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. 2022 May 6:2022:9469207.
doi: 10.1155/2022/9469207. eCollection 2022.

Identification of lncRNA Biomarkers and LINC01198 Promotes Progression of Chronic Rhinosinusitis with Nasal Polyps through Sponge miR-6776-5p

Affiliations

Identification of lncRNA Biomarkers and LINC01198 Promotes Progression of Chronic Rhinosinusitis with Nasal Polyps through Sponge miR-6776-5p

Xueping Wang et al. Biomed Res Int. .

Retraction in

Abstract

Background: Chronic sinusitis (CRS) was a chronic inflammation that originated in the nasal mucosa and affected the health of most people around the world. Chronic rhinosinusitis with nasal polyps (CRSwNP) was one kind of chronic sinusitis. Emerging research had suggested that long noncoding RNAs (lncRNAs) played vital parts in inflammatories and inflammation development.

Methods: We acquired GEO data to analyze the differential expression between the miRNA, immune genes, TF, and lncRNA data in CRSWNP and the corresponding control tissues. Bioinformatic analysis by coexpression of endogenous RNA network and competitive way enrichment, analysis, and forecasting functions of these noncoding RNA. The different pathway expressions in CRSwNP patients were confirmed using GSVA to analyze the differentially expressed immune genes and TF data sets in CRSwNP patients. The differential immune gene and transcription factor data set in CRSwNP perform functional notes and protein-protein interaction (PPI) network structure. We predicted the potential genes and RNAs related to CRSWNP by constructing a ceRNA network. In addition, we also used 19 hub immune genes to predict the potential drugs of CRSWNP. lncRNA biomarkers in CRSwNP were identified by lncRNAs LASSO regression. The CIBERSORT algorithm was used to contrast the divergence in immune infiltrations between CRSwNP and usual inferior turbinate organizations in 22 immunocyte subgroups.

Results: We identified a total of 48 miRNAs, 304 lncRNAs, 92 TFs, and 525 immune genes as CRSwNP-specific RNAs. GO and KEGG pathways both analyzed differentially expressed immune genes and transcription factor data sets. We predicted the potential genes GNG7, TUSC8, LINC01198, and has-miR-6776-5p by constructing ceRNA and PPI networks. At the same time, we found that the above genes were involved in two important pathways: chemokine signal path and PI3K/AKT signal path. In addition, we predicted 5 small molecule drugs to treat CRSwNP by analyzing 19 central immune genes, namely, danazol, ikarugamycin, semustine, cefamandole, and molindone. Finally, we identified 5 biomarkers in CRSwNP, namely, LINC01198, LINC01094, LINC01798, LINC01829, and LINC01320.

Conclusions: We had identified CRSwNP-related miRNAs, lncRNAs, TFs, and immune genes, which may be making use of latent therapeutic target for CRSwNP. At the same time, we identified 5 lncRNA biomarkers in CRSwNP. The results of this study showed that LINC01198 promoted the progression of CRSwNPs through spongy miR-6776-5p. Our studies provide a new way for further analyses of the pathogenesis of CRSwNP.

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Conflict of interest statement

The authors declare that they have no competing interest.

Figures

Figure 1
Figure 1
Volcano plots of abnormally expressed miRNA, immune genes, TF, and lncRNA in CRSwNP. Volcano plots of abnormally expressed miRNA (a), immune genes (b), TF, and lncRNA in CRSwNP and normal. Red and green points correspond to log2FC (∣log2FC | >0.5) up/down, respectively, and indicate FDR < 0.05. (a–d). Volcano plot of differential expression of miRNA (a), immune genes (b), transcription factors (c), and lncRNA (d) with log (fold change) as the abscissa and -log10 (P value) as the ordinate. Red and green splashes represent the genes that were significantly up- or downregulated in CRSwNP, respectively. Green splashes mean genes without significantly different expression. FDR < 0.05 and ∣logFC | >0.5.
Figure 2
Figure 2
The heat map of abnormally expressed miRNA, immune genes, TF, and lncRNA in normal and CRSwNP. The heat map of abnormally expressed miRNA, immune genes, TF, and lncRNA in normal and CRSwNP hierarchically clustered. Each list indicates a sample and every line indicates an miRNA (a), immune gene (b), transcription factors (c), and lncRNA (d). The expression value for each line was normalized by the z-score. Red indicates high relative expression and green indicates low relative expression (∣logFC | >1 and FDR < 0.05).
Figure 3
Figure 3
The enrichment of differentially expressed immune genes and transcription factors predicted targets in CRSwNP. Used to visualize the enrichment of differentially expressed immune genes and transcription factor prediction targets in CRSwNP. Detailed GO enrichment and KEGG information are presented in the bubble map. Gene Ontology (GO) Kyoto Encyclopedia of Genes and Genomes (KEGG) pathological analysis of immune and TF genes. Significantly enriched pathways featured P < 0.001. The analysis was conducted using R clusterProfilter. (a) GO enrichment analyses of TF and immune genes. y-axis represents GO terms, and x-axis represents GeneRatio. The number of genes enriched in the enrichment term is indicated by the size of the node. The importance of the GO term is indicated by the color, and the red indicates the highest significance. (b) KEGG pathway enrichment analyses of TF and immune genes. The y-axis represents the KEGG term. The x-axis represents GeneRatio. The number of genes enriched in the enrichment term is represented by the size of the node. The importance of the KEGG term is represented by color, and red represents the highest significance. (c) The heat map of differentially expressed immune genes for KEGG pathway horizontally represents KEGG terminology; longitudinal direction means sample, red means upregulation, and green means downregulation. (d) The heat map of differentially expressed immune genes for GO; horizontal means GO term, longitudinal means sample, red means upregulation, and green means downregulation.
Figure 4
Figure 4
Construction of TF-immune genes-pathway networks. (a) The protein-protein interaction (PPI) network. (b) TF-immune genes-pathway network. (c) The hub network by MCODE. (d) The hub network by CytoHubba.
Figure 5
Figure 5
The results of using mRNA to stepwise reverse predict miRNA and lncRNA and constructing a ceRNA network. (a) The flow chart of predicted miRNA target gene and network construction. (b) The hub lncRNA-miRNA-mRNA ceRNA pathway.
Figure 6
Figure 6
Predictive results of small molecule drug therapy for CRSwNP. Single-gene GSEA enrichment results of GNG7 gene and prediction results of potential small molecule drugs for the treatment of CRSwNP based on 19 hub immune genes. (a) lncRNA-miRNA-pathway ceRNA network. (b) Single-gene enrichment analysis of GNG7 gene (GSEA, gene set enrichment analysis). (c) Prediction results of targeted drugs.
Figure 7
Figure 7
Development and validation of lncRNA biomarkers. (a) The LASSO regression. (b) LASSO coefficient profiles of the 5 lncRNAs. (c) Receiver operating characteristic (ROC) curve of the CRSWNP for the training set (GSE136825). (d) Receiver operating characteristic (ROC) curve of the CRSWNP for validation set (GSE136825). (e) Receiver operating characteristic (ROC) curve of the CRSWNP for external data set (GSE36830). The x-axis represents the 1-specificity and y-axis represents the sensitivity. AUC: area under the curve. (f) Gene expression levels of LINC01198. (g) Gene expression levels of LINC01094. (h) Gene expression levels of LINC01798. (i) Gene expression levels of LINC01829. (j) Gene expression levels of LINC01320.
Figure 8
Figure 8
Visualization of immune cell infiltration and correlation between GNG7, TUSC8, and LINC01198 expressions and infiltrating immune cells. (a) The composition of infiltrating immune cells. (b) Violin plot of differences of immune cell infiltration. (c) Principal component analysis cluster plot of immune cell infiltration. (d) Correlation matrix of proportions of 22 types of infiltrating immune cells. (e) Correlation between GNG7 expression and plasma cells (R = 0.56, p = 4e − 07). (f) Correlation between LINC01198 expression and plasma cells (R = 0.21, p = 0.077). (g) Correlation between TUSC8 expression and plasma cells (R = 0.33, p = 0.0053). (h) Correlation between GNG7 expression and macrophage M2 (R = −0.33, p = 0.0055). (i) Correlation between LINC01198 expression and macrophage M2 (R = −0.16, p = 0.17). (j) Correlation between TUSC8 expression and macrophage M2 (R = −0.35, p = 0.0037).
Figure 9
Figure 9
Flow chart.

References

    1. Fokkens W. J., Lund V. J., Mullol J., et al. EPOS 2012: European position paper on rhinosinusitis and nasal polyps 2012. A summary for otorhinolaryngologists. Rhinology . 2012;50(1):1–12. doi: 10.4193/Rhino12.000. - DOI - PubMed
    1. DeConde A. S., Soler Z. M. Chronic rhinosinusitis: epidemiology and burden of disease. American Journal of Rhinology & Allergy . 2016;30(2):134–139. doi: 10.2500/ajra.2016.30.4297. - DOI - PubMed
    1. Jarvis D., Newson R., Lotvall J., et al. Asthma in adults and its association with chronic rhinosinusitis: the GA2LEN survey in Europe. Allergy . 2012;67(1):91–98. doi: 10.1111/j.1398-9995.2011.02709.x. - DOI - PubMed
    1. Kim Y. S., Kim N. H., Seong S. Y., Kim K. R., Lee G. B., Kim K. S. Prevalence and risk factors of chronic rhinosinusitis in Korea. American Journal of Rhinology & Allergy . 2011;25(3):117–121. doi: 10.2500/ajra.2011.25.3630. - DOI - PubMed
    1. Stevens W. W., Schleimer R. P., Kern R. C. Chronic rhinosinusitis with nasal polyps. The Journal of Allergy and Clinical Immunology. In Practice . 2016;4(4):565–572. doi: 10.1016/j.jaip.2016.04.012. - DOI - PMC - PubMed

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