Knowledge-based analyses reveal new candidate genes associated with risk of hepatitis B virus related hepatocellular carcinoma

Exploring a four-gene risk model based on doxorubicin resistance-associated lncRNAs in hepatocellular carcinoma

Background: Liver cancer is a lethal cancer type among which hepatocellular carcinoma (HCC) is the most common manifestation globally. Drug resistance is a central problem impeding the efficiency of HCC treatment. Long non-coding RNAs reportedly result in drug resistance. This study aimed to identify key lncRNAs associated with doxorubicin resistance and HCC prognosis. Materials and Methods: HCC samples with gene expression profiles and clinical data were accessed from public databases. We applied differential analysis to identify key lncRNAs that differed between HCC and normal samples and between drug-fast and control samples. We also used univariate Cox regression analysis to screen lncRNAs or genes associated with HCC prognosis. The least absolute shrinkage and selection operator (LASSO) was used to identify the key prognostic genes. Finally, we used receiver operating characteristic analysis to validate the effectiveness of the risk model. Results: The results of this study revealed RNF157-AS1 as a key lncRNA associated with both doxorubicin resistance and HCC prognosis. Metabolic pathways such as fatty acid metabolism and oxidative phosphorylation were enriched in RNF157-AS1-related genes. LASSO identified four protein-coding genes-CENPP, TSGA10, MRPL53, and BFSP1-to construct a risk model. The four-gene risk model effectively classified HCC samples into two risk groups with different overall survival. Finally, we established a nomogram, which showed superior performance in predicting the long-term prognosis of HCC. Conclusion: RNF157-AS1 may be involved in doxorubicin resistance and may serve as a potential therapeutic target. The four-gene risk model showed potential for the prediction of HCC prognosis.

Identifying novel host-based diagnostic biomarker panels for COVID-19: a whole-blood/nasopharyngeal transcriptome meta-analysis

BACKGROUND

Regardless of improvements in controlling the COVID-19 pandemic, the lack of comprehensive insight into SARS-COV-2 pathogenesis is still a sophisticated challenge. In order to deal with this challenge, we utilized advanced bioinformatics and machine learning algorithms to reveal more characteristics of SARS-COV-2 pathogenesis and introduce novel host response-based diagnostic biomarker panels.

METHODS

In the present study, eight published RNA-Seq datasets related to whole-blood (WB) and nasopharyngeal (NP) swab samples of patients with COVID-19, other viral and non-viral acute respiratory illnesses (ARIs), and healthy controls (HCs) were integrated. To define COVID-19 meta-signatures, Gene Ontology and pathway enrichment analyses were applied to compare COVID-19 with other similar diseases. Additionally, CIBERSORTx was executed in WB samples to detect the immune cell landscape. Furthermore, the optimum WB- and NP-based diagnostic biomarkers were identified via all the combinations of 3 to 9 selected features and the 2-phases machine learning (ML) method which implemented k-fold cross validation and independent test set validation.

RESULTS

The host gene meta-signatures obtained for SARS-COV-2 infection were different in the WB and NP samples. The gene ontology and enrichment results of the WB dataset represented the enhancement in inflammatory host response, cell cycle, and interferon signature in COVID-19 patients. Furthermore, NP samples of COVID-19 in comparison with HC and non-viral ARIs showed the significant upregulation of genes associated with cytokine production and defense response to the virus. In contrast, these pathways in COVID-19 compared to other viral ARIs were strikingly attenuated. Notably, immune cell proportions of WB samples altered in COVID-19 versus HC. Moreover, the optimum WB- and NP-based diagnostic panels after two phases of ML-based validation included 6 and 8 markers with an accuracy of 97% and 88%, respectively.

CONCLUSIONS

Based on the distinct gene expression profiles of WB and NP, our results indicated that SARS-COV-2 function is body-site-specific, although according to the common signature in WB and NP COVID-19 samples versus controls, this virus also induces a global and systematic host response to some extent. We also introduced and validated WB- and NP-based diagnostic biomarkers using ML methods which can be applied as a complementary tool to diagnose the COVID-19 infection from non-COVID cases.

An enhancer variant at 16q22.1 predisposes to hepatocellular carcinoma via regulating PRMT7 expression

Most cancer causal variants are found in gene regulatory elements, e.g., enhancers. However, enhancer variants predisposing to hepatocellular carcinoma (HCC) remain unreported. Here we conduct a genome-wide survey of HCC-susceptible enhancer variants through a three-stage association study in 11,958 individuals and identify rs73613962 (T > G) within the intronic region of PRMT7 at 16q22.1 as a susceptibility locus of HCC (OR = 1.41, P = 6.02 × 10^-10). An enhancer dual-luciferase assay indicates that the rs73613962-harboring region has allele-specific enhancer activity. CRISPR-Cas9/dCas9 experiments further support the enhancer activity of this region to regulate PRMT7 expression. Mechanistically, transcription factor HNF4A binds to this enhancer region, with preference to the risk allele G, to promote PRMT7 expression. PRMT7 upregulation contributes to in vitro, in vivo, and clinical HCC-associated phenotypes, possibly by affecting the p53 signaling pathway. This concept of HCC pathogenesis may open a promising window for HCC prevention/treatment.

Characterization of Hepatitis B Virus Integrations Identified in Hepatocellular Carcinoma Genomes

Hepatocellular carcinoma (HCC) is a leading cause of cancer-related mortality. Almost half of HCC cases are associated with hepatitis B virus (HBV) infections, which often lead to HBV sequence integrations in the human genome. Accurate identification of HBV integration sites at a single nucleotide resolution is critical for developing a better understanding of the cancer genome landscape and of the disease itself. Here, we performed further analyses and characterization of HBV integrations identified by our recently reported VIcaller platform in recurrent or known HCC genes (such as TERT, MLL4, and CCNE1) as well as non-recurrent cancer-related genes (such as CSMD2, NKD2, and RHOU). Our pathway enrichment analysis revealed multiple pathways involving the alcohol dehydrogenase 4 gene, such as the metabolism pathways of retinol, tyrosine, and fatty acid. Further analysis of the HBV integration sites revealed distinct patterns involving the integration upper breakpoints, integrated genome lengths, and integration allele fractions between tumor and normal tissues. Our analysis also implies that the VIcaller method has diagnostic potential through discovering novel clonal integrations in cancer-related genes. In conclusion, although VIcaller is a hypothesis free virome-wide approach, it can still be applied to accurately identify genome-wide integration events of a specific candidate virus and their integration allele fractions.

Zinc transporters and their functional integration in mammalian cells

Zinc is a ubiquitous biological metal in all living organisms. The spatiotemporal zinc dynamics in cells provide crucial cellular signaling opportunities, but also challenges for intracellular zinc homeostasis with broad disease implications. Zinc transporters play a central role in regulating cellular zinc balance and subcellular zinc distributions. The discoveries of two complementary families of mammalian zinc transporters (ZnTs and ZIPs) in the mid-1990s spurred much speculation on their metal selectivity and cellular functions. After two decades of research, we have arrived at a biochemical description of zinc transport. However, in vitro functions are fundamentally different from those in living cells, where mammalian zinc transporters are directed to specific subcellular locations, engaged in dedicated macromolecular machineries, and connected with diverse cellular processes. Hence, the molecular functions of individual zinc transporters are reshaped and deeply integrated in cells to promote the utilization of zinc chemistry to perform enzymatic reactions, tune cellular responsiveness to pathophysiologic signals, and safeguard cellular homeostasis. At present, the underlying mechanisms driving the functional integration of mammalian zinc transporters are largely unknown. This knowledge gap has motivated a shift of the research focus from in vitro studies of purified zinc transporters to in cell studies of mammalian zinc transporters in the context of their subcellular locations and protein interactions. In this review, we will outline how knowledge of zinc transporters has been accumulated from in-test-tube to in-cell studies, highlighting new insights and paradigm shifts in our understanding of the molecular and cellular basis of mammalian zinc transporter functions.

Lineage tracing: technology tool for exploring the development, regeneration, and disease of the digestive system

Lineage tracing is the most widely used technique to track the migration, proliferation, and differentiation of specific cells in vivo. The currently available gene-targeting technologies have been developing for decades to study organogenesis, tissue injury repairing, and tumor progression by tracing the fates of individual cells. Recently, lineage tracing has expanded the platforms available for disease model establishment, drug screening, cell plasticity research, and personalized medicine development in a molecular and cellular biology perspective. Lineage tracing provides new views for exploring digestive organ development and regeneration and techniques for digestive disease causes and progression. This review focuses on the lineage tracing technology and its application in digestive diseases.