Pan-cancer Alterations of the MYC Oncogene and Its Proximal Network across the Cancer Genome Atlas

Although the MYC oncogene has been implicated in cancer, a systematic assessment of alterations of MYC, related transcription factors, and co-regulatory proteins, forming the proximal MYC network (PMN), across human cancers is lacking. Using computational approaches, we define genomic and proteomic features associated with MYC and the PMN across the 33 cancers of The Cancer Genome Atlas. Pan-cancer, 28% of all samples had at least one of the MYC paralogs amplified. In contrast, the MYC antagonists MGA and MNT were the most frequently mutated or deleted members, proposing a role as tumor suppressors. MYC alterations were mutually exclusive with PIK3CA, PTEN, APC, or BRAF alterations, suggesting that MYC is a distinct oncogenic driver. Expression analysis revealed MYC-associated pathways in tumor subtypes, such as immune response and growth factor signaling; chromatin, translation, and DNA replication/repair were conserved pan-cancer. This analysis reveals insights into MYC biology and is a reference for biomarkers and therapeutics for cancers with alterations of MYC or the PMN. We present a computational study determining the frequency and extent of alterations of the MYC network across the 33 human cancers of TCGA. These data, together with MYC, positively correlated pathways as well as mutually exclusive cancer genes, will be a resource for understanding MYC-driven cancers and designing of therapeutics

Kinesiological Factors in Vertical Jump Performance: Differences Among Individuals

The purpose of this study was to investigate the kinesiological factors that distinguish good jumpers from poor ones, in an attempt to understand the critical factors in vertical jump performance (VJP). Fifty-two normal, physically active male college students each performed five maximal vertical jumps with arms akimbo. Ground reaction forces and video data were collected during the jumps. Subjects' strength was tested isometrically. Thirty-five potential predictor variables were calculated for statistical modeling by multiple-regression analysis. At the whole-body level of analysis, the best models (which included peak and average mechanical power) accounted for 88% of VJP variation (p < .0005). At the segmental level, the best models accounted for 60% of variation in VJP (p < .0005). Unexpectedly, coordination variables were not related to VJP. These data suggested that VJP was most strongly associated with the mechanical power developed during jump execution.The University of Michigan/[Rackham Predoctoral Fellowship]//Estados UniddosThe University of Michigan/[Rackham Dissertation Grant]//Estados UnidosUCR::Vicerrectoría de Docencia::Ciencias Sociales::Facultad de Educación::Escuela de Educación Físic