Medical image diagnosis of prostate tumor based on PSP-Net+VGG16 deep learning network.

BACKGROUND AND OBJECTIVE: Prostate cancer is the most common cancer of the male reproductive system. With the development of medical imaging technology, magnetic resonance images (MRI) have been used in the diagnosis and treatment of prostate cancer because of its clarity and non-invasiveness. Prostate MRI segmentation and diagnosis experience problems such as low tissue boundary contrast. The traditional segmentation method of manually drawing the contour boundary of the tissue cannot meet the clinical real-time requirements. How to quickly and accurately segment the prostate tumor has become an important research topic. METHODS: This paper proposes a prostate tumor diagnosis based on the deep learning network PSP-Net+VGG16. The deep convolutional neural network segmentation method based on the PSP-Net constructs a atrous convolution residual structure model extraction network. First, the three-dimensional prostate MRI is converted to two-dimensional image slices, and then the slice input of the two-dimensional image is trained based on the PSP-Net neural network; and the VGG16 network is used to analyze the region of interest and classify prostate cancer and normal prostate. RESULTS: According to the experimental results, the segmentation method based on the deep learning network PSP-Net is used to identify the data set samples. The segmentation accuracy is close to the Dice similarity coefficient and Hausdorff distance, and even exceeds the traditional prostate image segmentation method. The Dice index reached 91.3%, and the technique is superior in speed of processing. The predicted tumor markers are very close to the actual markers manually by clinicians; the classification accuracy and recognition rates of prostate MRI based on VGG16 are as high as 87.95% and 87.33%, and the accuracy rate and recall rate of the network model are relatively balanced. The area under curve index is also higher than other models, with good generalization ability. CONCLUSION: Experiments show that prostate cancer diagnosis based on the deep learning network PSP-Net+VGG16 is superior in accuracy and processing time compared to other algorithms, and can be well applied to clinical prostate tumor diagnosis.

METTL14-mediated Lnc-LSG1 m6A modification inhibits clear cell renal cell carcinoma metastasis via regulating ESRP2 ubiquitination.

Clear cell renal cell carcinoma (ccRCC) is the most lethal urological cancer and is characterized by a high rate of metastasis and relapse. N6-Methyladenosine (m6A) is implicated in various stages of cancer development. However, a thorough understanding of m6A-modified lncRNAs in ccRCC is lacking. The results showed that METTL14 had decreased expression in ccRCC tissues. In addition, the expression of METTL14 was negatively correlated to the prognosis, stage, and ccRCC tumor grade. The silencing of METTL14 was shown to significantly increase metastasis in vitro and in vivo. High-throughput methylated RNA immunoprecipitation sequencing (MeRIP-seq) showed that the m6A levels of Lnc-LSG1 could be regulated by METTL14. Lnc-LSG1 can directly bind to ESRP2 protein and promote ESRP2 degradation via facilitating ESRP2 ubiquitination. However, m6A modification on Lnc-LSG1 can block the interaction between Lnc-LSG1 and ESRP2 via the m6A reader, YTHDC1. Taken together, our findings unraveled the novel mechanism of METTL14 inhibiting ccRCC progression, and explored the correlation between m6A and lncRNA in ccRCC for the first time.

PIWI-interacting RNA 57125 restrains clear cell renal cell carcinoma metastasis by downregulating CCL3 expression.

Clear-cell renal cell carcinoma is one of the most common tumors disagnosed, with nearly one third of patients diagnosed with metastatic ccRCC. Although an increasing number of studies has revealed that piwi-interacting RNAs are aberrantly expressed in diverse types of cancers, few of them explored the detailed molecular mechanism of piRNAs in carcinogenesis, particularly in ccRCC. In this study, differentially expressed piRNAs associated with ccRCC were selected by using piRNA-sequencing combined with TCGA data analysis, and piR-57125 was identified. PiR-57125 was found remarkably downregulated in ccRCC samples. Functionally, knockdown of piR-57125 promoted migration and invasion of ccRCC, while overexpression of piR-57125 suppressed ccRCC metastasis. In vivo lung metastasis model also confirmed the same results. CCL3 was identified as the direct target of piR-57125 which could potentially reverse the inhibition effect of piR-57125 in ccRCC metastasis. Further study revealed that piR-57125 modulated ccRCC metastasis through the AKT/ERK pathway. These data indicate that piR-57125 restrains ccRCC metastasis by directly targeting CCL3 and inhibiting the AKT/ERK pathway, and could be a potential therapeutic target for ccRCC.

Association between N-Acetyltransferase 2 Polymorphism and Bladder Cancer Risk: a Meta-Analysis in a Single Ethnic Group.

BACKGROUND: Many studies have evaluated the correlation between N-acetyltransferase 2 (NAT2) slow acetylation genotype and bladder cancer risk. However, the results are inconsistent and remain to be confirmed in each ethnic group. To assess the effects of NAT2 acetylation status on the risk of bladder cancer in the Chinese population, a meta-analysis was performed. METHODS: Studies were identified using PubMed and Chinese databases through February 2016. The associations were assessed with pooled odds ratios (ORs) and 95% confidence intervals (CIs). RESULTS: This meta-analysis included 10 studies with 896 bladder cancer cases and 1188 controls. In the overall analysis, NAT2 slow acetylation phenotype was significantly associated with an increased risk of bladder cancer in the Chinese population (OR = 1.68, 95% CI = 1.11 - 2.53). In the subgroup analyses by geographic areas and sources of controls, significant risk was found in Mainland China (OR = 1.83, 95% CI = 1.04 - 3.20) and hospitalbased studies (OR = 1.74, 95% CI = 1.27 - 2.38), but not in Taiwan China. CONCLUSIONS: This meta-analysis suggested that the NAT2 slow acetylation genotype is associated with an increased bladder cancer risk in Chinese individuals.