The Akt/mTOR and Mitogen-Activated Protein Kinase Pathways in Lung Cancer Therapy

Effect of isolated grandivittin from Ferulago trifida Boiss. (Apiaceae) on the proliferation and apoptosis of human lung cancer A549 cells

Lung cancer is one of the deadliest cancers in the world. Introducing new promising agents can help the chemotherapeutic management of cancer. In the knowledge of oncology, plants are of special interest as a rich source of new antineoplastic and chemotherapeutic agents. Grandivittin (GRA) is one of the main constituents of Ferulago trifida Boiss. with established medicinal, phytochemical, and pharmacological properties. This study aimed to isolate and evaluate the antineoplastic potential of grandivittin and its underlying mechanisms in human lung cancer A549 cells. The viability of the A549 cells after being treated with 0.1, 0.4, 0.7, 1, and 1.3 mM of GRA for three following days was measured using the MTT method. The early apoptosis and late apoptosis were assessed by fluorescence-activated cell sorter analysis through annexin V/PI staining. The expression of apoptotic agents' genes (caspase 3, caspase 9, Bcl2, Bax, and P53) was evaluated by the RT-PCR method. GRA increased apoptotic cells and decreased cell viability in a dose- and time-dependent manner, in which only 50% of cells survived at a dose of 0.7 mM. The expression of Bax, P53, caspase 3, and caspase 9 genes in the A549 cells was significantly upregulated after GRA treatment compared to control cells (P < 0.05). On the other hand, Bcl2 was significantly downregulated after GRA treatment (P < 0.05). The results indicated that GRA can activate cell death in A549 lung carcinoma cells by inducing both DNA toxicity p53 and cascade-dependent pathways. Therefore, GRA may be a potential new therapeutic agent for the treatment of lung cancer.

Xanthohumol Impairs the PMA-Driven Invasive Behaviour of Lung Cancer Cell Line A549 and Exerts Anti-EMT Action

Xanthohumol (XN), the main prenylated flavonoid from hop cones, has been recently reported to exert significant proapoptotic, anti-proliferative, and growth inhibitory effects against lung cancer in both in vitro and in vivo studies. However, its anti-metastatic potential towards this malignancy is still unrevealed. Previously, we indicated that the human lung adenocarcinoma A549 cell line was sensitive to XN treatment. Therefore, using the same tumour cell model, we have studied the influence of XN on the phorbol-12-myristate-13-acetate (PMA)-induced cell migration and invasion. The effects of XN on the expression/activity of pro-invasive MMP-9 and MMP-2 and the expression of MMP inhibitors, i.e., TIMP-1 and TIMP-2 (anti-angiogenic factors), were evaluated. Additionally, the influence of XN on the production of the key pro-angiogenic cytokine, i.e., VEGF, and the release of TGF-β, which is both a pro-angiogenic cytokine and an epithelial-mesenchymal transition (EMT) stimulator, was studied. Furthermore, the influence of XN on the expression of EMT-associated proteins such as E-cadherin and α-E-catenin (epithelial markers), vimentin and N-cadherin (mesenchymal markers), and Snail-1 (transcriptional repressor of E-cadherin) was studied. To elucidate the molecular mechanism underpinning the XN-mediated inhibition of metastatic progression in PMA-activated cells, the phosphorylation levels of AKT, FAK, and ERK1/2 kinases, which are signalling molecules involved in EMT program activation, were assayed. The results showed that XN in non-cytotoxic concentrations impaired the PMA-driven migratory and invasive capacity of A549 cells by decreasing the level of expression of MMP-9 and concomitantly increasing the expression of the TIMP-1 protein, i.e., a specific blocker of pro-MMP-9 activation. Moreover, XN decreased the PMA-induced production of VEGF and TGF-β. Furthermore, the XN-treatment counteracted the PMA-induced EMT of the A549 cells by the upregulation of E-cadherin and α-E-catenin and the downregulation of N-cadherin, vimentin, and Snail-1 expression. The proposed mechanism underlying the anti-invasive XN activity involved the inhibition of the ERK/MAPK pathway and suppression of FAK and PI3/AKT signalling. Our results suggesting migrastatic properties of XN against lung cancer cells require further verification in in vivo assays.

Identification of potential tissue-specific cancer biomarkers and development of cancer versus normal genomic classifiers

Machine learning techniques for cancer prediction and biomarker discovery can hasten cancer detection and significantly improve prognosis. Recent “OMICS” studies which include a variety of cancer and normal tissue samples along with machine learning approaches have the potential to further accelerate such discovery. To demonstrate this potential, 2,175 gene expression samples from nine tissue types were obtained to identify gene sets whose expression is characteristic of each cancer class. Using random forests classification and ten-fold cross-validation, we developed nine single-tissue classifiers, two multi-tissue cancer-versus-normal classifiers, and one multi-tissue normal classifier. Given a sample of a specified tissue type, the single-tissue models classified samples as cancer or normal with a testing accuracy between 85.29% and 100%. Given a sample of non-specific tissue type, the multi-tissue bi-class model classified the sample as cancer versus normal with a testing accuracy of 97.89%. Given a sample of non-specific tissue type, the multi-tissue multi-class model classified the sample as cancer versus normal and as a specific tissue type with a testing accuracy of 97.43%. Given a normal sample of any of the nine tissue types, the multi-tissue normal model classified the sample as a particular tissue type with a testing accuracy of 97.35%. The machine learning classifiers developed in this study identify potential cancer biomarkers with sensitivity and specificity that exceed those of existing biomarkers and pointed to pathways that are critical to tissue-specific tumor development. This study demonstrates the feasibility of predicting the tissue origin of carcinoma in the context of multiple cancer classes.