Pathway-based analysis in genome-wide association study (GWAS) is being widely used to uncover novel multi-genic functional associations. Many of these pathway-based methods have been used to test the enrichment of the associated genes in the pathways, but exhibited low powers and were highly affected by free parameters. We present the novel method and software GSA-SNP2 for pathway enrichment analysis of GWAS P-value data. GSA-SNP2 provides high power, decent type I error control and fast computation by incorporating the random set model and SNP-count adjusted gene score. In a comparative study using simulated and real GWAS data, GSA-SNP2 exhibited high power and best prioritized gold standard positive pathways compared with six existing enrichment-based methods and two self-contained methods (alternative pathway analysis approach). Based on these results, the difference between pathway analysis approaches was investigated and the effects of the gene correlation structures on the pathway enrichment analysis were also discussed. In addition, GSA-SNP2 is able to visualize protein interaction networks within and across the significant pathways so that the user can prioritize the core subnetworks for further studies. GSA-SNP2 is freely available at https://sourceforge.net/projects/gsasnp2.

Cigarette smoking is associated with an increased risk of melanoma metastasis. Smokers show higher PD-L1 expression and better responses to PD-1/PD-L1 inhibitors than nonsmokers. Here, we investigate whether nicotine, a primary constituent of tobacco, induces PD-L1 expression and promotes melanoma cell proliferation and migration, which is mediated by the α9 nicotinic acetylcholine receptor (α9-nAChR). α9-nAChR overexpression in melanoma using melanoma cell lines, human melanoma tissues, and assessment of publicly available databases. α9-nAChR expression was significantly correlated with PD-L1 expression, clinical stage, lymph node status, and overall survival (OS). Overexpressing or knocking down α9-nAChR in melanoma cells up- or downregulated PD-L1 expression, respectively, and affected melanoma cell proliferation and migration. Nicotine-induced α9-nAChR activity promoted melanoma cell proliferation through stimulation of the α9-nAChR-mediated AKT and ERK signaling pathways. In addition, nicotine-induced α9-nAchR activity promoted melanoma cell migration via activation of epithelial-mesenchymal transition (EMT). Moreover, PD-L1 expression was upregulated in melanoma cells after nicotine treatment via the transcription factor STAT3 binding to the PD-L1 promoter. These results highlight that nicotine-induced α9-nAChR activity promotes melanoma cell proliferation, migration, and PD-L1 upregulation. This study may reveal important insights into the mechanisms underlying nicotine-induced melanoma growth and metastasis through α9-nAChR-mediated carcinogenic signals and PD-L1 expression.

Emotion recognition plays an indispensable role in human–machine interaction system. The process includes finding interesting facial regions in images and classifying them into one of seven classes: angry, disgust, fear, happy, neutral, sad, and surprise. Although many breakthroughs have been made in image classification, especially in facial expression recognition, this research area is still challenging in terms of wild sampling environment. In this paper, we used multi-level features in a convolutional neural network for facial expression recognition. Based on our observations, we introduced various network connections to improve the classification task. By combining the proposed network connections, our method achieved competitive results compared to state-of-the-art methods on the FER2013 dataset.

We present a novel approach to identify human microRNA (miRNA) regulatory modules (mRNA targets and relevant cell conditions) by biclustering a large collection of mRNA fold-change data for sequence-specific targets. Bicluster targets were assessed using validated messenger RNA (mRNA) targets and exhibited on an average 17.0% (median 19.4%) improved gain in certainty (sensitivity + specificity). The net gain was further increased up to 32.0% (median 33.4%) by incorporating functional networks of targets. We analyzed cancer-specific biclusters and found that the PI3K/Akt signaling pathway is strongly enriched with targets of a few miRNAs in breast cancer and diffuse large B-cell lymphoma. Indeed, five independent prognostic miRNAs were identified, and repression of bicluster targets and pathway activity by miR-29 was experimentally validated. In total, 29 898 biclusters for 459 human miRNAs were collected in the BiMIR database where biclusters are searchable for miRNAs, tissues, diseases, keywords and target genes.

Integration of single-cell RNA sequencing data between different samples has been a major challenge for analyzing cell populations. However, strategies to integrate differential expression analysis of single-cell data remain underinvestigated. Here, we benchmark 46 workflows for differential expression analysis of single-cell data with multiple batches. We show that batch effects, sequencing depth and data sparsity substantially impact their performances. Notably, we find that the use of batch-corrected data rarely improves the analysis for sparse data, whereas batch covariate modeling improves the analysis for substantial batch effects. We show that for low depth data, single-cell techniques based on zero-inflation model deteriorate the performance, whereas the analysis of uncorrected data using limmatrend, Wilcoxon test and fixed effects model performs well. We suggest several high-performance methods under different conditions based on various simulation and real data analyses. Additionally, we demonstrate that differential expression analysis for a specific cell type outperforms that of large-scale bulk sample data in prioritizing disease-related genes.