Cytonca

The STRING (v 10.0; https://string-db.org/cgi/input.pl, accessed on 20 July 2022) database was utilized for the analysis of the interactions between the DEG-encoded proteins [48 (link)]. All of the DEGs were set by Cytoscape software (v 3.2.0; https// cytoscape.org/, accessed on 20 July 2022). The cytonca [49 (link)] plug-in (v 2.1.6, cytonca">https://apps.cytoscape.org/apps/cytonca, accessed on 20 July 2022) was used to analyze the topological network properties of the node. The important nodes of the PPI network can be determined by ranking the score of every node. The most remarkable clustering modules in the PPI network were processed by the MCODE (v 1.4.2, MCODE">https://apps.cytoscape.org/apps/MCODE, accessed on 20 July 2022) [50 (link)] plug-in of the Cytoscape. A score of ≥5 was considered the threshold. The GO enrichment analysis was carried out for the significant clustering module genes.

On combining the Search Tool for the Retrieval of Interacting Genes (STRING, version 10.0, http://www.string-db.org/, combined score > 0.4) [19 (link)], Biological General Repository for Interaction Datasets (BioGRID, version 3.4, https://wiki.thebiogrid.org/) [20 (link)] and Human Protein Reference Database (release 9, http://www.hprd.org/) [21 (link)] interaction databases, PPI pairs among DSSM-associated genes were predicted. Subsequently, the PPI network was visualised for DSSM-associated genes using the Cytoscape software (http://www.cytoscape.org) [22 (link)]. Using the CytoNCA plug-in [23 (link)] (version 2.1.6, CytoNCA">http://apps.cytoscape.org/apps/CytoNCA) in Cytoscape, degree centrality (DC), betweenness centrality (BC), and closeness centrality of the nodes were analysed to obtain the hub proteins in the PPI network [24 (link)]. The parameter was set as “without weight.”
Based on the MCODE plug-in [25 ] (version 1.4.2; http://apps.cytoscape.org/apps/mcode; parameters set as degree cut-off = 2, maximum depth = 100, node score cut-off = 0.2, and K-core = 2) in Cytoscape, module analysis was conducted for the PPI network. Subsequently, KEGG pathway enrichment analysis was performed for the nodes of significant modules, with FDR < 0.05 as the cut-off criterion.

The clusterProfiler [12 (link)] package was used to perform gene ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses (p < 0.05). A statistically significant FDR was <0.05. The PPI network was developed utilizing STRING (https://cn.string-db.org/) data to better comprehend the relationship among the screened genes. A minimum necessary interaction score of high confidence (0.95) was selected. Cytoscape (version 3.9.1) was then used to visualize the PPI network. Subsequently, CytoNCA, a Cytoscape plugin for analyzing the centrality of PPI networks, was used to identify the network's crucial genes. The crucial genes were chosen based on their degree of centrality. Crucial genes were defined as genes with centrality values greater than two times the median centrality value in the PPI network.

An online database of known and predicted protein interactions, Search Tool for the Retrieval of Interacting Genes (STRING; http://string-db.org; version 10.0), was applied to predict the PPI network of DEGs (Franceschini et al., 2013 (link)) while interactions with a combined score > 0.4 were taken as statistically significant. Cytoscape (version 3.6.1, http://www.cytoscape.org; Smoot et al., 2011 (link)) was used to visualize molecular interaction networks, specifically using its plug-in CytoNCA to analyze the topological properties of nodes in the PPI network with parameters set as unweighted. By ranking the scores of each node, we obtained important nodes of protein interactions within the network. Considering most networks were scale-free, the hub genes with degree ≥ 20 were selected. Metascape³ was used to further verify the function enrichment of hub genes (Zhou et al., 2019 ) with P-value < 0.05 as the cutoff. Hub genes pathway analysis was performed and visualized by ClueGO (version 2.5.4) and CluePedia (version 1.5.4), also plug-ins of Cytoscape. P-value < 0.01 was considered to be statistically significant. A network of genes and their co-expression genes was analyzed via GeneMANIA⁴ (Warde-Farley et al., 2010 (link)). Finally, the expression of identified hub gene was verified in GSE14905 dataset.

The effective active ingredients acquired from TCMSP in the previous step were further searched to obtain the corresponding target proteins. The standardized genes were retrieved through the UniProt database (https://www.Uniprot.org/) [37 (link)]. The active genes of Panax Ginseng C. A. Mey, Atractylodes Macrocephala Koidz, Poria Cocos Wolf and licorice were sorted out and introduced into Cytoscape V3.7.2 to establish the "compound-target" network diagram to visualize the overall situation of drug compounds and targets. The network topology analysis plug-in CytoNCA [38 (link)] in Cytoscape V3.7.2 was used to obtain the core compound and target gene of SJZD with a degree of freedom > 34 (twice the median). "Degree" reflects the number of links with each node in the network and the interaction relationship between nodes.

We search and predict the key genes of the network using the significant parameters of degree centrality, betweenness centrality, closeness centrality and eigenvector centrality (Ostovari, Yu & Steele-Morris, 2018 (link)). The four centrality scores of each vertex were calculated by Cytoscape. Degree, betweenness and closeness were calculated using Network Analyzer of Cytoscape (Assenov et al., 2008 (link)) and eigenvector was calculated using Cytoscape app CytoNCA (Tang et al., 2015 (link)). The score file for these four parameters was downloaded from the Cytoscape software, the R language was used to describe the distribution of the four parameters and calculate the correlation among the four key centralities.