LitCovid-PubTator  

PMC:7532482 / 421-455 JSONTXT 3 Projects

Investigating the mechanism of ShuFeng JieDu capsule for the treatment of novel Coronavirus pneumonia (COVID-19) based on network pharmacology Abstract ShuFeng JieDu capsule (SFJDC), a traditional Chinese medicine, has been recommended for the treatment of COVID-19 infections. However, the pharmacological mechanism of SFJDC still remains vague to date. The active ingredients and their target genes of SFJDC were collected from TCMSP. COVID-19 is a type of Novel Coronavirus Pneumonia (NCP). NCP-related target genes were collected from GeneCards database. The ingredients-targets network of SFJDC and PPI networks were constructed. The candidate genes were screened by Venn diagram package for enrichment analysis. The gene-pathway network was structured to obtain key target genes. In total, 124 active ingredients, 120 target genes of SFJDC and 251 NCP-related target genes were collected. The functional annotations cluster 1 of 23 candidate genes (CGs) were related to lung and Virus infection. RELA, MAPK1, MAPK14, CASP3, CASP8 and IL6 were the key target genes. The results suggested that SFJDC cloud be treated COVID-19 by multi-compounds and multi-pathways, and this study showed that the mechanism of traditional Chinese medicine (TCM) in the treatment of disease from the overall perspective. Introduction Since December 2019, a novel coronavirus pneumonia (NCP) caused by new coronavirus (SARS-COV-2) has been prevalent in China and other countries, such as United States and Korea 1-3. WHO named this novel coronavirus pneumonia COVID-19 on February 11, 2020 4 and there was a total of 20 million reported cases of COVID-19 globally and 750,000 deaths as of August 10, 2020 5. Its transmission route is mainly through respiratory droplets, but also through contact transmission, which has the characteristics of rapid spread, strong infectivity and general susceptibility of various groups of people. COVID-19 mild patients present with fever, fatigue, dry cough and other symptoms, whereas severe patients can appear with dyspnea, acute respiratory distress syndrome (ARDS) or septic shock and other symptoms. There is no special drug at present 6,7. The treatment of COVID-19 mainly consisted of bed rest; intensive supportive treatment; oxygen therapy; antiviral therapy; antimicrobial therapy and Chinese medicine treatment. Critical cases need respiratory support (high flow nasal oxygen therapy, non-invasive ventilator or invasive mechanical ventilator); circulatory support for critically ill patients; plasma treatment from recovered patients and immunotherapy 8,9. Most of the infectious diseases caused by virus belong to the category of "plague" in ancient Chinese traditional medicine, which is caused by many evil spirits invading the body 10. The traditional medicine, including traditional Chinese medicine (TCM), has a good therapeutic effect on it 11,12. The Health and Health Commission of China and the State Administration of traditional Chinese Medicine in the "circular on the issuance of a new type of coronavirus infection pneumonia diagnosis and treatment program (version 5)" requested to strengthen the integration of Chinese and western medicine, and recommended a number of proprietary Chinese medicine in the process of diagnosis and treatment 13. On the basis of the national plan and in accordance with the principle of "three conditions and conditions", local prevention and control projects have also been successively issued according to local conditions 14. Recommended Chinese medicines include MaXing ShiGan Tang, QingFei PaiDu Tang, HuoXiang ZhengQi Capsules, JinHua QingGan Granules, LianHua QingWen Capsules or ShuFeng JieDu capsule 8. One clinical study showed that LianHua QingWen could improve the symptoms of COVID-19 patients and shorten the course of disease 15. A retrospective analysis study showed that the time of disappearance of clinical symptoms, recovery of body temperature, average length of stay in the integrated Chinese and western medicine treatment group (34) was significantly lower than that of the western medicine group (18) among the 52 COVID-19 patients 16.With QingFei PaiDu Tang combined with western medicine to treat the COVID-19 could significantly improve the patient's symptoms and achieved better results 17. ShuFeng JieDu capsule (SFJDC) is a traditional Chinese medicine used to treat influenza in China 18. SFJDC is composed of Polygoni Cuspidati Rhizoma Et Radix (PCRR), Forsythiae Fructus (FF), Isatidis Radix (IR), Herba Patriniae (HP), Phragmitis Rhizoma (PR), Verbenae Herb (VH), licorice (I), Radix Bupleuri (RB) (Table 1). SFJDC has antiviral, anti-inflammatory, antipyretic and immune regulatory effects 19. SFJDC was commonly used for upper respiratory tract infection, pulmonary infection, AECOPD and other disease 20.This drug now is also recommended for the treatment of COVID-19 infections in the latest Diagnosis and Treatment of Pneumonia Caused by COVID-19 (version 5) 13,21. Currently, SFJDC is recommended in the Diagnosis and Treatment of Pneumonia Caused by COVID-19 in 5 provinces and cities 22. Network pharmacology is a new discipline based on the theory of system biology, which analyzes the biological systems and selects specific signal nodes for multi-target drug molecular design. Network pharmacology emphasizes the multi-pathway regulation of signaling pathways and the regulation of multi-component, multi-target, multi-pathway, linking active components in traditional chinese medicine with target genes from molecular and biological aspects 23. Network pharmacology will help to understand the relationship among ingredients, genes and diseases and is suitable for the study of complex TCM or TCM compounds. The potential mechanism of preventing COVID-19 by HuoXiang ZhengQi oral solution was realized by network pharmacology and molecular docking 24. The research group Jing Zhao elucidated the mechanism of QingFei PaiDu Tang in the treatment of COVID-19 using network pharmacology 25. SFJDC could be efficacious for COVID-19, but active incredients, target genes and putative mechanism are not known. In the present study, the network pharmacological was used to investigate the possible mechanism and target of SFJDC in the treatment of COVID-19. COVID-19 is a type of Novel Coronavirus Pneumonia (NCP). The active ingredients and their target genes of SFJDC were collected from TCMSP. NCP-related target genes were collected from GeneCards database. The putative mechanism of SFJDC against NCP were analyzed by GO and KEGG pathway. The flowchart of network pharmacology was shown in Figure 1. The study provided possible theoretical reference for SFJDC in the prevention and treatment of COVID-19. Materials and Methods Screening of active Ingredients in SFJDC We identified the active ingredients of SFJDC from Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP http://tcmspw.com/tcmsp.php) 26. TCMSP is a unique herbal pharmacology platform that captures the relationship between drugs, target genes and diseases. The database includes the detection of natural compounds such as chemical, target and drug target networks. ADME is pharmacokinetics, which refers to the absorption, distribution, metabolism and excretion of exogenous chemicals by myosome. The four key parameters of ADME were blood-brain barrier (BBB), oral bioavailability (OB), Caco-2 permeability (Caco-2) and drug-likeness (DL) 27. In this study select candidate compounds which has OB≥30%, DL≥ 0.18, Caco-2≥-0.4, BBB≥-0.3.Then we sorted out each active ingredient for identification of targets. Identification of SFJDC putative target genes This study used the TCMSP platform to obtain the putative target genes of active ingredients of SFJDC. The Uniprot (https://www.uniprot.org/) 28 database provides a comprehensive, high quality and freely available source of protein sequence and function information. The putative target information corresponding to the active ingredients were input into UniProt database to obtain the standard name of the action target genes. Screening of NCP related targets COVID-19 is a type of Novel Coronavirus Pneumonia (NCP). So We collected NCP related targets from GeneCards (https://www.genecards.org/), which is a searchable, integrative database that provides comprehensive, user-friendly information on all annotated and predicted human genes 29. The key word “Novel Coronavirus Pneumonia” was used in the GeenCards database. PPI (Protein-Protein Interaction) network construction of SFJDC putative and NCP related target genes The PPI network of SFJDC putative and NCP related targets would be obtained from STRING (https://string-db.org/ ver11.0, update Jan 2019) 30. Active interaction sources were set as follows: Textmining, Co-expression, Neighborhood, Experiments, Databases, Gene Fusion and Co-occurrence. The required minimum interaction score was set at 0.4 in PPI network of SFJDC related targets, PPI network of NCP was set at 0.9. The barplot were generated by the R software (https://www.r-project.org/ver 3.6.2) based on counts value. Construction of SFJDC ingredient-target network Perl (https://www.perl.org/get.html) is a programming language suitable for writing simple scripts as well as complex applications. We used Strawberry Perl 5.30.1.1 to prepare the ingredient-target network. Cytoscape is a universal open source software for large-scale integrated development of molecular interaction networks working data. Then the ingredients-targets network of SFJDC was constructed using Cytoscape 3.7.2 software 31. PPI network construction of SFJDC against NCP In order to reveal the mechanism of SFJDC against NCP, a PPI network was constructed by the BisoGenet client which is a Cytoscape plugin was used to visualize. In this plugin, Protein-protein interactions information is taken from the DIP, BIOGRID, HPRD, INTACT, MINT, BIND 32. CytoNCA is a Cytoscape plugin integrating calculation, evaluation and visualization analysis for multiple centrality measure measures including Betweenness Centrality (BC), Degree Centrality (DC), Colseness Centrality (CC), Local average connectivity-based method (LAC), Eigenvector Centrality (EC) and Network Centrality (NC) 33. Identification of candidate genes (CGs) and enrichment analysis of CGs The CGs were filtered with R software using the Venn Diagram package (https://cran.r-project.org/web/packages/VennDiagram/index.html). The CGs would be used for Gene Ontology (GO) analysis (including biological processes (BP), molecular functions (MF), and cellular components (CC)) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. GO and KEGG pathway analyses results were processed by the “enrichplot” (http://www.bioconductor.org/packages/release/bioc/html/enrichplot.html) “clusterProfiler” (http://www.bioconductor.org/packages/release/bioc/html/clusterProfiler.html) and “ggplot2” packages by R software. A P value of less than 0.05 was used regarded as statistically significant. At the same time, we input CGs into DAVID (https://david.ncifcrf.gov/) for functional enrichment analysis to obtain disease clustering. Construction of gene-pathway network KEGG pathways that had significant changes of P<0.05 were further analyzed. The genes that significantly regulated pathways for gene-pathway network construction. The key target genes of SFJDC against NCP were screened by gene-pathway network. Results The active ingredients of each herb contained in SFJDC One hundred and thirty-seven active ingredients were screened out of TCMSP based on ADME, 4 in PCRR, 17 in FF, 25 in IR, 9 in HP, 7 in PR, 7 in VH, 1 in I, 67 in RB and 13 of which were repeated. Finally, 124 candidate active components of each herb contained in SFJDC were screened for further analysis after removing duplation (Table 2). Putative target genes of each herb in SFJDC and NCP related target genes The 124 candidate active components were imported into TCMSP database and Uniport database to identify the Putative target genes of each herb in SFJDC. One hundred and ten components were finally selected after removing 14 ingredients which did not link to any target genes. The target genes of 110 compounds were collected. 1705 genes were identified, 103 in PR, 209 in IR, 65 in HP, 1052 in RB, 75 in PCRR, 173 in FF and 27 in I. There were 1585 genes of the eight herbs overlapped, which was suggestive of potential interaction between the compounds of SFJDCA in the course of treatment. A total of 120 genes were identified after removing duplation (Table 3). And 251 NCP related target genes were identified from Gene Cards database (Table 4). PPI network of SFJDC putative and NCP related target genes In this study, we constructed the PPI network of SFJDC putative and NCP related target genes separately. The network of SFJDC putative target genes which minimum interaction score was set at 0.4 contained 119 nodes and 1108 edges which indicated the target genes interactions after removing the discrete points (Figure 2A). According the PPI network, the top thirty genes were listed in Figure 2B. After hiding the discrete points, NCP-related target genes PPI network contained 248 nodes and 1235 edges (Figure 2C). Similarly, the first 30 related genes were shown in Figure 2D. SFJDC ingredient-target network analysis The ingredient-target network of SFJDC was constructed using the screened ingredients and their targets as shown in Figure 3. The network contained 117 nodes and 419 edges which indicated the compound-target genes interaction. A median of 110 candidate compouds was 5 degrees which indicating that most compounds of SFJDC were affected by multiple target genes. The top three effective ingredient according were Wogonin, licochalcone a and acacetin. Wogonin, licochalcone a and acacetin have 42, 30 and 23 target genes, respectively. And the OB of Wogonin, licochalcone a and acacetin were 30.68, 40.79 and 34.97%, respectively. Hence, they might be the crucial effective compounds of SFJDC according the network. PPI network analysis of SFJDC against NCP PPI network of SFJDC against NCP were visualized using Cytoscape software. The network contained 2407 nodes and 53639 edges was shown in Figure 4A. The average degree of all nodes was 44.5692 and we selected the nodes with more than 44.5692 degrees as significant genes. A subnetwork of significant genes for SFJDC against NCP was constructed which consisted of 766 nodes and 28872 edges (Figure 4B). The average value of BC was 711.9504. The significant genes were further screened and a new network was constructed with 169 nodes and 4238 edges (Figure 4C). 169 genes were eventually identified for SFJDC against NCP including 156 other human genes and 13 target genes. Identification of candidate genes (CGs) and Enrichment analysis of CGs Twenty-three candidate genes (CGs) were identified by using the VennDiagram package (Figure 5). Then R software was used to perform GO and KEGG pathway analysis of the CGs. GO of CGs was analyzed based on BP, CC, MF. 1215 GO terms were significantly enriched (P<0.05), 1148 in BP, 28 in CC, 39 in MF. Top 20 terms were shown in Figure 6. The data of top 20 GO analysis were listed in Table 5. Based on these GO terms data, we found that most significantly terms were response to lipopolysaccharide, response to molecule of bacterial origin, membrane raft, membrane microdomain, BH domain binding and death domain binding, suggested that SFJDC could treat NCP with multiple synergies. The pathways that were significantly affected by SFJDC in the process of treating NCP were identified by KEGG pathway. 110 KEGG pathways were significantly enriched (P<0.05). Top twenty pathways were shown in Figure 7, color represented P value and size of the spot represented count of genes. Based on the analysis of KEGG pathway data (Table 6), the top five pathways such as Kaposi sarcoma-associated herpesvirus infection, AGE-RAGE signaling pathway in diabetic complications, Human cytomegalovirus infection, IL-17 signaling pathway and Hepatitis B, might be the core pharmacological mechanism of SFJDC for NCP. In this study, we chose the functional annotation clustering and set the classification stringency as high in DAVID. A total of 20 functional annotation clusters were obtained (Table 7). Annotation Cluster 1 (enrichment score 6.04) contains three categories: Asthma, Bronchiolitis Viral, Respiratory Syncytial Virus Infections, respiratory syncytial virus bronchiolitis, and all of them were lung related diseases and Virus infection disease. Gene-pathway network analysis The construction of gene-pathway network is based on significant enrichment pathway and regulated these ways, which was shown in Figure 8. The V shapes represented pathway and the squares represent target genes in the network. The network showed that RELA was the core target gene which had largest degree. Other five genes also had larger degree such as MAPK1, MAPK14, CASP3, CASP8 and IL6. They might be the key target genes using SFJDC in the process of treating NCP. All of the above analysis could reveal a new strategy for drug development on NCP. Discussion The theory of TCM has been formed and developed for thousands of years in China. In China, TCM has a good therapeutic effect on COVID-19, which has been written into the diagnosis and treatment guidelines. The guideline points out that the combination of traditional Chinese and western medicine should be strengthened in the treatment process 34. SFJDC is a traditional Chinese medicine, mainly used to treat upper respiratory tract infections, such as influenza, sore throat, mumps, streptococcus, etc. 21. Now, SFJDC has become an effective drug for the treatment of COVID-19 35. In recent years, the research on Chinese medicine prescriptions has developed to the level of effective parts, components, components. Network pharmacology can better understand and demonstrate the interaction between multi-component multi-target and disease 36. This study aims to analyze the active components and potential mechanism of SFJDC in the treatment of COVID-19 through network pharmacology. In the present study, the ingredients-targets network of SFJDC was constructed using 110 ingredients and 120 targets. The network contained 117 nodes and 419 edges which indicated the compound-target genes interaction. The results showed that most compounds of SFJDC were affected by multiple target genes, such as Wogonin, licochalcone a and acacetin acted on 42, 30 and 23 target genes, respectively. Various compounds of SFJDC may have the same targets to achieve synergy. Wogonin, a naturally occurring flavonoid, has been shown to multi-activity, such as anti-inflammatory, anti-fibrosis, anti-cancer and chondroprotective properties 37. Study showed that wogonin had an anti-infulenza activity by modulation of AMPK pathway 38. Licochalcone a, a flavonoid extracted from licorice toot, was known for its anti-inflammatory, anti-cancer, anti-oxidative and anti-bacterial bioactivity 39. Acacetin, a flavone compound, played an important role in anti-inflammatory and anti-peroxidative 40. In addition, they have high OB and acacetin from 2 herbs (PR, IR) of SFJDC. The three main ingredients were anti-inflammatory and COVID-19 caused by a series of inflammatory storms. Hence, they might be the crucial effective compounds of SFJDC according the network. PPI network of SFJDC against NCP were visualized using Cytoscape software to obtain the candidate target genes. In order to obtain the more accurate genes, two parameters including DC and BC were used to screen nodes and structure a new network. 169 genes were eventually identified for SFJDC against NCP including 156 other human genes and 13 target genes. Twenty-three candidate genes (CGs) were identified by using the VennDiagram package. CGs were enriched in BP, CC, MF by GO enrichment analysis. Based on GO terms data, we found that some terms were response to lipopolysaccharide or bacterial origin, membrane raft, membrane microdomain, BH domain binding and cytokine receptor binding. COVID-19 infections leaded to a strong immune response and inflammatory storm in which a large number of cytokines were activated, so SFJDC might regulate COVID-19 through the above biological processes. SFJDC, as a TCM formula, has multi-component, multi-target-gene, multi-pathway. In the present study, 110 KEGG pathways were significantly enriched. Seven of the top 20 pathways were associated with viral infection including Kaposi sarcoma-associated herpesvirus infection, Human cytomegalovirus infection, Hepatitis B, Influenza A, Epstein-Barr virus infection, Human immunodeficiency virus 1 infection and Measles, and three were associated with lung disease contained tuberculosis, pertussis and small cell lung cancer. Multiple targets of SFJDC may also inhibit the activation of cytokines and reduce inflammation by regulating cytokine pathways, such as IL-17 signaling pathway and TNF signaling pathway. In this study, we obtained 20 functional annotation clusters through DAVID. Annotation Cluster1 including Asthma, Bronchiolitis Viral, Respiratory Syncytial Virus Infections, respiratory syncytial virus bronchiolitis were lung related diseases and Virus infection disease. Gene-pathway network was constructed to the core and key target genes. The network showed that RELA had largest degree, was the core target gene. Other top five genes such as MAPK1, MAPK14, CASP3, CASP8 and IL6 might be the key target genes. The pathophysiological process of Severe Acute Respiratory Syndrome-Coronavirus-2 (SARS-COV-2) infection is similar to that of SARS-CoV infection, with a strong inflammatory response. The SARS-COV-2 virus mainly targets respiratory epithelial cells, alveolar epithelial cells, vascular endothelial cells and pulmonary macrophages, all of which express Angiotensin converting enzyme 2 (ACE2) receptor, triggering the generation of pro- inflammatory cytokines and chemokines (including IL-6, TNF, IL-10 and MCP1) 41. The NF-kB family member RELA is a widely expressed and effective transcriptional activator that activates the expression of many inflammatory through exposure to pathogens and inflammatory cytokines 42. RELA may play an important role in the infection of COVID-19. MAPK1 and MAPK14 are members of the MAPK family, which can regulate multiple cellular processes, such as response to oxidative stress, anti-inflammatory, immune response, apoptosis and cell proliferation 43. Joseph et al showed SASR-CoV-2 could induce severe inflammation by directly activating p38 MAPK pathway and many p38 MAPK inhibitors are in the clinical stage and should be considered for clinical trial for severe COVID-19 infection 44. CASP3 and CASP8, a family of cysteine-dependent proteases, play an important role in these events through activation of other apoptotic proteins mediated by proteolysis and cleavage of nuclear proteins 45. In Krahling's study, infection of 293/ACE2 cells with SARS-CoV activated apoptosis-associated events, such as caspase3, caspase 846. Therefore, we conclude that CASP3 and CASP8 may be activated and play an important role in the pathophysiological process of COVID-19. Higher plasma level of IL-6 was found in ICU patients with COVID-1947. Tocilizumab, a recombinant humanized anti-human IL-6 receptor monoclonal antibody, improved the clinical outcome in 20 severe and critical COVID-19 patients and is an effective treatment to reduce mortality 48. It has been clinically confirmed that SFJDC is effective in the treatment of COVID-19. Wang et al shown that conventional treatment combined with SFJDC treatment for 4 cases of COVID-19 patients could significantly improve symptoms and promote viral negative conversion 49. Another study including 70 COVID-19 patients found that SFJDC combined with Arbidol for COVID-19 compared with single using Arbidol could significantly shorten the time of clinical symptoms improvement and COVID-19 negative conversion 50. To summarise, the compound and targets of SFJDC were systematically studied by applying network pharmacology. Wogonin, licochalcone a and acacetin regulated the most targets associated with NCP. RELA, MAPK1, MAPK14, CASP3, CASP8 and IL6 were the core and key genes in the gene-network of SFJDC for the treatment of NCP. SFJDC regulated novel coronavirus pneumonia by multi-compound and multi-target, which provided theoretical support for SFJDC against COVID-19. More mechanism and roles require further clinical validation. Author contributions YQQ, XC designed the study; YHY and MYZ performed the data collection; JYL and RL analyzed the data; XC drafted the manuscript; YQQ revised the manuscript. All authors read and approved the final manuscript. Funding This work was supported by the fund from New Coronavirus Pneumonia emergency research project of Shandong University (2020XGA02). Data Accessibility Publicly available databases were analyzed in our study. The active ingredients and putative target genes of SFJDC from TCMSP can be found in http://tcmspw.com/tcmsp.php. NCP-related target genes were from GeneCards (https://www.genecards.org/). Abbreviations ACE2 Angiotensin converting enzyme 2 ARDS acute respiratory distress syndrome BBB blood-brain barrier BC Betweenness Centrality BP biological processes Caco-2 Caco-2 permeability CC cellular components CC Colseness Centrality CG candidate genes DC Degree Centrality DL drug-likeness (DL) EC Eigenvector Centrality FF Forsythiae Fructus GO Gene Ontology HP Herba Patriniae I licorice IR Isatidis Radix KEGG Kyoto Encyclopedia of Genes and Genomes LAC Local average connectivity-based method LHQWG LianHua QingWen granules MF molecular functions NC Network Centrality NCP Novel Coronavirus Pneumonia OB oral bioavailability PCRR Polygoni Cuspidati Rhizoma Et Radix PPI protein-protein interaction PR Phragmitis Rhizoma RB Radix Bupleuri SFJDC ShuFeng JieDu capsule SARS-COV-2 Severe Acute Respiratory Syndrome-Coronavirus-2 TCM Traditional Chinese Medicine TCMSP Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform VH Verbenae Herb Figure 1 The flowchart of the whole manuscript base on network pharmacology. Figure 2 PPI network of SFJDC putative and NCP related target genes and the Barplot of PPI. (A) PPI network of SFJDC putative target genes. (B) PPI network of NCP related target genes. (C) Barplot showing the significant genes in PPI network of SFJDC. (D) Barplot showing the significant genes in PPI network of NCP. PPI, protein-protein interaction; SFJDC: ShuFeng JieDu capsule; NCP: Novel Coronavirus Pneumonia. Figure 3 Ingredient-target network of SFJDC. The blue ovals represent target genes; the green, light blue, yellow, pink, purple and light yellow rectangulars represent the ingredients from PR, IR, HP, RB, PCRR, FF; the red rectangulars represent the ingredients from mlti-herb. PR: Phragmitis Rhizoma; IR: Isatidis Radix; HP: Herba Patriniae; RB:Radix Bupleuri; PCRR: Polygoni Cuspidati Rhizoma Et Radix; FF: Forsythiae Fructus. Figure 4 PPI network of SFJDC against NCP. (A)The whole network of SFJDC against NCP contained 2,407 nodes and 53,639 edges. (B) A subnetwork of significant genes from A consisted of 766 nodes and 28872 edges. (C) PPI network of more significant genes from B with 169 nodes and 4238 edges. BC: Betweenness Centrality; DC: Degree Centrality. Figure 5 Twenty-three overlapping genes between SFJDC and NCP. Figure 6 Gene ontology terms of CGs. The top 20 GO functional terms were selected (P<0.05). BP: biological processes; CC: cellular components; MF: molecular functions. Figure 7 KEGG pathway enrichment of CGs. The top 20 pathways were identified. Color represented P value and size of the spot represented count of genes. Figure 8 Gene-pathway network of SFJDC against NCP. The V shapes represented pathway and the squares represent target genes in the network. Table 1 Herb composition of Shu Feng Jie Du Capsule (SFJDC) English translation Latin name Chinese name Hu-Zhang Polygoni Cuspidati Rhizoma Et Radix 虎杖 Lian-Qiao Forsythiae Fructus 连翘 Ban-Lan-Gen Isatidis Radix 板蓝根 Chai-Hu Herba Patriniae 柴胡 Bai-Jiang-Cao Phragmitis Rhizoma 败酱草 Ma-Bian-Cao Verbenae Herb 马鞭草 Lu-Gen licorice 芦根 Gan-Cao Radix Bupleuri 甘草 Table 2 The active ingredients of each herb contained in SFJDC Mol ID Molecule Name OB (%) Caco-2 BBB DL Source MOL000173 wogonin 30.68 0.79 0.04 0.23 FF MOL000211 Mairin 55.38 0.73 0.22 0.78 FF; RB MOL000239 Jaranol 50.83 0.61 -0.22 0.29 RB MOL000358 beta-sitosterol 36.91 1.32 0.99 0.75 PR; PCRR; IR; FF; VH MOL000359 sitosterol 36.91 1.32 0.87 0.75 PR; IR; RB MOL000392 formononetin 69.67 0.78 0.02 0.21 RB MOL000449 Stigmasterol 43.83 1.44 1 0.76 PR; IR; HP; I; VH MOL000497 licochalcone a 40.79 0.82 -0.21 0.29 RB MOL000791 bicuculline 69.67 0.72 0.02 0.88 FF MOL000953 CLR 37.87 1.43 1.13 0.68 IR MOL001484 Inermine 75.18 0.89 0.4 0.54 RB MOL001645 Linoleyl acetate 42.1 1.36 1.08 0.2 HP MOL001663 (4aS,6aR,6aS,6bR,8aR,10R,12aR,14bS)-10-hydroxy-2,2,6a,6b,9,9,12a-heptamethyl-1,3,4,5,6,6a,7,8,8a,10,11,12,13,14b-tetradecahydropicene-4a-carboxylic acid 32.03 0.61 0.39 0.76 VH MOL001676 Vilmorrianine C 33.96 0.59 0.14 0.22 PR MOL001677 asperglaucide 58.02 0.28 -0.22 0.52 PR MOL001689 acacetin 34.97 0.67 -0.05 0.24 PR; IR MOL001697 Sinoacutine 63.39 0.72 0.36 0.53 PR MOL001749 ZINC03860434 43.59 1.04 0.6 0.35 IR MOL001755 24-Ethylcholest-4-en-3-one 36.08 1.46 1.22 0.76 IR MOL001756 quindoline 33.17 1.5 0.99 0.22 IR MOL001769 beta-sitosterol dodecantate 34.57 1.28 0.57 0.57 IR MOL001771 poriferast-5-en-3beta-ol 36.91 1.45 1.14 0.75 IR MOL001774 Ineketone 37.14 0.39 0.1 0.3 IR MOL001779 Sinoacutine 49.11 0.7 0.39 0.46 IR MOL001781 Indigo 38.2 0.83 0.02 0.26 IR MOL001782 (2Z)-2-(2-oxoindolin-3-ylidene)indolin-3-one 48.4 0.85 -0.06 0.26 IR MOL001783 2-(9-((3-methyl-2-oxopent-3-en-1-yl)oxy)-2-oxo-1,2,8,9-tetrahydrofuro[2,3-h]quinolin-8-yl)propan-2-yl acetate 64 0.39 -0.09 0.57 IR MOL001792 DFV 32.76 0.51 -0.29 0.18 IR; RB MOL001793 (E)-2-[(3-indole)cyanomethylene-]-3-indolinone 54.59 1.06 0.22 0.32 IR MOL001800 rosasterol 35.87 1.28 0.89 0.75 IR MOL001803 Sinensetin 50.56 1.12 0.04 0.45 IR MOL001804 Stigmasta-5,22-diene-3beta,7alpha-diol 43.04 1.35 0.84 0.82 IR MOL001806 Stigmasta-5,22-diene-3beta,7beta-diol 42.56 1.37 0.81 0.83 IR MOL001810 6-(3-oxoindolin-2-ylidene)indolo[2,1-b]quinazolin-12-one 45.28 1.19 0.48 0.89 IR MOL001814 (E)-3-(3,5-dimethoxy-4-hydroxy-benzylidene)-2-indolinone 57.18 0.69 0.16 0.25 IR MOL001820 (E)-3-(3,5-dimethoxy-4-hydroxyb-enzylidene)-2-indolinone 65.17 0.28 -0.17 0.25 IR MOL001828 3-[(3,5-dimethoxy-4-oxo-1-cyclohexa-2,5-dienylidene)methyl]-2,4-dihydro-1H-pyrrolo[2,1-b]quinazolin-9-one 51.84 0.81 0.03 0.56 IR MOL002311 Glycyrol 90.78 0.71 -0.2 0.67 RB MOL002565 Medicarpin 49.22 1 0.53 0.34 RB MOL002773 beta-carotene 37.18 2.25 1.52 0.58 VH MOL003281 20(S)-dammar-24-ene-3β,20-diol-3-acetate 40.23 0.93 0.28 0.82 FF MOL003290 (3R,4R)-3,4-bis[(3,4-dimethoxyphenyl)methyl]oxolan-2-one 52.3 0.78 0.17 0.48 FF MOL003295 (+)-pinoresinol monomethyl ether 53.08 0.69 0 0.57 FF MOL003306 ACon1_001697 85.12 0.76 0 0.57 FF MOL003308 (+)-pinoresinol monomethyl ether-4-D-beta-glucoside_qt 61.2 0.7 0.12 0.57 FF MOL003315 3beta-Acetyl-20,25-epoxydammarane-24alpha-ol 33.07 0.75 0.24 0.79 FF MOL003322 FORSYTHINOL 81.25 0.59 -0.08 0.57 FF MOL003330 (-)-Phillygenin 95.04 0.75 0.07 0.57 FF MOL003344 β-amyrin acetate 42.06 1.36 1.1 0.74 FF MOL003347 hyperforin 44.03 0.87 0.4 0.6 FF MOL003348 adhyperforin 44.03 0.93 0.58 0.61 FF MOL003365 Lactucasterol 40.99 0.88 0.5 0.85 FF MOL003370 Onjixanthone I 79.16 0.84 0.04 0.3 FF MOL003656 Lupiwighteone 51.64 0.68 -0.23 0.37 RB MOL003896 7-Methoxy-2-methyl isoflavone 42.56 1.16 0.56 0.2 RB MOL004598 3,5,6,7-tetramethoxy-2-(3,4,5-trimethoxyphenyl)chromone 31.97 0.75 0.08 0.59 HP MOL004609 Areapillin 48.96 0.6 -0.29 0.41 HP MOL004624 Longikaurin A 47.72 0.08 0.09 0.53 HP MOL004628 Octalupine 47.82 0.48 0.3 0.28 HP MOL004644 Sainfuran 79.91 0.9 0.23 0.23 HP MOL004653 (+)-Anomalin 46.06 0.46 0 0.66 HP MOL004718 α-spinasterol 42.98 1.28 0.79 0.76 HP MOL004805 (2S)-2-[4-hydroxy-3-(3-methylbut-2-enyl)phenyl]-8,8-dimethyl-2,3-dihydropyrano[2,3-f]chromen-4-one 31.79 1 0.25 0.72 RB MOL004806 euchrenone 30.29 1.09 0.39 0.57 RB MOL004808 glyasperin B 65.22 0.47 -0.09 0.44 RB MOL004810 glyasperin F 75.84 0.43 -0.15 0.54 RB MOL004811 Glyasperin C 45.56 0.71 0.07 0.4 RB MOL004814 Isotrifoliol 31.94 0.53 -0.25 0.42 RB MOL004815 (E)-1-(2,4-dihydroxyphenyl)-3-(2,2-dimethylchromen-6-yl)prop-2-en-1-one 39.62 0.66 -0.12 0.35 RB MOL004820 kanzonols W 50.48 0.63 0.04 0.52 RB MOL004828 Glepidotin A 44.72 0.79 0.06 0.35 RB MOL004829 Glepidotin B 64.46 0.46 -0.09 0.34 RB MOL004833 Phaseolinisoflavan 32.01 1.01 0.46 0.45 RB MOL004835 Glypallichalcone 61.6 0.76 0.23 0.19 RB MOL004838 8-(6-hydroxy-2-benzofuranyl)-2,2-dimethyl-5-chromenol 58.44 1 0.34 0.38 RB MOL004848 licochalcone G 49.25 0.64 -0.04 0.32 RB MOL004849 3-(2,4-dihydroxyphenyl)-8-(1,1-dimethylprop-2-enyl)-7-hydroxy-5-methoxy-coumarin 59.62 0.4 -0.23 0.43 RB MOL004855 Licoricone 63.58 0.53 -0.14 0.47 RB MOL004856 RBnin A 51.08 0.8 0.13 0.4 RB MOL004857 RBnin B 48.79 0.58 -0.1 0.45 RB MOL004863 3-(3,4-dihydroxyphenyl)-5,7-dihydroxy-8-(3-methylbut-2-enyl)chromone 66.37 0.52 -0.13 0.41 RB MOL004866 2-(3,4-dihydroxyphenyl)-5,7-dihydroxy-6-(3-methylbut-2-enyl)chromone 44.15 0.48 -0.28 0.41 RB MOL004879 Glycyrin 52.61 0.59 -0.13 0.47 RB MOL004882 Licocoumarone 33.21 0.84 0.06 0.36 RB MOL004883 Licoisoflavone 41.61 0.37 -0.27 0.42 RB MOL004884 Licoisoflavone B 38.93 0.46 -0.18 0.55 RB MOL004885 licoisoflavanone 52.47 0.39 -0.22 0.54 RB MOL004891 shinpterocarpin 80.3 1.1 0.68 0.73 RB MOL004907 Glyzaglabrin 61.07 0.34 -0.2 0.35 RB MOL004908 Glabridin 53.25 0.97 0.36 0.47 RB MOL004910 Glabranin 52.9 0.97 0.31 0.31 RB MOL004911 Glabrene 46.27 0.99 0.04 0.44 RB MOL004912 Glabrone 52.51 0.59 -0.11 0.5 RB MOL004913 1,3-dihydroxy-9-methoxy-6-benzofurano[3,2-c]chromenone 48.14 0.48 -0.19 0.43 RB MOL004915 Eurycarpin A 43.28 0.43 -0.06 0.37 RB MOL004941 (2R)-7-hydroxy-2-(4-hydroxyphenyl)chroman-4-one 71.12 0.41 -0.25 0.18 RB MOL004945 (2S)-7-hydroxy-2-(4-hydroxyphenyl)-8-(3-methylbut-2-enyl)chroman-4-one 36.57 0.72 -0.04 0.32 RB MOL004948 Isoglycyrol 44.7 0.91 0.05 0.84 RB MOL004957 HMO 38.37 0.79 0.25 0.21 RB MOL004959 1-Methoxyphaseollidin 69.98 1.01 0.48 0.64 RB MOL004966 3'-Hydroxy-4'-O-Methylglabridin 43.71 1 0.73 0.57 RB MOL004974 3'-Methoxyglabridin 46.16 0.94 0.47 0.57 RB MOL004978 2-[(3R)-8,8-dimethyl-3,4-dihydro-2H-pyrano[6,5-f]chromen-3-yl]-5-methoxyphenol 36.21 1.12 0.61 0.52 RB MOL004980 Inflacoumarin A 39.71 0.73 -0.24 0.33 RB MOL004985 icos-5-enoic acid 30.7 1.22 1.09 0.2 RB MOL004988 Kanzonol F 32.47 1.18 0.56 0.89 RB MOL004989 6-prenylated eriodictyol 39.22 0.4 -0.29 0.41 RB MOL004991 7-Acetoxy-2-methylisoflavone 38.92 0.74 0.16 0.26 RB MOL004996 gadelaidic acid 30.7 1.2 0.94 0.2 RB MOL005000 RBnin G 60.44 0.78 0.23 0.39 RB MOL005001 RBnin H 50.1 0.6 -0.14 0.78 RB MOL005003 Licoagrocarpin 58.81 1.23 0.61 0.58 RB MOL005007 Glyasperins M 72.67 0.49 -0.04 0.59 RB MOL005012 Licoagroisoflavone 57.28 0.71 0.09 0.49 RB MOL005016 Odoratin 49.95 0.42 -0.24 0.3 RB MOL005017 Phaseol 78.77 0.76 -0.06 0.58 RB MOL005018 Xambioona 54.85 1.09 0.52 0.87 RB MOL005020 dehydroglyasperins C 53.82 0.68 -0.12 0.37 RB MOL005229 Artemetin 49.55 0.81 -0.09 0.48 VH MOL005503 Cornudentanone 39.66 0.47 0.09 0.33 VH MOL008752 Dihydroverticillatine 42.69 0.56 0.11 0.84 VH MOL013281 6,8-Dihydroxy-7-methoxyxanthone 35.83 0.68 0.1 0.21 PCRR MOL013287 Physovenine 106.21 0.51 0.2 0.19 PCRR MOL013288 Picralinal 58.01 0.23 -0.21 0.75 PCRR Table 3 Putative target genes of each herb in SFJDC Herb Mol ID Molname Target genes FF MOL000173 wogonin ADRB2 AHSA1 AKT1 AR BAX BBC3 BCL2 CALM1 CASP3 CASP9 CCL2 CCND1 CDK2 CDKN1A CHEK1 DPP4 EIF6 ESR1 FSD1 GABRA1 GSK3B HSP90AA1 IL6 IL8RA JUN KDR MAPK14 MCL1 MMP1 NOS2 PPARG PRKCD PRSS1 PTGER3 PTGS1 PTGS2 RELA RXRA SCN5A TEP1 TNFSF15 TP63 RB FF MOL000211 Mairin PGR RB MOL000239 Jaranol AR CALM1 CDK2 CHEK1 DPP4 ESR2 HSP90AA1 NCOA2 NOS2 PRSS1 PTGS1 PTGS2 SCN5A PR IR; PCRR; FF MOL000358 beta-sitosterol ADRA1A ADRA1B ADRB2 BAX BCL2 CASP3 CASP8 CASP9 CHRM1 CHRM2 CHRM3 CHRM4 CHRNA2 DRD1 GABRA1 HSP90AA1 JUN KCNH2 MAP2 NCOA2 OPRM1 PGR PON1 PRKCA PTGS1 PTGS2 SCN5A SLC6A4 PR IR RB MOL000359 sitosterol NCOA2 NR3C2 PGR RB MOL000392 formononetin ACHE ADRA1A ADRB2 AR ATP5F1B CALM1 CCNA2 CDK2 CHEK1 CHRM1 DPP4 ESR1 ESR2 GSK3B HSD3B1 HSD3B2 HSP90AA1 HTR IL4 JUN MAOB MAPK14 ND6 NOS2 PKIA PPARG PPARG PRSS1 PTGS1 PTGS2 RXRA SLC6A3 SLC6A4 PR IR HP I MOL000449 Stigmasterol ADH1C ADRA1A ADRA1B ADRA2A ADRB1 ADRB2 AKR1B1 CHRM1 CHRM2 CHRM3 CTRB1 GABRA1 IGHG1 LTA4H MAOA MAOB NCOA1 NCOA2 NR3C2 PGR PLAU PTGS1 PTGS2 RXRA SCN5A SLC6A2 SLC6A3 RB MOL000497 licochalcone a ADRA1B ADRB2 AR BCL2 CA2 CALM1 CCNA2 CCND1 CDK2 CDK4 CHEK1 CHRM1 EIF6 ESR1 ESR2 FOSL2 GSK3B HSP90AA1 MAPK1 MAPK14 NCOA2 NOS2 PPARG PTGS1 PTGS2 RB1 RELA SCN5A SLC6A3 STAT3 FF MOL000791 bicuculline ACHE ALDH3A1 AR BMPR2 CRH FOS GABBR1 GJA1 GJB1 GNRH1 GNRHR GRIN2D GRM1 GRM5 HSP90AA1 HTR KCNH2 KDR PTGS1 PTGS2 SCN5A SLC6A2 VCP IR MOL000953 CLR NCOA2 NR3C2 PGR RB MOL001484 Inermine ADRA1B ADRA1D ADRB2 CALM1 CHRM1 CHRM3 HSP90AA1 HTR3A IGHG1 OPRM1 PRSS1 PTGS1 PTGS2 RXRA SCN5A HP MOL001645 Linoleyl acetate NCOA2 PTGS1 PTGS2 RXRA PR MOL001677 asperglaucide HTR KCNH2 PRSS1 PTGS2 PR; IR MOL001689 acacetin ADRB2 AR BAX BCL2 CALM1 CASP3 CASP8 CDK2 CDKN1A CHEK1 CYP19A1 DPP4 FASLG FASN HSP90AA1 NCOA1 NCOA2 NOS2 PRSS1 PTGS1 PTGS2 RELA TP63 PR MOL001697 Sinoacutine ACHE ADRA1A ADRA1B AR CHRM1 CHRM2 CHRM3 CHRM4 CHRM5 ESR1 ESR2 GABRA1 HTR OPRD1 OPRM1 PTGS1 PTGS2 SCN5A IR MOL001749 ZINC03860434 ADRB2 CHRM1 CHRM3 SCN5A IR MOL001755 24-Ethylcholest-4-en-3-one NR3C2 PGR IR MOL001756 quindoline MAOB NCOA2 PKIA PTGS1 PTGS2 IR MOL001771 poriferast-5-en-3beta-ol NCOA2 PGR IR MOL001774 Ineketone NR3C2 IR MOL001779 Sinoacutine ACHE ADRA1B AR CALM1 CHRM1 CHRM3 CHRM5 DPP4 ESR1 ESR2 HSP90AA1 HTR NOS2 OPRD1 OPRM1 PTGS1 PTGS2 RXRA SCN5A IR MOL001781 Indigo CCNA2 CDK2 PTGS1 PTGS2 RXRA IR MOL001782 (2Z)-2-(2-oxoindolin-3-ylidene)indolin-3-one AR CCNA2 CDK2 CHEK1 ESR1 GABRA1 GSK3B HSP90AA1 MAPK14 NOS2 PTGS1 PTGS2 RXRA IR MOL001783 2-(9-((3-methyl-2-oxopent-3-en-1-yl)oxy)-2-oxo-1,2,8,9-tetrahydrofuro[2,3-h]quinolin-8-yl)propan-2-yl acetate HSP90AA1 KCNH2 NCONA2 PRSS1 PTGS2 IR RB MOL001792 DFV ADRB2 ESR1 HSP90AA1 MAOB PKIA PTGS1 PTGS2 RXRA SLC6A4 IR MOL001793 (E)-2-[(3-indole)cyanomethylene-]-3-indolinone AR CCNA2 CDK2 CHEK1 ESR1 GSK3B HSP90AA1 MAPK14 NOS2 PTGS1 PTGS2 RXRA IR MOL001800 rosasterol PGR IR MOL001803 Sinensetin ACHE ADRA1B ADRB2 AR CALM1 CHEK1 DPP4 ESR2 F7 HSP90AA1 HTR KCNH2 NCOA1 NCOA2 NOS2 PRSS1 PTGS1 PTGS2 SCN5A IR MOL001804 Stigmasta-5,22-diene-3beta,7alpha-diol NCOA2 PGR IR MOL001810 6-(3-oxoindolin-2-ylidene)indolo[2,1-b]quinazolin-12-one ESR1 KDR PRSS1 PTGS1 PTGS2 IR MOL001814 (E)-3-(3,5-dimethoxy-4-hydroxy-benzylidene)-2-indolinone GABRA1 HSP90AA1 PTGS1 PTGS2 RXRA SCN5A IR MOL001820 (E)-3-(3,5-dimethoxy-4-hydroxyb-enzylidene)-2-indolinone ADRB2 CHRM1 GABRA1 HSP90AA1 PTGS1 PTGS2 RXRA SCN5A IR MOL001828 3-[(3,5-dimethoxy-4-oxo-1-cyclohexa-2,5-dienylidene)methyl]-2,4-dihydro-1H-pyrrolo[2,1-b]quinazolin-9-one F7 HSP90AA1 KCNH2 PRSS1 PTGS1 PTGS2 SCN5A RB MOL002311 Glycyrol CCNA2 CHEK1 ESR1 GSK3B HTR KDR MAPK14 NOS2 PPARG PTGS2 RB MOL002565 Medicarpin ADRA1A ADRA1B ADRA1D ADRB2 CALM1 CCNA2 CDK2 CHRM1 CHRM2 CHRM3 CHRM4 CHRM5 DPP4 DRD1 ESR1 ESR2 HSP90AA1 MAPK10 NOS2 OPRD1 OPRM1 PRSS1 PTGS1 PTGS2 RXRA SCN5A SLC6A3 SLC6A4 FF MOL003290 (3R,4R)-3,4-bis[(3,4-dimethoxyphenyl)methyl]oxolan-2-one ADRA1B ADRA1D ADRB2 CALM1 CHRM3 ESR1 F7 HSP90AA1 KCNH2 NCOA2 PTGS2 SCN5A SLC6A3 FF MOL003295 (+)-pinoresinol monomethyl ether ADRA1B ADRB2 CALM1 HSP90AA1 KCNH2 NCOA1 NCOA2 PTGS1 PTGS2 RXRA RXRB SCN5A FF MOL003306 ACon1_001697 ADRA1B ADRB2 CALM1 HSP90AA1 KCNH2 NCOA1 NCOA2 PTGS1 PTGS2 SCN5A FF MOL003308 (+)-pinoresinol monomethyl ether-4-D-beta-glucoside_qt ADRB2 CALM1 HSP90AA1 KCNH2 NCOA1 NCOA2 PTGS2 SCN5A FF MOL003315 3beta-Acetyl-20,25-epoxydammarane-24alpha-ol NR3C1 FF MOL003322 FORSYTHINOL ADRA1B ADRB2 CALM1 HSP90AA1 KCNH2 NCOA1 NCOA2 PTGS2 SCN5A FF MOL003330 (-)-Phillygenin ADRA1B ADRB2 CALM1 CHRM1 CHRM3 CHRM5 HSP90AA1 IGHG1 KCNH2 NCOA2 PTGS2 SCN5A FF MOL003347 hyperforin CYP3A4 ICAM1 IL8RA NR1I2 FF MOL003370 Onjixanthone I CALM1 CHEK1 DPP4 ESR2 HSP90AA1 NOS2 PTGS1 PTGS2 RXRA SCN5A RB MOL003656 Lupiwighteone AR CALM1 CCNA2 CDK2 CHEK1 DPP4 ESR1 ESR2 GSK3B HSP90AA1 HTR MAPK14 NCOA2 NOS2 PPARG PRSS1 PTGS2 SCN5A RB MOL003896 7-Methoxy-2-methyl isoflavone ACHE ADRA1B ADRA1D ADRB1 ADRB2 AR CALM1 CCNA2 CDK2 CHEK1 CHRM1 CHRM3 CHRM5 DPP4 DRD1 ESR1 ESR2 GABRA1 GSK3B HSP90AA1 HTR IGHG1 LTA4H MAOB MAPK14 NCOA1 NCOA2 NOS2 OPRM1 PKIA PPARG PRSS1 PTGS1 PTGS2 RXRA SCN5A SLC6A3 SLC6A4 HP MOL004598 3,5,6,7-tetramethoxy-2-(3,4,5-trimethoxyphenyl)chromone ACHE AR CALM1 ESR1 ESR2 F7 HTR NCOA2 PRSS1 PTGS2 HP MOL004609 Areapillin AR CALM1 DPP4 ESR2 F7 HSP90AA1 HTR IGHG1 NCOA1 NCOA2 NOS2 PRSS1 PTGS2 SCN5A HP MOL004624 Longikaurin A CHRM1 CHRM2 PRSS1 HP MOL004653 (+)-Anomalin DPP4 HTR KCNH2 PTGS2 HP MOL004718 α-spinasterol NCOA2 NR3C2 PGR RB MOL004805 (2S)-2-[4-hydroxy-3-(3-methylbut-2-enyl)phenyl]-8,8-dimethyl-2,3-dihydropyrano[2,3-f]chromen-4-one AR CALM1 ESR1 ESR2 GSK3B KCNH2 MAPK14 NOS2 PPARG PTGS2 RB MOL004806 euchrenone BACE1 CALM1 ESR1 ESR2 KCNH2 NOS2 PTGS2 SCN5A RB MOL004808 glyasperin B ACHE AR CALM1 CCNA2 CDK2 DPP4 ESR1 ESR2 F7 GSK3B HSP90AA1 HTR KDR NCOA2 NOS2 PPARG PRSS1 PTGS2 RB MOL004810 glyasperin F AR CALM1 CCNA2 CDK2 ESR1 ESR2 GSK3B HSP90AA1 MAPK14 NOS2 PPARG PRSS1 PTGS1 PTGS2 SCN5A RB MOL004811 Glyasperin C ACHE AR CALM1 CCNA2 CDK2 CHEK1 DPP4 ESR1 ESR2 GSK3B HSP90AA1 HTR KCNH2 MAPK14 NCOA2 NOS2 PPARG PRSS1 PTGS2 RXRA SCN5A RB MOL004814 Isotrifoliol AR CCNA2 CDK2 CHEK1 ESR1 ESR2 GSK3B HSP90AA1 MAPK14 NOS2 PTGS2 RB MOL004815 (E)-1-(2,4-dihydroxyphenyl)-3-(2,2-dimethylchromen-6-yl)prop-2-en-1-one ADRA1B AR CA2 CALM1 CCNA2 CDK2 CHEK1 ESR1 ESR2 GSK3B MAPK14 NCOA2 NOS2 PPARG PTGS1 PTGS2 RXRA SCN5A RB MOL004820 kanzonols W AR CALM1 CCNA2 CDK2 CHEK1 ESR1 ESR2 GSK3B MAPK14 NCOA1 NCOA2 NOS2 PPARG PRSS1 PTGS1 PTGS2 RXRA SCN5A RB MOL004828 Glepidotin A AR CALM1 CCNA2 CDK2 CHEK1 DPP4 ESR1 F7 GSK3B HSP90AA1 HTR IGHG1 KDR MAPK14 NOS2 PPARG PRSS1 PTGS1 PTGS2 RXRA SCN5A RB MOL004829 Glepidotin B ADRA1B CALM1 ESR1 F7 HSP90AA1 IGHG1 NCOA1 PTGS1 PTGS2 RXRA SCN5A RB MOL004833 Phaseolinisoflavan ACHE ADRA1B ADRB2 AR CALM1 CCNA2 CDK2 CHEK1 CHRM1 ESR1 ESR2 GSK3B MAPK14 NCOA1 NOS2 PPARG PRSS1 PTGS2 RXRA SCN5A RB MOL004835 Glypallichalcone ADRA1B ADRB2 AR CA2 CALM1 CCNA2 CDK2 CHEK1 CHRM1 ESR1 ESR2 GSK3B HSP90AA1 LTA4H MAOB MAPK14 NCOA1 NOS2 PKIA PPARG PTGS1 PTGS2 SCN5A SLC6A3 SLC6A4 RB MOL004838 8-(6-hydroxy-2-benzofuranyl)-2,2-dimethyl-5-chromenol ESR1 HSP90AA1 NOS2 PTGS2 RXRA RB MOL004848 licochalcone G AR CALM1 CCNA2 CDK2 ESR1 ESR2 GSK3B HSP90AA1 IGHG1 KDR MAPK14 NCOA2 NOS2 PPARG PTGS2 RB MOL004849 3-(2,4-dihydroxyphenyl)-8-(1,1-dimethylprop-2-enyl)-7-hydroxy-5-methoxy-coumarin AR CALM1 CDK2 CHEK1 DPP4 ESR1 ESR2 F7 GSK3B HSP90AA1 HTR KCNH2 KDR MAPK14 NCOA1 NCOA2 NOS2 PPARG PRSS1 PTGS2 RB MOL004855 Licoricone AR CALM1 CHEK1 ESR1 HTR KCNH2 KDR NCOA2 NOS2 PPARG PRSS1 PTGS2 RB MOL004856 RBnin A ACHE AR CALM1 CCNA2 CHEK1 DPP4 ESR1 ESR2 GSK3B HSP90AA1 HTR NCOA2 NOS2 PPARG PRSS1 PTGS2 SCN5A RB MOL004857 RBnin B ADRA1B ADRB2 AR CALM1 CCNA2 CHEK1 DPP4 ESR1 ESR2 F7 GSK3B HSP90AA1 HTR KDR NCOA2 NOS2 PPARG PRSS1 PTGS2 RB MOL004863 3-(3,4-dihydroxyphenyl)-5,7-dihydroxy-8-(3-methylbut-2-enyl)chromone AR CALM1 CCNA2 CDK2 CHEK1 ESR1 GSK3B HSP90AA1 HTR MAPK14 NCOA2 NOS2 PPARG PRSS1 PTGS2 RB MOL004866 2-(3,4-dihydroxyphenyl)-5,7-dihydroxy-6-(3-methylbut-2-enyl)chromone ADRB2 AR CALM1 CCNA2 CDK2 CHEK1 DPP4 F7 HSP90AA1 HTR PPARG PRSS1 PTGS2 SCN5A RB MOL004879 Glycyrin AR CALM1 CHEK1 DPP4 ESR1 ESR2 HTR KCNH2 KDR NCOA2 NOS2 PPARG PRSS1 PTGS2 RB MOL004882 Licocoumarone AR CCNA2 CDK2 ESR1 ESR2 GSK3B HSP90AA1 RB MOL004883 Licoisoflavone AR CALM1 CCNA2 CDK2 CHEK1 DPP4 ESR1 HSP90AA1 HTR KDR MAPK14 NCOA2 NOS2 PPARG PRSS1 PTGS2 RB MOL004884 Licoisoflavone B ACHE AR CALM1 CCNA2 CDK2 CHEK1 ESR1 ESR2 GSK3B HTR NOS2 PPARG PRSS1 PTGS2 RB MOL004885 licoisoflavanone ACHE AR CALM1 CCNA2 CDK2 ESR1 ESR2 F7 GSK3B HSP90AA1 NCOA1 NOS2 PPARG PRSS1 PTGS1 PTGS2 SCN5A RB MOL004891 shinpterocarpin ADRA1B ADRA1D ADRB2 AR CALM1 CCNA2 CDK2 CHRM1 CHRM3 ESR1 ESR2 GSK3B HTR3A KCNH2 MAPK14 NCOA1 NOS2 OPRD1 OPRM1 PPARG PRSS1 PTGS1 PTGS2 RXRA RXRB SCN5A RB MOL004907 Glyzaglabrin AR CCNA2 CDK2 CHEK1 DPP4 ESR1 ESR2 GSK3B HSP90AA1 MAPK14 NOS2 PPARG PRSS1 PTGS1 PTGS2 RB MOL004908 Glabridin ACHE ADRA1B ADRB2 AR CALM1 CCNA2 CDK2 CHEK1 CHRM1 ESR1 ESR2 GSK3B IGHG1 MAPK14 NCOA1 NCOA2 NOS2 PPARG PRSS1 PTGS2 RXRA RXRB SCN5A RB MOL004910 Glabranin CALM1 ESR1 HSP90AA1 NOS2 PTGS1 PTGS2 SCN5A RB MOL004911 Glabrene ADRB2 AR CALM1 CDK2 ESR1 ESR2 GSK3B HSP90AA1 MAPK14 NCOA2 NOS2 PPARG PRSS1 PTGS1 PTGS2 RXRA SCN5A RB MOL004912 Glabrone ACHE AR CALM1 CCNA2 CDK2 CHEK1 DPP4 ESR1 ESR2 GSK3B HTR MAPK14 NOS2 PPARG PRSS1 PTGS1 PTGS2 RXRA SCN5A RB MOL004913 1,3-dihydroxy-9-methoxy-6-benzofurano[3,2-c]chromenone CCNA2 CDK2 CHEK1 ESR1 ESR2 GSK3B HSP90AA1 MAPK14 PPARG RB MOL004915 Eurycarpin A AR CALM1 CCNA2 CDK2 CHEK1 DPP4 ESR1 ESR2 GSK3B HSP90AA1 HTR MAPK14 NOS2 PPARG PRSS1 PTGS2 SCN5A RB MOL004941 (2R)-7-hydroxy-2-(4-hydroxyphenyl)chroman-4-one PTGS1 ESR1 PTGS2 RXRA ADRB2 HSP90AA1 MAOB PKIA CALM1 GABRA1 SLC6A4 RB MOL004945 (2S)-7-hydroxy-2-(4-hydroxyphenyl)-8-(3-methylbut-2-enyl)chroman-4-one NOS2 RB MOL004948 Isoglycyrol AR DPP4 ESR1 GSK3B NOS2 PTGS2 RB MOL004957 HMO ADRB2 AR CALM1 CCNA2 CDK2 CHEK1 CHRM1 DPP4 ESR1 ESR2 GSK3B IGHG1 MAOB MAPK14 NOS2 PKIA PPARG PRSS1 PTGS1 PTGS2 RXRA SCN5A SLC6A3 SLC6A4 RB MOL004959 1-Methoxyphaseollidin ADRA1B ADRA1D ADRB2 AR CALM1 CCNA2 CDK2 ESR1 ESR2 GSK3B HSP90AA1 HTR KCNH2 KDR MAPK14 NCOA1 NCOA2 NOS2 PPARG PRSS1 PTGS1 PTGS2 RXRA SCN5A RB MOL004966 3'-Hydroxy-4'-O-Methylglabridin ADRA1B ADRB2 AR CALM1 CCNA2 CDK2 CHEK1 ESR1 ESR2 F7 GSK3B HSP90AA1 KCNH2 KDR MAPK14 NCOA1 NCOA2 NOS2 PPARG PRSS1 PTGS1 PTGS2 SCN5A RB MOL004974 3'-Methoxyglabridin ACHE ADRA1B ADRB2 AR CALM1 CCNA2 CDK2 CHEK1 ESR1 ESR2 F7 GSK3B HSP90AA1 KCNH2 MAPK14 NCOA1 NCOA2 NOS2 PPARG PRSS1 PTGS1 PTGS2 RXRA SCN5A RB MOL004978 2-[(3R)-8,8-dimethyl-3,4-dihydro-2H-pyrano[6,5-f]chromen-3-yl]-5-methoxyphenol ACHE ADRA1B ADRB2 AR CALM1 CCNA2 CDK2 CHEK1 CHRM1 CHRM3 ESR1 ESR2 GSK3B KCNH2 MAPK14 NCOA1 NCOA2 NOS2 PPARG PRSS1 PTGS1 PTGS2 RXRA RXRB SCN5A SLC6A3 RB MOL004980 Inflacoumarin A ADRB2 AR CALM1 DPP4 ESR1 HSP90AA1 HTR NCOA2 PPARG PRSS1 PTGS1 PTGS2 SCN5A RB MOL004985 icos-5-enoic acid NCOA2 RB MOL004988 Kanzonol F AR CALM1 ESR1 ESR2 NCOA2 PTGS2 RB MOL004989 6-prenylated eriodictyol CALM1 ESR1 F7 HSP90AA1 NOS2 PTGS2 SCN5A RB MOL004991 7-Acetoxy-2-methylisoflavone ACHE ADRA1B ADRA1D ADRB2 AR CALM1 CDK2 CHEK1 DPP4 ESR1 GABRA1 GSK3B HSP90AA1 HTR MAPK14 NCOA2 NOS2 PPARG PRSS1 PTGS1 PTGS2 RXRA SCN5A RB MOL004996 gadelaidic acid NCOA2 RB MOL005000 RBnin G AR CALM1 CCNA2 CHEK1 DPP4 ESR1 ESR2 GSK3B HSP90AA1 HTR MAPK14 NCOA2 NOS2 PPARG PRSS1 PTGS2 RB MOL005001 RBnin H AR CALM1 CCNA2 ESR1 HSP90AA1 KDR NCOA2 PRSS1 PTGS2 RB MOL005003 Licoagrocarpin ACHE ADRA1B ADRB2 AR CALM1 CCNA2 CDK2 CHRM1 CHRM3 CHRM5 ESR1 ESR2 GSK3B HSP90AA1 HTR KCNH2 MAPK14 NCOA2 NOS2 PPARG PRSS1 PTGS1 PTGS2 RXRA RXRB SCN5A RB MOL005007 Glyasperins M ACHE AR CALM1 CCNA2 CDK2 ESR1 ESR2 F7 GSK3B HSP90AA1 KCNH2 KDR NCOA1 NCOA2 NOS2 PPARD PPARG PRSS1 PTGS1 PTGS2 SCN5A RB MOL005012 Licoagroisoflavone AR CALM1 CCNA2 CDK2 CHEK1 DPP4 ESR1 ESR2 GSK3B HTR MAPK14 NOS2 PPARG PRSS1 PTGS2 SCN5A RB MOL005016 Odoratin AR CALM1 CCNA2 CDK2 CHEK1 DPP4 ESR1 ESR2 GSK3B HSP90AA1 MAPK14 NCOA2 NOS2 PPARG PRSS1 PTGS1 PTGS2 RXRA SCN5A RB MOL005017 Phaseol AR CCNA2 CDK2 CHEK1 ESR1 GSK3B HSP90AA1 HTR KDR MAPK14 PPARG PTGS2 RB MOL005018 Xambioona CALM1 ESR1 ESR2 NCOA2 NOS2 PTGS2 RB MOL005020 dehydroglyasperins C ADRB2 AR CALM1 CCNA2 CDK2 CHEK1 ESR1 ESR2 HSP90AA1 MAPK14 NCOA2 NOS2 PPARG PRSS1 PTGS2 SCN5A PCRR MOL013281 6,8-Dihydroxy-7-methoxyxanthone ADRB2 CA2 CDK2 CHEK1 DPP4 GSK3B HSP90AA1 MAPK14 PKIA PTGS1 PTGS2 PCRR MOL013287 Physovenine ACHE ADRA1A ADRA1B ADRA2B ADRB2 AR CA2 CCNA2 CDK2 CHRM1 CHRM2 CHRM3 CHRNA2 DRD1 ESR1 ESR2 GABRA1 GRIA2 GSK3B HSP90AA1 HTR NOS2 OPRD1 OPRM1 PRSS1 PTGS1 PTGS2 RXRA SCN5A SLC6A2 SLC6A3 SLC6A4 PCRR MOL013288 Picralinal AR OPRD1 OPRM1 SCN5A Table 4 Known therapeutic target genes for COVID-19 Gene GC Id Score Gene GC Id Score TNF GC06P033397 33.08 ITGAL GC16P030472 4.07 IL6 GC07P022765 31.28 STAT6 GC12M057095 4.04 CXCL8 GC04P073740 31.05 BAK1 GC06M033572 4.03 CD40LG GC0XP136649 30.56 PIK3CG GC07P106865 4.02 IL10 GC01M206767 30.33 FOS GC14P075278 4.01 IFNG GC12M068064 27.48 HELLS GC10P094501 4 CRP GC01M159715 25.76 CP GC03M149162 3.96 STAT1 GC02M190964 22.73 APOA1 GC11M116835 3.95 MBL2 GC10M052760 22.1 RPS27A GC02P055231 3.91 TP53 GC17M007661 19 CREBBP GC16M003726 3.87 CCL2 GC17P034255 18.13 TFRC GC03M196027 3.83 IL2 GC04M122451 17.68 LMAN1 GC18M059327 3.82 CCL5 GC17M035871 16.71 PLA2G4A GC01P186798 3.81 IFNA1 GC09P021478 16.65 CEACAM5 GC19P041709 3.65 EGFR GC07P055019 16.29 PRKCA GC17P066302 3.65 CXCL10 GC04M076021 15.3 EIF2S1 GC14P067359 3.65 TGFB1 GC19M041301 14.98 CLEC12A GC12P009951 3.61 IL1B GC02M112829 13.78 SUMO1 GC02M202206 3.59 ACE2 GC0XM015494 12.32 CCR3 GC03P046227 3.56 CSF2 GC05P132073 11.95 UBB GC17P016380 3.53 PPARG GC03P012287 11.93 MAPKAPK2 GC01P206684 3.48 CCR5 GC03P046384 11.37 CD3D GC11M118338 3.47 CXCL9 GC04M076001 11.3 CHKB GC22M050578 3.43 GPT GC08P144502 11.12 PPIA GC07P044808 3.43 MAPK1 GC22M021754 11.09 RUNX1 GC21M034787 3.42 CASP3 GC04M184627 10.88 BCL2L1 GC20M031664 3.4 IFNB1 GC09M021077 10.77 GZMA GC05P055102 3.38 ALB GC04P073397 10.68 IRF1 GC05M132481 3.35 FGF2 GC04P122826 10.53 CD81 GC11P002377 3.35 SFTPD GC10M079937 10.47 CST3 GC20M023608 3.29 CXCR3 GC0XM071615 10.18 PTGS1 GC09P122370 3.24 IL4 GC05P132673 10.12 F10 GC13P113122 3.22 HLA-B GC06M031289 9.84 CBL GC11P119206 3.18 CD79A GC19P041877 9.73 CXCL11 GC04M076033 3.13 CXCL2 GC04M074097 9.61 MAVS GC20P003827 3.12 ACE GC17P063477 9.6 KPNB1 GC17P047649 3.1 TMPRSS2 GC21M041464 9.59 SLC17A5 GC06M073593 3.07 IRF3 GC19M049659 9.51 ITGA5 GC12M054396 3 MAPK3 GC16M030117 9.37 ARF1 GC01P228082 2.99 IL17A GC06P052186 9.29 IFNL1 GC19P039296 2.97 IL5 GC05M132541 9.27 GRB2 GC17M075318 2.86 ICAM1 GC19P010270 9.22 CD3E GC11P118304 2.84 CCL3 GC17M036088 9.2 ATF2 GC02M175072 2.78 IL13 GC05P132656 9.19 CEACAM3 GC19P041796 2.72 MAPK8 GC10P048306 9.08 HAVCR2 GC05M157063 2.7 TTR GC18P031557 9.04 JAK1 GC01M064833 2.69 IL18 GC11M112143 8.58 NPM1 GC05P171387 2.67 ANPEP GC15M089784 8.58 TBK1 GC12P064451 2.64 PIK3R1 GC05P068215 8.57 F11 GC04P186265 2.63 CTSL GC09P087725 8.52 VHL GC03P010205 2.63 CD209 GC19M007739 8.45 IL16 GC15P081159 2.59 DDX58 GC09M032455 8.2 KPNA2 GC17P068035 2.57 FURIN GC15P090868 8.08 RELB GC19P045002 2.57 ADA GC20M044620 7.97 FCER2 GC19M007689 2.56 APOE GC19P044906 7.97 PIK3CB GC03M138652 2.55 MAPK14 GC06P046047 7.77 PRSS2 GC07P144731 2.54 DPP4 GC02M161992 7.64 RAPGEF3 GC12M047736 2.52 NFKB1 GC04P102501 7.61 BECN1 GC17M042810 2.51 HLA-A GC06P033211 7.44 HAVCR1 GC05M157007 2.48 SERPINE1 GC07P101127 7.43 ISG15 GC01P001001 2.41 PIK3CA GC03P179148 7.27 PML GC15P073994 2.41 PTGS2 GC01M186640 7.24 PRKCE GC02P045651 2.39 CD14 GC05M140631 7.16 CEACAM1 GC19M042507 2.38 MX1 GC21P041420 7.07 PIK3CD GC01P009629 2.37 IFIH1 GC02M162267 6.99 ERN1 GC17M064039 2.37 BCL2 GC18M063123 6.96 IFITM1 GC11P000313 2.36 FCGR2A GC01P161505 6.67 IRAK3 GC12P066188 2.35 CDK4 GC12M057743 6.64 NPTX1 GC17M080466 2.35 HSPA5 GC09M125234 6.59 HFE GC06P026087 2.34 BAX GC19P048954 6.53 TLR10 GC04M038773 2.33 CCL11 GC17P034285 6.47 SLC40A1 GC02M189560 2.3 CAT GC11P034460 6.43 LCK GC01P032251 2.29 HMOX1 GC22P035380 6.28 EIF2AK3 GC02M088637 2.27 SOD1 GC21P031659 6.25 POU5F1 GC06M031177 2.25 G6PD GC0XM154531 6.06 VAPA GC18P009904 2.15 CD4 GC12P006786 6.01 CARD9 GC09M136361 2.15 TF GC03P133666 5.96 TRIM25 GC17M056836 2.13 CTRL GC16M067927 5.95 HNRNPA1 GC12P054280 2.05 IL1A GC02M112773 5.93 CCND3 GC06M041934 1.99 PIK3C2A GC11M017165 5.92 MYOM2 GC08P002045 1.97 PARP1 GC01M226360 5.91 PRKRA GC02M178431 1.96 RELA GC11M065653 5.89 SOCS3 GC17M078356 1.95 NOS2 GC17M027756 5.85 LCN1 GC09P135521 1.91 EIF2AK2 GC02M037099 5.83 EIF4E GC04M098871 1.91 GAPDH GC12P006630 5.81 ICAM2 GC17M064002 1.89 NOS3 GC07P150990 5.77 BST2 GC19M017403 1.88 CTSB GC08M011842 5.72 IFITM2 GC11P000300 1.87 CCL4 GC17P036103 5.69 KPNA4 GC03M160494 1.83 CASP8 GC02P201233 5.65 DROSHA GC05M031401 1.78 ANXA5 GC04M121667 5.59 USP7 GC16M008892 1.78 F8 GC0XM154835 5.58 CD46 GC01P207752 1.74 CREB1 GC02P207529 5.55 AHSG GC03P186612 1.73 SH2D3A GC19M006752 5.54 BAG3 GC10P119651 1.72 HLA-DRB1 GC06M032578 5.48 TMPRSS11A GC04M067909 1.69 TMPRSS11D GC04M067820 5.38 APOD GC03M195568 1.66 BMP6 GC06P007726 5.32 PRKCB GC16P023872 1.64 SMAD3 GC15P067063 5.2 RHOB GC02P020447 1.64 MASP2 GC01M011026 5.13 ITGA6 GC02P172427 1.63 IFITM3 GC11M000319 5.11 STAT2 GC12M056341 1.62 HLA-C GC06M031272 5.11 CALM1 GC14P090396 1.61 BAD GC11M064273 5.04 OAS1 GC12P112906 1.6 CANX GC05P179678 4.97 BCL2L2 GC14P025033 1.6 MCL1 GC01M150673 4.77 IFI27 GC14P094104 1.6 CCL7 GC17P034270 4.71 PSMC6 GC14P052707 1.55 CASP6 GC04M109688 4.7 TFR2 GC07M100620 1.5 EGR1 GC05P138465 4.66 SPI1 GC11M059694 1.45 ITGB1 GC10M032900 4.64 IGKC GC02M089081 1.44 RNASE3 GC14P020891 4.63 PHB2 GC12M006965 1.44 STING1 GC05M139476 4.5 CD151 GC11P000883 1.42 CD34 GC01M207880 4.48 ITGA1 GC05P052788 1.42 DUSP1 GC05M172768 4.42 FAH GC15P080152 1.4 RB1 GC13P048303 4.41 NUDT2 GC09P034329 1.36 ADAM17 GC02M009488 4.4 AQP1 GC07P030911 1.35 HSPB1 GC07P076302 4.33 TMPRSS13 GC11M117900 1.32 EEF1A1 GC06M073515 4.33 CD3G GC11P118344 1.28 TOLLIP GC11M001274 4.31 PCSK5 GC09P075890 1.23 CCR1 GC03M046218 4.3 CBLB GC03M105655 1.21 EZR GC06M158765 4.27 TMEM233 GC12P119594 1.18 LCN2 GC09P128149 4.26 ANXA11 GC10M080150 1.13 TRAF3 GC14P104312 4.23 CLEC4D GC12P008509 1.1 SMAD7 GC18M048919 4.18 NMRAL1 GC16M004461 1.07 TXN GC09M110243 4.17 HPGDS GC04M094298 0.84 ICAM3 GC19M010335 4.15 SLC39A14 GC08P022367 0.83 VCP GC09M035056 4.15 OR8U9 GC11Pi00193 0.35 NLRP12 GC19M053793 4.14 C8G GC09P136944 0.31 ANXA2 GC15M060347 4.12 Table 5 The data of top twenty GO terms including BP, CC, MF GO category ID Description P-value P.adjust Count BP GO:0032496 response to lipopolysaccharide 1.27E-17 2.31E-14 13 BP GO:0002237 response to molecule of bacterial origin 2.10E-17 2.31E-14 13 BP GO:0071222 cellular response to lipopolysaccharide 1.39E-12 1.02E-09 9 BP GO:0071219 cellular response to molecule of bacterial origin 1.89E-12 1.04E-09 9 BP GO:0071216 cellular response to biotic stimulus 4.96E-12 2.18E-09 9 BP GO:2001234 negative regulation of apoptotic signaling pathway 1.96E-10 7.20E-08 8 BP GO:0010038 response to metal ion 2.37E-10 7.45E-08 9 BP GO:0048545 response to steroid hormone 3.89E-10 1.07E-07 9 BP GO:0022407 regulation of cell-cell adhesion 5.82E-10 1.42E-07 9 BP GO:0048608 reproductive structure development 1.05E-09 2.23E-07 9 BP GO:0061458 reproductive system development 1.12E-09 2.23E-07 9 BP GO:0009314 response to radiation 1.48E-09 2.65E-07 9 BP GO:0006979 response to oxidative stress 1.57E-09 2.65E-07 9 BP GO:0042110 T cell activation 2.01E-09 3.16E-07 9 BP GO:0034612 response to tumor necrosis factor 2.19E-09 3.22E-07 8 BP GO:0034349 glial cell apoptotic process 2.37E-09 3.25E-07 4 BP GO:0002573 myeloid leukocyte differentiation 3.55E-09 4.59E-07 7 BP GO:0070997 neuron death 5.17E-09 6.31E-07 8 BP GO:0097191 extrinsic apoptotic signaling pathway 6.78E-09 7.86E-07 7 BP GO:0070482 response to oxygen levels 1.36E-08 1.50E-06 8 CC GO:0045121 membrane raft 1.27E-06 6.17E-05 6 CC GO:0098857 membrane microdomain 1.30E-06 6.17E-05 6 CC GO:0098589 membrane region 1.61E-06 6.17E-05 6 CC GO:0005741 mitochondrial outer membrane 4.97E-05 0.001243834 4 CC GO:0005667 transcription factor complex 5.41E-05 0.001243834 5 CC GO:0031968 organelle outer membrane 7.97E-05 0.001361141 4 CC GO:0019867 outer membrane 8.29E-05 0.001361141 4 CC GO:0046930 pore complex 0.000324441 0.004663842 2 CC GO:1904813 ficolin-1-rich granule lumen 0.000392171 0.005011074 3 CC GO:0005819 spindle 0.000640704 0.007368092 4 CC GO:0090575 RNA polymerase II transcription factor complex 0.000869852 0.009093909 3 CC GO:0000307 cyclin-dependent protein kinase holoenzyme complex 0.001089346 0.010439568 2 CC GO:0101002 ficolin-1-rich granule 0.001253428 0.011088015 3 CC GO:0044798 nuclear transcription factor complex 0.001590231 0.013062616 3 CC GO:0005901 caveola 0.003891901 0.029837911 2 CC GO:1902554 serine/threonine protein kinase complex 0.004688015 0.03369511 2 CC GO:0035578 azurophil granule lumen 0.005004373 0.03385311 2 CC GO:0034774 secretory granule lumen 0.005946804 0.035510845 3 CC GO:1904949 ATPase complex 0.006127975 0.035510845 2 CC GO:0060205 cytoplasmic vesicle lumen 0.006856895 0.035510845 3 MF GO:0051400 BH domain binding 2.29E-07 1.91E-05 3 MF GO:0070513 death domain binding 2.29E-07 1.91E-05 3 MF GO:0019902 phosphatase binding 3.42E-06 0.000170898 5 MF GO:0033613 activating transcription factor binding 4.09E-06 0.000170898 4 MF GO:0002020 protease binding 2.08E-05 0.000629425 4 MF GO:0005126 cytokine receptor binding 2.82E-05 0.000629425 5 MF GO:0019903 protein phosphatase binding 2.96E-05 0.000629425 4 MF GO:0031625 ubiquitin protein ligase binding 3.02E-05 0.000629425 5 MF GO:0044389 ubiquitin-like protein ligase binding 4.02E-05 0.000746042 5 MF GO:0004707 MAP kinase activity 0.000145644 0.00243226 2 MF GO:0097153 cysteine-type endopeptidase activity involved in apoptotic process 0.000167918 0.002549304 2 MF GO:0004708 MAP kinase kinase activity 0.000191755 0.002668588 2 MF GO:0005123 death receptor binding 0.00021715 0.002789547 2 MF GO:0020037 heme binding 0.000687548 0.008201468 3 MF GO:0097718 disordered domain specific binding 0.000832459 0.00883078 2 MF GO:0046906 tetrapyrrole binding 0.000846063 0.00883078 3 MF GO:0001085 RNA polymerase II transcription factor binding 0.001026133 0.009707469 3 MF GO:0016248 channel inhibitor activity 0.001103999 0.009707469 2 MF GO:0016705 oxidoreductase activity, acting on paired donors, with incorporation or reduction of molecular oxygen 0.001104443 0.009707469 3 MF GO:0004712 protein serine/threonine/tyrosine kinase activity 0.001412492 0.011727316 2 Table 6 The data of top twenty KEGG pathway ID Description P-value P.adjust Count hsa05167 Kaposi sarcoma-associated herpesvirus infection 5.39E-16 8.46E-14 13 hsa04933 AGE-RAGE signaling pathway in diabetic complications 1.13E-15 8.85E-14 11 hsa05163 Human cytomegalovirus infection 6.58E-15 3.45E-13 13 hsa04657 IL-17 signaling pathway 4.27E-14 1.68E-12 10 hsa05161 Hepatitis B 2.60E-13 6.81E-12 11 hsa04668 TNF signaling pathway 2.60E-13 6.81E-12 10 hsa05164 Influenza A 1.78E-11 4.00E-10 10 hsa05133 Pertussis 2.57E-11 5.04E-10 8 hsa05152 Tuberculosis 3.16E-11 5.50E-10 10 hsa05169 Epstein-Barr virus infection 9.46E-11 1.48E-09 10 hsa05170 Human immunodeficiency virus 1 infection 1.60E-10 2.29E-09 10 hsa05142 Chagas disease (American trypanosomiasis) 2.86E-10 3.74E-09 8 hsa05140 Leishmaniasis 1.57E-09 1.90E-08 7 hsa04210 Apoptosis 2.89E-09 3.24E-08 8 hsa05162 Measles 3.24E-09 3.40E-08 8 hsa05132 Salmonella infection 4.64E-09 4.55E-08 9 hsa01522 Endocrine resistance 8.69E-09 8.03E-08 7 hsa04625 C-type lectin receptor signaling pathway 1.32E-08 1.15E-07 7 hsa05145 Toxoplasmosis 2.22E-08 1.83E-07 7 hsa05130 Pathogenic Escherichia coli infection 6.53E-08 5.13E-07 8 Table 7 The functional annotation clustering of CGs Annotation Cluster Term Count P-value Annotation Cluster 1 (Score:6.04) Asthma|Bronchiolitis, Viral|Respiratory Syncytial Virus Infections 7 8.50E-07 respiratory syncytial virus bronchiolitis 7 8.50E-07 Bronchiolitis, Viral|Respiratory Syncytial Virus Infections 7 1.04E-06 Annotation Cluster 2 (Score:4.91) Coronary Artery Disease|Inflammation 5 5.45E-07 non-Hodgkin lymphoma 4 1.90E-06 Recurrence|Venous Thromboembolism 5 2.48E-06 Arthritis 5 2.66E-06 Brain Ischemia|Hypertension|Osteoporosis|Stroke 5 3.69E-06 diabetes, type 1 6 1.38E-05 melanoma 5 2.10E-05 Inflammation|Venous Thromboembolism 4 2.24E-05 Chlamydia Infections|Inflammation|Trachoma 4 2.24E-05 Brain Ischemia|Inflammation|Stroke 4 2.24E-05 Pre-Eclampsia 4 3.32E-04 Migraine Disorders 4 4.52E-04 Annotation Cluster 3 (Score:4.89) Chorioamnionitis|Fetal Membranes, Premature Rupture|Infection of amniotic sac and membranes 7 4.94E-07 Chorioamnionitis|Fetal Membranes, Premature Rupture|Infection of amniotic sac and membranes|Obstetric Labor, Premature|Pre-Eclampsia|Premature Birth 7 5.10E-07 Coronary Artery Disease 7 1.56E-04 Alzheimer's disease 8 7.10E-04 Annotation Cluster 4 (Score:4.44) Hodgkin Disease|Inflammation 4 3.40E-06 Sarcoidosis 5 4.99E-06 Adenocarcinoma|Stomach Neoplasms 4 5.74E-05 kidney failure, chronic 5 2.02E-04 esophageal cancer 4 3.20E-04 Annotation Cluster 5(Score:4.26) Lymphoma, Non-Hodgkin|Lymphoma, Non-Hodgkin's 5 3.30E-05 Leukemia, Myelogenous, Chronic, BCR-ABL Positive|Neovascularization, Pathologic 4 4.02E-05 Leukemia, Myelogenous, Chronic, BCR-ABL Positive 4 1.29E-04 Annotation Cluster 6 (Score:4.09) Tuberculosis, Pulmonary 5 2.31E-06 systemic lupus erythematosus 5 8.88E-05 hepatitis C, chronic 4 3.56E-04 Tuberculosis 4 6.16E-04 Annotation Cluster 7 (Score:4.04) Helicobacter Infections|Inflammation|Precancerous Conditions|Stomach Neoplasms 4 9.23E-07 Stomach Neoplasms 5 3.54E-05 patent ductus arteriosus 5 6.13E-05 Cystic Fibrosis 4 1.29E-04 stomach cancer 4 5.46E-04 rheumatoid arthritis 4 0.003880586 Annotation Cluster 8 (Score:3.94) Infection|Inflammation|Premature Birth 5 5.26E-05 Inflammation|Premature Birth 5 5.77E-05 Connective Tissue Diseases|Fetal Diseases|Inflammation|Musculoskeletal Diseases|Pregnancy Complications, Hematologic|Premature Birth|Skin Diseases 5 5.77E-05 Asthma 4 9.96E-04 Annotation Cluster 9 (Score:3.84) Atherosclerosis 7 2.07E-05 Myocardial Infarction 7 2.05E-04 Alzheimer's disease 8 7.10E-04 Annotation Cluster 10 (Score:3.78) Brain Ischemia|Stroke 5 1.86E-05 Peripheral Vascular Diseases 4 4.02E-05 Cardiovascular Diseases 5 2.60E-04 Hypercholesterolemia|LDLC levels 4 0.004054968 Annotation Cluster 11 (Score:3.55) Restenosis 4 1.81E-04 Arthritis, Rheumatoid|Rheumatoid Arthritis 5 1.98E-04 Endometriosis 4 6.16E-04 Annotation Cluster 12 (Score:3.28) Alcoholism|Liver Cirrhosis, Alcoholic 3 2.12E-04 Esophageal Neoplasms|Hyperglycemia|Oesophageal neoplasm 3 2.12E-04 Biliary Tract Neoplasms|Inflammation 3 5.66E-04 Arthritis, Psoriatic|Psoriatic arthropathy 3 6.21E-04 cardiovascular 3 0.002488197 Annotation Cluster 13 (Score:3.18) Otitis Media|Recurrence 3 2.47E-04 Brucellosis 3 5.12E-04 Graft vs Host Disease|Hematologic Neoplasms|Neoplasm Recurrence, Local 3 5.12E-04 Kawasaki disease 3 6.21E-04 Atopy 3 0.003077333 Annotation Cluster 14 (Score:2.82) Atherosclerosis|Inflammation|Retinal Vein Occlusion 3 1.23E-04 Dermatitis, Atopic|Eczema allergic 3 7.41E-04 juvenile arthritis 3 0.001084437 graft-versus-host disease 3 0.002834545 Graft vs Host Disease 3 0.005354403 hepatitis C 3 0.008600238 Annotation Cluster 15 (Score:2.80) Uveitis, Anterior 3 1.23E-04 Pancreatitis, Chronic 3 8.05E-04 stroke, ischemic 3 0.004142278 Glomerulonephritis, IGA 3 0.016033469 Annotation Cluster 16 (Score:2.76) giant cell arteritis 3 5.12E-04 Malaria, Falciparum 3 0.002163435 Malaria 3 0.004882851 Annotation Cluster 17 (Score:2.72) Cardiovascular Diseases|Inflammation 3 1.50E-04 skin cancer, non-melanoma 3 0.001084437 Adenoma|Colorectal Neoplasms 3 0.002954755 Depression 3 0.028362664 Annotation Cluster 18 (Score:2.68) Endometriosis|Uterine Diseases 3 1.50E-04 Hepatitis B, Chronic 3 0.006357795 Pulmonary Disease, Chronic Obstructive 3 0.009415863 Annotation Cluster 19 (Score:2.63) respiratory syncytial virus 3 3.68E-04 Q fever 3 4.61E-04 Graves' disease|Graves' disease 3 0.001490775 Graves' disease 3 0.001579503 Diabetes Mellitus, Insulin-Dependent|Diabetes Mellitus, Type 1 3 0.006532757 Premature Birth 3 0.01182929 Kidney Diseases 3 0.012294446 Annotation Cluster 20 (Score:2.47) Carcinoma, Squamous Cell|Mouth Neoplasms 3 0.001084437 Helicobacter Infections|Stomach Neoplasms 3 0.002377534 Precursor Cell Lymphoblastic Leukemia-Lymphoma 3 0.015509841

Document structure show

Annnotations TAB TSV DIC JSON TextAE

last updated at 2021-04-26 04:34:34 UTC

  • Denotations: 1
  • Blocks: 0
  • Relations: 0