| ObjectiveAlthough there is a well-developed system for diagnosing and treating breast cancer,many patients still experience drug resistance or recurrence even after getting recommended care.Therefore,personalized and accurate breast cancer treatment protocols are the focus of current research.The occurrence,development and metastasis of breast cancer are closely related to lipid metabolism.LncRNA are non-coding RNA with more than 200 nucleotide fragments,and many of them are specifically high expressed in breast cancer and are associated with the typing,staging and drug resistance of breast cancer.It has been demonstrated that lncRNA have important research significance in lipid metabolism regulated breast cancer.Therefore,the construction of lncRNA prognostic models for breast cancer associated with lipid metabolism genes is a new research direction to improve the prognosis of breast cancer patients.MethodDifferential analysis of gene expression data sets related to lipid metabolism was performed.lncRNA was extracted from the sorted breast cancer gene expression data set.Next,lncRNA genes related to lipid metabolism and prognosis were screened to establish the lncRNA gene expression data set related to the study.The target genes of the model were screened by univariate and multivariate COX regression analysis,and the prognostic model was established by risk scoring formula.In addition,all the samples were divided into training set and validation set.According to the median risk score,the samples are divided into high-risk group and low-risk group.The independence of the model was verified by univariate COX regression analysis and multivariate COX regression analysis.Multifactor ROC curve was drawn to judge the predictive ability of the model.PCA principal component analysis was used to evaluate the differences between the two groups.Gene expression analysis,survival state analysis,survival curve analysis,and time-dependent ROC curve analysis of 1,3and 5 years were performed for the two groups in all samples,training set and verification set.The prognostic model was analyzed by GO and KEGG.The statistical differences of immune cell infiltration,immune pathway,immune checkpoint,drug sensitivity,targeted drug gene and tumor stem cell index between the high-risk group and low-risk group were compared to analyze the practical significance of the prognostic model in clinical application.Results334 lipid metabolizing lncrnas were obtained from the gene expression matrix of TCGA breast cancer.Through univariate and multivariate COX regression analysis,7types of lncrnas that constitute prognostic models were identified: AC004832.4,AC073896.3,AL356740.1,PRR34-AS1,AL359752.1,AL606834.2,AC007228.1.Meanwhile,a training set of 324 samples and a verification set of 323 samples were constructed.After the survival correlation coefficients of all samples were calculated,the formula for calculating the risk score of the prognosis model was obtained.Statistical analysis showed that this prognostic model was independent and better than the existing evaluation criteria.PCA analysis showed that the model group was better,and the overall prognosis of the low-risk group was better than that of the high-risk group.The high and low risk score groups showed significant statistical differences in immune cell infiltration,immune pathways,immune checkpoints,drug sensitivity,targeted drug genes,and tumor stem cell index,which indicated broad clinical application prospects.ConclusionBy analyzing lncRNA associated with lipid metabolism in breast cancer,this study constructed a new prognostic model based on seven genes: AC004832.4,AC073896.3,AL356740.1,PRR34-AS1,AL359752.1,AL606834.2,and AC007228.1.In the future,combined with prospective validation,it may improve the predictive accuracy and guide individual specifc therapy for patients with breast cancer. |
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