Transcriptomic analysis reveals shared gene signatures and molecular mechanisms between obesity and periodontitis

Methylation patterns associated with C-reactive protein in racially and ethnically diverse populations

Systemic low-grade inflammation is a feature of chronic disease. C-reactive protein (CRP) is a common biomarker of inflammation and used as an indicator of disease risk; however, the role of inflammation in disease is not completely understood. Methylation is an epigenetic modification in the DNA which plays a pivotal role in gene expression. In this study we evaluated differential DNA methylation patterns associated with blood CRP level to elucidate biological pathways and genetic regulatory mechanisms to improve the understanding of chronic inflammation. The racially and ethnically diverse participants in this study were included as 50% White, 41% Black or African American, 7% Hispanic or Latino/a, and 2% Native Hawaiian, Asian American, American Indian, or Alaska Native (total n = 13,433) individuals. We replicated 113 CpG sites from 87 unique loci, of which five were novel (CADM3, NALCN, NLRC5, ZNF792, and cg03282312), across a discovery set of 1,150 CpG sites associated with CRP level (p < 1.2E-7). The downstream pathways affected by DNA methylation included the identification of IFI16 and IRF7 CpG-gene transcript pairs which contributed to the innate immune response gene enrichment pathway along with NLRC5, NOD2, and AIM2. Gene enrichment analysis also identified the nuclear factor-kappaB transcription pathway. Using two-sample Mendelian randomization (MR) we inferred methylation at three CpG sites as causal for CRP levels using both White and Black or African American MR instrument variables. Overall, we identified novel CpG sites and gene transcripts that could be valuable in understanding the specific cellular processes and pathogenic mechanisms involved in inflammation.

Uncovering periodontitis-associated markers through the aggregation of transcriptomics information from diverse sources

Introduction

Periodontitis, a common chronic inflammatory disease, significantly impacted oral health. To provide novel biological indicators for the diagnosis and treatment of periodontitis, we analyzed public microarray datasets to identify biomarkers associated with periodontitis.

Method

The Gene Expression Omnibus (GEO) datasets GSE16134 and GSE106090 were downloaded. We performed differential analysis and robust rank aggregation (RRA) to obtain a list of differential genes. To obtain the core modules and core genes related to periodontitis, we evaluated differential genes through enrichment analysis, correlation analysis, protein-protein interaction (PPI) network and competing endogenous RNA (ceRNA) network analysis. Potential biomarkers for periodontitis were identified through comparative analysis of dual networks (PPI network and ceRNA network). PPI network analysis was performed in STRING. The ceRNA network consisted of RRA differentially expressed messenger RNAs (RRA_DEmRNAs) and RRA differentially expressed long non-coding RNAs (RRA_DElncRNAs), which regulated each other's expression by sharing microRNA (miRNA) target sites.

Results

RRA_DEmRNAs were significantly enriched in inflammation-related biological processes, osteoblast differentiation, inflammatory response pathways and immunomodulatory pathways. Comparing the core ceRNA module and the core PPI module, C1QA, CENPK, CENPU and BST2 were found to be the common genes of the two core modules, and C1QA was highly correlated with inflammatory functionality. C1QA and BST2 were significantly enriched in immune-regulatory pathways. Meanwhile, LINC01133 played a significant role in regulating the expression of the core genes during the pathogenesis of periodontitis.

Conclusion

The identified biomarkers C1QA, CENPK, CENPU, BST2 and LINC01133 provided valuable insight into periodontitis pathology.

OralExplorer: a web server for exploring the mechanisms of oral inflammatory diseases

BACKGROUND

Oral inflammatory diseases are localized infectious diseases primarily caused by oral pathogens with the potential for serious systemic complications. However, publicly available datasets for these diseases are underutilized. To address this issue, a web tool called OralExplorer was developed. This tool integrates the available data and provides comprehensive online bioinformatic analysis.

METHODS

Human oral inflammatory disease-related datasets were obtained from the GEO database and normalized using a standardized process. Transcriptome data were then subjected to differential gene expression analysis, immune infiltration analysis, correlation analysis, pathway enrichment analysis, and visualization. The single-cell sequencing data was visualized as cluster plot, feature plot, and heatmaps. The web platform was primarily built using Shiny. The biomarkers identified in OralExplorer were validated using local clinical samples through qPCR and IHC.

RESULTS

A total of 35 human oral inflammatory disease-related datasets, covering 6 main disease types and 901 samples, were included in the study to identify potential molecular signatures of the mechanisms of oral diseases. OralExplorer consists of 5 main analysis modules (differential gene expression analysis, immune infiltration analysis, correlation analysis, pathway enrichment analysis and single-cell analysis), with multiple visualization options. The platform offers a simple and intuitive interface, high-quality images for visualization, and detailed analysis results tables for easy access by users. Six markers (IL1β, SRGN, CXCR1, FGR, ARHGEF2, and PTAFR) were identified by OralExplorer. qPCR- and IHC-based experimental validation showed significantly higher levels of these genes in the periodontitis group.

CONCLUSIONS

OralExplorer is a comprehensive analytical platform for oral inflammatory diseases. It allows users to interactively explore the molecular mechanisms underlying the action and regression of these diseases. It also aids dental researchers in unlocking the potential value of transcriptomics data related to oral diseases. OralExplorer can be accessed at https://smuonco.shinyapps.io/OralExplorer/  (Alternate URL: http://robinl-lab.com/OralExplorer ).

P2RY13 is a prognostic biomarker and associated with immune infiltrates in renal clear cell carcinoma: A comprehensive bioinformatic study

Background and Aims

Clear cell renal cell carcinoma (ccRCC) is a common and aggressive form of cancer with a high incidence globally. This study aimed to investigate the role of P2RY13 in the progression of ccRCC and elucidate its mechanism of action.

Methods

Gene Expression Omnibus and The Cancer Genome Atlas databases were used to extract gene expression profiles of ccRCC. These profiles were annotated and visualized by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analyses, as well as Gene Set Enrichment Analysis (GSEA). The STRING database was used to establish a protein-protein interaction network and to analyze the functional similarity. The GEPIA2 database was used to predict survival associated with hub genes. Meanwhile, the TIMER2.0 database was used to assess immune cell infiltration and its link with the hub genes. Immunohistochemistry (IHC) was used to determine the difference between ccRCC and adjacent normal tissue.

Results

We identified 272 differentially expressed genes (DEGs). GO and KEGG analyses suggested that DEGs were primarily involved in lymphocyte activation, inflammatory response, immunological effector mechanism pathways. By cytohubba, the 20 highest-scoring hub genes were screened to identify critical genes in the protein-protein interaction network linked with ccRCC. Resting dendritic cells, CD8 T cells, and activated mast cells all showed a significant positive correlation with these hub genes. Moreover, a higher immune score was associated with increased prognostic risk scores, which in turn correlated with a poorer prognosis. IHC revealed that P2RY13 was expressed at higher levels in ccRCC compared to para-cancer tissues.

Conclusion

Identifying the DEGs will aid in the understanding of the causes and molecular mechanisms involved in ccRCC. P2RY13 may play a pivotal role in the progression and prognosis of ccRCC, potentially driving carcinogenesis though immune system mechanisms.

Immunoglobulin superfamily 6 is a molecule involved in the anti-tumor activity of macrophages in lung adenocarcinoma

BACKGROUND

Immunoglobulin superfamily 6 (IGSF6) is a novel member of the immunoglobulin superfamily and has been implicated in various diseases. However, the specific role of IGSF6 in the anti-tumor immunity within lung adenocarcinoma (LUAD) remains unclear.

METHODS

We analyzed the IGSF6 expression in LUAD using data from TCGA, and we performed qRT-PCR and western blotting to validate these findings using tissue samples obtained from LUAD patients. Images of IHC staining were obtained from HPA. To assess the clinical relevance of IGSF6 expression, we utilized UALCAN and SPSS to analyze its association with major clinical features of LUAD. Additionally, we employed ROC curves and survival analysis to evaluate the potential diagnostic and prognostic value of IGSF6 in LUAD. To gain insights into the functional implications of IGSF6, we performed enrichment analysis using the R software clusterProfiler package. Moreover, we utilized TIMER2.0 and TISIDB to investigate the relationship between IGSF6 and immune infiltrates in LUAD. The proportion of tumor-infiltrating immune cells in LUAD was assessed using FCM, and their correlation with IGSF6 expression in tumor tissues was analyzed. The localization of IGSF6 protein on macrophages was confirmed using the HPA and FCM. To determine the regulatory role of IGSF6 on macrophage activity in LUAD, we employed ELISA, FCM, and tumor-bearing models.

RESULTS

We discovered that both IGSF6 mRNA and protein levels were significantly decreased in LUAD. Additionally, we observed a negative correlation between IGSF6 expression and TNM stages as well as pathologic stages in LUAD. Notably, IGSF6 exhibited high sensitivity and specificity in diagnosing LUAD, and was positively associated with the survival rate of LUAD patients. Furthermore, IGSF6 expression was closely linked to gene sets involved in immune response. IGSF6 expression showed a positive correlation with immune infiltrates exhibiting anti-tumor activity, particularly M1 macrophages. We confirmed the predominant localization of the IGSF6 protein on the membrane of M1 macrophages. Importantly, the knockdown of IGSF6 resulted in a reduction in the anti-tumor activity of M1 macrophages, thereby promoting tumor progression.

CONCLUSION

IGSF6 is a molecule that is essential for the anti-tumor activity of macrophages in LUAD.