Identification of Key Modules, Hub Genes, and Noncoding RNAs in Chronic Rhinosinusitis with Nasal Polyps by Weighted Gene Coexpression Network Analysis

Identification of key genes and pathways in chronic rhinosinusitis with nasal polyps and asthma comorbidity using bioinformatics approaches

Patients with chronic rhinosinusitis with nasal polyps (CRSwNP) and asthma comorbidity (ACRSwNP) present severe symptoms and are more likely to relapse. However, the pathogenesis of ACRSwNP is not fully understood. The aim of this study was to explore the underlying pathogenesis of ACRSwNP using bioinformatics approaches. ACRSwNP-related differentially expressed genes (DEGs) were identified by the analysis of the GSE23552 dataset. The clusterProfiler R package was used to carry out functional and pathway enrichment analysis. A protein–protein interaction (PPI) network was built using the STRING database to explore key genes in the pathogenesis of ACRSwNP. The bioinformatics analysis results were verified through qRT-PCR. The Connectivity Map (CMap) database was used to predict potential drugs for the treatment of ACRSwNP. A total of 36 DEGs were identified, which were mainly enriched in terms of regulation of immune response and detection sensory perception of taste. Thirteen hub genes including AZGP1, AQP9, GAPT, PIP, and PRR4 were identified as potential hub genes in ACRSwNP from the PPI network. Analysis of the GSE41861 dataset showed that upregulation of CST1 in nasal mucosa was associated with asthma. qRT-PCR detection confirmed the bioinformatics analysis results. Tacrolimus and spaglumic acid were identified as potential drugs for the treatment of ACRSwNP from the CMap database. The findings of this study provide insights into the pathogenesis of ACRSwNP and may provide a basis for the discovery of effective therapeutic modalities for ACRSwNP.

Construction and analysis of a ceRNA network and patterns of immune infiltration in chronic rhinosinusitis with nasal polyps: based on data mining and experimental verification

Recent studies have revealed the significant role of the competing endogenous RNA (ceRNA) network in human diseases. However, systematic analysis of the ceRNA mechanism in chronic rhinosinusitis with nasal polyps (CRSwNP) is limited. In this study, we constructed a competitive endogenous RNA (ceRNA) network and identified a potential regulatory axis in CRSwNP based on bioinformatics analysis and experimental verification. We obtained lncRNA, miRNA, and mRNA expression profiles from the Gene Expression Omnibus. After analysis of CRSwNP patients and the control groups, we identified 565 DE-lncRNAs, 23 DE-miRNAs, and 1799 DE-mRNAs by the DESeq2 R package or limma R package. Enrichment analysis of 1799 DE-mRNAs showed that CRSwNP was associated with inflammation and immunity. Moreover, we identified 21 lncRNAs, 8 miRNAs and 8 mRNAs to construct the lncRNA-miRNA-mRNA ceRNA network. A potential MIAT/miR-125a/IRF4 axis was determined according to the degree and positive correlation between a lncRNA and its competitive endogenous mRNAs. The GSEA results suggested that IRF4 may be involved in immune cell infiltration. The validation of another dataset confirmed that MIAT and IRF4 were differentially expressed between the CRSwNP and control groups. The area under the ROC curve (AUC) of MIAT and IRF4 was 0.944. The CIBERSORT analysis revealed that eosinophils and M2 macrophages may be involved in the CRSwNP process. MIAT was correlated with dendritic cells and M2 macrophages, and IRF4 was correlated with dendritic cells. Finally, to validate the key genes, we performed in-silico validation using another dataset and experimental validation using immunohistochemistry, immunofluorescence, and Western blot. In summary, the constructed novel MIAT/miR-125a/IRF4 axis may play a critical role in the development and progression of CRSwNP. We believe that the ceRNA network and immune cell infiltration could offer further insight into novel molecular therapeutic targets for CRSwNP.

Identification of key genes associated with sepsis patients infected by staphylococcus aureus through weighted gene co-expression network analysis

The prevention and treatment of staphylococcus aureus septicemia is one of the thorniest problems in modern medicine. However, as the underlying pathogenesis of sepsis is still unclear, there is currently no golden standard for clinical diagnosis. In this study, we used GSE33341 dataset for differentially expressed gene (DEG) analysis and screened out 857 differentially expressed genes associated with staphylococcus aureus infection. The module having the highest correlation with clinical features of sepsis was screened by weighted gene co-expression network analysis (WGCNA). The genes in the selected module and the differentially expressed genes were represented in Venn diagram, and 59 pathogenic genes at the intersection were obtained. GO and KEGG analysis showed that these genes were mainly related to aerobic respiration, cellular stress response, mitochondrial electron transport, mitochondrial transport, oxidative phosphorylation. Kaplan-Meier was used to analyze the influence of the top 10 key genes on the prognosis of sepsis patients. The results showed that the high expression of NDUFA4, NDUFB3, COX7A2, ATP5J and COX7C was significantly correlated with the poor overall survival (OS) in patients with bacterial sepsis. These findings may potentially provide a reference for the diagnosis and treatment of bacterial septicemia.

Integration of segmented regression analysis with weighted gene correlation network analysis identifies genes whose expression is remodeled throughout physiological aging in mouse tissues

Gene expression alterations occurring with aging have been described for a multitude of species, organs, and cell types. However, most of the underlying studies rely on static comparisons of mean gene expression levels between age groups and do not account for the dynamics of gene expression throughout the lifespan. These studies also tend to disregard the pairwise relationships between gene expression profiles, which may underlie commonly altered pathways and regulatory mechanisms with age. To overcome these limitations, we have combined segmented regression analysis with weighted gene correlation network analysis (WGCNA) to identify high-confidence signatures of aging in the brain, heart, liver, skeletal muscle, and pancreas of C57BL/6 mice in a publicly available RNA-Seq dataset (GSE132040). Functional enrichment analysis of the overlap of genes identified in both approaches showed that immune- and inflammation-related responses are prominently altered in the brain and the liver, while in the heart and the muscle, aging affects amino and fatty acid metabolism, and tissue regeneration, respectively, which reflects an age-related global loss of tissue function. We also explored sexual dimorphism in the aging mouse transcriptome and found the liver and the muscle to have the most pronounced gender differences in gene expression throughout the lifespan, particularly in proteostasis-related pathways. While the data showed little overlap among the age-dysregulated genes between tissues, aging triggered common biological processes in distinct tissues, which we highlight as important features of murine tissue physiological aging.

Transcriptomic Signatures and Functional Network Analysis of Chronic Rhinosinusitis With Nasal Polyps

Chronic rhinosinusitis with nasal polyps (CRSwNP) is a chronic sinonasal inflammatory disease with limited treatment options of corticosteroids, sinus surgery, or both. CRSwNP is frequently associated with allergic rhinitis and asthma, but the molecular mechanisms underlying CRSwNP inflammation are not completely understood. We obtained four gene expression profiles (GSE136825, GSE36830, GSE23552, and GSE72713) from four Gene Expression Omnibus (GEO), which collectively included 65 nasal polyp samples from CRSwNP patients and 54 nasal mucosal samples from healthy controls. Using an integrated analysis approach, we identified 76 co-differentially expressed genes (co-DEGs, including 45 upregulated and 31 downregulated) in CRSwNP patients compared with the healthy controls. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses identified the terms including immune effector process, leukocyte migration, regulation of the inflammatory response, Staphylococcus aureus infection, and cytokine-cytokine receptor interaction. protein-protein interaction (PPI) network analysis and real-time quantitative PCR (RT-qPCR) showed that 7 genes might be crucial in CRSwNP pathogenesis. Repurposing drug candidates (Alfadolone, Hydralazine, SC-560, Iopamidol, Iloprost, etc) for CRSwNP treatment were identified from the Connectivity Map (CMap) database. Our results suggest multiple molecular mechanisms, diagnostic biomarkers, potential therapeutic targets, and new repurposing drug candidates for CRSwNP treatment.

Gene co-expression network analysis reveals immune cell infiltration as a favorable prognostic marker in non-uterine leiomyosarcoma

The present study aimed to improve the understanding of non-uterine leiomyosarcoma (NULMS) prognostic genes through system biology approaches. This cancer is heterogeneous and rare. Moreover, gene interaction networks have not been reported in NULMS yet. The datasets were obtained from the public gene expression databases. Seven co-expression modules were identified from 5000 most connected genes; using weighted gene co-expression network analysis. Using Cox regression, the modules showed favorable (HR = 0.6, 95% CI = 0.4-0.89, P = 0.0125), (HR = 0.65, 95% CI = 0.44-0.98, P = 0.04) and poor (HR = 1.55, 95% CI = 1.06-2.27, P = 0.025) prognosis to the overall survival (OS) (time = 3740 days). The first one was significant in multivariate HR estimates (HR = 0.4, 95% CI = 0.28-0.69, P = 0.0004). Enriched genes through the Database for Annotation, Visualization, and Integrated Discovery (DAVID) revealed significant immune-related pathways; suggesting immune cell infiltration as a favorable prognostic factor. The most significant protective genes were ICAM3, NCR3, KLRB1, and IL18RAP, which were in one of the significant modules. Moreover, genes related to angiogenesis, cell-cell adhesion, protein glycosylation, and protein transport such as PYCR1, SRM, and MDFI negatively affected the OS and were found in the other related module. In conclusion, our analysis suggests that NULMS might be a good candidate for immunotherapy. Moreover, the genes found in this study might be potential candidates for targeted therapy.

Correlations between stemness indices for hepatocellular carcinoma, clinical characteristics, and prognosis

Recent studies have shown that cancer stem cells (CSCs) are involved in the occurrence and development of hepatocellular carcinoma (HCC). However, potential mechanisms for this have not yet been elucidated. We constructed a model based on the Progenitor Cell Biology Consortium database to generate stemness indices. We then utilized RNA-seq data and clinical information from the Cancer Genome Atlas (CGA) and International Cancer Genome Consortium (ICGC) for model predictions and verification. An mRNA gene expression-based stemness index (mRNAsi) and a DNA methylation-based stemness index (mDNAsi) were both calculated through one-class logistic regression. By applying univariate Cox regression analysis, we found that the mRNAsi and the mDNAsi correlated significantly with overall survival. Functional prediction analyses were used to characterize implicated genes and their degree of involvement as network hubs through protein-protein interaction analysis, and Spearman's rank correlation coefficient test was used to assess the relationship between hub genes and indices for stemness. The mRNAsi values for CGA and ICGC carcinoma samples correlated significantly with negative clinical characteristics and overall survival, whereas gene and protein-protein interaction analyses revealed that SNAP25, KPT19, GABBR1, and EPCAM were negatively associated with clinical mDNAsi scores. Collectively, the data suggest that our new stemness model based on related genes may predict patient prognoses.