The crucial role of age and site in incidence and prognosis of female neuroendocrine neoplasms in the United States: a population-based study from 2000 to 2018.

BACKGROUND: As the incidence continues to rise, global concern about neuroendocrine neoplasms (NENs) is mounting. However, little is known about how NENs affect women patients. METHODS: The annual percentage change (APC) was calculated to describe the incidence. Cox proportional hazards multivariable regression was used to identify risk factors. The nomograms were employed to estimate prognosis. RESULTS: A total of 39,237 female NENs (fNENs) cases were identified. The incidence of fNENs increased annually (APC = 4.5, 95% CI 4.1-4.8, P < 0.05), and the incidence pattern and survival outcomes showed age and site-specificity. Appendiceal, rectal, and pulmonary fNENs were major contributors to the incidence of patients younger than 40, between 40-59, and over 60 years old, respectively. The Cox proportional hazards regression model revealed that age, tumor size, grade, stage, and primary sites were closely related to survival. The worst survival outcomes appeared in breast, reproductive system, and liver fNENs for patients under 40, between 40-49, and over 50 years old, respectively. A nomogram based on these developed with higher predictive accuracy of prognosis, with a C index of 0.906 in the training cohort and 0.901 in the validation cohort. CONCLUSIONS: Our findings revealed distinct site-specific tendencies in the incidence and survival patterns among fNEN patients across various age groups. Thus, reasonable patient screening and stratification strategies should be implemented, especially for young patients.

Prediction of immunotherapy response of bladder cancer with a pyroptosis-related signature indicating tumor immune microenvironment.

Background: Although prognostic models based on pyroptosis-related genes (PRGs) have been constructed in bladder cancer (BLCA), the comprehensive impact of these genes on tumor microenvironment (TME) and immunotherapeutic response has yet to be investigated. Methods: Based on expression profiles of 52 PRGs, we utilized the unsupervised clustering algorithm to identify PRGs subtypes and ssGSEA to quantify immune cells and hallmark pathways. Moreover, we screened feature genes of distinct PRGs subtypes and validated the associations with immune infiltrations in tissue using the multiplex immunofluorescence. Univariate, LASSO, and multivariate Cox regression analyses were employed to construct the scoring scheme. Results: Four PRGs clusters were identified, samples in cluster C1 were infiltrated with more immune cells than those in others, implying a favorable response to immunotherapy. While the cluster C2, which shows an extremely low level of most immune cells, do not respond to immunotherapy. CXCL9/CXCL10 and SPINK1/DHSR2 were identified as feature genes of cluster C1 and C2, and the specimen with high CXCL9/CXCL10 was characterized by more CD8 + T cells, macrophages and less Tregs. Based on differentially expressed genes (DEGs) among PRGs subtypes, a predictive model (termed as PRGs score) including five genes (CACNA1D, PTK2B, APOL6, CDK6, ANXA2) was built. Survival probability of patients with low-PRGs score was significantly higher than those with high-PRGs score. Moreover, patients with low-PRGs score were more likely to benefit from anti-PD1/PD-L1 regimens. Conclusion: PRGs are closely associated with TME and oncogenic pathways. PRGs score is a promising indicator for predicting clinical outcome and immunotherapy response.