Gene expression profile of hepatocellular carcinoma patients in Taiwan
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ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE101685 | GEO | 2019/07/31
REPOSITORIES: GEO
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Project description:Mongolia has the highest reported incidence of (and mortality from) hepatocellular carcinoma (HCC) in the world. This is the first molecular study aiming to characterize the genomic landscape of Mongolian HCC.
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Project description:CTNNB1 is the most frequently mutated gene in hepatocellular carcinoma (HCC). However, its clinical relevance remains controversial. We determined an evolutionarily conserved β-catenin signature by comparative analysis of gene expression data from human HCC and a mouse model (GSE43628). We generated gene expression data from the tumors of 88 HCC patients who underwent surgical resection as the primary treatment. We used these gene expression data to develop a new prognostification model for prognosis of HCC after surgery. We generated gene expression data from the tumors of 88 HCC patients who underwent surgical resection as the primary treatment.
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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)
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Project description:CTNNB1 is the most frequently mutated gene in hepatocellular carcinoma (HCC). However, its clinical relevance remains controversial. We determined an evolutionarily conserved β-catenin signature by comparative analysis of gene expression data from human HCC and a mouse model (GSE43628). We generated gene expression data from the tumors of 88 HCC patients who underwent surgical resection as the primary treatment. We used these gene expression data to develop a new prognostification model for prognosis of HCC after surgery.
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Project description:<p>Surveillance for hepatocellular carcinoma (HCC) with biannual ultrasound and serum alpha-fetoprotein, is recommended in patients with cirrhosis. However, its effectiveness for the detection of early-stage tumors is limited due to inadequate risk stratification and suboptimal performance of the current screening modalities. We developed a multi-center prospective cohort of patients with cirrhosis undergoing surveillance with contrast-enhanced MRI, and applied global untargeted metabolomics to 612 longitudinal samples collected from 203 patients. Among them, 37 developed HCC during follow-up. Linear mixed-effects models identified 150 metabolites among the 1263 detected, with significant abundance changes in serum samples collected prior to HCC diagnosis (Cases) compared to serum samples collected from patients who didn’t develop HCC during follow-up (Controls). The largest increases were observed for tauro-conjugated bile acids and gamma-glutamyl amino acids. The largest decreases were observed for derivatives of the bile acid deoxycholate, androgenic steroids, and acyl cholines. Using logistic regression analysis, 7 amino acids including serine and alanine, had stronger associations with HCC risk than AFP. The strongest protective effects were observed for N-acetylglycine and glycerophosphorylcholine (GPC). Analysis of serum samples collected after HCC treatment identified a reverse phenotype for metabolites associated with metabolism of amino acids, bile acids and purine & pyrimidine. Random forest machine learning algorithm using the 150 metabolites together with age, gender, PNPLA3 rs738409 and TMS6SF2 rs58542926 SNPs, identified 18 variables giving optimal model performance. Among them, N-acetylglycine, palmitoloelylcholine, GPC and alanine had better AUCs than AFP in discriminating between Cases and Controls. When restricting Cases to serum samples collected within 1 year prior to HCC diagnosis (Cases-12M), additional metabolites were identified, including several microbiota-derived metabolites. Random forest machine learning algorithm identified 20 top variables giving optimal model performance. The combination of the top 6 variables (AFP, 6-bromotryptophan, N-acetylglycine, salicyluric glucuronide, cortisol 21-sulfate and age) had good performance in discriminating Cases-12M from Controls (AUC=0.88 [0.82-0.94]). Finally, 28 of the 150 metabolites distinguished Cases with LIRAD-3 hepatic lesions who developed HCC during follow-up (Cases-LR3) from Controls with LIRAD-3 lesions who did not develop HCC during follow-up (Controls-LR3). Remarkably, the abundance of 7 acyl-cholines, GPC and GPC-related lysophospholipids were strongly reduced in Cases-LR3 compared to Controls-LR3. Altogether, this comprehensive study identified N-acetylglycine, amino acids, bile acids and choline-derived metabolites as main biomarkers of HCC risk. This study also revealed an important contribution of microbiota-derived metabolites to HCC development.</p>
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