PDF(Pubmed)
Abstract：
Lung cancer is one of the most dangerous malignant tumors affecting human health. Lung adenocarcinoma (LUAD) is the most common subtype of lung cancer. Both glycolytic and cholesterogenic pathways play critical roles in metabolic adaptation to cancer. A dataset of 585 LUAD samples was downloaded from The Cancer Genome Atlas database. We obtained co-expressed glycolysis and cholesterogenesis genes by selecting and clustering genes from Molecular Signatures Database v7.5. We compared the prognosis of different subtypes and identified differentially expressed genes between subtypes. Predictive outcome events were modeled using machine learning, and the top 9 most important prognostic genes were selected by Shapley additive explanation analysis. A risk score model was built based on multivariate Cox analysis. LUAD patients were categorized into four metabolic subgroups: cholesterogenic, glycolytic, quiescent, and mixed. The worst prognosis was the mixed subtype. The prognostic model had great predictive performance in the test set. Patients with LUAD were effectively typed by glycolytic and cholesterogenic genes and were identified as having the worst prognosis in the glycolytic and cholesterogenic enriched gene groups. The prognostic model can provide an essential basis for clinicians to predict clinical outcomes for patients. The model was robust on the training and test datasets and had a great predictive performance.
摘要：
肺癌是影响人类健康最危险的恶性肿瘤之一。肺腺癌(LUAD)是肺癌最常见的亚型。糖酵解和胆固醇生成途径在癌症的代谢适应中起关键作用。从癌症基因组图谱数据库下载585个LUAD样品的数据集。通过从分子特征数据库v7.5中选择和聚类基因，我们获得了共表达的糖酵解和胆固醇生成基因。我们比较了不同亚型的预后，并确定了亚型之间的差异表达基因。预测结果事件使用机器学习建模，并通过Shapley加性解释分析选择了前9个最重要的预后基因。建立基于多变量Cox分析的风险评分模型。LUAD患者分为四个代谢亚组：糖酵解,静止,和混合。预后最差的是混合亚型。预后模型在测试集中具有很好的预测性能。LUAD患者通过糖酵解和胆固醇生成基因有效分型，并在糖酵解和胆固醇生成富集基因组中被确定为预后最差。该预后模型可以为临床医生预测患者的临床结局提供必要的依据。该模型在训练和测试数据集上具有鲁棒性，并具有良好的预测性能。
