Phytochemicals from Ayurvedic plants as potential medicaments for ovarian cancer: an in silico analysis

Phytotoxic phenols from the needles of Cedrus deodara

During the course of screening for anti-seed germination phytochemicals, the methanol fraction of the Cedrus deodara fresh needles showed potent activity. Bioactivity-guided fractionation led to the isolation of thirty-eight phenolic compounds. Four ones were identified as previously undescribed including (7S,8S)-3-methoxy-9'-acetoxy-3',7-epoxy-8,4'-oxyneoligna-4,9-diol (7), (7S,8R)-dihydro-3'-hydroxy-8-acetoxymethyl-7-(4-hydroxy-3-methoxy-phenyl)-1'-benzofuranpropanol (10), (8S)-4,9,9'-trihydroxy-3,3'-dimethoxy-8,4'-oxyneolignan (11) and (7S,8S)-4,7,9'-trihydroxy-3,3'-dimethoxy-9-acetoxy-8,4'-oxyneolignan (16), respectively. The potential phytotoxic effects of these compounds on the seed germination and root elongation of Arabidopsis thaliana were evaluated by the filter paper assay developed in our laboratory. Bioassay results indicated that caffeic acid (36) displayed most significant inhibitory activities against the seed germination and root elongation of A. thaliana, stronger than those of the commercial herbicides acetochlor and glyphosate at the same concentration of 200 μg/mL. Ditetrahydrofuran lignan (1), dihydrochalcone (25), and eight simple phenols (28, 29, 31, 33-35, 37 and 38) completely inhibited the seed germination of A. thaliana at the concentration of 400 μg/mL, which were as active as acetochlor. Dihydroflavone (21) and the simple phenols 32-34 displayed stronger inhibitory effects on the root elongation of A. thaliana than that of glyphosate. The inhibitory effects of these active compounds on the seed germination and root elongation of Amaranthus tricolor and Lactuca sativa were evaluated as well. The phytotoxic activity of 11, 16, 22, 25, 31, 34, 37 and 38 were detected for the first time. In addition, the structure-activity relationships of the same class of these phytochemicals were discussed.

In silico studies on the phytochemical components of Lagenaria siceraria targeting aromatase receptors against breast cancer

In India, breast cancer is the most common cause of mortality for women and has the potential to spread to other body organs. As a transcription factor, interactions with the estrogen receptor (ER) alpha are primarily responsible for the development of malignant tumors. Aromatase inhibitors are the most often used treatment for ER(+) breast cancer. Various synthetic compounds have been developed over the years to block the aromatase receptor, however, the majority of them are hazardous and cause multidrug resistance. So, combating these natural drugs can be prioritized. The current study was conducted to investigate the anticancer potential of Lagenaria siceraria phytoconstituents against breast cancer target protein (PDB ID: 3EQM) based on a literature review. In this study, 34 Lagenaria siceraria ligands were chosen, and the structure of the human aromatase receptor was acquired from the protein data bank. For those natural chemicals, molecular docking, drug-likeness, toxicity, and molecular dynamics were used to evaluate and analyse their anti-breast cancer activity. Five substances, 2,3-Diphenyl quinoxaline, 17-Acetoxy pregnolone, Benzyl-d-glucoside, Ergostenol acetate, and Stigmast-7-en-3-ol, shown higher binding affinity than Tamoxifen, signaling their potential use in breast cancer treatment.

DLC1 as Druggable Target for Specific Subsets of Gastric Cancer: An RNA-seq-Based Study

Background: Gastric cancer has been ranked the third leading cause of cancer death worldwide. Its detection at the early stage is difficult because patients mostly experience vague and non-specific symptoms in the early stages. Methods: The RNA-seq datasets of both gastric cancer and normal samples were considered and processed. The obtained differentially expressed genes were then subjected to functional enrichment analysis and pathway analysis. An implicit atomistic molecular dynamics simulation was executed on the selected protein receptor for 50 ns. The electrostatics, surface potential, radius of gyration, and macromolecular energy frustration landscape were computed. Results: We obtained a large number of DEGs; most of them were down-regulated, while few were up-regulated. A DAVID analysis showed that most of the genes were prominent in the KEGG and Reactome pathways. The most prominent GAD disease classes were cancer, metabolic, chemdependency, and infection. After an implicit atomistic molecular dynamics simulation, we observed that DLC1 is electrostatically optimized, stable, and has a reliable energy frustration landscape, with only a few maximum energy frustrations in the loop regions. It has a good functional and binding affinity mechanism. Conclusions: Our study revealed that DLC1 could be used as a potential druggable target for specific subsets of gastric cancer.

Differential expression analysis in ovarian cancer: A functional genomics and systems biology approach

BACKGROUND

Ovarian cancer is one of the rarest lethal oncologic diseases that have hardly any specific biomarkers. The availability of high-throughput genomic data and advancement in bioinformatics tools allow us to predict gene biomarkers and apply systems biology approaches to get better diagnosis, and prognosis of the disease with a tentative drug that may be repurposed.

OBJECTIVE

To perform genome-wide association studies using microarray gene expression of ovarian cancer and identify gene biomarkers, construction and analyze networks, perform survival analysis, and drug interaction studies for better diagnosis, prognosis, and treatment of ovarian cancer.

METHOD

The gene expression profiles of both healthy and serous ovarian cancer epithelial samples were considered. We applied a series of bioinformatics methods and tools, including fold-change statistics for differential expression analysis, DisGeNET and NCBI-Gene databases for gene-disease association mapping, DAVID 6.8 for GO enrichment analysis, GeneMANIA for network construction, Cytoscape 3.8 with its plugins for network visualization, analysis, and module detection, the UALCAN for patient survival analysis, and PubChem, DrugBank and DGIdb for gene-drug interaction.

RESULTS

We identified 8 seed genes that were subjected for drug-gene interaction studies. Because of over-expression in all the four stages of ovarian cancer, we discern that genes HMGA1 and PSAT1 are potential therapeutic biomarkers for its diagnosis at an early stage (stage I). Our analysis suggests that there are 11 drugs common in the seed genes. However, hypermethylated seed genes HMGA1 and PSAT1 showcased a good interaction affinity with drugs cisplatin, cyclosporin, bisphenol A, progesterone, and sunitinib, and are crucial in the proliferation of ovarian cancer.

CONCLUSION

Our study reveals that HMGA1 and PSAT1 can be deployed for initial screening of ovarian cancer and drugs cisplatin, bisphenol A, cyclosporin, progesterone, and sunitinib are effective in curbing the epigenetic alteration.