Clinicopathological and molecular characterization of chromophobe hepatocellular carcinoma

2021 ◽  
Author(s):  
Hyo Jeong Kang ◽  
Ji‐Hye Oh ◽  
Yeon Wook Kim ◽  
Wonkyung Kim ◽  
Jihyun An ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Molecular Characterization

Molecular characterization of hepatocellular carcinoma in patients with non-alcoholic steatohepatitis

2021 ◽  
Author(s):  
Roser Pinyol ◽  
Sara Torrecilla ◽  
Huan Wang ◽  
Carla Montironi ◽  
Marta Piqué-Gili ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Molecular Characterization ◽  
Alcoholic Steatohepatitis

scholarly journals Molecular Characterization of HBV DNA Integration in Patients with Hepatitis and Hepatocellular Carcinoma

2018 ◽  
Vol 9 (18) ◽  
pp. 3225-3235 ◽  
Author(s):  
Liu Yang ◽  
Song Ye ◽  
Xinyi Zhao ◽  
Liyan Ji ◽  
Yinxin Zhang ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Molecular Characterization ◽  
Hbv Dna ◽  
Dna Integration ◽  
Hbv Dna Integration

Abstract 2412: Molecular characterization of hepatocellular carcinoma (HCC) patient-derived explant models

2011 ◽  
Author(s):  
Lan Zhang ◽  
Jingchuan Zhang ◽  
Liang Xie ◽  
Xiaoying Xie ◽  
Qiuli Guo ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Molecular Characterization

scholarly journals Multi-omics subtyping of hepatocellular carcinoma patients using a Bayesian network mixture model

2021 ◽  
Author(s):  
Polina Suter ◽  
Eva Dazert ◽  
Jack Kuipers ◽  
Charlotte K.Y. Ng ◽  
Tuyana Boldanova ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Bayesian Network ◽  
Molecular Characterization ◽  
Mixture Model ◽  
Clinical Stage ◽  
Molecular Characteristics ◽  
Clustering Methods ◽  
Cancer Subtypes ◽  
Molecular Phenotypes

Comprehensive molecular characterization of cancer subtypes is essential for predicting clinical outcomes and searching for personalized treatments. We present bnClustOmics, a statistical model and computational tool for multi-omics unsupervised clustering, which serves a dual purpose: Clustering patient samples based on a Bayesian network mixture model and learning the networks of omics variables representing these clusters. The discovered networks encode interactions among all omics variables and provide a molecular characterization of each patient subgroup. We conducted simulation studies that demonstrated the advantages of our approach compared to other clustering methods in the case where the generative model is a mixture of Bayesian networks. We applied bnClustOmics to a hepatocellular carcinoma (HCC) dataset comprising genome (mutation and copy number), transcriptome, proteome, and phosphoproteome data. We identified three main HCC subtypes together with molecular characteristics, some of which are associated with survival even when adjusting for the clinical stage. Cluster-specific networks shed light on the links between genotypes and molecular phenotypes of samples within their respective clusters and suggest targets for personalized treatments.

Molecular characterization of hepatocellular carcinoma in Mongolia delineates unique genomic features

2020 ◽  
Vol 73 ◽  
pp. S627-S629
Author(s):  
Marc Puigvehí ◽  
Daniela Sia ◽  
Miguel Torres-Martin ◽  
Miho Maeda ◽  
Thais Leonel ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Molecular Characterization ◽  
Genomic Features

Proteomic analysis and molecular characterization of tissue ferritin light chain in hepatocellular carcinoma

Hepatology ◽  
2002 ◽  
Vol 35 (6) ◽  
pp. 1459-1466 ◽  
Author(s):  
Kang-Sik Park ◽  
Hoguen Kim ◽  
Nam-Gyun Kim ◽  
Sang Yun Cho ◽  
Kun-Ho Choi ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Molecular Characterization ◽  
Light Chain ◽  
Proteomic Analysis
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