BackgroundSalivary Adenoid cystic carcinoma (SACC) patients with local invasion and lung metastasis are often resistant to conventional therapy such as operation, chemotherapy and radiotherapy. To explore the underling mechanisms, we studied the roles of miRNA in regulating invasiveness of SACC cells.MethodsMicroRNA profiling was done in SACC cells with microarray. MiRNA mimics or antisense oligonucleotide was transfected and invasiveness of SACC cells was evaluated by adhesion assay and transwell assay. The target gene of miRNA was identified by luciferase reporter assay and “rescue” experiment. Tumor metastasis was evaluated by BALB/c-nu mice xenografts. MiRNA and its target gene expression were identified by in-situ hybridization and immunohistochemistry respectively, in 302 patients from affiliated hospitals of Sun Yat-sen University and in 148 patients from affiliated hospitals of Central South University, and correlated to the clinicopathological status of the patients.ResultsMiR-320a was down-regulated in high lung metastatic ACCM and SACC-LM cells compared with the corresponding low metastatic ACC2 and SACC-83 cells, and inhibited adhesion, invasion and migration of SACC cells by targeting integrin beta 3 (ITGB3). In vivo, enforced miR-320a expression suppressed metastasis of SACC xenografts. In the two independent sets, miR-320a was downregulated in primary SACCs with metastasis compared to those without metastasis, and low expression of this miRNA predicts poor patient survival and rapid metastasis. Multivariate analysis showed that miR-320a expression was an independent indicator of lung metastasis.ConclusionsMiR-320a inhibits metastasis in SACCs by targeting ITGB3 and may serve as a therapeutic target and prognostic marker in salivary cancers.Electronic supplementary materialThe online version of this article (doi:10.1186/s12943-015-0344-y) contains supplementary material, which is available to authorized users.

Lidar odometry (LO) is a key technology in numerous reliable and accurate localization and mapping systems of autonomous driving. The state-of-the-art LO methods generally leverage geometric information to perform point cloud registration. Furthermore, obtaining the point cloud semantic information describing the environment more abundantly will facilitate the registration. We present a novel semantic lidar odometry method based on self-designed parameterized semantic features (PSFs) to achieve low-drift ego-motion estimation for autonomous vehicle in real time. We first use a convolutional neural network-based algorithm to obtain point-wise semantics from the input laser point cloud, and then use semantic labels to separate road, building, traffic sign and pole-like point cloud and fit them separately to obtain corresponding PSFs. A fast PSF-based matching enables us to refine geometric features (GeFs) registration, thereby reducing the impact of blurred submap surface on the accuracy of GeFs matching. Besides, we design an efficient instance-level method to accurately recognize and remove the dynamic objects while retaining static ones in the semantic point cloud, which are beneficial to further improve the accuracy of LO. We evaluate our method, namely PSF-LO, on the public dataset KITTI Odometry Benchmark and rank #1 among semantic lidar methods with an average translational error of 0.82% in the test dataset.

Plasma miR-92a-3p and miR-486-5p might prove to be potential biomarkers to indicate responses to mercury exposure. However, further studies are necessary to prove the causal association between miRNAs changes and mercury exposure, and to determine whether these two miRNAs are clear biomarkers to mercury exposure.

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