Exosomes are emerging as important mediators of the cross-talk between tumor cells and the microenvironment. However, the mechanisms by which exosomes modulate tumor development under hypoxia in pancreatic cancer remain largely unknown. Here, we found that hypoxic exosomes derived from pancreatic cancer cells activate macrophages to the M2 phenotype in a HIF1a or HIF2a-dependent manner, which then facilitates the migration, invasion, and epithelial-mesenchymal transition of pancreatic cancer cells. Given that exosomes have been shown to transport miRNAs to alter cellular functions, we discovered that miR-301a-3p was highly expressed in hypoxic pancreatic cancer cells and enriched in hypoxic pancreatic cancer cell-derived exosomes. Circulating exosomal miR-301a-3p levels positively associated with depth of invasion, lymph node metastasis, late TNM stage, and poor prognosis of pancreatic cancer. Hypoxic exosomal miR-301a-3p induced the M2 polarization of macrophages via activation of the PTEN/PI3Kγ signaling pathway. Coculturing of pancreatic cancer cells with macrophages in which miR-301a-3p was upregulated or treated with hypoxic exosomes enhanced their metastatic capacity. Collectively, these data indicate that pancreatic cancer cells generate miR-301a-3p-rich exosomes in a hypoxic microenvironment, which then polarize macrophages to promote malignant behaviors of pancreatic cancer cells. Targeting exosomal miR-301a-3p may provide a potential diagnosis and treatment strategy for pancreatic cancer. These findings identify an exosomal miRNA critical for microenvironmental cross-talk that may prove to be a potential target for diagnosis and treatment of pancreatic cancer. http://cancerres.aacrjournals.org/content/canres/78/16/4586/F1.large.jpg .

Deep learning has been proved to be an advanced technology for big data analysis with a large number of successful cases in image processing, speech recognition, object detection, and so on. Recently, it has also been introduced in food science and engineering. To our knowledge, this review is the first in the food domain. In this paper, we provided a brief introduction of deep learning and detailedly described the structure of some popular architectures of deep neural networks and the approaches for training a model. We surveyed dozens of articles that used deep learning as the data analysis tool to solve the problems and challenges in food domain, including food recognition, calories estimation, quality detection of fruits, vegetables, meat and aquatic products, food supply chain, and food contamination. The specific problems, the datasets, the preprocessing methods, the networks and frameworks used, the performance achieved, and the comparison with other popular solutions of each research were investigated. We also analyzed the potential of deep learning to be used as an advanced data mining tool in food sensory and consume researches. The result of our survey indicates that deep learning outperforms other methods such as manual feature extractors, conventional machine learning algorithms, and deep learning as a promising tool in food quality and safety inspection. The encouraging results in classification and regression problems achieved by deep learning will attract more research efforts to apply deep learning into the field of food in the future.

Caveolin-1 (Cav-1), a principal structural component of caveolar membrane domains, contributes to cancer development but its precise functional roles and regulation remain unclear. In this study, we determined the oncogenic function of Cav-1 in preclinical models of pancreatic cancer and in human tissue specimens. Cav-1 expression levels correlated with metastatic potential and epithelial-to-mesenchymal transition (EMT) in both mouse and human pancreatic cancer cells. Elevated levels in cells promoted EMT, migration, invasion and metastasis in animal models, whereas RNAi-mediated knockdown inhibited these processes. We determined that levels of Cav-1 and the Forkhead transcription factor FoxM1 correlated directly in pancreatic cancer cells and tumor tissues. Enforced expression of FoxM1 increased Cav-1 levels, whereas RNAi-mediated knockdown of FoxM1 had the opposite effect. FoxM1 directly bound to the promoter region of Cav-1 gene and positively transactivated its activity. Collectively, our findings defined Cav-1 as an important downstream oncogenic target of FoxM1, suggesting that dysregulated signaling of this novel FoxM1-Cav-1 pathway promotes pancreatic cancer development and progression.