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GSE26942
Homo sapiens
217 Downloadable Samples
Illumina HumanHT-12 V3.0 expression beadchip
Description
This SuperSeries is composed of the SubSeries listed below.
Publication Title
No associated publication
Sample Metadata Fields
Sex, Age, Specimen part, Subject
View Samples- Homo sapiens
- 144 Downloadable Samples
- Illumina HumanHT-12 V4.0 expression beadchip
Description
While we and others have uncovered and validated several genomic predictors for metastatic recurrences, a molecular or genomic predictor that can reliably identify high-risk patients for late de novo recurrence has not been firmly established. We analyzed previously reported gene expression data from human livers that underwent partial hepatectomy or transplantation, which were representative physiological conditions that trigger liver regeneration signals. We generated gene expression data from tumor and matched non-tumor surrounding tissues of 72 hepatocellular carcinoma (HCC) patients who underwent surgical resection as the primary treatment. We used these gene expression data to develop a new prognostification model for recurrence of HCC after surgery.
Publication Title
Genomic predictors for recurrence patterns of hepatocellular carcinoma: model derivation and validation.
Sample Metadata Fields
Sex, Age, Specimen part, Disease, Disease stage
View Samples- Homo sapiens
- 118 Downloadable Samples
- Illumina human-6 v2.0 expression beadchip
Description
Despite continual efforts to rationalize a prognostic stratification of patients with esophageal adenocarcinoma (EAC) before treatment, current staging system only shows limited success owing to the lack of molecular and genetic markers that reflect prognostic features of the tumor. To develop molecular predictors of prognosis, we used systems-level characterization of tumor transcriptome. Using DNA microarray, genome-wide gene expression profiling was performed on 75 biopsy samples from patients with untreated EAC. Various statistical and informatical methods were applied to gene expression data to identify potential biomarkers associated with prognosis. Potential marker genes were validated in an independent cohort using quantitiative RT-PCR to measure gene expression. Distinct subgroups of EAC were uncovered by systems-level characterization of tumor transcriptome. We also identified a six-gene expression signature that could be used to predict overall survival (OS) of EAC patients. In particular, expression of SPARC and SPP1 was a strong independent predictor of OS, and a combined gene expression signature with these two genes was associated with prognosis (P < 0.024), even when all relevant pathological variables were considered together in multivariate Cox hazard regression analysis. Our findings suggest that molecular features reflected in gene expression signatures may dictate the prognosis of EAC patients, and these gene expression signatures can be used to predict the likelihood of prognosis at the time of diagnosis and before treatment.
Publication Title
Prognostic biomarkers for esophageal adenocarcinoma identified by analysis of tumor transcriptome.
Sample Metadata Fields
No sample metadata fields
View Samples- Homo sapiens
- 109 Downloadable Samples
Illumina HumanHT-12 V3.0 expression beadchip
Description
Clinical heterogeneity of gastric cancer reflected in unequal outcome of treatment is poorly defined in molecular level, and molecular subtypes and their associated biomarkers have not been established to improve prognostification and treatment of gastric cancer. Using microarray technologies, we analyzed gene expression profiling data from gastric cancer patients and uncovered potential prognostic subtypes and identify gene expression signature associated with prognosis and response to adjuvant chemotherapy.
Publication Title
No associated publication
Sample Metadata Fields
Sex, Age, Specimen part, Subject
View Samples- Homo sapiens
- 108 Downloadable Samples
- Illumina HumanHT-12 V3.0 expression beadchip
Description
Clinical heterogeneity of gastric cancer reflected in unequal outcome of treatment is poorly defined in molecular level, and molecular subtypes and their associated biomarkers have not been established to improve prognostification and treatment of gastric cancer. Using microarray technologies, we analyzed gene expression profiling data from gastric cancer patients and uncovered potential prognostic subtypes and identify gene expression signature associated with prognosis and response to adjuvant chemotherapy.
Publication Title
No associated publication
Sample Metadata Fields
Sex, Age, Specimen part, Subject
View Samples- Mus musculus, Homo sapiens
- 49 Downloadable Samples
- Illumina human-6 v2.0 expression beadchip
Description
To test gene expression changes of human cancer cells and mouse surrounding tissue cells during tumor progression, 4 different types of cancer cells (MDA-MB231Br3, PC14Br4, KM12M, A375SM) were injected into mouse brain, skin and orthotopic sites. RNAs containing human cancer cells and mouse surrounding tissue cells were extracted and hybridized into human and mouse arrays at the same times and it revealed the brain microenvironment induced complete reprogramming of metasized cancer cells, resulting in a gain of neuronal cell characteristic , mimicking neurogenesis during development.
Publication Title
No associated publication
Sample Metadata Fields
Specimen part
View Samples- Homo sapiens
- 90 Downloadable Samples
- Illumina HumanWG-6 v3.0 expression beadchip
Description
Despite continual efforts to establish pre-operative prognostic model of gastric cancer by using clinical and pathological parameters, a staging system that reliably separates patients with early and advanced gastric cancer into homogeneous groups with respect to prognosis does not exist. With use of microarray and quantitative RT-PCR technologies, we exploited series of experiments in combination with complementary data analyses on tumor specimens from 161 gastric cancer patients. Various statistical analyses were applied to gene expression data to uncover subgroups of gastric cancer, to identify potential biomarkers associated with prognosis, and to construct molecular predictor of risk from identified prognostic biomarkers.Two subgroups of gastric cancer with strong association with prognosis were uncovered. The robustness of prognostic gene expression signature was validated in independent patient cohort with use of support vector machines prediction model. For easy translation of our finding to clinics, we develop scoring system based on expression of six genes that can predict the likelihood of recurrence after curative resection of tumors. In multivariate analysis, our novel risk score was an independent predictor of recurrence (P=0.004) in cohort of 96 patients, and its robustness was validated in two other independent cohorts. We identified novel prognostic subgroups of gastric cancer that are distinctive in gene expression patterns. Six-gene signature and risk score derived from them has been validated for predicting the likelihood of survival at diagnosis.
Publication Title
Gene expression signature-based prognostic risk score in gastric cancer.
Sample Metadata Fields
No sample metadata fields
View Samples- Homo sapiens
- 88 Downloadable Samples
- Illumina HumanHT-12 V4.0 expression beadchip
Description
CTNNB1 is the most frequently mutated gene in hepatocellular carcinoma (HCC). However, its clinical relevance remains controversial. We determined an evolutionarily conserved -catenin signature by comparative analysis of gene expression data from human HCC and a mouse model (GSE43628). We generated gene expression data from the tumors of 88 HCC patients who underwent surgical resection as the primary treatment. We used these gene expression data to develop a new prognostification model for prognosis of HCC after surgery.
Publication Title
Activating CAR and β-catenin induces uncontrolled liver growth and tumorigenesis.
Sample Metadata Fields
Sex, Age, Specimen part, Disease, Disease stage
View Samples- Homo sapiens
- 28 Downloadable Samples
- Illumina human-6 v2.0 expression beadchip
Description
This SuperSeries is composed of the SubSeries listed below.
Publication Title
No associated publication
Sample Metadata Fields
No sample metadata fields
View Samples- Mus musculus
- 37 Downloadable Samples
- Affymetrix Mouse Gene 1.0 ST Array (mogene10st)
Description
To determine the molecular basis of gene regulation in pancreatic ductal epithelial cells, we developed methods for the isolation of this cell population during mouse development and normal adult homeostasis, as well as in conditions with ductal features (acinar-to-ductal metaplasia (ADM), pancreatic intraepithelial neoplasia (PanIN) and pancreatic ductal adenocarcinoma (PDAC)). Our technique utilizes the specificity of Dolichos biflorus Agglutinin (DBA) lectin marking the entire normal ductal tree, including terminal intercalated ducts (putative sites of stem or progenitor cells) and ductal structures in ADM and PanIN. We used ferromagnetic-labeled DBA lectin to isolate ductal structures. Ductal cells were isolated under the following conditions: (1) Embryonic Development in wild type mice: E14.5, E15.5, E16.5, and postnatal day 1 (P1); (2) Injury and regeneration (pancreatitis) 0, 1, 3, 5 days following cerulein-induced acute pancreatitis. Cerulein is a cholecystokinin analog which produces a self-limited pancreatitis with injury and subsequent regeneration and repair, completed five days after insult; and (3) Pdx1-Cre;LSL-KrasG12D/+ mice aged 10 and 20 weeks that harbor PanIN lesions and a subset develop PDAC. Ductal/PanIN cells were isolated from these mice and appropriate control mice (Pdx1-Cre;Kras+/+).
Publication Title
The Prrx1 homeodomain transcription factor plays a central role in pancreatic regeneration and carcinogenesis.
Sample Metadata Fields
Age, Specimen part, Treatment, Time
View Samples