Background Several somatic mutation hotspots were recently identified in the TERT promoter region in human cancers. Large scale studies of these mutations in multiple tumor types are limited, in particular in Asian populations. This study aimed to: analyze TERT promoter mutations in multiple tumor types in a large Chinese patient cohort, investigate novel tumor types and assess the functional significance of the mutations. Methods TERT promoter mutation status was assessed by Sanger sequencing for 13 different tumor types and 799 tumor tissues from Chinese cancer patients. Thymic epithelial tumors, gastrointestinal leiomyoma, and gastric schwannoma were included, for which the TERT promoter has not been previously sequenced. Functional studies included TERT expression by RT-qPCR, telomerase activity by the TRAP assay, and promoter activity by the luciferase reporter assay. Results TERT promoter mutations were highly frequent in glioblastoma (83.9%), urothelial carcinoma (64.5%), oligodendroglioma (70.0%), medulloblastoma (33.3%), and hepatocellular carcinoma (31.4%). C228T and C250T were the most common mutations. In urothelial carcinoma, several novel rare mutations were identified. TERT promoter mutations were absent in GIST, thymic epithelial tumors, gastrointestinal leiomyoma, gastric schwannoma, cholangiocarcinoma, gastric and pancreatic cancer. TERT promoter mutations highly correlated with upregulated TERT mRNA expression and telomerase activity in adult gliomas. These mutations differentially enhanced the transcriptional activity of the TERT core promoter. Conclusions TERT promoter mutations are frequent in multiple tumor types and have similar distributions in Chinese cancer patients. The functional significance of these mutations reflect the importance to telomere maintenance and hence tumorigenesis, making them potential therapeutic targets.

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ObjectivesTo examine the expression of ALDOB in gastric cancer (GC) tissue and to reveal its potential clinicopathological and prognostic significance.Materials and methodsWe screened for genes that were differentially expressed between GC and nontumor tissues using a microarray, specifically the Affymetrix U133 Plus 2.0 Array platform. We then verified the transcriptional and translational levels of ALDOB by performing quantitative real-time polymerase chain reaction (qRT-PCR) and immunohistochemistry (IHC). In addition, a merged data set based on the Gene Expression Omnibus was generated and a survival analysis performed.ResultsThe microarray analysis revealed that ALDOB was downregulated (more than sevenfold) in GC compared with nontumor tissue. Both qRT-PCR and IHC validated the decrease of ALDOB in GC tissue. Moreover, we found that the expression of ALDOB was significantly related to tumor-invasion depth, lymph-node metastasis, distant metastasis, and TNM stage. The survival analysis, based on the IHC and merged data set, indicated that the overall survival was better in patients with high ALDOB expression. The Cox regression analysis showed that ALDOB expression was an independent prognostic factor for GC.ConclusionThe expression of ALDOB in GC tissue was significantly related to the clinicopathological features and prognosis of the disease, thus suggesting that ALDOB could act as a novel molecular marker for GC.

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Increased expression of galectin-1 (Gal-1) in carcinoma-associated fibroblasts (CAFs) has been reported to correlate with progression and prognosis in many cancers. However, rarely have reports sought to determine whether high Gal-1 expression in CAFs in gastric cancer is involved in the tumor process, and the specific mechanism by which it promotes the evolution of gastric cancer is still unknown. In this study, we cultured gastric cancer CAFs, which showed strong expression of Gal-1, and established a co-culture system of CAFs with gastric cancer cells. Specific siRNA and in vitro migration and invasion assays were used to explore the effects of the interaction between Gal-1 expression of CAFs and gastric cancer cells on cell migration and invasion. We found that the overexpression of Gal-1 in CAFs enhanced gastric cancer cell migration and invasion, and these stimulatory effects could be blocked by specific siRNA which reduced the Gal-1 expression level. A set of cancer invasion-associated genes were then chosen to identify the possible mechanism of Gal-1-induced cell invasion. Among these genes, integrin β1 expression in cancer cells was considered to be associated with Gal-1 expression. Pre-blocking of the integrin β1 expression in gastric cancer cells with siRNA could interrupt the invasion-promoting effect of CAFs with high Gal-1 expression. Furthermore, immunohistochemical assay confirmed a positive correlation between Gal-1 and integrin β1 expression. Our results showed that high expression of Gal-1 in CAFs might facilitate gastric cancer cell migration and invasion by upregulating integrin β1 expression in gastric cancer.

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Abstract-Weakly-supervised image segmentation is a challenging problem with multidisciplinary applications in multimedia content analysis and beyond. It aims to segment an image by leveraging its image-level semantics (i.e., tags). This paper presents a weakly-supervised image segmentation algorithm that learns the distribution of spatially structural superpixel sets from image-level labels. More specifically, we first extract graphlets from a given image, which are small-sized graphs consisting of superpixels and encapsulating their spatial structure. Then, an efficient manifold embedding algorithm is proposed to transfer labels from training images into graphlets. It is further observed that there are numerous redundant graphlets that are not discriminative to semantic categories, which are abandoned by a graphlet selection scheme as they make no contribution to the subsequent segmentation. Thereafter, we use a Gaussian mixture model (GMM) to learn the distribution of the selected post-embedding graphlets (i.e., vectors output from the graphlet embedding). Finally, we propose an image segmentation algorithm, termed representative graphlet cut, which leverages the learned GMM prior to measure the structure homogeneity of a test image. Experimental results show that the proposed approach outperforms state-of-the-art weakly-supervised image segmentation methods, on five popular segmentation data sets. Besides, our approach performs competitively to the fully-supervised segmentation models.