Integrative in-silico analysis on phytocompounds of Pajanelia longifolia against hepatocellular carcinoma: gene network target identification, molecular docking, simulation and ADMET profiling

Hepatocellular carcinoma (HCC) is one of the most common types of liver cancer, with a high mortality rate. Despite significant advances in HCC detection and treatment, problem remain persistence. Plant-derived natural products have always been a boon for novel medication development; in this case, Pajanelia longifolia is a traditional medicinal plant recognised for its therapeutic usefulness in a variety of diseases. However, its efficacy against HCC has never been tested. Therefore, this study was initiated using an integrated in-silico approach to determine whether Pajanelia longifolia -derived compounds could target hub genes associated in HCC. Extracted Differentially expressed genes from two GEO database and TCGA-LIHC using GEO2R tool and R software. Overlapped DEG, were then utilised to conduct cluster analysis and performed three centrality techniques which were then intersected to obtain hub genes. The compounds extracted from bark of P.longifolia using ethyl acetate and identified via mass spectrometry. These hub genes were then docked with the selected compounds obtained from bark extract of and Simultaneous drug-likeness was assessed through ADMET followed by validation using MD simulation. Six hub genes BUB1, BUB1B, CCNA2, CCNB1 and KIF11 were seen significantly upregulated in hepatocellular carcinoma. Out of 65 compounds only C2, C57, and C65 of P. longifolia displayed strong binding affinity as well as favourable drug-likeness. Molecular dynamics simulation validated the stability of these complexes' structures, but localised loop flexibility facilitated adaptation. At last, this pioneer study shown the potential of Pajanelia longifolia as promising leads to HCC open avenue for future research.