Objective. Head and neck squamous cell carcinoma (HNSCC) is a highly heterotopic malignant tumor. Alternative splicing (AS) and RNA modification have been reported to be involved in tumorigenesis. Therefore, we constructed RNA modification-associated AS (RMA-AS) signature model to predict the prognosis of HNSCC. Methods. AS events and RNA-modified gene expression information were downloaded from TCGA-HNSCC database. Univariate Cox regression analysis was employed for analyzing prognosis-related AS events. RMA-AS events were obtained by constructing a coexpression network between RNA modification-associated genes and AS events using WGCNA package. The prognostic signatures were analyzed by LASSO, univariate Cox, and multivariate Cox regression. Kaplan-Meier survival analysis, proportional hazard model, and ROC curve were performed to verify the prognostic value. “ESTIMATE” R package, ssGSEA algorithm, and CIBERSORT method were used to detect immune microenvironment in HNSCC. Cytoscape was utilized to build a regulatory network of splicing factor-regulated RMA-AS. Results. There were 16,574 prognostic AS events and 4 differentially expressed RNA modification-associated genes in HNSCC. Based on RMA-AS events, we obtained a risk model consisting of 14 AS events, named RMA-AS_Score. The samples were divided into RMA-AS_Score high- and RMA-AS_Score low-risk groups, according to the risk score. The RMA-AS_Score high group was related to poor prognosis. Moreover, the RMA-AS_Score signature was an independent prognostic predictor and was related to tumor grade. Meanwhile, the AUC value of RMA-AS_Score was 0.652, which is better than other clinical characteristics. Besides, a nomogram prediction model of quantitative prognosis has also been developed, which has robust effectiveness in predicting prognosis. In addition, the prognostic signature was observably related to immune microenvironment and immune checkpoint. Finally, 14 splicing factors were identified and constructed into a network of splicing factor-regulated RMA-AS. Conclusion. We identified the RMA-AS signature of HNSCC. This signature could be treated as an independent prognostic predictor.

IntroductionMany scales are designed to screen for obstructive sleep apnoea‐hypopnoea syndrome (OSAHS); however, there is a lack of an efficiently and easily diagnostic tool, especially for Chinese. Therefore, we conduct a cross‐sectional study in China to develop and validate an efficient and simple clinical diagnostic model to help screen patients at risk of OSAHS.MethodsThis study based on 782 high‐risk patients (aged >18 years) admitted to the Sleep Medicine department of the Sixth Affiliated Hospital, Sun Yat‐sen University from 2015 to 2021. Totally 34 potential predictors were evaluated. We divided all patients into training and validation dataset to develop diagnostic model. The univariable and multivariable logistic regression model were used to build model and nomogram was finally built.ResultsAmong 602 high‐risk patients with median age of 46 (37, 56) years, 23.26% were women. After selecting using the univariate logistic model, 15 factors were identified. We further used the stepwise method to build the final model with five factors: age, BMI, total bilirubin levels, high Berlin score, and symptom of morning dry mouth or mouth breathing. The AUC was 0.780 (0.711, 0.848), with sensitivity of 0.848 (0.811, 0.885), specificity of 0.629 (0.509, 0.749), accuracy of 0.816 (0.779, 0.853). The discrimination ability had been verified in the validation dataset. Finally, we established a nomogram model base on the above final model.ConclusionWe developed and validated a predictive model with five easily acquire factors to diagnose OSAHS patient in high‐risk population with well discriminant ability. Accordingly, we finally build the nomogram model.