Multi-omics and machine learning identify novel biomarkers and therapeutic targets of long-term prevention COVID-19

Yumei Zhou¹Pengbei Fan²Haiyun Zhang¹Shuai Han³Minghua Bai¹Ji Wang^1*Qi Wang^1*

• ¹National Institute of TCM Constitution and Preventive Treatment of Disease, Wangqi Academy of Beijing University of Chinese Medicine, Beijing University of Chinese Medicine, Beijing, China
• ²School of Traditional Chinese Medicine, Southern Medical University, Guangzhou, China
• ³Inner Mongolia Medical University, Hohhot, China

Background: 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. Conclusions: 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.