目的:建立基于乳腺癌脂肪酸代谢(fatty acid metabolism,FAM)相关基因的预后模型,并对乳腺癌的FAM模型进行评估预后。方法:整理乳腺癌单细胞RNA测序(single-cell RNA sequencing,scRNA-seq)数据集,鉴定出乳腺上皮肿瘤细胞,通过表达水平曲线下面积(area under the curve for expression level,AUCell)分析得出乳腺癌FAM在不同肿瘤细胞内的活化程度,再与加权基因共表达分析(weighted gene co-expression network analysis,WGCNA)结果取交集得到乳腺癌FAM相关差异基因。通过单因素和多因素Cox回归建立767例患者的预后风险模型。多指标受试者工作特征曲线(receiver operating characteristic,ROC),决策分析曲线(decision analysis curve,DAC),一致性指数(concordance index,C-index)用于评估模型的准确性。最后使用GSE3143数据集验证。结果:单细胞组学分析发现597个差异基因,单变量和多变量Cox回归确定了12个与OS相关的风险相关基因:FOXQ1、TFPI2、MACC1、ACOT7、MCEE、SLC27A2、QPRT、SLC2A1、ACAA1、NDRG1、KYNU、YOD1。多指标ROC分析,DAC以及C-index均提示该模型预测准确率较高。结论:基于12个乳腺癌FAM相关基因预后模型具有一定的准确性,可更好地用于指导临床治疗。
Objective:To establish a prognostic model of fatty acid metabolism (FAM) for breast cancer based on FAM related genes, and to evaluate the prognosis of the FAM model. Methods: Breast cancer single-cell RNA sequencing (scRNA-seq) data set was sorted out to identify breast epithelial tumor cells, and the activation degree of breast cancer FAM in different tumor cells was obtained using area under the curve for expression level (AUCell) analysis. The weighted gene co-expression network analysis (WGCNA) was used to obtain FAM-related differential genes in breast cancer. The prognostic risk model of 767 patients was established using univariate and multivariate COX regression. Receiver operating characteristic curve (ROC), decision analysis curve (DAC) and concordance index (C-index) were used to evaluate the accuracy of the model. Finally, the GSE3143 dataset was used for validation. Results: A total of 597 differential genes were identified by single-cell transcriptomic analysis, and 12 risk-related genes associated with OS were identified by univariate and multivariate COX regression, which were FOXQ1, TFPI2, MACC1, ACOT7, MCEE, SLC27A2, QPRT, SLC2A1, ACAA1, NDRG1, KYNU and YOD1. Multi-index ROC analysis, DAC and C-index indicated that the model had high prediction accuracy. Conclusion: The prognostic model based on 12 FAM-related genes for breast cancer has certain accuracy, which could be used to guide clinical treatment more accurately.
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