Homeobox and Polycomb target gene methylation in human solid tumors.

DNA methylation is an epigenetic mark that plays an important role in defining cancer phenotypes, with global hypomethylation and focal hypermethylation at CpG islands observed in tumors. These methylation marks can also be used to define tumor types and provide an avenue for biomarker identification. The homeobox gene class is one that has potential for this use, as well as other genes that are Polycomb Repressive Complex 2 targets. To begin to unravel this relationship, we performed a pan-cancer DNA methylation analysis using sixteen Illumina HM450k array datasets from TCGA, delving into cancer-specific qualities and commonalities between tumor types with a focus on homeobox genes. Our comparisons of tumor to normal samples suggest that homeobox genes commonly harbor significant hypermethylated differentially methylated regions. We identified two homeobox genes, HOXA3 and HOXD10, that are hypermethylated in all 16 cancer types. Furthermore, we identified several potential homeobox gene biomarkers from our analysis that are uniquely methylated in only one tumor type and that could be used as screening tools in the future. Overall, our study demonstrates unique patterns of DNA methylation in multiple tumor types and expands on the interplay between the homeobox gene class and oncogenesis.

Genetic and environmental factors influencing neonatal resting-state functional connectivity.

Functional magnetic resonance imaging has been used to identify complex brain networks by examining the correlation of blood-oxygen-level-dependent signals between brain regions during the resting state. Many of the brain networks identified in adults are detectable at birth, but genetic and environmental influences governing connectivity within and between these networks in early infancy have yet to be explored. We investigated genetic influences on neonatal resting-state connectivity phenotypes by generating intraclass correlations and performing mixed effects modeling to estimate narrow-sense heritability on measures of within network and between-network connectivity in a large cohort of neonate twins. We also used backwards elimination regression and mixed linear modeling to identify specific demographic and medical history variables influencing within and between network connectivity in a large cohort of typically developing twins and singletons. Of the 36 connectivity phenotypes examined, only 6 showed narrow-sense heritability estimates greater than 0.10, with none being statistically significant. Demographic and obstetric history variables contributed to between- and within-network connectivity. Our results suggest that in early infancy, genetic factors minimally influence brain connectivity. However, specific demographic and medical history variables, such as gestational age at birth and maternal psychiatric history, may influence resting-state connectivity measures.