Dataset Information

Development Of A Three-Gene Prognostic Signature For Hepatitis B Virus Associated Hepatocellular Carcinoma Based On Integrated Transcriptomic Analysis.

ABSTRACT: Integration of public genome-wide gene expression data together with Cox regression analysis is a powerful weapon to identify new prognostic gene signatures for cancer diagnosis and prognosis. Hepatitis B virus (HBV) is a major cause of hepatocellular carcinoma (HCC), however, it remains largely unknown about the specific gene prognostic signature of HBV-associated HCC. Using Robust Rank Aggreg (RRA) method to integrate seven whole genome expression datasets, we identified 82 up-regulated genes and 577 down-regulated genes in HBV-associated HCC patients. Combination of several enrichment analysis, univariate and multivariate Cox proportional hazards regression analysis, we revealed that a three-gene (SPP2, CDC37L1, and ECHDC2) prognostic signature could act as an independent prognostic indicator for HBV-associated HCC in both the discovery cohort and the internal testing cohort. Gene set enrichment analysis showed that the high-risk group with lower expression levels of the three genes was enriched in bladder cancer and cell cycle pathway, whereas the low-risk group with higher expression levels of the three genes was enriched in drug metabolism-cytochrome P450, PPAR signaling pathway, fatty acid and histidine metabolisms. This indicates that patients of HBV-associated HCC with higher expression of these three genes may preserve relatively good hepatic cellular metabolism and function, which may also protect HCC patients from persistent drug toxicity in response to various medication. Our findings suggest a three-gene prognostic model that serves as a specific prognostic signature for HBV-associated HCC.

SUBMITTER: Yang Y

PROVIDER: S-EPMC5995946 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Development Of A Three-Gene Prognostic Signature For Hepatitis B Virus Associated Hepatocellular Carcinoma Based On Integrated Transcriptomic Analysis.

Yang Yao Y Lu Qian Q Shao Xuejun X Mo Banghui B Nie Xuqiang X Liu Wei W Chen Xianhua X Tang Yuan Y Deng Youcai Y Yan Jun J

Journal of Cancer 20180430 11

Integration of public genome-wide gene expression data together with Cox regression analysis is a powerful weapon to identify new prognostic gene signatures for cancer diagnosis and prognosis. Hepatitis B virus (HBV) is a major cause of hepatocellular carcinoma (HCC), however, it remains largely unknown about the specific gene prognostic signature of HBV-associated HCC. Using Robust Rank Aggreg (RRA) method to integrate seven whole genome expression datasets, we identified 82 up-regulated genes ...[more]

PMID: 29896284

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Project description:There is limited data on the molecular mechanisms whereby hepatitis D virus (HDV) promotes liver cancer. Therefore, serum and liver specimens obtained at the time of liver transplantation from well-characterized patients with HDV-HCC (n = 5) and with non-HCC HDV cirrhosis (n = 7) were studied using an integrated genomic approach. Transcriptomic profiling was performed using laser capture-microdissected (LCM) malignant and nonmalignant hepatocytes, tumorous and nontumorous liver tissue from patients with HDV-HCC, and liver tissue from patients with non-HCC HDV cirrhosis. HDV-HCC was also compared with hepatitis B virus (HBV) HBV-HCC alone, and hepatitis C virus (HCV) HCV-HCC. HDV malignant hepatocytes were characterized by an enrichment of upregulated transcripts associated with pathways involved in cell-cycle/DNA replication, damage, and repair (Sonic Hedgehog, GADD45, DNA-damage-induced 14-3-3σ, cyclins and cell-cycle regulation, cell cycle: G2-M DNA-damage checkpoint regulation, and hereditary breast cancer). Moreover, a large network of genes identified functionally relate to DNA repair, cell cycle, mitotic apparatus, and cell division, including 4 cancer testis antigen genes, attesting to the critical role of genetic instability in this tumor. Besides being overexpressed, these genes were also strongly coregulated. Gene coregulation was high not only when compared with nonmalignant hepatocytes, but also to malignant hepatocytes from HBV-HCC alone or HCV-HCC. Activation and coregulation of genes critically associated with DNA replication, damage, and repair point to genetic instability as an important mechanism of HDV hepatocarcinogenesis. This specific HDV-HCC trait emerged also from the comparison of the molecular pathways identified for each hepatitis virus-associated HCC. Despite the dependence of HDV on HBV, these findings suggest that HDV and HBV promote carcinogenesis by distinct molecular mechanisms.Implications: This study identifies a molecular signature of HDV-associated hepatocellular carcinoma and suggests the potential for new biomarkers for early diagnostics. Mol Cancer Res; 16(9); 1406-19. ©2018 AACR.

Project description:Background: Hepatocellular carcinoma (HCC) is considered the most common type of liver cancer and the fourth leading cause of cancer-related deaths in the world. Since the disease is usually diagnosed at advanced stages, it has poor prognosis. Therefore, reliable biomarkers are urgently needed for early diagnosis and prognostic assessment. Methods: We used genome-wide gene expression profiling datasets from human and rat early HCC (eHCC) samples to perform integrated genomic and network-based analyses, and discovered gene markers that are expressed in blood and conserved in both species. We then used independent gene expression profiling datasets for peripheral blood mononuclear cells (PBMCs) for eHCC patients and from The Cancer Genome Atlas (TCGA) database to estimate the diagnostic and prognostic performance of the identified gene signature. Furthermore, we performed functional enrichment, interaction networks and pathway analyses. Results: We identified 41 significant genes that are expressed in blood and conserved across species in eHCC. We used comprehensive clinical data from over 600 patients with HCC to verify the diagnostic and prognostic value of 41-gene-signature. We developed a prognostic model and a risk score using the 41-geneset that showed that a high prognostic index is linked to a worse disease outcome. Furthermore, our 41-gene signature predicted disease outcome independently of other clinical factors in multivariate regression analysis. Our data reveals a number of cancer-related pathways and hub genes, including EIF4E, H2AFX, CREB1, GSK3B, TGFBR1, and CCNA2, that may be essential for eHCC progression and confirm our gene signature’s ability to detect the disease in its early stages in patients’ biological fluids instead of invasive procedures and its prognostic potential. Conclusion: Our findings indicate that integrated cross-species genomic and network analysis may provide reliable markers that are associated with eHCC that may lead to better diagnosis, prognosis, and treatment options.

Dataset Information

Development Of A Three-Gene Prognostic Signature For Hepatitis B Virus Associated Hepatocellular Carcinoma Based On Integrated Transcriptomic Analysis.

Publications

Development Of A Three-Gene Prognostic Signature For Hepatitis B Virus Associated Hepatocellular Carcinoma Based On Integrated Transcriptomic Analysis.

Similar Datasets

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