| Background:According to 2020 statistics,female breast cancer has superseded lung cancer as the most commonly diagnosed cancer worldwide.Fatty acid metabolism augmented tumorigenesis,disease progression and therapy resistance via strengthened synthesis,storage,and catabolism.Breast cancer is highly linked to the biology function of fatty acid metabolism due to the presence of many adipocytes in breast tissue.The significance of fatty acid metabolism in breast cancer needs to be further researched.Objectives:We used a bioinformatics approach to construct prognostic models related to fatty acid metabolism in breast cancer.And functional analysis was used to explore the biological significance of fatty acid metabolism gene prognostic models.Methods:We obtained total 991 breast cancer samples gene expression profiles,and important clinical information from TCGA database as a training set.The gene expression profiles,survival time and survival status of GSE20685 dataset were downloaded from GEO database as a validation set.The genes related to fatty acid metabolism were downloaded from the GSEA,and all the genes were screened for prognosis-related by univariate Cox regression analysis in the training set.Then,Lasso Cox hazards regression analysis and random survival forest was applied to the fatty acid metabolism-related genes expression profiles to acquire prognostic models based on 7 overall survived related genes,respectively.All samples were divided into high-risk and low-risk groups based median score.We used Kaplan-Meier survival analysis to verify the survival difference between the high-risk and low-risk groups,and we used ROC curves and independent prognostic analysis to investigate the prognostic value of prognostic models.We used external dataset GSE20685 to validate prognostic model reliability.Gene set enrichment analysis(GSEA)was performed to decipher the major enriched signaling pathways and biological functions between two groups.We analyze the immune score,infiltration levels of immune cells in the high-risk and low-risk groups by Wilcoxon.We also extracted and analyzed the three common immune checkpoints’ expression matrices between two groups.Compare the AUC value between Lasso Cox hazards regression analysis model and random survival forest prediction model.We analysis all data by R and p values< 0.05 considered significant.Results:(1)Univariate Cox regression were used to filter out seven fatty acid metabolism genes(CEL、APOC3、APOA5、PTGES3、PLA2G1B、GSTM4、PSME1)related to prognosis in breast cancer.(2)Based on the Lasso Cox regression analysis method and randomized survival forest method,we constructed two 7-gene prognostic models in the training set data respectively.We confirmed that two models are indeed associated with prognosis,and the overall survival rate in high-risk group is lower than that in low-risk group.The model is verified for dependability using univariate Cox regression and multivariate Cox regression analysis.We use external dataset GSE20685 to validate accuracy from GEO dataset,and the similar results we obtained which confirm the accuracy of the prognostic models constructed.(3)To further reveal the landscape of TME we analysis the immune cell infiltration level and three common immune checkpoints’ expression level.The result showed that the patients in low-risk group had higher levels of immune cell infiltration and immune checkpoint gene expression.(4)In the training set and valuation set,we predicated 1,3,5-year survival AUC values by two models.Comparing the AUC values of two models,both can predict the survival of breast cancer patients well,and the random survival forest model is better than the Lasso cox regression analysis model.Conclusions:(1)By using seven fatty acids metabolism genes related to prognosis(CEL,APOC3,APOA5,PTGES3,PLA2G1 B,GSTM4,PSME1),we construct prognostic models based on Lasso Cox regression and random survival forest.(2)Both models could better classified patients into high-risk group and low-risk group.Compared with high-risk group,patients in the low-risk group have better prognosis.(3)Compared with high-risk group,the patients in low-risk group showed high level of immune cell infiltration and immune checkpoint gene expression,which showed that the patients in the low-risk group are sensitive to immunotherapy.(4)The predictive performance of the randomized survival forest model in the prognosis of breast cancer patients is better than of the Lasso Cox regression analysis model. |
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