Version 3

A pharmacology network is comprised of nodes and edges. The entities that make up the nodes of the networks are resveratrol NDs and related target genes. The Cytoscape version 3.7.2 was used to constructed networks, which is a java based open source software (Demchak et al., 2014 (link)). Functional pathways of resveratrol related to NDs were analyzed using GO enrichment and KEGG pathways analysis based upon the database for Annotation, Visualization and Integrated Discovery (DAVID) version 6.8 (https://david.ncifcrf.gov/) (Ke et al., 2019 (link)). P < 0.05 suggested the enrichment degree had statistically significant and the pathway results might be essential functional mechanisms of resveratrol in the treatment of NDs.

The network of active compounds and therapeutic targets was constructed by linking the compounds and therapeutic targets to understand the complex interactions between the compounds of Dangshen and the therapeutic targets of OS. The network of therapeutic targets and organs was established by linking therapeutic targets and their distribution in organs to clarify the relationship between the therapeutic targets and organs with increased expression of the target. The therapeutic targets' PPI network was built by linking the therapeutic targets to their interacting targets. Next, Cytoscape version 3.7.2 (http://www.cytoscape.org/) was used to present the networks mentioned above, which is a software program for network visualization [34 (link)]. Lastly, NetworkAnalyzer [35 (link)] was used to calculate three topological parameters of each node in the network, including the degree, betweenness centrality, and closeness centrality [36 (link)].

The putative target genes of ADI and lung cancer were overlapped to identify the shared target genes for ADI to treat lung cancer. These common putative target genes were input into the search tool for the retrieval of interacting genes (STRING) 11.0 database (https://string-db.org/) [32 (link)] to construct the protein–protein interaction (PPI) network. To guarantee the robustness of outcomes, the screening threshold in the STRING database was set as interactions score ≥ 0.9. Next, the PPI networks were visualized and analyzed using Cytoscape (version 3.72) [33 (link)]. Degree, betweenness, closeness were three important indexes to describe a protein's topological importance in the network. In the PPI network, nodes met with all the following topology value criteria were considered as hub target genes in the network: (1) with the degree greater than double of the median degree; (2) with betweenness greater than the median betweenness; (3) with closeness greater than the median closeness.

Various proteins have numerous reactions in the human body because there are many protein–protein interactions in the human body [11 (link),12 (link)]. PPI analyses were performed using the STRING database (https://string-db.org/, version 11.5, accessed on 14 April 2023) with a medium confidence score (≥0.400). The PPI’s topology was analyzed and visualized using Cytoscape version 3.7.2 (https://cytoscape.org/, accessed on 14 April 2023), with node colors of red (large nodes) and blue (small nodes) [13 (link)].

Before establishing the model, the ferroptosis-related genes from WGCNA were tested by univariate Cox regression analysis (p <0.001). Furthermore, ferroptosis-related lncRNAs co-expressed with ferroptosis-related genes were screened by Pearson correlation test (correlation coefficient >0.4, and p <0.001). The lncRNAs were further screened by univariate Cox regression analysis (p <0.001). Then, the selected ferroptosis-related genes and lncRNAs were merged to establish the model. A network containing the ferroptosis-related mRNA-lncRNA network was constructed and visualized by Cytoscape (version 3.7.2)
HCC patients from the TCGA liver hepatocellular carcinoma (LIHC) cohort were randomly divided into a training group, and another 50% were set as the test group. The LASSO Cox regression algorithm was applied to select the ferroptosis-related signature. Finally, a formula for the risk score was established, and we calculated the risk score for each patient as follows:
Coefi indicates the correlation coefficient of each ferroptosis-related signature, and X indicates the level of gene expression. The median risk score in the training cohort was set as the cutoff value, and the training group and test group were divided into high-risk and low-risk groups according to the cutoff.

PPI networks are constructed from protein targets. that interact among themselves to execute certain biological processes such as cell signaling, regulation of gene expressions, metabolic pathways like energy production, and regulation of cell cycle checkpoints [12 (link)]. The STRING database v.11 (https://string-db.org/) was utilized to identify potential therapeutic targets of UA related to SCI pathology within the PPI network, setting the species type to “Homo sapiens” and a high confidence score (0.9) [13 (link)]. Following this, the Cytoscape (version 3.7.2) software (https://cytoscape.org) was used to visually output the PPI network.