Metabolomics,Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

Gene expression in skeletal muscle of cancer patients before and after potentially curative surgery

ABSTRACT: The mechanisms underlying muscle wasting in cancer patients remain poorly understood, and consequently there remains an unmet clinical need for new biomarkers and treatment strategies. Microarrays were used to examine the transcriptome in single biopsies from healthy controls (n=6) and in paired biopsies [pre-resection baseline (weight-loss 7%) and 8 month post-resection follow-up (disease-free /weight-stable for previous 2 months)] from quadriceps muscle of patients with upper gastrointestinal cancer (UGIC) (n=12).

ORGANISM(S): Homo sapiens

SUBMITTER: Iain Gallagher

PROVIDER: E-GEOD-34111 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Publications

Suppression of skeletal muscle turnover in cancer cachexia: evidence from the transcriptome in sequential human muscle biopsies.

Gallagher Iain J IJ Stephens Nathan A NA MacDonald Alisdair J AJ Skipworth Richard J E RJ Husi Holger H Greig Carolyn A CA Ross James A JA Timmons James A JA Fearon Kenneth C H KC

Clinical cancer research : an official journal of the American Association for Cancer Research 20120327 10

<h4>Purpose</h4>The mechanisms underlying muscle wasting in patients with cancer remain poorly understood, and consequently there remains an unmet clinical need for new biomarkers and treatment strategies.<h4>Experimental design</h4>Microarrays were used to examine the transcriptome in single biopsies from healthy controls (n = 6) and in paired biopsies [pre-resection baseline (weight-loss 7%) and 8 month post-resection follow-up (disease-free/weight-stable for previous 2 months)] from quadricep ...[more]

PMID: 22452944

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Project description:One of the most common smoking-related diseases, chronic obstructive pulmonary disease (COPD), results from a dysregulated, multi-tissue inflammatory response to cigarette smoke. We hypothesized that systemic inflammatory signals in genome-wide blood gene expression can identify clinically important COPD-related disease subtypes, and we leveraged pre-existing gene interaction networks to guide unsupervised clustering of blood microarray expression data. Using network-informed non-negative matrix factorization, we analyzed genome-wide blood gene expression from 229 former smokers in the ECLIPSE Study, and we identified novel, clinically relevant molecular subtypes of COPD. These network-informed clusters were more stable and more strongly associated with measures of lung structure and function than clusters derived from a network-naÃ¯ve approach, and they were associated with subtype-specific enrichment for inflammatory and protein catabolic pathways. These clusters were successfully reproduced in an independent sample of 135 smokers from the COPDGene Study. Briefly, gene expression was derived from whole blood samples in ECLIPSE subjects and peripheral blood mononuclear cells (PBMCs) for the COPDGene subjects. Gene expression profiling was performed using the Affymetrix Human U133 Plus2 array. Gene expression data were log-transformed, and background correction and normalization were performed for the merged ECLIPSE and COPDGene samples using robust multi-array averaging and quantile normalization as implemented in the affy Bioconductor package[27]. Of the 136 COPDGene subjects reported in a previous publication[13], one self-reported African-American subject was removed from analysis, which was conducted on the remaining 135 non-Hispanic white subjects. To identify a set of genes associated with COPD, we performed differential expression analysis for 38,519 probesets in ECLIPSE that passed quality control measures. Normalized probeset intensities were related to measures indicative of two primary dimensions of pulmonary impairment in COPD airway obstruction as indicated by two measures of spirometric lung function (FEV1 (% of predicted) and FEV1/FVC) and lung parenchymal destruction, i.e., emphysema (as quantified by the percentage of low attenuation area less than -950 Hounsfield units on lung computed tomography, %LAA-950). The analysis was conducted using the limma Bioconductor package, and the false discovery rate was controlled at 5%. The following covariates were included in the differential expression analysis age, pack-years of cigarette smoke exposure, and gender. After standardizing gene expression data from 229 ECLIPSE subjects by the variance of each probe set, we applied NMF[29] and NBS[6] to identify meta-patients (i.e. subtypes or subject clusters) and meta-genes (i.e. representative subtype expression profiles). Cross-sectional study of smokers. 229 subjects from the ECLIPSE study were analyzed in the model discovery phase. 135 subjects from the COPDGene Study (GSE42057) were used for replication. Please note that the entire data set for total 364 samples including the re-analyzed samples is provided in the *364samples.txt files.

Dataset Information

Gene expression in skeletal muscle of cancer patients before and after potentially curative surgery

Publications

Suppression of skeletal muscle turnover in cancer cachexia: evidence from the transcriptome in sequential human muscle biopsies.

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