Construction and Validation of a Prognostic Risk Model for Triple-Negative Breast Cancer Based on Autophagy-Related Genes.

BACKGROUND

Autophagy plays an important role in triple-negative breast cancer (TNBC). However, the prognostic value of autophagy-related genes (ARGs) in TNBC remains unknown. In this study, we established a survival model to evaluate the prognosis of TNBC patients using ARGs signature.

METHODS

A total of 222 autophagy-related genes were downloaded from The Human Autophagy Database. The RNA-sequencing data and corresponding clinical data of TNBC were obtained from The Cancer Genome Atlas (TCGA) database. Differentially expressed autophagy-related genes (DE-ARGs) between normal samples and TNBC samples were determined by the DESeq2 package. Then, univariate Cox, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analyses were performed. According to the LASSO regression results based on univariate Cox, we identified a prognostic signature for overall survival (OS), which was further validated by using the Gene Expression Omnibus (GEO) cohort. We also found an independent prognostic marker that can predict the clinicopathological features of TNBC. Furthermore, a nomogram was drawn to predict the survival probability of TNBC patients, which could help in clinical decision for TNBC treatment. Finally, we validated the requirement of an ARG in our model for TNBC cell survival and metastasis.

RESULTS

There are 43 DE-ARGs identified between normal and tumor samples. A risk model for OS using CDKN1A, CTSD, CTSL, EIF4EBP1, TMEM74, and VAMP3 was established based on univariate Cox regression and LASSO regression analysis. Overall survival of TNBC patients was significantly shorter in the high-risk group than in the low-risk group for both the training and validation cohorts. Using the Kaplan-Meier curves and receiver operating characteristic (ROC) curves, we demonstrated the accuracy of the prognostic model. Multivariate Cox regression analysis was used to verify risk score as an independent predictor. Subsequently, a nomogram was proposed to predict 1-, 3-, and 5-year survival for TNBC patients. The calibration curves showed great accuracy of the model for survival prediction. Finally, we found that depletion of EIF4EBP1, one of the ARGs in our model, significantly reduced cell proliferation and metastasis of TNBC cells.

CONCLUSION

Based on six ARGs (CDKN1A, CTSD, CTSL, EIF4EBP1, TMEM74, and VAMP3), we developed a risk prediction model that can help clinical doctors effectively predict the survival status of TNBC patients. Our data suggested that EIF4EBP1 might promote the proliferation and migration in TNBC cell lines. These findings provided a novel insight into the vital role of the autophagy-related genes in TNBC and may provide new therapeutic targets for TNBC.