LILRB4 regulates multiple myeloma development through STAT3-PFKFB1 pathway.

Although multiple myeloma (MM) responds well to immunotherapeutic treatment, certain portions of MM are still unresponsive or relapse after immunotherapy. Other immune molecules are needed for the immunotherapy of MM. Here, we revealed that leukocyte immunoglobulin-like receptor B4 (LILRB4) was highly expressed in multiple myeloma cell lines and patient samples and that the expression of LILRB4 was adversely correlated with the overall survival of MM patients. Knockdown of LILRB4 efficiently delayed the growth of MM cells both in vitro and in vivo. Mechanistically, IKZF1 transactivated LILRB4 expression to trigger the downstream of STAT3-PFKFB1 pathways to support MM cell proliferation. Blockade of LILRB4 signaling by blocking antibodies can effectively inhibit MM progression. Our data show that targeting LILRB4 is potentially an additional therapeutic strategy for the immunotherapeutic treatment of MM.

A novel approach for ASD recognition based on graph attention networks.

Early detection and diagnosis of Autism Spectrum Disorder (ASD) can significantly improve the quality of life for affected individuals. Identifying ASD based on brain functional connectivity (FC) poses a challenge due to the high heterogeneity of subjects' fMRI data in different sites. Meanwhile, deep learning algorithms show efficacy in ASD identification but lack interpretability. In this paper, a novel approach for ASD recognition is proposed based on graph attention networks. Specifically, we treat the region of interest (ROI) of the subjects as node, conduct wavelet decomposition of the BOLD signal in each ROI, extract wavelet features, and utilize them along with the mean and variance of the BOLD signal as node features, and the optimized FC matrix as the adjacency matrix, respectively. We then employ the self-attention mechanism to capture long-range dependencies among features. To enhance interpretability, the node-selection pooling layers are designed to determine the importance of ROI for prediction. The proposed framework are applied to fMRI data of children (younger than 12 years old) from the Autism Brain Imaging Data Exchange datasets. Promising results demonstrate superior performance compared to recent similar studies. The obtained ROI detection results exhibit high correspondence with previous studies and offer good interpretability.

Upregulation of circRNA_0023685 promotes gastric cancer progression via a circRNA-miRNA-mRNA interaction network.

Circular RNAs (circRNAs) have been extensively studied for their critical roles as noncoding RNAs (ncRNAs) in gastric cancer (GC). In this study, we focused on the expression, function and molecular mechanism of circRNA_0023685 in gastric cancer (GC) to provide new ways for the diagnosis and treatment of GC. Firstly, a novel differentially expressed circRNA, circRNA_0023685, was identified, and its differential expression in GC plasma, tissue, and cell lines was further verified by RT-qPCR. Next, circRNA_0023685 was verified to promote the proliferation, migration and apoptosis of GC cells in vitro. CircRNA_0023685 was also proved to enhance the growth of GC tumors in xenograft models. Finally, for excavating the mechanism to promote GC, downstream microRNAs (miRNAs) and mRNAs were screened by bioinformatics analyses. After intersecting the target genes and genes enriched in GO analysis, a circRNA competing endogenous RNAs (ceRNAs) network was built. A protein-protein interaction (PPI) network was then constructed to find the candidate gene, APP. Our study confirmed that the highly expressed circRNA_0023685 could promote GC, which provided a new clinical diagnostic biomarker and therapeutic target for GC.