Identifying Key Somatic Copy Number Alterations Driving Dysregulation of Cancer Hallmarks in Lower-Grade Glioma

The DTX Protein Family: An Emerging Set of E3 Ubiquitin Ligases in Cancer

Until recently, Deltex (DTX) proteins have been considered putative E3 ligases, based on the presence of an E3 RING domain in their protein coding sequence. The human DTX family includes DTX1, DTX2, DTX3, DTX3L and DTX4. Despite the fact that our knowledge of this class of E3-ubiquitin ligases is still at an early stage, our understanding of their role in oncogenesis is beginning to unfold. In fact, recently published studies allow us to define specific biological scenarios and further consolidate evidence-based working hypotheses. According to the current evidence, all DTX family members are involved in the regulation of Notch signaling, suggesting a phylogenetically conserved role in the regulation of this pathway. Indeed, additional evidence reveals a wider involvement of these proteins in other signaling complexes and cancer-promoting mechanisms beyond NOTCH signaling. DTX3, in particular, had been known to express two isoform variants (DTX3a and DTX3b). The recent identification and cloning of a third isoform variant in cancer (DTX3c), and its specific involvement in EphB4 degradation in cancer cells, sheds further light on this group of proteins and their specific role in cancer. Herein, we review the cumulative knowledge of this family of E3 Ubiquitin ligases with a specific focus on the potential oncogenic role of DTX isoforms in light of the rapidly expanding findings regarding this protein family's cellular targets and regulated signaling pathways. Furthermore, using a comparative and bioinformatic approach, we here disclose a new putative motif of a member of this family which may help in understanding the biological and contextual differences between the members of these proteins.

Development of a Hallmark Pathway-Related Gene Signature Associated with Immune Response for Lower Grade Gliomas

Although some biomarkers have been used to predict prognosis of lower-grade gliomas (LGGs), a pathway-related signature associated with immune response has not been developed. A key signaling pathway was determined according to the lowest adjusted p value among 50 hallmark pathways. The least absolute shrinkage and selection operator (LASSO) and stepwise multivariate Cox analyses were performed to construct a pathway-related gene signature. Somatic mutation, drug sensitivity and prediction of immunotherapy analyses were conducted to reveal the value of this signature in targeted therapies. In this study, an allograft rejection (AR) pathway was considered as a crucial signaling pathway, and we constructed an AR-related five-gene signature, which can independently predict the prognosis of LGGs. High-AR LGG patients had higher tumor mutation burden (TMB), Immunophenscore (IPS), IMmuno-PREdictive Score (IMPRES), T cell-inflamed gene expression profile (GEP) score and MHC I association immunoscore (MIAS) than low-AR patients. Most importantly, our signature can be validated in four immunotherapy cohorts. Furthermore, IC50 values of the six classic chemotherapeutic drugs were significantly elevated in the low-AR group compared with the high-AR group. This signature might be regarded as an underlying biomarker in predicting prognosis for LGGs, possibly providing more therapeutic strategies for future clinical research.

Network Control Models With Personalized Genomics Data for Understanding Tumor Heterogeneity in Cancer

Due to rapid development of high-throughput sequencing and biotechnology, it has brought new opportunities and challenges in developing efficient computational methods for exploring personalized genomics data of cancer patients. Because of the high-dimension and small sample size characteristics of these personalized genomics data, it is difficult for excavating effective information by using traditional statistical methods. In the past few years, network control methods have been proposed to solve networked system with high-dimension and small sample size. Researchers have made progress in the design and optimization of network control principles. However, there are few studies comprehensively surveying network control methods to analyze the biomolecular network data of individual patients. To address this problem, here we comprehensively surveyed complex network control methods on personalized omics data for understanding tumor heterogeneity in precision medicine of individual patients with cancer.