No abstract

◥Due to its intricate heterogeneity and limited treatment, hepatocellular carcinoma (HCC) has been considered a major cause of cancer-related mortality worldwide. Increasing evidence indicates that G-protein-coupled estrogen receptor 1 (GPER1) can promote estrogen-dependent hepatocellular proliferation by activating AKT signaling. The mTOR complex 2 (mTORC2), whose integrity and activity are modulated by its subunit Sin1, controls the activation of AKT by phosphorylation at position S473. In this study, we investigate the modulation of Sin1 and how estrogen signaling may influence the mTORC2-AKT cascade in HCC cells and a DEN-induced mouse model. We have found that estradiol-dependent Sin1 expression is transcriptionally modulated by GPER1 as well as ERa. GPER1 is able to regulate Sin1 stability via nuclear translocation, therefore increasing Sin1-mTORC2-AKT activation. Moreover, Sin1 interacts with ERa and further enhances its transcriptional activity. Sin1 is highly expressed in acute liver injury and in cases of HCC harboring high expression of GPER1 and constitutive activation of mTORC2-AKT signaling. GPER1 inhibition using the antagonist G-15 reverses DEN-induced acute liver injury by suppressing Sin1 expression and mTORC2-AKT activation. Notably, SIN1 expression varies between male and female mice in the context of both liver injury and liver cancer. In addition, high SIN1 expression is predictive of good prognosis in both male and female patients with HCC who are free from hepatitis virus infection and who report low alcohol consumption. Hence, here we demonstrate that Sin1 can be regulated by GPER1 both through nongenomic and indirect genomic signaling.Implications: This study suggests that Sin1 may be a novel HCC biomarker which is gender-dependent and sensitive to particular risk factor.

SARS-CoV-2 has caused a worldwide pandemic. Existing research on coronavirus mutations is based on small data sets, and multiple sequence alignment using a global-scale data set has yet to be conducted. Statistical analysis of integral mutations and global spread are necessary and could help improve primer design for nucleic acid diagnosis and vaccine development. Here, we optimized multiple sequence alignment using a conserved sequence search algorithm to align 24,768 sequences from the GISAID data set. A phylogenetic tree was constructed using the maximum likelihood (ML) method. Coronavirus subtypes were analyzed via t-SNE clustering. We performed haplotype network analysis and t-SNE clustering to analyze the coronavirus origin and spread. Overall, we identified 33 sense, 17 nonsense, 79 amino acid loss, and 4 amino acid insertion mutations in full-length open reading frames. Phylogenetic trees were successfully constructed and samples clustered into subtypes. The COVID-19 pandemic differed among countries and continents. Samples from the United States and western Europe were more diverse, and those from China and Asia mainly contained specific subtypes. Clades G/GH/GR are more likely to be the origin clades of SARS-CoV-2 compared with clades S/L/V. Conserved sequence searches can be used to segment long sequences, making large-scale multisequence alignment possible, facilitating more comprehensive gene mutation analysis. Mutation analysis of the SARS-CoV-2 can inform primer design for nucleic acid diagnosis to improve virus detection efficiency. In addition, research into the characteristics of viral spread and relationships among geographic regions can help formulate health policies and reduce the increase of imported cases.

Esophageal cancer (EC) is one of the deadliest cancers worldwide. However, reliable biomarkers for early diagnosis, or those for the prognosis of therapy, remain unfulfilled goals for its subtype esophageal squamous cell carcinoma (ESCC). The purpose of this study was to identify reliable biomarkers for the diagnosis and prognosis of ESCC by gene chip re-annotation technique and downstream bioinformatics analysis. In our research, the GSE53624 dataset was downloaded from the GEO database. Then, we reannotated the gene expression probe and obtained the gene expression matrix. Differential expressed genes (DEGs) were found by R packages and they were subjected to Gene Ontology enrichment analysis and protein–protein interaction (PPI) network construction. As a result, a total of 28,885 mRNA probes were reannotated, among which 210 down-regulated and 80 up-regulated DEGs were screened out. By combining these genes set in clinical prognosis information and Western blot analysis, we found four genes with diagnostic and prognostic significance, including MMP134SPP1, MMP10, and COL1A1. Furthermore, markers of infiltrating immune cells exhibited different DEG-related immune infiltration patterns.