A novel metabolic gene signature-based nomogram to predict overall survival in breast cancer.
A novel metabolic gene signature-based nomogram to predict overall survival in breast cancer.
复制标题
一种基于代谢基因特征的列线图来预测乳腺癌的总体生存率
DOI：
10.21037/atm-20-4813
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发表时间：
2021-03
影响因子：
--
通讯作者：
Shen K
中科院分区：
文献类型：
--
作者：
Sun X;Zhou ZR;Fang Y;Ding S;Lu S;Wang Z;Wang H;Chen X;Shen K
背景乳腺癌风险预测通常基于临床病理特征，尽管基因表达具有高度异质性。代谢改变是癌症的标志，因此，代谢特征与临床参数的整合对于预测乳腺癌的疾病结果是必要的。方法从基因集富集分析（GSEA）数据集中下载代谢基因。在单变量分析中具有统计学显著性的基因被应用于最小绝对收缩和选择算子（LASSO）分析以在GSE 20685数据集中构建基因签名。将临床病理特征和具有预后意义的风险评分纳入诺模图，以预测患者的总生存期（OS）。使用癌症基因组图谱（TCGA）和GSE 866166数据集作为验证数据集。使用时间依赖性受试者工作特征（tROC）曲线和校准图评估模型的准确性和区分度。结果构建了一个包含55个基因的代谢基因签名（MGS），在发现（P <0.001）和验证（P <0.001）数据集中，MGS与OS显著相关。MGS是一个独立的预后因素，可以将患者分为高风险组和低风险组，而不管他们对微阵列50（PAM 50）亚型的不同预测分析。时间依赖性ROC曲线显示基于MGS的风险评分[ROC曲线下面积（AUC）：0.931]优于基于美国癌症联合委员会（AJCC）分期（AUC：0.781）和PAM 50（AUC：0.675）的风险评分。基于AJCC分期和风险评分的诺模图可以预测OS，并且校准曲线与实际结果显示出良好的一致性，表明该诺模图可能具有实用价值。京都基因和基因组百科全书（KEGG）和基因本体论（GO）分析表明该MGS主要富集在氨基酸途径中。结论MGS评分优于PAM50和AJCC分期等现有的危险预测指标，具有上级意义。结合临床因素（AJCC分期）和MGS，构建了一个诺模图，并显示出良好的预测能力，乳腺癌的OS。
Background Breast cancer risk prediction is often based on clinicopathological characteristics despite the high heterogeneity derived from gene expression. Metabolic alteration is a hallmark of cancer, and thus, the integration of a metabolic signature with clinical parameters is necessary to predict disease outcomes in breast cancers. Methods Metabolic genes were downloaded from the Gene Set Enrichment Analysis (GSEA) dataset. Genes with statistical significance in the univariate analysis were applied in the least absolute shrinkage and selection operator (LASSO) analysis to build a gene signature in the GSE20685 dataset. Clinicopathological characteristics and risk scores with prognostic significance were incorporated into the nomogram to predict the overall survival (OS) of patients. The Cancer Genome Atlas (TCGA) and GSE866166 datasets were used as the validation datasets. Time-dependent receiver operating characteristic (tROC) curves and calibration plots were used to assess the accuracy and discrimination of the model. Results A 55-gene metabolic gene signature (MGS) was constructed, and was significantly related to OS both in the discovery (P<0.001) and validation (P<0.001) datasets. The MGS was an independent prognostic factor and could divide patients into high- and low-risk groups regardless of their different prediction analysis of microarray 50 (PAM50) subtypes. Time-dependent ROC curves indicated that the risk scores based on the MGS [area under the ROC curve (AUC): 0.931] were superior to the those based on the American Joint Committee on Cancer (AJCC) stage (AUC: 0.781) and PAM50 (AUC: 0.675). A nomogram based on the AJCC stage and risk score could predict OS, and the calibration curves showed good agreement to the actual outcome, indicating that the nomogram may have practical utility. Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analysis indicated that this MGS was primarily enriched in amino acid pathways. Conclusions Our results demonstrated that the MGS was superior to existing risk predictors such as PAM50 and AJCC stage. By combining clinical factors (AJCC stage) and the MGS, a nomogram was constructed and showed good predictive ability for OS in breast cancer.
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作者：
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