Human Hepatocellular Carcinoma study
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE1898 | GEO | 2004/10/28
SECONDARY ACCESSION(S): PRJNA90985
REPOSITORIES: GEO
Similar Datasets
Project description:Global gene expression patterns of 91 human hepatocellular carcinomas (HCC) were analyzed to define the molecular characteristics of the tumors and to test the prognostic value of the expression profiles. Total RNAs were isolated from frozen liver tissue using CsCl density gradient centrifugation methods. Total RNA from 18 normal livers were pooled and used as reference in entire microarray experiments. To obtain gene expression profile data from human HCC, 20 μg of total RNAs from tissues were used to drive fluorescently (Cy-5 or Cy-3) labeled cDNA. At least two hybridizations were carried out for each tissue using dye-swap strategy to eliminate dye labeling bias. Unsupervised classification methods revealed two distinctive subclasses of HCC that are highly associated with survival of the patients. This association was validated by five independent supervised learning methods. Genes most strongly associated with survival were identified by using the Cox proportional hazards survival analysis. This approach identified a limited number of genes that accurately predicted the length of survival and provides new molecular insight into the pathogenesis of HCC. Tumors from the low survival subclass have strong cell proliferation and anti-apoptosis gene expression signatures. In addition, the low survival subclass displayed higher expression of genes involved in ubiquitination and histone modification suggesting an etiological involvement of these processes in accelerating the progression of HCC. The biological differences identified in the HCC subclasses should provide an attractive source for the development of therapeutic targets (e.g. HIF1a) for selective treatment(s) of HCC patients.
2004-10-28 | E-GEOD-1898 | biostudies-arrayexpress
Project description:Genetically modified mice have been extensively used for analyzing the molecular events that occur during the tumor development. However, in many cases, if not all, it is uncertain to what extent the mouse models reproduce features observed in the corresponding human conditions. This is due largely to lack of precise methods for direct and comprehensive comparison at the molecular level of the mouse and human tumors. We used global gene expression patterns of 68 hepatocellular carcinoma (HCC) from 7 different mouse models and 91 human HCC from pre-defined subclasses to obtain direct comparison of the molecular features of mouse and human HCC. Total RNAs were isolated from frozen liver tissue using CsCl density gradient centrifugation methods. Total RNA from the livers of 10 wild type mice were pooled and used as reference in entire microarray experiments. To obtain gene expression profile data from four transgenic HCC mouse models, 20 μg of total RNAs from tissues were used to drive fluorescently (Cy-5 or Cy-3) labeled cDNA. At least two hybridizations were carried out for each tissue using dye-swap strategy to eliminate dye labeling bias. Keywords: other
2004-10-28 | GSE1897 | GEO
Project description:Genetically modified mice have been extensively used for analyzing the molecular events that occur during the tumor development. However, in many cases, if not all, it is uncertain to what extent the mouse models reproduce features observed in the corresponding human conditions. This is due largely to lack of precise methods for direct and comprehensive comparison at the molecular level of the mouse and human tumors. We used global gene expression patterns of 68 hepatocellular carcinoma (HCC) from 7 different mouse models and 91 human HCC from pre-defined subclasses to obtain direct comparison of the molecular features of mouse and human HCC. Total RNAs were isolated from frozen liver tissue using CsCl density gradient centrifugation methods. Total RNA from the livers of 10 wild type mice were pooled and used as reference in entire microarray experiments. To obtain gene expression profile data from four transgenic HCC mouse models, 20 μg of total RNAs from tissues were used to drive fluorescently (Cy-5 or Cy-3) labeled cDNA. At least two hybridizations were carried out for each tissue using dye-swap strategy to eliminate dye labeling bias.
2004-10-28 | E-GEOD-1897 | biostudies-arrayexpress
Project description:Background & Aims: Hepatocellular carcinoma (HCC) is the second most lethal cancer due to lack of effective therapies. Although promising, HCC molecular classification, which enriches potential responders to specific therapies, has not yet been assessed in clinical trials of anti-HCC drugs. We aimed to overcome these challenges by developing clinicopathological surrogate indices of HCC molecular classification. Methods: HCC classification defined in our previous transcriptome meta-analysis (S1, S2, and S3 subclasses) was implemented in an FDA-approved diagnostic platform (Elements assay, NanoString). Ninety-six HCC tumors (training set) were assayed to develop molecular subclass-predictive indices based on clinicopathological features, which were independently validated in 99 HCC tumors (validation set). Molecular deregulations associated with the histopathological features were determined by pathway analysis. Sample sizes for HCC clinical trials enriched with specific molecular subclasses were determined. Results: HCC subclass-predictive indices were: steatohepatitic (SH)-HCC variant and immune cell infiltrate for S1 subclass, macrotrabecular/compact pattern, lack of pseudoglandular pattern, and high serum alpha-fetoprotein (>400 ng/mL) for S2 subclass, and microtrabecular pattern, lack of SH-HCC and clear cell variants, and lower histological grade for S3 subclass. Macrotrabecular/compact pattern, a predictor of S2 subclass, was associated with activation of therapeutically targetable oncogene YAP and stemness markers EPCAM/KRT19. BMP4 was associated with pseudoglandular pattern. Subclass-predictive indices-based patient enrichment reduced clinical trial sample sizes from 121, 184, and 53 to 30, 43, and 22 for S1, S2, and S3 subclass-targeting therapies, respectively. Conclusions: HCC molecular subclasses can be enriched by clinicopathological indices tightly associated with deregulation of therapeutically targetable molecular pathways.
2015-06-17 | GSE59548 | GEO
Project description:Two subclasses of lung squamous cell carcinoma with different gene expression profiles and prognosis identified by hierarchical clustering and validated by non-negative matrix factorization . BACKGROUND: Current clinical and histopathological criteria used to define lung squamous cell carcinomas (SCCs) are insufficient to predict clinical outcome. We attempted to make a clinically-useful classification based on gene expression profiling. METHODS: We used cDNA microarrays with 40386 elements to analyze the gene expression profiles of 48 surgically resected samples of lung SCC. 9 samples of lung adenocarcinoma and 30 of normal lung were also included to give a total of 87 samples analyzed. After gene filtering, the data were subjected to hierarchical clustering and consensus clustering with the non-negative matrix factorization (NMF) approach. FINDINGS: Initial analysis by hierarchical clustering allowed division of SCCs into two distinct subclasses. An additional independent round of hierarchical clustering and consensus clustering with the NMF approach provided a validation for the classification. Kaplan-Meier analysis with the log rank test pointed to a non-significant difference in survival (p=0.071) but the likelihood of survival to 6 years was significantly different between the two groups (40.5% vs 81.8%, p=0.014, Z-test). Biological process categories characteristic for each subclass were identified statistically and up-regulation of cell-proliferation related genes was evident in the subclass with a poor prognosis. In the subclass with the better survival, genes involved in differentiated intracellular functions, such as the MAPKKK cascade, ceramide metabolism, or regulation of transcription, were up-regulated. Keywords: repeat sample
2009-04-01 | E-GEOD-2088 | biostudies-arrayexpress
Project description:Background: Septic shock is a heterogeneous syndrome within which probably exist several biological subclasses. Discovery and identification of septic shock subclasses could provide the foundation for the design of more specifically targeted therapies. Herein we tested the hypothesis that pediatric septic shock subclasses can be discovered through genome-wide expression profiling. Methods: Genome-wide expression profiling was conducted using whole blood-derived RNA from 98 children with septic shock, followed by a series of bioinformatic approaches targeted at subclass discovery and characterization. Results: Three putative subclasses (subclasses A, B, and C) were initially identified based on an empiric, discovery-oriented expression filter and unsupervised hierarchical clustering. Statistical comparison of the 3 putative subclasses (ANOVA, Bonferonni correction, p < 0.05) identified 6,934 differentially regulated genes. K means clustering of these 6,934 genes generated 10 coordinately regulated gene clusters corresponding to multiple signaling and metabolic pathways, all of which were differentially regulated across the 3 subclasses. Leave one out cross validation procedures indentified 100 genes having the strongest predictive values for subclass identification. Forty-four of these 100 genes corresponded to signaling pathways relevant to the adaptive immune system and glucocorticoid receptor signaling, the majority of which were repressed in subclass A patients. Subclass A patients were also characterized by repression of genes corresponding to zinc-related biology. Phenotypic analyses revealed that subclass A patients were younger, had a higher illness severity, and a higher mortality rate than patients in subclasses B and C. Conclusions: Genome-wide expression profiling can identify pediatric septic shock subclasses having clinically relevant phenotypes. Expression data from 98 children with septic shock and 32 normal controls were generated using whole blood-derived RNA samples representing the first 24 hours of admission to the pediatric intensive care unit. The controls were used for normalization. Subsequently, we used the expression data to derive expression-based subclasses of patients using discovery oriented expression and statistical filters, followed by unsupervised hierarchical clustering.
2011-01-13 | E-GEOD-26440 | biostudies-arrayexpress
Project description:Background: Septic shock is a heterogeneous syndrome within which probably exist several biological subclasses. Discovery and identification of septic shock subclasses could provide the foundation for the design of more specifically targeted therapies. Herein we tested the hypothesis that pediatric septic shock subclasses can be discovered through genome-wide expression profiling. Methods: Genome-wide expression profiling was conducted using whole blood-derived RNA from 98 children with septic shock, followed by a series of bioinformatic approaches targeted at subclass discovery and characterization. Results: Three putative subclasses (subclasses A, B, and C) were initially identified based on an empiric, discovery-oriented expression filter and unsupervised hierarchical clustering. Statistical comparison of the 3 putative subclasses (ANOVA, Bonferonni correction, p < 0.05) identified 6,934 differentially regulated genes. K means clustering of these 6,934 genes generated 10 coordinately regulated gene clusters corresponding to multiple signaling and metabolic pathways, all of which were differentially regulated across the 3 subclasses. Leave one out cross validation procedures indentified 100 genes having the strongest predictive values for subclass identification. Forty-four of these 100 genes corresponded to signaling pathways relevant to the adaptive immune system and glucocorticoid receptor signaling, the majority of which were repressed in subclass A patients. Subclass A patients were also characterized by repression of genes corresponding to zinc-related biology. Phenotypic analyses revealed that subclass A patients were younger, had a higher illness severity, and a higher mortality rate than patients in subclasses B and C. Conclusions: Genome-wide expression profiling can identify pediatric septic shock subclasses having clinically relevant phenotypes.
2011-01-13 | GSE26440 | GEO
Project description:The variability in the prognosis of hepatocellular carcinoma (HCC) patients suggests that HCC may comprise several distinctive biological phenotypes. These phenotypes may result from different neoplastic pathways during the tumorigenesis and/or from a different cell of origin. Here we address if the transcriptional characteristics of the HCC would provide insight into the cellular origin of the tumors. We integrated gene expression data from rat fetal hepatoblasts and adult hepatocytes, HCC from mouse models, and human HCC. The HCC patients who shared gene expression patterns with fetal hepatoblasts showed extremely poor prognosis when compared with those lacking the hepatoblast signature. The gene expression program that distinguishes this novel subtype from the rest of HCC includes well known markers of hepatic oval cells, suggesting that HCC in this subtype may arise from hepatic progenitor cells. Two independent gene network analyses of the gene expression signature characteristic for the tumors sharing the hepatoblast expression patterns revealed that activation of AP-1 transcription factors might play key roles in tumor development in the newly identified HCC subtype. In addition, by applying hepatoblast-specific and genome-wide global signatures, HCC patients were further stratified into three distinct subgroups with a significant association with overall survival and recurrence. Total RNAs from 19 normal livers were pooled and used as the reference for all microarray experiments. To obtain gene expression profile data from 49 human HCC, 20 µg of total RNAs from tissues were used to drive fluorescently (Cy-5 or Cy-3) labeled cDNA. At least two hybridizations were carried out for each tissue using a dye-swap strategy to eliminate dye labeling bias.
2010-07-01 | E-GEOD-4024 | biostudies-arrayexpress
Project description:Long noncoding RNAs (lncRNAs) are a class of non-coding RNAs longer than 200 nt that function in endogenous gene regulation and tumorigenesis. Hepatocellular carcinoma (HCC) is a heterogeneous disease with different treatment outcome. It is a challenge to develop a prognostic marker to identify HCC patients who are at greatest risk for recurrence or death. In this study, we try to screen lncRNAs whose expression levels are associated with recurrence or death of HCC patients through an extensive lncRNA profiling study on a cohort of 59 HCC patients. For these experiments, we used RNA extracted from 59 HCC tissues and 20 normal livers. Total RNAs from the 20 normal livers were pooled and used as a reference for all microarray experiments. For each microarray experiment, Cy5-labeled probes derived from the DNase-treated total RNA from each HCC sample was hybridized against Cy3-labeled probes derived from common reference on Arraystar Human LncRNA Microarray (Arraystar, Rockville, USA). LncRNAs whose expression was significantly associated with disease-specific survival and time to recurrence were selected based on microarray data. The univariate Cox proportional hazards model was used to assess the association of lncRNAs with survival. We computed a statistical significance level (P value) for two endpointsM-bM-^@M-^Tthe time to cancer-related death and time to recurrence, based on univariate Cox proportional hazards models in BRB-ArrayTools version 4.2.0.
2012-08-16 | E-GEOD-40144 | biostudies-arrayexpress
Project description:Two subclasses of lung squamous cell carcinoma with different gene expression profiles and prognosis identified by hierarchical clustering and validated by non-negative matrix factorization . BACKGROUND: Current clinical and histopathological criteria used to define lung squamous cell carcinomas (SCCs) are insufficient to predict clinical outcome. We attempted to make a clinically-useful classification based on gene expression profiling. METHODS: We used cDNA microarrays with 40386 elements to analyze the gene expression profiles of 48 surgically resected samples of lung SCC. 9 samples of lung adenocarcinoma and 30 of normal lung were also included to give a total of 87 samples analyzed. After gene filtering, the data were subjected to hierarchical clustering and consensus clustering with the non-negative matrix factorization (NMF) approach. FINDINGS: Initial analysis by hierarchical clustering allowed division of SCCs into two distinct subclasses. An additional independent round of hierarchical clustering and consensus clustering with the NMF approach provided a validation for the classification. Kaplan-Meier analysis with the log rank test pointed to a non-significant difference in survival (p=0.071) but the likelihood of survival to 6 years was significantly different between the two groups (40.5% vs 81.8%, p=0.014, Z-test). Biological process categories characteristic for each subclass were identified statistically and up-regulation of cell-proliferation related genes was evident in the subclass with a poor prognosis. In the subclass with the better survival, genes involved in differentiated intracellular functions, such as the MAPKKK cascade, ceramide metabolism, or regulation of transcription, were up-regulated. Keywords: repeat sample
2009-04-01 | GSE2088 | GEO