Analysis of Genetic Alterations in Ocular Adnexal Mucosa-Associated Lymphoid Tissue Lymphoma With Whole-Exome Sequencing

FAT1 inhibits the proliferation of DLBCL cells via increasing the m6A modification of YAP1 mRNA

Emerging evidence shows that FAT atypical cadherin 1 (FAT1) mutations occur in lymphoma and are associated with poorer overall survival. Considering that diffuse large B cell lymphoma (DLBCL) is the category of lymphoma with the highest incidence rate, this study aims to explore the role of FAT1 in DLBCL. The findings demonstrate that FAT1 inhibits the proliferation of DLBCL cell lines by downregulating the expression of YAP1 rather than by altering its cellular localization. Mechanistic analysis via meRIP-qPCR/luciferase reporter assays showed that FAT1 increases the m6A modification of YAP1 mRNA 3'UTR and the subsequent binding of heterogeneous nuclear ribonucleoprotein D (HNRNPD) to the m6A modified YAP1 mRNA, thus decreasing the stability of YAP1 mRNA. Furthermore, FAT1 increases YAP1 mRNA 3'UTR m6A modification by decreasing the activity of the TGFβ-Smad2/3 pathway and the subsequent expression of ALKBH5, which is regulated at the transcriptional level by Smad2/3. Collectively, these results reveal that FAT1 inhibits the proliferation of DLBCL cells by increasing the m6A modification of the YAP1 mRNA 3'UTR via the TGFβ-Smad2/3-ALKBH5 pathway. The findings of this study therefore indicate that FAT1 exerts anti-tumor effects in DLBCL and may represent a novel target in the treatment of this form of lymphoma.

Transcriptomic analysis of mRNAs in human whole blood identified age-specific changes in healthy individuals

Older age is one of the most important shared risk factors for multiple chronic diseases, increasing the medical burden to contemporary societies. Current research focuses on identifying aging biomarkers to predict aging trajectories and developing interventions aimed at preventing and delaying the progression of multimorbidity with aging. Here, a transcriptomic changes analysis of whole blood genes with age was conducted. The age-related whole blood gene-expression profiling datasets were downloaded from the Gene Expression Omnibus (GEO) database. We screened the differentially expressed genes (DEGs) between healthy young and old individuals and performed functional enrichment analysis. Cytoscape with Cytohubba and MCODE was used to perform an interaction network of DEGs and identify hub genes. In addition, ROC curves and correlation analysis were used to evaluate the accuracy of hub genes. In total, we identified 29 DEGs between young and old samples that were enriched mainly in immunoglobulin binding and complex, humoral immune response, and immune response-activating signaling pathways. In combination with the PPI network and topological analysis, 4 hub genes (IGLL5, Jchain, POU2AF1, and Bach2) were identified. Pearson analysis showed that the expression changes of these hub genes were highly correlated with age. Among them, 3 hub genes (IGLL5, POU2AF1, and Bach2) were identified with good accuracy (AUC score > 0.7), indicating that these genes were the best indicators of age. Together, our results provided potential biomarkers IGLL5, POU2AF1, and Bach2 to identify individuals at high early risk of age-related disease to be targeted for early interventions and contribute to understanding the molecular mechanisms in the progression of aging.