Identification of the Mechanism of Matrine Combined with Glycyrrhizin for Hepatocellular Carcinoma Treatment through Network Pharmacology and Bioinformatics Analysis

Identification of molecular targets of Trigonelline for treating breast cancer through network pharmacology and bioinformatics-based prediction

Breast cancer, a highly prevalent and fatal cancer that affects the female population worldwide, stands as a significant health challenge. Despite the abundance of chemotherapy drugs, the adverse side effects associated with them have initiated an investigation into natural plant-based compounds. Trigonelline, an alkaloid found in Trigonella foenum-graecum, was previously reported for its anticancer properties by the researchers. In this present study, we have identified the molecular targets of Trigonelline in breast cancer and predicted its drug-like properties and toxicity. By analyzing breast cancer targets from databases including TTD, TCGA, Gene cards, and Trigonelline targets obtained from CTD, we identified 14 specific targets of Trigonelline in the context of breast cancer. The protein-protein interaction (PPI) network of the 14 Trigonelline targets provided insights into the complex relationships between different genes and targets. Heatmap analysis demonstrated the expression patterns of these 14 genes at the protein and RNA levels in breast cancer cells and breast tissues. Notably, four genes, namely EGF, BAX, EGFR, and MTOR, were enriched in the breast cancer pathway. At the same time, PARP1, DDIT3, BAX, and TNF were associated with the apoptosis pathway according to KEGG pathway enrichment analyses. Molecular docking studies between Trigonelline and target proteins from the Protein Data Bank (PDB) revealed favorable binding affinity. Furthermore, mutation analysis of target genes within a dataset of 1918 samples from cBioPortal revealed the absence of mutations. Remarkably, Trigonelline also exhibited binding affinity towards two mutant proteins, and based on these findings, we predicted that Trigonelline could be utilized to target breast cancer genes and their mutants through network pharmacology. Additionally, this was supported by molecular dynamic simulation studies. As our study is preliminary, further validation through in vitro and in vivo studies is essential to confirm the efficacy of Trigonelline in breast cancer treatment.

Application of network pharmacology in synergistic action of Chinese herbal compounds

Herbal medicines are frequently blended in the form of multi-drug combinations primarily based on the precept of medicinal compatibility, to achieve the purpose of treating diseases. However, due to the lack of appropriate techniques and the multi-component and multi-target nature of Chinese medicine compounding, it is tough to explain how the drugs interact with each other. As a rising discipline, cyber pharmacology has formed a new approach characterized by using holistic and systematic "network targets" via the cross-fertilization of computer technology, bioinformatics, and different multidisciplinary disciplines. It can broadly screen the active ingredients of traditional Chinese medicine, enhance the effective utilization of drugs, and elucidate the mechanism of drug action. We will overview the principles of Chinese medicine compounding and dispensing, the research methods of network pharmacology, and the software of network pharmacology in the lookup of compounded Chinese medicines, aiming to supply thoughts for the better application of network pharmacology in the research of Chinese medicines.

Comparative Efficacy of East Asian Herbal Formulae Containing Astragali Radix-Cinnamomi Ramulus Herb-Pair against Diabetic Peripheral Neuropathy and Mechanism Prediction: A Bayesian Network Meta-Analysis Integrated with Network Pharmacology

The Astragali Radix-Cinnamomi Ramulus herb-pair (ACP) has been widely used in the treatment of diabetic peripheral neuropathy (DPN) as part of East Asian herbal medicine (EAHM). Eligible randomized controlled trials (RCTs) were identified by searching 10 databases. The outcomes investigated were response rate, sensory nerve conduction velocity (SNCV), and motor nerve conduction velocity (MNCV) in four regions of the body. The compounds in the ACP and their targets of action, disease targets, common targets, and other relevant information were filtered using network pharmacology. Forty-eight RCTs, with 4308 participants, and 16 different interventions were identified. Significant differences were observed in the response rate, MNCV, and SNCV, as all EAHM interventions were superior to conventional medicine or lifestyle modification. The EAHM formula containing the ACP ranked highest in more than half of the assessed outcomes. Furthermore, major compounds, such as quercetin, kaempferol, isorhamnetin, formononetin, and beta-sitosterol, were found to suppress the symptoms of DPN. The results of this study suggest that EAHM may increase therapeutic efficacy in DPN management, and EAHM formulations containing the ACP may be more suitable for improving treatment response rates to NCV and DPN therapy.

Basement membrane-related regulators for prediction of prognoses and responses to diverse therapies in hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) remains a global health threat. Finding a novel biomarker for assessing the prognosis and new therapeutic targets is vital to treating this patient population. Our study aimed to explore the contribution of basement membrane-related regulators (BMR) to prognostic assessment and therapeutic response prediction in HCC.

MATERIAL AND METHODS

The RNA sequencing and clinical information of HCC were downloaded from TCGA-LIHC, ICGC-JP, GSE14520, GSE104580, and CCLE datasets. The BMR signature was created by the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm and used to separate HCC patients into low- and high-risk groups. We conducted analyses using various R 4.1.3 software packages to compare prognoses and responses to immunotherapy, transcatheter arterial chemoembolization (TACE), and chemotherapeutic drugs between the groups. Additionally, stemness indices, molecular functions, and somatic mutation analyses were further explored in these subgroups.

RESULTS

The BMR signature included 3 basement membrane-related genes (CTSA, P3H1, and ADAM9). We revealed that BMR signature was an independent risk contributor to poor prognosis in HCC, and high-risk group patients presented shorter overall survival. We discovered that patients in the high-risk group might be responsive to immunotherapy, while patients in the low-risk group may be susceptible to TACE therapy. Over 300 agents were screened to identify effective drugs for the two subgroups.

CONCLUSION

Overall, basement membrane-related regulators represent novel biomarkers in HCC for assessing prognosis, response to immunotherapy, the effectiveness of TACE therapy, and drug susceptibility.

Genes Modulating Butyrate Metabolism for Assessing Clinical Prognosis and Responses to Systematic Therapies in Hepatocellular Carcinoma

Butyrate, one of the major products of the gut microbiota, has played notable roles in diverse therapies for multiple tumors. Our study aimed to determine the roles of genes that modulate butyrate metabolism (BM) in predicting the clinical prognosis and responses to systemic therapies in hepatocellular carcinoma (HCC). The genes modulating BM were available from the GeneCard database, and gene expression and clinical information were obtained from TCGA-LIHC, GEO, ICGC-JP, and CCLE databases. Candidate genes from these genes that regulate BM were then identified by univariate Cox analysis. According to candidate genes, the patients in TCGA were grouped into distinct subtypes. Moreover, BM- related gene signature (BMGs) was created via the LASSO Cox algorithm. The roles of BMGs in identifying high-risk patients of HCC, assessing the prognoses, and predicting systematic therapies were determined in various datasets. The statistical analyses were fulfilled with R 4.1.3, GraphPad Prism 8.0 and Perl 5.30.0.1 software. In the TCGA cohort, most butyrate-related genes were over-expressed in the B cluster, and patients in the B cluster showed worse prognoses. BMGs constructed by LASSO were composed of eight genes. BMGs exhibited a strong performance in evaluating the prognoses of HCC patients in various datasets, which may be superior to 33 published biomarkers. Furthermore, BMGs may contribute to the early surveillance of HCC, and BMGs could play active roles in assessing the effectiveness of immunotherapy, TACE, ablation therapy, and chemotherapeutic drugs for HCC. BMGs may be served as novel promising biomarkers for early identifying high-risk groups of HCC, as well as assessing prognoses, drug sensitivity, and the responses to immunotherapy, TACE, and ablation therapy in patients with HCC.