RNA-seq analysis of hepatocellular carcinoma
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE184733 | GEO | 2022/07/11
REPOSITORIES: GEO
Similar Datasets
Project description:Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer causing more than half a million annual deaths world-wide. Understanding the molecular mechanisms contributing to HCC development and progression is highly desirable for improved surveillance, diagnosis and treatment. Liver tissue metabolomics has the potential to reflect the physiological changes behind HCC development. Also, it allows researchers to investigate racial disparities in HCC. The use of both gas chromatography – mass spectrometry (GC-MS) and liquid chromatography – mass spectrometry (LC-MS) platforms helps increase the metabolome coverage, allowing researchers to better unravel the relationships of metabolites and HCC. The objective of this study is to identify HCC-associated metabolites by analysis of liver tissues from HCC patients using both GC-MS and LC-MS platforms. Paired tumor and non-tumor tissues from 40 patients were analyzed by GC-MS and LC-MS. The patients consist of 14 African-Americans (AA), 10 Asian-Americans (AS), and 16 European-Americans (EA). The levels of the metabolites extracted from the solid liver tissue of the HCC area and adjacent non-HCC area were compared. Among the analytes detected by GC-MS and LC-MS with significant alterations, 17 were selected based on availability of putative metabolite identifications. These metabolites belong to TCA cycle, glycolysis, purines, and lipid metabolism, and have been previously reported in liver metabolomics studies where high correlation with HCC progression was implied. We demonstrated that metabolites that are related to HCC pathogenesis can be identified through metabolomics analysis of liver tissues by both GC-MS and LC-MS. In addition, this analysis has led to the identification of metabolites associated with HCC in a race-specific manner.
2019-03-07 | ST001152 | MetabolomicsWorkbench
Project description:BACKGORUND & AIMS: Hepatocellular carcinoma (HCC) disproportionally affects Asian Americans due to the high prevalence of chronic hepatitis B virus (HBV) infection and among Asian ethnic groups. The Hmong are diagnosed with HCC at a younger age, with more advanced stage disease, and have the highest cause specific mortality of any Asian ethnic group. Poor survival from HCC has also been associated with differential gene expression in HCC tumors leading us to hypothesize that quantitative traits loci lead to differences in the expression of genes in Hmong which reduce their HCC survival. METHODS: We performed bulk RNA-sequencing of whole blood total RNA from HBV-infected Hmong (n=30) and non-Hmong Asians (Chinese (n=46), and Vietnamese (n=19)). RESULTS: No significant differences between groups were identified in demographic, clinical, or viral factors. A total of 1,491 differentially-expressed transcripts (DETs) were identified across the 3 ethnic groups. When restricted to those with an HBV viral load >2,000 IU/L, 768 DETs were identified and accurately separated individuals by ethincity. Among 273 DETs identified by functional enrichment and pathway analysis and associated with annotated sets of HCC-related genes, 9 were found in 2 gene sets highly expressed in HCC with poor survival. After adjusting for age and sex, expression levels of 4 of these, including CD164, ECT2, HDAC2, and UBE2T, remained significantly elevated in Hmong compared to Chinese and Vietnamese. CONCLUSIONS: These results suggest expression quantitative trait loci predispose Hmong to a more agrressive form of HCC and may explain the poor survival among this Asian ethnic group.
2021-11-01 | GSE173897 | GEO
Project description:Our study aimed to develop an IRG-based survival prediction model for hepatocellular carcinoma (HCC) patients. To validate the expression levels of the model signature genes, we then performed gene expression profiling analysis using data obtained from RNA-seq of 65 HCC patients. Finally, we confirmed the 5-IRG signature has good predictive power by RNA-seq data from tumor tissues and paired adjacent normal tissues.
2022-10-18 | GSE214846 | GEO
Project description:Hepatocellular carcinoma (HCC) is a highly heterogeneous disease, and prior attempts to develop genomic-based classification for HCC have yielded highly divergent results, indicating difficulty in identifying unified molecular anatomy. We performed a meta-analysis of gene expression profiles in data sets from eight independent patient cohorts across the world. In addition, aiming to establish the real world applicability of a classification system, we profiled 118 formalin-fixed, paraffin-embedded tissues from an additional patient cohort. A total of 603 patients were analyzed, representing the major etiologies of HCC (hepatitis B and C) collected from Western and Eastern countries. We observed three robust HCC subclasses (termed S1, S2, and S3), each correlated with clinical parameters such as tumor size, extent of cellular differentiation, and serum alpha-fetoprotein levels. An analysis of the components of the signatures indicated that S1 reflected aberrant activation of the WNT signaling pathway, S2 was characterized by proliferation as well as MYC and AKT activation, and S3 was associated with hepatocyte differentiation. Functional studies indicated that the WNT pathway activation signature characteristic of S1 tumors was not simply the result of beta-catenin mutation but rather was the result of transforming growth factor-beta activation, thus representing a new mechanism of WNT pathway activation in HCC. These experiments establish the first consensus classification framework for HCC based on gene expression profiles and highlight the power of integrating multiple data sets to define a robust molecular taxonomy of the disease. Surgically resected 118 tumor tissues from patients with hepatocellular carcinoma (HCC)
2009-09-08 | E-GEOD-10186 | biostudies-arrayexpress
Project description:An increasing number of non-coding RNAs (ncRNAs) are implicated in various human diseases including cancer; however ncRNA transcriptome of hepatocellular carcinoma (HCC) remains largely unexplored. We use CAGE (Cap Analysis of Gene Expression) to comprehensively map transcription start sites (TSSs) across different etiologies of human HCC as well as mouse HCC, with particular emphasis on ncRNAs distant from protein-coding genes. We find thousands of significantly up-regulated distal ncRNAs in HCC tumors compared to their matched non-tumors, which are as many as protein-coding genes. Moreover, we identify many LTR retroviral promoters activated in HCC tissues and expressed in a subfamily-specific manner, which account for approximately 20% of the up-regulated distal ncRNAs. The transcripts derived from LTRs, determined by 3' RACE, are multi-exon nuclear ncRNAs typically 0.5-2kb in length. This study sheds light on ncRNA transcriptome of human and mouse HCC. Expression profiles using CAGE for 37 mouse HCC. The human data are archived at dbGaP (phs000885.v1.p1). An umbrella BioProject has been created to associate the GEO and dbGaP BioProjects: PRJNA278792
2015-09-10 | E-GEOD-60982 | biostudies-arrayexpress
Project description:Multi-omics approaches were utilized to comprehensively analyze the molecular characteristics of hepatocellular carcinoma (HCC) in South Texas (STX) Hispanics. Exome sequencing revealed high mutation frequencies of AXIN2 and CTNNB1, suggesting a predominant activation of the Wnt/beta-catenin pathway. Cell cycles were positively-, and liver functions were negatively-enriched on gene set enrichment analysis of our transcriptomic and proteomic datasets. Enrichments of gene sets representing specific liver metabolic pathways were found to be associated with the dysregulation of corresponding metabolites. Significant reduction of the majority of lipids seen in serum samples of HCC patients and many fatty acids in HCC tumors corroborate negative adipogenesis and lipid metabolism enrichments. We identified two subtypes of HCC using enrichment scores from paired tumor-nontumor samples of STX-Hispanics or TCGA-LIHC. The subtype with better overall survival revealed pronounced positive enrichments of immune-, angiogenesis-, and negative enrichments of liver function-related hallmark gene sets than the other. The former had higher levels of immune checkpoint and immune exhaustion markers. Together, we report molecular features of HCC, specific and non-specific to Hispanics.
2023-07-11 | MSV000092403 | MassIVE
Project description:BACKGROUND & AIMS: In approximately 70% of patients with hepatocellular carcinoma (HCC) treated by resection or ablation, disease recurs within 5 years. Although gene expression signatures have been associated with outcome, there is no method to predict recurrence based on combined clinical, pathology, and genomic data (from tumor and cirrhotic tissue). We evaluated gene expression signatures associated with outcome in a large cohort of patients with early stage (Barcelona-Clinic Liver Cancer 0/A), single-nodule HCC and heterogeneity of signatures within tumor tissues. METHODS: We assessed 287 HCC patients undergoing resection and tested genome-wide expression platforms using tumor (n = 287) and adjacent nontumor, cirrhotic tissue (n = 226). We evaluated gene expression signatures with reported prognostic ability generated from tumor or cirrhotic tissue in 18 and 4 reports, respectively. In 15 additional patients, we profiled samples from the center and periphery of the tumor, to determine stability of signatures. Data analysis included Cox modeling and random survival forests to identify independent predictors of tumor recurrence. RESULTS: Gene expression signatures that were associated with aggressive HCC were clustered, as well as those associated with tumors of progenitor cell origin and those from nontumor, adjacent, cirrhotic tissues. On multivariate analysis, the tumor-associated signature G3-proliferation (hazard ratio [HR], 1.75; P = .003) and an adjacent poor-survival signature (HR, 1.74; P = .004) were independent predictors of HCC recurrence, along with satellites (HR, 1.66; P = .04). Samples from different sites in the same tumor nodule were reproducibly classified. CONCLUSIONS: We developed a composite prognostic model for HCC recurrence, based on gene expression patterns in tumor and adjacent tissues. These signatures predict early and overall recurrence in patients with HCC, and complement findings from clinical and pathology analyses. Center and peripheral portions of hepatocellular carcinoma tumors as well as surrounding non-tumor cirrhotic liver tissues were obtained from fresh frozen surgically resected tissues, and isolated total RNA samples were analyzed for genome-wide expression profiles.
2014-03-25 | E-GEOD-56140 | biostudies-arrayexpress
Project description:Detection of HCC can improve the survival rate for patients with HCC. Thus, effective methods and biomarkers are required to improve the rate of early-HCC detection. We analyzed glycopeptides from AFP in the sera of patients with HCC and liver cirrhosis for the detection of HCC using LC-PRM with immunoprecipitation. Our results suggest that glycopeptide-based LC-PRM is a promising detection tool and that the relative percentage of fucosylated AFP is a clinically useful parameter for the detection of HCC.
2021-02-15 | MSV000086881 | MassIVE
Project description:We compared transcriptomic profiles of 23 ICC tumor specimens to hepatocellular carcinoma (HCC) specimens using Affymetrix mRNA array and the miRNA array platforms to search for unique gene signatures linked to patient prognosis. ICC and HCC share common stem-like molecular characteristics and stem-like tumor features associated with poor prognosis. Gene expression profiling of 16 intrahepatic cholangiocarcinoma (ICC), 7 mixed type of combined HCC and ICC (CHC), 2 Hepatic Adenoma, 5 Focal Nodular Hyperplasia, and 7 Non-Tumor Tissues were performed.
2012-09-28 | E-GEOD-32879 | biostudies-arrayexpress
Project description:Hepatocellular carcinoma (HCC) is a highly heterogeneous disease, and prior attempts to develop genomic-based classification for HCC have yielded highly divergent results, indicating difficulty in identifying unified molecular anatomy. We performed a meta-analysis of gene expression profiles in data sets from eight independent patient cohorts across the world. In addition, aiming to establish the real world applicability of a classification system, we profiled 118 formalin-fixed, paraffin-embedded tissues from an additional patient cohort. A total of 603 patients were analyzed, representing the major etiologies of HCC (hepatitis B and C) collected from Western and Eastern countries. We observed three robust HCC subclasses (termed S1, S2, and S3), each correlated with clinical parameters such as tumor size, extent of cellular differentiation, and serum alpha-fetoprotein levels. An analysis of the components of the signatures indicated that S1 reflected aberrant activation of the WNT signaling pathway, S2 was characterized by proliferation as well as MYC and AKT activation, and S3 was associated with hepatocyte differentiation. Functional studies indicated that the WNT pathway activation signature characteristic of S1 tumors was not simply the result of beta-catenin mutation but rather was the result of transforming growth factor-beta activation, thus representing a new mechanism of WNT pathway activation in HCC. These experiments establish the first consensus classification framework for HCC based on gene expression profiles and highlight the power of integrating multiple data sets to define a robust molecular taxonomy of the disease.
2009-09-08 | GSE10186 | GEO