The association between PICALM rs3851179 variant and Alzheimer's disease (AD) has been well established by previous genome-wide association studies (GWAS) and candidate gene studies in European population. Recent studies investigated the association between PICALM rs3851179 and AD susceptibility in Chinese population. However, these studies reported consistent and inconsistent results. Here, we selected 9435 samples including 3704 AD cases and 5731 controls from previous studies and evaluated this association using a meta-analysis method for additive model. We did not observe significant genetic heterogeneity in Chinese population. Our results indicate significant association between PICALM rs3851179 and AD in Chinese population. The sensitivity analysis indicates that the association between rs3851179 and AD did not vary substantially. The regression analysis suggests no significant publication bias. In summary, this updated meta-analysis highlights the involvement of PICALM rs3851179 variant in Alzheimer's disease susceptibility in Chinese population.

Tumor metastasis is the major cause of mortality from cancer. From this perspective, detecting cancer gene expression and transcriptome changes is important for exploring tumor metastasis molecular mechanisms and cellular events. Precisely estimating a patient’s cancer state and prognosis is the key challenge to develop a patient’s therapeutic schedule. In the recent years, a variety of machine learning techniques widely contributed to analyzing real-world gene expression data and predicting tumor outcomes. In this area, data mining and machine learning techniques have widely contributed to gene expression data analysis by supplying computational models to support decision-making on real-world data. Nevertheless, limitation of real-world data extremely restricted model predictive performance, and the complexity of data makes it difficult to extract vital features. Besides these, the efficacy of standard machine learning pipelines is far from being satisfactory despite the fact that diverse feature selection strategy had been applied. To address these problems, we developed directed relation-graph convolutional network to provide an advanced feature extraction strategy. We first constructed gene regulation network and extracted gene expression features based on relational graph convolutional network method. The high-dimensional features of each sample were regarded as an image pixel, and convolutional neural network was implemented to predict the risk of metastasis for each patient. Ten cross-validations on 1,779 cases from The Cancer Genome Atlas show that our model’s performance (area under the curve, AUC = 0.837; area under precision recall curve, AUPRC = 0.717) outstands that of an existing network-based method (AUC = 0.707, AUPRC = 0.555).

No abstract

No abstract

No abstract

No abstract