Dataset Information

Identification of novel lipid biomarkers in xmrk- and Myc-induced models of hepatocellular carcinoma in zebrafish.

ABSTRACT:

Background

Hepatocellular carcinoma (HCC) is the predominant form of liver cancer and is accompanied by complex dysregulation of lipids. Increasing evidence suggests that particular lipid species are associated with HCC progression. Here, we aimed to identify lipid biomarkers of HCC associated with the induction of two oncogenes, xmrk, a zebrafish homolog of the human epidermal growth factor receptor (EGFR), and Myc, a regulator of EGFR expression during HCC.

Methods

We induced HCC in transgenic xmrk, Myc, and xmrk/Myc zebrafish models. Liver specimens were histologically analyzed to characterize the HCC stage, Oil-Red-O stained to detect lipids, and liquid chromatography/mass spectrometry analyzed to assign and quantify lipid species. Quantitative real-time polymerase chain reaction was used to measure lipid metabolic gene expression in liver samples. Lipid species data was analyzed using univariate and multivariate logistic modeling to correlate lipid class levels with HCC progression.

Results

We found that induction of xmrk, Myc and xmrk/Myc caused different stages of HCC. Lipid deposition and class levels generally increased during tumor progression, but triglyceride levels decreased. Myc appears to control early HCC stage lipid species levels in double transgenics, whereas xmrk may take over this role in later stages. Lipid metabolic gene expression can be regulated by either xmrk, Myc, or both oncogenes. Our computational models showed that variations in total levels of several lipid classes are associated with HCC progression.

Conclusions

These data indicate that xmrk and Myc can temporally regulate lipid species that may serve as effective biomarkers of HCC progression.

SUBMITTER: Monroe JD

PROVIDER: S-EPMC8981695 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature

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Publications

Identification of novel lipid biomarkers in xmrk- and Myc-induced models of hepatocellular carcinoma in zebrafish.

Monroe Jerry D JD Fraher Daniel D Huang Xiaoqian X Mellett Natalie A NA Meikle Peter J PJ Sinclair Andrew J AJ Lirette Seth T ST Maihle Nita J NJ Gong Zhiyuan Z Gibert Yann Y

Cancer & metabolism 20220404 1

<h4>Background</h4>Hepatocellular carcinoma (HCC) is the predominant form of liver cancer and is accompanied by complex dysregulation of lipids. Increasing evidence suggests that particular lipid species are associated with HCC progression. Here, we aimed to identify lipid biomarkers of HCC associated with the induction of two oncogenes, xmrk, a zebrafish homolog of the human epidermal growth factor receptor (EGFR), and Myc, a regulator of EGFR expression during HCC.<h4>Methods</h4>We induced HC ...[more]

PMID: 35379333

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Project description:Hepatocellular carcinoma (HCC) has a high mortality rate owing to its complexity. Identification of abnormally expressed genes in HCC tissues compared to those in normal liver tissues is a viable strategy for investigating the mechanisms of HCC tumorigenesis and progression as a means of developing novel treatments. A significant advantage of the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) is that the data therein were collected from different independent researchers and may be integrated, allowing for a more robust data analysis. Accordingly, in the present study, the gene expression profiles for HCC and control samples were downloaded from the GEO and TCGA. Functional enrichment analysis was performed using a Metascape dataset, and a protein-protein interaction (PPI) network was constructed using the Search Tool for the Retrieval of Interacting Genes/proteins (STRING) online database. The prognostic value of mRNA for HCC was assessed using the Kaplan-Meier Plotter, a public online tool. A gene mRNA heatmap and DNA amplification numbers were obtained from cBioPortal. A total of 2,553 upregulated genes were identified. Functional enrichment analysis revealed that these differentially expressed genes (DEGs) were mainly accumulated in metabolism of RNA and the cell cycle. Considering the complexity and heterogeneity of the molecular alterations in HCC, multiple genes for the prognostication of patients with HCC are more reliable than a single gene. Thus, the PPI network and univariate Cox regression analysis were applied to screen candidate genes (small nuclear ribonucleoprotein polypeptide B and B1, nucleoporin 37, Rac GTPase activating protein 1, kinesin family member 20A, minichromosome maintenance 10 replication initiation factor, ubiquitin conjugating enzyme E2 C and hyaluronan mediated motility receptor) that are associated with the overall survival and progression-free survival of patients with HCC. In conclusion, the present study identified a set of genes that are associated with overall survival and progression-free survival of patients with HCC, providing valuable information for the prognosis of HCC.

Dataset Information

Identification of novel lipid biomarkers in xmrk- and Myc-induced models of hepatocellular carcinoma in zebrafish.

Background

Methods

Results

Conclusions

Publications

Identification of novel lipid biomarkers in xmrk- and Myc-induced models of hepatocellular carcinoma in zebrafish.

Similar Datasets

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