Background: Hepatocellular carcinoma (HCC) is a very common neoplasm worldwide, and competitive endogenous RNA (ceRNA) plays an important role in the development of HCC. The purpose of this study is to investigate the molecular mechanisms of ceRNAs in HCC. Methods: This study detects potential ceRNAs from HCC through whole genome analysis of lncRNA, miRNA and mRNA expression. We then performed high-throughput sequencing of tissues from five hepatitis B related HCC patients to screen ceRNAs and those screened ceRNAs expressions were verified on tissues from an independent group of six patients. Finally, the function of ceRNAs of interest was illustrated in vitro. Result: Functional and pathway analysis of The Cancer Genome Atlas revealed ceRNA networks. The high-throughput sequencing identified 985 upregulated and 1612 downregulated lncRNAs and 887 upregulated and 1116 downregulated mRNAs in HCC patients. Differentially expressed genes were parallel to cancer-associated processes, comprising 18 upregulated and 35 downregulated significantly enriched pathways including alcoholism and viral carcinogenesis. Among them, a potential ceRNA network was detected and verified in six HCC patients. CeRNAs of the lncRNA MAGI2-AS3/miR-374-5p/FOXO1 pathway were significantly dysregulated in HCC, and validation in vitro showed that FOXO1 is positively regulated by MAGI2-AS3 through the induction of miR-374a/b-5p in HCC cells. In addition, the overexpression of FOXO1 is associated with proliferation, migration, and invasion of HCC cells and increases apoptosis of HCC cells. MiR-374a/b-5p caused an opposite effect by directly suppressing FOXO1 in HCC cells. Conclusion: CeRNA networks were found in HCC and aberrantly expressed ceRNAs of lncRNA MAGI2-AS3/miR-374-5p/FOXO1 plays a crucial role in HCC, assisting in diagnosis and providing a method for treatment.

Cancer consists of a group of diseases with the salient properties of an uncontrolled cell cycle, metastasis, and evasion of the immune response, mainly driven by the genomic instability of somatic cells and the physicochemical environment. Long noncoding RNAs (lncRNAs) are defined as noncoding RNAs with a length of more than 200 nucleotides. LncRNA dysregulation participates in diverse disease types and is tightly associated with patient clinical features, such as age, disease stage, and prognosis. In addition, an increasing number of lncRNAs have been confirmed to regulate a series of biological and pathological processes through numerous mechanisms. The lncRNA epidermal growth factor receptor antisense RNA 1 (EGFR-AS1) was recently discovered to be aberrantly expressed in many types of diseases, particularly in cancers. A high level of EGFR-AS1 was demonstrated to correlate with multiple patient clinical characteristics. More importantly, EGFR-AS1 was found to be involved in the mediation of various cellular activities, including cell proliferation, invasion, migration, chemosensitivity, and stemness. Therefore, EGFR-AS1 is a promising marker for cancer management. In this review, we introduce the expression profile, molecular mechanisms, biological functions, and clinical value of EGFR-AS1 in cancers.

Cigarette smoking is one of the largest causes of preventable death worldwide. This study aimed to identify susceptibility loci for age at smoking initiation (ASI) by performing an exome-wide association analysis. A total of 2510 smokers of either African-American (AA) or European-American (EA) origin were genotyped and analyzed at both the single nucleotide polymorphism (SNP) and gene levels. After removal of those SNPs with a minor allele frequency (<0.01), 48091 and 34933 SNPs for AAs and EAs, respectively, were used to conduct a SNP-based association analysis. Gene-based analyses were then performed for all SNPs examined within each gene. Further, we estimated the proportion of variance explained by all common SNPs included in the analysis. The strongest signals were detected for SNPs rs17849904 in the pitrilysin metallopeptidase 1 gene (PITRM1) in the AA sample (p = 9.02 × 10⁻⁷) and rs34722354 in the discoidin domain of the receptor tyrosine kinase 2 gene (DDR2) in the EA sample (p = 9.74 × 10⁻⁷). Both SNPs remained significant after Bonferroni correction for the number of SNPs tested. Subsequently, the gene-based association analysis revealed a significantly associated gene, DHRS7, in the AA sample (p = 5.00 × 10^–6), a gene previously implicated in nicotine metabolism. Our study revealed two susceptibility loci for age of smoking initiation in the two ethnic samples, with the first being PITRM1 for AA smokers and the second DDR2 for EA smokers. In addition, we found DHRS7 to be a plausible candidate for ASI in the AA sample from our gene-based association analysis. PITRM1 and DHRS7 for African-American smokers and DDR2 for European-American smokers are new candidate genes for smoking initiation. These genes represent new additions to smoking initiation, an important but less studied phenotype in nicotine dependence research.

Clinical proteomics analysis is of great significance for analyzing pathological mechanisms and discovering disease-related biomarkers. Using computational methods to accurately predict disease types can effectively improve patient disease diagnosis and prognosis. However, how to eliminate the errors introduced by peptide precursor identification and protein identification for pathological diagnosis remains a major unresolved issue. Here, we develop a powerful end-to-end deep learning model, termed “MS1Former”, that is able to classify hepatocellular carcinoma tumors and adjacent non-tumor (normal) tissues directly using raw MS1 spectra without peptide precursor identification. Our model provides accurate discrimination of subtle m/z differences in MS1 between tumor and adjacent non-tumor tissue, as well as more general performance predictions for data-dependent acquisition, data-independent acquisition, and full-scan data. Our model achieves the best performance on multiple external validation datasets. Additionally, we perform a detailed exploration of the model’s interpretability. Prospectively, we expect that the advanced end-to-end framework will be more applicable to the classification of other tumors.

Premalignant alteration assessment in liver-like tissue derived from embryonic stem cells by aristolochic acid I exposure

Single nucleotide polymorphisms (SNPs) and genes associated with susceptibility to hepatitis B virus (HBV) infection that have been identified by genome-wide association studies explain only a limited portion of the known heritability, indicating more genetic variants remain to be discovered. In this study, we adopted a new research strategy to identify more susceptibility genes and variants for HBV infection. We first performed genetic association analysis of 300 sib-pairs and 3,087 case-control samples, which revealed that 36 SNPs located in 31 genes showed nominal associations with HBV infection in both samples. Of these genes, we selected SEC24D for further molecular analysis according to the following two main lines of evidence. First, a time course analysis of the expression profiles from HBV-infected primary human hepatocytes (PHH) demonstrated that SEC24D expression increased markedly as time passed after HBV infection (P = 4.0 × 10⁻⁴). Second, SNP rs76459466 in SEC24D was adversely associated with HBV risk (OR_meta = 0.82; P_meta = 0.002), which again indicated that SEC24D represents a novel susceptibility gene for HBV infection. Moreover, SEC24D appeared to be protective against HBV infection in vitro. Consistently, we found that SEC24D expression was significantly enhanced in non-infected liver tissues (P = 0.002). We conclude that SEC24D is a novel candidate gene linked to susceptibility to HBV infection.