Chinese journal of otorhinolaryngology head and neck surgery最新文献

Objective: To analysis the molecular characteristics of chronic rhinosinusitis with nasal polyps (CRSwNP), to unravel its pathophysiological mechanisms, and to develop a prognostic model capable of effectively predicting postoperative recurrence. Methods: The data from three datasets (GSE198950, GSE179265, and GSE136825) were integrated, comprising 39 control cases, 16 cases of chronic rhinosinusitis without nasal polyps, and 89 cases of CRSwNP. Differential expression genes (DEGs) were identified based on adjusted P<0.05 and Log2FC>1. KEGG and GO enrichment analyses, as well as STRING node scoring, were conducted. Variable selection was performed using random forest and least absolute shrinkage and selection operator regression (LASSO), with key nodes identified through intersection analysis. Mann-Whitney U test was applied, and variables with P<0.05 were included in the model. A prognostic model for CRSwNP was constructed using logistic regression, externally validated using RNA-seq data, and evaluated with receiver operating characteristic (ROC) curve analysis to calculate the area under the curve (AUC). Results: This research illustrated both upregulated and downregulated DEGs in CRSwNP, activating pathways like neuroactive ligand-receptor interaction and IL-17 signaling, while inhibiting calcium signaling and gap junctions. Key nodes identified through random forest and LASSO, including G protein subunit γ4 (U=3.00 P=0.028), Cholecystokinin (U=0.50, P=0.006), Epidermal growth factor (U=1.00 P=0.008), and Neurexin-1 (U=0.00, P=0.004), showing statistical significance in external validation. The prognostic model, visualized in a line graph, exhibited high reliability (C-index=0.875,AUC=0.866). The ROC curve in external validation indicated its effectiveness in predicting postoperative recurrence (AUC=0.859). Conclusions: This study integrates multiple datasets on CRSwNP to provide a comprehensive description of its molecular features. The prognostic model, built upon key nodes identified through random forest and LASSO analyses, demonstrates high accuracy in both internal and external validations, thus providing robust support for the development of personalized treatment strategies for CRSwNP.

Objective: To analyze the characteristics of patients with chronic rhinosinusitis (CRS) in the South China region based on pathological tissue biomarkers for regional comparison. Methods: The study population consisted of CRS in-patients in the First Affiliated Hospital of Sun Yat-sen University from October 2019 to June 2022. Among all the 181 cases, 123 of them were male and 58 were female, with an average age of 40. Retrospectively collected clinical data included demographic information, preoperative symptom scores, preoperative endoscopic images, preoperative paranasal sinus computed tomography scanning images, and inflammatory serological features. In addition, 52 variables of pathological tissue biomarkers including cytokines, chemokines and remodeling factors were collected for analysis. Cluster analysis was performed on the integrated data of training set through centroid-based clustering algorithm, and the inflammatory characteristics, post-operation control status, and airway diseases comorbidity of each endotype were analyzed. R project (version 4.2.2) was used in statistical analysis. Results: Cluster analysis divided 181 patients with CRS into 4 endotypes. Cluster 1 (n=101, 55.80%) showed a locally low inflammatory status. Cluster 2 (n=23, 12.71%) showed a mixed type of inflammation with predominantly neutrophilic inflammation and tissue remodeling. Cluster 3 (n=11, 6.08%) was characterized by type Ⅱ inflammation without tissue remodeling. Cluster 4 (n=46, 25.41%) was mainly characterized by type Ⅱ inflammation with tissue remodeling, showing higher comorbidity rate of asthma and allergic rhinitis. This cluster presented more severe symptoms, significant olfactory dysfunction, extensive overall inflammation based on objective examination results, a notable increase in total eosinophil count and proportion in peripheral blood, and the highest uncontrolled rate observed one year post-surgery. In comparison to other regions, the endotype classification of CRS in Southern China was characterized by a predominant pattern of locally low inflammatory status, a moderate level of type Ⅱ inflammation with tissue remodeling, and a lesser presence of neutrophilic inflammation. Conclusion: CRS distribution in Southern China is mainly characterized by low inflammatory endotype and type Ⅱ inflammation with tissue remodeling. The latter shows more severe clinical manifestations and higher uncontrol rate after surgery.

Objective: To evaluate the predictive efficacy of sinus CT radiomics for treatment outcomes in nasal polyp patients undergoing endoscopic sinus surgery. Methods: A retrospective cohort study was conducted at the First Affiliated Hospital of Sun Yat-sen University, including 194 patients with nasal polyps treated between January 2015 and December 2019. The cohort comprised 132 males and 62 females, aged 16 to 75 years. Patients were divided into a training set (n=135) and an internal validation set (n=59). An external validation set (n=34), consisting of 22 males and 12 females aged 16 to 59 years, was included from January 2020 to December 2021. Disease control was evaluated using the criteria from the European Position Paper on Rhinosinusitis and Nasal Polyps 2020 (EPOS 2020). Radiomic features were extracted from sinus CT images and analyzed using the least absolute shrinkage and selection operator (LASSO) regression. Models combining radiomic and clinical features were developed to predict treatment efficacy. Results: The radiomics and combined models, based on four selected features, outperformed the clinical feature model in the training set, with AUC values of 0.901 and 0.915, versus 0.874, respectively. In the internal validation set, AUCs were 0.839, 0.832, and 0.716. Despite reduced AUCs in the external set, the radiomics model maintained good generalizability (0.748, 0.764, 0.620). Decision curve analysis showed significant clinical benefits in both radiomics and combined models. Conclusion: The CT-based radiomics model demonstrates significant predictive power in identifying refractory nasal polyps, suggesting its potential for clinical application in treatment outcome prediction.

Objective: To investigate the molecular mechanisms of chronic rhinosinusitis (CRS), to identify key cell subgroups and genes, to construct effective diagnostic models, and to screen for potential therapeutic drugs. Methods: Key cell subgroups in CRS were identified through single-cell transcriptomic sequencing data. Essential genes associated with CRS were selected and diagnostic models were constructed by hdWGCNA (high dimensional weighted gene co-expression network analysis) and various machine learning algorithms. Causal inference analysis was performed using Mendelian randomization and colocalization analysis. Potential therapeutic drugs were identified using molecular docking technology, and the results of bioinformatics analysis were validated by immunofluorescence staining. Graphpad Prism, R, Python, and Adobe Illustrator software were used for data and image processing. Results: An increased proportion of basal and suprabasal cells was observed in CRS, especially in eosinophilic CRS with nasal polyps (ECRSwNP), with P=0.001. hdWGCNA revealed that the "yellow module" was closely related to basal and suprabasal cells in CRS. Univariate logistic regression and LASSO algorithm selected 13 key genes (CTSC, LAMB3, CYP2S1, TRPV4, ARHGAP21, PTHLH, CDH26, MRPS6, TENM4, FAM110C, NCKAP5, SAMD3, and PTCHD4). Based on these 13 genes, an effective CRS diagnostic model was developed using various machine learning algorithms (AUC=0.958). Mendelian randomization analysis indicated a causal relationship between CTSC and CRS (inverse variance weighted: OR=1.06, P=0.006), and colocalization analysis confirmed shared genetic variants between CTSC and CRS (PPH4/PPH3>2). Molecular docking results showed that acetaminophen binded well with CTSC (binding energy:-5.638 kcal/mol). Immunofluorescence staining experiments indicated an increase in CTSC⁺cells in CRS. Conclusion: This study integrates various bioinformatics methods to identify key cell types and genes in CRS, constructs an effective diagnostic model, underscores the critical role of the CTSC gene in CRS pathogenesis, and provides new targets for the treatment of CRS.