Systematic Expression Analysis of the Ferroptosis and Iron-Metabolism Transcriptomic Profile for Hepatocellular Carcinoma

2020 ◽  
Author(s):  
Bufu Tang ◽  
Jinyu Zhu ◽  
Jie Li ◽  
Kai Fan ◽  
Yang Gao ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Expression Analysis ◽  
Iron Metabolism ◽  
Transcriptomic Profile

scholarly journals Gene expression analysis of biopsy samples reveals critical limitations of transcriptome-based molecular classifications of hepatocellular carcinoma

2016 ◽  
Vol 2 (2) ◽  
pp. 80-92 ◽  
Author(s):  
Zuzanna Makowska ◽  
Tujana Boldanova ◽  
David Adametz ◽  
Luca Quagliata ◽  
Julia E. Vogt ◽  
...  
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Expression Analysis ◽  
Gene Expression Analysis

scholarly journals Identification of Candidate Biomarkers for Hepatocellular Carcinoma Through Pre-Cancerous Expression Analysis in an HBx Transgenic Mouse

2007 ◽  
Vol 6 (10) ◽  
pp. 1532-1538 ◽  
Author(s):  
Qiang Sun ◽  
Yingai Zhang ◽  
Fang Liu ◽  
Xiaohang Zhao ◽  
Xiao Yang
Keyword(s):  
Hepatocellular Carcinoma ◽  
Transgenic Mouse ◽  
Expression Analysis

scholarly journals Expression analysis of liver-specific circulating microRNAs in HCV-induced hepatocellular Carcinoma in Egyptian patients

2018 ◽  
Vol 19 (5) ◽  
pp. 400-406 ◽  
Author(s):  
Lobna Mourad ◽  
Eman El-Ahwany ◽  
Mona Zoheiry ◽  
Hoda Abu-Taleb ◽  
Marwa Hassan ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Expression Analysis ◽  
Circulating Micrornas ◽  
Egyptian Patients

scholarly journals Long Non-Coding RNA PVT1/miR-150/ HIG2 Axis Regulates the Proliferation, Invasion and the Balance of Iron Metabolism of Hepatocellular Carcinoma

2018 ◽  
Vol 49 (4) ◽  
pp. 1403-1419 ◽  
Author(s):  
Yunxiuxiu Xu ◽  
Xinxi Luo ◽  
Wenguang He ◽  
Guangcheng Chen ◽  
Yanshan Li ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Cell Proliferation ◽  
Cell Lines ◽  
Iron Metabolism ◽  
Cell Apoptosis ◽  
Luciferase Reporter ◽  
Migration And Invasion ◽  
Non Coding Rna ◽  
Long Non Coding Rna

Background/Aims: To investigate the biological roles and underlying molecular mechanisms of long non-coding RNA (lncRNA) PVT1 in Hepatocellular carcinoma (HCC). Methods: qRT-PCR was performed to measure the expression of miRNA and mRNA. Western blot was performed to measure the protein expression. CCK-8 assay was performed to determine cell proliferation. Flow cytometry was performed to detect cell apoptosis. Wounding-healing assay and Transwell assay was performed to detect cell migration and invasion. Dual luciferase reporter assay was performed to verify the target relationship. Quantichrom iron assay was performed to check uptake level of cellular iron. Results: PVT1 expression was up-regulated in HCC tissues and cell lines. Function studies revealed that PVT1 knockdown significantly suppressed cell proliferation, migration and invasion, and induced cell apoptosis in vitro. Furthermore, PVT1 could directly bind to microRNA (miR)-150 and down-regulate miR-150 expression. Hypoxia-inducible protein 2 (HIG2) was found to be one target gene of miR-150, and PVT1 knockdown could inhibit the expression of HIG2 through up-regulating miR-150 expression. In addition, the expression of miR-150 was down-regulated, while the expression of HIG2 was up-regulated in HCC tissues and cell lines. Moreover, inhibition of miR-150 could partly reverse the biological effects of PVT1 knockdown on proliferation, motility, apoptosis and iron metabolism in vitro, which might be associated with dysregulation of HIG2. In vivo results showed that PVT1 knockdown suppressed tumorigenesis and iron metabolism disorder by regulating the expression of miR-150 and HIG2. Conclusion: Taken together, the present study demonstrates that PVT1/miR-150/HIG2 axis may lead to a better understanding of HCC pathogenesis and provide potential therapeutic targets for HCC.

scholarly journals Expression analysis of apolipoproteins AI & AIV in hepatocellular carcinoma: A protein-based hepatocellular carcinoma-associated study

2018 ◽  
Vol 147 (4) ◽  
pp. 361 ◽  
Author(s):  
Premashis Kar ◽  
Dipu Bharali ◽  
BasuDev Banerjee ◽  
Mausumi Bharadwaj ◽  
SyedAkhtar Husain
Keyword(s):  
Hepatocellular Carcinoma ◽  
Expression Analysis

Iron metabolism gene expression and prognostic features of hepatocellular carcinoma

2018 ◽  
Vol 119 (11) ◽  
pp. 9178-9204 ◽  
Author(s):  
Ying Shen ◽  
Xin Li ◽  
Bin Zhao ◽  
Yanru Xue ◽  
Shenghang Wang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Iron Metabolism ◽  
Prognostic Features ◽  
Metabolism Gene

Ang-2 polymorphisms in relation to outcome in advanced HCC patients receiving sorafenib.

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. e15666-e15666 ◽  
Author(s):  
Giorgia Marisi ◽  
Mario Scartozzi ◽  
Luca Faloppi ◽  
Giuseppe Francesco Foschi ◽  
Gianfranco Lauletta ◽  
...  
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Expression Analysis ◽  
Gene Expression Analysis ◽  
Direct Sequencing ◽  
Angiogenic Factor ◽  
Polymorphism Analysis ◽  
Advanced Hepatocellular Carcinoma ◽  
Significant Activity ◽  
Angiopoietin 2

e15666 Background: Angiopoietin-2 (Ang-2) is a crucial angiogenic factor. By binding to its receptor Tie2, Ang-2 cooperates with the VEGF pathway to maintain normal physiological functions. In the presence of VEGF, Ang-2 destabilizes blood vessels and promotes vascular sprouting. In cancers, Ang-2 is linked to not only angiogenesis but also invasive and metastatic phenotypes. Although sorafenib exerts no significant activity against Tie2, the predictive value of Ang-2 has been explored in 2 studies. Llovet et al conducted a large biomarker study based on SHARP study. The authors found that a high baseline plasma Ang-2 level was an independent factor for poorer OS but not for reduced sorafenib efficacy. Polymorphism analysis seems to have more advantages than protein or gene expression analysis. Gene expression analysis is performed on biological material collected at a specific time in the natural history of the disease. It is also subject to the influence of a number of laboratory biases. Conversely, polymorphism analysis can be performed at any time during the course of the disease, is not substantially influenced by laboratory biases and is less expensive. In our study we analysed the role of ANG-2 polymorphisms in relation to clinical outcome in patients with hepatocellular carcinoma treated with sorafenib. Methods: We analyzed 135 patients with hepatocellular carcinoma treated with sorafenib. Peripheral blood samples or FFPE tumor tissues were available for DNA extraction and genotyping analysis. Nine Ang-2 polymorphisms were analyzed by direct sequencing or Real Time PCR method. Results: With regard to Ang4 rs55633437 was observed that patients carrying the allele GG were associated with a better PFS and OS. The variants GG were associated with a median OS of 16.9 months vs 6.5 months of variants GT and TT (p = 0.016). The variants GT and TT were associated with a median PFS of 2.94 months vs 4.67 months of variants GG (p = 0.03). These data were confirmed by multivariate analysis Conclusions: Ang4 rs55633437 could represent prognostic markers in patients with advanced hepatocellular carcinoma treated with sorafenib.

scholarly journals A Computational Framework to Identify Transcriptional and Network Differences between Hepatocellular Carcinoma and Normal Liver Tissue and Their Applications in Repositioning Drugs

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Aimin Hu ◽  
Zheng Wei ◽  
Zuxiang Zheng ◽  
Bichao Luo ◽  
Jieming Yi ◽  
...  
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Differentially Expressed Genes ◽  
Expression Analysis ◽  
Molecular Mechanisms ◽  
Drug Repositioning ◽  
Differential Expression Analysis ◽  
Differentially Expressed ◽  
Computational Framework ◽  
Normal Tissues

Hepatocellular carcinoma (HCC) is one of the most common and lethal malignancies worldwide. Although there have been extensive studies on the molecular mechanisms of its carcinogenesis, FDA-approved drugs for HCC are rare. Side effects, development time, and cost of these drugs are the major bottlenecks, which can be partially overcome by drug repositioning. In this study, we developed a computational framework to study the mechanisms of HCC carcinogenesis, in which drug perturbation-induced gene expression signatures were utilized for repositioning of potential drugs. Specifically, we first performed differential expression analysis and coexpression network module analysis on the HCC dataset from The Cancer Genome Atlas database. Differential gene expression analysis identified 1,337 differentially expressed genes between HCC and adjacent normal tissues, which were significantly enriched in functions related to various pathways, including α-adrenergic receptor activity pathway and epinephrine binding pathway. Weighted gene correlation network analysis (WGCNA) suggested that the number of coexpression modules was higher in HCC tissues than in normal tissues. Finally, by correlating differentially expressed genes with drug perturbation-related signatures, we prioritized a few potential drugs, including nutlin and eribulin, for the treatment of hepatocellular carcinoma. The drugs have been reported by a few experimental studies to be effective in killing cancer cells.

Identification of Iron Metabolism-Related Genes Signature and Novel Role of FLVCR1 in Hepatocellular Carcinoma

2020 ◽  
Author(s):  
Jianhui Chen ◽  
Chuan HU ◽  
Reguang Pan ◽  
Xuedan Du ◽  
Haotian Fu ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Iron Metabolism ◽  
Clustering Analysis ◽  
Cox Regression ◽  
Treatment Strategies ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Consensus Clustering ◽  
Lasso Regression ◽  
Prognostic Signature

Abstract Background: Hepatocellular carcinoma (HCC) is the main and highly malignant histological subtype of liver cancer. We tried to construct a novel signature with iron metabolism-related genes to provide new therapeutic targets and improve the prognosis for HCC patients.Methods: The gene expression data of 70 iron metabolism-related genes and its relevant clinical information were obtained from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) databases. Consensus clustering analysis was performed to determine clusters of HCC patients with different OS. Cox regression and LASSO regression analyses were used to establish a prognostic signature. Receiver operating characteristic (ROC) and Kaplan–Meier analyses were carried out to examine the predicated performance of the signature.Results: Consensus clustering analysis determined two clusters of HCC patients with different OS(p<0.01), TNM stage(p<0.05) and pathological grade(p<0.05). A nine-gene prognostic signature established with iron metabolism-related genes can independently predicate the prognostic of HCC patients. The ROC curves showed a great performance of the signature. In addition, FLVCR1, a hub gene with the highest mutation frequency in our signature, showed the significantly prognostic value in HCC patients. High FLVCR1 expression was significantly associated with poor prognosis and aggressive progression in HCC patients. The promoter methylation level of FLVCR1 was lower in HCC samples with aggressive progression status. The FLVCR1 expression was positively correlated with the infiltration level of B cell, CD4+ T cell, macrophage, neutrophil and dendritic cell. Conclusion: Our study first established a signature related to iron metabolism and identified FLVCR1 as a potential therapeutic target. These findings provided more treatment strategies for HCC patients.

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