miR-4286 promotes prostate cancer progression by targeting the expression of SALL1.

BACKGROUND: Prostate cancer (PCa) is a serious health threat for humans worldwide. Recent studies have revealed that microRNAs are associated with the progression of human cancers, including PCa. However, no study has been performed aiming to investigate the role of microNA-4286 (miR-4286) on PCa. METHODS: A quantitative reverse transcriptase-polymerase chain reaction was conducted to analyze the expression level of miR-4286 in PCa cells. The connection of miR-4286 and spalt like transcription factor 1 (SALL1) was analyzed with a bioinformatic analysis tool, a dual-luciferase activity reporter assay and western blotting. The effects of miR-4286 and SALL1 on PCa cell behaviors were examined in vitro. RESULTS: We showed miR-4286 expression was significantly increased in PCa cells compared to a normal cell line. Knockdown of miR-4286 could inhibit PCa cell proliferation but promote cell apoptosis by targeting SALL1. CONCLUSIONS: The results of the present study suggest that miR-4286 overexpression represents a tumor promoter role in PCa.

Magnetic Resonance Imaging Image Feature Analysis Algorithm under Convolutional Neural Network in the Diagnosis and Risk Stratification of Prostate Cancer.

This work aimed to explore the accuracy of magnetic resonance imaging (MRI) images based on the convolutional neural network (CNN) algorithm in the diagnosis of prostate cancer patients and tumor risk grading. A total of 89 patients with prostate cancer and benign prostatic hyperplasia diagnosed by MRI examination and pathological examination in hospital were selected as the research objects in this study (they passed the exclusion criteria). The MRI images of these patients were collected in two groups and divided into two groups before and after treatment according to whether the CNN algorithm was used to process them. The number of diagnosed diseases and the number of cases of risk level inferred based on the tumor grading were compared to observe which group was closer to the diagnosis of pathological biopsy. Through comparative analysis, compared with the positive rate of pathological diagnosis (44%), the positive rate after the treatment of the CNN algorithm (42%) was more similar to that before the treatment (34%), and the comparison was statistically marked (P < 0.05). In terms of risk stratification, the grading results after treatment (37 cases) were closer to the results of pathological grading (39 cases) than those before treatment (30 cases), and the comparison was statistically obvious (P < 0.05). In addition, it was obvious that the MRT images would be clearer after treatment through the observation of the MRT images before and after treatment. In conclusion, MRI image segmentation algorithm based on CNN was more accurate in the diagnosis and risk stratification of prostate cancer than routine MRI. According to the evaluation of Dice similarity coefficient (DSC) and Hausdorff I distance (HD), the CNN segmentation method used in this study was more perfect than other segmentation methods.