Multi-omics and machine learning identify novel biomarkers and therapeutic targets of COVID-19
Yumei Zhou, Pengbei Fan, Haiyun Zhang, Shuai Han, Minghua Bai, Ji Wang, Qi Wang
Frontiers in Immunology, doi:10.3389/fimmu.2025.1671936
Introduction: COVID-19 has caused over 7 million deaths worldwide since its onset in 2019, and the virus remains a significant health threat. Identifying sensitive and specific biomarkers, along with elucidating immune-mediated mechanisms, is essential for improving the diagnosis, treatment, and prevention of COVID-19. To predict key molecular markers of COVID-19 using an established multi-omics framework combined with machine learning models. Methods: We conducted an integrated analysis of single-cell RNA sequencing (scRNA-seq), bulk RNA sequencing, and proteomics data to identify critical biomarkers associated with COVID-19. The multi-omics approach enabled the characterization of gene expression dynamics and alterations in immune cell subsets in COVID-19 patients. Machine learning techniques and molecular docking analyses were employed to identify biomarkers and therapeutic targets within the disease's pathophysiological network. Results: Principal component analysis effectively grouped samples based on clinical characteristics. Using random forest and SVM-RFE models, we identified clinical indicators capable of accurately distinguishing COVID-19 patients. Transcriptomic analysis, including scRNA-seq, highlighted the pivotal role of CD8 + T cells, and WGCNA identified related module genes. Proteomic analysis, integrated with machine learning, revealed 36 DEPs. Further investigation identified several genes associated with monocyte proportions. Correlation analysis showed that BTD, CFL1, PIGR, and SERPINA3 were strongly linked to CD8 + T cell abundance in COVID-19 patients. ROC curve analysis demonstrated that these genes could effectively distinguish between COVID-19 patients and healthy individuals. Concordant findings from both transcriptomic and proteomic levels support BTD, CFL1, PIGR, and SERPINA3 as potential auxiliary diagnostic markers. Finally, AlphaFold-based molecular docking analysis suggested these biomarkers may also serve as candidate therapeutic targets.
Ethics statement The studies involving humans were approved by the Ethics Committee of the Second Affiliated Hospital of Mudanjiang Medical College (Approval No. 202328 ). The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.
Author contributions
Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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doi:10.1038/s41392-024-02043-4DOI record:
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"abstract": "<jats:sec><jats:title>Introduction</jats:title><jats:p>COVID-19 has caused over 7 million deaths worldwide since its onset in 2019, and the virus remains a significant health threat. Identifying sensitive and specific biomarkers, along with elucidating immune-mediated mechanisms, is essential for improving the diagnosis, treatment, and prevention of COVID-19. To predict key molecular markers of COVID-19 using an established multi-omics framework combined with machine learning models.</jats:p></jats:sec><jats:sec><jats:title>Methods</jats:title><jats:p>We conducted an integrated analysis of single-cell RNA sequencing (scRNA-seq), bulk RNA sequencing, and proteomics data to identify critical biomarkers associated with COVID-19. The multi-omics approach enabled the characterization of gene expression dynamics and alterations in immune cell subsets in COVID-19 patients. Machine learning techniques and molecular docking analyses were employed to identify biomarkers and therapeutic targets within the disease’s pathophysiological network.</jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p>Principal component analysis effectively grouped samples based on clinical characteristics. Using random forest and SVM-RFE models, we identified clinical indicators capable of accurately distinguishing COVID-19 patients. Transcriptomic analysis, including scRNA-seq, highlighted the pivotal role of CD8<jats:sup>+</jats:sup> T cells, and WGCNA identified related module genes. Proteomic analysis, integrated with machine learning, revealed 36 DEPs. Further investigation identified several genes associated with monocyte proportions. Correlation analysis showed that BTD, CFL1, PIGR, and SERPINA3 were strongly linked to CD8<jats:sup>+</jats:sup> T cell abundance in COVID-19 patients. ROC curve analysis demonstrated that these genes could effectively distinguish between COVID-19 patients and healthy individuals. Concordant findings from both transcriptomic and proteomic levels support BTD, CFL1, PIGR, and SERPINA3 as potential auxiliary diagnostic markers. Finally, AlphaFold-based molecular docking analysis suggested these biomarkers may also serve as candidate therapeutic targets.</jats:p></jats:sec><jats:sec><jats:title>Discussion</jats:title><jats:p>Preliminary findings indicate that BTD, CFL1, PIGR, and SERPINA3 are vital molecular biomarkers related of CD8+ T cell, providing new insights into the molecular mechanisms and long-term prevention of COVID-19.</jats:p></jats:sec>",
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