Citations

The need for liver transplantation (LT) among older patients is increasing, but the role of LT in the elderly (≥70 years) is not well defined. We retrospectively reviewed all primary LTs from 1998 through 2016 at our center. Survival and associated risk factors were analyzed with Cox regression and Kaplan‐Meier methods for LT recipients in 3 age groups: <60, 60‐69, and ≥70 years. Among 2281 LT recipients, the median age was 56 years (range, 15‐80 years), and 162 were aged ≥70 years. The estimated 5‐ and 10‐year patient survival probabilities for elderly LT recipients were lower (70.8% and 43.6%) than for recipients aged 60‐69 years (77.2% and 64.6%) and <60 years (80.7% and 67.6%). Patient and graft survival rates associated with LT improved over time from the pre–Model for End‐Stage Liver Disease era to Share 15, pre–Share 35, and Share 35 for the cohort overall (P < 0.001), but rates remained relatively stable in septuagenarians throughout the study periods (all P > 0.45). There was no incremental negative effect of age at LT among elderly patients aged 70‐75 years (log‐rank P = 0.32). Among elderly LT recipients, greater requirement for packed red blood cells and longer warm ischemia times were significantly associated with decreased survival (P < 0.05). Survival of LT recipients, regardless of age, markedly surpassed that of patients who were denied LT, but it was persistently 20%‐30% lower than the expected survival of the general US population (P < 0.001). With the aging of the population, select older patients with end‐stage liver diseases can benefit from LT, which largely restores their expected life spans.

Abstract DNA methylation is instrumental for gene regulation. Global changes in the epigenetic landscape have been recognized as a hallmark of cancer. However, the role of DNA methylation in epithelial ovarian cancer (EOC) remains unclear. In this study, high-density genetic and DNA methylation data in white blood cells from the Framingham Heart Study (N = 1,595) were used to build genetic models to predict DNA methylation levels. These prediction models were then applied to the summary statistics of a genome-wide association study (GWAS) of ovarian cancer including 22,406 EOC cases and 40,941 controls to investigate genetically predicted DNA methylation levels in association with EOC risk. Among 62,938 CpG sites investigated, genetically predicted methylation levels at 89 CpG were significantly associated with EOC risk at a Bonferroni-corrected threshold of P &lt; 7.94 × 10−7. Of them, 87 were located at GWAS-identified EOC susceptibility regions and two resided in a genomic region not previously reported to be associated with EOC risk. Integrative analyses of genetic, methylation, and gene expression data identified consistent directions of associations across 12 CpG, five genes, and EOC risk, suggesting that methylation at these 12 CpG may influence EOC risk by regulating expression of these five genes, namely MAPT, HOXB3, ABHD8, ARHGAP27, and SKAP1. We identified novel DNA methylation markers associated with EOC risk and propose that methylation at multiple CpG may affect EOC risk via regulation of gene expression. Significance: Identification of novel DNA methylation markers associated with EOC risk suggests that methylation at multiple CpG may affect EOC risk through regulation of gene expression.

BackgroundFunctional genomics employs dozens of OMICs technologies to explore the functions of DNA, RNA and protein regulators in gene regulation processes. Despite each of these technologies being powerful tools on their own, like the parable of blind men and an elephant, any one single technology has a limited ability to depict the complex regulatory system. Integrative OMICS approaches have emerged and become an important area in biology and medicine. It provides a precise and effective way to study gene regulations.ResultsThis article reviews current popular OMICs technologies, OMICs data integration strategies, and bioinformatics tools used for multi‐dimensional data integration. We highlight the advantages of these methods, particularly in elucidating molecular basis of biological regulatory mechanisms.ConclusionsTo better understand the complexity of biological processes, we need powerful bioinformatics tools to integrate these OMICs data. Integrating multi‐dimensional OMICs data will generate novel insights into system‐level gene regulations and serves as a foundation for further hypothesis‐driven research.