脂肪酸代谢相关基因预后模型在肾透明细胞癌中的应用

Application of prognostic model of fatty acid metabolism related genes in renal cell carcinoma

目的 探索基于脂肪酸代谢相关基因的预后模型在肾透明细胞癌患者中应用的可行性。方法 下载TCGA数据库中肾透明细胞癌组织及癌旁组织mRNA表达矩阵,R语言筛选差异表达的脂肪酸代谢相关基因,结合患者生存数据,利用多元Cox比例风险回归模型建立脂肪酸代谢相关多基因预后模型,计算患者的风险评分(risk score, RS),按RS的中位数将患者分为高、低风险组,利用Kaplan-Meier生存曲线分析高、低风险组之间患者的生存差异。将RS与临床病理因素纳入Cox回归分析,验证RS是否为影响患者生存的独立危险因素。受试者工作特征(ROC)曲线评估基于脂肪酸代谢相关基因的预测模型的准确性。比较RS及其相关基因在各临床病理因素分组中的差异。GSEA富集分析比较筛选高、低风险组中的KEGG差异基因集。结果 共筛选出4个差异基因(CPT1B、HADH、CYP4A11、ACADSB)建立肾癌脂肪酸代谢相关基因预后模型。预后风险评分(RS)公式为:RS=0.490×CPT1B-0.428×HADH-0.11×CYP4A11-0.372×ACADSB。低风险组的患者预后较好(P<0.001)。RS是影响肾透明细胞癌患者预后的独立危险因素(P<0.01)。基于肾透明细胞癌脂肪酸代谢相关基因模型计算的RS的5年生存ROC曲线AUC为0.802。GSEA分析筛选出在低风险组81个差异基因(FDR<0.25,normalizedP<0.05)。结论 肾癌脂肪酸代谢相关多基因预后模型能够比较准确预测肾透明细胞癌患者的预后。

Objective To establish a prognostic model of fatty acid metabolism related genes for predicting the prognosis of renal clear cell carcinoma. Methods The differentially expressed fatty acid metabolism-related genes in renal clear cell carcinoma samples and normal samples in TCGA database were screened by R language software. The Cox proportional hazard regression model was used to select and establish a multigene prognostic model and the prognostic score was calculated. Patients were divided into high-risk group and low-risk group according to the median prognostic score. Kaplan-Meier survival curve was used to analyze the difference in two groups. The clinical pathological factors and prognostic score factors were included in the Cox regression model to analyze the factors affecting the survival of patients with renal clear cell carcinoma. ROC receiver operating curve analysis was used to evaluate the accuracy of the prognostic prediction model. The prognostic model of fatty acid metabolism-related genes and their correlation with clinical factors were analyzed. GSEA enrichment analysis analyzed the differences of gene sets in risk groups. Results A total of 4 differential genes(CPT1B, HADH, CYP4A11, and ACADSB) were selected to establish a prognostic model for genes related to fatty acid metabolism in renal cell carcinoma. The prognostic risk score(RS) formula is as follows: RS=0.490×CPT1B-0.428×HADH-0.11 × CYP4A11-0.372 × ACADSB. Kaplan-Meier survival analysis confirmed that the overall survival rate of patients with low-risk prognostic score was significantly higher in patients with overall renal clear cell carcinoma, and the difference was statistically significant(P<0.001). Cox regression analysis showed that the prognostic model of genes related to age and fatty acid metabolism is an independent influencing factor for the prognosis of patients with renal clear cell carcinoma(P<0.01).The 5 years’ AUC of the renal clear cell carcinoma ROC curve of the renal cancer fatty acid metabolism related gene model was 0.802.GSEA analysis showed that the difference of 81gene sets in the low-risk group was statistically significant(P<0.05).Conclusion The prognostic model of renal cancer fatty acid metabolism-related genes can be used to predict the prognosis of patients with renal clear cell carcinoma,which is conducive to further guide clinical treatment.