Analysis of the potential biological significance of glycosylation in triple-negative breast cancer on patient prognosis

Background: Breast cancer is the most common malignancy in women, with its prognosis varying greatly according to its subtype. Triple-negative breast cancer (TNBC) has the worst prognosis among all subtypes. Glycosylation is a critical factor influencing the prognosis of patients with TNBC. Our aim is to develop a tumor prognosis model by analyzing genes related to glycosylation to predict patient outcomes.

Methods: The dataset used in this study was downloaded from the Cancer Genome Atlas Program (TCGA) database, and predictive genes were identified through Cox one-way regression analysis. The model genes with the highest risk scores among the 18 samples were obtained by lasso regression analysis to establish the model. We analyzed the pathways affecting the progression of TNBC and discovered key genes for subsequent research.

Results: Our model was constructed using data from TCGA database and validated through Kaplan-Meier curve analysis and Receiver Operating Characteristic (ROC) curve assessment. Our analysis revealed that a high expression of tumor-related chemokines in the high-risk group may be associated with poor tumor prognosis. Furthermore, we conducted a random survival forest analysis and identified two significant genes, namely DPM2 and PINK1, which have been selected for further investigation.

Conclusion: The prognostic analysis model, developed based on the glycosylation genes in TNBC, exhibits excellent validation efficacy. This model is valuable for the prognostic analysis of patients with TNBC.