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Focal gains of VEGFA and molecular classification of hepatocellular carcinoma

ABSTRACT: The data are derived from anonymized patient samples for which demographic information is not provided This SuperSeries is composed of the SubSeries listed below.

ORGANISM(S): Homo sapiens

PROVIDER: GSE9829 | GEO | 2008/06/22

SECONDARY ACCESSION(S): PRJNA103797

REPOSITORIES: GEO

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Focal gains of VEGFA and molecular classification of hepatocellular carcinoma

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RNA biotype-associated molecular classification in hepatitis B-related hepatocellular carcinoma

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.

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Expression profiles of 30-gene hepatocellular carcinoma molecular classification signature in surgically resected Japanese hepatocellular carcinoma patients

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

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