ABSTRACT
The intricate molecular landscape of cancer pathogenesis continues to captivate researchers worldwide, with Circular RNAs (circRNAs) emerging as pivotal players in the dynamic regulation of biological functions. The study investigates the elusive link between circRNAs and the Transforming Growth Factor-ß (TGF-ß) signalling pathway, exploring their collective influence on cancer progression and metastasis. Our comprehensive investigation begins by profiling circRNA expression patterns in diverse cancer types, revealing a repertoire of circRNAs intricately linked to the TGF-ß pathway. Through integrated bioinformatics analyses and functional experiments, we elucidate the specific circRNA-mRNA interactions that modulate TGF-ß signalling, unveiling the regulatory controls governing this crucial pathway. Furthermore, we provide compelling evidence of the impact of circRNA-mediated TGF-ß modulation on key cellular processes, including epithelial-mesenchymal transition (EMT), migration, and cell proliferation. In addition to their mechanistic roles, circRNAs have shown promise as diagnostic and prognostic biomarkers, as well as potential molecular targets for cancer therapy. Their ability to modulate critical pathways, such as the TGF-ß signalling axis, underscores their significance in cancer biology and clinical applications. The intricate interplay between circRNAs and TGF-ß is dissected, uncovering novel regulatory circuits that contribute to the complexity of cancer biology. This review unravels a previously unexplored dimension of carcinogenesis, emphasizing the crucial role of circRNAs in shaping the TGF-ß signalling landscape.
ABSTRACT
INTRODUCTION: Although the transition of a lead candidate into a drug is currently structured by well-defined milestone, it is still most challenging and offers no guarantee in success to the end. In fact, ligand-based pharmacophore modeling has become a key motive force for retrieving potential leads across several therapeutic areas. METHODS: An urgent need towards the development of novel antidepressant agents led us to generate a pharmacophore model from an existing 44 compounds dataset. The best model with one hydrophobic, two ring aromatic, and one positive ionization features was chosen on behalf of the correlation coefficient, sensitivity, specificity, yield of actives and accuracy measures using HypoGen module of Discovery Studio. In house library consisting of 10,000 substituted 1,3,5 triazine derivatives were shortlisted to select four insilico hits. All shortlisted compounds were synthesized and characterized by FTIR, 1H-& 13C-NMR spectroscopy and finally tested for antidepressant-like activity using behavioral models on rats viz. Forced Swim Test (FST) and Elevated Plus Maze (EPM). RESULTS: Two shortlisted compounds with optimal fit values showed a significant decrease in the duration of immobility as compared to standard drug Imipramine in FST while time spent in open arm in enhanced in case of EPM.
