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The long noncoding RNA (lncRNA) H19 has been recently characterized as an oncogenic lncRNA in some tumors. However, the role of H19 in pancreatic ductal adenocarcinoma (PDAC) remains unclear. In this study, we found that not only the levels of H19 was overexpressed in PDAC compared with adjacent normal tissues, but also H19 expression was upregulated remarkably in primary tumors which subsequently metastasized, compared to those did not metastasis. Subsequently, the efficacy of knockdown of H19 by H19-small interfering RNA (siRNA) was evaluated in vitro, and we found that downregulation of H19 impaired PDAC cell invasion and migration. We further demonstrated that H19 promoted PDAC cell invasion and migration at least partially by increasing HMGA2-mediated epithelial-mesenchymal transition (EMT) through antagonizing let-7. This study suggests an important role of H19 in regulating metastasis of PDAC and provides some clues for elucidating the lncRNA-miRNA functional network in cancer.

Purpose To develop data acquisition and image reconstruction methods to enable high-resolution 1H MR spectroscopic imaging (MRSI) of the brain, using the recently proposed subspace-based spectroscopic imaging framework called SPICE (SPectroscopic Imaging by exploiting spatiospectral CorrElation). Theory and Methods SPICE is characterized by the use of a subspace model for both data acquisition and image reconstruction. For data acquisition, we propose a novel spatiospectral encoding scheme that provides hybrid data sets for determining the subspace structure and for image reconstruction using the subspace model. More specifically, we use a hybrid chemical shift imaging (CSI)/echo-planar spectroscopic imaging (EPSI) sequence for two-dimensional (2D) MRSI and a dual-density, dual-speed EPSI sequence for three-dimensional (3D) MRSI. For image reconstruction, we propose a method that can determine the subspace structure and the high-resolution spatiospectral reconstruction from the hybrid data sets generated by the proposed sequences, incorporating field inhomogeneity correction and edge-preserving regularization. Results Phantom and in vivo brain experiments were performed to evaluate the performance of the proposed method. For 2D MRSI experiments, SPICE is able to produce high SNR spatiospectral distributions with an approximately 3 mm nominal in-plane resolution from a 10-min acquisition. For 3D MRSI experiments, SPICE is able to achieve an approximately 3 mm in-plane and 4 mm through-plane resolution in about 25 min. Conclusion Special data acquisition and reconstruction methods have been developed for high-resolution 1H-MRSI of the brain using SPICE. Using these methods, SPICE is able to produce spatiospectral distributions of 1H metabolites in the brain with high spatial resolution, while maintaining a good SNR. These capabilities should prove useful for practical applications of SPICE.

High-dimensional MR imaging often requires long data acquisition time, thereby limiting its practical applications. This paper presents a low-rank tensor based method for accelerated high-dimensional MR imaging using sparse sampling. This method represents high-dimensional images as low-rank tensors (or partially separable functions) and uses this mathematical structure for sparse sampling of the data space and for image reconstruction from highly undersampled data. More specifically, the proposed method acquires two datasets with complementary sampling patterns, one for subspace estimation and the other for image reconstruction; image reconstruction from highly undersampled data is accomplished by fitting the measured data with a sparsity constraint on the core tensor and a group sparsity constraint on the spatial coefficients jointly using the alternating direction method of multipliers. The usefulness of the proposed method is demonstrated in MRI applications; it may also have applications beyond MRI.