Focal gains of VEGFA and molecular classification of hepatocellular carcinoma
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ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE9829 | GEO | 2008/06/22
SECONDARY ACCESSION(S): PRJNA103797
REPOSITORIES: GEO
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Project description:The data are derived from anonymized patient samples for which demographic information is not provided This SuperSeries is composed of the SubSeries listed below. Refer to individual Series
2008-06-22 | E-GEOD-9829 | biostudies-arrayexpress
Project description:Hepatocellular carcinomas represent the third leading cause of cancer-related deaths worldwide. The vast majority of cases arise in the context of chronic liver injury due to hepatitis B virus or hepatitis C virus infection. To identify genetic mechanisms of hepatocarcinogenesis, we characterized copy number alterations and gene expression profiles from the same set of tumors associated with hepatitis C virus. Most tumors harbored 1q gain, 8q gain, or 8p loss, with occasional alterations in 13 additional chromosome arms. In addition to amplifications at 11q13 in 6 of 103 tumors, 4 tumors harbored focal gains at 6p21 incorporating vascular endothelial growth factor A (VEGFA). Fluorescence in situ hybridization on an independent validation set of 210 tumors found 6p21 high-level gains in 14 tumors, as well as 2 tumors with 6p21 amplifications. Strikingly, this locus overlapped with copy gains in 4 of 371 lung adenocarcinomas. Overexpression of VEGFA via 6p21 gain in hepatocellular carcinomas suggested a novel, non-cell-autonomous mechanism of oncogene activation. Hierarchical clustering of gene expression among 91 of these tumors identified five classes, including "CTNNB1", "proliferation", "IFN-related", a novel class defined by polysomy of chromosome 7, and an unannotated class. These class labels were further supported by molecular data; mutations in CTNNB1 were enriched in the "CTNNB1" class, whereas insulin-like growth factor I receptor and RPS6 phosphorylation were enriched in the "proliferation" class. The enrichment of signaling pathway alterations in gene expression classes provides insights on hepatocellular carcinoma pathogenesis. Furthermore, the prevalence of VEGFA high-level gains in multiple tumor types suggests indications for clinical trials of antiangiogenic therapies.
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Project description:Focal gains of VEGFA and molecular classification of hepatocellular carcinoma
| PRJNA103797 | ENA
Project description:Hepatocellular carcinoma (HCC) is a disease with unique management complexity because it displays high heterogeneity of molecular phenotypes. We herein aimed to characterize the molecular features of HCC by the development of a classification system that was based on the gene expression profile of metabolic genes. Integrative analysis was performed with a metadata set featuring 371 and 231 HCC human samples from the Cancer Genome Atlas and the International Cancer Genome Consortium, respectively. All samples were linked with clinical information. RNA sequencing data of 2752 previously characterized metabolism-related genes were used for non-negative matrix factorization clustering, and three subclasses of HCC (C1, C2, and C3) were identified. We then analyzed the metadata set for metabolic signatures, prognostic value, transcriptome features, immune infiltration, clinical characteristics, and drug sensitivity of subclasses, and compared the resulting subclasses with previously published classifications. Subclass C1 displayed high metabolic activity, low α-fetoprotein (AFP) expression, and good prognosis. Subclass C2 was associated with low metabolic activities and displayed high expression of immune checkpoint genes, demonstrating drug sensitivity toward cytotoxic T-lymphocyte-associated protein-4 inhibitors and the receptor tyrosine kinase inhibitor cabozantinib. Subclass C3 displayed intermediate metabolic activity, high AFP expression level, and bad prognosis. Finally, a 90-gene classifier was generated to enable HCC classification. This study establishes a new HCC classification based on the gene expression profiles of metabolic genes, thereby furthering the understanding of the genetic diversity of human HCC.
| S-EPMC7138397 | biostudies-literature
Project description:Hepatocellular carcinoma (HCC) is the second most lethal cancer caused by 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.Hepatocellular carcinoma 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 tumours (training set) were assayed to develop molecular subclass-predictive indices based on clinicopathological features, which were independently validated in 99 HCC tumours (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.Hepatocellular carcinoma 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-foetoprotein (>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 the 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.Hepatocellular carcinoma molecular subclasses can be enriched by clinicopathological indices tightly associated with deregulation of therapeutically targetable molecular pathways.
| S-EPMC4674393 | biostudies-literature
Project description:Genomic profiling of hepatocellular carcinoma (HCC) tumors has elucidated recurrent molecular aberrations common or specific to disease etiology, patient race or geographic regions, allowing the classification of HCC tumors into subclasses sharing similar molecular and clinical characteristics. Previously reported transcriptome-based molecular subclasses have highlighted several common themes. Aggressive tumors are characterized by TP53 inactivation mutations and activation of pro-oncogenic signaling pathways, and further subclassified according to expression of stemness markers. The stemness marker-negative aggressive tumors display preferential TGF-? activation. Another group of less aggressive tumors contains a subclass characterized by CTNNB1 mutations accompanied with overexpression of liver-specific WNT targets such as GLUL. Molecular therapies selectively targeting features of the HCC subclasses have suggested their utility in enriching potential responders in clinical trials and guiding therapeutic decision-making for HCC patients.
| S-EPMC4662420 | biostudies-literature
Project description:Immunity plays an important role in tumor development. In this study, we aimed to investigate molecular classification and its prognostic value in hepatocellular carcinoma (HCC) based on immune signature. Gene set enrichment analysis (GSEA) was used to calculate scores of immune pathways for HCC and hierarchical clustering in two databases (The Cancer Genome Atlas [TCGA], Liver Cancer-RIKEN, JP [LIRI_JP]). The scores of the immune microenvironment and the proportions of 22 immune cells were also calculated. Single-sample GSEA (ssGSEA) was used to screen survival prognosis-related immune pathways and calculate the hazard radio of differentially expressed immune-related genes (IRGs), which were validated in clinical samples and multiple datasets. Based on the immune characteristics, we identified three HCC subtypes, namely immunity high (Immunity_H), immunity medium (Immunity_M), and immunity low (Immunity_L), and confirmed that the classification was reliable and predictable. Immunity_H with a higher immune and stromal score indicated better survival rate. Cox regression analysis showed that IL18RAP and IL7R were the protective genes. Immune risk score was the independent risk factor of overall survival in HCC patients. These results indicated that immunogenomic classification could distinguish HCC patients with different immune status, which could impact the prognosis of the patients with HCC.
| S-EPMC8332372 | biostudies-literature
Project description:The roles of ubiquitin-related genes in hepatocellular carcinoma (HCC) have not been thoroughly investigated. This study aimed to systematically examine ubiquitin-related genes and identify subtypes and stratify prognosis of HCC by using ubiquitin-related signatures. Survival, biological processes, tumor microenvironment (TME), and genomic alterations of the HCC subtypes were investigated. Patients with HCC were classified into two subtypes (clusters 1 and 2) with distinct survival outcomes, pathways, and genomic alterations. Cluster 2 had better prognosis than did cluster 1. Hepatitis B, hepatitis C, Janus tyrosine kinase (JAK)-signal transducer and activator of transcription (STAT) pathway, and natural killer cell-mediated cytotoxicity were enriched in cluster 1. Moreover, cluster 2 had a higher immune score and immune cell infiltrations, whereas cluster 1 had a lower immune score and immune infiltrations. Additionally, mutations, amplifications, and deletions among the phosphatidylinositol 3-kinase (PI3K)-AKT, p53, and receptor tyrosine kinase (RTK)-RAS pathways more frequently occurred in cluster 1, while those among the Hippo, MYC, and Notch signaling pathways were found in cluster 2. Finally, a prognostic signature, consisting of eight ubiquitin-related genes, was established and validated. In brief, our study established a new classification and developed a prognostic signature for HCC.
| S-EPMC8138213 | biostudies-literature
Project description:Recent advance of RNA-seq technology enabled us to profile the diverse variations of RNA transcripts precisely, including the variants from alternative splicing as well as non-coding transcripts. Here, by performing transcriptome profiling including coding and noncoding transcripts, we identify four molecular subtypes of hepatitis B-related hepatocellular carcinoma (HCC) patients that are characterized by enriched expression of the RNA biotypes of noncoding (NC) and immune-related transcripts (IM) (i.e., IM+NC+, IM-NC+, IM+NC-, IM-NC-). The subtype IM+NC+ shows better prognostic outcome, while the subtype IM+NC- shows the worst prognostic outcome, respectively. Further interrogation of the subtypes identifies long noncoding transcripts (i.e., LINC00844, C3P1, and TRPG1-AS1) as well as an alternatively spliced event of USO1 that play pivotal roles in HCC progression. In addition, we report an oncogenic fusion transcript SLC39A14-PIWIL2 that promotes an aggressive phenotype of HCC. Our comprehensive and systematic analysis of HCC transcriptome identify RNA biotype-based molecular classification, revealing novel driver transcriptome variants that can be potential biomarkers and/or therapeutic targets for precision medicine.
2022-04-01 | GSE113617 | GEO
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