Tetrastigma hemsleyanum suppresses neuroinflammation in febrile seizures rats via regulating PKC-δ/caspase-1 signaling pathway

Molecular mechanisms of Tetrastigma hemsleyanum Diels&Gilg against lung squamous cell carcinoma: From computational biology and experimental validation

ETHNOPHARMACOLOGICAL RELEVANCE

Tetrastigma hemsleyanum （T. hemsleyanum）, valued in traditional medicine for its potential to boost immunity and combat tumors, contains uncharacterized active compounds and mechanisms. This represents a significant gap in our understanding of its ethnopharmacological relevance.

AIM OF THE STUDY

To involve the mechanism of anti-lung cancer effect of T. hemsleyanum by means of experiment and bioinformatics analysis.

MATERIALS AND METHODS

The anticancer mechanism of T. hemsleyanum against lung squamous carcinoma (LUSC) in zebrafish was investigated. The LUSC model was established by injecting NCI-H2170 cells in the zebrafish and evaluating its anti-tumor efficacy. Next, component targets and key genes were obtained by molecular complex detection (MCODE) analysis and protein-protein interaction (PPI) network analysis. Component analysis of T. hemsleyanum was performed by UPLC-Q-TOF-MS. Molecular docking was used to simulate the binding activities of key potential active components to core targets were simulated using. Prognostic and pan-cancer analyses were then performed to validate the signaling pathways involved in the prognostic genes using gene set enrichment analysis (GSEA). Subsequently, Molecular dynamics simulations were then performed for key active components and core targets. Finally, cellular experiments were used to verify the expression of glutamate metabotropic receptor 3 (GRM3) and glutamate metabotropic receptor 7 (GRM7) in the anticancer effect exerted of T. hemsleyanum.

RESULTS

We experimentally confirmed the inhibitory effect of T. hemsleyanum on LUSC by transplantation of NCI-H2170 cells into zebrafish. There are 20 main compounds in T. hemsleyanum, such as procyanidin B1, catechin, quercetin, and kaempferol, etc. A total of 186 component targets of T. hemsleyanum and sixteen hub genes were screened by PPI network and MCODE analyses. Molecular docking and molecular dynamics simulation results showed that Gingerglycolipid B and Rutin had higher affinity with GRM3 and GRM7, respectively. Prognostic analysis, Pan-cancer analysis and verification experiment also confirmed that GRM3 and GRM7 were targets for T. hemsleyanum to exert anti-tumor effects and to participate in immune and mutation processes. In vitro experiments suggested that the inhibitory effect of T. hemsleyanum on cancer cells was correlated with GRM3 and GRM7.

CONCLUSION

In vivo, in vitro and in silico results confirmed the potential anticancer effects against LUSC of T. hemsleyanum, which further consolidated the claim of its traditional uses.

Network-Based Methods and Their Applications in Drug Discovery

Drug discovery is time-consuming, expensive, and predominantly follows the "one drug → one target → one disease" paradigm. With the rapid development of systems biology and network pharmacology, a novel drug discovery paradigm, "multidrug → multitarget → multidisease", has emerged. This new holistic paradigm of drug discovery aligns well with the essence of networks, leading to the emergence of network-based methods in the field of drug discovery. In this Perspective, we initially introduce the concept and data sources of networks and highlight classical methodologies employed in network-based methods. Subsequently, we focus on the practical applications of network-based methods across various areas of drug discovery, such as target prediction, virtual screening, prediction of drug therapeutic effects or adverse drug events, and elucidation of molecular mechanisms. In addition, we provide representative web servers for researchers to use network-based methods in specific applications. Finally, we discuss several challenges of network-based methods and the directions for future development. In a word, network-based methods could serve as powerful tools to accelerate drug discovery.