Activation of c-Met, a receptor tyrosine kinase, induces radiation therapy resistance in non-small cell lung cancer (NSCLC). The activated residual of c-Met is located in lipid rafts (Duhon et al. Mol Carcinog 49:739-49, 2010). Therefore, we hypothesized that disturbing the integrity of lipid rafts would restrain the activation of the c-Met protein and reverse radiation resistance in NSCLC. In this study, a series of experiments was performed to test this hypothesis. NSCLC A549 and H1993 cells were incubated with methyl-β-cyclodextrin (MβCD), a lipid raft inhibitor, at different concentrations for 1 h before the cells were X-ray irradiated. The following methods were used: clonogenic (colony-forming) survival assays, flow cytometry (for cell cycle and apoptosis analyses), immunofluorescence microscopy (to show the distribution of proteins in lipid rafts), Western blotting, and biochemical lipid raft isolation (purifying lipid rafts to show the distribution of proteins in lipid rafts). Our results showed that X-ray irradiation induced the aggregation of lipid rafts in A549 cells, activated c-Met and c-Src, and induced c-Met and c-Src clustering to lipid rafts. More importantly, MβCD suppressed the proliferation of A549 and H1993 cells, and the combination of MβCD and radiation resulted in additive increases in A549 and H1993 cell apoptosis. Destroying the integrity of lipid rafts inhibited the aggregation of c-Met and c-Src to lipid rafts and reduced the expression of phosphorylated c-Met and phosphorylated c-Src in lipid rafts. X-ray irradiation induced the aggregation of lipid rafts and the clustering of c-Met and c-Src to lipid rafts through both lipid raft-dependent and lipid raft-independent mechanisms. The lipid raft-dependent activation of c-Met and its downstream pathways played an important role in the development of radiation resistance in NSCLC cells mediated by c-Met. Further studies are still required to explore the molecular mechanisms of the activation of c-Met and c-Src in lipid rafts induced by radiation.

Background: Pancreatic cancer is a fatal malignancy of the digestive system and the 5-year survival rate remains low. Therefore, new molecular therapeutic targets are required to improve treatments, prognosis, and the survival of patients. N6-methyladenosine (m6A) is the most prevalent reversible methylation in mammalian messenger RNA (mRNA) and has critical roles in the tumorigenesis and metastasis of various malignancies. However, the role of m6A in pancreatic cancer is still unclear. Exploring genetic alterations and functional networks of m6A regulators in pancreatic cancer may provide new strategies for its treatment. Methods: In this study, we used data from the Cancer Genome Atlas (TCGA) database and other public databases through cBioPortal, LinkedOmics, UALCAN, GEPIA, STRING, and the database for annotation, visualization, and integrated discovery (DAVID) to systematically analyze the molecular alterations and functions of 20 main m6A regulators in pancreatic cancer. Results: We found that m6A regulators had widespread genetic alterations, and that their expression levels were significantly correlated with pancreatic cancer malignancy. Moreover, m6A regulators were associated with the prognosis of pancreatic cancer patients. Conclusions: m6A regulators play a crucial part in the occurrence and development of pancreatic cancer. Our study will guide further studies of m6A RNA modification in pancreatic cancer and could potentially provide new strategies for pancreatic cancer treatment.

Background: The relationship between molecular heterogeneity and clinical features of pancreatic cancer remains unclear. In this study, pancreatic cancer was divided into different subgroups to explore its specific molecular characteristics and potential therapeutic targets. Patients and methods: Expression profiling data were downloaded from The Cancer Genome Atlas database and standardized. Bioinformatics techniques such as unsupervised hierarchical clustering was used to explore the optimal molecular subgroups in pancreatic cancer. Clinical pathological features and pathways in each subgroup were also analyzed to find out the potential clinical applications and initial promotive mechanisms of pancreatic cancer. Results: Pancreatic cancer was divided into three subgroups based on different gene expression features. Patients included in each subgroup had specific biological features and responded significantly different to chemotherapy. Conclusion: Three distinct subgroups of pancreatic cancer were identified, which means that patients in each subgroup might benefit from targeted individual management.

Objective: To construct a key prognosis-related regulatory network and to identify the epigenomic alterations of RNAs that have crucial functions in cancer pathogenesis.Methods: RNA expression profiles of miRNAs, mRNAs and circRNAs were extracted from the NCBI GEO and EBI ArrayExpress databases. The identification of differentially expressed genes was performed by R language. Databases such as starBase and TCGA were used for annotation, visualization, and integrated discovery. GO and KEGG pathway enrichment analyses were performed by DAVID 6.8. The key prognosis-related regulatory network was constructed with Cytoscape software. The involved circRNAs were verified by quantitative real-time PCR (qRT-PCR) in five pancreatic cancer cell lines. Proliferation ability was assessed by the CCK-8 assay, apoptosis was detected by flow cytometry, and migratory and invasive abilities were assessed by Transwell assays.Results: In total, 798 differentially expressed genes, consisting of 85 circRNAs, 19 miRNAs and 694 mRNAs, were obtained. A miRNA interaction network was predicted and used to construct a prognosis-related regulatory network. GO annotation and KEGG pathway analysis revealed important biological processes and pathways in pancreatic cancers. The key regulatory factors in the network were miR-146b-5p and miR-152. Eight circRNAs included in the network were verified, and hsa_circ_0006502 was downregulated in the five tested pancreatic cancer cell lines and functioned as a tumor suppressor, as predicted.Conclusion: In conclusion, a key prognosis-related regulatory network in pancreatic cancers was constructed through bioinformatics analysis of public RNA databases, and miR-146b-5p and miR-152 were the key regulatory factors. Hsa_circ_0006502 was identified as a tumor suppressor that interacted closely with miR-146b-5p and might serve as a potential therapeutic agent.