ABSTRACT
Background: This study aimed to identify prognostic signatures to predict the prognosis of breast cancer (BRCA) patients based on a series of comprehensive analyses of gene expression data. Methods: The RNA-sequencing expression data and corresponding BRCA patient clinical data were collected from the Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) datasets. Firstly, the differently expressed genes (DEGs) related to prognosis between tumor tissues and normal tissues were ascertained by performing R package "limma". Secondly, the DEGs were used to construct a polygenic risk scoring model by the weighted gene co-expression network analysis (WGCNA) and the least absolute shrinkage and selection operator Cox regression (Lasso-cox) analysis method. Thirdly, survival analysis was performed to investigate the risk score values in the TCGA cohort. And the enrichment analysis, immune cell infiltration levels analysis, and protein-protein internet (PPI) analysis were performed. Simultaneously, the GEO cohort was used to validate the model. Lastly, we constructed a nomogram to explore the influence of polygenic risk score and other clinical factors on the survival probability of patients with BRCA. Results: A total of 1000 DEGs including 396 upregulated genes and 604 downregulated genes were identified from the TCGA-BRCA dataset. We obtained 5 prognosis-related genes, as the key biomarkers by Lasso-cox analysis (FBXL19, HAGHL, PHKG2, PKMYT1, and TXNDC17), all of which were significantly upregulated in breast tumors. The prognostic prediction of the 5 genes model was great in training and validation cohorts. Moreover, the high-risk group had a poorer prognosis. The Cox regression analysis showed that the comprehensive risk score for 5 genes was an independent prognosis factor. Conclusion: The 5 genes risk model constructed in this study had an independent predictive ability to distinguish patients with a high risk of death from those with a low-risk score, and it can be used as a practical and reliable prognostic tool for BRCA.
ABSTRACT
Background: Hepatitis B virus (HBV) infection has been associated with the risk and prognosis of many malignancies. Nevertheless, the association between HBV and the prognosis of breast cancer is unclear. The objectives of this study were to investigate the prognostic role of hepatitis B surface antigen (HBsAg) and to integrate HBsAg to establish nomograms for better prognostic prediction of very young patients with breast cancer. Methods: This analysis was performed retrospectively in a cohort of 1,012 consecutive very young (≤35 at diagnosis) patients who received curative resection for breast cancer. The significance of HBsAg in the prognosis of these patients was investigated. Univariate and multivariate analyses were used to identify independent variables for disease-free survival (DFS) and overall survival (OS). Nomograms were built based on those identified variables. Results: Overall, 140 of the 1,012 patients (13.8%) were seropositive for HBsAg. The median follow-up was 67.9 (95% CI, 64.4-71.4) months for the entire population. The HBsAg-positive cohort had significantly inferior DFS (HR, 1.66; 95% CI, 1.07-2.56; P = 0.021) and OS (HR, 1.75; 95% CI, 1.10-2.79; P = 0.016) as compared with the HBsAg-negative cohort. The rates of 10-year DFS and OS were 77.4 and 73.0% in the HBsAg-positive group and 84.1 and 85.6% in the HBsAg-negative group, respectively. In multivariable analysis, HBsAg status was identified as an independent significant unfavorable prognostic factor for DFS (P = 0.01) and OS (P = 0.04) in very young patients with breast cancer. Nomograms were established and displayed good calibration and acceptable discrimination. The C-index values for DFS and OS were 0.656 (95% CI: 0.620-0.691) and 0.738 (95% CI: 0.697-0.779), respectively. Based on the total prognostic scores (TPS) of the nomograms, 3 different prognosis groups were identified for DFS and OS. Conclusions: HBsAg is an independent unfavorable prognostic factor for DFS and OS in very young patients with curatively resected breast cancer, and nomograms integrating HBsAg provide individual survival prediction to benefit prognosis evaluation and individualized therapy.
