Development of prognostic signature and nomogram for patients with breast cancer.

To identify prognostic signature that could predict the survival of patients with breast cancer (BC).Breast cancer samples and normal breast tissues in the TCGA-BRCA and GSE7390 were included. Differentially expressed genes (DEGs) were identified using the "limma" method. Overall survival (OS) associated with DEGs were obtained using univariate and multivariable Cox proportional-hazards regression analysis, and the corresponding prognostic signature and nomogram were constructed. Calibration analysis and decision curve analysis (DCA) were performed.In all, 742 DEGs were identified, 19 of which were independently correlated with the OS of BC patients. The OS of patients in the 19-gene signature low-risk group was significantly better than that in high-risk group (hazard ratio [HR] 0.3506, 95% confidence interval [CI] 0.2488-0.4939), and the 19-gene based signature was demonstrated to be an independent prognostic factor in patient with BC in the TCGA-BRCA cohort (HR 1.501, 95% CI 1.374-1.640) and validation cohort GSE7392 ((HR 0.3557, 95% CI 0.2155-0.5871, P < .0001)). The primary and internally validated C-indexes for the 19-gene signature-based nomogram were 0.817 and 8.013, respectively. The results of calibration analysis and DCA analysis confirmed the robustness and the clinical usability of the nomogram.We constructed a prognostic signature and nomogram for patient with BC, which showed good application prospect.

Combined effects of GSTM1 and GSTT1 polymorphisms on breast cancer risk: A MOOSE-compliant meta-analysis and false-positive report probabilities test.

Many molecular epidemiology studies have reported an association between the combined effects of glutathione S-transferase M1 (GSTM1) and glutathione S-transferase T1 (GSTT1) polymorphisms on breast cancer risk. However, the results have been controversial.A meta-analysis was performed to clarify this issue.Meta-analysis of observational studies in epidemiology guidelines was used. Pooled the crude odds ratios (ORs) and 95% confidence intervals (CIs) were calculated using a random-effects model or fixed-effects model. Several subgroup analyses were conducted by ethnicity, source of control, matching, and menopausal status. In addition, we also performed sensitivity analysis and publication bias. Moreover, a false-positive report probability (FPRP) test was applied to assess positive results.A significantly increased breast cancer risk was observed in overall population (GSTM1 null/GSTT1 present [- +] vs GSTM1 present/GSTT1 present [+ +]: OR = 1.19, 95% CI: 1.03-1.36, GSTM1 null/GSTT1 null [- -] vs + +: OR = 1.63, 95% CI: 1.29-2.06, (- +) + GSTM1 present/GSTT1 null (+ -) vs + +: OR = 1.17, 95% CI: 1.05-1.31, (- +) + (+ -) + (- -) vs + +: OR = 1.27, 95% CI: 1.12-1.44, and - - vs (- +) + (+ -) + (+ +): OR = 1.39, 95% CI: 1.17-1.66) and several subgroup analyses, such as Caucasians, Indians, postmenopausal women, and so on. However, positive results were only considered noteworthy in overall population (- - vs + +: FPRP = 0.150 and (- +) + (+ -) + (- -) vs + +: FPRP = 0.162). Moreover, no significant association was observed when we used the trim and fill method to adjust the pooled data from all populations. Further, none of positive results of sensitivity analysis were considered noteworthy (FPRP >0.2).These positive findings should be interpreted with caution and indicate that an increased breast cancer risk may most likely result from false-positive results, rather than from true associations or biological factors on the combined effects of GSTM1 and GSTT1. Future studies should be based on sample sizes well-powered and attention needs to be paid to study design to further identify this issue.