HGFAC expression decreased in liver cancer and its low expression correlated with DNA hypermethylation and poor prognosis

miR-4270 suppresses hepatocellular carcinoma progression by inhibiting DNMT3A-mediated methylation of HGFAC promoter

Background

miR-4270 is a regulatory factor has been linked with the progression of various cancers, such as nasopharyngeal carcinoma, hepatocellular carcinoma (HCC), and gastric cancer. However, the underlying mechanisms through which miR-4270 modulates HCC development are not fully understood.

Methods

miR-4270 expression levels were analyzed in various HCC cell lines and tissue samples. An online bioinformatics tool was then utilized to predict the miR-4270 target gene. The binding relationship between miR-4270 and its target gene DNMT3A was verified using dual-luciferase reporter and Ago2-RIP assays. Then, co-immunoprecipitation (Co-IP) and chromatin immunoprecipitation (ChIP) assays were conducted to investigate the association between DNMT3A and the hepatocyte growth factor activator (HGFAC) promoter region. To assess the methylation level of the HGFAC promoter, methylation-specific PCR (MSP) was employed. Furthermore, rescue analyses were carried out to evaluate the functional relevance of miR-4270 and HGFAC in the modulation of the malignant properties of HCC cells. Finally, HepG2 cells overexpressing miR-4270 were subcutaneously injected into nude mice to estimate the impact of miR-4270 on the xenograft tumor growth of HCC.

Results

A substantial miR-4270 downregulation was revealed in HCC patient samples and cell lines. miR-4270 upregulation suppressed both cell proliferation and invasion while promoting apoptosis. At the molecular level, miR-4270 was found to bind to the 3'untranslated region (3'UTR) of DNMT3A, thereby inhibiting DNMT3A-mediated methylation of the HGFAC promoter. Functional assays indicated that inhibition of miR-4270 stimulated HCC cell growth, an effect counteracted by overexpression of HGFAC. In vivo assays further verified that miR-4270 effectively suppressed the progression of HCC xenograft tumors.

Conclusions

miR-4270 was found to mitigate the malignant characteristics of HCC by inhibiting DNMT3A-mediated methylation of the HGFAC promoter, suggesting a potential therapeutic avenue for the management of HCC.

The REASON score: an epigenetic and clinicopathologic score to predict risk of poor survival in patients with early stage oral squamous cell carcinoma

BACKGROUND

Oral squamous cell carcinoma (OSCC) is a capricious cancer with poor survival rates, even for early-stage patients. There is a pressing need to develop more precise risk assessment methods to appropriately tailor clinical treatment. Genome-wide association studies have not produced a viable biomarker. However, these studies are limited by using heterogeneous cohorts, not focusing on methylation although OSCC is a heavily epigenetically-regulated cancer, and not combining molecular data with clinicopathologic data for risk prediction. In this study we focused on early-stage (I/II) OSCC and created a risk score called the REASON score, which combines clinicopathologic characteristics with a 12-gene methylation signature, to predict the risk of 5-year mortality.

METHODS

We combined data from an internal cohort (n = 515) and The Cancer Genome Atlas (TCGA) cohort (n = 58). We collected clinicopathologic data from both cohorts to derive the non-molecular portion of the REASON score. We then analyzed the TCGA cohort DNA methylation data to derive the molecular portion of the risk score.

RESULTS

5-year disease specific survival was 63% for the internal cohort and 86% for the TCGA cohort. The clinicopathologic features with the highest predictive ability among the two the cohorts were age, race, sex, tobacco use, alcohol use, histologic grade, stage, perineural invasion (PNI), lymphovascular invasion (LVI), and margin status. This panel of 10 non-molecular features predicted 5-year mortality risk with a concordance (c)-index = 0.67. Our molecular panel consisted of a 12-gene methylation signature (i.e., HORMAD2, MYLK, GPR133, SOX8, TRPA1, ABCA2, HGFAC, MCPH1, WDR86, CACNA1H, RNF216, CCNJL), which had the most significant differential methylation between patients who survived vs. died by 5 years. All 12 genes have already been linked to survival in other cancers. Of the genes, only SOX8 was previously associated with OSCC; our study was the first to link the remaining 11 genes to OSCC survival. The combined molecular and non-molecular panel formed the REASON score, which predicted risk of death with a c-index = 0.915.

CONCLUSIONS

The REASON score is a promising biomarker to predict risk of mortality in early-stage OSCC patients. Validation of the REASON score in a larger independent cohort is warranted.

Comprehensive analyses of competing endogenous RNA networks reveal potential biomarkers for predicting hepatocellular carcinoma recurrence

Background

Hepatocellular carcinoma (HCC) is one of the most common and deadly malignant tumors, with a high rate of recurrence worldwide. This study aimed to investigate the mechanism underlying the progression of HCC and to identify recurrence-related biomarkers.

Methods

We first analyzed 132 HCC patients with paired tumor and adjacent normal tissue samples from the Gene Expression Omnibus (GEO) database to identify differentially expressed genes (DEGs). The expression profiles and clinical information of 372 HCC patients from The Cancer Genome Atlas (TCGA) database were next analyzed to further validate the DEGs, construct competing endogenous RNA (ceRNA) networks and discover the prognostic genes associated with recurrence. Finally, several recurrence-related genes were evaluated in two external cohorts, consisting of fifty-two and forty-nine HCC patients, respectively.

Results

With the comprehensive strategies of data mining, two potential interactive ceRNA networks were constructed based on the competitive relationships of the ceRNA hypothesis. The ‘upregulated’ ceRNA network consists of 6 upregulated lncRNAs, 3 downregulated miRNAs and 5 upregulated mRNAs, and the ‘downregulated’ network includes 4 downregulated lncRNAs, 12 upregulated miRNAs and 67 downregulated mRNAs. Survival analysis of the genes in the ceRNA networks demonstrated that 20 mRNAs were significantly associated with recurrence-free survival (RFS). Based on the prognostic mRNAs, a four-gene signature (ADH4, DNASE1L3, HGFAC and MELK) was established with the least absolute shrinkage and selection operator (LASSO) algorithm to predict the RFS of HCC patients, the performance of which was evaluated by receiver operating characteristic curves. The signature was also validated in two external cohort and displayed effective discrimination and prediction for the RFS of HCC patients.

Conclusions

In conclusion, the present study elucidated the underlying mechanisms of tumorigenesis and progression, provided two visualized ceRNA networks and successfully identified several potential biomarkers for HCC recurrence prediction and targeted therapies.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-08173-0.

Bioinformatics analysis of key biomarkers and potential molecular mechanisms in hepatocellular carcinoma induced by hepatitis B virus

BACKGROUND

Hepatocellular carcinoma (HCC) accounts for up to 90% of all primary hepatic malignancies; it is the sixth most common cancer and the second most common cause of cancer mortality worldwide. Numerous studies have shown that hepatitis B virus and its products, HBV integration, and mutation can induce HCC. However, the molecular mechanisms underpinning the regulation of HCC induced by HBV remain unclear.

METHODS

We downloaded 2 gene expression profiling datasets, of HBV and of HCC induced by HBV, from the gene expression omnibus (GEO) database. Differentially expressed genes (DEGs) between HCC and HBV were identified to explore any predisposing changes in gene expression associated with HCC. DEGs between HCC and adjacent healthy tissues were investigated to identify genes that may play a key role in HCC. Any overlapping genes among these DEGs were included in our bioinformatics analysis. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses of overlapping genes were performed using the Metascape online database; the protein-protein interaction (PPI) network was analyzed using the STRING online database; and we obtained the hub genes of the PPI network using Cytoscape software. An overall survival (OS) analysis of hub genes was performed using km-plotter and the gene expression profiling interactive analysis (GEPIA) online database. The expression levels of hub genes were determined using the TCGA and GEPIA databases. Finally, the relationships between hub genes and tumors were analyzed using the comparative toxicogenomics database (CTD).

RESULTS

We identified 113 overlapping genes from the 2 datasets. Using functional and pathway analyses, we found that the overlapping genes were mainly related to the AMPK signaling pathway and cellular responses to cadmium ions. C8A, SPP2, KLKB1, PROZ, C6, FETUB, MBL2, HGFAC, C8B, and ANGPTL3 were identified as hub genes and C8A, SPP2, PROZ, C6, HGFAC, and C8B were found to be significant for survival.

CONCLUSION

The DEGs re-analyzed between HCC and hepatitis B enable a systematic understanding of the molecular mechanisms of HCC reliant on hepatitis B virus.