Use of a composite motor, cognitive, and global functional clinical outcome measure in HD provides an improved measure of clinical progression more related to measures of progressive brain atrophy and provides an opportunity for enhanced clinical trial efficiency relative to currently used individual motor, cognitive, and functional outcome measures.

In this study, we aimed to investigate the effects of lncRNA CASC11 on gastric cancer (GC) cell progression through regulating miR-340-5p and cell cycle pathway. Expressions of lncRNA CASC11 in gastric cancer tissues and cell lines were determined by qRT-PCR. Differentially expressed lncRNAs, mRNAs and miRNAs were screened through microarray analysis. The relationship among CASC11, CDK1 and miR-340-5p was predicted by TargetScan and validated through dual luciferase reporter assay. Western blot assay examined the protein level of CDK1 and several cell cycle regulatory proteins. GO functional analysis and KEGG pathway analysis were used to predict the association between functions and related pathways. Cell proliferation was determined by CCK-8 assays. Cell apoptosis and cell cycle were detected by flow cytometry assay. CASC11 was highly expressed in GC tissues and cell lines. Knockdown of CASC11 inhibited GC cell proliferation, promoted cell apoptosis and blocked cell cycle. KEGG further indicated an enriched cell cycle pathway involving CDK1. QRT-PCR showed that miR-340-5p was down-regulated in GC cells tissues, while CDK1 was up-regulated. Furthermore, CASC11 acted as a sponge of miR-340-5p which directly targeted CDK1. Meanwhile, miR-340-5p overexpression promoted GC cell apoptosis and induced cell cycle arrest, while CDK1 overexpression inhibited cell apoptosis and accelerated cell cycle. Our study revealed the mechanism of CASC11/miR-340-5p/CDK1 network in GC cell line, and suggested that CASC11 was a novel facilitator that exerted a biological effect by activating the cell cycle signaling pathway. This finding provides a potential therapeutic target for GC.

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Recent advances in genome sequencing and imputation technologies provide an exciting opportunity to comprehensively study the contribution of genetic variants to complex phenotypes. However, our ability to translate genetic discoveries into mechanistic insights remains limited at this point. In this paper, we propose an efficient knockoff-based method, GhostKnockoff, for genome-wide association studies (GWAS) that leads to improved power and ability to prioritize putative causal variants relative to conventional GWAS approaches. The method requires only Z-scores from conventional GWAS and hence can be easily applied to enhance existing and future studies. The method can also be applied to meta-analysis of multiple GWAS allowing for arbitrary sample overlap. We demonstrate its performance using empirical simulations and two applications: (1) a meta-analysis for Alzheimer’s disease comprising nine overlapping large-scale GWAS, whole-exome and whole-genome sequencing studies and (2) analysis of 1403 binary phenotypes from the UK Biobank data in 408,961 samples of European ancestry. Our results demonstrate that GhostKnockoff can identify putatively functional variants with weaker statistical effects that are missed by conventional association tests.

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