Construction of a Comprehensive Multiomics Map of Hepatocellular Carcinoma and Screening of Possible Driver Genes

Single-nucleus RNA sequencing of pre-malignant liver reveals disease-associated hepatocyte state with HCC prognostic potential

Current approaches to staging chronic liver diseases have limited utility for predicting liver cancer risk. Here, we employed single-nucleus RNA sequencing (snRNA-seq) to characterize the cellular microenvironment of healthy and pre-malignant livers using two distinct mouse models. Downstream analyses unraveled a previously uncharacterized disease-associated hepatocyte (daHep) transcriptional state. These cells were absent in healthy livers but increasingly prevalent as chronic liver disease progressed. Copy number variation (CNV) analysis of microdissected tissue demonstrated that daHep-enriched regions are riddled with structural variants, suggesting these cells represent a pre-malignant intermediary. Integrated analysis of three recent human snRNA-seq datasets confirmed the presence of a similar phenotype in human chronic liver disease and further supported its enhanced mutational burden. Importantly, we show that high daHep levels precede carcinogenesis and predict a higher risk of hepatocellular carcinoma development. These findings may change the way chronic liver disease patients are staged, surveilled, and risk stratified.

The Cancer Genome Atlas (TCGA) based m6A methylation-related genes predict prognosis in rectosigmoid cancer

N6-methyladenosine (m6A) methylation plays an important role in the occurrence and development of tumors. This study aimed to explore the effects of m6A methylation regulatory genes on rectosigmoid cancer (RSC). RNA-seq data and related clinical information in The Cancer Genome Atlas database were analyzed. The Wilcoxon test was used to analyze the different expression levels of m6A methylation regulatory genes between the tumor and normal samples. Least absolute shrinkage and selection operator Cox regression analysis was used to construct a risk prognosis model between the m6A methylation regulatory genes and RSC. The median risk score was used to classify RSC patients into high and low-risk groups. Kaplan-Meier survival analysis and receiver operating characteristic curves were used to evaluate the sensitivity and specificity of the prediction model. The expression of m6A methylation regulation genes was different between the tumor and normal samples, 6 genes were overexpressed in tumor and 2 genes were down-regulated. Four m6A methylation regulatory genes, YTHDF3, KIAA1429, ALKBH5 and METTL3, were screened by least absolute shrinkage and selection operator Cox regression analysis. The overall survival of high-risk group was significantly lower than that of low-risk group (P = 4.681 × 10-4). The area under the curve value in the receiver operating characteristic curve was 0.935, indicating that the prediction model was effective. Univariate and multivariate Cox regression were used to test the effectiveness of the model. m6A methylation regulators YTHDF3, KIAA1429, ALKBH5, and METTL3 can be used to construct predictive models to predict overall survival in different clinical subgroups of RSC patients.

Sofosbuvir induces gene expression for promoting cell proliferation and migration of hepatocellular carcinoma cells

Direct-acting antivirals (DAAs) have achieved a sustained virological response (SVR) rate of 95–99% in treating HCV. Several studies suggested that treatment with sofosbuvir (SOF), one type of DAAs, may be associated with increased risk of developing HCC. The aim of this study is to investigate the potential mechanisms of SOF on the development of HCC. OR-6 (harboring full-length genotype 1b HCV) and Huh 7.5.1 cells were used to examine the effects of SOF on cell proliferation and migration of HCC cells. SOF-upregulated genes in OR-6 cells were inspected using next generation sequencing (NGS)and the clinical significance of these candidate genes was analyzed using The Cancer Genome Atlas (TCGA) database. We found that SOF increased cell proliferation and cell migration in OR-6 and Huh 7.5.1 cells. Several SOF-upregulated genes screened from NGS were confirmed by real-time PCR in OR-6 cells. Among these genes, PHOSPHO2, KLHL23, TRIM39, TSNAX-DISC1 and RPP21 expression were significantly elevated in the tumor tissues compared with the non-tumor tissues of HCC according to TCGA database. High expression of PHOSPHO2 and RPP21 was associated with poor overall survival of HCC patients. Moreover, knockdown of PHOSPHO2-KLHL23, TSNAX-DISC1, TRIM39 and RPP21 diminished cell proliferation and migration increased by SOF in OR-6 and Huh 7.5.1 cells. In conclusion, SOF-upregulated genes promoted HCC cell proliferation and migration, which might be associated with the development of HCC.

Multiomics technologies: role in disease biomarker discoveries and therapeutics

 

Medical research has been revolutionized after the publication of the full human genome. This was the major landmark that paved the way for understanding the biological functions of different macro and micro molecules. With the advent of different high-throughput technologies, biomedical research was further revolutionized. These technologies constitute genomics, transcriptomics, proteomics, metabolomics, etc. Collectively, these high-throughputs are referred to as multi-omics technologies. In the biomedical field, these omics technologies act as efficient and effective tools for disease diagnosis, management, monitoring, treatment and discovery of certain novel disease biomarkers. Genotyping arrays and other transcriptomic studies have helped us to elucidate the gene expression patterns in different biological states, i.e. healthy and diseased states. Further omics technologies such as proteomics and metabolomics have an important role in predicting the role of different biological molecules in an organism. It is because of these high throughput omics technologies that we have been able to fully understand the role of different genes, proteins, metabolites and biological pathways in a diseased condition. To understand a complex biological process, it is important to apply an integrative approach that analyses the multi-omics data in order to highlight the possible interrelationships of the involved biomolecules and their functions. Furthermore, these omics technologies offer an important opportunity to understand the information that underlies disease. In the current review, we will discuss the importance of omics technologies as promising tools to understand the role of different biomolecules in diseases such as cancer, cardiovascular diseases, neurodegenerative diseases and diabetes.

SUMMARY POINTS

Enhancing Cancer Driver Gene Prediction by Protein-Protein Interaction Network

With the advances in gene sequencing technologies, millions of somatic mutations have been reported in the past decades, but mining cancer driver genes with oncogenic mutations from these data remains a critical and challenging area of research. In this study, we proposed a network-based classification method for identifying cancer driver genes with merging the multi-biological information. In this method, we construct a cancer specific genetic network from the human protein-protein interactome (PPI) to mine the network structure attributes, and combine biological information such as mutation frequency and differential expression of genes to achieve accurate prediction of cancer driver genes. Across seven different cancer types, the proposed algorithm always achieves high prediction accuracy, which is superior to the existing advanced methods. In the analysis of the predicted results, about 40 percent of the top 10 candidate genes overlap with the Cancer Gene Census database. Interestingly, the feature comparison indicates that the network based features are still more important than the biological features, including the mutation frequency and genetic differential expression. Further analyses also show that the integration of network structure attributes and biological information is valuable for predicting new cancer driver genes.