Unknown,Transcriptomics,Genomics,Proteomics

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Transcription profiling by array of human familial hypercholesterolemia to identify atherosclerotic markers in human blood

ABSTRACT: Atherosclerosis is characterized by thickening of the arterial wall and is the primary cause of the coronary artery disease and cerebrovascular disease, two of the most common causes of illness and death worldwide. One of the leading risk factors for development of atherosclerosis is familial hypercholesterolemia. Familial hypercholesterolemia is an autosomal dominant disorder, which is caused by mutations mainly located in the low-density lipoprotein receptor (LDLR) gene. It is characterized by elevated levels of low-density lipoprotein cholesterol, the presence of tendon xanthomas, and premature cardiovascular disease. Aim of this study was to find atherosclerotic markers in white blood cells of patients compared to healthy controls. None of these patients exhibited symptoms of atherosclerosis by standard diagnostic methods; however, transcriptome analysis of blood RNA indicated changes in ubiquitin proteolysis and cell adhesion system as expected in initiation steps of atherosclerosis. Experiment Overall Design: Five patients diagnosed with Familial hypercholesterolemia and five age, sex, BMI and smoking status matched controls contributed blood from which total RNA from white blood cells was isolated. RNA samples were analyzed using Affymetrix microarrays and two groups were compared for differentially expressed genes.

ORGANISM(S): Homo sapiens

SUBMITTER: Peter Juvan

PROVIDER: E-GEOD-13985 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Project description:Background: Inter-patient prostate cancer (PrCa) heterogeneity results in highly variable patient outcomes. Multi-purpose biomarkers to dissect this heterogeneity are urgently required to improve treatment and accelerate drug development in PrCa. Circulating biomarkers are most practical for evaluating this disease. We pursued the analytical validation and clinical qualification of blood mRNA expression arrays. Methods: Whole blood samples were collected into PaxGeneTM tubes from PrCa patients: 31 good prognosis patients selected for active surveillance (AS) and 63 advanced castration resistant PrCa (CRPC) patients. RNA was extracted, amplified, biotinylated, and hybridised to Affymetrix U133plus2 microarrays and analysis of genome-wide expression profiles were analysed using Bayesian Latent Process Decomposition (LPD). Findings: LPD analysis of the mRNA expression data divided the evaluable patients (n=94) into 4 separate groups. LPD1 and LPD2 consisted almost entirely of CRPC patients (14/14; 17/18); LPD3 (15/31) and LDP4 (12/21) comprised both AS and CRPC. Ten patients were unclassifiable by LPD analysis. LPD1 CRPC patients had significantly poorer overall survival (median 10.7 months, CI-95% 4.2-17.2) than CRPC patients in LPD2 to 4 (median 26.5 months, CI-95% 18.1-34.9, p=0.00007). LPD 1 membership remained the strongest prognostic factor in a multivariate analysis (HR 5.0, CI-95% 2.1-11.9, p=0.0002). Gene signatures in the poor prognosis LPD group 1 were associated with increased CD71+ early erythroid cells and decreased B-cell and T-cell immune response. A 9-gene signature, that was validated by RT-PCR studies, classified patients as group LPD 1 with a very low misclassification rate (1.2%). Interpretation: Poor PrCa outcome can be predicted using gene expression signatures from whole blood and merits further evaluation in predictive and pharmacodynamic biomarker studies for novel anticancer drugs including immune therapies. 107 blood samples were collected in paxgene tubes from 94 prostate cancer patients (31 good prognosis & 63 advanced castration resistant PrCa). Replicates include 9 biological replicates and four technical replicates

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Transcription profiling by array of human familial hypercholesterolemia to identify atherosclerotic markers in human blood

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