The mRNA–miRNA–lncRNA Regulatory Network and Factors Associated with Prognosis Prediction of Hepatocellular Carcinoma

The aim of this study was to identify novel prognostic mRNA and microRNA (miRNA) biomarkers for hepatocellular carcinoma (HCC) using methods in systems biology. Differentially expressed mRNAs, miRNAs, and long non-coding RNAs (lncRNAs) were compared between HCC tumor tissues and normal liver tissues in The Cancer Genome Atlas (TCGA) database. Subsequently, a prognosis-associated mRNA co-expression network, an mRNA–miRNA regulatory network, and an mRNA–miRNA–lncRNA regulatory network were constructed to identify prognostic biomarkers for HCC through Cox survival analysis. Seven prognosis-associated mRNA co-expression modules were obtained by analyzing these differentially expressed mRNAs. An expression module including 120 mRNAs was significantly correlated with HCC patient survival. Combined with patient survival data, several mRNAs and miRNAs, including CHST4, SLC22A8, STC2, hsa-miR-326, and hsa-miR-21 were identified from the network to predict HCC patient prognosis. Clinical significance was investigated using tissue microarray analysis of samples from 258 patients with HCC. Functional annotation of hsa-miR-326 and hsa-miR-21-5p indicated specific associations with several cancer-related pathways. The present study provides a bioinformatics method for biomarker screening, leading to the identification of an integrated mRNA–miRNA–lncRNA regulatory network and their co-expression patterns in relation to predicting HCC patient survival.     

Plasmin-sensitive dibasic sequences in the third fibronectin-like domain of L1-cell adhesion molecule (CAM) facilitate homomultimerization and concomitant integrin recruitment

L1 is a multidomain transmembrane neural recognition molecule essential for neurohistogenesis. While moieties in the immunoglobulin-like domains of L1 have been implicated in both heterophilic and homophilic binding, the function of the fibronectin (FN)-like repeats remains largely unresolved. Here, we demonstrate that the third FN-like repeat of L1 (FN3) spontaneously homomultimerizes to form trimeric and higher order complexes. Remarkably, these complexes support direct RGD-independent interactions with several integrins, including alpha(v)beta(3) and alpha(5)beta(1). A pep- tide derived from the putative C-C' loop of FN3 (GSQRKHSKRHIHKDHV(852)) also forms trimeric complexes and supports alpha(v)beta(3) and alpha(5)beta(1) binding. Substitution of the dibasic RK(841) and KR(845) sequences within this peptide or the FN3 domain limited multimerization and abrogated integrin binding. Evidence is presented that the multimerization of, and integrin binding to, the FN3 domain is regulated both by conformational constraints imposed by other domains and by plasmin- mediated cleavage within the sequence RK( downward arrow)HSK( downward arrow)RH(846). The integrin alpha(9)beta(1), which also recognizes the FN3 domain, colocalizes with L1 in a manner restricted to sites of cell-cell contact. We propose that distal receptor ligation events at the cell-cell interface may induce a conformational change within the L1 ectodomain that culminates in receptor multimerization and integrin recruitment via interaction with the FN3 domain.     

How the Internally Organized Direction Sense Is Used to Navigate

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The Kinetochore-Microtubule Coupling Machinery Is Repurposed in Sensory Nervous System Morphogenesis

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老年人认知训练的神经机制研究

伴随老化,老年人的认知和脑功能会表现出一定的下降趋势.尽管如此,人类的大脑到老年期都会保有一定的可塑性,认知训练的方式是延缓认知和脑功能衰退的有效手段.本文回顾了以往针对老年人不同类型的认知训练研究,探讨了认知训练的理论基础(包括放大观和补偿观),深入分析了老年人认知训练的神经机制,并在此基础上指出以往研究中理论基础冲突的不足和对未来研究老年人训练任务适配性的展望.     

An Approach Based on Mutually Informed Neural Networks to Optimize the Generalization Capabilities of Decision Support Systems Developed for Heart Failure Prediction

Available clinical methods for heart failure (HF) diagnosis are expensive and require a high-level of experts intervention. Recently, various machine learning models have been developed for the prediction of HF where most of them have an issue of over-fitting. Over-fitting occurs when machine learning based predictive models show better performance on the training data yet demonstrate a poor performance on the testing data and the other way around. Developing a machine learning model which is able to produce generalization capabilities (such that the model exhibits better performance on both the training and the testing data sets) could overall minimize the prediction errors. Hence, such prediction models could potentially be helpful to cardiologists for the effective diagnose of HF. This paper proposes a two-stage decision support system to overcome the over-fitting issue and to optimize the generalization factor. The first stage uses a mutual information based statistical model while the second stage uses a neural network. We applied our approach to the HF subset of publicly available Cleveland heart disease database. Our experimental results show that the proposed decision support system has optimized the generalization capabilities and has reduced the mean percent error (MPE) to 8.8% which is significantly less than the recently published studies. In addition, our model exhibits a 93.33% accuracy rate which is higher than twenty eight recently developed HF risk prediction models that achieved accuracy in the range of 57.85% to 92.31%. We can hope that our decision support system will be helpful to cardiologists if deployed in clinical setup.     

Prioritization of SNPs for genome-wide association studies using an interaction model of genetic variation,gene expression,and trait variation

The identification of true causal loci to unravel the statistical evidence of genotype-phenotype correlations and the biological relevance of selected single-nucleotide polymorphisms (SNPs) is a challenging issue in genome-wide association studies (GWAS). Here, we introduced a novel method for the prioritization of SNPs based on p-values from GWAS. The method uses functional evidence from populations, including phenotype-associated gene expressions. Based on the concept of genetic interactions, such as perturbation of gene expression by genetic variation, phenotype and gene expression related SNPs were prioritized by adjusting the p-values of SNPs. We applied our method to GWAS data related to drug-induced cytotoxicity. Then, we prioritized loci that potentially play a role in druginduced cytotoxicity. By generating an interaction model, our approach allowed us not only to identify causal loci, but also to find intermediate nodes that regulate the flow of information among causal loci, perturbed gene expression, and resulting phenotypic variation.