Purpose. The aim of this study was to develop and assess a nomogram to predict noninflammatory skin involvement of invasive breast cancer. Methods. We developed a prediction model based on SEER database, a training dataset of 89202 patients from January 2010 to December 2016. Multivariable logistic regression analysis was applied to build a predicting model incorporating the feature selected in the least absolute shrinkage and selection operator regression model. Discrimination, calibration, and clinical usefulness of the predicting model were assessed using the C-index, calibration plot, and decision curve analysis. Internal validation was assessed using the bootstrapping validation. Results. Predictors contained in the prediction nomogram included use of age, race, grade, tumor size, stage-N, ER status, PR status, and Her-2 status. The model shows good discrimination with a C-index of 0.857 (95% confidence interval: 0.807–0.907) and good calibration. High C-index value of 0.847 could still be reached in the internal validation. Conclusion. This study constructed a novel nomogram with accuracy to help clinicians access the risk of noninflammatory skin involvement by tumor. The assessment of clinicopathologic factors can predict the individual probability of skin involvement and provide assistance to the clinical decision-making.
Ubiquitination related genes (URGs) are important biomarkers and therapeutic targets in cancer. However, URG prognostic prediction models have not been established in breast cancer (BC) before. Our study aimed to identify URGs to serve as potential prognostic indicators in patients with BC.The URGs were downloaded from the ubiquitin and ubiquitin-like conjugation database. GSE42568 and The Cancer Genome Atlas were exploited to screen differentially expressed URGs in BC. The univariate Cox proportional hazards regression analysis, least absolute shrinkage and selection operator analysis, and multivariate Cox proportional hazards regression analysis were employed to construct multi-URG signature in the training set (GSE42568). Kaplan–Meier curve and log-rank method analysis, and ROC curve were applied to validate the predictive ability of the multi-URG signature in BC. Next, we validated the signature in test set (GSE20685). Finally, we performed GSEA analysis to explore the mechanism.We developed a 4-URG (CDC20, PCGF2, UBE2S, and SOCS2) signature with good performance for patients with BC. According to this signature, BC patients can be classified into a high-risk and a low-risk group with significantly different overall survival. The predictive ability of this signature was favorable in the test set. Univariate and multivariate Cox regression analysis showed that the 4-URG signature was independent risk factor for BC patients. GSEA analysis showed that the 4-URG signature may related to the function of DNA replication, DNA repair, and cell cycle.Our study developed a novel 4-URG signature as a potential indicator for BC.
Background: Ubiquitination-related genes (URGs) are important biomarkers and therapeutic targets in cancer. However, URG prognostic prediction models have not been established in triple-negative breast cancer (TNBC) before. Our study aimed to explore the roles of URGs in TNBC.Methods: The Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) and the Gene Expression Omnibus (GEO) databases were used to identify URG expression patterns in TNBC. Non-negative matrix factorization (NMF) analysis was used to cluster TNBC patients. The least absolute shrinkage and selection operator (LASSO) analysis was used to construct the multi-URG signature in the training set (METABRIC). Next, we evaluated and validated the signature in the test set (GSE58812). Finally, we evaluated the immune-related characteristics to explore the mechanism.Results: We identified four clusters with significantly different immune signatures in TNBC based on URGs. Then, we developed an 11-URG signature with good performance for patients with TNBC. According to the 11-URG signature, TNBC patients can be classified into a high-risk group and a low-risk group with significantly different overall survival. The predictive ability of this 11-URG signature was favorable in the test set. Moreover, we constructed a nomogram comprising the risk score and clinicopathological characteristics with favorable predictive ability. All of the immune cells and immune-related pathways were higher in the low-risk group than in the high-risk group.Conclusion: Our study indicated URGs might interact with the immune phenotype to influence the development of TNBC, which contributes to a further understanding of molecular mechanisms and the development of novel therapeutic targets for TNBC.
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