Functional and topological properties in hepatocellular carcinoma transcriptome

Anti-tumor target screening of sea cucumber saponin Frondoside A: a bioinformatics and molecular docking analysis

Cancer remains the leading cause of death worldwide. In spite of significant advances in targeted and immunotherapeutic approaches, clinical outcomes for cancer remain poor. The aim of the present study was to investigate the potential mechanisms and therapeutic targets of Frondoside A for the treatment of liver, pancreatic, and bladder cancers. The data presented in our study demonstrated that Frondoside A reduced the viability and migration of HepG2, Panc02, and UM-UC-3 cancer cell in vitro. Moreover, we utilized the GEO database to screen and identify for differentially expressed genes (DEGs) in liver, pancreatic, and bladder cancers, which resulted in the identification of 714, 357, and 101 DEGs, respectively. Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway annotation were performed using the Metascape database for DEGs that were significantly associated with cancer development. The protein-protein interaction (PPI) networks of the identified DEGs in liver, pancreatic, and bladder cancers were analyzed using Cytoscape 3.9.0 software, and subsequently identified potential key genes that were associated with these networks. Subsequently, their prognostic values were assessed by gene expression level analysis and Kaplan-Meier survival analysis (GEPIA). Furthermore, we utilized TIMER 2.0 to investigate the correlation between the expression of the identified key gene and cancer immune infiltration. Finally, molecular docking simulations were performed to assess the affinity of Frondoside A and key genes. Our results showed a significant correlation between these DEGs and cancer progression. Combined, these analyses revealed that Frondoside A involves in the regulation of multiple pathways, such as drug metabolism, cell cycle in liver cancer by inhibiting the expression of CDK1, TOP2A, CDC20, and KIF20A, and regulates protein digestion and absorption, receptor interaction in pancreatic cancer by down-regulation of ASPM, TOP2A, DLGAP5, TPX2, KIF23, MELK, LAMA3, and ANLN. While in bladder cancer, Frondoside A regulates muscle contraction, complement and coagulation cascade by increase FLNC expression. In conclusion, the present study offers valuable insights into the molecular mechanism underlying the anticancer effects of Frondoside A, and suggests that Frondoside A can be used as a functional food supplement or further developed as a natural anti-cancer drug.

HMGB2 is a potential diagnostic marker and therapeutic target for liver fibrosis and cirrhosis

BACKGROUND

High mobility group proteins 1 and 2 (HMGB1 and HMGB2) are 80% conserved in amino acid sequence. The function of HMGB1 in inflammation and fibrosis has been extensively characterized. However, an unaddressed central question is the role of HMGB2 on liver fibrosis. In this study, we provided convincing evidence that the HMGB2 expression was significantly upregulated in human liver fibrosis and cirrhosis, as well as in several mouse liver fibrosis models.

METHODS

The carbon tetrachloride (CCl4) induced liver fibrosis mouse model was used. AAV8-Hmgb2 was utilized to overexpress Hmgb2 in the liver, while Hmgb2-/- mice were used for loss of function experiments. The HMGB2 inhibitor inflachromene and liposome-shHMGB2 (lipo-shHMGB2) were employed for therapeutic intervention.

RESULTS

The serum HMGB2 levels were also markedly elevated in patients with liver fibrosis and cirrhosis. Deletion of Hmgb2 in Hmgb2-/- mice or inhibition of HMGB2 in mice using a small molecule ICM slowed the progression of CCl4-induced liver fibrosis despite constant HMGB1 expression. In contrast, AAV8-mediated overexpression of Hmgb2 enchanced CCl4-incuded liver fibrosis. Primary hepatic stellate cells (HSCs) isolated from Hmgb2-/- mice showed significantly impaired transdifferentiation and diminished activation of α-SMA, despite a modest induction of HMGB1 protein. RNA-seq analysis revealed the induction of top 45 CCl4-activated genes in multiple signaling pathways including integrin signaling and inflammation. The activation of these genes by CCl4 were abolished in Hmgb2-/- mice or in ICM-treated mice. These included C-X3-C motif chemokine receptor 1 (Cx3cr1) associated with inflammation, cyclin B (Ccnb) associated with cell cycle, DNA topoisomerase 2-alpha (Top2a) associated with intracellular component, and fibrillin (Fbn) and fibromodulin (Fmod) associated with extracellular matrix.

CONCLUSION

We conclude that HMGB2 is indispensable for stellate cell activation. Therefore, HMGB2 may serve as a potential therapeutic target to prevent HSC activation during chronic liver injury. The blood HMGB2 level may also serve as a potential diagnostic marker to detect early stage of liver fibrosis and cirrhosis in humans.

Differential methylation patterns in lean and obese non-alcoholic steatohepatitis-associated hepatocellular carcinoma

BACKGROUND

Nonalcoholic fatty liver disease affects about 24% of the world's population and may progress to nonalcoholic steatohepatitis (NASH), cirrhosis, and hepatocellular carcinoma (HCC). While more common in those that are obese, NASH-HCC can develop in lean individuals. The mechanisms by which HCC develops and the role of epigenetic changes in the context of obesity and normal weight are not well understood.

METHODS

In this study, we used previously generated mouse models of lean and obese HCC using a choline deficient/high trans-fat/fructose/cholesterol diet and a choline supplemented/high trans-fat/fructose/cholesterol diet, respectively, to evaluate methylation differences in HCC progression in lean versus obese mice. Differentially methylated regions were determined using reduced representation bisulfite sequencing.

RESULTS

A larger number of differentially methylated regions (DMRs) were seen in NASH-HCC progression in the obese mice compared to the non-obese mice. No overlap existed in the DMRs with the largest methylation differences between the two models. In lean NASH-HCC, methylation differences were seen in genes involved with cancer progression and prognosis (including HCC), such as CHCHD2, FSCN1, and ZDHHC12, and lipid metabolism, including PNPLA6 and LDLRAP1. In obese NASH- HCC, methylation differences were seen in genes known to be associated with HCC, including RNF217, GJA8, PTPRE, PSAPL1, and LRRC8D. Genes involved in Wnt-signaling pathways were enriched in hypomethylated DMRs in the obese NASH-HCC.

CONCLUSIONS

These data suggest that differential methylation may play a role in hepatocarcinogenesis in lean versus obese NASH. Hypomethylation of Wnt signaling pathway-related genes in obese mice may drive progression of HCC, while progression of HCC in lean mice may be driven through other signaling pathways, including lipid metabolism.

Network analysis of hepatocellular carcinoma liquid biopsies augmented by single-cell sequencing data

Liquid biopsy, the analysis of body fluids, represents a promising approach for disease diagnosis and prognosis with minimal intervention. Sequencing cell-free RNA derived from liquid biopsies has been very promising for the diagnosis of several diseases. Cancer research, in particular, has emerged as a prominent candidate since early diagnosis has been shown to be a critical determinant of disease prognosis. Although high-throughput analysis of liquid biopsies has uncovered many differentially expressed genes in the context of cancer, the functional connection between these genes is not investigated in depth. An important approach to remedy this issue is the construction of gene networks which describes the correlation patterns between different genes, thereby allowing to infer their functional organization. In this study, we aimed at characterizing extracellular transcriptome gene networks of hepatocellular carcinoma patients compared to healthy controls. Our analysis revealed a number of genes previously associated with hepatocellular carcinoma and uncovered their association network in the blood. Our study thus demonstrates the feasibility of performing gene co-expression network analysis from cell-free RNA data and its utility in studying hepatocellular carcinoma. Furthermore, we augmented cell-free RNA network analysis with single-cell RNA sequencing data which enables the contextualization of the identified network modules with cell-type specific transcriptomes from the liver.

Exploring the Multicomponent Synergy Mechanism of Yinzhihuang Granule in Inhibiting Inflammation-Cancer Transformation of Hepar Based on Integrated Bioinformatics and Network Pharmacology

Background

The Chinese patent drug Yinzhihuang granule (YZHG) is used to treat hepatitis B. This research is aimed at exploring the multicomponent synergistic mechanism of YZHG in the treatment of inflammation-cancer transformation of hepar and at providing new evidence and insights for its clinical application.

Methods

To retrieve the components and targets of Yinzhihuang granules. The differentially expressed genes (DEGs) of hepar inflammation-cancer transformation were obtained from TTD, PharmGKB, and GEO databases. Construct the compound-prediction target network and the key module network using Cytoscape 3.7.1.

Results

The results show that hepatitis B and hepatitis C shared a common target, MMP2. CDK1 and TOP2A may play an important role in the treatment with YZHG in hepatitis B inflammatory cancer transformation. KEGG pathway enrichment showed that key genes of modules 1, 2, and 4 were mainly enriched in the progesterone-mediated oocyte maturation signaling pathway and oocyte meiosis signaling pathway.

Conclusion

The multicomponent, multitarget, and multichannel pharmacological benefits of YZHG in the therapy of inflammation-cancer transition of hepar are directly demonstrated by network pharmacology, providing a scientific basis for its mechanism.

Conjoint analysis for hepatic carcinoma with hub genes and multi-slice spiral CT

Hepatic carcinoma (HCC) is a common malignant tumor, with insidious onset and poor prognosis. However, more hub genes associated with hepatocellular carcinoma are unknown. And there are few researches about the conjoint analysis with the hub genes and multi-slice spiral computerized tomography (CT).A total of 100 HCC participates were recruited, who all received the examination of multi-slice spiral CT. Two expression profile data sets (GSE101728 and GSE101685) were downloaded from the Gene Expression Omnibus (GEO) database. GEO2R can perform a command to compare gene expression profiles between groups in order to identify differently expressed genes (DEGs). Functional annotation of DEGs via Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis was made with Database for Annotation, Visualization, and Integrated Discovery (DAVID). Construction and analysis of protein-protein interaction network were performed. Furthermore, the study could mine of hub genes and explore the correlation with the multi-slice CT. Real-time quantitative polymerase chain reaction (RT-qPCR) assay was used the exam the expression of hub genes.A total of 10 genes were identified as hub genes with degrees ≥10. The hub genes (NIMA Related Kinase 2 [NEK2], Anillin Actin Binding Protein [ANLN], DNA Topoisomerase II Alpha [TOP2A], Centromere Protein F [CENPF], Assembly Factor For Spindle Microtubules [ASPM], Cell Division Cycle 20 [CDC20], Cyclin Dependent Kinase 1 [CDK1], Cyclin B1 [CCNB1], Epithelial Cell Transforming 2 [ECT2], Cyclin B2 [CCNB2]) were identified from the Molecular Complex Detection (MCODE) network. These hub genes were highly expressed in HCC tissues, and when these genes were highly expressed, the survival prognosis of HCC patients was poor. The type of CT enhancement was significantly related with the expression of NEK2 (P < .001), ANLN (P < .001), and TOP2A (P = .006).The combination between the gene expression (NEK2, ANLN, and TOP2A) and type of CT enhancement might provide a new idea for future basic research and targeted therapy of HCC.

Stemness Related Genes Revealed by Network Analysis Associated With Tumor Immune Microenvironment and the Clinical Outcome in Lung Adenocarcinoma

Lung adenocarcinoma (LUAD) is one of the leading fatal malignancy with high morbidity and mortality worldwide. However, due to its complicated mechanism and lack of effective clinical therapeutics, early diagnosis and prognosis are still unsatisfactory. Most of the previous studies focused on cancer stem cells (CSCs), the relationship between cancer stemness (stem-like characteristics) and anti-tumor immunity has not been clearly revealed. Therefore, this study aimed to comprehensively analyze the role of cancer stemness and tumor microenvironment (TME) in LUAD using weighted gene co-expression network analysis (WGCNA). We constructed a gene co-expression network, identified key modules, and hub genes, and further explored the relationship between hub gene expression and cancer immunological characteristics through a variety of algorithms, including Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE) and Gene Set Enrichment Analysis (GSEA). The hub genes were renamed stemness related genes (SRGs), whose functions were examined at the transcription and protein levels through survival analysis with additional samples, Oncomine database, immunohistochemistry, single cell RNA sequencing (scRNA-seq) and single-sample Gene Set Enrichment Analysis (ssGSEA). Subsequently, Tumor Immune Dysfunction and Exclusion (TIDE) and Connectivity Map (CMap) were implemented for treatment and prognosis analyses. As a result, 15 co-expressed SRGs (CCNA2, CCNB1, CDC20, CDCA5, CDCA8, FEN1, KIF2C, KPNA2, MCM6, NUSAP1, RACGAP1, RRM2, SPAG5, TOP2A, and TPX2) were identified. The overexpression of which was discovered to be associated with reduced immune infiltration in LUAD. It was discovered that there was a general negative correlation between cancer stemness and immunity. The expression of SRGs could probably affect our tumor occurrence, progression, the efficacy of chemotherapy and immunotherapy, and clinical outcomes. In conclusion, the 15 SRGs reported in our study may be used as potential candidate biomarkers for prognostic indicators and therapeutic targets after further validation.

Identification of Time-Invariant Biomarkers for Non-Genotoxic Hepatocarcinogen Assessment

Non-genotoxic hepatocarcinogens (NGHCs) can only be confirmed by 2-year rodent studies. Toxicogenomics (TGx) approaches using gene expression profiles from short-term animal studies could enable early assessment of NGHCs. However, high variance in the modulation of the genes had been noted among exposure styles and datasets. Expanding from our previous strategy in identifying consensus biomarkers in multiple experiments, we aimed to identify time-invariant biomarkers for NGHCs in short-term exposure styles and validate their applicability to long-term exposure styles. In this study, nine time-invariant biomarkers, namely A2m, Akr7a3, Aqp7, Ca3, Cdc2a, Cdkn3, Cyp2c11, Ntf3, and Sds, were identified from four large-scale microarray datasets. Machine learning techniques were subsequently employed to assess the prediction performance of the biomarkers. The biomarker set along with the Random Forest models gave the highest median area under the receiver operating characteristic curve (AUC) of 0.824 and a low interquartile range (IQR) variance of 0.036 based on a leave-one-out cross-validation. The application of the models to the external validation datasets achieved high AUC values of greater than or equal to 0.857. Enrichment analysis of the biomarkers inferred the involvement of chronic inflammatory diseases such as liver cirrhosis, fibrosis, and hepatocellular carcinoma in NGHCs. The time-invariant biomarkers provided a robust alternative for NGHC prediction.

Identifying novel biomarkers in hepatocellular carcinoma by weighted gene co-expression network analysis

Hepatocellular carcinoma (HCC) is a highly malignant tumor found in the bile duct epithelial cells, and the second most common tumor of the liver. However, the pivotal roles of most molecules of tumorigenesis in HCC are still unclear. Hence, it is essential to detect the tumorigenic mechanism and develop novel prognostic biomarkers for clinical application. The data of HCC mRNA-seq and clinical information from The Cancer Genome Atlas (TCGA) database were analyzed by weighted gene co-expression network analysis (WGCNA). Co-expression modules and clinical traits were constructed by the Pearson correlation analysis, interesting modules were selected and gene ontology and pathway enrichment analysis were performed. Intramodule analysis and protein-protein interaction construction of selected modules were conducted to screen hub genes. In addition, upstream transcription factors and microRNAs of hub genes were predicted by miRecords and NetworkAnalyst database. Afterward, a high connectivity degree of hub genes from two networks was picked out to perform the differential expression validation in the Gene Expression Profiling Interactive Analysis database and Human Protein Atlas database and survival analysis in Kaplan-Meier plotter online tool. By utilizing WGCNA, several hub genes that regulate the mechanism of tumorigenesis in HCC were identified, which was associated with clinical traits including the pathological stage, histological grade, and liver function. Surprisingly, ZWINT, CENPA, RACGAP1, PLK1, NCAPG, OIP5, CDCA8, PRC1, and CDK1 were identified statistically as hub genes in the blue module, which were closely implicated in pathological T stage and histologic grade of HCC. Moreover, these genes also were strongly associated with the HCC cell growth and division. Network and survival analyses found that nine hub genes may be considered theoretically as indicators to predict the prognosis of patients with HCC or clinical treatment target, it will be necessary for basic experiments and large-scale cohort studies to validate further.

Network-based integrated analysis of omics data reveal novel players of TGF-β1-induced EMT in human peritoneal mesothelial cells

Long-term peritoneal dialysis is associated with progressive fibrosis of the peritoneum. Epithelial-mesenchymal transition (EMT) of mesothelial cells is an important mechanism involved in peritoneal fibrosis, and TGF-β1 is considered central in this process. However, targeting currently known TGF-β1-associated pathways has not proven effective to date. Therefore, there are still gaps in understanding the mechanisms underlying TGF-β1-associated EMT and peritoneal fibrosis. We conducted network-based integrated analysis of transcriptomic and proteomic data to systemically characterize the molecular signature of TGF-β1-stimulated human peritoneal mesothelial cells (HPMCs). To increase the power of the data, multiple expression datasets of TGF-β1-stimulated human cells were employed, and extended based on a human functional gene network. Dense network sub-modules enriched with differentially expressed genes by TGF-β1 stimulation were prioritized and genes of interest were selected for functional analysis in HPMCs. Through integrated analysis, ECM constituents and oxidative stress-related genes were shown to be the top-ranked genes as expected. Among top-ranked sub-modules, TNFAIP6, ZC3H12A, and NNT were validated in HPMCs to be involved in regulation of E-cadherin, ZO-1, fibronectin, and αSMA expression. The present data shows the validity of network-based integrated analysis in discovery of novel players in TGF-β1-induced EMT in peritoneal mesothelial cells, which may serve as new prognostic markers and therapeutic targets for peritoneal dialysis patients.

Overexpression of the ASPM gene is associated with aggressiveness and poor outcome in bladder cancer

Abnormal spindle-like microcephaly-associated (ASPM) protein is essential for mitotic spindle function during cell replication. The present study aimed to evaluate the hypothesis that ASPM serves a critical role in cancer invasiveness and may act as a prognostic biomarker in bladder cancer. In total, 6 independent worldwide bladder cancer microarray mRNA expression datasets (n=1,355) with clinical and follow-up annotations were collected from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. Reverse transcription-quantitative polymerase chain reaction analysis revealed that ASPM mRNA expression was higher in bladder cancer tissue compared with adjacent normal bladder mucosae in 10 paired human tissue samples (P=0.004). ASPM overexpression in human bladder cancer samples was consistent with the mRNA expression datasets from GEO and TCGA. Bioinformatics analysis indicated that ASPM mRNA expression was significantly associated with grade and tumor node metastasis (TNM) stage in bladder cancer, based on pooled GEO and TCGA datasets (P<0.05). Stratification analysis indicated that the clinical significance of ASPM was particularly pronounced in low-grade or papillary subtypes of bladder cancer. Individual Cox and pooled Kaplan-Meier analyses suggested that ASPM expression was significantly directly correlated with poor overall (OS) and progression-free survival (PFS) in bladder cancer. Multivariate and stratification analyses demonstrated that the prognostic significance of ASPM was evident in low-grade or papillary bladder cancers, yet not in high-grade or non-papillary subgroups. Increased expression of ASPM was associated with poor OS in muscle-invasive bladder cancer and with poor PFS in non-muscle-invasive bladder cancer (P<0.05). Bioinformatics analysis identified the top 11 ASPM-related genes on STRING-DB.org. The expression of the majority of these genes was associated with poor outcomes of bladder cancer with statistical significance. Gene set enrichment analysis indicated that the high expression of ASPM could enrich gene signatures involved in mitosis, differentiation and metastasis in bladder cancer. Further analysis of TCGA datasets indicated that increased ASPM expression was significantly associated with higher Gleason score, T stage, N stage and poor clinical outcome in prostate cancer. It was also significantly associated with late TNM stage and poor PFS in renal cell carcinoma. In summary, ASPM may serve as a novel prognostic biomarker for low-grade or papillary bladder cancer.

Effects of harmaline on cell growth of human liver cancer through the p53/p21 and Fas/FasL signaling pathways

The effects of harmaline on the viability and apoptosis of human liver carcinoma were investigated in vitro. HepG2 cells were treated with harmaline (0-10 µM), and the proliferation and apoptosis of HepG2 cells were investigated using an MTT assay and flow cytometry, respectively. The protein expression of cellular tumor antigen p53 (p53), cyclin-dependent kinase inhibitor 1 (p21), tumor necrosis factor receptor superfamily member 6 (Fas), Fas ligand (FasL) and caspase-8 was subsequently measured using western blotting. In addition, an ELISA was used to analyze caspase-8/3 activity. Harmaline significantly increased p53, p21, Fas and FasL protein expression in HepG2 cells. Additionally, treatment with harmaline significantly increased the expression of caspase-8 and caspase-8/3 activity. The results from the present study suggest that harmaline suppresses the viability, but induces the apoptosis, of human liver carcinoma cells through upregulation of the p53/p21 and Fas/FasL signaling pathways.

NIMA-related kinase 2 regulates hepatocellular carcinoma cell growth and proliferation

NIMA-related kinase 2 (Nek2) is often upregulated in human cancer and is important in regulating the cell cycle and gene expression, and maintaining centrosomal structure and function. The present study aimed to investigate the expression pattern, clinical significance, and biological function of Nek2 in hepatocellular carcinoma (HCC). mRNA and protein levels of Nek2 were examined in HCC and corresponding normal liver tissues. The MTT and soft agar colony formation assays, and flow cytometry were employed to assess the roles of Nek2 in cell proliferation and growth. In addition, western blot analysis was performed to assess the expression of cell cycle- and proliferation-related proteins. The results revealed that Nek2 was upregulated in HCC tissues and cell lines. The clinical significance of Nek2 expression was also analyzed. Inhibiting Nek2 expression by siRNA suppressed cell proliferation, growth, and colony formation in hepatocellular carcinoma cell line HepG2 cells, induced cell cycle arrest in the G2/M phase by retarding the S-phase, and promoted apoptosis. Furthermore, Nek2 depletion downregulated β-catenin expression in HepG2 cells and diminished expression of Myc proto-oncogene protein (c-Myc), cyclins D1, B1, and E and cyclin-dependent kinase 1, whilst increasing protein levels of p27. This demonstrates that overexpression of Nek2 is associated with the malignant evolution of HCC. Targeting Nek2 may inhibit HCC cell growth and proliferation through the regulation of β-catenin by the Wnt/β-catenin pathway and therefore may be developed as a novel therapeutic strategy to treat HCC.

Novel chemoimmunotherapeutic strategy for hepatocellular carcinoma based on a genome-wide association study

Pharmacotherapeutic options are limited for hepatocellular carcinoma (HCC). Recently, we identified the anti-tumor ligand MHC class I polypeptide-related sequence A (MICA) gene as a susceptibility gene for hepatitis C virus-induced HCC in a genome-wide association study (GWAS). To prove the concept of HCC immunotherapy based on the results of a GWAS, in the present study, we searched for drugs that could restore MICA expression. A screen of the FDA-approved drug library identified the anti-cancer agent vorinostat as the strongest hit, suggesting histone deacetylase inhibitors (HDACis) as potent candidates. Indeed, the HDACi-induced expression of MICA specific to HCC cells enhanced natural killer (NK) cell-mediated cytotoxicity in co-culture, which was further reinforced by treatment with an inhibitor of MICA sheddase. Similarly augmented anti-tumor activity of NK cells via NK group 2D was observed in vivo. Metabolomics analysis revealed HDACi-mediated alterations in energy supply and stresses for MICA induction and HCC inhibition, providing a mechanism for the chemoimmunotherapeutic actions. These data are indicative of promising strategies for selective HCC innate immunotherapy.

Increased NEK2 in hepatocellular carcinoma promotes cancer progression and drug resistance by promoting PP1/Akt and Wnt activation

NIMA-related expressed kinase 2 (NEK2) participates in the carcinogenesis and progression of certain types of cancer, however, its expression and roles in the development of hepatocellular carcinoma (HCC) remains unknown. Here, we found that NEK2 expression was significantly upregulated in both human HCC tissues and cell lines, and increased NEK2 expression in HCC was significantly correlated with clinical progression of HCC in patients. Knockdown of NEK2 in HCC cells inhibited HCC progression, as determined by the suppressed cell proliferation, invasion and metastasis. Furthermore, knockdown of NEK2 inhibited drug resistance of HCC cells, as shown by the promoted suppression of cell viability in 5-fluorouracil (5‑FU)‑treated HCC cells. Mechanistically, protein phosphatase 1 (PP1)/Akt and Wnt signaling activation are significantly inhibited by NEK2 knockdown, which is responsible for the HCC progression and involved in NEK2‑induced cancer cell abnormal biological behavior. Thus, enhanced NEK2 expression in HCC promotes HCC progression and drug resistance by promoting PP1/Akt and Wnt pathway activation, which may represent a new therapeutic target for HCC.

CDKN3 expression is negatively associated with pathological tumor stage and CDKN3 inhibition promotes cell survival in hepatocellular carcinoma

Aberrant expression of CDKN3 may be involved in carcinogenesis of liver cancer. The effect of CDKN3 on tumorigenesis and the molecular mechanisms involved have not been fully elucidated. Immunohistochemistry was performed to detect CDKN3 expression levels in tumor tissues. CDKN3 siRNA was used to knockdown CDKN3 in QGY7701 hepatocellular carcinoma (HCC) cells. Colony formation assay was used to measure the clonogenic capacity of the tumor cells. Cell viability was determined by MTT assay. Logistic regression was performed to analyze the association between CDKN3 expression level and the HCC clinical pathology index. The CDKN3 expression level was significantly decreased in HCC tumor tissues compared with normal liver tissue and liver cirrhosis tissue. Additionally, CDKN3 expression was negatively-associated with the pathological stage of the tumor. Inhibition of CKDN3 promoted the clonogenic capacity and chemotherapeutic tolerance in HCC tissues compared with controls. Knockdown of CDKN3 resulted in downregulation of p53 and p21 protein levels, whereas, AKT serine/threonine kinase 1 expression was upregulated. Thus, CDKN3 expression may reduce the survival of tumor cells and alter the sensitivity to therapeutic agents via the AKT/P53/P21 signaling pathway. Therefore, CDKN3 may be involved in tumor differentiation and self-renewal.

Expression of aurora kinase A correlates with the Wnt-modulator RACGAP1 in gastric cancer

Canonical Wnt signaling is involved in gastric carcinogenesis. The aim of this study was to identify the link between Wnt signaling and aurora kinase A (AURKA), a target for the treatment of gastrointestinal cancers. Publicly available microarray data were used to identify phenotype‐specific protein–protein interaction (PPI) subnetworks. The in silico analysis revealed a gastric cancer‐specific PPI subnetwork consisting of 2745 proteins and 50,935 interactions. We focused on the link of AURKA to a Wnt‐specific interaction module consisting of 92 proteins. There was a direct association of AURKA with Rac GTPase‐activating protein 1 (RACGAP1), as well as with CTNBB1 (β‐catenin) and CDKN1A as second‐order interactors. Differential expression analysis revealed a significant downregulation of both AURKA and RACGAP1 in gastric cancer compared to noncancer controls. Biopsies from a prospective cohort of 56 patients with gastric cancer (32 intestinal type, 24 diffuse type) and 20 noncancer controls were used for validation of the identified targets. The RT‐PCR data confirmed a strong correlation of AURKA and RACGAP1 gene expression both in the tumor, the tumor‐adjacent and the tumor‐distant mucosa. RACGAP1 in the tumor was also associated with CTNBB1 expression, and inversely associated with CDKN1A gene expression. Immunohistochemistry confirmed expression of the RACGAP1 protein in gastric cancer and the tumor‐adjacent mucosa. RACGAP1 expression was not associated with tumor stage, grading, Lauren type, Helicobacter pylori infection, or age. In conclusion, AURKA is directly associated with the expression of RACGAP1, a modulator of the canonical Wnt signaling pathway.

Diffusion of information throughout the host interactome reveals gene expression variations in network proximity to target proteins of hepatitis C virus

Hepatitis C virus infection is one of the most common and chronic in the world, and hepatitis associated with HCV infection is a major risk factor for the development of cirrhosis and hepatocellular carcinoma (HCC). The rapidly growing number of viral-host and host protein-protein interactions is enabling more and more reliable network-based analyses of viral infection supported by omics data. The study of molecular interaction networks helps to elucidate the mechanistic pathways linking HCV molecular activities and the host response that modulates the stepwise hepatocarcinogenic process from preneoplastic lesions (cirrhosis and dysplasia) to HCC. Simulating the impact of HCV-host molecular interactions throughout the host protein-protein interaction (PPI) network, we ranked the host proteins in relation to their network proximity to viral targets. We observed that the set of proteins in the neighborhood of HCV targets in the host interactome is enriched in key players of the host response to HCV infection. In opposition to HCV targets, subnetworks of proteins in network proximity to HCV targets are significantly enriched in proteins reported as differentially expressed in preneoplastic and neoplastic liver samples by two independent studies. Using multi-objective optimization, we extracted subnetworks that are simultaneously “guilt-by-association” with HCV proteins and enriched in proteins differentially expressed. These subnetworks contain established, recently proposed and novel candidate proteins for the regulation of the mechanisms of liver cells response to chronic HCV infection.

RacGTPase-activating protein 1 interacts with hepatitis C virus polymerase NS5B to regulate viral replication

Hepatitis C virus (HCV) is a positive-strand RNA virus responsible for chronic liver disease and hepatocellular carcinoma (HCC). RacGTPase-activating protein 1 (RacGAP1) plays an important role during GTP hydrolysis to GDP in Rac1 and CDC42 protein and has been demonstrated to be upregulated in several cancers, including HCC. However, the molecular mechanism leading to the upregulation of RacGAP1 remains poorly understood. Here, we showed that RacGAP1 levels were enhanced in HCV cell-culture-derived (HCVcc) infection. More importantly, we illustrated that RacGAP1 interacts with the viral protein NS5B in mammalian cells. The small interfering RNA (siRNA)-mediated knockdown of RacGAP1 in human hepatoma cell lines inhibited replication of HCV RNA, protein, and production of infectious particles of HCV genotype 2a strain JFH1. Conversely, these were reversed by the expression of a siRNA-resistant RacGAP1 recombinant protein. In addition, viral protein NS5B polymerase activity was significantly reduced by silencing RacGAP1 and, vice versa, was increased by overexpression of RacGAP1 in a cell-based reporter assay. Our results suggest that RacGAP1 plays a crucial role in HCV replication by affecting viral protein NS5B polymerase activity and holds importance for antiviral drug development.

Gene network-based analysis identifies two potential subtypes of small intestinal neuroendocrine tumors

BACKGROUND

Tumor transcriptomes contain information of critical value to understanding the different capacities of a cell at both a physiological and pathological level. In terms of clinical relevance, they provide information regarding the cellular "toolbox" e.g., pathways associated with malignancy and metastasis or drug dependency. Exploration of this resource can therefore be leveraged as a translational tool to better manage and assess neoplastic behavior. The availability of public genome-wide expression datasets, provide an opportunity to reassess neuroendocrine tumors at a more fundamental level. We hypothesized that stringent analysis of expression profiles as well as regulatory networks of the neoplastic cell would provide novel information that facilitates further delineation of the genomic basis of small intestinal neuroendocrine tumors.

RESULTS

We re-analyzed two publically available small intestinal tumor transcriptomes using stringent quality control parameters and network-based approaches and validated expression of core secretory regulatory elements e.g., CPE, PCSK1, secretogranins, including genes involved in depolarization e.g., SCN3A, as well as transcription factors associated with neurodevelopment (NKX2-2, NeuroD1, INSM1) and glucose homeostasis (APLP1). The candidate metastasis-associated transcription factor, ST18, was highly expressed (>14-fold, p < 0.004). Genes previously associated with neoplasia, CEBPA and SDHD, were decreased in expression (-1.5 - -2, p < 0.02). Genomic interrogation indicated that intestinal tumors may consist of two different subtypes, serotonin-producing neoplasms and serotonin/substance P/tachykinin lesions. QPCR validation in an independent dataset (n = 13 neuroendocrine tumors), confirmed up-regulated expression of 87% of genes (13/15).

CONCLUSIONS

An integrated cellular transcriptomic analysis of small intestinal neuroendocrine tumors identified that they are regulated at a developmental level, have key activation of hypoxic pathways (a known regulator of malignant stem cell phenotypes) as well as activation of genes involved in apoptosis and proliferation. Further refinement of these analyses by RNAseq studies of large-scale databases will enable definition of individual master regulators and facilitate the development of novel tissue and blood-based tools to better understand diagnose and treat tumors.

Identification of the RNA recognition element of the RBPMS family of RNA-binding proteins and their transcriptome-wide mRNA targets

Recent studies implicated the RNA-binding protein with multiple splicing (RBPMS) family of proteins in oocyte, retinal ganglion cell, heart, and gastrointestinal smooth muscle development. These RNA-binding proteins contain a single RNA recognition motif (RRM), and their targets and molecular function have not yet been identified. We defined transcriptome-wide RNA targets using photoactivatable-ribonucleoside-enhanced crosslinking and immunoprecipitation (PAR-CLIP) in HEK293 cells, revealing exonic mature and intronic pre-mRNA binding sites, in agreement with the nuclear and cytoplasmic localization of the proteins. Computational and biochemical approaches defined the RNA recognition element (RRE) as a tandem CAC trinucleotide motif separated by a variable spacer region. Similar to other mRNA-binding proteins, RBPMS family of proteins relocalized to cytoplasmic stress granules under oxidative stress conditions suggestive of a support function for mRNA localization in large and/or multinucleated cells where it is preferentially expressed.

Deregulation of microcephalin and ASPM expression are correlated with epithelial ovarian cancer progression

Mutations in the MCPH1 (Microcephalin) and ASPM (abnormal spindle-like microcephaly associated) genes cause primary microcephaly. Both are centrosomal associated proteins involved in mitosis. Microcephalin plays an important role in DNA damage response and ASPM is required for correct division of proliferative neuro-epithelial cells of the developing brain. Reduced MCPH1 mRNA expression and ASPM mRNA over-expression have been implicated in the development of human carcinomas. Epithelial ovarian cancer (EOC) is characterised by highly aneuploid tumours. Previously we have reported low Microcephalin and high ASPM protein levels and associations with clinico-pathological parameters in malignant cells from ascitic fluids. To confirm these previous findings on a larger scale Microcephalin and ASPM expression levels and localisations were evaluated by immunohistochemistry in two cohorts; a training set of 25 samples and a validation set of 322 EOC tissue samples. Results were correlated to the associated histopathological data. In normal ovarian tissues the Microcephalin nuclear staining pattern was consistently strong. In the cancer tissues, we identified low nuclear Microcephalin expression in high grade and advanced stage tumours (p<0.0001 and p = 0.0438 respectively). ASPM had moderate to high nuclear and low to moderate cytoplasmic expression in normal tissue. Cytoplasmic ASPM expression decreased with tumour grade and stage in the serous subtype of EOC (p = 0.023 and p = 0.011 respectively). Cytoplasmic ASPM increased with tumour stage in the endometrioid subtype (p = 0.023). Increasing tumour invasiveness (T3) and lymph node involvement (N1) also correlated with a decrease in cytoplasmic ASPM in EOC (p = 0.02 and p = 0.04 respectively). We have validated previous findings of deregulated expression of Microcephalin and ASPM in EOC by confirming associations for low nuclear Microcephalin levels and high cytoplasmic ASPM levels in a larger scale tumour tissue study. Microcephalin and ASPM may prove useful biomarkers in EOC.

Identifying hepatocellular carcinoma-related genes and pathways by system biology analysis

BACKGROUND

Previous researches have been focused on revealing the functions of each individual gene and/or pathway in the initiation, progression and maintenance of hepatocellular carcinoma (HCC). However, the mechanistic relationships among different genes and/or pathways are largely unknown.

AIMS

In this study, we tended to uncover the potential molecular networks and critical genes which play important roles in HCC progression.

METHODS

The transcriptional profiles from normal and HCC patient samples were analyzed and compared using bioinformatic methods, including differentially expressed gene (DEG) analysis, hierarchical clustering, construction of protein-protein interaction (PPI) network and GO-Elite analysis.

RESULTS

Initially, the normal and HCC sample data were processed and 679 most dramatic DEGs were identified. The PPI network analysis indicates the significance of multiple biological processes as well as signaling pathways in affecting liver function and HCC progression. In addition, hierarchical clustering analysis showed the most significant modules and identified the relationship between different genes, and some important genes such as FOS, IGF1, ADH4, ITGA2 and LEF1 were found to be hubs which master each individual module.

CONCLUSION

Our study greatly improves the understanding of the HCC development in a systematic manner and provides the potential clue for exploiting drugs which might target the most significant genes and/or signaling pathways.