Network pharmacology plays a pivotal role in systems biology, bridging the gap between traditional Chinese medicine (TCM) theory and contemporary pharmacological research. Network pharmacology enables researchers to construct multilayered networks that systematically elucidate TCM’s multi-component, multi-target mechanisms of action. This review summarizes key databases commonly used in network pharmacology, including those focused on herbs, components, diseases, and dedicated platforms for network pharmacology analysis. Additionally, we explore the growing use of network pharmacology in TCM, citing literature from Web of Science, PubMed, and CNKI over the past two decades with keywords like “network pharmacology”, “TCM network pharmacology”, and “herb network pharmacology”. The application of network pharmacology in TCM is widespread, covering areas such as identifying the material basis of TCM efficacy, unraveling mechanisms of action, and evaluating toxicity, safety, and novel drug development. However, challenges remain, such as the lack of standardized data collection across databases and insufficient consideration of processed herbs in research. Questions also persist regarding the reliability of study outcomes. This review aims to offer valuable insights and reference points to guide future research in precision TCM network pharmacology.

Background and Objective: Despite striking advances in multimodality management, gastric cancer (GC) remains the third cause of cancer mortality globally and identifying novel diagnostic and prognostic biomarkers is urgently demanded. The study aimed to identify potential key genes associated with the pathogenesis and prognosis of GC. Methods: Differentially expressed genes between GC and normal gastric tissue samples were screened by an integrated analysis of multiple gene expression profile datasets. Key genes related to the pathogenesis and prognosis of GC were identified by employing protein–protein interaction network and Cox proportional hazards model analyses. Results: We identified nine hub genes (TOP2A, COL1A1, COL1A2, NDC80, COL3A1, CDKN3, CEP55, TPX2, and TIMP1) which might be tightly correlated with the pathogenesis of GC. A prognostic gene signature consisted of CST2, AADAC, SERPINE1, COL8A1, SMPD3, ASPN, ITGBL1, MAP7D2, and PLEKHS1 was constructed with a good performance in predicting overall survivals. Conclusion: The findings of this study would provide some directive significance for further investigating the diagnostic and prognostic biomarkers to facilitate the molecular targeting therapy of GC.

Lung adenocarcinoma (LUAD) is the most prevalent subtype of non-small cell lung cancer. Additionally, disulfidptosis, a newly discovered type of cell death, has been found to be closely associated with the onset and progression of tumors. The study first identified genes related to disulfidptosis through correlation analysis. These genes were then screened using univariate cox regression and LASSO regression, and a prognostic model was constructed through multivariate cox regression. A nomogram was also created to predict the prognosis of LUAD. The model was validated in three independent data sets: GSE72094, GSE31210, and GSE37745. Next, patients were grouped based on their median risk score, and differentially expressed genes between the two groups were analyzed. Enrichment analysis, immune infiltration analysis, and drug sensitivity evaluation were also conducted. In this study, we examined 21 genes related to disulfidptosis and developed a gene signature that was found to be associated with a poorer prognosis in LUAD. Our model was validated using three independent datasets and showed AUC values greater than 0.5 at 1, 3, and 5 years. Enrichment analysis revealed that the disulfidptosis-related genes signature had a multifaceted impact on LUAD, particularly in relation to tumor development, proliferation, and metastasis. Patients in the high-risk group exhibited higher tumor purity and lower stromal score, ESTIMATE score, and Immune score. This study constructed a gene signature related to disulfidptosis in lung adenocarcinoma and analyzed its impact on the disease and its association with the tumor microenvironment. The findings of this research provide valuable insights into the understanding of lung adenocarcinoma and could potentially lead to the development of new treatment strategies.

Background and Objective: Non-small cell lung cancer (NSCLC) accounts for 80–85% of all patients with lung cancer and 5-year relative overall survival (OS) rate is less than 20%, so that identifying novel diagnostic and prognostic biomarkers is urgently demanded. The present study attempted to identify potential key genes associated with the pathogenesis and prognosis of NSCLC. Methods: Four GEO datasets (GSE18842, GSE19804, GSE43458, and GSE62113) were obtained from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) between NSCLC samples and normal ones were analyzed using limma package, and RobustRankAggreg (RRA) package was used to conduct gene integration. Moreover, Search Tool for the Retrieval of Interacting Genes database (STRING), Cytoscape, and Molecular Complex Detection (MCODE) were utilized to establish protein–protein interaction (PPI) network of these DEGs. Furthermore, functional enrichment and pathway enrichment analyses for DEGs were performed by Funrich and OmicShare. While the expressions and prognostic values of top genes were carried out through Gene Expression Profiling Interactive Analysis (GEPIA) and Kaplan Meier-plotter (KM) online dataset. Results: A total of 249 DEGs (113 upregulated and 136 downregulated) were identified after gene integration. Moreover, the PPI network was established with 166 nodes and 1784 protein pairs. Topoisomerase II alpha (TOP2A), a top gene and hub node with higher node degrees in module 1, was significantly enriched in mitotic cell cycle pathway. In addition, Interleukin-6 (IL-6) was enriched in amb2 integrin signaling pathway. The mitotic cell cycle was the most significant pathway in module 1 with the highest P-value. Besides, five hub genes with high degree of connectivity were selected, including TOP2A, CCNB1, CCNA2, UBE2C, and KIF20A, and they were all correlated with worse OS in NSCLC. Conclusion: The results showed that TOP2A, CCNB1, CCNA2, UBE2C, KIF20A, and IL-6 may be potential key genes, while the mitotic cell cycle pathway may be a potential pathway contribute to progression in NSCLC. Further, it could be used as a new biomarker for diagnosis and to direct the synthesis medicine of NSCLC.

Background: Acute myocardial infarction (AMI) is a common disease leading threat to human health around the world. Here we aimed to explore new biomarkers and potential therapeutic targets in AMI through adopting integrated bioinformatics tools. Methods: The gene expression Omnibus (GEO) database was used to obtain genes data of AMI and no-AMI whole blood. Furthermore, differentially expressed genes (DEGs) were screened using the “Limma” package in R 3.6.1 software. Functional and pathway enrichment analyses of DEGs were performed via “Bioconductor” and “GOplot” package in R 3.6.1 software. In order to screen hub DEGs, the STRING version 11.0 database, Cytoscape and molecular complex detection (MCODE) were applied. Correlation among the hub DEGs was evaluated using Pearson's correlation analysis. Results: By performing DEGs analysis, 289 upregulated and 62 downregulated DEGs were successfully identified from GSE66360, respectively. And they were mainly enriched in the terms of neutrophil activation, immune response, cytokine, nuclear factor kappa-B (NF-κB) signaling pathway, IL-17 signaling pathway, and tumor necrosis factor (TNF) signaling pathway. Based on the data of protein–protein interaction (PPI), the top 10 hub genes were ranked, including interleukin-8 (CXCL8), TNF, N-formyl peptide receptor 2 (FPR2), growth-regulated alpha protein (CXCL1), transcription factor AP-1 (JUN), interleukin-1 beta (IL1B), platelet basic protein (PPBP), matrix metalloproteinase-9 (MMP9), toll-like receptor 2 (TLR2), and high affinity immunoglobulin epsilon receptor subunit gamma (FCER1G). What's more, the results of correlation analysis demonstrated that there was positive correlation between the 10 hub DEGs. Conclusion: Ten DEGs were identified as potential candidate diagnostic biomarkers for patients with AMI in present study. However, further experiments are needed to confirm the functional pathways and hub genes associated with AMI.