Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

Transcriptome analysis on gastric cancer

ABSTRACT: Reliable identification of cancer markers can have substantial implications to early detection of cancer. We report here an integrated computational and experimental study on identification of gastric cancer markers in patients’ tissue and sera based on (i) genome-scale transcriptomic analyses on 80 paired gastric cancer/reference tissues, with the aim of identifying abnormally expressed genes at various subtypes/stages of gastric carcinoma (ii) a computational identification of differentially expressed genes that may have their proteins secreted into blood circulation, followed by experimental validations. 160 Total samples were analyzed on paried tumor and adjacent normal tissues from 80 gastric cancer patients. Differential analysis identified genes with a fold-change over 1.5.

ORGANISM(S): Homo sapiens

SUBMITTER: Juan Cui

PROVIDER: E-GEOD-27342 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

ACCESS DATA

Similar Datasets

Project description:While blood transcriptional profiling has improved diagnosis and understanding of disease pathogenesis of adult tuberculosis (TB), no studies applying gene expression profiling of children with TB have been described so far. In this study, we have compared whole blood gene expression in childhood TB patients, as well as in healthy latently infected (LTBI) and uninfected (HC) children in a cohort of Warao Amerindians in the Delta Amacuro in Venezuela. We identified a 116-gene signature set by means of random forest analysis that showed an average prediction error of 11% for TB vs. LTBI and for TB vs. LTBI vs. HC in our dataset. Furthermore, a minimal set of only 9 genes showed a significant predictive value for all previously published adult studies using whole blood gene expression, with average prediction errors between 17% and 23%. Additionally, a minimal gene set of 42 genes with a comparable predictive value to the 116-gene set in both our dataset and the previously published literature cohorts for the comparsion of TB vs. LTBI vs. HC was identified. In order to identify a robust representative gene set that would hold stand among different ethnic populations, we selected ten genes that were highly discriminative between TB, LTBI and HC in all literature datasets as well as in our dataset. Functional annotation of these ten genes highlights a possible role for genes involved in calcium signaling and calcium metabolism as biomarkers for active TB. These ten genes were validated by quantitative real-time polymerase chain reaction in an additional cohort of 54 Warao Amerindian children with LTBI, HC and non-TB pneumonia. Decision tree analysis indicated that five of the ten genes were sufficient to diagnose 78% of the TB cases correctly with 100% specificity. We conclude that our data justify the further exploration of our signature set as biomarkers to diagnose childhood TB. Furthermore, as the identification of different biomarkers in ethnically distinct cohorts is apparent, it is important to cross-validate newly identified markers in all available cohorts. In this study, 27 children 1 to 15 years of age with TB (n=9), LTBI (n=9) and HC (n=9) were recruited between May 2010 and December 2010. Tuberculin skin test (TST) and QuantiFERON-TB Gold In-Tube assay (QFT-GIT) were performed on all children. A sputum sample was collected from all children with expectoration and a gastric aspirate was taken from all children under 6 years of age. Children with active TB were diagnosed based on culture of M. tuberculosis (n=2) or on the basis of clinical, epidemiological and radiological features (n=7). The latter group were children with a TST = 10 mm or a positive QFT-GIT result who presented all of: persistent fever >38M-BM-0C objectively recorded daily for at least two weeks, persistent cough for more than three weeks, weight loss (>5% reduction in weight compared with the highest weight recorded in last three months) or failure to thrive (documented crossing of percentile lines in the preceding three months), persistent lethargy or decrease in playfulness/activity reported by the parent and absence of clinical response on broad-spectrum antibiotics. Standard antero-posterior and lateral chest radiographs (CXRs) were taken from all children. Two independent experts, blinded to all clinical information, evaluated the CXRs and documented their findings on a standard report form. Where the two objective experts disagreed, a third expert was consulted and final consensus was achieved. A diagnosis of TB was only made when the CXR was consistent with TB9 and the child showed a positive clinical response to anti-TB treatment. Children were followed up clinically, radiologically and, in case of a negative TST at inclusion, by means of TST at six and 12 months after inclusion. LTBI was defined as a TST = 10mm and a positive QFT-GIT with a negative culture result on inclusion in the absence of radiological and clinical evidence of TB disease on inclusion as well as on t=6 and t=12 months. HC were children with a TST = 0 mm at inclusion and at t=6 and t=12 months. The HC had a negative QFT-GIT and a negative culture result at inclusion without radiological or clinical evidence of TB disease on inclusion nor on t=6 and t=12 months. TB patients were sampled before initiation of anti-TB treatment. Of three of the nine TB patients, a follow-up sample was taken when the patient was in anti-TB treatment for five months. All children were HIV-negative. 27 samples in total where analyzed, active TB infection (TB, n=9), Latent TB infection (LTBI, n=9) and healthy controls (HC, n=9) . Gene expression values were log2-transformed and differentially expressed genes were identified based on log2 fold changes (M-values). P values were calculated with a Bayes-regularized one-way ANOVA. Random Forest recursive feature elimination was used to find a signature geneset capable of discrimination active TB from latent TB and from non-infected individuals.

Dataset Information

Transcriptome analysis on gastric cancer

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets