Image_5_The Potential Bioactive Components of Nine TCM Prescriptions Against COVID-19 in Lung Cancer Were Explored Based on Network Pharmacology and M.TIF (432.75 kB)
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Image_5_The Potential Bioactive Components of Nine TCM Prescriptions Against COVID-19 in Lung Cancer Were Explored Based on Network Pharmacology and Molecular Docking.TIF

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posted on 20.01.2022, 04:33 authored by Lin Du, Yajie Xiao, Yijun Xu, Feng Chen, Xianghui Chu, Yuqi Cao, Xun Zhang
Objective

The purpose of this study was to screen active components and molecular targets of nine prescriptions recommended by the National Health Commission (NHC) of China by network pharmacology, and to explore the potential mechanism of the core active components against COVID-19 with molecular docking.

Methods

Differentially expressed genes of lung adenocarcinoma (LUAD) screened by edgeR analysis were overlapped with immune-related genes in MMPORT and COVID-19-related genes in GeneCards. The overlapped genes were also COVID-19 immune-related genes in LUAD. TCMSP platform was used to identify active ingredients of the prescription, potential targets were identified by the UniProt database, and the cross genes with COVID-19 immune-related genes in LUAD were used to construct a Chinese Medicine-Logy-immune target network. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed on the target genes of each prescription. Finally, the key active components were selected for molecular docking simulation with ACE2.

Results

We obtained 15 overlapping immunization target genes from FPQXZ, HSYFZ, HSZFZ, and QFPDT, 16 overlapping immunization target genes from QYLFZ, SDYFZ, SRYFZ, and YDBFZ, and 17 overlapping immunization target genes from QYLXZ. ADRB2, FOS, HMOX1, ICAM1, IL6, JUN, NFKBIA, and STAT1 also had the highest-ranked therapeutic targets for 9 prescriptions, and their expressions were positively correlated with TME-related stromal score, immune score, and ESTIMATE score. Among 9 compounds with the highest frequency of occurrence in the 9 prescriptions, baicalein had the highest ACE2 binding affinity and can be well-combined into the active pocket of ACE2 It is stabilized by forming hydrogen bonds with ASN290 and ILE291 in ACE2 and hydrophobic interaction with PHE438, ILE291, and PRO415.

Conclusion

The nine Chinese medicine prescriptions may play an anti-SARS-CoV-2 role via regulating viral transcription and immune function through multi-component, multi-target, and multi-pathway.

History

References