Multimetric feature selection for analyzing multicategory outcomes of colorectal cancer: random forest and multinomial logistic regression models.

Colorectal cancer (CRC) is one of the most common cancers worldwide, and a leading cause of cancer deaths. Better classifying multicategory outcomes of CRC with clinical and omic data may help adjust treatment regimens based on individual's risk. Here, we selected the features that were useful for classifying four-category survival outcome of CRC using the clinical and transcriptomic data, or clinical, transcriptomic, microsatellite instability and selected oncogenic-driver data (all data) of TCGA. We also optimized multimetric feature selection to develop the best multinomial logistic regression (MLR) and random forest (RF) models that had the highest accuracy, precision, recall and F1 score, respectively. We identified 2073 differentially expressed genes of the TCGA RNASeq dataset. MLR overall outperformed RF in the multimetric feature selection. In both RF and MLR models, precision, recall and F1 score increased as the feature number increased and peaked at the feature number of 600-1000, while the models' accuracy remained stable. The best model was the MLR one with 825 features based on sum of squared coefficients using all data, and attained the best accuracy of 0.855, F1 of 0.738 and precision of 0.832, which were higher than those using clinical and transcriptomic data. The top-ranked features in the MLR model of the best performance using clinical and transcriptomic data were different from those using all data. However, pathologic staging, HBS1L, TSPYL4, and TP53TG3B were the overlapping top-20 ranked features in the best models using clinical and transcriptomic, or all data. Thus, we developed a multimetric feature-selection based MLR model that outperformed RF models in classifying four-category outcome of CRC patients. Interestingly, adding microsatellite instability and oncogenic-driver data to clinical and transcriptomic data improved models' performances. Precision and recall of tuned algorithms may change significantly as the feature number changes, but accuracy appears not sensitive to these changes.

The Ubiquitin-specific Protease USP36 Associates with the Microprocessor Complex and Regulates miRNA Biogenesis by SUMOylating DGCR8.

miRNA biogenesis is a cellular process that produces mature miRNAs from their primary transcripts, pri-miRNAs, via two RNAse III enzyme complexes: the Drosha-DGCR8 microprocessor complex in the nucleus and the Dicer-TRBP complex in the cytoplasm. Emerging evidence suggests that miRNA biogenesis is tightly regulated by posttranscriptional and posttranslational modifications and aberrant miRNA biogenesis is associated with various human diseases including cancer. DGCR8 has been shown to be modified by SUMOylation. Yet, the SUMO ligase mediating DGCR8 SUMOylation is currently unknown. Here, we report that USP36, a nucleolar ubiquitin-specific protease essential for ribosome biogenesis, is a novel regulator of DGCR8. USP36 interacts with the microprocessor complex and promotes DGCR8 SUMOylation, specifically modified by SUMO2. USP36-mediated SUMOylation does not affect the levels of DGCR8 and the formation of the Drosha-DGCR8 complex, but promotes the binding of DGCR8 to pri-miRNAs. Consistently, abolishing DGCR8 SUMOylation significantly attenuates its binding to pri-miRNAs and knockdown of USP36 attenuates pri-miRNA processing, resulting in marked reduction of tested mature miRNAs. Induced expression of a SUMOylation-defective mutant of DGCR8 inhibits cell proliferation. Together, these results suggest that USP36 plays an important role in regulating miRNA biogenesis by SUMOylating DGCR8. Significance: This study identifies that USP36 mediates DGCR8 SUMOylation by SUMO2 and is critical for miRNA biogenesis. As USP36 is frequently overexpressed in various human cancers, our study suggests that deregulated USP36-miRNA biogenesis pathway may contribute to tumorigenesis.