摘要：Originally discovered in neuronal guidance, the Slit-Robo pathway is emerging as an important player in human cancers. However, its involvement and mechanism in colorectal cancer (CRC) remains to be elucidated. Here, we report that Slit2 expression is reduced in CRC tissues compared with adjacent noncancerous tissues. Extensive promoter hypermethylation of the Slit2 gene has been observed in CRC cells, which provides a mechanistic explanation for the Slit2 downregulation in CRC. Functional studies showed that Slit2 inhibits CRC cell migration in a Robo-dependent manner. Robo-interacting ubiquitin-specific protease 33 (USP33) is required for the inhibitory function of Slit2 on CRC cell migration by deubiquitinating and stabilizing Robo1. USP33 expression is downregulated in CRC samples, and reduced USP33 mRNA levels are correlated with increased tumor grade, lymph node metastasis and poor patient survival. Taken together, our data reveal USP33 as a previously unknown tumor-suppressing gene for CRC by mediating the inhibitory function of Slit-Robo signaling on CRC cell migration. Our work suggests the potential value of USP33 as an independent prognostic marker of CRC.
摘要：Identification and detection of dendritic spines in neuron images are of high interest in diagnosis and treatment of neurological and psychiatric disorders (e.g., Alzheimer's disease, Parkinson's diseases, and autism). In this paper, we have proposed a novel automatic approach using wavelet-based conditional symmetric analysis and regularized morphological shared-weight neural networks (RMSNN) for dendritic spine identification involving the following steps: backbone extraction, localization of dendritic spines, and classification. First, a new algorithm based on wavelet transform and conditional symmetric analysis has been developed to extract backbone and locate the dendrite boundary. Then, the RMSNN has been proposed to classify the spines into three predefined categories (mushroom, thin, and stubby). We have compared our proposed approach against the existing methods. The experimental result demonstrates that the proposed approach can accurately locate the dendrite and accurately classify the spines into three categories with the accuracy of 99.1% for "mushroom" spines, 97.6% for "stubby" spines, and 98.6% for "thin" spines.