Clinical Significance of Nuclear Yin-Yang Overexpression Evaluated by Immunohistochemistry in Tissue Microarrays and Digital Pathology Analysis: A Useful Prognostic Tool for Breast Cancer

Yin Yang 1 (YY1) is a multifunctional transcription factor implicated in gene regulation, cell proliferation, and survival. While its role in breast cancer (BC) has been explored, its prognostic significance remains controversial. In this study, we evaluated nuclear YY1 expression in 276 BC tissue samples using immunohistochemistry (IHC), tissue microarrays (TMAs), and digital pathology (DP). Nuclear staining was quantified using Aperio ImageScope software, focusing on tumor regions to avoid confounding from stromal or non-tumor tissues. This selective and standardized approach enabled precise quantification of YY1 expression. Our results show elevated median YY1 expression in tumor vs. normal matched tissues (<i>p</i> < 0.001). The optimal cutoff for medium-intensity nuclear YY1 expression in tumor areas for overall survival (OS) was established by a receiver operating characteristic (ROC) curve (AUC = 0.718, 95% CI: 0.587-0.849, <i>p</i> = 0.008). In contrast, ROC curves showed no prognostic impact (AUC and <i>p</i>-value) for YY1 quantification in whole spots (tumor + normal). As a categorical variable, high YY1 expression was correlated with more aggressive BC features, including tumor size > 3 cm (57.7% vs. 44.2% <i>p</i> = 0.037), the triple-negative breast cancer (TNBC) molecular subtype (27.3% vs. 13.9% <i>p</i> = 0.026), and advanced prognostic stage (III) (31.8% vs. 16.7% <i>p</i> = 0.003), while as a continuous variable, YY1 was associated with higher histological (<i>p</i> = 0.003) and nuclear grades (<i>p</i> = 0.022). High YY1 expression was significantly associated with a reduced OS of BC patients, as shown by Kaplan-Meier curves (HR = 2.227, <i>p</i> = 0.002). Since YY1 was significantly enriched in TNBC, we evaluated its prognostic resolution in this subgroup. But, probably due to the small number of patients within this subset, our results were not statistically significant (HR = 1.317, 95% CI: 0.510-3.405, <i>p</i> = 0.566). Next, we performed multivariate Cox regression, confirming YY1 as an independent prognostic factor for overall survival (HR = 1.927, 95% CI: 1.144-3.247, <i>p</i> = 0.014). In order to improve prognostic value, we constructed a mathematical model derived from the multivariate Cox regression results, including YYI, AJCC prognostic stage (STA), and axillary lymph node dissection (ALN), with the following equation: h(t) = h₀(t) × exp (0.695 × YY1 + 1.103 × STA - 0.503 × ALN). ROC analysis of this model showed a better AUC of 0.915, similar sensitivity (83.3%), and much higher specificity (92%). Bioinformatic analysis of public datasets supported these findings in BC, showing YY1 overexpression in multiple cancer types and its association with poor outcomes in BC. These results suggest that YY1 may play a role in tumor progression and serve as a valuable prognostic biomarker in BC. DP combined with molecular data enhanced biomarker accuracy, supporting clinical applications of YY1 in routine diagnostics and personalized therapy. Additionally, developing a combined score based on the modeling of multiple prognostic factors significantly enhanced survival predictions, representing a practical tool for risk stratification and the guidance of therapeutic decisions.