Network propagation-based prioritization of long tail genes in 17 cancer types.

BACKGROUND: The diversity of genomic alterations in cancer poses challenges to fully understanding the etiologies of the disease. Recent interest in infrequent mutations, in genes that reside in the "long tail" of the mutational distribution, uncovered new genes with significant implications in cancer development. The study of cancer-relevant genes often requires integrative approaches pooling together multiple types of biological data. Network propagation methods demonstrate high efficacy in achieving this integration. Yet, the majority of these methods focus their assessment on detecting known cancer genes or identifying altered subnetworks. In this paper, we introduce a network propagation approach that entirely focuses on prioritizing long tail genes with potential functional impact on cancer development. RESULTS: We identify sets of often overlooked, rarely to moderately mutated genes whose biological interactions significantly propel their mutation-frequency-based rank upwards during propagation in 17 cancer types. We call these sets "upward mobility genes" and hypothesize that their significant rank improvement indicates functional importance. We report new cancer-pathway associations based on upward mobility genes that are not previously identified using driver genes alone, validate their role in cancer cell survival in vitro using extensive genome-wide RNAi and CRISPR data repositories, and further conduct in vitro functional screenings resulting in the validation of 18 previously unreported genes. CONCLUSION: Our analysis extends the spectrum of cancer-relevant genes and identifies novel potential therapeutic targets.

Investigation of anticancer activities of STA-9090 (ganetespib) as a second generation HSP90 inhibitor in Saos-2 osteosarcoma cells.

Osteosarcoma is common childhood tumour type of the bone. Chemotherapy is the most important step in treatment of osteosarcoma. Despite advanced diagnosis methods and target specific cancer therapeutics, osteosarcoma has still a high mortality rate and a tendency to metastasize. Therefore, new therapeutic strategies are evaluated in osteosarcoma treatment in pre-clinical and clinical studies. In the last ten years, heat shock protein 90 (HSP90) has been important biological target to design target specific cancer drugs. HSP90 play vital roles in proper folding, stabilization and maintenance of oncogenic client proteins in tumorigenesis. Therefore, inhibition of HSP90 has been significant therapeutic aspects in cancer drug design. STA-9090 (ganetespib) is a second generation small molecule HSP90 inhibitor which blocks tumurogenesis in cancer cells. STA-9090 inhibited ATP hydrolysis and protein folding process of HSP90. In this study, STA-9090 decreased Saos-2 cell proliferation and IC50 dose of STA-9090 was found out as 18.71 µM and 10.25 µM at 24 h and 48 h, respectively. STA-9090 inhibited HSP90 ATPase function and disrupted oncogenic client protein folding activity. Also, STA-9090 decreased protein level of the HSP90 in osteosarcoma cells. Expression analysis of osteosarcoma and bone metabolism related genes was performed by RT2 Profiler PCR Array. This study has found the down-regulation of the expression levels of oncogenic genes: DKK1, TWIST1, WNT10B, WNT3A, RANK, RANKL, PTH, FGFR1, FGFR2, LTBP2, IL6, TGFß1, MMP2 and SPARC genes, in STA-9090 treated Saso-2 cells. Furthermore, expression levels of osteosarcoma related genes, OPG, ERα, ERß, IL15, BMP2 and BMP7, were found to have increased significantly. Biological activities of STA-9090 on Saos-2 cell line show its potential as a target specific drug to inhibit osteosarcoma and its metastasis.

Identifying "Many-to-Many" Relationships between Gene-Expression Data and Drug-Response Data via Sparse Binary Matching.

Identifying gene-drug patterns is a critical step in pharmacology for unveiling disease mechanisms and drug discovery. The availability of high-throughput technologies accumulates massive large-scale pharmacological and genomic data, and thus provides a new substantial opportunity to deeply understand how the oncogenic genes and the therapeutic drugs relate to each other. However, most previous studies merely used the pharmacological and genomic datasets without any prior knowledge to infer the gene-drug patterns. Here, we proposed a novel network-guided sparse binary matching model (NSBM) to decode these relationships hidden in the datasets. Not only the large-scale gene-expression data and drug-response data are jointly analyzed in our method, but also the additional prior information of genes and drugs are integrated into the form of network-based regularization. The essential structure of the NSBM model is a convex quadratic minimization problem with network-based penalties. It was demonstrated to be superior when compared with two benchmark methods through extensive experiments on both synthetic and empirical data. Posterior validation, including gene-ontology and enrichment analysis, confirmed the effectiveness of NSBM in revealing gene-drug patterns on a large-scale heterogeneous data source.

Identification of a Radiosensitivity Molecular Signature Induced by Enzalutamide in Hormone-sensitive and Hormone-resistant Prostate Cancer Cells.

Prostate cancer (PCa) is the most common cancer amongst men. A novel androgen receptor (AR) antagonist, enzalutamide (ENZA) has recently been demonstrated to enhance the effect of radiation (XRT) by impairing the DNA damage repair process. This study aimed to identify a radiosensitive gene signature induced by ENZA in the PCa cells and to elucidate the biological pathways which influence this radiosensitivity. We treated LNCaP (AR-positive, hormone-sensitive PCa cells) and C4-2 (AR-positive, hormone-resistant PCa cells) cells with ENZA alone and in combination with androgen deprivation therapy (ADT) and XRT. Using one-way ANOVA on the gene expression profiling, we observed significantly differentially expressed (DE) genes in inflammation-and metabolism-related genes in hormone-sensitive and hormone-resistant PCa cell lines respectively. Survival analysis in both the TCGA PRAD and GSE25136 datasets suggested an association between the expression of these genes and time to recurrence. These results indicated that ENZA alone or in combination with ADT enhanced the effect of XRT through immune and inflammation-related pathways in LNCaP cells and metabolic-related pathways in C4-2 cells. Kaplan-Meier analysis and Cox proportional hazard models showed that low expression of all the candidate genes except for PTPRN2 were associated with tumor progression and recurrence in a PCa cohort.

Predicting drug-target interaction network using deep learning model.

BACKGROUND: Traditional methods for drug discovery are time-consuming and expensive, so efforts are being made to repurpose existing drugs. To find new ways for drug repurposing, many computational approaches have been proposed to predict drug-target interactions (DTIs). However, due to the high-dimensional nature of the data sets extracted from drugs and targets, traditional machine learning approaches, such as logistic regression analysis, cannot analyze these data sets efficiently. To overcome this issue, we propose LASSO (Least absolute shrinkage and selection operator)-based regularized linear classification models and a LASSO-DNN (Deep Neural Network) model based on LASSO feature selection to predict DTIs. These methods are demonstrated for repurposing drugs for breast cancer treatment. METHODS: We collected drug descriptors, protein sequence data from Drugbank and protein domain information from NCBI. Validated DTIs were downloaded from Drugbank. A new similarity-based approach was developed to build the negative DTIs. We proposed multiple LASSO models to integrate different combinations of feature sets to explore the prediction power and predict DTIs. Furthermore, building on the features extracted from the LASSO models with the best performance, we also introduced a LASSO-DNN model to predict DTIs. The performance of our newly proposed DNN model (LASSO-DNN) was compared with the LASSO, standard logistic (SLG) regression, support vector machine (SVM), and standard DNN models. RESULTS: Experimental results showed that the LASSO-DNN over performed the SLG, LASSO, SVM and standard DNN models. In particular, the LASSO models with protein tripeptide composition (TC) features and domain features were superior to those that contained other protein information, which may imply that TC and domain information could be better representations of proteins. Furthermore, we showed that the top ranked DTIs predicted using the LASSO-DNN model can potentially be used for repurposing existing drugs for breast cancer based on risk gene information. CONCLUSIONS: In summary, we demonstrated that the efficient representations of drug and target features are key for building learning models for predicting DTIs. The disease-associated risk genes identified from large-scale genomic studies are the potential drug targets, which can be used for drug repurposing.

Differential gene expression induced by Verteporfin in endometrial cancer cells.

Endometrial cancer (EMCA) is a clinically heterogeneous disease. Previously, we tested the efficacy of Verteporfin (VP) in EMCA cells and observed cytotoxic and anti-proliferative effects. In this study, we analyzed RNA sequencing data to investigate the comprehensive transcriptomic landscape of VP treated Type 1 EMCA cell lines, including HEC-1-A and HEC-1-B. There were 549 genes with differential expression of two-fold or greater and P < 0.05 after false discovery rate correction for the HEC-1-B cell line. Positive regulation of TGFß1 production, regulation of lipoprotein metabolic process, cell adhesion, endodermal cell differentiation, formation and development, and integrin mediated signaling pathway were among the significantly associated terms. A functional enrichment analysis of differentially expressed genes after VP treatment revealed extracellular matrix organization Gene Ontology as the most significant. CDC23 and BUB1B, two genes crucially involved in mitotic checkpoint progression, were found to be the pair with the best association from STRING among differentially expressed genes in VP treated HEC-1-B cells. Our in vivo results indicate that subcutaneous tumors in mice were regressed after VP treatment by inhibiting cell cycle pathway proteins. The present study revealed multiple key genes of pathological significance in EMCA, thereby improving our understanding of molecular profiles of EMCA cells.

Role of Metastasis-Related Genes in Cisplatin Chemoresistance in Gastric Cancer.

The role of metastasis-related genes in cisplatin (CDDP) chemoresistance in gastric cancer is poorly understood. Here, we examined the expression of four metastasis-related genes (namely, c-met, HMGB1, RegIV, PCDHB9) in 39 cases of gastric cancer treated with neoadjuvant therapy with CDDP or CDDP+5-fluorouracil and evaluated its association with CDDP responsiveness. Comparison of CDDP-sensitive cases with CDDP-resistant cases, the expression of c-met, HMGB1, and PCDHB9 was correlated with CDDP resistance. Among them, the expression of HMGB1 showed the most significant correlation with CDDP resistance in multivariate analysis. Treatment of TMK-1 and MKN74 human gastric cancer cell lines with ethyl pyruvate (EP) or tanshinone IIA (TAN), which are reported to inhibit HMGB1 signaling, showed a 4-5-fold increase in inhibition by CDDP. Treatment with EP or TAN also suppressed the expression of TLR4 and MyD88 in the HMGB1 signal transduction pathway and suppressed the activity of NFκB in both cell lines. These results suggest that the expression of these cancer metastasis-related genes is also related to anticancer drug resistance and that suppression of HMGB1 may be particularly useful for CDDP sensitization.

Zearalenone exposure elevated the expression of tumorigenesis genes in mouse ovarian granulosa cells.

Zearalenone (ZEA) is one of mycotoxins which are from corn, sorghum and wheat. As an estrogenic compound, ZEA mainly affects animal growth and reproduction with causing abnormal reproduction capability. Previous studies have shown that ZEA poses adverse effects on follicular development, but the mechanism of genetic toxicity of ZEA is not understood. The purpose of this study was to explore the effects of ZEA exposure on granulosa cells which play vital roles during follicular development. Mouse granulosa cells were exposed to 10 µM or 30 µM ZEA for 72 h in vitro, and the differences in gene expression patterns between control and ZEA exposures were analyzed by RNA-seq. The data demonstrated that 30 µM ZEA had a significant effect on the gene expression, especially ZEA exposure increased the expression of many genes related to different kinds of cancers and cancer related pathways like Hippo signaling pathway and the related genes, such as Ccnd1, Smad3, Tead3, Yap1 and Wwtr1. Furthermore, immunohistochemistry confirmed the increase in the protein levels of YAP1, WWTR1 and CCND1 in 30 µM ZEA exposure group. Collectively, this investigation indicated that ZEA exposure promoted the expression of tumorigenesis genes in mouse granulosa cells to.

Genomic testing for pancreatic cancer in clinical practice as real-world evidence.

BACKGROUND: Precision medicine guided by comprehensive genome sequencing represents a potential treatment strategy for pancreatic cancer. However, clinical sequencing for pancreatic cancer entails several practical difficulties. We have launched an in-house clinical sequencing system and started genomic testing for patients with cancer in clinical practice. We have analyzed the clinical utility of this system in pancreatic cancer. METHODS: We retrospectively reviewed 20 patients with pancreatic cancer who visited our division. Genomic DNA was extracted from both tumor tissue and peripheral blood mononuclear cells obtained from the patients. We performed a comprehensive genomic testing using targeted amplicon sequencing for 160 cancer-related genes. The primary endpoints were the detection rates of potential actionable and druggable gene alterations. The secondary endpoints were the detection rate of secondary germline findings, the rate of re-biopsy required for genome sequencing, survival time after the initial visit (post-sequencing survival time), and turnaround time. RESULTS: Although re-biopsy was required for 25% (5/20) of all patients, genomic testing was performed in all patients. Actionable and druggable gene alterations were detected in 100% (20/20) and 35% (7/20) of patients, respectively, whereas secondary germline findings were detected in 5% (1/20) of patients. The median turnaround times for physicians and patients were 20 and 26 days, respectively. The median post-sequencing survival time was 10.3 months. Only 10% (2/20) of all patients were treated with therapeutic agents based on the outcomes of genomic testing. CONCLUSIONS: The clinical application of comprehensive genomic testing for pancreatic cancer was feasible and promising in clinical practice.

A High-throughput Quantitative Expression Analysis of Cancer-related Genes in Human HepG2 Cells in Response to Limonene, a Potential Anticancer Agent.

BACKGROUND: Citrus bioactive compounds, as active anticancer agents, have been under focus by several studies worldwide. However, the underlying genes responsible for the anticancer potential have not been sufficiently highlighted. OBJECTIVES: The current study investigated the gene expression profile of hepatocellular carcinoma, HepG2, cells after treatment with Limonene. METHODS: The concentration that killed 50% of HepG2 cells was used to elucidate the genetic mechanisms of limonene anticancer activity. The apoptotic induction was detected by flow cytometry and confocal fluorescence microscope. Two of the pro-apoptotic events, caspase-3 activation and phosphatidylserine translocation were manifested by confocal fluorescence microscopy. Highthroughput real-time PCR was used to profile 1023 cancer-related genes in 16 different gene families related to the cancer development. RESULTS: In comparison to untreated cells, limonene increased the percentage of apoptotic cells up to 89.61%, by flow cytometry, and 48.2% by fluorescence microscopy. There was a significant limonene- driven differential gene expression of HepG2 cells in 15 different gene families. Limonene was shown to significantly (>2log) up-regulate and down-regulate 14 and 59 genes, respectively. The affected gene families, from the most to the least affected, were apoptosis induction, signal transduction, cancer genes augmentation, alteration in kinases expression, inflammation, DNA damage repair, and cell cycle proteins. CONCLUSION: The current study reveals that limonene could be a promising, cheap, and effective anticancer compound. The broad spectrum of limonene anticancer activity is interesting for anticancer drug development. Further research is needed to confirm the current findings and to examine the anticancer potential of limonene along with underlying mechanisms on different cell lines.

GDISC: a web portal for integrative analysis of gene-drug interaction for survival in cancer.

Summary: Survival analysis has been applied to The Cancer Genome Atlas (TCGA) data. Although drug exposure records are available in TCGA, existing survival analyses typically did not consider drug exposure, partly due to naming inconsistencies in the data. We have spent extensive effort to standardize the drug exposure data, which enabled us to perform survival analysis on drug-stratified subpopulations of cancer patients. Using this strategy, we integrated gene copy number data, drug exposure data and patient survival data to infer gene-drug interactions that impact survival. The collection of all analyzed gene-drug interactions in 32 cancer types are organized and presented in a searchable web-portal called gene-drug Interaction for survival in cancer (GDISC). GDISC allows biologists and clinicians to interactively explore the gene-drug interactions identified in the context of TCGA, and discover interactions associated to their favorite cancer, drug and/or gene of interest. In addition, GDISC provides the standardized drug exposure data, which is a valuable resource for developing new methods for drug-specific analysis. Availability and Implementation: GDISC is available at https://gdisc.bme.gatech.edu/. Contact: peng.qiu@bme.gatech.edu.

Trinucleotide repeat expansions in human breast cancer-susceptibility genes: relevant targets for aspirin chemoprevention?

PURPOSE: Defining novel molecular mechanisms pertinent to aspirin chemoprevention of breast cancer (BC) and to explain controversial epidemiological results in this regard. METHODS: Literature search in relevant databases with the following key words; aspirin, nucleotide repeat expansions, breast cancer. Human genome contains nucleotide repeat expansions and exon-1 of the androgen receptor gene AR contains a CAG string with an average of 20 repeats. Longer AR CAG repeats associate with lower AR protein functioning leading relatively higher estrogen receptor signals and higher risk of hormone receptor-positive BC. Nucleotide repeat expansions also exist in E2F4 and POLG genes in BC. In cell culture models, aspirin reduces CAG.CTG expansions in kidney cells and restores myogenic differentiation in cells obtained from tissues with myotonic dystrophy, a disorder caused by large CTG expansions. CONCLUSIONS: We hypothesize that aspirin reduction of trinucleotide repeat expansions in breast cancer-susceptibility genes may be one of the relevant mechanisms of its chemopreventive effects.

Centchroman altered the expressions of tumor-related genes through active chromatin modifications in mammary cancer.

Centchroman (CC), a female oral contraceptive, has been shown to possess breast anti-cancer activities. Recently, we have shown CC-mediated antimetastatic effect through reversal of epithelial-to-mesenchymal transition (EMT) in breast cancer. The loss of tumor suppressor genes (TSGs) has been shown to promote EMT in breast cancer. Therefore, in the present study, we investigated the effect of CC-treatment on the expression of tumor-related genes including both tumor suppressor- and tumor promoter genes in breast cancer. CC treatment resulted in G0 /G1 phase cell cycle arrest in human breast cancer MDA-MB-231, SK-BR-3, and ZR-75-1 cells with the concomitant induction of TSGs such as p21WAF1/CIP1 , p16INK4a , and p27Kip1 . In addition, CC treatment also resulted in the downregulation of tumor promoter gene, human telomerase reverse transcriptase (hTERT). The induction of TSGs and downregulation of hTERT was found to be correlated with decreased expression levels of histone deacetylases (HDACs) and DNA methyltransferases (DNMTs). Further, mechanistic studies revealed CC-induced global DNA demethylation and alterations in the enrichment of chromatin modification markers at the promoters of p21 and hTERT. These in vitro results were corroborated with in vivo findings in 4T1-syngeneic mouse model, where CC-treatment resulted in tumor growth reduction accompanied with the induction of TSGs and alterations in the expression levels of HDACs, DNMT1, and histone modification markers. Overall, our findings suggest that CC-treatment induces the expression of TSGs and downregulates hTERT through histone modifications and DNA methylation changes. Therefore, CC could be further developed into a promising drug candidate against breast cancer. © 2015 Wiley Periodicals, Inc.

A network flow-based method to predict anticancer drug sensitivity.

Predicting anticancer drug sensitivity can enhance the ability to individualize patient treatment, thus making development of cancer therapies more effective and safe. In this paper, we present a new network flow-based method, which utilizes the topological structure of pathways, for predicting anticancer drug sensitivities. Mutations and copy number alterations of cancer-related genes are assumed to change the pathway activity, and pathway activity difference before and after drug treatment is used as a measure of drug response. In our model, Contributions from different genetic alterations are considered as free parameters, which are optimized by the drug response data from the Cancer Genome Project (CGP). 10-fold cross validation on CGP data set showed that our model achieved comparable prediction results with existing elastic net model using much less input features.

Using distance covariance for improved variable selection with application to learning genetic risk models.

Variable selection is of increasing importance to address the difficulties of high dimensionality in many scientific areas. In this paper, we demonstrate a property for distance covariance, which is incorporated in a novel feature screening procedure together with the use of distance correlation. The approach makes no distributional assumptions for the variables and does not require the specification of a regression model and hence is especially attractive in variable selection given an enormous number of candidate attributes without much information about the true model with the response. The method is applied to two genetic risk problems, where issues including uncertainty of variable selection via cross validation, subgroup of hard-to-classify cases, and the application of a reject option are discussed.

Alteration of BRCA1 expression affects alcohol-induced transcription of RNA Pol III-dependent genes.

Emerging evidence has indicated that alcohol consumption is an established risk factor for breast cancer. Deregulation of RNA polymerase III (Pol III) transcription enhances cellular Pol III gene production, leading to an increase in translational capacity to promote cell transformation and tumor formation. We have reported that alcohol intake increases Pol III gene transcription to promote cell transformation and tumor formation in vitro and in vivo. Studies revealed that tumor suppressors, pRb, p53, PTEN and Maf1 repress the transcription of Pol III genes. BRCA1 is a tumor suppressor and its mutation is tightly related to breast cancer development. However, it is not clear whether BRCA1 expression affects alcohol-induced transcription of Pol III genes. At the present studies, we report that restoring BRCA1 in HCC 1937 cells, which is a BRCA1 deficient cell line, represses Pol III gene transcription. Expressing mutant or truncated BRCA1 in these cells does not affect the ability of repression on Pol III genes. Our analysis has demonstrated that alcohol induces Pol III gene transcription. More importantly, overexpression of BRCA1 in estrogen receptor positive (ER+) breast cancer cells (MCF-7) decreases the induction of tRNA(Leu) and 5S rRNA genes by alcohol, whereas reduction of BRCA1 by its siRNA slightly increases the transcription of the class of genes. This suggests that BRCA1 is associated with alcohol-induced deregulation of Pol III genes. These studies for the first time demonstrate the role of BRCA1 in induction of Pol III genes by alcohol and uncover a novel mechanism of alcohol-associated breast cancer.

Defining molecular sensors to assess long-term effects of pesticides on carcinogenesis.

The abundance of dioxins and dioxin-like pollutants has massively increased in the environment due to human activity. These chemicals are particularly persistent and accumulate in the food chain, which raises major concerns regarding long-term exposure to human health. Most dioxin-like pollutants activate the aryl hydrocarbon receptor (AhR) transcription factor, which regulates xenobiotic metabolism enzymes that belong to the cytochrome P450 1A family (that includes CYP1A1 and CYP1B1). Importantly, a crosstalk exists between estrogen receptor α (ERα) and AhR. More specifically, ERα represses the expression of the CYP1A1 gene, which encodes an enzyme that converts 17ß-estradiol into 2-hydroxyestradiol. However, (ERα) does not repress the CYP1B1 gene, which encodes an enzyme that converts 17ß-estradiol into 4-hydroxyestradiol, one of the most genotoxic estrogen metabolites. In this review, we discuss how chronic exposure to xenobiotic chemicals, such as pesticides, might affect the expression of genes regulated by the AhR-ERα crosstalk. Here, we focus on recent advances in the understanding of molecular mechanisms that mediate this crosstalk repression, and particularly on how ERα represses the AhR target gene CYP1A1, and could subsequently promote breast cancer. Finally, we propose that genes implicated in this crosstalk could constitute important biomarkers to assess long-term effects of pesticides on human health.

The synthetic glucocorticoids prednisolone and dexamethasone regulate the same genes in acute lymphoblastic leukemia cells.

BACKGROUND: Glucocorticoids (GCs) cause apoptosis in malignant cells of lymphoid lineage by transcriptionally regulating a plethora of genes. As a result, GCs are included in almost all treatment protocols for lymphoid malignancies, particularly childhood acute lymphoblastic leukemia (chALL). The most commonly used synthetic GCs in the clinical setting are prednisolone and dexamethasone. While the latter has a higher activity and more effectively reduces the tumor load in patients, it is also accompanied by more serious adverse effects than the former. Whether this difference might be explained by regulation of different genes by the two GCs has never been addressed. RESULTS: Using a recently developed GC bioassay based on a GC-responsive reporter construct in human Jurkat T-ALL cells, we found ~7-fold higher biological activity with dexamethasone than prednisolone. Similarly, 1.0e-7 M dexamethasone and 7.0e-7 M prednisolone triggered similar cell death rates in CCRF-CEM-C7H2 T-chALL cells after 72 hours of treatment. Using microarray-based whole genome expression profiling and a variety of statistical and other approaches, we compared the transcriptional response of chALL cells to 6 hour exposure to both synthetic GCs at the above concentrations. Our experiments did not detect any gene whose regulation by dexamethasone differed significantly from that by prednisolone. CONCLUSIONS: Our findings suggest that the reported differences in treatment efficacy and cytotoxicity of dexamethasone and prednisolone are not caused by inherent differences of the 2 drugs to regulate the expression of certain genes, but rather result either from applying them in biologically in-equivalent concentrations and/or from differences in their pharmacokinetics and - dynamics resulting in different bioactivities in tumor cells and normal tissues.

Biological and clinical implications of retinoic acid-responsive genes in human hepatocellular carcinoma cells.

BACKGROUND & AIMS: Accumulating data from epidemiological and experimental studies have suggested that retinoids, which are vitamin A derivatives, exert antitumor activity in various organs. We performed a gene screening based on in silico analysis of retinoic acid response elements (RAREs) to identify the genes facilitating the antitumor activity of retinoic acid (RA) and investigated their clinical significance in hepatocellular carcinoma (HCC). METHODS: In silico analysis of RAREs was performed in the 5-kb upstream region of EST clusters. Chromatin immunoprecipitation analysis of the retinoic acid receptors and gene expression analysis were performed in HuH7, HepG2, and MCF7 cells treated with all-trans RA (ATRA). mRNA expression of RA-responsive genes was investigated using tumor and non-tumor tissues of clinical HCC samples from 171 patients. The association between gene expression and survival of patients was examined by Cox regression analysis. RESULTS: We identified 201 candidate genes with promoter regions containing consensus RARE and finally selected 26 RA-responsive genes. Of these, downregulation of OTU domain-containing 7B (OTUD7B) gene, which was upregulated by ATRA, in tumor tissue was associated with a low cancer-specific survival of HCC patients. Functional analyses revealed that OTUD7B negatively regulates nuclear factor κB (NF-κB) signaling and decreases the survival of HCC cells. CONCLUSIONS: We identified RA-responsive genes which are regulated by retinoid signal and found that low-OTUD7B mRNA expression is associated with a poor prognosis for HCC patients. OTUD7B-mediated inhibition of NF-κB signaling may be an effective target for antitumor therapy for HCC.