Abstract
The number of patients with COVID-19 caused by severe acute respiratory syndrome coronavirus 2 is still increasing. In the case of COVID-19 and tuberculosis (TB), the presence of one disease affects the infectious status of the other. Meanwhile, coinfection may result in complications that make treatment more difficult. However, the molecular mechanisms underpinning the interaction between TB and COVID-19 are unclear. Accordingly, transcriptome analysis was used to detect the shared pathways and molecular biomarkers in TB and COVID-19, allowing us to determine the complex relationship between COVID-19 and TB. Two RNA-seq datasets (GSE114192 and GSE163151) from the Gene Expression Omnibus were used to find concerted differentially expressed genes (DEGs) between TB and COVID-19 to identify the common pathogenic mechanisms. A total of 124 common DEGs were detected and used to find shared pathways and drug targets. Several enterprising bioinformatics tools were applied to perform pathway analysis, enrichment analysis and networks analysis. Protein–protein interaction analysis and machine learning was used to identify hub genes (GAS6, OAS3 and PDCD1LG2) and datasets GSE171110, GSE54992 and GSE79362 were used for verification. The mechanism of protein-drug interactions may have reference value in the treatment of coinfection of COVID-19 and TB.
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ZH, JK, JXL, and PC designed the study, acquired all datasets, analyzed the data, prepared the figure and table and wrote the main manuscript. JL and YH analyzed the data, collected the specimens and interpreted the data. QL, HC, NH, and TL supervised the project and revised the manuscript. XG: evaluated and guided the full text and granted final approval of the version to be submitted. All authors reviewed and approved the final manuscript.
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Huang, ZM., Kang, JQ., Chen, PZ. et al. Identifying the Interaction Between Tuberculosis and SARS-CoV-2 Infections via Bioinformatics Analysis and Machine Learning. Biochem Genet (2023). https://doi.org/10.1007/s10528-023-10563-x
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DOI: https://doi.org/10.1007/s10528-023-10563-x