Linking Complement C3 and B Cells in Nasal Polyposis

The multi-omics single-cell landscape of sinus mucosa in uncontrolled severe chronic rhinosinusitis with nasal polyps

Uncontrolled severe chronic rhinosinusitis with nasal polyps (CRSwNP) is associated with elevated levels of type 2 inflammatory cytokines and raised immunoglobulin concentrations in nasal polyp tissue. By using single-cell RNA sequencing, transcriptomics, surface proteomics, and T cell and B cell receptor sequencing, we found the predominant cell types in nasal polyps were shifted from epithelial and mesenchymal cells to inflammatory cells compared to nasal mucosa from healthy controls. Broad expansions of CD4 T effector memory cells, CD4 tissue-resident memory T cells, CD8 T effector memory cells and all subtypes of B cells in nasal polyp tissues. The T and B cell receptor repertoires were skewed in NP. This study highlights the deviated immune response and remodeling mechanisms that contribute to the pathogenesis of uncontrolled severe CRSwNP. CLINICAL IMPLICATIONS: We identified differences in the cellular compositions, transcriptomes, proteomes, and deviations in the immune profiles of T cell and B cell receptors as well as alterations in the intercellular communications in uncontrolled severe CRSwNP patients versus healthy controls, which might help to define potential therapeutic targets in the future.

Pathogenesis of chronic rhinosinusitis with nasal polyp and a prominent T2 endotype

Chronic rhinosinusitis is a heterogenous and multifactorial disease, characterized by persistent inflammation of the nose and paranasal sinuses, which causes nasal obstruction, nasal discharge, facial pain, and smell disturbance. Chronic rhinosinusitis is divided into two phenotypes: chronic rhinosinusitis with nasal polyp and chronic rhinosinusitis without nasal polyp. Nasal polyps can be associated with many inflammatory cells including eosinophil cells, neutrophil cells, plasma cells, and lymphocytes. T2 endotype is characterized by the type-2 immune response and nasal polyps are associated with eosinophilic dominant infiltration. In contrast, in the T1 and T3 endotypes, chronic rhinosinusitis can be associated with neutrophilic dominant infiltration. In addition, there are mixed types of inflammation with different proportions of eosinophils-neutrophils in chronic rhinosinusitis. In the T2 endotype, there is an increase in the production of Th2 cytokines, including interleukin-4, interleukin-5, and interleukin-13, high levels of immunoglobulin-E in polyp tissue, and eosinophilia. Stimulation of Th2 cells, type-2 innate lymphoid cells, epithelial cell damage, Staphylococcus aureus enterotoxins, and autoimmune antibodies have important roles in the enhancement of Th2 cytokines and pathogenesis of chronic rhinosinusitis with nasal polyp. Monoclonal antibodies target type-2 inflammation, decrease nasal polyp size, and improve the clinical symptoms of CRSwNP patients. The present review will focus on factors involved in the pathogenesis of chronic rhinosinusitis and its treatment.

Using Machine Learning Modeling to Explore New Immune-Related Prognostic Markers in Non-Small Cell Lung Cancer

OBJECTIVE

To find new immune-related prognostic markers for non-small cell lung cancer (NSCLC).

METHODS

We found GSE14814 is related to NSCLC in GEO database. The non-small cell lung cancer observation (NSCLC-OBS) group was evaluated for immunity and divided into high and low groups for differential gene screening according to the score of immune evaluation. A single factor COX regression analysis was performed to select the genes related to prognosis. A prognostic model was constructed by machine learning, and test whether the model has a test efficacy for prognosis. A chip-in-chip non-small cell lung cancer chemotherapy (NSCLC-ACT) sample was used as a validation dataset for the same validation and prognostic analysis of the model. The coexpression genes of hub genes were obtained by pearson analysis and gene enrichment, function enrichment and protein interaction analysis. The tumor samples of patients with different clinical stages were detected by immunohistochemistry and the expression difference of prognostic genes in tumor tissues of patients with different stages was compared.

RESULTS

By screening, we found that LYN, C3, COPG2IT1, HLA.DQA1, and TNFRSF17 is closely related to prognosis. After machine learning, we constructed the immune prognosis model from these 5 genes, and the model AUC values were greater than 0.9 at three time periods of 1, 3, and 5 years; the total survival period of the low-risk group was significantly better than that of the high-risk group. The results of prognosis analysis in ACT samples were consistent with OBS groups. The coexpression genes are mainly involved B cell receptor signaling pathway and are mainly enriched in apoptotic cell clearance. Prognostic key genes are highly correlated with PDCD1, PDCD1LG2, LAG3, and CTLA4 immune checkpoints. The immunohistochemical results showed that the expression of COPG2IT1 and HLA.DQA1 in stage III increased significantly and the expression of LYN, C3, and TNFRSF17 in stage III decreased significantly compared with that of stage I. The experimental results are consistent with the previous analysis.

CONCLUSION

LYN, C3, COPG2IT1, LA.DQA1, and NFRSF17 may be new immune markers to judge the prognosis of patients with non-small cell lung cancer.