Body mass index and risk of pancreatic cancer in a Chinese population

10.1371/journal.pone.0085149PLoS ONE91-POLN

Suffocating cancer: hypoxia-associated epimutations as targets for cancer therapy

Lower than normal levels of oxygen (hypoxia) is a hallmark of all solid tumours rendering them frequently resistant to both radiotherapy and chemotherapy regimes. Furthermore, tumour hypoxia and activation of the hypoxia inducible factor (HIF) transcriptional pathway is associated with poorer prognosis. Driven by both genetic and epigenetic changes, cancer cells do not only survive but thrive in hypoxic conditions. Detailed knowledge of these changes and their functional consequences is of great clinical utility and is already helping to determine phenotypic plasticity, histological tumour grading and overall prognosis and survival stratification in several cancer types. As epigenetic changes - contrary to genetic changes - are potentially reversible, they may prove to be potent therapeutic targets to add to the cancer physicians' armorarium in the future

Nanoscale Metallic Iron for Environmental Remediation: Prospects and Limitations

The amendment of the subsurface with nanoscale metallic iron particles (nano-Fe0) has been discussed in the literature as an efficient in situ technology for groundwater remediation. However, the introduction of this technology was controversial and its efficiency has never been univocally established. This unsatisfying situation has motivated this communication whose objective was a comprehensive discussion of the intrinsic reactivity of nano-Fe0 based on the contemporary knowledge on the mechanism of contaminant removal by Fe0 and a mathematical model. It is showed that due to limitations of the mass transfer of nano-Fe0 to contaminants, available concepts cannot explain the success of nano-Fe0 injection for in situ groundwater remediation. It is recommended to test the possibility of introducing nano-Fe0 to initiate the formation of roll-fronts which propagation would induce the reductive transformation of both dissolved and adsorbed contaminants. Within a roll-front, FeII from nano-Fe0 is the reducing agent for contaminants. FeII is recycled by biotic or abiotic FeIII reduction. While the roll-front concept could explain the success of already implemented reaction zones, more research is needed for a science-based recommendation of nano- Fe0 for subsurface treatment by roll-front

Pan-cancer analysis of whole genomes

Cancer is driven by genetic change, and the advent of massively parallel sequencing has enabled systematic documentation of this variation at the whole-genome scale(1-3). Here we report the integrative analysis of 2,658 whole-cancer genomes and their matching normal tissues across 38 tumour types from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). We describe the generation of the PCAWG resource, facilitated by international data sharing using compute clouds. On average, cancer genomes contained 4-5 driver mutations when combining coding and non-coding genomic elements; however, in around 5% of cases no drivers were identified, suggesting that cancer driver discovery is not yet complete. Chromothripsis, in which many clustered structural variants arise in a single catastrophic event, is frequently an early event in tumour evolution; in acral melanoma, for example, these events precede most somatic point mutations and affect several cancer-associated genes simultaneously. Cancers with abnormal telomere maintenance often originate from tissues with low replicative activity and show several mechanisms of preventing telomere attrition to critical levels. Common and rare germline variants affect patterns of somatic mutation, including point mutations, structural variants and somatic retrotransposition. A collection of papers from the PCAWG Consortium describes non-coding mutations that drive cancer beyond those in the TERT promoter(4); identifies new signatures of mutational processes that cause base substitutions, small insertions and deletions and structural variation(5,6); analyses timings and patterns of tumour evolution(7); describes the diverse transcriptional consequences of somatic mutation on splicing, expression levels, fusion genes and promoter activity(8,9); and evaluates a range of more-specialized features of cancer genomes(8,10-18).Peer reviewe

Impact of Systemic Inflammation and Autoimmune Diseases on apoA-I and HDL Plasma Levels and Functions

The cholesterol of high-density lipoproteins (HDLs) and its major proteic component, apoA-I, have been widely investigated as potential predictors of acute cardiovascular (CV) events. In particular, HDL cholesterol levels were shown to be inversely and independently associated with the risk of acute CV diseases in different patient populations, including autoimmune and chronic inflammatory disorders. Some relevant and direct anti-inflammatory activities of HDL have been also recently identified targeting both immune and vascular cell subsets. These studies recently highlighted the improvement of HDL function (instead of circulating levels) as a promising treatment strategy to reduce inflammation and associated CV risk in several diseases, such as systemic lupus erythematosus and rheumatoid arthritis. In these diseases, anti-inflammatory treatments targeting HDL function might improve both disease activity and CV risk. In this narrative review, we will focus on the pathophysiological relevance of HDL and apoA-I levels/functions in different acute and chronic inflammatory pathophysiological conditions

Statistical and integrative system-level analysis of DNA methylation data

Epigenetics plays a key role in cellular development and function. Alterations to the epigenome are thought to capture and mediate the effects of genetic and environmental risk factors on complex disease. Currently, DNA methylation is the only epigenetic mark that can be measured reliably and genome-wide in large numbers of samples. This Review discusses some of the key statistical challenges and algorithms associated with drawing inferences from DNA methylation data, including cell-type heterogeneity, feature selection, reverse causation and system-level analyses that require integration with other data types such as gene expression, genotype, transcription factor binding and other epigenetic information