A novel prognostic tool for triple-negative breast cancer: creating and testing a parthanatos-related gene model.

Background: Parthanatos is a poly (ADP-ribose) polymerase 1 (PARP1)-dependent programmed cell death pathway. However, the association between parthanatos and triple-negative breast cancer (TNBC) as well as its impact on patient prognosis remains unclear. This study aims to investigate the prognostic value of parthanatos in TNBC and to develop a predictive model for identifying potential prognostic biomarkers. Methods: Gene expression profiles of TNBC cases were acquired from the publicly available dataset of The Cancer Genome Atlas (TCGA). After reviewing existing literature on parthanatos, we pinpointed 31 genes related with this process, hereafter termed parthanatos-related genes (PRGs). Using correlation analysis, we identified 1,569 messenger RNAs (mRNAs) showing strong associations (correlation coefficient >0.6) with these PRGs, which we defined as parthanatos-related mRNAs (PR-mRNAs). Combining these PR-mRNAs with the initial 31 PRGs yielded a comprehensive gene set of 1,600 genes. Initial screening via univariate Cox analysis identified 27 candidate genes demonstrating significant prognostic value (P<0.05) and a prognostic risk model was developed using least absolute shrinkage and selection operator (LASSO) regression analysis. To systematically evaluate the predictive performance of the constructed model, we conducted comprehensive validation using Kaplan-Meier survival analysis, receiver operating characteristic (ROC) curve analysis and calibration plots. Finally, by analyzing the genes in the model, NECTIN2 was selected for evaluation of its potential biological functions. Results: This investigation developed a 12 PR-mRNAs signature for prognostic risk stratification in TNBC. The model demonstrated strong predictive accuracy across temporal assessments, with area under the curve (AUC) values exceeding 0.89 in the training set (1-year: 0.943, 3-year: 0.980, 5-year: 0.896) and maintaining robust performance in both validation cohorts (testing set: 1-year 0.968, 3-year 0.795, 5-year 0.876; combined cohort: 1-year 0.947, 3-year 0.922, 5-year 0.887). Bootstrap resampling validation confirmed the model's stability and reproducibility in clinical outcome prediction. Notably, elevated NECTIN2 expression correlated significantly with poorer overall survival (P<0.01), a finding supported by functional studies showing that NECTIN2 knockdown in MDA-MB-468 cells significantly attenuated both proliferative capacity and migratory potential. Conclusions: Through systematic analysis, we developed a 12 PR-mRNAs signature that effectively predicts clinical outcomes in TNBC patients. The newly established risk assessment model not only provides a reliable tool for prognostic evaluation but also reveals NECTIN2 as a clinically significant biomarker, with elevated expression correlating with poorer survival outcomes.