hospital Group)Dongying People’s Hospital (Dongying Hospital of Shandong Provincial Hospital Group)Dongying People’s Hospital (Dongying Hospital of Shandong Provincial Hospital Group)Guangzhou University of Chinese MedicineDongying People’s Hospital (Dongying Hospital of Shandong Provincial Hospital Group)Abstract Background This study delves into the intricate landscape of atherosclerosis (AS), a chronic inflammatory disorder with significant implications for cardiovascular health. AS poses a considerable burden on global healthcare systems, elevating both mortality and morbidity rates. The pathological underpinnings of AS involve a marked metabolic disequilibrium, particularly within pyrimidine metabolism (PyM), a crucial enzymatic network central to nucleotide synthesis and degradation. While the therapeutic relevance of pyrimidine metabolism in diverse diseases is acknowledged, the explicit role of pyrimidine metabolism genes (PyMGs) in the context of AS remains elusive. Utilizing bioinformatics methodologies, this investigation aims to reveal and substantiate PyMGs intricately linked with AS. Methods A set of 41 candidate PyMGs was scrutinized through differential expression analysis. GSEA and GSVA were employed to illuminate potential biological pathways and functions associated with the identified PyMGs. Simultaneously, Lasso regression and SVM-RFE were utilized to distill core genes and assess the diagnostic potential of four quintessential PyMGs (CMPK1, CMPK2, NT5C2, RRM1) in discriminating AS. The relationship between key PyMGs and clinical presentations was also explored. Validation of the expression levels of the four PyMGs was performed using the GSE43292 and GSE9820 datasets. Results This investigation identified four PyMGs, with NT5C2 and RRM1 emerging as key players, intricately linked to AS pathogenesis. Functional analysis underscored their critical involvement in metabolic processes, including pyrimidine-containing compound metabolism and nucleotide biosynthesis. Diagnostic evaluation of these PyMGs in distinguishing AS showcased promising results. Conclusion In conclusion, this exploration has illuminated a constellation of four PyMGs with a potential nexus to AS pathogenesis. These findings unveil emerging biomarkers, paving the way for novel approaches to disease monitoring and progression, and providing new avenues for therapeutic intervention in the realm of atherosclerosis.https://doi.org/10.1186/s12872-024-03819-wAtherosclerosis (AS)Pyrimidine Metabolism Genes (PyMGs)Lasso regressionSVM-RFEBioinformatics |
| spellingShingle | Fanli Bu Xiao Qin Tiantian Wang Na Li Man Zheng Zixuan Wu Kai Ma Unlocking potential biomarkers bridging coronary atherosclerosis and pyrimidine metabolism-associated genes through an integrated bioinformatics and machine learning approach BMC Cardiovascular Disorders Atherosclerosis (AS) Pyrimidine Metabolism Genes (PyMGs) Lasso regression SVM-RFE Bioinformatics |
| title | Unlocking potential biomarkers bridging coronary atherosclerosis and pyrimidine metabolism-associated genes through an integrated bioinformatics and machine learning approach |
| title_full | Unlocking potential biomarkers bridging coronary atherosclerosis and pyrimidine metabolism-associated genes through an integrated bioinformatics and machine learning approach |
| title_fullStr | Unlocking potential biomarkers bridging coronary atherosclerosis and pyrimidine metabolism-associated genes through an integrated bioinformatics and machine learning approach |
| title_full_unstemmed | Unlocking potential biomarkers bridging coronary atherosclerosis and pyrimidine metabolism-associated genes through an integrated bioinformatics and machine learning approach |
| title_short | Unlocking potential biomarkers bridging coronary atherosclerosis and pyrimidine metabolism-associated genes through an integrated bioinformatics and machine learning approach |
| title_sort | unlocking potential biomarkers bridging coronary atherosclerosis and pyrimidine metabolism associated genes through an integrated bioinformatics and machine learning approach |
| topic | Atherosclerosis (AS) Pyrimidine Metabolism Genes (PyMGs) Lasso regression SVM-RFE Bioinformatics |
| url | https://doi.org/10.1186/s12872-024-03819-w |
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