A phase II trial of apalutamide for intermediate-risk prostate cancer and molecular correlates.

Objectives: To determine whether 6 months of preoperative apalutamide for intermediate-risk prostate cancer (IRPCa) reduces the aggregate postoperative radiotherapy risk and to evaluate associations of molecular perturbations with clinical outcomes in this study cohort.

Patients And Methods: Between May 2018 and February 2020, eligible patients with IRPCa (Gleason 3 + 4 or 4 + 3 and clinical T2b-c or prostate-specific antigen level of 10-20 ng/mL) were treated with apalutamide 240 mg/day for 6 months followed by radical prostatectomy (RP) in this single-arm, phase II trial. The primary endpoint was presence of any adverse pathological feature at risk of pelvic radiation (pathological T stage after neoadjuvant therapy [yp]T3 or ypN1 or positive surgical margins).

FuncPEP v2.0: An Updated Database of Functional Short Peptides Translated from Non-Coding RNAs.

Over the past decade, there have been reports of short novel functional peptides (less than 100 aa in length) translated from so-called non-coding RNAs (ncRNAs) that have been characterized using mass spectrometry (MS) and large-scale proteomics studies. Therefore, understanding the bivalent functions of some ncRNAs as transcripts that encode both functional RNAs and short peptides, which we named ncPEPs, will deepen our understanding of biology and disease. In 2020, we published the first database of functional peptides translated from non-coding RNAs-FuncPEP.

RETRACTED: Liu et al. Prediction of Ovarian Cancer Response to Therapy Based on Deep Learning Analysis of Histopathology Images. 2023, , 4044.

The journal and authors wish to retract the article entitled 'Prediction of Ovarian Cancer Response to Therapy Based on Deep Learning Analysis of Histopathology Images' cited above [...

PCA-Plus: Enhanced principal component analysis with illustrative applications to batch effects and their quantitation.

Background: Principal component analysis (PCA), a standard approach to analysis and visualization of large datasets, is commonly used in biomedical research for detecting similarities and differences among groups of samples. We initially used conventional PCA as a tool for critical quality control of batch and trend effects in multi-omic profiling data produced by The Cancer Genome Atlas (TCGA) project of the NCI. We found, however, that conventional PCA visualizations were often hard to interpret when inter-batch differences were moderate in comparison with intra-batch differences; it was also difficult to quantify batch effects objectively.