Molecular Targets and Mechanisms of Hedyotis diffusa-Scutellaria barbata Herb Pair for the Treatment of Colorectal Cancer Based on Network Pharmacology and Molecular Docking

Network-based pharmacology-based research on the effect and mechanism of the Hedyotis diffusa-Scutellaria Barbata pair in the treatment of hepatocellular carcinoma

The Hedyotis diffusa-Scutellaria officinalis pair (HD-SB) has therapeutic effects on a variety of cancers. Our study was to explore the mechanism of HD-SB in the treatment of hepatocellular carcinoma (HCC). A total of 217 active ingredients of HD-SB and 1196 HCC-related targets were reserved from the TCMSP and the SwissTarget Prediction database, and we got 63 intersection targets from GeneCards. We used a Venn diagram, and Cytoscape found that the three core ingredients were quercetin, luteolin, and baicalein. The PPI analysis showed that the core targets were TP53, CDK2, XPO1, and APP. Molecular docking results showed that these core ingredients had good binding potential with the core targets. HD-SB acts simultaneously on various HCC-related signaling pathways, including proteoglycans in cancer and the P53 signaling pathway. In vitro experiments confirmed that HD-SB can inhibit HepG2 cell proliferation by increasing TP53 and APP levels and decreasing XPO1 and CDK2 levels. This study analyzed active ingredients, core targets, and central mechanisms of HD-SB in the treatment of HCC. It reveals the role of HD-SB in targeting the P53 signaling pathway in the treatment of HCC. We hope that our research could provide a new perspective to the therapy of HCC and find new anticancer drugs.

Exploring the mechanism of action bitter melon in the treatment of breast cancer by network pharmacology

BACKGROUND

Bitter melon has been used to stop the growth of breast cancer (BRCA) cells. However, the underlying mechanism is still unclear.

AIM

To predict the therapeutic effect of bitter melon against BRCA using network pharmacology and to explore the underlying pharmacological mechanisms.

METHODS

The active ingredients of bitter melon and the related protein targets were taken from the Indian Medicinal Plants, Phytochemistry and Therapeutics and SuperPred databases, respectively. The GeneCards database has been searched for BRCA-related targets. Through an intersection of the drug's targets and the disease's objectives, prospective bitter melon anti-BRCA targets were discovered. Gene ontology and kyoto encyclopedia of genes and genomes enrichment analyses were carried out to comprehend the biological roles of the target proteins. The binding relationship between bitter melon's active ingredients and the suggested target proteins was verified using molecular docking techniques.

RESULTS

Three key substances, momordicoside K, kaempferol, and quercetin, were identified as being important in mediating the putative anti-BRCA effects of bitter melon through the active ingredient-anti-BRCA target network study. Heat shock protein 90 AA, proto-oncogene tyrosine-protein kinase, and signal transducer and activator of transcription 3 were found to be the top three proteins in the protein-protein interaction network study. The several pathways implicated in the anti-BRCA strategy for an active component include phosphatidylinositol 3-kinase/protein kinase B signaling, transcriptional dysregulation, axon guidance, calcium signaling, focal adhesion, janus kinase-signal transducer and activator of transcription signaling, cyclic adenosine monophosphate signaling, mammalian target of rapamycin signaling, and phospholipase D signaling.

CONCLUSION

Overall, the integration of network pharmacology, molecular docking, and functional enrichment analyses shed light on potential mechanisms underlying bitter melon's ability to fight BRCA, implicating active ingredients and protein targets, as well as highlighting the major signaling pathways that may be altered by this natural product for therapeutic benefit.

Optimal ranking and directional signature classification using the integral strategy of multi-objective optimization-based association rule mining of multi-omics data

Introduction: Association rule mining (ARM) is a powerful tool for exploring the informative relationships among multiple items (genes) in any dataset. The main problem of ARM is that it generates many rules containing different rule-informative values, which becomes a challenge for the user to choose the effective rules. In addition, few works have been performed on the integration of multiple biological datasets and variable cutoff values in ARM. Methods: To solve all these problems, in this article, we developed a novel framework MOOVARM (multi-objective optimized variable cutoff-based association rule mining) for multi-omics profiles. Results: In this regard, we identified the positive ideal solution (PIS), which maximized the profit and minimized the loss, and negative ideal solution (NIS), which minimized the profit and maximized the loss for all gene sets (item sets), belonging to each extracted rule. Thereafter, we computed the distance (d +) from PIS and distance (d -) from NIS for each gene set or product. These two distances played an important role in determining the optimized associations among various pairs of genes in the multi-omics dataset. We then globally estimated the relative closeness to PIS for ranking the gene sets. When the relative closeness score of the rule is greater than or equal to the pre-defined threshold value, the rule can be considered a final resultant rule. Moreover, MOOVARM evaluated the relative score of the rule based on the status of all genes instead of individual genes. Conclusions: MOOVARM produced the final rank of the extracted (multi-objective optimized) rules of correlated genes which had better disease classification than the state-of-the-art algorithms on gene signature identification.