In vitro and In vivo Based Multi-Target Therapy and Prognostic Bio-modeling in Liver Cancer

Hepatocellular Carcinoma, being 3rd most aggressive malignancy with complex multifactorial etiologies become a major public health issue worldwide. The critical and unknown molecular environment raised multiple challenges for oncologists, which is resulting in poor earlier detection with unsatisfactory treatment options in patients.

This study determined the cancer regulatory networks and interaction mechanism of five distinctive genes (CTNNB1TTN, TERT, ALB, OBSCN) in HCC by using comprehensive statistical-based machine learning and system biology approaches. This work implemented redundancy analysis of genes by CRISPRCas9 and RNAi technique, accompanied by identification of transcription factors, kinases, and intermediate proteins associated with key genes. Network Pharmacology with molecular docking and dynamics simulation followed by in-vitro verification is used to evaluate anti-cancer drug activity against TERT.

The results declared a high frequency of genetic alteration in genes at the allelic level with enrichment in several oncogenic signaling pathways, including stem cell proliferation and transferase activity, etc. Among key genes, TERT is being analyzed for drug discovery in HCC. The molecular docking studies of TERT with ambrisentan revealed high binding energy, which was further investigated by molecular dynamics studies and ADMET evaluation. An in vitro wet lab experiment (MTT assay) is designed to determine the cytotoxicity of the drug in the HepG2 cell line.

Current research has discovered the most effective pharmacological targets through a full understanding of their genetic mechanisms. Biomarker-based treatments provide a more precise diagnostic assessment of HCC genetics and progression.

The findings of this study enlightened identified targets as the scientific foundation for HCC therapeutics using integrated network analysis and molecular dynamics simulations.