Previous studies have indicated a correlation between nut intake and cancer risk in humans. This meta-analysis aimed to determine the relationship between nut consumption and the risks of cancer incidence and mortality. The PubMed, Embase, and Web of Science databases were searched up to August 2019. Relative risks and 95% confidence intervals were calculated using random-effects and fixed-effects models. We included 38 studies on nut consumption and cancer risk and 9 studies on cancer-specific mortality. Compared with no nut intake, nut intake was associated with a lower cancer risk (Relative Risk=0.90; 95% confidence interval, 0.86-0.94). Inverse associations were observed with colorectal cancer, gastric cancer, pancreatic cancer, and lung cancer in subgroup analyses. Tree nut consumption was found to reduce cancer risk (Relative Risk=0.88; 95% confidence interval, 0.79-0.99). Dose-response curves suggested that protective benefits against cancer increased with increased nut intake (P=0.005, P-nonlinearity=0.0414). An inverse correlation with cancer-specific mortality (Odd Ratio=0.90; 95% confidence interval, 0.88-0.92) was observed. In conclusion, nut consumption is inversely associated with the risks of cancer incidence and mortality; a higher intake is significantly associated with a lower cancer risk.

To identify a glycolysis-related gene signature for the evaluation of prognosis in patients with breast cancer, we analyzed the data of a training set from TCGA database and four validation cohorts from the GEO and ICGC databases which included 1,632 patients with breast cancer. We conducted GSEA, univariate Cox regression, LASSO, and multiple Cox regression analysis. Finally, an 11-gene signature related to glycolysis for predicting survival in patients with breast cancer was developed. And Kaplan–Meier analysis and ROC analyses suggested that the signature showed a good prognostic ability for BC in the TCGA, ICGC, and GEO datasets. The analyses of univariate Cox regression and multivariate Cox regression revealed that it’s an important prognostic factor independent of multiple clinical features. Moreover, a prognostic nomogram, combining the gene signature and clinical characteristics of patients, was constructed. These findings provide insights into the identification of breast cancer patients with a poor prognosis.

IMPORTANCE Breast cancer (BC), a common malignant tumor, ranks first among cancers in terms of morbidity and mortality among female patients. Currently, identifying effective prognostic models has a significant association with the prediction of the overall survival of patients with BC and guidance of clinicians in early diagnosis and treatment. OBJECTIVES To identify a potential DNA repair-related prognostic signature through a comprehensive evaluation and to further improve the accuracy of prediction of the overall survival of patients with BC. DESIGN, SETTING, AND PARTICIPANTS In this prognostic study, conducted from October 9, 2019, to February 3, 2020, the gene expression profiles and clinical data of patients with BC were collected from The Cancer Genome Atlas database. This study consisted of a training set from The Cancer Genome Atlas database and 2 validation cohorts from the Gene Expression Omnibus, which included 1096 patients with BC. A prognostic signature based on 8 DNA repair-related genes (DRGs) was developed to predict overall survival among female patients with BC. MAIN OUTCOMES AND MEASURES Primary screening prognostic biomarkers were analyzed using univariate Cox proportional hazards regression analysis and the least absolute shrinkage and selection operator Cox proportional hazards regression. A risk model was completely established through multivariate Cox proportional hazards regression analysis. Finally, a prognostic nomogram, combining the DRG signature and clinical characteristics of patients, was constructed. To examine the potential mechanisms of the DRGs, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were performed. RESULTS In this prognostic study based on samples from 1096 women with BC (mean [SD] age, 59.6 [13.1] years), 8 DRGs (MDC1, RPA3, MED17, DDB2, SFPQ, XRCC4, CYP19A1, and PARP3) were identified as prognostic biomarkers. The time-dependent receiver operating characteristic curve analysis suggested that the 8-gene signature had a good predictive accuracy. In the training cohort, the areas under the curve were 0.708 for 3-year survival and 0.704 for 5-year survival. In the validation cohort, the areas under the curve were 0.717 for 3-year survival and 0.772 for 5-year survival in the GSE9893 data set and 0.691 for 3-year survival and 0.718 for 5-year survival in the GSE42568 data set. This DRG signature mainly involved some regulation pathways of vascular endothelial cell proliferation. CONCLUSIONS AND RELEVANCE In this study, a prognostic signature using 8 DRGs was developed that successfully predicted overall survival among female patients with BC. This risk model provides new clinical evidence for the diagnostic accuracy and targeted treatment of BC.

The association between vitamin C intake and breast cancer is unclear. This meta-analysis aimed to precisely assess the association of vitamin C intake with breast cancer risk and mortality. We searched the PubMed, Embase, and Web of Science databases up to June 2020 and found 69 studies relevant to breast cancer risk (54 studies) and survival (15 studies). Relative risks and 95% confidence intervals were calculated using the random-effects models. Pooled results suggested that the highest versus lowest vitamin C intake was significantly associated with a lower risk of breast cancer incidence (Relative Risk = 0.86; 95% confidence interval, 0.81–0.92). Dietary vitamin C but not supplements was found to reduce breast cancer risk (Relative Risk = 0.89; 95% confidence interval, 0.82–0.96). For the highest versus lowest vitamin C intake, the pooled hazard risk for breast cancer-specific mortality was 0.78 (95% confidence interval, 0.69–0.88), totality mortality was 0.82 (95% confidence interval, 0.74–0.91), and recurrence was 0.81 (95% confidence interval, 0.67–0.99). Our analysis suggests that higher vitamin C intake is significantly associated with reduced breast cancer incidence and mortality. However, the intake of vitamin C supplements has no significant effect on breast cancer prevention.

Background Ovarian cancer (OV) is deemed the most lethal gynecological cancer in women. The aim of this study was to construct an effective gene prognostic model for predicting overall survival (OS) in patients with OV. Methods The expression profiles of glycolysis‐related genes (GRGs) and clinical data of patients with OV were extracted from The Cancer Genome Atlas (TCGA) database. Univariate, multivariate, and least absolute shrinkage and selection operator Cox regression analyses were conducted, and a prognostic signature based on GRGs was constructed. The predictive ability of the signature was analyzed using training and test sets. Results A gene risk signature based on nine GRGs (ISG20, CITED2, PYGB, IRS2, ANGPTL4, TGFBI, LHX9, PC, and DDIT4) was identified to predict the survival outcome of patients with OV. The signature showed a good prognostic ability for OV, particularly high‐grade OV, in the TCGA dataset, with areas under the curve (AUC) of 0.709 and 0.762 for 3‐ and 5‐year survival, respectively. Similar results were found in the test sets, and the AUCs of 3‐, 5‐year OS were 0.714 and 0.772 in the combined test set. And our signature was an independent prognostic factor. Moreover, a nomogram combining the prediction model and clinical factors was developed. Conclusion Our study established a nine‐GRG risk model and nomogram to better predict OS in patients with OV. The risk model represents a promising and independent prognostic predictor for patients with OV. Moreover, our study on GRGs could offer guidance for the elucidation of underlying mechanisms in future studies.