Prognostic modeling of glycosylation in TNBC and screening of related key genes through a comprehensive analysis of multi-omics studies

Abstract

Abstract Background The most common malignancy in women is breast cancer, and the prognosis varies greatly according to its typing, among which the worst prognosis is TNBC. The glycosylation is one of the most priorities among reasons influencing the prognosis with TNBC of patients. We aim to develop a tumor prognosis model by analyzing genes related to glycosylation in order to predict patient prognosis. Methods The dataset was downloaded from the TCGA databank and the 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, and the model was established. The related pathways affecting the progression of TNBC were analyzed, and the key genes of the disease were discovered for subsequent research. Results The model was constructed using TCGA database data, and The model underwent verification through K-M curve analysis and ROC curve. Then, we analyzed that the high expression of tumor-related chemokines in high-risk group may be associated with poor tumor prognosis. Finally, 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 constructed by the TNBC glycosylation gene has excellent validation efficacy. It can be used for prognostic analysis of relevant TNBC patients.