Cyclooxygenase-2 (COX-2) plays an important role in the carcinogenesis and progression of gastric cancer. It has been demonstrated that COX-2 overexpression depends on different cellular pathways, involving both transcriptional and post-transcriptional regulation. MicroRNAs (miRNAs) are small, noncoding RNAs that function as post-transcriptional regulators. Here, we characterize miR-101 expression and its role in the regulation of COX-2 expression, which in turn, will provide us with additional insights into the potential therapeutic benefits of exogenous miR-101 for treatment of gastric cancer. Our results showed that miR-101 levels in gastric cancer tissues were significantly lower than those in the matched normal tissue (P < 0.01). Furthermore, lower levels of miR-101 were associated with increased tumor invasion and lymph node metastasis (P < 0.05). We also found an inverse correlation between miR-101 and COX-2 expression in both gastric cancer specimens and cell lines. Significant decreases in COX-2 mRNA and COX-2 levels were observed in the pre-miR-101-infected gastric cancer cells. One possible mechanism of interaction is that miR-101 inhibited COX-2 expression by directly binding to the 3′-UTR of COX-2 mRNA. Overexpression of miR-101 in gastric cancer cell lines also inhibited cell proliferation and induced apoptosis in vitro, as well as inhibiting tumor growth in vivo. These results collectively indicate that miR-101 may function as a tumor suppressor in gastric cancer, with COX-2 as a direct target.

Automatic and accurate esophageal lesion classification and segmentation is of great significance to clinically estimate the lesion status of esophageal disease and make suitable diagnostic schemes. Due to individual variations and visual similarities of lesions in shapes, colors and textures, current clinical methods remain subject to potential high-risk and time-consumption issues. In this paper, we propose an Esophageal Lesion Network (ELNet) for automatic esophageal lesion classification and segmentation using deep convolutional neural networks (DCNNs). The underlying method automatically integrates dual-view contextual lesion information to extract global features and local features for esophageal lesion classification of four esophageal image types (Normal, Inflammation, Barrett, and Cancer) and proposes lesion-specific segmentation network for automatic esophageal lesion annotation of three esophageal lesion types at pixel level. For established clinical large-scale database of 1051 white-light endoscopic images, ten-fold cross-validation is used in method validation. Experiment results show that the proposed framework achieves classification with sensitivity of 0.9034, specificity of 0.9718 and accuracy of 0.9628, and the segmentation with sensitivity of 0.8018, specificity of 0.9655 and accuracy of 0.9462. All of these indicate that our method enables an efficient, accurate and reliable esophageal lesion diagnosis in clinical.The main contributions of our work can be generalized as follows: 1 For the first time, proposed ELNet enables an automatically and reliably comprehensive esophageal lesions classification of four esophageal lesion types (Normal, Inflammation, Barrett, and Cancer) and lesion-specific segmentation from clinically white-light esophageal images to make suitable and repaid diagnostic schemes for clinicians. 2 A novel Dual-Stream network (DSN) is proposed for esophageal lesion classification. DSN automatically integrates dual-view contextual lesion information using two CNN streams to complementarily extract the global features from the holistic esophageal images and the local features from the lesion patches. 3 Lesion-specific esophageal lesion annotation with Segmentation Network with Classification (SNC) strategy is proposed to automatically annotate three lesion types (Inflammation, Barrett, Cancer) at pixel level to reduce the intra-class differences of esophageal lesions. 4 A clinically large-scale database esophageal database is established for esophageal lesions classification and segmentation. This database includes 1051 white-light esophageal images, which consists of endoscopic images in four different lesion types. Each image in this database has a classification label and its corresponding segmentation annotation.

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BACKGROUND Gastric cancer is one of the most common malignant tumors of the digestive system worldwide, posing a serious danger to human health. Cyclooxygenase (COX)-2 plays an important role in the carcinogenesis and progression of gastric cancer. Acetyl-11-keto-β-boswellic acid (AKBA) is a promising drug for cancer therapy, but its effects and mechanism of action on human gastric cancer remain unclear. AIM To evaluate whether the phosphatase and tensin homolog (PTEN)/Akt/COX-2 signaling pathway is involved in the anti-tumor effect of AKBA in gastric cancer. METHODS Human poorly differentiated BGC823 and moderately differentiated SGC7901 gastric cancer cells were routinely cultured in Roswell Park Memorial Institute 1640 medium supplemented with 10% fetal bovine serum and 1% penicillin/streptomycin. Gastric cancer cell proliferation was determined by methyl thiazolyl tetrazolium colorimetric assay. Apoptosis was measured by flow cytometry. Cell migration was assessed using the wound-healing assay. Expression of Bcl-2, Bax, proliferating cell nuclear antigen, PTEN, p-Akt, and COX-2 were detected by Western blot analysis. A xenograft nude mouse model of human gastric cancer was established to evaluate the anti-cancer effect of AKBA in vivo . RESULTS AKBA significantly inhibited the proliferation of gastric cancer cells in a dose- and time-dependent manner, inhibited migration in a time-dependent manner, and induced apoptosis in a dose-dependent manner in vitro ; it also inhibited tumor growth in vivo . AKBA up-regulated the expression of PTEN and Bax, and down-regulated the expression of proliferating cell nuclear antigen, Bcl-2, p-Akt, and COX-2 in a dose-dependent manner. The PTEN inhibitor bpv (Hopic) reversed the high expression of PTEN and low expression of p-Akt and COX-2 that were induced by AKBA. The Akt inhibitor MK2206 combined with AKBA down- regulated the expression of p-Akt and COX-2, and the combined effect was better than that of AKBA alone. CONCLUSION AKBA inhibits the proliferation and migration and promotes the apoptosis of gastric cancer cells through the PTEN/Akt/COX-2 signaling pathway.

The potential effect of PKC412, a small molecular multi-kinase inhibitor, in colorectal cancer (CRC) cells was evaluated here. We showed that PKC412 was cytotoxic and anti-proliferative against CRC cell lines (HT-29, HCT-116, HT-15 and DLD-1) and primary CRC cells. PKC412 provoked caspase-dependent apoptotic death, and induced G2-M arrest in the CRC cells. AKT activation was inhibited by PKC412 in CRC cells. Reversely, expression of constitutively-active AKT1 (CA-AKT1) decreased the PKC412's cytotoxicity against HT-29 cells. We propose that Bcl-2 could be a primary resistance factor of PKC412. ABT-737, a Bcl-2 inhibitor, or Bcl-2 siRNA knockdown, dramatically potentiated PKC412's lethality against CRC cells. Forced Bcl-2 over-expression, on the other hand, attenuated PKC412's cytotoxicity. Significantly, PKC412 oral administration suppressed AKT activation and inhibited HT-29 tumor growth in nude mice. Mice survival was also improved with PKC412 administration. These results indicate that PKC412 may have potential value for CRC treatment.

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