Improving Model Performance on the Stratification of Breast Cancer Patients by Integrating Multiscale Genomic Features.

In clinical cancer research, it is a hot topic on how to accurately stratify patients based on genomic data. With the development of next-generation sequencing technology, more and more types of genomic features, such as mRNA expression level, can be used to distinguish cancer patients. Previous studies commonly stratified patients by using a single type of genomic features, which can only reflect one aspect of the cancer.

Transcriptome Analysis Identifies Piwi-Interacting RNAs as Prognostic Markers for Recurrence of Prostate Cancer.

Prostate cancer remains the second leading cause of male cancer death, and there is an unmet need for biomarkers to identify patients with such aggressive disease. Piwi-inteacting RNAs (piRNAs) have been classified as transcriptional and posttranscriptional regulators in somatic cells. In this study, we discovered three piRNAs as novel prognostic markers and their association with prostate cancer biochemical recurrence was confirmed in validation data set.

Drug-Induced Rhabdomyolysis Atlas (DIRA) for idiosyncratic adverse drug reaction management.

Drug-induced rhabdomyolysis (DIR) is an idiosyncratic and fatal adverse drug reaction (ADR) characterized in severe muscle injuries accompanied by multiple-organ failure. Limited knowledge regarding the pathophysiology of rhabdomyolysis is the main obstacle to developing early biomarkers and prevention strategies. Given the lack of a centralized data resource to curate, organize, and standardize widespread DIR information, here we present a Drug-Induced Rhabdomyolysis Atlas (DIRA) that provides DIR-related information, including: a classification scheme for DIR based on drug labeling information; postmarketing surveillance data of DIR; and DIR drug property information.