Gigantol Targets MYC for Ubiquitin-proteasomal Degradation and Suppresses Lung Cancer Cell Growth

Research progress on the regulatory and pharmacological mechanism of chemical components of Dendrobium

Dendrobium is a precious Chinese herbal medicine, which belongs to the genus Orchidaceae. Ancient records and modern pharmacological research show that Dendrobium has pharmacological effects such as anti-tumor, antioxidant regulating immunity and blood glucose, and anti-aging. Dendrobium contains polysaccharides, alkaloids, bibenzyl, sesquiterpenes, phenanthrene, polyphenols and other types of chemicals. Its pharmacological activity is closely related to these chemical components. For example, dendrobium extracts can achieve anti-tumor effects by inhibiting tumor cell proliferation and metastasis, promoting cell apoptosis and ferroptosis, or increasing cell sensitivity to chemotherapy drugs. It enhances immunity by regulating immune cell activity or cytokine release. In addition, it can alleviate neurodegenerative diseases by protecting nerve cells from apoptotic damage. In recent years, research reports on biologically active compounds in Dendrobium have shown a blowout growth, which makes us realize that it is meaningful to continuously update the research progress on the components and pharmacological regulatory mechanism of this traditional Chinese medicine. By classifying the collected chemical components according to different chemical structures and summarizing their pharmacological mechanisms, we investigated the current research progress of Dendrobium and provide a more comprehensive scientific foundation for the further development and clinical transformation of Dendrobium in the future.

Dendrobium nobile active ingredient Dendrobin A against hepatocellular carcinoma via inhibiting nuclear factor kappa-B signaling

OBJECTIVE

Dendrobin A, a typical active ingredient of the traditional Chinese medicine Dendrobium nobile, has potential clinical application in cancer treatment; however, its effect and mechanism in anti-hepatocellular carcinoma (HCC) remain unsolved.

METHOD

The effects of Dendrobin A on the viability, migration, invasion, cycle, apoptosis, and epithelial-mesenchymal transition of HepG2 and SK-HEP-1 cells were verified by in vitro experiments. mRNA sequencing was performed to screen the differentially expressed genes (DEGs) of HCC cells before and after Dendrobin A treatment, following GO enrichment and KEGG signaling pathway analyses. Mechanistically, molecular docking was used to evaluate the binding of Dendrobin A with proteins p65 and p50, before further verifying the activation of nuclear factor kappa-B (NF-κB) signaling. Finally, the antiproliferative effect of Dendrobin A on HCC cells was explored through animal experiments.

RESULTS

Dendrobin A arrested cell cycle, induced apoptosis, and inhibited proliferation, migration, invasion, and blocked epithelial-mesenchymal transition in HepG2 and SK-HEP-1 cells. mRNA sequencing identified 830 DEGs, involving various biological processes. KEGG analysis highlighted NF-κB signaling. Molecular docking revealed strong binding of Dendrobin A with p65 and p50 proteins, and western blotting confirmed reduced levels of p-p65 and p-p50 in HCC cells post Dendrobin A treatment. NF-κB agonist PMA reversed Dendrobin A-inhibited cell proliferation migration and invasion. In vivo experiments showed that Dendrobin A inhibited HCC cell growth.

CONCLUSION

Our findings suggest that Dendrobin A exhibits anti-HCC properties by inhibiting the activation of the NF-κB pathway. These results provide a scientific basis for utilizing Dendrobium nobile in anti-HCC therapies.

Review of Personalized Medicine and Pharmacogenomics of Anti-Cancer Compounds and Natural Products

In recent years, the FDA has approved numerous anti-cancer drugs that are mutation-based for clinical use. These drugs have improved the precision of treatment and reduced adverse effects and side effects. Personalized therapy is a prominent and hot topic of current medicine and also represents the future direction of development. With the continuous advancements in gene sequencing and high-throughput screening, research and development strategies for personalized clinical drugs have developed rapidly. This review elaborates the recent personalized treatment strategies, which include artificial intelligence, multi-omics analysis, chemical proteomics, and computation-aided drug design. These technologies rely on the molecular classification of diseases, the global signaling network within organisms, and new models for all targets, which significantly support the development of personalized medicine. Meanwhile, we summarize chemical drugs, such as lorlatinib, osimertinib, and other natural products, that deliver personalized therapeutic effects based on genetic mutations. This review also highlights potential challenges in interpreting genetic mutations and combining drugs, while providing new ideas for the development of personalized medicine and pharmacogenomics in cancer study.

Therapeutic Potential and Predictive Pharmaceutical Modeling of Stilbenes in Cannabis sativa

Cannabis sativa is a plant used for recreational and therapeutic purposes; however, many of the secondary metabolites in the plant have not been thoroughly investigated. Stilbenes are a class of compounds with demonstrated anti-inflammatory and antioxidant properties and are present in cannabis. Many stilbenes present in cannabis have been investigated for their therapeutic effects. Fourteen stilbenes have been identified to be present in cannabis, all of which are structurally dihydrostilbenoids, with half possessing a prenylated moiety. The stilbenes summarized in this analysis show varying degrees of therapeutic benefits ranging from anti-inflammatory, antiviral, and anti-cancer to antioxidant effects. Many of the identified stilbenes have been researched to a limited extent for potential health benefits. In addition, predictive in silico modeling was performed on the fourteen identified cannabis-derived stilbenes. This modeling provides prospective activity, pharmacokinetic, metabolism, and permeability data, setting the groundwork for further investigation into these poorly characterized compounds.

Integrated analysis of Dendrobium nobile extract Dendrobin A against pancreatic ductal adenocarcinoma based on network pharmacology, bioinformatics, and validation experiments

Background: Dendrobium nobile (D. nobile), a traditional Chinese medicine, has received attention as an anti-tumor drug, but its mechanism is still unclear. In this study, we applied network pharmacology, bioinformatics, and in vitro experiments to explore the effect and mechanism of Dendrobin A, the active ingredient of D. nobile, against pancreatic ductal adenocarcinoma (PDAC). Methods: The databases of SwissTargetPrediction and PharmMapper were used to obtain the potential targets of Dendrobin A, and the differentially expressed genes (DEGs) between PDAC and normal pancreatic tissues were obtained from The Cancer Genome Atlas and Genotype-Tissue Expression databases. The protein-protein interaction (PPI) network for Dendrobin A anti-PDAC targets was constructed based on the STRING database. Molecular docking was used to assess Dendrobin A anti-PDAC targets. PLAU, one of the key targets of Dendrobin A anti-PDAC, was immunohistochemically stained in clinical tissue arrays. Finally, in vitro experiments were used to validate the effects of Dendrobin A on PLAU expression and the proliferation, apoptosis, cell cycle, migration, and invasion of PDAC cells. Results: A total of 90 genes for Dendrobin A anti-PDAC were screened, and a PPI network for Dendrobin A anti-PDAC targets was constructed. Notably, a scale-free module with 19 genes in the PPI indicated that the PPI is highly credible. Among these 19 genes, PLAU was positively correlated with the cachexia status while negatively correlated with the overall survival of PDAC patients. Through molecular docking, Dendrobin A was found to bind to PLAU, and the Dendrobin A treatment led to an attenuated PLAU expression in PDAC cells. Based on clinical tissue arrays, PLAU protein was highly expressed in PDAC cells compared to normal controls, and PLAU protein levels were associated with the differentiation and lymph node metastatic status of PDAC. In vitro experiments further showed that Dendrobin A treatment significantly inhibited the proliferation, migration, and invasion, inducing apoptosis and arresting the cell cycle of PDAC cells at the G2/M phase. Conclusion: Dendrobin A, a representative active ingredient of D. nobile, can effectively fight against PDAC by targeting PLAU. Our results provide the foundation for future PDAC treatment based on D. nobile.

ECE2 is a prognostic biomarker associated with m6A modification and involved in immune infiltration of lung adenocarcinoma

BACKGROUND

The targeted therapy for lung cancer relies on prognostic genes and requires further research. No research has been conducted to determine the effect of endothelin-converting enzyme 2 (ECE2) in lung cancer.

METHODS

We analyzed the expression of ECE2 in lung adenocarcinoma (LUAD) and normal adjacent tissues and its relationship with clinicopathological characteristics from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus database (GEO). Immunohistochemical staining was used to further validate the findings. GO/KEGG enrichment analysis and gene set enrichment analysis (GSEA) of ECE2 co-expression were performed using R software. Data from TIMER, the GEPIA database, and TCGA were analyzed to determine the relationship between ECE2 expression and LUAD immune infiltration. To investigate the relationship between ECE2 expression levels and LUAD m6A modification, TCGA data and GEO data were analyzed.

RESULTS

ECE2 is highly expressed in various cancers including LUAD. ECE2 showed high accuracy in distinguishing tumor and normal sample results. The expression level of ECE2 in LUAD was significantly correlated with tumor stage and prognosis. GO/KEGG enrichment analysis showed that ECE2 was closely related to mitochondrial gene expression, ATPase activity and cell cycle. GSEA analysis showed that ECE2-related differential gene enrichment pathways were related to mitotic cell cycle, MYC pathway, PLK1 pathway, DNA methylation pathway, HIF1A pathway and Oxidative stress-induced cellular senescence. Analysis of the TIMER, GEPIA database, and TCGA datasets showed that ECE2 expression levels were significantly negatively correlated with B cells, CD4+ cells, M2 macrophages, neutrophils, and dendritic cells. TCGA and GEO datasets showed that ECE2 was significantly associated with m6A modification-related genes HNRNPC, IGF2BP1, IGF2BP3 and RBM1.

CONCLUSION

ECE2 is associated with m6A modification and immune infiltration and is a prognostic biomarker in LUAD.

Analysis of the Protein-Protein Interaction Network Identifying c-Met as a Target of Gigantol in the Suppression of Lung Cancer Metastasis

BACKGROUND/AIM

c-Met (mesenchymal-epithelial transition factor) facilitates cancer progression and is recognized as a promising drug target. The molecular target of gigantol from Dendrobium draconis in suppressing cancer metastasis is largely unknown.

MATERIALS AND METHODS

Proteins affected by gigantol treatment were subjected to proteomic and bioinformatic analysis. Protein-Protein interaction (PPI) networks were constructed by the Search Tool for the Retrieval of Interacting Genes (STRING). The Kyoto Encyclopedia of Genes and Genomes (KEGG) database and hub gene were used to enrich the dominant pathways. Western blot analysis and immunofluorescence were used to validate the effect of gigantol on the target protein and signaling.

RESULTS

Gigantol down-regulates 41 adhesion proteins and 39-migratory proteins, while it up-regulates 30 adhesion-related proteins and 22 proteins controlling cell migration. The key components of our constructed PPI network comprised 41 proteins of cell adhesion enriched in 40 nodes with 25 edges, 39 proteins of cell migration enriched in 39 nodes with 76 edges in down-regulated proteins, 30 proteins of cell adhesion enriched in 30 nodes with 21 edges, and 22 proteins of cell migration enriched in 22 nodes with 22 edges in up-regulated protein. c-Met was identified as a central protein of the PPI network in the largest degree. KEGG mapper further suggested that c-Met, PI3K, and AKT were the regulatory proteins affected by gigantol. To confirm, the effects of gigantol on c-Met, the p-PI3K, PI3K, p-AKT, and AKT proteins were investigated by western blotting and the results showed a consistent effect of gigantol in the suppression of the c-Met/PI3K/AKT signal. Next, immunofluorescence showed a dramatic decrease in c-Met, PI3K and AKT activation in response to gigantol.

CONCLUSION

c-Met is an important target of gigantol treatment in lung cancer cells. Gigantol suppresses metastasis-related cell motility through decreasing c-Met resulting in PI3K/AKT signaling disruption.