Abstract
BackgroundAutophagy 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.MethodsThe 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.ResultsTotally, 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).ConclusionsOur 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.
Highlights
Autophagy is a natural phenomenon that regulates cell metabolism inside
While the Least Absolute Shrinkage and Selection Operator (LASSO regression) was to construct the model of autophagic gene prognosis and to calculate patients risk score through R software package “glmnet”.Through including clinical and pathological factors, the multivariate COX regression could filter out independent risk factors, which were jointly incorporated into the model construction
The univariate Cox regression analysis was performed on 501 Breast Cancer (BC) samples, and 33 prognostic-related autophagy genes (AGs) were obtained, in which 23 upregulated and ones and 10 down-regulated ones (Fig. 1A)
Summary
The immune cell autophagy process could eliminate senile organelles and abnormal long-lived proteins in the body, which will be conducive to maintaining immune cell homeostasis. Li et al BMC Bioinformatics (2021) 22:580 autophagy working in preventing the accumulation of toxic or carcinogenic damaged proteins and organelles and inhibiting simultaneously cell cancelation. If it occurred dysfunctional autophagy process, a tumor could be coming soon [1]. It has been implicated that tumorigenesis is closely related to cell autophagy [2]. 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. The model can guide clinicians to make up individualized strategies and stratify patients aged 40–60 years based on risk level
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