4d735c40-03df-47de-bb50-14fe449650ee - samples
Ontology highlight
ABSTRACT:
PROVIDER: EGAD00001005797 | EGA |
REPOSITORIES: EGA
Similar Datasets
Project description:Fibrolamellar carcinoma (FLC) is a rare liver cancer. Expression of miR-375 is significantly lost in primary FLC tumors compared to non-malignant liver. Here, we treated a FLC cell line with miR-375 mimic or scramble control to determine the function of miR-375 in FLC.
2019-03-07 | GSE125602 | GEO
Project description:A Cartes d'Identite des Tumeurs (CIT) project from the french Ligue Nationale Contre le Cancer (http://cit.ligue-cancer.net) | Affymetrix HG-U133 Plus 2.0 : 5 mixed fibrolamellar carcinoma (m-FLC), 17 pure fibrolamellar carcinoma (p-FLC), 7 hepatocelluar carcinoma arising in non-cirrhotic liver (nc-HCC) and 10 non-tumoral livers (NL).
2014-08-15 | E-MTAB-1503 | biostudies-arrayexpress
Project description:Fibrolamellar carcinoma (FLC) is a rare, therapeutically intractable liver cancer that disproportionately affects youth. Here we used chromatin run-on sequencing to characterize the enhancer landscape in FLC and RNA sequencing to identify dysregulated genes in FLC.
| EGAS00001004169 | EGA
Project description:Background & Aims: Fibrolamellar hepatocellular carcinoma (FLC) is a rare primary hepatic cancer usually developed in non-cirrhotic livers of children and young adults with unknown etiology. Treatment is limited to surgical intervention. To date, molecular pathogenesis of FLC has been poorly characterized. Herein, we aim to provide an integrative genomic analysis from a large series of FLC patients. Methods: A clinically annotated cohort of 77 FLCs was analyzed through wholetranscriptome, SNP-array and whole-exome sequencing. Non-negative matrix factorization was performed for class discovery, and GSEA, NTP, IPA and immunohistochemistry for functional annotation. GISTIC algorithm identified chromosomal aberrations; Mutect and VarScan2, somatic mutations, and Random survival forest the prognostic signature, validated in an independent cohort. Results: Unsupervised gene expression clustering revealed 3 robust molecular classes: Proliferation-51%, enriched with liver cancer proliferation signatures and mTOR signaling activation, Inflammation-26%, with pro-inflammatory cytokines signatures, and Unannotated-23%, with non-liver-related cancer signatures. Neuroendocrine genes and cholangiocyte and hepatocyte histological markers were present in all classes. FLC showed few copy number variations, being the most frequent: focal amplification at 8q24.3(12.5%), and deletions at 19p13(28%) and 22q13.32(25%). DNAJB1-PRKACA fusion transcript was observed in 79% of cases. FLC tumors had 32 damaging mutations on average, affecting uncommon genes in liver neoplasms (BRCA2, U2AF1). An 8-gene prognostic signature predicted survival in FLC patients. Conclusions: FLC genomic analysis reveals a unique molecular portrait characterized by uncommon damaging mutations and chromosomal aberrations, and a highly prevalent fusion protein. Three molecular classes, including Proliferation and Inflammation, define the biological behavior. Prognostic signature will allow better patient stratification. Gene-expression profiles of fresh frozen human fibrolamellar hepatocellular carcinoma
2015-02-12 | E-GEOD-57726 | biostudies-arrayexpress
Project description:Background & Aims: Fibrolamellar hepatocellular carcinoma (FLC) is a rare primary hepatic cancer usually developed in non-cirrhotic livers of children and young adults with unknown etiology. Treatment is limited to surgical intervention. To date, molecular pathogenesis of FLC has been poorly characterized. Herein, we aim to provide an integrative genomic analysis from a large series of FLC patients. Methods: A clinically annotated cohort of 77 FLCs was analyzed through whole transcriptome, SNP-array and whole-exome sequencing. Non-negative matrix factorization was performed for class discovery, and GSEA, NTP, IPA and immunohistochemistry for functional annotation. GISTIC algorithm identified chromosomal aberrations; Mutect and VarScan2, somatic mutations, and Random survival forest the prognostic signature, validated in an independent cohort. Results: Unsupervised gene expression clustering revealed 3 robust molecular classes: Proliferation-51%, enriched with liver cancer proliferation signatures and mTOR signaling activation, Inflammation-26%, with pro-inflammatory cytokines signatures, and Unannotated-23%, with non-liver-related cancer signatures. Neuroendocrine genes and cholangiocyte and hepatocyte histological markers were present in all classes. FLC showed few copy number variations, being the most frequent: focal amplification at 8q24.3(12.5%), and deletions at 19p13(28%) and 22q13.32(25%). DNAJB1-PRKACA fusion transcript was observed in 79% of cases. FLC tumors had 32 damaging mutations on average, affecting uncommon genes in liver neoplasms (BRCA2, U2AF1). An 8-gene prognostic signature predicted survival in FLC patients. Conclusions: FLC genomic analysis reveals a unique molecular portrait characterized by uncommon damaging mutations and chromosomal aberrations, and a highly prevalent fusion protein. Three molecular classes, including Proliferation and Inflammation, define the biological behavior. Prognostic signature will allow better patient stratification. Gene-expression profiles of formalin-fixed, paraffin-embedded human fibrolamellar hepatocellular carcinoma
2015-02-12 | E-GEOD-57725 | biostudies-arrayexpress
Project description:Fibrolamellar carcinoma (FLC) is a rare liver cancer. Here we characterized the small RNA expression landscape of primary FLC, a FLC patient-derived xenograft model, and normal maturational liver lineage stages
2019-03-07 | GSE114974 | GEO
Project description:Fibrolamellar hepatocellular carcinoma (FLC) is a rare type of primary liver cancer that often arises in children, adolescents and young adults. At the pathological level, FLC display pure morphology or can present with mixed morphology involving a conventional HCC component. Owing to the rarity of the disease, its genetic landscape is fully unknown. Pure FLC showed less chromosomic aberrations than mixed FLC and hepatocellular carcinoma arising in non-cirrhotic liverﾵ. Nevertheless; they displayed more gains in 16q23 and more LOH in 21q22. We also analyzed the mutational landscape of 8 FLC by whole-exome sequencing and showed mutations in the coagulation pathway.
2015-06-15 | E-MTAB-2675 | biostudies-arrayexpress
Project description:Fibrolamellar carcinoma (FLC) is an aggressive liver cancer with low survival rates and no standard-of-care. The vast majority of FLC patients carry a causative somatic mutation that leads to the gene fusion, DNAJB1-PRKACA. To work toward novel therapeutic strategies, it is important to understand the mechanisms underlying FLC etiology and progression. To that end, in this study, we analyzed a large sample cohort (52 patients) using multiple genome-scale assays to define genes subject primarily to post-transcriptional regulation by microRNAs. These studies revealed a candidate master microRNA regulator in FLC, miR-10b. Functional experiments in cells established from a patient-derived xenograft model revealed that miR-10b contributes to FLC cell metabolic health and proliferation.
2021-11-03 | GSE181922 | GEO
Project description:The etiology of fibrolamellar hepatocellular carcinoma (FL-HCC), a liver cancer occurring increasingly in children to young adults, is poorly understood. We performed high-throughput sequencing on mRNA isolated from a newly developed model of FL-HCC and 4 different maturational lineages of the hepatobiliary system. Our results indicate that the transcriptome of FL-HCC is most similar to that of biliary tree stem cells.
2015-10-06 | GSE73114 | GEO
Project description:Fibrolamellar hepatocellular carcinoma (FLC) is a rare primary hepatic cancer usually developed in non-cirrhotic livers of children and young adults with unknown etiology. Treatment is limited to surgical intervention. To date, molecular pathogenesis of FLC has been poorly characterized. A cohort of FLCs was analyzed through SNP-array. GISTIC algorithm identified chromosomal aberrations.
2015-02-12 | GSE59443 | GEO