60 research outputs found
Randomized multicenter trial on the effect of radiotherapy for plantar Fasciitis (painful heel spur) using very low doses – a study protocol
Expression quantitative trait loci of genes predicting outcome are associated with survival of multiple myeloma patients
Canadian Institutes of Health Research, Grant/
Award Number: 81274; Huntsman Cancer
Institute Pilot Funds; Leukemia Lymphoma
Society, Grant/Award Number: 6067-09; the
National Institute of Health/National Cancer
Institute, Grant/Award Numbers: P30
CA016672, P30 CA042014, P30 CA13148,
P50 CA186781, R01 CA107476, R01
CA134674, R01 CA168762, R01 CA186646,
R01 CA235026, R21 CA155951, R25 CA092049, R25 CA47888, U54 CA118948;
Utah Population Database, Utah Cancer
Registry, Huntsman Cancer Center Support
Grant, Utah State Department of Health,
University of Utah; VicHealth, Cancer Council
Victoria, Australian National Health and
Medical Research Council, Grant/Award
Numbers: 1074383, 209057, 396414;
Victorian Cancer Registry, Australian Institute
of Health and Welfare, Australian National
Death Index, Australian Cancer Database;
Mayo Clinic Cancer Center; University of Pisa
and DKFZThe authors thank all site investigators that contributed to the studies
within the Multiple Myeloma Working Group (Interlymph Consortium),
staff involved at each site and, most importantly, the study participants
for their contributions that made our study possible. This work was partially
supported by intramural funds of University of Pisa and DKFZ. This
work was supported in part by the National Institute of Health/National
Cancer Institute (R25 CA092049, P30 CA016672, R01 CA134674, P30
CA042014, R01 CA186646, R21 CA155951, U54 CA118948, P30
CA13148, R25 CA47888, R01 CA235026, R01 CA107476, R01
CA168762, P50 CA186781 and the NCI Intramural Research Program),
Leukemia Lymphoma Society (6067-09), Huntsman Cancer Institute
Pilot Funds, Utah PopulationDatabase, Utah Cancer Registry, Huntsman
Cancer Center Support Grant, Utah StateDepartment of Health, University
of Utah, Canadian Institutes of Health Research (Grant number
81274), VicHealth, Cancer Council Victoria, Australian National Health
and Medical Research Council (Grants 209057, 396414, 1074383), Victorian
Cancer Registry, Australian Institute of Health and Welfare,
Australian National Death Index, Australian Cancer Database and the
Mayo Clinic Cancer Center.Open Access funding enabled and organized
by ProjektDEAL.The data that support the findings of this study are available on
request from the corresponding author. The data are not publicly
available due to privacy or ethical restrictions.Gene expression profiling can be used for predicting survival in multiple myeloma (MM) and identifying patients who will benefit from particular types of therapy. Some germline single nucleotide polymorphisms (SNPs) act as expression quantitative trait loci (eQTLs) showing strong associations with gene expression levels. We performed an association study to test whether eQTLs of genes reported to be associated with prognosis of MM patients are directly associated with measures of adverse outcome. Using the genotype-tissue expression portal, we identified a total of 16 candidate genes with at least one eQTL SNP associated with their expression with P < 10(-7) either in EBV-transformed B-lymphocytes or whole blood. We genotyped the resulting 22 SNPs in 1327 MM cases from the International Multiple Myeloma rESEarch (IMMEnSE) consortium and examined their association with overall survival (OS) and progression-free survival (PFS), adjusting for age, sex, country of origin and disease stage. Three polymorphisms in two genes (TBRG4-rs1992292, TBRG4-rs2287535 and ENTPD1-rs2153913) showed associations with OS at P < .05, with the former two also associated with PFS. The associations of two polymorphisms in TBRG4 with OS were replicated in 1277 MM cases from the International Lymphoma Epidemiology (InterLymph) Consortium. A meta-analysis of the data from IMMEnSE and InterLymph (2579 cases) showed that TBRG4-rs1992292 is associated with OS (hazard ratio = 1.14, 95% confidence interval 1.04-1.26, P = .007). In conclusion, we found biologically a plausible association between a SNP in TBRG4 and OS of MM patients.Canadian Institutes of Health Research (CIHR)
81274Huntsman Cancer Institute Pilot FundsLeukemia and Lymphoma Society
6067-09United States Department of Health & Human Services
National Institutes of Health (NIH) - USA
NIH National Cancer Institute (NCI)
P30 CA016672
P30 CA042014
P30 CA13148
P50 CA186781
R01 CA107476
R01 CA134674
R01 CA168762
R01 CA186646
R01 CA235026
R21 CA155951
R25 CA092049
R25 CA47888
U54 CA118948Utah Population Database, Utah Cancer Registry, Huntsman Cancer Center Support Grant, Utah State Department of Health, University of UtahVicHealth, Cancer Council Victoria, Australian National Health and Medical Research Council
1074383
209057
396414Victorian Cancer Registry, Australian Institute of Health and Welfare, Australian National Death Index, Australian Cancer DatabaseMayo Clinic Cancer CenterUniversity of PisaHelmholtz Associatio
Colorectal cancer stages transcriptome analysis
Colorectal cancer (CRC) is the third most common cancer and the second leading cause of
cancer-related deaths in the United States. The purpose of this study was to evaluate the
gene expression differences in different stages of CRC. Gene expression data on 433 CRC
patient samples were obtained from The Cancer Genome Atlas (TCGA). Gene expression
differences were evaluated across CRC stages using linear regression. Genes with
p 0.001 in expression differences were evaluated further in principal component analysis
and genes with p 0.0001 were evaluated further in gene set enrichment analysis. A total of
377 patients with gene expression data in 20,532 genes were included in the final analysis.
The numbers of patients in stage I through IV were 59, 147, 116 and 55, respectively. NEK4
gene, which encodes for NIMA related kinase 4, was differentially expressed across the four
stages of CRC. The stage I patients had the highest expression of NEK4 genes, while the
stage IV patients had the lowest expressions (p = 9*10−6
). Ten other genes (RNF34,
HIST3H2BB, NUDT6, LRCh4, GLB1L, HIST2H4A, TMEM79, AMIGO2, C20orf135 and
SPSB3) had p value of 0.0001 in the differential expression analysis. Principal component
analysis indicated that the patients from the 4 clinical stages do not appear to have distinct
gene expression pattern. Network-based and pathway-based gene set enrichment analyses
showed that these 11 genes map to multiple pathways such as meiotic synapsis and packaging of telomere ends, etc. Ten of these 11 genes were linked to Gene Ontology terms
such as nucleosome, DNA packaging complex and protein-DNA interactions. The protein
complex-based gene set analysis showed that four genes were involved in H2AX complex
II. This study identified a small number of genes that might be associated with clinical stages
of CRC. Our analysis was not able to find a molecular basis for the current clinical staging
for CRC based on the gene expression patterns
Mixing, hypersalinity and gradients in Hervey Bay, Australia
Hervey Bay, a large coastal embayment situated off the central eastern coast of Australia, is a shallow tidal area (average depth = 15 m), close to the continental shelf. It shows features of an inverse estuary, due to the high evaporation rate (approx. 2 m/year), low precipitation (less than 1 m/year) and on average almost no freshwater input from rivers that drain into the bay. The hydro- and thermodynamical structure of Hervey Bay and their variability are presented here for the first time, using a combination of four-dimensional modelling and observations from field studies. The numerical studies are performed with the Coupled Hydrodynamical Ecological model for RegioNal Shelf seas (COHERENS). Due to the high tidal range (> 3.5 m) the bay is considered as a vertically well-mixed system and therefore only horizontal fronts a likely. Recent field measurements, but also the numerical simulations indicate characteristic features of an inverse/hypersaline estuary with low salinities (35.5 psu) in the open ocean and peak values (> 39.0 psu) in the head water of the bay. The model further predicts a nearly persistent mean salinity gradient of 0.5 psu across the bay (with higher salinities close to the shore)
Policy Analysis and the Incorporation of Biological Objectives into Fishery Management Decisions
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