SETD4 inhibits prostate cancer development by promoting H3K27me3-mediated NUPR1 transcriptional repression and cell cycle arrest.

The suppressor of variegation enhancer of zeste-trithorax (SET) domain methyltransferases have been reported to function as key regulators in multiple tumor types by catalyzing histone lysine methylation. Nevertheless, our understanding on the role of these lysine methyltransferases, including SETD4, in prostate cancer (PCa) remains limited. Hence, the specific role of SETD4 in PCa was investigated in this study. The expression of SETD4 in PCa cells and tissue samples was downregulated in PCa cells and tissue specimens, and decreased SETD4 expression led to inferior clinicopathological characteristics in patients with PCa. knockdown of SETD4 facilitated the proliferation of PCa cells and accelerated cell cycle progression. Mechanistically, SETD4 repressed NUPR1 transcription by methylating H3K27 to generate H3K27me3, subsequently inactivated Akt pathway and impeded the tumorigenesis of PCa. Our results highlight that SETD4 prevents the development of PCa by catalyzing the methylation of H3K27 and suppressing NUPR1 transcription, subsequently inactivating the Akt signaling pathway. The findings suggest the potential application of SETD4 in PCa prognosis and therapeutics.

Coexpression of TLR9 and VEGF-C is associated with lymphatic metastasis in prostate cancer.

Prostate cancer (PCa) is one of the most frequent cancers in men, and its biomolecular targets have been extensively studied. This study aimed to analyze the expression of toll-like receptor 9 (TLR9) and vascular endothelial growth factor C (VEGF-C) and the clinical value of the coexpression of TLR9 and VEGF-C in PCa. We retrospectively evaluated 55 patients with clinically localized, intermediate-risk, or high-risk PCa who underwent laparoscopic radical prostatectomy (LRP) and extended pelvic lymph node dissection (ePLND) without neoadjuvant hormonal therapy at a single institution from June 2013 to December 2016. In all 55 patients, the median number of lymph nodes (LNs) resected was 23 (range: 18-31), and a total of 1269 LNs were removed, of which 78 LNs were positive. Seventeen patients had positive LNs, with a positive rate of 30.9%. In addition, the immunohistochemical results in the above patients revealed that high TLR9 expression was correlated with higher Gleason score (GS) (P = 0.049), increased LN metastasis (P = 0.004), and more perineural invasion (PNI) (P = 0.033). Moreover, VEGF-C expression was associated with GS (P = 0.040), pathological stage (pT stage) (P = 0.022), LN metastasis (P = 0.003), and PNI (P = 0.001). Furthermore, a significant positive correlation between TLR9 and VEGF-C was found (P < 0.001), and the TLR9/VEGF-C phenotype was associated with LN metastasis (P = 0.047). Collectively, we propose that TLR9 stimulation may promote LN metastasis in PCa cells through the upregulation of VEGF-C expression, thereby affecting the prognosis of PCa patients. Therefore, these markers may serve as valuable targets for the treatment of PCa.

Joint artifact correction and super-resolution of image slicing and mapping system via a convolutional neural network.

As the key component of the image mapping spectrometer, the image mapper introduces complex image degradation in the reconstructed images, including low spatial resolution and intensity artifacts. In this paper, we propose a novel image processing method based on the convolutional neural network to perform artifact correction and super-resolution (SR) simultaneously. The proposed joint network contains two branches to handle the artifact correction task and SR task in parallel. The artifact correction module is designed to remove the artifacts in the image and the SR module is used to improve the spatial resolution. An attention fusion module is constructed to combine the features extracted by the artifact correction and SR modules. The fused features are used to reconstruct an artifact-free high-resolution image. We present extensive simulation results to demonstrate that the proposed joint method outperforms state-of-the-art methods and can be generalized to other image mapper designs. We also provide experimental results to prove the efficiency of the joint network.