The putative tumor suppressor gene EphA7 is a novel BMI-1 target

Crystal structure of clinically reported mutations Gly656Arg, Gly656Glu and Asp751His identified in the kinase domain of EphA7

Erythropoietin producing hepatocellular (Eph) forms the largest family of receptor tyrosine kinases (RTK). As a family, Eph regulates physiological events such as cell-cell interaction, cell migration, and adhesion. The Kinase domain is the catalytic core of the Eph receptor and is highly conserved sequentially. EphA7 has been recently regarded as a cancer driver gene and comprises several clinically important mutations. Three of the EphA7 mutations Gly656Glu, Gly656Arg, and Asp751His, present in the kinase domain, are predicted to be highly pathogenic. Furthermore, Gly656Glu and Gly656Arg are reported to be hotspot mutations. Considering the importance of mutations, crystals structure of EphA7 Gly656Glu, Gly656Arg, and Asp751His mutants has been explored. Changes in folding pattern and intramolecular interactions were observed in mutant structures. Secondary structural changes were observed in the hinge region of EphA7 Gly656Arg and Asp751His structure, affecting the transition of kinase domain between open and closed conformations. EphA7 Asp751His mutant structure shows a distorted nucleotide-binding groove. Differences were observed in hydrogen bonding and hydrophobic interactions between the catalytic and highly conserved DFG motif in the EphA7 mutants, which may influence the catalytic activity of kinase domain.

Medulloblastoma genomics in the modern molecular era

Medulloblastoma (MB) represents a spectrum of biologically and clinically distinct entities. Initially described histopathologically as a small, round blue cell tumor arising in the cerebellum, MB has emerged as a paradigm for molecular classification in cancer. Recent advances in genomic, transcriptomic and epigenomic profiling of MB have further refined molecular classification and complemented conventional histopathological diagnosis. Herein, we review the main clinical and molecular features of the four consensus subgroups of MB (WNT, SHH, Group 3 and Group 4). We also highlight hereditary predisposition syndromes associated with increased risk of MB. Finally, we explore advances in the classification of the consensus molecular groups while also presenting cutting-edge frontiers in identifying intratumoral heterogeneity and cellular origins of MB.

Integrative analysis of the lncRNA-associated ceRNA network reveals lncRNAs as potential prognostic biomarkers in human muscle-invasive bladder cancer

Background

Long noncoding RNAs (lncRNAs) play important roles in competing endogenous RNA (ceRNA) networks involved in the development and progression of various cancers, including muscle-invasive bladder cancer (MIBC).

Purpose

This study aims to construct the lncRNA-associated ceRNA network and identify lncRNA signatures correlated with the clinical features of MIBC tissue samples from The Cancer Genome Atlas (TGCA) database.

Methods

The differential expression profiles of MIBC associated lncRNAs, miRNAs and mRNAs were obtained from TCGA. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed to determine the principal functions of significantly dysregulated mRNAs. The dysregulated lncRNA-associated ceRNA network of MIBC was constructed based on the bioinformatics data, and the correlations between lncRNA expression and clinical features were analyzed using a weighted gene coexpression network analysis (WGCNA). Six cancer specific lncRNAs from the ceRNA network were randomly selected to detect their expression in 32 paired MIBC tissue samples and 5 bladder cancer cell lines using quantitative real-time polymerase chain reaction (qRT-PCR).

Results

The ceRNA network was constructed with 30 lncRNAs, 13 miRNAs and 32 mRNAs. Seventeen lncRNAs in the ceRNA network correlated with certain clinical features, and only 1 lncRNA (MIR137HG) correlated with the overall survival (OS) of patients with MIBC (log-rank test P<0.05). GO and KEGG analyses revealed roles for the potential mRNA targets of MIR137HG in epithelial cell differentiation and the peroxisome proliferator-activated receptor (PPAR) and tumor necrosis factor (TNF) signaling pathways. The expression data from TCGA were highly consistent with the verification results of the MIBC tissue samples and bladder cancer cell lines.

Conclusion

These findings improve our understanding of the regulatory mechanism of the lncRNA-miRNA-mRNA ceRNA network and reveal potential lncRNAs as prognostic biomarkers of MIBC.

ShRNA-mediated BMI-1 gene silencing inhibits gastrointestinal stromal tumor cell telomerase activity and enhances apoptosis

Gastrointestinal stromal tumors (GISTs) are the most frequently occurring mesenchymal tumors of the gastrointestinal tract. Telomerase activity is well acknowledged as a critical factor in oncogenesis. The objective of the present study is to evaluate the effect of BMI gene silencing on proliferation, apoptosis and telomerase activity in human GIST882 cells. GIST882 cells were transfected with a eukaryotic expression vector of an shRNA fragment. The silencing efficiency in the GIST882 cells was determined by RT-qPCR and a western blot analysis. After the shRNA-BMI-1 plasmid was transfected into the GIST882 cells and nude mice, a cell counting kit-8 (CCK-8) assay and flow cytometry were utilized to detect the GIST882 cell proliferation, the apoptosis rate and the cell cycle. Tumor growth was observed by tumor xenograft in nude mice. Telomerase activity and telomere length were detected by a Southern blot and a target region amplified polymorphism. The shRNA-BMI-1 recombinant plasmid was successfully constructed. The mRNA and protein expression of the BMI-1 gene in GIST882 cells was suppressed by the shRNA-BMI-1 recombinant plasmid. Meanwhile, BMI-1 gene silencing inhibited the cell proliferation, tumor growth, and cell cycle in the GIST882 cells. However, cell apoptosis was increased and telomerase activity was decreased with the silencing of the BMI-1 gene. Collectively, the results of this study suggest that silencing the BMI-1 gene may provide a new target for the treatment of GISTs.

Multitrait genome association analysis identifies new susceptibility genes for human anthropometric variation in the GCAT cohort

Background

Heritability estimates have revealed an important contribution of SNP variants for most common traits; however, SNP analysis by single-trait genome-wide association studies (GWAS) has failed to uncover their impact. In this study, we applied a multitrait GWAS approach to discover additional factor of the missing heritability of human anthropometric variation.

Methods

We analysed 205 traits, including diseases identified at baseline in the GCAT cohort (Genomes For Life- Cohort study of the Genomes of Catalonia) (n=4988), a Mediterranean adult population-based cohort study from the south of Europe. We estimated SNP heritability contribution and single-trait GWAS for all traits from 15 million SNP variants. Then, we applied a multitrait-related approach to study genome-wide association to anthropometric measures in a two-stage meta-analysis with the UK Biobank cohort (n=336 107).

Results

Heritability estimates (eg, skin colour, alcohol consumption, smoking habit, body mass index, educational level or height) revealed an important contribution of SNP variants, ranging from 18% to 77%. Single-trait analysis identified 1785 SNPs with genome-wide significance threshold. From these, several previously reported single-trait hits were confirmed in our sample with LINC01432 (p=1.9×10⁻⁹) variants associated with male baldness, LDLR variants with hyperlipidaemia (ICD-9:272) (p=9.4×10⁻¹⁰) and variants in IRF4 (p=2.8×10⁻⁵⁷), SLC45A2 (p=2.2×10⁻¹³⁰), HERC2 (p=2.8×10⁻¹⁷⁶), OCA2 (p=2.4×10⁻¹²¹) and MC1R (p=7.7×10⁻²²) associated with hair, eye and skin colour, freckling, tanning capacity and sun burning sensitivity and the Fitzpatrick phototype score, all highly correlated cross-phenotypes. Multitrait meta-analysis of anthropometric variation validated 27 loci in a two-stage meta-analysis with a large British ancestry cohort, six of which are newly reported here (p value threshold <5×10⁻⁹) at ZRANB2-AS2, PIK3R1, EPHA7, MAD1L1, CACUL1 and MAP3K9.

Conclusion

Considering multiple-related genetic phenotypes improve associated genome signal detection. These results indicate the potential value of data-driven multivariate phenotyping for genetic studies in large population-based cohorts to contribute to knowledge of complex traits.