Five crucial prognostic-related autophagy genes stratified female breast cancer patients aged 40–60 years

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Abstract

Background: Autophagy is closely related to the progression of breast cancer. The aim at this study is to establish a prognostic-related model comprised of hub autophagy genes (AGs) to assess patient prognosis. Simultaneously, the model can guide clinicians to make up individualized strategies and stratify patients aged 40–60 years based on risk level. Methods: The hub AGs were identified with univariate COX regression and LASSO regression. The functions and alterations of these selected AGs were analyzed as well. Moreover, the multivariate COX regression and correlation analysis between hub AGs and clinicopathological parameters were done. Results: Totally, 33 prognostic-related AGs were obtained from the univariate COX regression (P < 0.05). SERPINA1, HSPA8, HSPB8, MAP1LC3A, and DIRAS3 were identified to constitute the prognostic model by the LASSO regression. The survival curve of patients in the high-risk and low-risk groups was statistically significant (P < 0.05). The 3-year and 5-year ROC displayed that their AUC value reached 0.762 and 0.825, respectively. Stage and risk scores were independent risk factors relevant to prognosis. RB1CC1, RPS6KB1, and BIRC6 were identified as the most predominant mutant genes. It was found that AGs were mainly involved in regulating the endopeptidases synthesis and played important roles in the ErbB signal pathway. SERPIN1, risk score was closely related to the stage (P < 0.05); HSPA8, risk score were closely related to T stag (P < 0.05); HSPB8 was closely related to N stag (P < 0.05). Conclusions: Our prognostic model had the relatively robust predictive ability on prognosis for patients aged 40–60 years. If the stage was added into the prognostic model, the predictive ability would be more powerful.

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Li, X., Zhang, H., Liu, J., Li, P., & Sun, Y. (2021). Five crucial prognostic-related autophagy genes stratified female breast cancer patients aged 40–60 years. BMC Bioinformatics, 22(1). https://doi.org/10.1186/s12859-021-04503-y

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