USP15, activated by TFAP4 transcriptionally, stabilizes SHC1 via deubiquitination and deteriorates renal cell carcinoma.

Ubiquitin-specific peptidase 15 (USP15), a critical deubiquitinating enzyme, has been demonstrated to improve substrate stabilization by hydrolyzing the bond between the substrate and ubiquitin, and is implicated in multiple carcinogenic processes. Prompted by the information cited from The Cancer Genome Atlas (TCGA) database and the Cancer Proteogenomic Data Analysis Site (cProSite), USP15 is selectively overexpressed in clear cell renal cell carcinoma (ccRCC) samples. We aimed to investigate the function of USP15 on ccRCC malignant features, which was emphasized in its deubiquitination of SHC adaptor protein 1 (SHC1).

SCATTERING STATISTICS OF GENERALIZED SPATIAL POISSON POINT PROCESSES.

We present a machine learning model for the analysis of randomly generated discrete signals, modeled as the points of an inhomogeneous, compound Poisson point process. Like the wavelet scattering transform introduced by Mallat, our construction is naturally invariant to translations and reflections, but it decouples the roles of scale and frequency, replacing wavelets with Gabor-type measurements. We show that, with suitable nonlinearities, our measurements distinguish Poisson point processes from common self-similar processes, and separate different types of Poisson point processes.

A Hybrid Scattering Transform for Signals with Isolated Singularities.

The scattering transform is a wavelet-based model of Convolutional Neural Networks originally introduced by S. Mallat. Mallat's analysis shows that this network has desirable stability and invariance guarantees and therefore helps explain the observation that the filters learned by early layers of a Convolutional Neural Network typically resemble wavelets.

A Combined CRISP3 and SPINK1 Prognostic Grade in EPS-Urine and Establishment of Models to Predict Prognosis of Patients With Prostate Cancer.

Background: Prostate cancer (PCa) is characterized by significant heterogeneity. Thus, novel prognostic indicators are required to improve prognosis and treatment.

Methods: Cysteine rich secretory protein 3 (CRISP3) and serine peptidase inhibitor Kazal type 1 (SPINK1) levels in expressed prostatic secretion (EPS)-urine collected during digital rectal examination of 496 patients histologically diagnosed with PCa were detected enzyme-linked immunosorbent assay.

Establishment of Novel Prostate Cancer Risk Subtypes and A Twelve-Gene Prognostic Model.

Prostate cancer (PCa) is the most common malignancy among men worldwide. However, its complex heterogeneity makes treatment challenging. In this study, we aimed to identify PCa subtypes and a gene signature associated with PCa prognosis.

Establishment of Novel DNA Methylation-Based Prostate Cancer Subtypes and a Risk-Predicting Eight-Gene Signature.

Prostate cancer (PCa) is the most common malignant tumor affecting males worldwide. The substantial heterogeneity in PCa presents a major challenge with respect to molecular analyses, patient stratification, and treatment. Least absolute shrinkage and selection operator was used to select eight risk-CpG sites.

Immune-Related Gene-Based Novel Subtypes to Establish a Model Predicting the Risk of Prostate Cancer.

Background: There is significant heterogeneity in prostate cancer (PCa), but immune status can reflect its prognosis. This study aimed to explore immune-related gene-based novel subtypes and to use them to create a model predicting the risk of PCa.

Methods: We downloaded the data of 487 PCa patients from The Cancer Genome Atlas (TCGA) database.