Purpose: Huashi Baidu formula (HSBDF) was developed to treat the patients with severe COVID-19 in China. The purpose of this study was to explore its active compounds and demonstrate its mechanisms against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) through network pharmacology and molecular docking. Methods: All the components of HSBDF were retrieved from the pharmacology database of TCM system. The genes corresponding to the targets were retrieved using UniProt and GeneCards database. The herb-compound-target network was constructed by Cytoscape. The target protein-protein interaction network was built using STRING database. The core targets of HSBDF were analyzed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). The main active compounds of HSBDF were docked with SARS-CoV-2 and angiotensin converting enzyme II (ACE2). Results: Compound-target network mainly contained 178 compounds and 272 corresponding targets. Key targets contained MAPK3, MAPK8, TP53, CASP3, IL6, TNF, MAPK1, CCL2, PTGS2, etc. There were 522 GO items in GO enrichment analysis (p < .05) and 168 signaling pathways (p < .05) in KEGG, mainly including TNF signaling pathway, PI3K-Akt signaling pathway, NOD-like receptor signaling pathway, MAPK signaling pathway, and HIF-1 signaling pathway. The results of molecular docking showed that baicalein and quercetin were the top two compounds of HSBDF, which had high affinity with ACE2. Conclusion: Baicalein and quercetin in HSBDF may regulate multiple signaling pathways through ACE2, which might play a therapeutic role on COVID-19.

Objective Sijunzi decoction (SJZD) was used to treat patients with colorectal cancer (CRC) as an adjuvant method. The aim of the study was to investigate the therapeutic targets and pathways of SJZD towards the tumor microenvironment of CRC via network pharmacology and the ESTIMATE algorithm. Methods The ESTIMATE algorithm was used to calculate immune and stromal scores to predict the level of infiltrating immune and stromal cells. The active targets of SJZD were searched in the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) and UniProt database. The core targets were obtained by matching the differentially expressed genes in CRC tissues and the targets of SJZD. Then, GO, KEGG and validation in TCGA were carried out. Results According to the ESTIMATE algorithm and survival analysis, the median survival time of the low stromal score group was significantly higher than that of the high stromal score group (P = 0.018), while the patients showed no significant difference of OS between different immune groups (P = 0.19). A total of 929 genes were upregulated and 115 genes were downregulated between the stromal score groups (|logFC| > 2, adjusted P < 0.05); 357 genes were upregulated and 472 genes were downregulated between the immune score groups. The component-target network included 139 active components and 52 related targets. The core targets were HSPB1, SPP1, IGFBP3, and TGFB1, which were significantly associated with poor prognosis in TCGA validation. GO terms included the response to hypoxia, the extracellular space, protein binding and the TNF signaling pathway. Immunoreaction was the main enriched pathway identified by KEGG analysis. Conclusion The core genes (HSPB1, SPP1, IGFBP3 and TGFB1) affected CRC development and prognosis by regulating hypoxia, protein binding and epithelial-mesenchymal transition in the extracellular matrix.

Background: Colorectal cancer (CRC) is a heterogeneous disease at the cellular and molecular levels. Its pathogenesis is complex and involves abnormalities of malignant cells and tumor microenvironment (TME). The characteristics of malignant cells and TME based on single cells are unknown. This study aimed to deeply understand the intratumoral heterogeneity of CRC, and to identify cell subclusters associated with poor prognosis.Methods: scRNA-seq analyses of the CRC patients were performed. Cell subclusters of TME (immune cells and stromal cells) and malignant cells were identified. Immune Checkpoint analysis, functional enrichment analysis, SCENIC analysis, pseudotime trajectory analysis and intercellular signal communication analysis were utilized to explore the characteristics of intratumoral heterogeneity. The correlation of cell subclusters and the prognosis of CRC patients were explored using the transcriptome sequencing data of CRC from The Cancer Genome Atlas (TCGA) database.Results: A total of 11 major cell types and 18 cell subclusters were identified. Malignant cells exhibited abnormal metabolic patterns, in which the leptin signaling pathway was downregulated and the arachidonic acid metabolic pathway was upregulated. Macrophages exhibited M2 polarization. TREM2 and SPP1 genes were highly expressed in tumor-associated macrophages (TAMs). CD8+ T cells tend to be exhausted. Based on the fraction of each cell cluster, TAMs, exhausted CD8+ T cells and myo-cancer-associated fibroblasts (mCAFs) were associated with a poor prognosis. The patients from the TCGA COAD-READ were divided into 3 groups. The patients in Group 2, which showed a higher proportion of exhausted CD8+ T cells, had a poor prognosis. Conlusions: There were heterogeneity in malignant cells and TME of CRC. TAMs, exhausted CD8+ T cells and mCAFs were closely related to the poor prognosis of CRC.