Screening and function analysis of hub genes and pathways in hepatocellular carcinoma via bioinformatics approaches

Role and mechanism of NCAPD3 in promoting malignant behaviors in gastric cancer

Background

Gastric cancer (GC) is one of the major malignancies threatening human lives and health. Non-SMC condensin II complex subunit D3 (NCAPD3) plays a crucial role in the occurrence of many diseases. However, its role in GC remains unexplored.

Materials and Methods

The Cancer Genome Atlas (TCGA) database, clinical samples, and cell lines were used to analyze NCAPD3 expression in GC. NCAPD3 was overexpressed and inhibited by lentiviral vectors and the CRISPR/Cas9 system, respectively. The biological functions of NCAPD3 were investigated in vitro and in vivo. Gene microarray, Gene set enrichment analysis (GSEA) and ingenuity pathway analysis (IPA) were performed to establish the potential mechanisms.

Results

NCAPD3 was highly expressed in GC and was associated with a poor prognosis. NCAPD3 upregulation significantly promoted the malignant biological behaviors of gastric cancer cell, while NCAPD3 inhibition exerted a opposite effect. NCAPD3 loss can directly inhibit CCND1 and ESR1 expression to downregulate the expression of downstream targets CDK6 and IRS1 and inhibit the proliferation of gastric cancer cells. Moreover, NCAPD3 loss activates IRF7 and DDIT3 to regulate apoptosis in gastric cancer cells.

Conclusion

Our study revealed that NCAPD3 silencing attenuates malignant phenotypes of GC and that it is a potential target for GC treatment.

The role of Cyclin Dependent Kinase Inhibitor 3 (CDKN3) in promoting human tumors: Literature review and pan-cancer analysis

Background

Although many experiments and clinical studies have proved the link between the expression of CDKN3 and human tumors, we have not been able to identify any bioinformatics study in which the extensive tumor-promoting effect of CDKN3 was systematically analyzed.

Objective

Explore the extensive tumor-promoting effects of CDKN3 and review the research progress of CDKN3 in cancer.

Methods

We systematically reviewed the literature on CDKN3 and tumors. We explored the potential tumor-promoting effects of CDKN3 on different tumors in the TCGA database and the GTEx database using multiple platforms and websites. We studied the expression level of CDKN3, survival, prognosis, diagnosis, genetic variation, immune infiltration, and enrichment analysis using databases such as TIMER 2.0, GEPIA2, cBioPortal, and STRING.

Results

We found that CDKN3 is highly expressed in most tumors. The expression of CDKN3 is closely related to the prognosis of some tumors. And CDKN3 may have diagnostic value. The conclusion of our literature review is roughly the same, but there are differences, which are worthy of further study. Moreover, CDKN3 may be related to immune cell infiltration in tumor tissues. The genetic alteration of LUAD, STAD, SARC, PCPG, and ESCA with "Amplification" as the main type. In addition, through enrichment analysis, we found that CDKN3 affects tumors mainly through the control of the cell cycle and mitosis.

Conclusion

CDKN3 is highly expressed in most tumor tissues and has a statistical correlation with survival prognosis. It has extensive tumor-promoting effects that may be related to mechanisms such as immune infiltration.

A systematic study on the treatment of hepatitis B-related hepatocellular carcinoma with drugs based on bioinformatics and key target reverse network pharmacology and experimental verification

BACKGROUND

Chronic hepatitis B virus (HBV) infection is the major etiology of hepatocellular carcinoma (HCC). However, the mechanism of hepatitis B-related hepatocellular carcinoma (HBV-related HCC) is still unclear. Therefore, understanding the pathogenesis and searching for drugs to treat HBV-related HCC was an effective strategy to treat this disease.

PURPOSE

Bioinformatics was used to predict the potential targets of HBV-related HCC. The reverse network pharmacology of key targets was used to analyze the clinical drugs, traditional Chinese medicine (TCM) and small molecules of TCM in the treatment of HBV-related HCC.

METHODS

In this study, three microarray datasets totally containing 330 tumoral samples and 297 normal samples were selected from the GEO database. These microarray datasets were used to screen DEGs. And the expression profile and survival of 6 key genes were analyzed. In addition, Comparative Toxicogenomics Database and Coremine Medical database were used to enrich clinical drugs and TCM of HBV-related HCC by the 6 key targets. Then the obtained TCM were classified based on the Chinese Pharmacopoeia. Among these top 6 key genes, CDK1 and CCNB1 had the most connection nodes and the highest degree and were the most significantly expressed. In general, CDK1 and CCNB1 tend to form a complex, which is conducive to cell mitosis. Hence, this study mainly studied CDK1 and CCNB1. HERB database was used to predict small molecules TCM. The inhibition effect of quercetin, celastrol and cantharidin on HepG2.2.15 cells and Hep3B cells was verified by CCK8 experiment. The effects of quercetin, celastrol and cantharidin on CDK1 and CCNB1 of HepG2.2.15 cells and Hep3B cells were determined by Western Blot.

RESULTS

In short, 272 DEGs (53 upregulated and 219 downregulated) were identified. Among these DEGs, 6 key genes with high degree were identified, which were AURKA, BIRC5, CCNB1, CDK1, CDKN3 and TYMS. Kaplan-Meier plotter analysis showed that higher expression levels of AURKA, BIRC5, CCNB1, CDK1, CDKN3 and TYMS were associated with poor OS. According to the first 6 key targets, a variety of drugs and TCM were identified. These results showed that clinical drugs included targeted drugs, such as sorafenib, palbociclib and Dasatinib. and chemotherapy drugs, such as cisplatin and doxorubicin. TCM, such as the TCM flavor was mainly warm and bitter, and the main meridians were liver and lung. Small molecules of TCM included flavonoids, terpenoids, alkaloids and glycosides, such as quercetin, celastrol, cantharidin, hesperidin, silymarin, casticin, berberine and ursolic acid, which have great potential in anti-HBV-related HCC. For molecular docking of chemical components, the molecules with higher scores were flavonoids, alkaloids, etc. Three representative types of TCM small molecules were verified respectively, and it was found that quercetin, celastrol and cantharidin inhibited the proliferation of HepG2.2.15 cells and Hep3B cells along concentration gradient. Quercetin, celastrol and cantharidin decreased CDK1 expression in HepG2.2.15 and Hep3B cells, but for CCNB1, only cantharidin decreased CCNB1 expression in the two strains of cells.

CONCLUSION

In conclusion, AURKA, BIRC5, CCNB1, CDK1, CDKN3 and TYMS could be potential targets for the diagnosis and prognosis of HBV-related HCC. Clinical drugs include chemotherapeutic and targeted drug, traditional Chinese medicine is mainly bitter and warm TCM. Small molecular of TCM including flavonoids, terpenoids and glycosides and alkaloids, which have great potential in anti-HBV-related HCC. This study provides potential therapeutic targets and novel strategies for the treatment of HBV-related HCC.

Clinical impact of artificial intelligence-based solutions on imaging of the pancreas and liver

Artificial intelligence (AI) has experienced substantial progress over the last ten years in many fields of application, including healthcare. In hepatology and pancreatology, major attention to date has been paid to its application to the assisted or even automated interpretation of radiological images, where AI can generate accurate and reproducible imaging diagnosis, reducing the physicians’ workload. AI can provide automatic or semi-automatic segmentation and registration of the liver and pancreatic glands and lesions. Furthermore, using radiomics, AI can introduce new quantitative information which is not visible to the human eye to radiological reports. AI has been applied in the detection and characterization of focal lesions and diffuse diseases of the liver and pancreas, such as neoplasms, chronic hepatic disease, or acute or chronic pancreatitis, among others. These solutions have been applied to different imaging techniques commonly used to diagnose liver and pancreatic diseases, such as ultrasound, endoscopic ultrasonography, computerized tomography (CT), magnetic resonance imaging, and positron emission tomography/CT. However, AI is also applied in this context to many other relevant steps involved in a comprehensive clinical scenario to manage a gastroenterological patient. AI can also be applied to choose the most convenient test prescription, to improve image quality or accelerate its acquisition, and to predict patient prognosis and treatment response. In this review, we summarize the current evidence on the application of AI to hepatic and pancreatic radiology, not only in regard to the interpretation of images, but also to all the steps involved in the radiological workflow in a broader sense. Lastly, we discuss the challenges and future directions of the clinical application of AI methods.

Cell cycle kinases (AUKA, CDK1, PLK1) are prognostic biomarkers and correlated with tumor-infiltrating leukocytes in HBV related HCC

Hepatocellular carcinoma (HCC) is one of the high incidence cancers and third leading cause of cancer-related mortality. HBV is the top most risk factor accounting for 50-80% of the HCC cases. Kinases: Aurora kinase A (AURKA), cyclin-dependent kinase (CDK1) and Polo-like kinase 1 (PLK1), the key regulators of cell mitosis are overexpressed in varieties of cancers including HCC. However, the exact role of these genes in prognosis of HCC is not fully unveiled. In addition, there is no such an accurate prognostic biomarker for HBV-related HCC. To address this issue, we performed a multidimensional analysis of AURKA, CDK1 and PLK1 with a series of publicly available databases in multiple cancers and with experimental validation in HBV-related HCC tissues. Overexpression of AURKA, CDK1 and PLK1 was found in multiple cancers including HCC. Elevated expression of these genes could result from lowered DNA methylation and genomic alterations. Transcriptional overexpression was significantly correlated with poor prognosis of HCC patients. The expression levels were also significantly positively associated with tumor grades and stages. Furthermore, the expression levels of these genes had a strong correlation with infiltration of immune cells. Our analysis shows that AURKA, CDK1 and PLK1 are correlated with immune infiltration and are the prognostic biomarkers for HBV-induced HCC.Communicated by Ramaswamy H. Sarma.

Identification of Novel Hub Genes Associated with Psoriasis Using Integrated Bioinformatics Analysis

Psoriasis is a chronic, prolonged, and recurrent inflammatory skin disease and the current therapeutics can only alleviate the symptoms rather than cure it completely. Therefore, we aimed to identify the molecular signatures and specific biomarkers of psoriasis to provide novel clues for psoriasis and targeted therapy. In the present study, the Gene Expression Omnibus (GEO) database was used to retrieve three microarray datasets (GSE166388, GSE50790 and GSE42632) and to explore the differentially expressed genes (DEGs) in psoriasis using the Affy package in R software. The gene ontology (GO) and Kyoto Encyclopedia of Gene and Genome (KEGG) pathway enrichment were utilized to determine the common DEGs and their capabilities. The STRING database was used to develop DEG-encoded proteins and a protein-protein interaction network (PPI) and the Cytohubba plugin to classify hub genes. Using the NetworkAnalyst platform, we detected transcription factors (TFs), microRNAs and drug candidates interacting with hub genes. In addition, the expression levels of hub genes in HaCaT cells were detected by western blot. We screened the up- and downregulated DEGs from the transcriptome microarrays of corresponding psoriasis patients. Functional enrichment of DEGs in psoriasis was mainly associated with positive regulation of leukocyte cell-cell adhesion and T cell activation, cytokine binding, cytokine activity and the Wnt signaling pathway. Through further data processing, we obtained 57 intersecting genes in the three datasets and probed them in STRING to determine the interaction of their expressed proteins and we obtained the critical 10 hub genes in the Cytohubba plugin, including TOP2A, CDKN3, MCM10, PBK, HMMR, CEP55, ASPM, KIAA0101, ESC02, and IL-1β. Using these hub genes as targets, we obtained 35 TFs and 213 miRNAs that may regulate these genes and 33 potential therapeutic agents for psoriasis. Furthermore, the expression levels of TOP2A, MCM10, PBK, ASPM, KIAA0101 and IL-1β were observably increased in HaCaT cells. In conclusion, we identified potential biomarkers, risk factors and drugs for psoriasis.

BUB1 predicts poor prognosis and immune status in liver hepatocellular carcinoma

Accurate assessment of the tumour immune microenvironment promotes individualized immunotherapy regimens and screens dominant populations suitable for immunotherapy. Therefore, potential molecular markers were investigated to make an overall assessment of the immune microenvironment status of liver hepatocellular carcinoma (LIHC). In this study, a total of 121 differentially expressed genes (DEGs) were identified, and DEGs were enriched in the epithelial-mesenchymal transition, hypoxia, myogenesis, and p53 pathways. A total of 20 hub genes were selected and a strong correlation was identified between these hub genes and prognosis. The expression of budding uninhibited by benzimidazoles 1 (BUB1) was found to be upregulated in LIHC and was strongly related to immune cells and immune checkpoint molecule expression. Immunohistochemistry (IHC) indicated that BUB1 expression was higher in LIHC tissues than in normal liver tissues. BUB1 knockdown resulted in reduced proliferation and vertical migration ability of LIHC cells, and reduced the expression of phospho-SMAD family member 2 and phospho-SMAD family member 3 proteins. IHC showed that BUB1 expression was accompanied by immune cell infiltration into LIHC tissues. These results suggest that BUB1 may serve as a potential prognostic biomarker for LIHC and as an indicator of its immune status.

Study on the expression of TOP2A in hepatocellular carcinoma and its relationship with patient prognosis

Background

Hepatocellular carcinoma (HCC) is a primary liver cancer with a high mortality rate. However, the molecular mechanism of HCC formation remains to be explored and studied.

Objective

To investigate the expression of TOP2A in hepatocellular carcinoma (HCC) and its prognosis.

Methods

The data set of hepatocellular carcinoma was downloaded from GEO database for differential gene analysis, and hub gene was identified by Cytoscape. GEPIA was used to verify the expression of HUB gene and evaluate its prognostic value. Then TOP2A was selected as the research object of this paper by combining literature and clinical sample results. Firstly, TIMER database was used to study TOP2A, and the differential expression of TOP2A gene between normal tissues and cancer tissues was analyzed, as well as the correlation between TOP2A gene expression and immune infiltration of HCC cells. Then, the expression of top2a-related antibodies was analyzed using the Human Protein Atlas database, and the differential expression of TOP2A was verified by immunohistochemistry. Then, SRTING database and Cytoscape were used to establish PPI network for TOP2A and protein–protein interaction analysis was performed. The Oncomine database and cBioPortal were used to express and identify TOP2A mutation-related analyses. The expression differences of TOP2A gene were identified by LinkedOmics, and the GO and KEGG pathways were analyzed in combination with related genes. Finally, Kaplan–Meier survival analysis was performed to analyze the clinical and prognosis of HCC patients.

Results

TOP2A may be a new biomarker and therapeutic target for hepatocellular carcinoma.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-021-02439-0.

Identification of Gene Signature as Diagnostic and Prognostic Blood Biomarker for Early Hepatocellular Carcinoma Using Integrated Cross-Species Transcriptomic and Network Analyses

Background: Hepatocellular carcinoma (HCC) is considered the most common type of liver cancer and the fourth leading cause of cancer-related deaths in the world. Since the disease is usually diagnosed at advanced stages, it has poor prognosis. Therefore, reliable biomarkers are urgently needed for early diagnosis and prognostic assessment. Methods: We used genome-wide gene expression profiling datasets from human and rat early HCC (eHCC) samples to perform integrated genomic and network-based analyses, and discovered gene markers that are expressed in blood and conserved in both species. We then used independent gene expression profiling datasets for peripheral blood mononuclear cells (PBMCs) for eHCC patients and from The Cancer Genome Atlas (TCGA) database to estimate the diagnostic and prognostic performance of the identified gene signature. Furthermore, we performed functional enrichment, interaction networks and pathway analyses. Results: We identified 41 significant genes that are expressed in blood and conserved across species in eHCC. We used comprehensive clinical data from over 600 patients with HCC to verify the diagnostic and prognostic value of 41-gene-signature. We developed a prognostic model and a risk score using the 41-geneset that showed that a high prognostic index is linked to a worse disease outcome. Furthermore, our 41-gene signature predicted disease outcome independently of other clinical factors in multivariate regression analysis. Our data reveals a number of cancer-related pathways and hub genes, including EIF4E, H2AFX, CREB1, GSK3B, TGFBR1, and CCNA2, that may be essential for eHCC progression and confirm our gene signature's ability to detect the disease in its early stages in patients' biological fluids instead of invasive procedures and its prognostic potential. Conclusion: Our findings indicate that integrated cross-species genomic and network analysis may provide reliable markers that are associated with eHCC that may lead to better diagnosis, prognosis, and treatment options.

Antitumor Effect of Lenvatinib Combined with Alisertib in Hepatocellular Carcinoma by Targeting the DNA Damage Pathway

METHODS

Immunohistochemical staining, sequencing, and genetic analysis of liver cancer tissues were performed. The antitumor efficacy of single-agent or combination treatment was measured by cell counting kit-8 assay and colony formation assays. Their antiproliferative and apoptosis activity is evaluated by cell cycle analyses and wound healing assays. The DNA-related proteins were also measured by Western blotting and immunohistochemical staining. The HepG2 xenograft model was used to detect the effects of lenvatinib-alisertib on the antitumor activity.

RESULTS

AURKA was found to be upregulated in HCC tissues (77.3%, 17/22). Combined alisertib and lenvatinib treatment significantly enhanced the inhibition of proliferation and migration in HepG2 and Hep3B cell lines compared to single-agent treatments (all Ps < 0.01). Alisertib alone or in combination with lenvatinib demonstrated a significant increase in the percentage of super-G2 cells (lenvatinib 1 μM vs. lenvatinib 1 μM + alisertib 0.1 μM 8.84 ± 0.84 vs. 34.0 ± 1.54, P < 0.001). Discontinuous spindles and missegregated chromosomes in HCC cells treated with alisertib in combination with lenvatinib were observed. We further revealed that combined treatment inhibited the expression of DNA damage pathway proteins compared to those of single-agent treatments. In nude mice, combined administration of alisertib combined with lenvatinib significantly enhanced the suppression of tumor growth and induced apoptosis (all Ps < 0.01).

CONCLUSIONS

Our findings provide evidence for the possible use of alisertib in combination with lenvatinib in the treatment of HCC for better therapeutic outcomes.

Identification of genes predicting unfavorable prognosis in hepatitis B virus-associated hepatocellular carcinoma

Background

To identify potential key genes predicting unfavorable prognosis in hepatitis B virus (HBV)-associated hepatocellular carcinoma (HCC).

Methods

Gene expression profiles of GSE121248, GSE62232, and GSE55092 from the GEO database were obtained and analyzed. Differentially expressed genes (DEGs) between HBV-associated HCC tissues and adjacent normal tissues were screened by the limma package and Venn diagram software. Functional assessment of DEGs was performed by Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG). Hub genes were selected by the protein-protein interaction (PPI) network and further validated by GSE14520 clinical data.

Results

A total of 26 up-regulated genes and 76 down-regulated genes were identified by analyzing three databases. GO and KEGG analysis demonstrated that these genes were involved in cell division, metabolism-related biological processes, the p53 pathway, and the cell cycle, among others. PPI network suggested that 14 hub DEGs (TOP2A, HMMR, DTL, CCNB1, NEK2, PBK, RACGAP1, PRC1, CDK1, RRM2, ECT2, BUB1B, ANLN, and ASPM) were most dysregulated and had potential to distinguish between HBV-associated HCC and noncancerous tissues. Further survival analysis of hub genes demonstrated that high expression of TOP2A was significantly associated with poor clinical outcomes of HBV-associated HCC.

Conclusions

TOP2A might serve as a key gene for prognosis and as a therapeutic target for HBV-associated HCC.

Identification of potential hub genes associated with the pathogenesis and prognosis of hepatocellular carcinoma via integrated bioinformatics analysis

Objective

The objective was to identify potential hub genes associated with the pathogenesis and prognosis of hepatocellular carcinoma (HCC).

Methods

Gene expression profile datasets were downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) between HCC and normal samples were identified via an integrated analysis. A protein–protein interaction network was constructed and analyzed using the STRING database and Cytoscape software, and enrichment analyses were carried out through DAVID. Gene Expression Profiling Interactive Analysis and Kaplan–Meier plotter were used to determine expression and prognostic values of hub genes.

Results

We identified 11 hub genes (CDK1, CCNB2, CDC20, CCNB1, TOP2A, CCNA2, MELK, PBK, TPX2, KIF20A, and AURKA) that might be closely related to the pathogenesis and prognosis of HCC. Enrichment analyses indicated that the DEGs were significantly enriched in metabolism-associated pathways, and hub genes and module 1 were highly associated with cell cycle pathway.

Conclusions

In this study, we identified key genes of HCC, which indicated directions for further research into diagnostic and prognostic biomarkers that could facilitate targeted molecular therapy for HCC.

Bioinformatics Analysis of Candidate Genes and Pathways Related to Hepatocellular Carcinoma in China: A Study Based on Public Databases

Background and Objective: Hepatocellular carcinoma (HCC) is a highly aggressive malignant tumor of the digestive system worldwide. Chronic hepatitis B virus (HBV) infection and aflatoxin exposure are predominant causes of HCC in China, whereas hepatitis C virus (HCV) infection and alcohol intake are likely the main risk factors in other countries. It is an unmet need to recognize the underlying molecular mechanisms of HCC in China.

Methods: In this study, microarray datasets (GSE84005, GSE84402, GSE101685, and GSE115018) derived from Gene Expression Omnibus (GEO) database were analyzed to obtain the common differentially expressed genes (DEGs) by R software. Moreover, the gene ontology (GO) functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed by using Database for Annotation, Visualization and Integrated Discovery (DAVID). Furthermore, the protein-protein interaction (PPI) network was constructed, and hub genes were identified by the Search Tool for the Retrieval of Interacting Genes (STRING) and Cytoscape, respectively. The hub genes were verified using Gene Expression Profiling Interactive Analysis (GEPIA), UALCAN, and Kaplan-Meier Plotter online databases were performed on the TCGA HCC dataset. Moreover, the Human Protein Atlas (HPA) database was used to verify candidate genes’ protein expression levels.

Results: A total of 293 common DEGs were screened, including 103 up-regulated genes and 190 down-regulated genes. Moreover, GO analysis implied that common DEGs were mainly involved in the oxidation-reduction process, cytosol, and protein binding. KEGG pathway enrichment analysis presented that common DEGs were mainly enriched in metabolic pathways, complement and coagulation cascades, cell cycle, p53 signaling pathway, and tryptophan metabolism. In the PPI network, three subnetworks with high scores were detected using the Molecular Complex Detection (MCODE) plugin. The top 10 hub genes identified were CDK1, CCNB1, AURKA, CCNA2, KIF11, BUB1B, TOP2A, TPX2, HMMR and CDC45. The other public databases confirmed that high expression of the aforementioned genes related to poor overall survival among patients with HCC.

Conclusion: This study primarily identified candidate genes and pathways involved in the underlying mechanisms of Chinese HCC, which is supposed to provide new targets for the diagnosis and treatment of HCC in China.

Expression of FOXM1 and Aurora-A predicts prognosis and sorafenib efficacy in patients with hepatocellular carcinoma

BACKGROUND:

Effective prognostic biomarkers and powerful target-therapeutic drugs are needed for improving the treatment of Hepatocellular carcinoma (HCC).

OBJECTIVE:

This study aimed to evaluate the expression of FOXM1 and Aurora-A and their prognostic value in HCC.

METHODS:

We determined the differentially expressed genes signature in HCC using the Gene Set Enrichment Analysis (GSEA), and then evaluated the expression of FOXM1 and Aurora-A in TCGA and KMUH cohort. Associations between co-expression of FOXM1 and Aurora-A and clinical variables were calculated. Overall survival (OS) and recurrence-free survival (RFS) were estimated with different FOXM1 and Aurora-A expression status.

RESULTS:

FOXM1-related gene sets were mostly associated with cell cycle regulation in HCC tissues. We found a positive correlation between the expression of FOXM1 and Aurora-A. Overexpression of FOXM1 and Aurora-A was associated with larger tumor size, advanced stage, higher grade, and double-positive for HBV and HCV. The coordinated overexpression of FOXM1 and Aurora-A was the most significant independent prognostic factor for OS and RFS. Furthermore, the concomitant high expression of FOXM1 and Aurora-A predicted the worst OS of sorafenib-treated patients with HCC.

CONCLUSIONS:

The co-expression of FOXM1 and Aurora-A could be a reliable biomarker to predict the sorafenib response and prognosis of HCC patients.

Identification and Analysis of Potential Key Genes Associated With Hepatocellular Carcinoma Based on Integrated Bioinformatics Methods

BACKGROUND

Hepatocellular carcinoma (HCC) is a type of primary liver tumor with poor prognosis and high mortality, and its molecular mechanism remains incompletely understood. This study aimed to use bioinformatics technology to identify differentially expressed genes (DEGs) in HCC pathogenesis, hoping to identify novel biomarkers or potential therapeutic targets for HCC research.

METHODS

The bioinformatics analysis of our research mostly involved the following two datasets: Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA). First, we screened DEGs based on the R packages (limma and edgeR). Using the DAVID database, the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of DEGs were carried out. Next, the protein-protein interaction (PPI) network of the DEGs was built in the STRING database. Then, hub genes were screened through the cytoHubba plug-in, followed by verification using the GEPIA and Oncomine databases. We demonstrated differences in levels of the protein in hub genes using the Human Protein Atlas (HPA) database. Finally, the hub genes prognostic values were analyzed by the GEPIA database. Additionally, using the Comparative Toxicogenomics Database (CTD), we constructed the drug-gene interaction network.

RESULTS

We ended up with 763 DEGs, including 247 upregulated and 516 downregulated DEGs, that were mainly enriched in the epoxygenase P450 pathway, oxidation-reduction process, and metabolism-related pathways. Through the constructed PPI network, it can be concluded that the P53 signaling pathway and the cell cycle are the most obvious in module analysis. From the PPI, we filtered out eight hub genes, and these genes were significantly upregulated in HCC samples, findings consistent with the expression validation results. Additionally, survival analysis showed that high level gene expression of CDC20, CDK1, MAD2L1, BUB1, BUB1B, CCNB1, and CCNA2 were connected with the poor overall survival of HCC patients. Toxicogenomics analysis showed that only topotecan, oxaliplatin, and azathioprine could reduce the gene expression levels of all seven hub genes.

CONCLUSION

The present study screened out the key genes and pathways that were related to HCC pathogenesis, which could provide new insight for the future molecularly targeted therapy and prognosis evaluation of HCC.

Splicing machinery dysregulation drives glioblastoma development/aggressiveness: oncogenic role of SRSF3

Glioblastomas remain the deadliest brain tumour, with a dismal ∼12–16-month survival from diagnosis. Therefore, identification of new diagnostic, prognostic and therapeutic tools to tackle glioblastomas is urgently needed. Emerging evidence indicates that the cellular machinery controlling the splicing process (spliceosome) is altered in tumours, leading to oncogenic splicing events associated with tumour progression and aggressiveness. Here, we identify for the first time a profound dysregulation in the expression of relevant spliceosome components and splicing factors (at mRNA and protein levels) in well characterized cohorts of human high-grade astrocytomas, mostly glioblastomas, compared to healthy brain control samples, being SRSF3, RBM22, PTBP1 and RBM3 able to perfectly discriminate between tumours and control samples, and between proneural-like or mesenchymal-like tumours versus control samples from different mouse models with gliomas. Results were confirmed in four additional and independent human cohorts. Silencing of SRSF3, RBM22, PTBP1 and RBM3 decreased aggressiveness parameters in vitro (e.g. proliferation, migration, tumorsphere-formation, etc.) and induced apoptosis, especially SRSF3. Remarkably, SRSF3 was correlated with patient survival and relevant tumour markers, and its silencing in vivo drastically decreased tumour development and progression, likely through a molecular/cellular mechanism involving PDGFRB and associated oncogenic signalling pathways (PI3K-AKT/ERK), which may also involve the distinct alteration of alternative splicing events of specific transcription factors controlling PDGFRB (i.e. TP73). Altogether, our results demonstrate a drastic splicing machinery-associated molecular dysregulation in glioblastomas, which could potentially be considered as a source of novel diagnostic and prognostic biomarkers as well as therapeutic targets for glioblastomas. Remarkably, SRSF3 is directly associated with glioblastoma development, progression, aggressiveness and patient survival and represents a novel potential therapeutic target to tackle this devastating pathology.

Bioinformatics analysis reveals meaningful markers and outcome predictors in HBV-associated hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is the most common type of malignant neoplasm of the liver with high morbidity and mortality. Extensive research into the pathology of HCC has been performed; however, the molecular mechanisms underlying the development of hepatitis B virus-associated HCC have remained elusive. Thus, the present study aimed to identify critical genes and pathways associated with the development and progression of HCC. The expression profiles of the GSE121248 dataset were downloaded from the Gene Expression Omnibus database and the differentially expressed genes (DEGs) were identified. Gene Ontology (GO) and Kyoto Encyclopedia of Gene and Genome (KEGG) analyses were performed by using the Database for Annotation, Visualization and Integrated Discovery. Subsequently, protein-protein interaction (PPI) networks were constructed for detecting hub genes. In the present study, 1,153 DEGs (777 upregulated and 376 downregulated genes) were identified and the PPI network yielded 15 hub genes. GO analysis revealed that the DEGs were primarily enriched in ‘protein binding’, ‘cytoplasm’ and ‘extracellular exosome’. KEGG analysis indicated that DEGs were accumulated in ‘metabolic pathways’, ‘chemical carcinogenesis’ and ‘fatty acid degradation’. After constructing the PPI network, cyclin-dependent kinase 1, cyclin B1, cyclin A2, mitotic arrest deficient 2 like 1, cyclin B2, DNA topoisomerase IIα, budding uninhibited by benzimidazoles (BUB)1, TTK protein kinase, non-SMC condensin I complex subunit G, NDC80 kinetochore complex component, aurora kinase A, kinesin family member 11, cell division cycle 20, BUB1B and abnormal spindle microtubule assembly were identified as hub genes based on the high degree of connectivity by using Cytoscape software. In addition, overall survival (OS) and disease-free survival (DFS) analyses were performed using the Gene Expression Profiling Interactive Analysis online database, which revealed that the increased expression of all hub genes were associated with poorer OS and DFS outcomes. Receiver operating characteristic curves were constructed using GraphPad prism 7.0 software. The results confirmed that 15 hub genes were able to distinguish HCC form normal tissues. Furthermore, the expression levels of three key genes were analyzed in tumor and normal samples of the Human Protein Atlas database. The present results may provide further insight into the underlying mechanisms of HCC and potential therapeutic targets for the treatment of this disease.

Multifaceted Role of PRDM Proteins in Human Cancer

The PR/SET domain family (PRDM) comprise a family of genes whose protein products share a conserved N-terminal PR [PRDI-BF1 (positive regulatory domain I-binding factor 1) and RIZ1 (retinoblastoma protein-interacting zinc finger gene 1)] homologous domain structurally and functionally similar to the catalytic SET [Su(var)3-9, enhancer-of-zeste and trithorax] domain of histone methyltransferases (HMTs). These genes are involved in epigenetic regulation of gene expression through their intrinsic HMTase activity or via interactions with other chromatin modifying enzymes. In this way they control a broad spectrum of biological processes, including proliferation and differentiation control, cell cycle progression, and maintenance of immune cell homeostasis. In cancer, tumor-specific dysfunctions of PRDM genes alter their expression by genetic and/or epigenetic modifications. A common characteristic of most PRDM genes is to encode for two main molecular variants with or without the PR domain. They are generated by either alternative splicing or alternative use of different promoters and play opposite roles, particularly in cancer where their imbalance can be often observed. In this scenario, PRDM proteins are involved in cancer onset, invasion, and metastasis and their altered expression is related to poor prognosis and clinical outcome. These functions strongly suggest their potential use in cancer management as diagnostic or prognostic tools and as new targets of therapeutic intervention.

BUB1 promotes proliferation of liver cancer cells by activating SMAD2 phosphorylation

Budding uninhibited by benzimidazoles 1 (BUB1) is a mitotic checkpoint serine/threonine kinase that has been reported as an oncogene or tumor suppressor gene in various types of cancer, including breast cancer, pancreatic ductal adenocarcinoma, prostate and gastric cancers. However, its role in liver cancer remains unclear. The present study aimed to explore the biological function of BUB1 in liver cancer. The present study demonstrated that BUB1 mRNA expression levels and the intensity of immunohistochemical staining were significantly increased in liver cancer tissues compared with normal tissues. The role of BUB1 in cell proliferation was also determined. Overexpression of BUB1 significantly promoted cell proliferation, whereas knockdown of BUB1 expression inhibited the proliferation of liver cancer cell lines. In experiments investigating the underlying mechanism, overexpression of BUB1 increased the levels of SMAD2 phosphorylation, whereas knockdown of BUB1 reduced the levels of SMAD2 phosphorylation. Therefore, BUB1 may promote proliferation of liver cancer cells by activating phosphorylation of SMAD2, and BUB1 may serve as a potential target in the diagnosis and/or treatment of liver cancer.

Super-Enhancer-Associated Hub Genes In Chronic Myeloid Leukemia Identified Using Weighted Gene Co-Expression Network Analysis

Purpose

Super-enhancer (SE)-associated oncogenes extensively potentiate the uncontrolled proliferation capacity of cancer cells. In this study, we aimed to identify the SE-associated hub genes associated with the clinical characteristics of chronic myeloid leukemia (CML).

Methods

Eigengenes from CML clinical modules were determined using weighted gene co-expression network analysis (WGCNA). Overlapping genes between eigengenes and SE-associated genes were used to construct protein–protein interaction (PPI) networks and annotate for pathway enrichment analysis. Expression patterns of the top-ranked SE-associated hub genes were further determined in CML patients and healthy controls via real-time PCR. After treatment of K562 cells with the BRD4 inhibitor, JQ1, for 24 hrs, mRNA and protein levels of SE-associated hub genes were evaluated using real-time PCR and Western blotting, respectively. H3K27ac, H3K4me1 and BRD4 ChIP-seq signal peaks were used to predict and identify SEs visualized by the Integrative Genomics Viewer.

Results

The yellow module was significantly related to the status and pathological phase of CML. SE-associated hub candidate genes were mainly enriched in the cell cycle pathway. Based on the PPI networks of hub genes and the top rank of degree, five SE-associated genes were identified: specifically, BUB1, CENPO, KIF2C, ORC1, and RRM2. Elevated expression of these five genes was not only related to CML status and phase but also positively regulated by SE and suppressed by the BRD4 inhibitor, JQ1, in K562 cells. Strong signal peaks of H3K27ac, H3K4me1 and BRD4 ChIP-seq of the five genes were additionally observed close to the predicted SE regions.

Conclusion

This is the first study to characterize SE-associated genes linked to clinical characteristics of CML via weighted gene co-expression network analysis. Our results support a novel mechanism involving aberrant expression of hub SE-associated genes in CML patients and K562 cells, and these genes will be potential new therapeutic targets for human leukemia.

NCAPG Promotes The Proliferation Of Hepatocellular Carcinoma Through PI3K/AKT Signaling

Purpose

Studies show that high expression of non-SMC condensin I complex subunit G (NCAPG) is associated with many tumors. In this study, we explore the mechanism by which NCAPG promotes proliferation in hepatocellular carcinoma (HCC).

Patients and methods

Liver cancer and paracancerous tissue specimens of 90 HCC patients were collected, and expression levels of NCAPG in these tissues and cell lines were evaluated by Western blotting and immunohistochemistry. HCC cells were transfected with siRNAs and plasmids, and pathway activators or inhibitors were added. The 5-ethynyl-2ʹ-deoxyuridine (EdU) proliferation assay was used to measure cell proliferation. Flow cytometry was used to evaluate cell apoptosis. Western blot assays were performed as a standard procedure to detect total protein expression. Treated HCC cells were subcutaneously injected into nude mice.

Results

Analysis using the Oncomine database showed that NCAPG was upregulated in HCC and immunohistochemistry and Western blot assays showed it was upregulated in both HCC tissues and HCC cell lines. The overexpression of NCAPG could promote HCC cell proliferation and reduce HCC cell apoptosis. More importantly, RNA-sequencing analysis predicted that NCAPG plays a role in the HCC via PI3K-AKT signaling pathway. The PI3K/AKT/FOXO4 pathway was aberrantly activated, and the expressions of apoptosis-related protein were altered when NCAPG was overexpressed or silenced both in vitro and in vivo. LY294002, a PI3K inhibitor, could eliminate the NCAPG role of promoting HCC cell proliferation and reducing HCC cell apoptosis, while 740Y-P, a PI3K activator, contributed to the opposite effect.

Conclusion

NCAPG functions as an oncogene in HCC and plays a role in promoting cell proliferation and antiapoptosis through activating the PI3K/AKT/FOXO4 pathway.

Bioinformatics Analysis based on Multiple Databases Identifies Hub Genes Associated with Hepatocellular Carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) is the most common liver cancer and the mechanisms of hepatocarcinogenesis remain elusive.

OBJECTIVE

This study aims to mine hub genes associated with HCC using multiple databases.

METHODS

Data sets GSE45267, GSE60502, GSE74656 were downloaded from GEO database. Differentially expressed genes (DEGs) between HCC and control in each set were identified by limma software. The GO term and KEGG pathway enrichment of the DEGs aggregated in the datasets (aggregated DEGs) were analyzed using DAVID and KOBAS 3.0 databases. Protein-protein interaction (PPI) network of the aggregated DEGs was constructed using STRING database. GSEA software was used to verify the biological process. Association between hub genes and HCC prognosis was analyzed using patients' information from TCGA database by survminer R package.

RESULTS

From GSE45267, GSE60502 and GSE74656, 7583, 2349, and 553 DEGs were identified respectively. A total of 221 aggregated DEGs, which were mainly enriched in 109 GO terms and 29 KEGG pathways, were identified. Cell cycle phase, mitotic cell cycle, cell division, nuclear division and mitosis were the most significant GO terms. Metabolic pathways, cell cycle, chemical carcinogenesis, retinol metabolism and fatty acid degradation were the main KEGG pathways. Nine hub genes (TOP2A, NDC80, CDK1, CCNB1, KIF11, BUB1, CCNB2, CCNA2 and TTK) were selected by PPI network and all of them were associated with prognosis of HCC patients.

CONCLUSION

TOP2A, NDC80, CDK1, CCNB1, KIF11, BUB1, CCNB2, CCNA2 and TTK were hub genes in HCC, which may be potential biomarkers of HCC and targets of HCC therapy.

Identification of prognosis markers for endometrial cancer by integrated analysis of DNA methylation and RNA-Seq data

Endometrial cancer is highly malignant and has a poor prognosis in the advanced stage, thus, prediction of its prognosis is important. DNA methylation has rapidly gained clinical attention as a biomarker for diagnostic, prognostic and predictive purposes in various cancers. In present study, differentially methylated positions and differentially expressed genes were identified according to DNA methylation and RNA-Seq data. Functional analyses and interaction network were performed to identify hub genes, and overall survival analysis of hub genes were validated. The top genes were evaluated by immunohistochemical staining of endometrial cancer tissues. The gene function was evaluated by cell growth curve after knockdown CDC20 and CCNA2 of endometrial cancer cell line. A total of 329 hypomethylated highly expressed genes and 359 hypermethylated lowly expressed genes were identified, and four hub genes were obtained according to the interaction network. Patients with low expression of CDC20 and CCNA2 showed better overall survival. The results also were demonstrated by the immunohistochemical staining. Cell growth curve also demonstrated that knockdown CDC20 and CCNA2 can suppress the cell proliferation. We have identified two aberrantly methylated genes, CDC20 and CCNA2 as novel biomarkers for precision diagnosis in EC.

Hepatocellular carcinoma: Therapeutic advances in signaling, epigenetic and immune targets

Hepatocellular carcinoma (HCC) remains a global medical burden with rising incidence due to chronic viral hepatitis and non-alcoholic fatty liver diseases. Treatment of advanced disease stages is still unsatisfying. Besides first and second generation tyrosine kinase inhibitors, immune checkpoint inhibitors have become central for the treatment of HCC. New modalities like epigenetic therapy using histone deacetylase inhibitors (HDACi) and cell therapy approaches with chimeric antigen receptor T cells (CAR-T cells) are currently under investigation in clinical trials. Development of such novel drugs is closely linked to the availability and improvement of novel preclinical and animal models and the identification of predictive biomarkers. The current status of treatment options for advanced HCC, emerging novel therapeutic approaches and different preclinical models for HCC drug discovery and development are reviewed here.

Transcriptional regulation of Bcl-2 gene by the PR/SET domain family member PRDM10

Bcl-2 (B-cell lymphoma 2) protein is localized in the outer membrane of mitochondria, where it plays an important role in promoting cellular survival and inhibiting the actions of pro-apoptotic proteins. PRDM10 is a member of the PR/SET family of epigenetic regulators and may play a role in development and cell differentiation. Here we show that human PRDM10 contributes to the transcriptional regulation of human Bcl-2 gene. We found that PRDM10-depletion in human cells reduced the expression of Bcl-2 protein and over-expression of PRDM10 promoted Bcl-2 protein expression. Furthermore, luciferase reporter activity of Bcl-2 gene P1 promoter was significantly increased in cells co-transfected with PRDM10, and PRDM10 was able to bind to the Bcl-2 P1 promoter in vivo. Using The Cancer Genome Atlas (TCGA) data set, we found weak positive correlation between PRDM10 and Bcl-2 in several cancer types including cancers of the breast, colon, and lung tissues. These data identify a novel function for PRDM10 protein and provide insights on the transcriptional control of Bcl-2 expression.