Risk Factors and a Nomogram Model for Residual Symptoms of Cured Benign Paroxysmal Positional Vertigo.

BACKGROUND: We aimed to analyze the independent risk factors that affect the treatment outcomes of residual symptoms of cured benign paroxysmal positional vertigoand to construct a nomogram model. METHODS: A total of 186 benign paroxysmal positional vertigo patients who were treated in our hospital from June 2019 to August 2021 were selected. According to whether there were residual symptoms, they were divided into a group with residual symptoms (n=82) and a group without residual symptoms (n = 104). The logistic regression model was used to analyze the independent risk factors affecting the treatment outcomes, and the results were incorporated into R software to establish a nomogram model for verification. RESULTS: The incidence rate of residual symptoms in the 186 patients was 44.09% (82/186). Logistic regression analysis showed that age, course of disease, number of maneuvers, anxiety state, diabetes mellitus, and hypertension were independent risk factors affecting the treatment outcomes of residual symptoms after cured benign paroxysmal positional vertigo. The area under the receiver operating characteristic curve of the nomogram model was 0.938. The calibration curve was fitted well (χ2 = 8.165, P = .417). CONCLUSION: The nomogram model constructed based on age, course of disease, number of maneuvers, anxiety state, diabetes mellitus, and hypertension had a high predictive value for the treatment outcomes of residual symptoms in benign paroxysmal positional vertigo patients.

Prognostic Value and Immunological Characteristics of a Novel RNA Binding Protein Signature in Cutaneous Melanoma.

BACKGROUND: The existing studies indicate that RNA binding proteins (RBPs) are closely correlated with the genesis and development of cancers. However, the role of RBPs in cutaneous melanoma remains largely unknown. Therefore, the present study aims to establish a reliable prognostic signature based on RBPs to distinguish cutaneous melanoma patients with different prognoses and investigate the immune infiltration of patients. METHODS: After screening RBPs from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, Cox and least absolute shrinkage and selection operator (LASSO) regression analysis were then used to establish a prediction model. The relationship between the signature and the abundance of immune cell types, the tumor microenvironment (TME), immune-related pathways, and immune checkpoints were also analyzed. RESULTS: In total, 7 RBPs were selected to establish the prognostic signature. Patients categorized as a high-risk group demonstrated worse overall survival (OS) rates compared to those of patients categorized as a low-risk group. The signature was validated in an independent external cohort and indicated a promising prognostic ability. Further analysis indicated that the signature wasan independent prognostic indicator in cutaneous melanoma. A nomogram combining risk score and clinicopathological features was then established to evaluate the 3- and 5-year OS in cutaneous melanoma patients. Analyses of immune infiltrating, the TME, immune checkpoint, and drug susceptibility revealed significant differences between the two groups. GSEA analysis revealed that basal cell carcinoma, notch signaling pathway, melanogenesis pathways were enriched in the high-risk group, resulting in poor OS. CONCLUSION: We established and validated a robust 7-RBP signature that could be a potential biomarker to predict the prognosis and immunotherapy response of cutaneous melanoma patients, which provides new insights into cutaneous melanoma immunotherapeutic strategies.