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An expression signature of phenotypic resistance to hepatocellular carcinoma identified by cross-species gene expression analysis.

ABSTRACT: Hepatocarcinogenesis is under polygenic control. We analyzed gene expression patterns of dysplastic liver nodules (DNs) and hepatocellular carcinomas (HCCs) chemically-induced in F344 and BN rats, respectively susceptible and resistant to hepatocarcinogenesis.Expression profiles were performed by microarray and validated by quantitative RT-PCR and Western blot.Cluster analysis revealed two distinctive gene expression patterns, the first of which included normal liver of both strains and BN nodules, and the second one F344 nodules and HCC of both strains. We identified a signature predicting DN and HCC progression, characterized by highest expression of oncosuppressors Csmd1, Dmbt1, Dusp1, and Gnmt, in DNs, and Bhmt, Dmbt1, Dusp1, Gadd45g, Gnmt, Napsa, Pp2ca, and Ptpn13 in HCCs of resistant rats. Integrated gene expression data revealed highest expression of proliferation-related CTGF, c-MYC, and PCNA, and lowest expression of BHMT, DMBT1, DUSP1, GADD45g, and GNMT, in more aggressive rat and human HCC. BHMT, DUSP1, and GADD45g expression predicted patients' survival.Our results disclose, for the first time, a major role of oncosuppressor genes as effectors of genetic resistance to hepatocarcinogenesis. Comparative functional genomic analysis allowed discovering an evolutionarily conserved gene expression signature discriminating HCC with different propensity to progression in rat and human.

SUBMITTER: Frau M

PROVIDER: S-EPMC4517440 | biostudies-literature | 2012 Jun

REPOSITORIES: biostudies-literature

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An expression signature of phenotypic resistance to hepatocellular carcinoma identified by cross-species gene expression analysis.

Frau Maddalena M Simile Maria M MM Tomasi Maria L ML Demartis Maria I MI Daino Lucia L Seddaiu Maria A MA Brozzetti Stefania S Feo Claudio F CF Massarelli Giovanni G Solinas Giuliana G Feo Francesco F Lee Ju-Seog JS Pascale Rosa M RM

Cellular oncology (Dordrecht, Netherlands) 20120321 3

<h4>Background and aims</h4>Hepatocarcinogenesis is under polygenic control. We analyzed gene expression patterns of dysplastic liver nodules (DNs) and hepatocellular carcinomas (HCCs) chemically-induced in F344 and BN rats, respectively susceptible and resistant to hepatocarcinogenesis.<h4>Methods</h4>Expression profiles were performed by microarray and validated by quantitative RT-PCR and Western blot.<h4>Results</h4>Cluster analysis revealed two distinctive gene expression patterns, the first ...[more]

PMID: 22434528

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Project description:BackgroundHepatocellular carcinoma (HCC) is a life-threatening and refractory malignancy with poor outcome. Genetic mutations are the hallmark of cancer. Thus far, there is no comprehensive prognostic model constructed by mutation-gene transcriptome in HCC. The prognostic value of mutation-gene signature in HCC remains elusive.MethodsRNA expression profiles and the corresponding clinical information were recruited from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) databases. The least absolute shrinkage and selection operator (LASSO) Cox regression analysis was employed to establish gene signature. Kaplan-Meier curve and time-dependent receiver operating characteristic curve were implemented to evaluate the prognostic value. The Wilcoxon test was performed to analyze the expression of immune checkpoint genes, cell cycle genes, and tumor drug resistance genes in different risk groups. Finally, quantitative real-time PCR (qRT-RCR) and immunohistochemistry (IHC) were performed to validate the mRNA and protein expression between HCC and adjacent nontumorous tissues in an independent cohort.ResultsA prognostic model consisting of five mutated genes was established by LASSO Cox regression analysis. The prognostic model classified patients into high- and low-risk groups. Compared with the low-risk group, patients in the high-risk group had significantly worse survival results. The prognostic model can accurately predict the overall survival of HCC patients and predict overall survival more accurately when combined with stage. Furthermore, the immune checkpoint genes, cell cycle genes, and tumor drug resistance genes were higher expressed in the high-risk group compared in the low-risk group. In addition, the expression level of prognostic signature genes was validated in an independent sample cohort, which was consistent with RNA sequencing expression in the TCGA database.ConclusionThe prediction model of HCC constructed using mutation-related genes is of great significance for clinical decision making and the personalized treatment of patients with HCC.

Project description:Metabolic changes are the markers of cancer and have attracted wide attention in recent years. One of the main metabolic features of tumor cells is the high level of glycolysis, even if there is oxygen. The transformation and preference of metabolic pathways is usually regulated by specific gene expression. The aim of this study is to develop a glycolysis-related risk signature as a biomarker via four common cancer types. Only hepatocellular carcinoma was shown the strong relationship with glycolysis. The mRNA sequencing and chip data of hepatocellular carcinoma, breast invasive carcinoma, renal clear cell carcinoma, colorectal adenocarcinoma were included in the study. Gene set enrichment analysis was performed, profiling three glycolysis-related gene sets, it revealed genes associated with the biological process. Univariate and multivariate Cox proportional regression models were used to screen out prognostic-related gene signature. We identified six mRNAs (DPYSL4, HOMER1, ABCB6, CENPA, CDK1, STMN1) significantly associated with overall survival in the Cox proportional regression model for hepatocellular carcinoma. Based on this gene signature, we were able to divide patients into high-risk and low-risk subgroups. Multivariate Cox regression analysis showed that prognostic power of this six gene signature is independent of clinical variables. Further, we validated this data in our own 55 paired hepatocellular carcinoma and adjacent tissues. The results showed that these proteins were highly expressed in hepatocellular carcinoma tissues compared with adjacent tissue. The survival time of high-risk group was significantly shorter than that of low-risk group, indicating that high-risk group had poor prognosis. We calculated the correlation coefficients between six proteins and found that these six proteins were independent of each other. In conclusions, we developed a glycolysis-related gene signature that could predict survival in hepatocellular carcinoma patients. Our findings provide novel insight to the mechanisms of glycolysis and it is useful for identifying patients with hepatocellular carcinoma with poor prognoses.

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.

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An expression signature of phenotypic resistance to hepatocellular carcinoma identified by cross-species gene expression analysis.

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An expression signature of phenotypic resistance to hepatocellular carcinoma identified by cross-species gene expression analysis.

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