Glioblastoma (GBM) mesenchymal (MES) transition can be regulated by long non-coding RNAs (lncRNAs) via modulation of various factors (Epithelial-to-Mesenchymal (EMT) markers, biological signalling, an Show more
Glioblastoma (GBM) mesenchymal (MES) transition can be regulated by long non-coding RNAs (lncRNAs) via modulation of various factors (Epithelial-to-Mesenchymal (EMT) markers, biological signalling, and the extracellular matrix (ECM)). However, understanding of these mechanisms in terms of lncRNAs is largely sparse. This review systematically analysed the mechanisms by which lncRNAs influence MES transition in GBM from a systematic search of the literature (using PRISMA) performed in five databases (PubMed, MEDLINE, EMBASE, Scopus, and Web of Science). We identified a total of 62 lncRNAs affiliated with GBM MES transition, of which 52 were upregulated and 10 were downregulated in GBM cells, where 55 lncRNAs were identified to regulate classical EMT markers in GBM (E-cadherin, N-cadherin, and vimentin) and 25 lncRNAs were reported to regulate EMT transcription factors (ZEB1, Snai1, Slug, Twist, and Notch); a total of 16 lncRNAs were found to regulate the associated signalling pathways (Wnt/β-catenin, PI3k/Akt/mTOR, TGFβ, and NF-κB) and 14 lncRNAs were reported to regulate ECM components (MMP2/9, fibronectin, CD44, and integrin-β1). A total of 25 lncRNAs were found dysregulated in clinical samples (TCGA vs. GTEx), of which 17 were upregulated and 8 were downregulated. Gene set enrichment analysis predicted the functions of HOXAS3, H19, HOTTIP, MEG3, DGCR5, and XIST at the transcriptional and translational levels based on their interacting target proteins. Our analysis observed that the MES transition is regulated by complex interplays between the signalling pathways and EMT factors. Nevertheless, further empirical studies are required to elucidate the complexity in this process between these EMT factors and the signalling involved in the GBM MES transition. Show less
Next-generation phenotyping (NGP) is an application of advanced methods of computer vision on medical imaging data such as portrait photos of individuals with rare disorders. NGP on portraits results Show more
Next-generation phenotyping (NGP) is an application of advanced methods of computer vision on medical imaging data such as portrait photos of individuals with rare disorders. NGP on portraits results in gestalt scores that can be used for the selection of appropriate genetic tests, and for the interpretation of the molecular data. Here, we report on an exceptional case of a young girl that was presented at the age of 8 and 15 and enrolled in NGP diagnostics on the latter occasion. The girl had clinical features associated with Koolen-de Vries syndrome (KdVS) and a suggestive facial gestalt. However, chromosomal microarray (CMA), Sanger sequencing, multiplex ligation-dependent probe analysis (MLPA), and trio exome sequencing remained inconclusive. Based on the highly indicative gestalt score for KdVS, the decision was made to perform genome sequencing to also evaluate noncoding variants. This analysis revealed a 4.7 kb de novo deletion partially affecting intron 6 and exon 7 of the KANSL1 gene. This is the smallest reported structural variant to date for this phenotype. The case illustrates how NGP can be integrated into the iterative diagnostic process of test selection and interpretation of sequencing results. Show less