Genetic variants of cell cycle pathway genes predict disease-free survival of hepatocellular carcinoma

[Dealcoholized red wine inhibits occurrence and progression of hepatocellular carcinoma possibly by inducing cell cycle arrest and apoptosis]

OBJECTIVE

To investigate the inhibitory effect of dealcoholized red wine (DRW) on occurrence and progression of hepatocellular carcinoma (HCC) and explore its possible mechanisms.

METHODS

Three HCC cell lines (Huh7, HepG2 and SKHep-1) treated with 5, 10, 25, 50 and 100 μL/mL DRW were examined for changes in proliferation and colony formation ability using CCK-8 assay and colony formation assay. A nude mouse model bearing subcutaneous HCC xenograft was used to test the effect of 300 μL/day DRW for 4 weeks on tumor growth. The inhibitory effect of 300 μL/day DRW for 6 weeks on tumor growth was also observed in a mouse model of chemically induced HCC by examining the tumor number, largest tumor diameter and the liver/body ratio. RNA-seq technique was used for transcriptome sequencing of Huh7 cells treated with DRW (75 μL/mL) for 48 h, and gene-set enrichment analysis (GSEA) was performed to identify the changes in genes and pathways. Flow cytometry assay was used to analyze the changes in cell cycle and apoptosis of the cells.

RESULTS

DRW inhibited the proliferation of the HCC cell lines in a concentration-and time-dependent manner, and concentration-dependently inhibited colony formation of the cells. Treatment with DRW significantly reduced the volume of subcutaneous tumor xenograft in the tumor-bearing nude mice (P < 0.05), and lowered the number of tumors (P < 0.001), the largest tumor diameter (P < 0.05) and the liver/body ratio (P < 0.01) in mice with chemically induced HCC. RNA-seq showed that 634 genes were significantly up-regulated and 478 were down-regulated in Huh7 cells after treatment with DRW. Gene-set enrichment analysis revealed that DRW significantly down-regulated cell cycle-related pathways (E2F Targets, G2M Checkpoint and MYC Targets) and up-regulated apoptosis pathways. Flow cytometry assay showed that DRW induced cell cycle arrest in G1 phase and apoptosis of Huh7 cells.

CONCLUSION

DRW inhibits the occurrence and progression of HCC, and this effect is mediated possibly by inducing cell cycle arrest and apoptosis.

A pyroptosis-related gene signature provides an alternative for predicting the prognosis of patients with hepatocellular carcinoma

BACKGROUND

Hepatocellular Carcinoma (HCC) is a common malignant neoplasm with limited treatment options and poor outcomes. Thus, there is an urgent need to find sensitive biomarkers for HCC.

METHODS

Gene expression and clinicopathological information were obtained from public databases, based on which a pyroptosis-related gene signature was constructed by the least absolute shrinkage and selection operator Cox regression. The applicability of the signature was evaluated via Kaplan-Meier curve and time-dependent ROC curve. TIMER, QUANTISEQ, MCPCOUNTER, EPIC, CIBERSORT, ssGSEA, and ESTIMATE were employed to assess the immune status. Comparisons between groups were analyzed with Wilcoxon test. Pearson and Spearman correlation analyses were adopted for linear correlation analysis. Genetic knockdown was conducted using siRNA transfection and the mRNA expression levels of interest genes were measured using quantitative reverse transcription PCR. Finally, protein levels in 10 paired tumor tissues and adjacent non-tumor tissues from HCC patients were measured using immunohistochemistry.

RESULTS

A pyroptosis-related gene signature was established successfully to calculate independent prognostic risk scores. It was found that survival outcomes varied significantly between different risk groups. In addition, an attenuated antitumor immune response was found in the high-risk group. Meanwhile, multiple immune checkpoints were up-regulated in high-risk score patients. Cell cycle-related genes, angiogenesis-related genes and tumor drug resistance genes were also markedly elevated. Knockdown of prognostic genes in the signature significantly inhibited the expression of immune checkpoint genes and angiogenesis-related genes. Besides, each prognostic gene was expressed at a higher level in HCC tissues than in adjacent normal tissues.

CONCLUSIONS

We successfully established a novel pyroptosis-related gene signature which could help predict the overall survival and assess the immune status of HCC patients.

Identification of a Functional ceRNA Network to Explore Potential Biomarkers for Hepatocellular Carcinoma

PURPOSE

To establish a novel circRNA-miRNA-mRNA network associated with the poor prognosis of hepatocellular carcinoma (HCC).

MATERIALS AND METHODS

Quantitative real-time PCR was used to verify the differentially expressed circRNA. Moreover, the competing endogenous RNA networks were established using bioinformatics methods. Meanwhile, the prognostic value and potential mechanism of ceRNA network in hepatocellular carcinoma (HCC) were analyzed.

RESULTS

This work found that circ_0130911 was highly expressed in HCC tissues and early recurring HCC. Further, we effectively constructed a ceRNA network. The ceRNA network regulated by circ_0130911 might influence the prognosis of HCC by regulating cell cycle-related pathways.

CONCLUSION

The ceRNA network proposed here can be used as a novel biomarker for the prognosis of HCC, thereby providing new insights for the targeted therapy of HCC.

Integrated Analysis of an lncRNA-Associated ceRNA Network Reveals Potential Biomarkers for Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is a common malignant tumor worldwide. In this study, we aimed to explore the potential biomarkers and key regulatory pathways related to HCC using integrated bioinformatic analysis and validation. The microarray data of GSE12717 and GSE54238 were downloaded from the Gene Expression Omnibus database. A competing endogenous RNA (ceRNA) network was constructed based on potential long-noncoding RNA (lncRNA)-microRNA (miRNA)-mRNA interactions. A total of 191 mRNAs, 8 miRNAs, and 5 lncRNAs were selected to construct the ceRNA network. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were used to predict their biological functions. The PI3K-Akt signaling pathway was significantly enriched. Kaplan-Meier survival analysis based on the Gene Expression Profiling Interactive Analysis (GEPIA) database was conducted for the weighted mRNAs and lncRNAs. The results showed that SRC, GMPS, CDK2, FEN1, EZH2, ZWINT, MTHFD1L, GINS2, and MAPKAPK5-AS1 were significantly upregulated in tumor tissues. The relative expression levels of these genes were significantly upregulated in HCC patients based on the StarBase database. For further validation, the expression levels of these genes were detected by real-time quantitative reverse transcription-polymerase chain reaction in 20 HCC tumor tissues and paired paracancerous tissues. Receiver operating characteristic analysis revealed that CDK2, MTHFD1L, SRC, ZWINT, and MAPKAPK5-AS1 had significant diagnostic value in HCC, but further studies are needed to explore their mechanisms in HCC.

Identification of Hub Genes Associated With Hepatocellular Carcinoma Using Robust Rank Aggregation Combined With Weighted Gene Co-expression Network Analysis

Background

Bioinformatics provides a valuable tool to explore the molecular mechanisms underlying pathogenesis of hepatocellular carcinoma (HCC). To improve prognosis of patients, identification of robust biomarkers associated with the pathogenic pathways of HCC remains an urgent research priority.

Methods

We employed the Robust Rank Aggregation method to integrate nine qualified HCC datasets from the Gene Expression Omnibus. A robust set of differentially expressed genes (DEGs) between tumor and normal tissue samples were screened. Weighted gene co-expression network analysis was applied to cluster DEGs and the key modules related to clinical traits identified. Based on network topology analysis, novel risk genes derived from key modules were mined and biological verification performed. The potential functions of these risk genes were further explored with the aid of miRNA–mRNA regulatory networks. Finally, the prognostic ability of these genes was assessed by constructing a clinical prediction model.

Results

Two key modules showed significant association with clinical traits. In combination with protein–protein interaction analysis, 29 hub genes were identified. Among these genes, 19 from one module showed a pattern of upregulation in HCC and were associated with the tumor node metastasis stage, and 10 from the other module displayed the opposite trend. Survival analyses indicated that all these genes were significantly related to patient prognosis. Based on the miRNA-mRNA regulatory network, 29 genes strongly linked to tumor activity were identified. Notably, five of the novel risk genes, ABAT, DAO, PCK2, SLC27A2, and HAO1, have rarely been reported in previous studies. Gene set enrichment analysis for each gene revealed regulatory roles in proliferation and prognosis of HCC. Least absolute shrinkage and selection operator regression analysis further validated DAO, PCK2, and HAO1 as prognostic factors in an external HCC dataset.

Conclusion

Analysis of multiple datasets combined with global network information presents a successful approach to uncover the complex biological mechanisms of HCC. More importantly, this novel integrated strategy facilitates identification of risk hub genes as candidate biomarkers for HCC, which could effectively guide clinical treatments.

Identification of diagnostic and prognostic biomarkers, and candidate targeted agents for hepatitis B virus-associated early stage hepatocellular carcinoma based on RNA-sequencing data

Primary liver cancer is a rapidly progressing neoplasm with high morbidity and mortality rates. The present study aimed to identify potential diagnostic and prognostic biomarkers, and candidate targeted agents for hepatitis B virus (HBV)-associated early stage hepatocellular carcinoma (HCC). The gene expression profiles were extracted from the Gene Expression Omnibus database. Differentially expressed genes (DEGs), hub genes and the enrichment of signaling pathways were filtered out via a high-throughput sequencing method. The association between hub genes and the effects of the abnormal expression of hub genes on the rate of genetic variation, overall survival (OS), relapse-free survival (RFS), progression-free survival (PFS) and disease-free survival (DSS) of patients with HCC, as well as pathological stage and grade, were analyzed using different databases. A total of 1,582 DEGs were identified. Gene Ontology analysis revealed that the DEGs were mainly involved in the ‘oxidation-reduction process’, ‘steroid metabolic process’, ‘metabolic process’ and ‘fatty acid beta-oxidation’. Enrichment analysis of Kyoto Encyclopedia of Genes and Genomes pathways revealed that the DEGs were mainly associated with ‘metabolic pathways’, ‘PPAR signaling pathway’, ‘fatty acid degradation’ and the ‘cell cycle’. A total of 8 hub genes were extracted. Additionally, the abnormal expression levels of hub genes were closely associated with the OS, RFS, PFS and DSS of patients, the pathological stage and the grade. Furthermore, abnormal expression levels of the 8 hub genes were found in >30% of all samples. Several small molecular compounds that may reverse the altered DEGs were identified based on Connectivity Map analysis, including phenoxybenzamine, GW-8510, resveratrol, 0175029-0000 and daunorubicin. In conclusion, the dysfunction of fat metabolic pathways, the cell cycle, oxidation-reduction processes and viral carcinogenesis may serve critical roles in the occurrence of HBV-associated early stage HCC. The identified 8 hub genes may act as robust biomarkers for diagnosis and prognosis. Some small molecular compounds may be promising targeted agents against HBV-associated early stage HCC.

Five Core Genes Related to the Progression and Prognosis of Hepatocellular Carcinoma Identified by Analysis of a Coexpression Network

The molecular mechanism of tumorigenesis of the prevalent cancer hepatocellular carcinoma (HCC) is unclear. In this study, through weighted gene coexpression network analysis, a coexpression network was constructed by selecting the top 25% most variant genes in the dataset GSE62232. The average linkage hierarchical clustering identified 24 modules, and among them, the pink module associated with prognosis of HCC was screened. Five gene candidates (PCNA, RFC4, PTTG1, H2AFZ, and RRM1) with a common network in the module were screened after the protein-protein interaction network complex was combined with the coexpression network. After progression and survival analysis, all candidates were identified as real core genes. According to the Human Protein Atlas and the Oncomine database, these genes were dysregulated in HCC samples. The receiver operating characteristic curve proved that the expression levels of the core genes had high diagnostic efficacy. The results of gene set enrichment analysis and functional enrichment analysis demonstrated the importance of the cell cycle-related pathways in HCC progression and prognosis. In conclusion, the five real core genes and cell cycle-related pathways identified in this study could greatly improve the knowledge about HCC progression and contribute to HCC treatment.

Upregulation of long non-coding RNA FOXP4-AS1 and its regulatory network in hepatocellular carcinoma

Objective

FOXP4-AS1 (FOXP4 antisense RNA 1) is putatively a functional oncogene in colorectal cancer. This study constructed a regulatory network involving FOXP4-AS1 for better understanding of its function in hepatocellular carcinoma (HCC).

Methods

FOXP4-AS1 was assessed in HCC and adjacent normal (control) liver samples via quantitative real-time PCR. Differentially expressed micro RNAs (DEmiRNAs) were predicted. Their target genes were verified via the gene expression profiling interaction analysis (GEPIA) database, and subjected to gene ontology (GO) annotation and KEGG (Kyoto Encyclopedia of Genes and Genome) pathway enrichment analysis. Protein-protein interaction (PPI) networks were established and hub genes identified with Cytoscape software. The GEPIA database was used to assess the prognostic roles of 20 hub genes in liver cancer. The cBioPortal database was used to exhibit alterations of the genes.

Results

The HCC samples had significantly higher levels of FOXP4-AS1 compared with the control (P=0.001). Six upregulated and 4 downregulated DEmiRNAs were identified. Over- and under-expressed predicted target genes (183 and 147, respectively) were selected for GO annotation and KEGG pathway enrichment analysis. The downregulated genes were significantly prominent in the PI3K-Akt signaling pathway; the upregulated genes in the cell cycle. The PPI networks indicated IGFBP3 and PRC1 as hub genes with the highest node degrees. Higher expressions of 9 (6) genes were associated with worse (better) prognosis in HCC.

Conclusion

An HCC-associated FOXP4-AS1-miRNA-mRNA regulatory network was constructed, and molecular mechanisms involved in HCC development were elucidated. This work provides direction for finding new HCC therapeutic targets.

Upregulation of BUB1B, CCNB1, CDC7, CDC20, and MCM3 in Tumor Tissues Predicted Worse Overall Survival and Disease-Free Survival in Hepatocellular Carcinoma Patients

OBJECTIVE

To evaluate the association between upregulated differentially expressed genes (DEGs) and the outcomes of patients with hepatocellular carcinoma (HCC).

METHODS

Using Gene Expression Omnibus (GEO) datasets including GSE45436, GSE55092, GSE60502, GSE84402, and GSE17548, we detected upregulated DEGs in tumors. KEGG, GO, and Reactome enrichment analysis of the DEGs was conducted to clarify their function. The impact of the upregulated DEGs on patients' survival was analyzed based on TCGA profile.

RESULTS

161 shared upregulated DEGs were identified among GSE45436, GSE55092, GSE60502, and GSE84402 profiles. Cell cycle was the shared pathway/biological process in the gene sets investigation among databases of KEGG, GO, and Reactome. After being validated in GSE17548, 13 genes including BUB1B, CCNA2, CCNB1, CCNE2, CDC20, CDC6, CDC7, CDK1, CDK4, CDKN2A, CHEK1, MAD2L1, and MCM3 in cell cycle pathway were shared in the three databases for enrichment. The expression of BUB1B, CCNB1, CDC7, CDC20, and MCM3 was upregulated in HCC tissues when compared with adjacent normal tissues in 6.67%, 7.5%, 8.06%, 5.56%, and 9.72% of HCC patients, respectively. Overexpression of BUB1B, CCNB1, CDC7, CDC20, and MCM3 in HCC tissues accounted for poorer overall survival (OS) and disease-free survival (DFS) in HCC patients (all log rank P < 0.05). BUB1B, CCNB1, CDC7, CDC20, and MCM3 were all overexpressed in HCC patients with neoplasm histologic grade G3-4 compared to those with G1-2 (all P < 0.05). BUB1B, CCNB1, and CDC20 were significantly upregulated in HCC patients with vascular invasion (all P < 0.05). Additionally, levels of BUB1B, CCNB1, CDC7, and CDC20 were significantly higher in HCC patients deceased, recurred, or progressed (all P < 0.05).

CONCLUSION

Correlated with advanced histologic grade and/or vascular invasion, upregulation of BUB1B, CCNB1, CDC7, CDC20, and MCM3 in HCC tissues predicted worse OS and DFS in HCC patients. These genes could be novel therapeutic targets for HCC treatment.

Genetic variants of cell cycle pathway genes predict disease-free survival of hepatocellular carcinoma

Disruption of the cell cycle pathway has previously been related to development of human cancers. However, associations between genetic variants of cell cycle pathway genes and prognosis of hepatocellular carcinoma (HCC) remain largely unknown. In this study, we evaluated the associations between 24 potential functional single nucleotide polymorphisms (SNPs) of 16 main cell cycle pathway genes and disease‐free survival (DFS) of 271 HCC patients who had undergone radical surgery resection. We identified two SNPs, i.e., SMAD3 rs11556090 A>G and RBL2 rs3929G>C, that were independently predictive of DFS in an additive genetic model with false‐positive report probability (FPRP) <0.2. The SMAD3 rs11556090G allele was associated with a poorer DFS, compared with the A allele [hazard ratio (HR) = 1.46, 95% confidential interval (95% CI) = 1.13–1.89, P = 0.004]; while the RBL2 rs3929 C allele was associated with a superior DFS, compared with the G allele (HR = 0.74, 95% CI = 0.57–0.96, P = 0.023). Additionally, patients with an increasing number of unfavorable genotypes (NUGs) of these loci had a significant shorter DFS (P_trend = 0.0001). Further analysis using receiver operating characteristic (ROC) curves showed that the model including the NUGs and known prognostic clinical variables demonstrated a significant improvement in predicting the 1‐year DFS (P = 0.011). Moreover, the RBL2 rs3929 C allele was significantly associated with increased mRNA expression levels of RBL2 in liver tissue (P = 1.8 × 10⁻⁷) and the whole blood (P = 3.9 × 10⁻¹⁴). Our data demonstrated an independent or a joint effect of SMAD3 rs11556090 and RBL2 rs3929 in the cell cycle pathway on DFS of HCC, which need to be validated by large cohort and biological studies.