ABSTRACT
BACKGROUND@#The disease burdens for endometrial cancer (EC) vary across different countries and geographical regions and change every year. Herein, we reported the updated results of the Global Burden of Disease Study 2019 on EC with respect to age-standardized incidence and mortality from 1990 to 2019.@*METHODS@#The annual percentage change (APC) of incidence and mortality was evaluated using joinpoint regression analysis to examine the temporal trends during the same timeframe in terms of the global landscape, different sociodemographic indices (SDI), and geographic regions. The relationship between Human Development Index (HDI) and incidence and mortality was additionally explored.@*RESULTS@#The age-standardized incidence rates (ASIRs) revealed a significant average global elevation by 0.5% per year (95% confidence interval [CI], 0.3-0.7; P <0.001). The age-standardized mortality rates (ASMRs), in contrast, fell by an average of 0.8% per year (95% CI, -1.0 to -0.7; P <0.001) worldwide. The ASIRs and ASMRs for EC varied across different SDIs and geographical regions. We noted four temporal trends and a significant reduction by 0.5% per year since 2010 in the ASIR, whereas we detected six consecutively decreasing temporal trends in ASMR during the entire period. Notably, the estimated APCs were significantly positively correlated with HDIs (ρ = 0.22; 95% CI, 0.07-0.35; P = 0.003) with regard to incident cases in 2019.@*CONCLUSIONS@#Incidence rates for EC reflected a significant increase overall (although we observed a decline since 2010), and the death rates declined consecutively from 1990 to 2019. We posit that more precise strategies can be tailored and then implemented based on the distinct age-standardized incidence and mortality burden in different geographical areas.
Subject(s)
Humans , Female , Global Burden of Disease , Incidence , Endometrial Neoplasms/epidemiology , Cost of IllnessABSTRACT
Objective@#The role of the protein-coding gene arylacetamide deacetylase (AADAC) in the prognostication of ovarian cancer remains uncertain. We aimed to identify and validate its prognostic value using integrated bioinformatics analyses. @*Methods@#Gene expression profiles of RNA-sequencing and microarray data were retrieved from The Cancer Genome Atlas and Gene Expression Omnibus. Univariate and multivariate Cox regression models were used to evaluate the prognostic value of gene expression. The predictive accuracy of the gene signature model was evaluated using a time-dependent receiver operating characteristic (ROC) curve. In addition, the correlation between immune infiltration and AADAC was identified. A nomogram of the gene signature with clinical parameters was constructed to estimate the clinical application of the signature for survival prediction in patients with ovarian cancer. @*Results@#Univariate and multivariate Cox regression analyses in the training and validation cohorts indicated that a high AADAC expression signature was significantly and independently correlated with better survival outcomes in ovarian cancer. AADAC upregulation positively correlated with the infiltration of CD4+ memory T cells. Immunological signature gene sets were significantly enriched in CD4+ T cell regulation pathways. The area under the curve of the time-dependent ROC for overall survival indicated that the constructed nomogram had a moderate predictive ability for prognostic prediction in ovarian cancer. @*Conclusion@#AADAC expression signature significantly and independently correlated with the survival outcome and CD4+ memory T cell infiltration in ovarian cancer, indicating its potential applicability in the prediction of prognosis and immunotherapy efficacy.