Transcriptomic and Proteomic Analysis of Steatohepatitic Hepatocellular Carcinoma Reveals Novel Distinct Biologic Features

2020 ◽  
Vol 155 (1) ◽  
pp. 87-96
Author(s):  
Benjamin J Van Treeck ◽  
Taofic Mounajjed ◽  
Roger K Moreira ◽  
Mushfig Orujov ◽  
Daniela S Allende ◽  
...  

Abstract Objectives Steatohepatitic hepatocellular carcinoma is a distinct variant of hepatocellular carcinoma strongly associated with underlying nonalcoholic steatohepatitis. The molecular biology of steatohepatitic hepatocellular carcinoma is not fully elucidated, and thus we aimed to investigate the molecular underpinnings of this entity. Methods Transcriptomic analysis using RNAseq was performed on eight tumor-nonneoplastic pairs of steatohepatitic hepatocellular carcinoma with comparison to conventional hepatocellular carcinoma transcriptomes curated in The Cancer Genome Atlas. Immunohistochemistry was used to validate key RNA-level findings. Results Steatohepatitic hepatocellular carcinoma demonstrated a distinctive differential gene expression profile compared with The Cancer Genome Atlas curated conventional hepatocellular carcinomas (n = 360 cases), indicating the distinctive steatohepatitic hepatocellular carcinoma morphology is associated with a unique gene expression profile. Pathway analysis comparing tumor-nonneoplastic pairs revealed significant upregulation of the hedgehog pathway based on GLI1 overexpression and significant downregulation of carnitine palmitoyltransferase 2 transcript. Glutamine synthetase transcript was significantly upregulated, and fatty acid binding protein 1 transcript was significantly downregulated and immunohistochemically confirmed, indicating steatohepatitic hepatocellular carcinoma tumor cells display a zone 3 phenotype. Conclusions Steatohepatitic hepatocellular carcinoma demonstrates a distinctive morphology and gene expression profile, phenotype of zone 3 hepatocytes, and activation of the hedgehog pathway and repression of carnitine palmitoyltransferase 2, which may be important in tumorigenesis.

2019 ◽  
Vol 15 (22) ◽  
pp. 2603-2617 ◽  
Author(s):  
Siti A Sulaiman ◽  
Nadiah Abu ◽  
Nurul-Syakima Ab-Mutalib ◽  
Teck Yew Low ◽  
Rahman Jamal

Aim: Micro and macro vascular invasion (VI) are known as independent predictors of tumor recurrence and poor survival after surgical treatment of hepatocellular carcinoma (HCC). Here, we aimed to re-analyze The Cancer Genome Atlas of liver hepatocellular carcinoma datasets to identify the VI-expression signatures. Materials & methods: We filtered The Cancer Genome Atlas liver hepatocellular carcinoma (LIHC) datasets into three groups: no VI (NVI = 198); micro VI (MIVI = 89) and macro VI (MAVI = 16). We performed differential gene expression, methylation and microRNA analyses. Results & conclusion: We identified 12 differentially expressed genes and 55 differentially methylated genes in MAVI compared with no VI. The GPD1L gene appeared in all of the comparative analyses. Higher GPD1L expression was associated with VI and poor outcomes in the HCC patients.


2018 ◽  
Author(s):  
SR Rosario ◽  
MD Long ◽  
HC Affronti ◽  
AM Rowsam ◽  
KH Eng ◽  
...  

AbstractUnderstanding the levels of metabolic dysregulation in different disease settings is vital for the safe and effective incorporation of metabolism-targeted therapeutics in the clinic. Using transcriptomic data from 10,704 tumor and normal samples from The Cancer Genome Atlas, across 26 disease sites, we developed a novel bioinformatics pipeline that distinguishes tumor from normal tissues, based on differential gene expression for 114 metabolic pathways. This pathway dysregulation was confirmed in separate patient populations, further demonstrating the robustness of this approach. A bootstrapping simulation was then applied to assess whether these alterations were biologically meaningful, rather than expected by chance. We provide distinct examples of the types of analysis that can be accomplished with this tool to understand cancer specific metabolic dysregulation, highlighting novel pathways of interest in both common and rare disease sites. Utilizing a pathway mapping approach to understand patterns of metabolic flux, differential drug sensitivity, can accurately be predicted. Further, the identification of Master Metabolic Transcriptional Regulators, whose expression was highly correlated with pathway gene expression, explains why metabolic differences exist in different disease sites. We demonstrate these also have the ability to segregate patient populations and predict responders to different metabolism-targeted therapeutics.


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