[Show abstract][Hide abstract] ABSTRACT: Using expression profiles from postmortem prefrontal cortex samples of 624 dementia patients and non-demented controls, we investigated global disruptions in the co-regulation of genes in two neurodegenerative diseases, late-onset Alzheimer's disease (AD) and Huntington's disease (HD). We identified networks of differentially co-expressed (DC) gene pairs that either gained or lost correlation in disease cases relative to the control group, with the former dominant for both AD and HD and both patterns replicating in independent human cohorts of AD and aging. When aligning networks of DC patterns and physical interactions, we identified a 242-gene subnetwork enriched for independent AD/HD signatures. This subnetwork revealed a surprising dichotomy of gained/lost correlations among two inter-connected processes, chromatin organization and neural differentiation, and included DNA methyltransferases, DNMT1 and DNMT3A, of which we predicted the former but not latter as a key regulator. To validate the inter-connection of these two processes and our key regulator prediction, we generated two brain-specific knockout (KO) mice and show that Dnmt1 KO signature significantly overlaps with the subnetwork (P = 3.1 × 10−12), while Dnmt3a KO signature does not (P = 0.017).
Molecular Systems Biology 07/2014; 10(7). · 11.34 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Hepatocellular carcinoma (HCC) is one of the most deadly cancers worldwide and has no effective treatment, yet the molecular basis of hepatocarcinogenesis remains largely unknown. Here we report findings from a whole genome sequencing (WGS) study of 88 matched HCC tumor/normal pairs, 81 of which are HBV positive, seeking to identify genetically altered genes and pathways implicated in HBV-associated HCC. We find beta catenin to be the most frequently mutated oncogene (15.9%) and TP53 the most frequently mutated tumor suppressor (35.2%). The Wnt/beta catenin and JAK/STAT pathways, altered in 62.5% and 45.5% of cases respectively, are likely to act as two major oncogenic drivers in HCC. This study also identifies several prevalent and potentially actionable mutations including activating mutations of Janus Kinase 1 (JAK1) in 9.1% of patients and provides a path towards therapeutic intervention of the disease.
[Show abstract][Hide abstract] ABSTRACT: The genetics of complex disease produce alterations in the molecular interactions of cellular pathways whose collective effect may become clear through the organized structure of molecular networks. To characterize molecular systems associated with late-onset Alzheimer's disease (LOAD), we constructed gene-regulatory networks in 1,647 postmortem brain tissues from LOAD patients and nondemented subjects, and we demonstrate that LOAD reconfigures specific portions of the molecular interaction structure. Through an integrative network-based approach, we rank-ordered these network structures for relevance to LOAD pathology, highlighting an immune- and microglia-specific module that is dominated by genes involved in pathogen phagocytosis, contains TYROBP as a key regulator, and is upregulated in LOAD. Mouse microglia cells overexpressing intact or truncated TYROBP revealed expression changes that significantly overlapped the human brain TYROBP network. Thus the causal network structure is a useful predictor of response to gene perturbations and presents a framework to test models of disease mechanisms underlying LOAD.
[Show abstract][Hide abstract] ABSTRACT: Complex diseases result from molecular changes induced by multiple genetic factors and the environment. To derive a systems view of how genetic loci interact in the context of tissue-specific molecular networks, we constructed an F2 intercross comprised of >500 mice from diabetes-resistant (B6) and diabetes-susceptible (BTBR) mouse strains made genetically obese by the Leptin(ob/ob) mutation (Lep(ob)). High-density genotypes, diabetes-related clinical traits, and whole-transcriptome expression profiling in five tissues (white adipose, liver, pancreatic islets, hypothalamus, and gastrocnemius muscle) were determined for all mice. We performed an integrative analysis to investigate the inter-relationship among genetic factors, expression traits, and plasma insulin, a hallmark diabetes trait. Among five tissues under study, there are extensive protein-protein interactions between genes responding to different loci in adipose and pancreatic islets that potentially jointly participated in the regulation of plasma insulin. We developed a novel ranking scheme based on cross-loci protein-protein network topology and gene expression to assess each gene's potential to regulate plasma insulin. Unique candidate genes were identified in adipose tissue and islets. In islets, the Alzheimer's gene App was identified as a top candidate regulator. Islets from 17-week-old, but not 10-week-old, App knockout mice showed increased insulin secretion in response to glucose or a membrane-permeant cAMP analog, in agreement with the predictions of the network model. Our result provides a novel hypothesis on the mechanism for the connection between two aging-related diseases: Alzheimer's disease and type 2 diabetes.
[Show abstract][Hide abstract] ABSTRACT: Genome-wide association studies (GWAS) have identified loci reproducibly associated with pulmonary diseases; however, the molecular mechanism underlying these associations are largely unknown. The objectives of this study were to discover genetic variants affecting gene expression in human lung tissue, to refine susceptibility loci for asthma identified in GWAS studies, and to use the genetics of gene expression and network analyses to find key molecular drivers of asthma. We performed a genome-wide search for expression quantitative trait loci (eQTL) in 1,111 human lung samples. The lung eQTL dataset was then used to inform asthma genetic studies reported in the literature. The top ranked lung eQTLs were integrated with the GWAS on asthma reported by the GABRIEL consortium to generate a Bayesian gene expression network for discovery of novel molecular pathways underpinning asthma. We detected 17,178 cis- and 593 trans- lung eQTLs, which can be used to explore the functional consequences of loci associated with lung diseases and traits. Some strong eQTLs are also asthma susceptibility loci. For example, rs3859192 on chr17q21 is robustly associated with the mRNA levels of GSDMA (P = 3.55×10(-151)). The genetic-gene expression network identified the SOCS3 pathway as one of the key drivers of asthma. The eQTLs and gene networks identified in this study are powerful tools for elucidating the causal mechanisms underlying pulmonary disease. This data resource offers much-needed support to pinpoint the causal genes and characterize the molecular function of gene variants associated with lung diseases.
[Show abstract][Hide abstract] ABSTRACT: To survey hepatitis B virus (HBV) integration in liver cancer genomes, we conducted massively parallel sequencing of 81 HBV-positive and 7 HBV-negative hepatocellular carcinomas (HCCs) and adjacent normal tissues. We found that HBV integration is observed more frequently in the tumors (86.4%) than in adjacent liver tissues (30.7%). Copy-number variations (CNVs) were significantly increased at HBV breakpoint locations where chromosomal instability was likely induced. Approximately 40% of HBV breakpoints within the HBV genome were located within a 1,800-bp region where the viral enhancer, X gene and core gene are located. We also identified recurrent HBV integration events (in ≥ 4 HCCs) that were validated by RNA sequencing (RNA-seq) and Sanger sequencing at the known and putative cancer-related TERT, MLL4 and CCNE1 genes, which showed upregulated gene expression in tumor versus normal tissue. We also report evidence that suggests that the number of HBV integrations is associated with patient survival.
[Show abstract][Hide abstract] ABSTRACT: Common inflammatome gene signatures as well as disease-specific signatures were identified by analyzing 12 expression profiling data sets derived from 9 different tissues isolated from 11 rodent inflammatory disease models. The inflammatome signature significantly overlaps with known drug targets and co-expressed gene modules linked to metabolic disorders and cancer. A large proportion of genes in this signature are tightly connected in tissue-specific Bayesian networks (BNs) built from multiple independent mouse and human cohorts. Both the inflammatome signature and the corresponding consensus BNs are highly enriched for immune response-related genes supported as causal for adiposity, adipokine, diabetes, aortic lesion, bone, muscle, and cholesterol traits, suggesting the causal nature of the inflammatome for a variety of diseases. Integration of this inflammatome signature with the BNs uncovered 151 key drivers that appeared to be more biologically important than the non-drivers in terms of their impact on disease phenotypes. The identification of this inflammatome signature, its network architecture, and key drivers not only highlights the shared etiology but also pinpoints potential targets for intervention of various common diseases.
Molecular Systems Biology 01/2012; 8:594. · 11.34 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: The prognosis of hepatocellular carcinoma (HCC) varies following surgical resection and the large variation remains largely unexplained. Studies have revealed the ability of clinicopathologic parameters and gene expression to predict HCC prognosis. However, there has been little systematic effort to compare the performance of these two types of predictors or combine them in a comprehensive model.
Tumor and adjacent non-tumor liver tissues were collected from 272 ethnic Chinese HCC patients who received curative surgery. We combined clinicopathologic parameters and gene expression data (from both tissue types) in predicting HCC prognosis. Cross-validation and independent studies were employed to assess prediction.
HCC prognosis was significantly associated with six clinicopathologic parameters, which can partition the patients into good- and poor-prognosis groups. Within each group, gene expression data further divide patients into distinct prognostic subgroups. Our predictive genes significantly overlap with previously published gene sets predictive of prognosis. Moreover, the predictive genes were enriched for genes that underwent normal-to-tumor gene network transformation. Previously documented liver eSNPs underlying the HCC predictive gene signatures were enriched for SNPs that associated with HCC prognosis, providing support that these genes are involved in key processes of tumorigenesis.
When applied individually, clinicopathologic parameters and gene expression offered similar predictive power for HCC prognosis. In contrast, a combination of the two types of data dramatically improved the power to predict HCC prognosis. Our results also provided a framework for understanding the impact of gene expression on the processes of tumorigenesis and clinical outcome.
[Show abstract][Hide abstract] ABSTRACT: Although Dickkopf-1 (DKK1) is known to be a negative regulator of the Wnt/β-catenin pathway, it has been recently found to be upregulated in cancers.
We investigated the clinical and prognostic significance of both serum and transcript DKK1 and its functional roles in human hepatocellular carcinoma (HCC).
We evaluated the expression level of DKK1 in both tissue and serum samples from patients with HCC using GeneChip microarray and real-time-quantitative PCR and sandwich ELISA system respectively. The clinicopathological and prognostic significance of serum and tissue DKK1 levels was examined. Functional characterization of DKK1 with regard to cell migration, invasion and tumour growth was performed.
Both DKK1 transcript and serum protein were upregulated in a stepwise manner in human HCCs. Its transcript levels were associated with more aggressive tumour behaviour, in terms of venous invasion (P = 0.003), advanced tumour stage (P = 0.003). DKK1 transcript correlated with shorter overall (P = 0.006) and disease-free survival (P = 0.012), and higher serum DKK1 levels correlated with shorter disease-free survival (P = 0.046). Knockdown of DKK1 significantly reduced both migratory and invasive abilities of HCC cells, whereas overexpression of DKK1 enhanced the tumour formation efficiency and tumour growth in vivo.
Serum and tissue DKK1 levels increased in a stepwise manner in multistep hepatocarcinogenesis and had prognostic significance. DKK1 plays a functional role in cell migration, invasion and tumour growth.
Liver international: official journal of the International Association for the Study of the Liver 08/2011; 31(10):1494-504. · 3.87 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: One of the primary objectives in cancer research is to identify causal genomic alterations, such as somatic copy number variation (CNV) and somatic mutations, during tumor development. Many valuable studies lack genomic data to detect CNV; therefore, methods that are able to infer CNVs from gene expression data would help maximize the value of these studies.
We developed a framework for identifying recurrent regions of CNV and distinguishing the cancer driver genes from the passenger genes in the regions. By inferring CNV regions across many datasets we were able to identify 109 recurrent amplified/deleted CNV regions. Many of these regions are enriched for genes involved in many important processes associated with tumorigenesis and cancer progression. Genes in these recurrent CNV regions were then examined in the context of gene regulatory networks to prioritize putative cancer driver genes. The cancer driver genes uncovered by the framework include not only well-known oncogenes but also a number of novel cancer susceptibility genes validated via siRNA experiments.
To our knowledge, this is the first effort to systematically identify and validate drivers for expression based CNV regions in breast cancer. The framework where the wavelet analysis of copy number alteration based on expression coupled with the gene regulatory network analysis, provides a blueprint for leveraging genomic data to identify key regulatory components and gene targets. This integrative approach can be applied to many other large-scale gene expression studies and other novel types of cancer data such as next-generation sequencing based expression (RNA-Seq) as well as CNV data.
BMC Systems Biology 08/2011; 5:121. · 2.98 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Hepatocellular carcinoma (HCC) is a heterogeneous and highly aggressive malignancy, for which there are no effective cures. Identification of a malignant stemlike subtype of HCC may offer patients with a dismal prognosis a potential targeted therapy using c-MET and Wnt pathway inhibitors. MicroRNAs (miRNAs) show promise as diagnostic and prognostic tools for cancer detection and stratification. Using a TRE-c-Met-driven transgenic HCC mouse model, we identified a cluster of 23 miRNAs that is encoded within the Dlk1-Gtl2 imprinted region on chromosome 12qF1 overexpressed in all of the isolated liver tumors. Interestingly, this region is conserved among mammalian species and maps to the human DLK1-DIO3 region on chromosome 14q32.2. We thus examined the expression of the DLK1-DIO3 miRNA cluster in a cohort of 97 hepatitis B virus-associated HCC patients and identified a subgroup (n = 18) of patients showing strong coordinate overexpression of miRNAs in this cluster but not in other cancer types (breast, lung, kidney, stomach, and colon) that were tested. Expression levels of imprinted gene transcripts from neighboring loci in this 14q32.2 region and from a subset of other imprinted sites were concomitantly elevated in human HCC. Interestingly, overexpression of the DLK1-DIO3 miRNA cluster was positively correlated with HCC stem cell markers (CD133, CD90, EpCAM, Nestin) and associated with a high level of serum α-fetoprotein, a conventional biomarker for liver cancer, and poor survival rate in HCC patients. In conclusion, our findings suggest that coordinate up-regulation of the DLK1-DIO3 miRNA cluster at 14q32.2 may define a novel molecular (stem cell-like) subtype of HCC associated with poor prognosis.
Journal of Biological Chemistry 07/2011; 286(35):30706-13. · 4.65 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Recent work has revealed the causative links between deregulation of microRNAs (miRNAs) and cancer development. In hepatocellular carcinoma (HCC), aberrant expression of miRNAs has been observed, but the molecular mechanisms that contribute to such changes remains to be elucidated. Here, we reported the analysis of miRNA expression in 94 pairs of tumor and adjacent nontumor tissues from HBV-associated HCC in Chinese patients. We found miRNAs were aberrantly expressed in HCC tissues. To investigate the cause of such deregulation, we detected changes in DNA copy number by measuring locus-specific hybridization intensity, and found changes in expression of several miRNAs are correlated with genomic amplification or deletion. For example, the genomic regions of miR-30d and miR-151 were amplified in ∼50% of HCC tumor tissues, and the expressions of these miRNAs are significantly correlated with DNA copy number. We also employed cDNA microarray data, and provide evidence that key regulators of the miRNA biosynthetic pathway, including DROSHA, DGCR8, AGO1, and AGO2, are frequently overexpressed in HCC. This study provides molecular clues that may contribute to the global changes of miRNA expression in HCC.
Omics: a journal of integrative biology 02/2011; 15(3):187-91. · 2.29 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: In hepatocellular carcinoma (HCC) genes predictive of survival have been found in both adjacent normal (AN) and tumor (TU) tissues. The relationships between these two sets of predictive genes and the general process of tumorigenesis and disease progression remains unclear.
Here we have investigated HCC tumorigenesis by comparing gene expression, DNA copy number variation and survival using ∼250 AN and TU samples representing, respectively, the pre-cancer state, and the result of tumorigenesis. Genes that participate in tumorigenesis were defined using a gene-gene correlation meta-analysis procedure that compared AN versus TU tissues. Genes predictive of survival in AN (AN-survival genes) were found to be enriched in the differential gene-gene correlation gene set indicating that they directly participate in the process of tumorigenesis. Additionally the AN-survival genes were mostly not predictive after tumorigenesis in TU tissue and this transition was associated with and could largely be explained by the effect of somatic DNA copy number variation (sCNV) in cis and in trans. The data was consistent with the variance of AN-survival genes being rate-limiting steps in tumorigenesis and this was confirmed using a treatment that promotes HCC tumorigenesis that selectively altered AN-survival genes and genes differentially correlated between AN and TU.
This suggests that the process of tumor evolution involves rate-limiting steps related to the background from which the tumor evolved where these were frequently predictive of clinical outcome. Additionally treatments that alter the likelihood of tumorigenesis occurring may act by altering AN-survival genes, suggesting that the process can be manipulated. Further sCNV explains a substantial fraction of tumor specific expression and may therefore be a causal driver of tumor evolution in HCC and perhaps many solid tumor types.
PLoS ONE 01/2011; 6(7):e20090. · 3.53 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Biomarkers derived from gene expression profiling data may have a high false-positive rate and must be rigorously validated using independent clinical data sets, which are not always available. Although animal model systems could provide alternative data sets to formulate hypotheses and limit the number of signatures to be tested in clinical samples, the predictive power of such an approach is not yet proven. The present study aims to analyze the molecular signatures of liver cancer in a c-MET-transgenic mouse model and investigate its prognostic relevance to human hepatocellular carcinoma (HCC). Tissue samples were obtained from tumor (TU), adjacent non-tumor (AN) and distant normal (DN) liver in Tet-operator regulated (TRE) human c-MET transgenic mice (n = 21) as well as from a Chinese cohort of 272 HBV- and 9 HCV-associated HCC patients. Whole genome microarray expression profiling was conducted in Affymetrix gene expression chips, and prognostic significances of gene expression signatures were evaluated across the two species. Our data revealed parallels between mouse and human liver tumors, including down-regulation of metabolic pathways and up-regulation of cell cycle processes. The mouse tumors were most similar to a subset of patient samples characterized by activation of the Wnt pathway, but distinctive in the p53 pathway signals. Of potential clinical utility, we identified a set of genes that were down regulated in both mouse tumors and human HCC having significant predictive power on overall and disease-free survival, which were highly enriched for metabolic functions. In conclusions, this study provides evidence that a disease model can serve as a possible platform for generating hypotheses to be tested in human tissues and highlights an efficient method for generating biomarker signatures before extensive clinical trials have been initiated.
PLoS ONE 01/2011; 6(9):e24582. · 3.53 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Tumorigenesis involves multistep genetic alterations. To elucidate the microRNA (miRNA)-gene interaction network in carcinogenesis, we examined their genome-wide expression profiles in 96 pairs of tumor/non-tumor tissues from hepatocellular carcinoma (HCC). Comprehensive analysis of the coordinate expression of miRNAs and mRNAs reveals that miR-122 is under-expressed in HCC and that increased expression of miR-122 seed-matched genes leads to a loss of mitochondrial metabolic function. Furthermore, the miR-122 secondary targets, which decrease in expression, are good prognostic markers for HCC. Transcriptome profiling data from additional 180 HCC and 40 liver cirrhotic patients in the same cohort were used to confirm the anti-correlation of miR-122 primary and secondary target gene sets. The HCC findings can be recapitulated in mouse liver by silencing miR-122 with antagomir treatment followed by gene-expression microarray analysis. In vitro miR-122 data further provided a direct link between induction of miR-122-controlled genes and impairment of mitochondrial metabolism. In conclusion, miR-122 regulates mitochondrial metabolism and its loss may be detrimental to sustaining critical liver function and contribute to morbidity and mortality of liver cancer patients.
Molecular Systems Biology 08/2010; 6:402. · 11.34 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Copy number variation (CNV) is one of the most profound forms of somatic DNA changes that underlie most human cancers. However, the degree of complexity within and between DNA and mRNA variations in cancer cohorts has yet to be fully characterized. Here we characterized the connectivity of CNV/CNV and its contribution to transcriptome in human cancer cell lines. Strikingly, we found there is a significant nonrandom correlation of many unlinked DNA loci and also a significant association between CNV and mRNA expression in cis and in trans (called eCNV). Both distributions of DNA/DNA and DNA/mRNA associations exhibit a scale-free structure showing that, for DNA/DNA, a few loci correlate to many other loci, whereas most loci correlate to only a few loci; and for DNA/mRNA, certain chromosomal loci associate with many mRNAs and that many mRNAs are controlled by more than one locus. This suggests that a small number of DNA loci act as hubs in a hierarchical structure that is highly nonrandom in nature, and genes linking to these hot spots tend to be involved in similar biological functions. Derivation of highly connected structures suggests a process of undirected copy number changes followed by selection of those advantageous to tumor cells during tumorigenesis. Given that the cohort includes many tissue types, our observations may identify a common and important underlying structure present in human tumors.
Omics: a journal of integrative biology 02/2010; 14(1):91-7. · 2.29 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Using comparative proteomic and genomic approaches, the authors identified eukaryotic translation initiation factor 5A (eIF5A) as an oncofetal molecule highly abundant in mouse embryonic livers and human hepatocellular carcinoma (HCC) cell lines. To evaluate the oncogenic role and prognostic significance of eIF5A in HCC, we investigate the expression patterns of the two isoforms (eIF5A1 and eIF5A2) in a cohort of 258 HCC cases by cDNA microarray. Both eIF5A isoforms were expressed in the tumors, and clinically correlated eIF5A1 with more numbers of tumor nodules and eIF5A2 with tumor venous infiltration in HCC. In a separate cohort of 50 HCCs, high level of eIF5A2, but not eIF5A1, was associated with elevated levels of deoxyhypusine synthase and deoxyhypusine hydroxylase that catalyze post-translational hypusination of eIF5A protein. Interestingly, N1-guanyl-1,7-diaminoheptane (GC7), which is an inhibitor for the first step of eIF5A hypusination, was shown to significantly impair the cell proliferation and invasion of primary HCC cells (HepG2 and Hep3B). To further demonstrate the tumorigenic role associated with eIF5A, a drastic reduction of cell proliferation was associated with suppression of eIF5A2 by transfecting Hep3B, H2-P and H2-M HCC cells expressing high level of this isoform using small interfering RNA (siRNA) against eIF5A2. For these assays, a milder response was usually observed in normal hepatocyte cell line. Therefore, these findings suggest that eIF5A plays an important role in HCC tumorigenesis and metastasis, and targeting eIF5A hypusination by GC7 inhibitor or eIF5A2 by RNA interference (RNAi) may offer new therapeutic alternatives to HCC patients.
International Journal of Cancer 12/2009; 127(4):968-76. · 6.20 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Surgical resection is one important curative treatment for hepatocellular carcinoma (HCC), but the prognosis following surgery differs substantially and such large variation is mainly unexplained. A review of the literature yields a number of clinicopathologic parameters associated with HCC prognosis. However, the results are not consistent due to lack of systemic approach to establish a prediction model incorporating all these parameters.
We conducted a retrospective analysis on the common clinicopathologic parameters from a cohort of 572 ethnic Chinese HCC patients who received curative surgery. The cases were randomly divided into training (n = 272) and validation (n = 300) sets. Each parameter was individually tested and the significant parameters were entered into a linear classifier for model building, and the prediction accuracy was assessed in the validation set
Our findings based on the training set data reveal 6 common clinicopathologic parameters (tumor size, number of tumor nodules, tumor stage, venous infiltration status, and serum alpha-fetoprotein and total albumin levels) that were significantly associated with the overall HCC survival and disease-free survival (time to recurrence). We next built a linear classifier model by multivariate Cox regression to predict prognostic outcomes of HCC patients after curative surgery This analysis detected a considerable fraction of variance in HCC prognosis and the area under the ROC curve was about 70%. We further evaluated the model using two other protocols; leave-one-out procedure (n = 264) and independent validation (n = 300). Both were found to have excellent prediction power. The predicted score could separate patients into distinct groups with respect to survival (p-value = 1.8e-12) and disease free survival (p-value = 3.2e-7).
This described model will provide valuable guidance on prognosis after curative surgery for HCC in clinical practice. The adaptive nature allows easy accommodation for future new biomarker inputs, and it may serve as the foundation for future modeling and prediction for HCC prognosis after surgical treatment.
[Show abstract][Hide abstract] ABSTRACT: Hepatocellular carcinoma (HCC) is a lethal malignancy for which there are no effective therapies. To develop rational therapeutic approaches for treating this disease, we are performing proof-of-principle studies targeting molecules crucial for the development of HCC. Here, we show that cadherin-17 (CDH17) adhesion molecule is up-regulated in human liver cancers and can transform premalignant liver progenitor cells to produce liver carcinomas in mice. RNA interference-mediated knockdown of CDH17 inhibited proliferation of both primary and highly metastatic HCC cell lines in vitro and in vivo. The antitumor mechanisms underlying CDH17 inhibition involve inactivation of Wnt signaling, because growth inhibition and cell death were accompanied by relocalization of beta-catenin to the cytoplasm and a concomitant reduction in cyclin D1 and an increase in retinoblastoma. CONCLUSION: Our results identify CDH17 as a novel oncogene in HCC and suggest that CDH17 is a biomarker and attractive therapeutic target for this aggressive malignancy.
[Show abstract][Hide abstract] ABSTRACT: A principal task in dissecting the genetics of complex traits is to identify causal genes for disease phenotypes. We previously developed a method to infer causal relationships among genes through the integration of DNA variation, gene transcription and phenotypic information. Here we have validated our method through the characterization of transgenic and knockout mouse models of genes predicted to be causal for abdominal obesity. Perturbation of eight out of the nine genes, with Gas7, Me1 and Gpx3 being newly confirmed, resulted in significant changes in obesity-related traits. Liver expression signatures revealed alterations in common metabolic pathways and networks contributing to abdominal obesity and overlapped with a macrophage-enriched metabolic network module that is highly associated with metabolic traits in mice and humans. Integration of gene expression in the design and analysis of traditional F(2) intercross studies allows high-confidence prediction of causal genes and identification of pathways and networks involved.