AUTHOR=Liu Jie , Zhu Jing , Wang Xin , Zhou Zhisheng , Liu Haiyan , Zhu Dajiang 

TITLE=A Novel YTHDF3-Based Model to Predict Prognosis and Therapeutic Response in Breast Cancer

JOURNAL=Frontiers in Molecular Biosciences

VOLUME=Volume 9 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/molecular-biosciences/articles/10.3389/fmolb.2022.874532

DOI=10.3389/fmolb.2022.874532

ISSN=2296-889X

ABSTRACT=Background: Due to the high tumor heterogeneity, breast cancer (BC) patients still suffered poor survival outcome. YTHDF3 played a critical role in the prognosis of BC patients. Hence, we aimed to construct a YTHDF3-based model for the prediction of the overall survival (OS) and the sensitivity of therapeutic agents in BC patients.
Methods: Based on the Cancer Genome Atlas (TCGA, https://portal.gdc.cancer.gov/) database, we obtained BC patients (n=999) with YTHDF3 expression profiles. The association between YTHDF3 expression and 5-year OS was determined via Cox proportional hazards regression (CPHR) analysis. By integrating the variables, we established a prognostic nomogram. The model was estimated via discrimination, calibration ability, and decision curve analysis (DCA). The performance of model was compared with the TNM stage system through receiver operating characteristic (ROC) curves and DCA. By means of the Genomics of Drug Sensitivity in Cancer (GDSC) database (https://www.cancerrxgene.org/), the therapeutic agents’ response was estimated. Gene set enrichment analysis (GSEA) demonstrated possible biological mechanisms related to YTHDF3. TIMER and CIBERSORTx were employed to analyze the association between YTHDF3 and tumor-infiltrating immune cells.
Results: The high YTHDF3 expression was significantly correlated with poor 5-year OS in BC patients. Through multivariate CPHR, four independent prognostic variables (age, TNM stage, YTHDF3 expression, molecular subtype) were determined. On the basis of the four factors, a YTHDF3-based nomogram was built. The area under the curve (AUC) of ROC curve for the model surpassed that of the TNM stage system (0.72 vs 0.63, p = 0.00028). The model predictions showed close consistence with the actual observations via the calibration plot. Therapeutic response prediction was conducted in high- and low-risk groups and compared with each other. The BC patients with higher risk score contained more therapeutic resistance than who with lower risk score.
Conclusions: YTHDF3 was verified as a prognostic biomarker of BC and a novel YTHDF3-based model was conducted to predict the 5-year OS of BC patients. Our model could be applied to effectively predict the therapeutic response of common-used agents for BC patients.