This study aimed to identify new susceptibility modules and genes by analyzing the transcriptional profiles of peri-implantitis and periodontitis within the same host environment, using weighted gene co-expression network analysis (WGCNA).
The tumor microenvironment (TME) within mucosal neoplastic tissue in oral cancer (ORCA) is greatly influenced by tumor-infiltrating lymphocytes (TILs). Here, a clustering method was performed using CIBERSORT profiles of ORCA data that were filtered from the publicly accessible data of patients with head and neck cancer in The Cancer Genome Atlas (TCGA) using hierarchical clustering where patients were regrouped into binary risk groups based on the clustering-measuring scores and survival patterns associated with individual groups. Based on this analysis, clinically reasonable differences were identified in 16 out of 22 TIL fractions between groups. A deep neural network classifier was trained using the TIL fraction patterns. This internally validated classifier was used on another individual ORCA dataset from the International Cancer Genome Consortium data portal, and patient survival patterns were precisely predicted. Seven common differentially expressed genes between the two risk groups were obtained. This new approach confirms the importance of TILs in the TME and provides a direction for the use of a novel deep-learning approach for cancer prognosis.
ABSTRACT Acute Interstitial Pneumonia (AIP) represents a severe form of diffuse lung injury within the idiopathic interstitial pneumonia spectrum. Given the limited understanding of its molecular basis, this study aims to elucidate AIP's genomic and transcriptomic profiles to uncover its pathophysiological underpinnings and identify potential therapeutic targets. We conducted a comprehensive analysis of genomic and transcriptomic data from lung tissues of 15 AIP patients. This included assessing differentially expressed genes (DEGs) and identifying mutations in exonic coding variants, as well as analysing expression quantitative trait loci (eQTL) profiles to link non‐coding SNP genotypes with gene expression levels. Transcriptomic analysis revealed a significant upregulation of genes linked to the Type I interferon receptor and keratin filament, and a downregulation of genes related to focal adhesion and endothelial integrity, compared to healthy individuals. These patterns were distinct from those observed in idiopathic pulmonary fibrosis (IPF) and non‐IPF interstitial lung diseases (ILDs). Genomic analysis highlighted mutations in genes associated with keratin and the extracellular matrix. Additionally, eQTL profiling provided insights into the genetic regulation of gene expression in AIP. Our findings reveals AIP's unique molecular landscape, differentiating it from other ILDs and laying the groundwork for future diagnostic and therapeutic research.
Peri-implantitis is similar to periodontitis, but there are some differences. For the effective control of peri-implantitis, it is necessary to clarify its similarities and differences with periodontitis in terms of gene expression.
Introduction It is well known that the presence of diabetes significantly affects the progression of periodontitis and that periodontitis has negative effects on diabetes and diabetes-related complications. Although this two-way relationship between type 2 diabetes and periodontitis could be understood through experimental and clinical studies, information on common genetic factors would be more useful for the understanding of both diseases and the development of treatment strategies. Materials and Methods Gene expression data for periodontitis and type 2 diabetes were obtained from the Gene Expression Omnibus database. After preprocessing of data to reduce heterogeneity, differentially expressed genes (DEGs) between disease and normal tissue were identified using a linear regression model package. Gene ontology and Kyoto encyclopedia of genes and genome pathway enrichment analyses were conducted using R package ‘ vsn ’. A protein-protein interaction network was constructed using the search tool for the retrieval of the interacting genes database. We used molecular complex detection for optimal module selection. CytoHubba was used to identify the highest linkage hub gene in the network. Results We identified 152 commonly DEGs, including 125 upregulated and 27 downregulated genes. Through common DEGs, we constructed a protein-protein interaction and identified highly connected hub genes. The hub genes were up-regulated in both diseases and were most significantly enriched in the Fc gamma R-mediated phagocytosis pathway. Discussion We have identified three up-regulated genes involved in Fc gamma receptor-mediated phagocytosis, and these genes could be potential therapeutic targets in patients with periodontitis and type 2 diabetes.
N6-methyladenosine (m6A) modification in RNA affects various aspects of RNA metabolism and regulates gene expression. This modification is modulated by many regulatory proteins, such as m6A methyltransferases (writers), m6A demethylases (erasers), and m6A-binding proteins (readers). Previous studies have suggested that alterations in m6A regulatory proteins induce genome-wide alternative splicing in many cancer cells. However, the functional effects and molecular mechanisms of m6A-mediated alternative splicing have not been fully elucidated. To understand the consequences of this modification on RNA splicing in cancer cells, we performed RNA sequencing and analyzed alternative splicing patterns in METTL3-knockdown osteosarcoma U2OS cells. We detected 1,803 alternatively spliced genes in METTL3-knockdown cells compared to the controls and found that cell cycle-related genes were enriched in differentially spliced genes. A comparison of the published MeRIP-seq data for METTL14 with our RNA sequencing data revealed that 70-87% of alternatively spliced genes had an m6A peak near 1 kb of alternative splicing sites. Among the 19 RNA-binding proteins enriched in alternative splicing sites, as revealed by motif analysis, expression of SFPQ highly correlated with METTL3 expression in 12,839 TCGA pan-cancer patients. We also found that cell cycle-related genes were enriched in alternatively spliced genes of other cell lines with METTL3 knockdown. Taken together, we suggest that METTL3 regulates m6A-dependent alternative splicing, especially in cell cycle-related genes, by regulating the functions of splicing factors such as SFPQ.
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