Integrative Analysis of Microarray Data to Reveal Regulation Patterns in the Pathogenesis of Hepatocellular Carcinoma

Echinacoside inhibits hepatocellular carcinoma progression by targeting the miR-30c-5p/FOXD1/KLF12 axis

BACKGROUND

Hepatocellular carcinoma (HCC) is the third leading cause of cancer-attributed mortality and the primary liver malignancy in the world. Echinacoside is a phenylethanoid glycoside derived from traditional Chinese medicinal herbs which possessed multiple health benefits on humans, including anti-tumor effects.

OBJECTIVE

This study aimed to demonstrate the function of echinacoside in HCC progression and the involvement of miR-30c-5p/FOXD1/KLF12 axis.

METHODS

The HepG2 cells were treated by different dose of echinacoside, miR-30c-5p mimic, miR-30c-5p inhibitor, and FOXD1 overexpression lentiviruses or siRNA individually or simultaneously. The cell invasion and migration were measured by transwell assay. RNA and protein levels were tested by RT-PCR and western blot, respectively. The regulatory function of miR-30c-5p on Forkhead box D1 (FOXD1), FOXD1 on Krüppel-like factor 12 (KLF12) was tested by luciferase reporter assay or/and ChIP assay. Meanwhile, a liver cancer lung metastasis mice model was used to examine the functions of echinacoside and miR-30c-5p on HCC metastasis in vivo. Moreover, the correlations among miR-30c-5p, FOXD1, KLF12, and HCC prognosis was analyzed using clinical sample and TCGA database.

RESULTS

Based on both in vitro and in vivo investigations, we found that echinacoside could inhibit HCC cell migration, invasiveness, and tumor metastasis, and associated with the enhanced miR-30c-5p/FOXD1/KLF12 axis. Furthermore, through analyzing the interactions among intermediate molecules, we revealed that miR-30c-5p, FOXD1, and KLF12üere clinically relevant with each other in HCC patients, correlated with HCC prognosis, and regulated by echinacoside to contribute in the inhibition of HCC progression.

CONCLUSIONS

These findings suggest that echinacoside could inhibit HCC progression, and the mechanism related to the enhanced miR-30c-5p/FOXD1/KLF12 axis. Moreover, the abovementioned intermediate molecules might serve as prospective biomarkers for HCC prognosis.

Transcription factor ZNF263 enhances EGFR-targeted therapeutic response and reduces residual disease in lung adenocarcinoma

EGF receptor (EGFR) tyrosine kinase inhibitors (TKIs) have achieved clinical success in lung adenocarcinoma (LUAD). However, tumors often show profound but transient initial response and then gain resistance. We identify transcription factor ZNF263 as being significantly decreased in osimertinib-resistant or drug-tolerant persister LUAD cells and clinical residual tumors. ZNF263 overexpression improves the initial response of cells and delays the formation of persister cells with osimertinib treatment. We further show that ZNF263 binds and recruits DNMT1 to the EGFR gene promoter, suppressing EGFR transcription with DNA hypermethylation. ZNF263 interacts with nuclear EGFR, impairing the EGFR-STAT5 interaction to enhance AURKA expression. Overexpressing ZNF263 also makes tumor cells with wild-type EGFR expression or refractory EGFR mutations more susceptible to EGFR inhibition. More importantly, lentivirus or adeno-associated virus (AAV)-mediated ZNF263 overexpression synergistically suppresses tumor growth and regrowth with osimertinib treatment in xenograft animal models. These findings suggest that enhancing ZNF263 may achieve complete response in LUAD with EGFR-targeted therapies.

Prediction of liver cancer prognosis based on immune cell marker genes

Introduction

Monitoring the response after treatment of liver cancer and timely adjusting the treatment strategy are crucial to improve the survival rate of liver cancer. At present, the clinical monitoring of liver cancer after treatment is mainly based on serum markers and imaging. Morphological evaluation has limitations, such as the inability to measure small tumors and the poor repeatability of measurement, which is not applicable to cancer evaluation after immunotherapy or targeted treatment. The determination of serum markers is greatly affected by the environment and cannot accurately evaluate the prognosis. With the development of single cell sequencing technology, a large number of immune cell-specific genes have been identified. Immune cells and microenvironment play an important role in the process of prognosis. We speculate that the expression changes of immune cell-specific genes can indicate the process of prognosis.

Method

Therefore, this paper first screened out the immune cell-specific genes related to liver cancer, and then built a deep learning model based on the expression of these genes to predict metastasis and the survival time of liver cancer patients. We verified and compared the model on the data set of 372 patients with liver cancer.

Result

The experiments found that our model is significantly superior to other methods, and can accurately identify whether liver cancer patients have metastasis and predict the survival time of liver cancer patients according to the expression of immune cell-specific genes.

Discussion

We found these immune cell-specific genes participant multiple cancer-related pathways. We fully explored the function of these genes, which would support the development of immunotherapy for liver cancer.

The Prognostic Significance of FOXD1 Expression in Head and Neck Squamous Cell Carcinoma

It has been reported that forkhead box D1 (FOXD1) plays an established role in human early embryonic development and is broadly involved in various malignancies. However, there is limited information regarding FOXD1 expression in head and neck squamous cell carcinoma (HNSCC). This present study aimed to explore the clinical significance of FOXD1 in patients with HNSCC. Tissue microarrays of 334 primary HNSCC patients who underwent surgery between 2008 and 2010 at Sun Yat-sen University Cancer Center were investigated by immunohistochemistry regarding FOXD1 expression. χ2 test was used to estimate the relationship of FOXD1 expression with clinicopathologic characteristics. Univariate and multivariate analyses were performed to identify FOXD1 expression as an independent prognostic indicator of overall survival (OS) and disease-free survival (DFS). FOXD1 expression is closely associated with postoperative recurrence. HNSCC patients with high FOXD1 expression have poorer prognoses than the low-expression group (p < 0.05). According to multivariate analysis, FOXD1 was an independent prognostic factor for OS and DFS. The results revealed that FOXD1 could be a prognostic factor for HNSCC and might serve as a potential target for novel therapies.

Nonsense-Mediated Decay Targeted RNA (ntRNA): Proposal of a ntRNA–miRNA–lncRNA Triple Regulatory Network Usable as Biomarker of Prognostic Risk in Patients with Kidney Cancer

The most prevalent subtype of renal cell carcinoma (RCC), kidney renal clear cell carcinoma (KIRC) may be associated with a poor prognosis in a high number of cases, with a stage-specific prognostic stratification currently in use. No reliable biomarkers have been utilized so far in clinical practice despite the efforts in biomarker research in the last years. Nonsense-mediated mRNA decay (NMD) is a critical safeguard against erroneous transcripts, particularly mRNA transcripts containing premature termination codons (called nonsense-mediated decay targeted RNA, ntRNA). In this study, we first characterized 296 differentially expressed ntRNAs that were independent of the corresponding gene, 261 differentially expressed miRNAs, and 4653 differentially expressed lncRNAs. Then, we constructed a hub ntRNA–miRNA–lncRNA triple regulatory network associated with the prognosis of KIRC. Moreover, the results of immune infiltration analysis indicated that this network may influence the changes of the tumor immune microenvironment. A prognostic model derived from the genes and immune cells associated with the network was developed to distinguish between high- and low-risk patients, which was a better prognostic than other models, constructed using different biomarkers. Additionally, correlation of methylation and ntRNAs in the network suggested that some ntRNAs were regulated by methylation, which is helpful to further study the causes of abnormal expression of ntRNAs. In conclusion, this study highlighted the possible clinical implications of ntRNA functions in KIRC, proposing potential significant biomarkers that could be utilized to define the prognosis and design personalized treatment plans in kidney cancer management in the next future.

Dissecting novel mechanisms of hepatitis B virus related hepatocellular carcinoma using meta-analysis of public data

BACKGROUND

Hepatitis B virus (HBV) is a cause of hepatocellular carcinoma (HCC). Interestingly, this process is not necessarily mediated through cirrhosis and may in fact involve oncogenic processes. Prior studies have suggested specific oncogenic gene expression pathways were affected by viral regulatory proteins. Thus, identifying these genes and associated pathways could highlight predictive factors for HCC transformation and has implications in early diagnosis and treatment.

AIM

To elucidate HBV oncogenesis in HCC and identify potential therapeutic targets.

METHODS

We employed our Search, Tag, Analyze, Resource platform to conduct a meta-analysis of public data from National Center for Biotechnology Information’s Gene Expression Omnibus. We performed meta-analysis consisting of 155 tumor samples compared against 185 adjacent non-tumor samples and analyzed results with ingenuity pathway analysis.

RESULTS

Our analysis revealed liver X receptors/retinoid X receptor (RXR) activation and farnesoid X receptor/RXR activation as top canonical pathways amongst others. Top upstream regulators identified included the Ras family gene rab-like protein 6 (RABL6). The role of RABL6 in oncogenesis is beginning to unfold but its specific role in HBV-related HCC remains undefined. Our causal analysis suggests RABL6 mediates pathogenesis of HBV-related HCC through promotion of genes related to cell division, epigenetic regulation, and Akt signaling. We conducted survival analysis that demonstrated increased mortality with higher RABL6 expression. Additionally, homeobox A10 (HOXA10) was a top upstream regulator and was strongly upregulated in our analysis. HOXA10 has recently been demonstrated to contribute to HCC pathogenesis in vitro. Our causal analysis suggests an in vivo role through downregulation of tumor suppressors and other mechanisms.

CONCLUSION

This meta-analysis describes possible roles of RABL6 and HOXA10 in the pathogenesis of HBV-related HCC. RABL6 and HOXA10 represent potential therapeutic targets and warrant further investigation.

Hepatic ARID3A facilitates liver cancer malignancy by cooperating with CEP131 to regulate an embryonic stem cell-like gene signature

Liver cancer stemness refers to the stem cell-like phenotype of hepatocarcinoma cells and is closely related to a high degree of tumour malignancy. Here, we identified AT-rich interacting domain 3A (ARID3A) as one of the most upregulated stemness-related transcription factors in liver cancer by an in vitro functional screen. ARID3A can promote liver cancer cell viability and metastasis both in vitro and in vivo. Mechanistically, ARID3A interacts with CEP131 and transcriptionally activates KDM3A by co-occupying its promoter element, further upregulating the expression of downstream embryonic stem (ES) signature genes via demethylation of H3K9me2. ARID3A and CEP131 promote an ES cell gene signature through activation of KDM3A and contribute to the poor prognosis of liver cancer patients. Collectively, these results provide evidence highlighting a transcription-dependent mechanism of ARID3A in stemness regulation in liver cancer. The ARID3A/CEP131-KDM3A regulatory circuit could serve as a prognostic indicator and potential therapeutic target for liver cancer.

Low Level of PALMD Contributes to the Metastasis of Uveal Melanoma

Uveal melanoma (UM) is a highly aggressive disease. There is an urgent need to develop the metastasis prediction markers of UM. This study aims to detect the key role of PALMD in UM metastasis. Transcriptome sequencing results of 2 sets of UM metastatic samples (GSE22138 and GSE156877) were downloaded from the Gene Expression Omnibus (GEO), and 18 overlapping differentially expressed genes were screened out, including PALMD. PALMD was significantly underexpressed in metastatic UM tissue. Low expression of PALMD was associated with poor prognosis in UM patients. The decreased expression of PALMD promoted the invasion and migration of 92-1 and Mel270 cells, while the high expression of PALMD inhibited the invasion and migration of UM cells. Furthermore, the levels of matrix metallopeptidase (MMP) 2 and MMP9 increased after transfection of siRNAs specifically targeting PALMD, whereas the levels of MMP2 and MMP9 were decreased after PALMD overexpression. However, PALMD did not affect the proliferation of UM cells. In addition, ZNF263 promoted the transcription of PALMD through the putative binding sequence using the JASPAR database, luciferase reporter gene analysis and chromatin immunoprecipitation assay. In summary, the expression of PALMD regulated by ZNF263 plays an important role in UM metastasis.

ARID3A promotes the chemosensitivity of colon cancer by inhibiting AKR1C3

ARID3A is upregulated in colorectal cancer and can promote the proliferation and metastasis of cancer cells. However, patients with higher level of ARID3A have a better prognosis. This study aimed to uncover the mechanism by which ARID3A benefits the prognosis of colon cancer. Our results indicated that ARID3A upregulation enhanced the chemosensitivity of colon cancer cells to 5-Fluorouracil (5-FU), whereas ARID3A downregulation inhibited the chemosensitivity of colon cancer cells to 5-FU. Through database analysis, we found that AKR1C3, a drug resistance related gene, was the target of ARID3A. Moreover, AKR1C3 was downregulated in colon cancer tissues compared to normal tissues. Next, we assessed the interaction between AKR1C3 and ARID3A, and found that ARID3A inhibited the transcription of AKR1C3, leading to the downregulation of AKR1C3 in colon cancer cells. We also verified that AKR1C3 inhibited the chemosensitivity of colon cancer cells to 5-FU. Moreover, patients with higher ratio of ARID3A to AKR1C3 had a better prognosis. This study suggested that ARID3A promoted chemosensitivity of colon cancer cells by inhibiting AKR1C3 in colon cancer. The ratio of ARID3A to AKR1C3 is a good marker to predict the prognosis of colon cancer patients. This article is protected by copyright. All rights reserved.

Mechanistic Modeling of Gene Regulation and Metabolism Identifies Potential Targets for Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is the predominant form of liver cancer and has long been among the top three cancers that cause the most deaths worldwide. Therapeutic options for HCC are limited due to the pronounced tumor heterogeneity. Thus, there is a critical need to study HCC from a systems point of view to discover effective therapeutic targets, such as through the systematic study of disease perturbation in both regulation and metabolism using a unified model. Such integration makes sense for cancers as it links one of the dominant physiological features of cancers (growth, which is driven by metabolic networks) with the primary available omics data source, transcriptomics (which is systematically integrated with metabolism through the regulatory-metabolic network model). Here, we developed an integrated transcriptional regulatory-metabolic model for HCC molecular stratification and the prediction of potential therapeutic targets. To predict transcription factors (TFs) and target genes affecting tumorigenesis, we used two algorithms to reconstruct the genome-scale transcriptional regulatory networks for HCC and normal liver tissue. which were then integrated with corresponding constraint-based metabolic models. Five key TFs affecting cancer cell growth were identified. They included the regulator CREB3L3, which has been associated with poor prognosis. Comprehensive personalized metabolic analysis based on models generated from data of liver HCC in The Cancer Genome Atlas revealed 18 genes essential for tumorigenesis in all three subtypes of patients stratified based on the non-negative matrix factorization method and two other genes (ACADSB and CMPK1) that have been strongly correlated with lower overall survival subtype. Among these 20 genes, 11 are targeted by approved drugs for cancers or cancer-related diseases, and six other genes have corresponding drugs being evaluated experimentally or investigationally. The remaining three genes represent potential targets. We also validated the stratification and prognosis results by an independent dataset of HCC cohort samples (LIRI-JP) from the International Cancer Genome Consortium database. In addition, microRNAs targeting key TFs and genes were also involved in established cancer-related pathways. Taken together, the multi-scale regulatory-metabolic model provided a new approach to assess key mechanisms of HCC cell proliferation in the context of systems and suggested potential targets.

BIOMEX: an interactive workflow for (single cell) omics data interpretation and visualization

The amount of biological data, generated with (single cell) omics technologies, is rapidly increasing, thereby exacerbating bottlenecks in the data analysis and interpretation of omics experiments. Data mining platforms that facilitate non-bioinformatician experimental scientists to analyze a wide range of experimental designs and data types can alleviate such bottlenecks, aiding in the exploration of (newly generated or publicly available) omics datasets. Here, we present BIOMEX, a browser-based software, designed to facilitate the Biological Interpretation Of Multi-omics EXperiments by bench scientists. BIOMEX integrates state-of-the-art statistical tools and field-tested algorithms into a flexible but well-defined workflow that accommodates metabolomics, transcriptomics, proteomics, mass cytometry and single cell data from different platforms and organisms. The BIOMEX workflow is accompanied by a manual and video tutorials that provide the necessary background to navigate the interface and get acquainted with the employed methods. BIOMEX guides the user through omics-tailored analyses, such as data pretreatment and normalization, dimensionality reduction, differential and enrichment analysis, pathway mapping, clustering, marker analysis, trajectory inference, meta-analysis and others. BIOMEX is fully interactive, allowing users to easily change parameters and generate customized plots exportable as high-quality publication-ready figures. BIOMEX is open source and freely available at https://www.vibcancer.be/software-tools/biomex.

Dietary Erythrodiol Modifies Hepatic Transcriptome in Mice in a Sex and Dose-Dependent Way

Erythrodiol is a terpenic compound found in a large number of plants. To test the hypotheses that its long-term administration may influence hepatic transcriptome and this could be influenced by the presence of APOA1-containing high-density lipoproteins (HDL), Western diets containing 0.01% of erythrodiol (10 mg/kg dose) were provided to Apoe- and Apoa1-deficient mice. Hepatic RNA-sequencing was carried out in male Apoe-deficient mice fed purified Western diets differing in the erythrodiol content. The administration of this compound significantly up- regulated 68 and down-regulated 124 genes at the level of 2-fold change. These genes belonged to detoxification processes, protein metabolism and nucleic acid related metabolites. Gene expression changes of 21 selected transcripts were verified by RT-qPCR. Ccl19-ps2, Cyp2b10, Rbm14-rbm4, Sec61g, Tmem81, Prtn3, Amy2a5, Cyp2b9 and Mup1 showed significant changes by erythrodiol administration. When Cyp2b10, Dmbt1, Cyp2b13, Prtn3 and Cyp2b9 were analyzed in female Apoe-deficient mice, no change was observed. Likewise, no significant variation was observed in Apoa1- or in Apoe-deficient mice receiving doses ranging from 0.5 to 5 mg/kg erythrodiol. Our results give evidence that erythrodiol exerts a hepatic transcriptional role, but this is selective in terms of sex and requires a threshold dose. Furthermore, it requires an APOA1-containing HDL.

A zinc finger family protein, ZNF263, promotes hepatocellular carcinoma resistance to apoptosis via activation of ER stress-dependent autophagy

Hepatocellular carcinoma (HCC) is one of the leading causes of cancer death worldwide. Endoplasmic reticulum stress (ERS) is generally activated in HCC and is important for the sensitivity of HCC to anticancer drugs. ERS-dependent autophagy is a crucial mechanism affecting the sensitivity of HCC to anticancer drugs, but the mechanism by which ERS regulates autophagy is not well understood. Zinc finger protein 263 (ZNF263) is a transcription factor member of the zinc finger family. However, its functional role in HCC remains to be studied. In the current study, we investigated the role of ZNF263 in regulating ERS-induced chemoresistance in HCC and its possible mechanism. We found that ZNF263 was the most significant ERS-specific super-enhancer bounding transcriptional factor and was up-regulated in HCC patients and cell lines. Further, ZNF263 expression correlated with ERS, clinical stage and shorter survival in HCC patients. ZNF263 knockdown by RNA interference results in decreased proliferation, apoptosis resistance, and chemoresistance. Further study showed that ZNF263 increased chemoresistance by activating ERS-related autophagy. In conclusion, our study highlights ZNF263 as a functional ERS-related tumor activator and indicates it as a potential target for HCC therapy.

ZNF263 is a transcriptional regulator of heparin and heparan sulfate biosynthesis

Heparin is the most widely prescribed biopharmaceutical in production globally. Its potent anticoagulant activity and safety makes it the drug of choice for preventing deep vein thrombosis and pulmonary embolism. In 2008, adulterated material was introduced into the heparin supply chain, resulting in several hundred deaths and demonstrating the need for alternate sources of heparin. Heparin is a fractionated form of heparan sulfate derived from animal sources, predominantly from connective tissue mast cells in pig mucosa. While the enzymes involved in heparin biosynthesis are identical to those for heparan sulfate, the factors regulating these enzymes are not understood. Examination of the promoter regions of all genes involved in heparin/heparan sulfate assembly uncovered a transcription factor-binding motif for ZNF263, a C2H2 zinc finger protein. CRISPR-mediated targeting and siRNA knockdown of ZNF263 in mammalian cell lines and human primary cells led to dramatically increased expression levels of HS3ST1, a key enzyme involved in imparting anticoagulant activity to heparin, and HS3ST3A1, another glucosaminyl 3-O-sulfotransferase expressed in cells. Enhanced 3-O-sulfation increased binding to antithrombin, which enhanced Factor Xa inhibition, and binding of neuropilin-1. Analysis of transcriptomics data showed distinctively low expression of ZNF263 in mast cells compared with other (non-heparin-producing) immune cells. These findings demonstrate a novel regulatory factor in heparan sulfate modification that could further advance the possibility of bioengineering anticoagulant heparin in cultured cells.

Upregulation of CENPM promotes hepatocarcinogenesis through mutiple mechanisms

Background

Hepatocellular carcinoma (HCC) still remains a dominating medical challenge in early diagnosis and clinical therapy. Centromere protein M (CENPM) has been proved to be over-expressed in HCC tissues, but carcinogenic mechanism of CENPM contributing to liver cancer is poorly understood.

Methods

In this study, we first explored mRNA and protein levels of CENPM in HCC samples, matching adjacent non-tumor tissues and six hepatoma cell lines by polymerase chain reaction (PCR), western blotting and immunohistochemistry (IHC). Clinical data of HCC patients downloaded from The Cancer Genome Atlas (TCGA) were also analyzed. The character of CENPM concerned with HCC progression through several functional experimentations in vitro and in vivo was researched. Bioinformatics was carried out to further discover biological functions of CENPM.

Results

CENPM was positively up-regulated in HCC and connected with a poor prognosis. Silencing CENPM repressed cell proliferation in vivo and in vitro, and knock-down CENPM inhibited cell migration and invasion. Additionally, depletion of CENPM can promote cell apoptosis and arrested cell cycle. Furthermore, single-gene gene set enrichment analysis (GSEA) analysis indicated that CENPM was linked to the P53 signaling pathway and cell cycle pathway, and our research supported this prediction. Finally, we also found that miR-1270 was a negative regulator and participated in post-transcriptional regulation of CENPM, and hepatitis B virus X protein (HBx) can promote hepatocellular carcinoma by suppressing miR1270.

Conclusion

CENPM was closely associated with HCC progression and it could be considered as a new possible biomarker along with a therapeutic target for HCC.

Classification of early and late stage liver hepatocellular carcinoma patients from their genomics and epigenomics profiles

Background

Liver Hepatocellular Carcinoma (LIHC) is one of the major cancers worldwide, responsible for millions of premature deaths every year. Prediction of clinical staging is vital to implement optimal therapeutic strategy and prognostic prediction in cancer patients. However, to date, no method has been developed for predicting the stage of LIHC from the genomic profile of samples.

Methods

The Cancer Genome Atlas (TCGA) dataset of 173 early stage (stage-I), 177 late stage (stage-II, Stage-III and stage-IV) and 50 adjacent normal tissue samples for 60,483 RNA transcripts and 485,577 methylation CpG sites, was extensively analyzed to identify the key transcriptomic expression and methylation-based features using different feature selection techniques. Further, different classification models were developed based on selected key features to categorize different classes of samples implementing different machine learning algorithms.

Results

In the current study, in silico models have been developed for classifying LIHC patients in the early vs. late stage and cancerous vs. normal samples using RNA expression and DNA methylation data. TCGA datasets were extensively analyzed to identify differentially expressed RNA transcripts and methylated CpG sites that can discriminate early vs. late stages and cancer vs. normal samples of LIHC with high precision. Naive Bayes model developed using 51 features that combine 21 CpG methylation sites and 30 RNA transcripts achieved maximum MCC (Matthew’s correlation coefficient) 0.58 with an accuracy of 78.87% on the validation dataset in discrimination of early and late stage. Additionally, the prediction models developed based on 5 RNA transcripts and 5 CpG sites classify LIHC and normal samples with an accuracy of 96–98% and AUC (Area Under the Receiver Operating Characteristic curve) 0.99. Besides, multiclass models also developed for classifying samples in the normal, early and late stage of cancer and achieved an accuracy of 76.54% and AUC of 0.86.

Conclusion

Our study reveals stage prediction of LIHC samples with high accuracy based on the genomics and epigenomics profiling is a challenging task in comparison to the classification of cancerous and normal samples. Comprehensive analysis, differentially expressed RNA transcripts, methylated CpG sites in LIHC samples and prediction models are available from CancerLSP (http://webs.iiitd.edu.in/raghava/cancerlsp/).

Potential biomarkers of HCC based on gene expression and DNA methylation profiles

The aim of the present study was to identify potential biomarkers of hepatocellular carcinoma (HCC). Three gene expression profiles of GSE95698, GSE49515 and GSE76427 and a DNA methylation profile of GSE73003 were downloaded from the Gene Expression Omnibus (GEO) database, each comprising data regarding HCC and control tissue samples. The differentially expressed genes (DEGs) between the HCC group and the control group were identified using the limma software package. The Database for Annotation, Visualization and Integrated Discovery (DAVID) was used to perform Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of the overlapping DEGs. The PPI network of the overlapping DEGs was constructed using the Search Tool for the Retrieval of Interacting Genes/Proteins. A total of 41 DEGs were identified in HCC the group compared with control group. The overlapping DEGs were enriched in 11 GO terms and 3 KEGG pathways. A total of 6,349 DMSs were identified, and 6 of the differentially expressed genes were also differentially methylated [Denticleless protein homolog (DTL), Dual specificity phosphatase 1 (DUSP1), Eomesodermin, Endothelial cell specific molecule 1, Nuclear factor κ-light-chain gene enhancer of activated B cells inhibitor, α (NFKBIA) and suppressor of cytokine signaling 2 (SOCS2)]. The present study suggested that DTL, DUSP1, NFKBIA and SOCS2 may be potential biomarkers of HCC, and the tumor protein 'p53 signaling', 'forkhead box O1' signaling and 'metabolic' pathways may serve roles in the pathogenesis of HCC.

CYP3A4 Gene Is a Novel Biomarker for Predicting a Poor Prognosis in Hepatocellular Carcinoma

BACKGROUND/AIM

Project HOPE (High-tech Omics-based Patient Evaluation) began in 2014 using integrated gene expression profiling (GEP) of cancer tissues as well as diathesis of each patient who underwent operation at our Institution. The aim of this study was to identify novel genes displaying altered gene expression related to the survival and early recurrence after hepatectomy for hepatocellular carcinoma (HCC) using the results of integrated GEP analysis.

MATERIALS AND METHODS

The present study included 92 patients. Genes with aberrant expression were selected by the difference of expression levels with ≥10-fold change between tumor and non-tumor tissues.

RESULTS

GEP analysis showed that down-regulation was frequently observed in the PRSS8 (64%), CYP3A4 (61%) and EPCAM (57%) genes. Multivariate analysis revealed tumor stage ≥II (p=0.008) and down-regulation of the CYP3A4 gene (p=0.036) as independent predictor for overall survival. Furthermore, multivariate analysis identified maximum tumor diameter ≥74mm (p=0.008), presence of intrahepatic-metastasis (p=0.020), and down-regulation of CYP3A4 gene (p=0.019) as independent predictors for early recurrence.

CONCLUSION

CYP3A4 was identified as a novel tumor suppressor gene related to a poor prognosis in HCC.

The multisystemic functions of FOXD1 in development and disease

Transcription factors (TFs) participate in a wide range of cellular processes due to their inherent function as essential regulatory proteins. Their dysfunction has been linked to numerous human diseases. The forkhead box (FOX) family of TFs belongs to the "winged helix" superfamily, consisting of proteins sharing a related winged helix-turn-helix DNA-binding motif. FOX genes have been extensively present during vertebrates and invertebrates' evolution, participating in numerous molecular cascades and biological functions, such as embryonic development and organogenesis, cell cycle regulation, metabolism control, stem cell niche maintenance, signal transduction, and many others. FOXD1, a forkhead TF, has been related to different key biological processes such as kidney and retina development and embryo implantation. FOXD1 dysfunction has been linked to different pathologies, thereby constituting a diagnostic biomarker and a promising target for future therapies. This paper aims to present, for the first time, a comprehensive review of FOXD1's role in mouse development and human disease. Molecular, structural, and functional aspects of FOXD1 are presented in light of physiological and pathogenic conditions, including its role in human disease aetiology, such as cancer and recurrent pregnancy loss. Taken together, the information given here should enable a better understanding of FOXD1 function for basic science researchers and clinicians.

FOXD1 is targeted by miR-30a-5p and miR-200a-5p and suppresses the proliferation of human ovarian carcinoma cells by promoting p21 expression in a p53-independent manner

High-grade serous ovarian carcinoma (HGSOC) accounts for the highest number of deaths among patients with epithelial ovarian cancer. However, the molecular mechanisms underlying HGSOC tumorigenesis are currently unclear. In the present study, a lentiviral expression system was employed to manipulate forkhead box D1 (FOXD1) expression in ovarian cancer cells. Immunohistochemical staining was used to examine the expression of FOXD1 in tissue samples. Clonogenic and MTT assays were employed to evaluate cell proliferation, and flow cytometry was applied for cell cycle analysis. Dual-luciferase reporter and chromatin immunoprecipitation assays were used to determine the role of FOXD1 in regulating p21 expression. The results demonstrated that FOXD1 expression was downregulated in HGSOC, and high expression levels of FOXD1 were found to be a predictor of good prognosis. FOXD1 significantly inhibited the proliferation of human ovarian cancer cells and induced cell cycle arrest at G1 phase in vitro. In addition, exogenous FOXD1 expression inhibited ovarian cancer cell growth in vivo. Furthermore, microRNA (miR)-30a-5p and miR-200a-5p were observed to be upregulated in HGSOC, and function as direct negative regulators of FOXD1 by targeting its 3'-untranslated region. The present study also revealed that FOXD1 promotes p21 expression in a p53-independent manner. In conclusion, the results of the present study indicate a direct association between FOXD1 and p21 that may be mediated by miR-30a-5p and miR-200a-5p. The authors hypothesize that FOXD1 may serve as a biomarker or therapeutic target in HGSOC.