Construction of an oxidative stress-associated genes signature in breast cancer by machine learning algorithms.

OBJECTIVE: To construct a prognostic model of a breast cancer-related oxidative stress-related gene (OSRG) signature using machine learning algorithms. METHODS: The OSRGs of breast cancer were constructed by least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analysis. The Cancer Genome Atlas (TCGA) was used to analyse the gene expression and prognostic value. The Human Protein Atlas was used to analyse the protein expression of hub genes. Receiver operating characteristic analysis, calibration curve and decision curve analysis were used to predict the stability of this model. RESULTS: The area under the curve of 1-, 3- and 5-year overall survival were 0.751, 0.707 and 0.645 in the TCGA training dataset; and 0.692, 0.678 and 0.602 in the TCGA testing dataset, respectively. Calibration plot showed good agreement between predicted probabilities and observed outcomes. Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Set Enrichment Analysis (GSEA) pathway analysis indicated that multiple cancer-related pathways were highly enriched in the high-risk group. Immune infiltration analysis showed immune cells and their functions may play a key role in the development and mechanism of breast cancer. CONCLUSIONS: This new OSRG signature was associated with the immune infiltration and it might be useful in predicting the prognosis in patients with breast cancer.

Depressive symptoms increase the risk of falls and injurious falls in Chinese adults aged ≥ 45 years: A 3-year cohort study.

Background: Falls and depressive symptoms are both public health concerns in China, but the effects of depressive symptoms on falls and injurious falls have not been thoroughly investigated. Methods: This population-based prospective cohort study used data derived from adults aged ≥45 years acquired from the 2015 and 2018 China Health and Retirement Longitudinal Study. Data were analyzed from August 2021 to December 2021. Self-reported depressive symptoms were determined using a 10-item Center for Epidemiologic Studies Depression scale (CESD-10) with a total score range of 0-30. Item responses of 3-4 or 5-7 days were deemed indicative of specific depressive symptoms. The outcome variables were self-reported accidental falls and injurious falls. Results: Of the 12,392 participants included in the study, 3,671 (29.6%) had high baseline depressive symptoms (CESD-10 scores ≥ 10), 1,892 (15.3%) experienced falls, and 805 (6.5%) experienced injurious falls during 2015-2018 follow-up. High depressive symptoms increased the risk of falls [odds ratio (OR) 1.34, 95% confidence interval (CI) 1.19-1.50] and injurious falls (OR 1.28, 95% CI 1.09-1.51) in a multivariable logistic regression model adjusted for major demographic, health-related, and anthropometric covariates. All of the 10 specific depressive symptoms except "felt hopeless" were associated with falls, and four specific symptoms significantly increased the risk of injurious falls; "had trouble concentrating" (OR 1.32, 95% CI 1.13-1.55); "felt depressed" (OR 1.32, 95% CI 1.12-1.55); "everything was an effort" (OR 1.23, 95% CI 1.04-1.45); and "restless sleep" (OR 1.18, 95% CI 1.02-1.40). Conclusion: High depressive symptoms are significantly related to risk of falls and injurious falls. Four specific symptoms (had trouble concentrating, felt depressed, everything was an effort, and restless sleep) increase the risk of injurious falls in Chinese adults aged ≥ 45 years.