Abstract
Purpose: Lichens are well-known as a source of pharmacologically active compounds. This includes anticancer compounds which have biomass constraints including using traditional techniques of lichen bioprospecting. This current study reports the use of cutting-edge metabolomics and a computational approach to discover anticancer biomarkers from Indonesian lichens.
Methods: Seven lichen crude extracts were evaluated against cervical cell lines HeLa using a MTT assay and secondary metabolites were profiled and recorded via a gas chromatography-mass spectrometry (GC-MS) protocol. A multivariate analysis orthogonal partial least-squares-discriminant analysis (OPLS-DA) was employed to determine anticancer biomarker of the lichens. A structure-based computational study against the HeLa cancer cell related protein targets (BCL-2 (4MAN), AKT-1 (4GV1), MCL-1 (5FDO), and BRAF (5VAM)) was used to determine the most potent biomarker.
Results: The MTT assessment indicated the seven lichens possessed strong, medium and weak cytotoxicity. Multivariate analysis showed an OPLS-DA score plot with distinct separation among the strong, medium and weak cytotoxic groups. The biplot OPLS-DA and GC-MS analysis proposed 13 compounds of Parmelia caroliniana and 12 compounds of Physcia cf. millegrana as anticancer biomarker candidates. Docking experiments revealed 6-amino-3,4,7-triphenylpyrido[2’,3’:4,5]thieno[2,3-c]pyridazine 4 from P. caroliniana to possess the highest binding affinity against BCL-2 (4MAN), AKT-1 (4GV1), MCL-1 (5FDO), and BRAF (5VAM) proteins with affinity energy values of -10.0, -11.6, -10.4, -12.6, respectively.
Conclusion: The study successfully revealed compound 4 as the anticancer biomarker against HeLa cell cancer of P. caroliniana in which can be further explored through in vitro and in vivo studies. Further, the metabolomic protocol established can be adapted as a tool for biomarker discoveries from other medicinal plants.