DataSheet1_Predicting Differences in Treatment Response and Survival Time of Lung Adenocarcinoma Patients Based on a Prognostic Risk Model of Glycolysis-Related Genes.PDF

Background: Multiple factors influence the survival of patients with lung adenocarcinoma (LUAD). Specifically, the therapeutic outcomes of treatments and the probability of recurrence of the disease differ among patients with the same stage of LUAD. Therefore, effective prognostic predictors need to be identified.
Methods: Based on the tumor mutation burden (TMB) data obtained from The Cancer Genome Atlas (TCGA) database, LUAD patients were divided into high and low TMB groups, and differentially expressed glycolysis-related genes between the two groups were screened. The least absolute shrinkage and selection operator (LASSO) and Cox regression were used to obtain a prognostic model. A receiver operating characteristic (ROC) curve and a calibration curve were generated to evaluate the nomogram that was constructed based on clinicopathological characteristics and the risk score. Two data sets (GSE68465 and GSE11969) from the Gene Expression Omnibus (GEO) were used to verify the prognostic performance of the gene. Furthermore, differences in immune cell distribution, immune-related molecules, and drug susceptibility were assessed for their relationship with the risk score.
Results: We constructed a 5-gene signature (FKBP4, HMMR, B4GALT1, SLC2A1, STC1) capable of dividing patients into two risk groups. There was a significant difference in overall survival (OS) times between the high-risk group and the low-risk group (p < 0.001), with the low-risk group having a better survival outcome. Through multivariate Cox analysis, the risk score was confirmed to be an independent prognostic factor (HR = 2.709, 95% CI = 1.981–3.705, p < 0.001), and the ROC curve and nomogram exhibited accurate prediction performance. Validation of the data obtained in the GEO database yielded similar results. Furthermore, there were significant differences in sensitivity to immunotherapy, cisplatin, paclitaxel, gemcitabine, docetaxel, gefitinib, and erlotinib between the low-risk and high-risk groups.
Conclusion: Our results reveal that glycolysis-related genes are feasible predictors of survival and the treatment response of patients with LUAD.

研究背景：肺腺癌（lung adenocarcinoma, LUAD）患者的生存状况受多种因素影响。具体而言，即使处于同一分期的LUAD患者，其治疗效果与疾病复发概率也存在显著差异，因此亟需筛选出有效的预后预测因子。
研究方法：本研究从癌症基因组图谱（The Cancer Genome Atlas, TCGA）数据库中提取肿瘤突变负荷（tumor mutation burden, TMB）数据，将LUAD患者划分为高TMB组与低TMB组，并筛选两组间差异表达的糖酵解相关基因。采用最小绝对收缩和选择算子（least absolute shrinkage and selection operator, LASSO）与Cox回归构建预后模型。通过绘制受试者工作特征（receiver operating characteristic, ROC）曲线与校准曲线，评估基于临床病理特征与风险评分构建的列线图。从基因表达综合数据库（Gene Expression Omnibus, GEO）中获取GSE68465与GSE11969两个数据集，用以验证该基因特征的预后预测效能。此外，本研究还分析了免疫细胞分布、免疫相关分子与药物敏感性的差异，并探讨其与风险评分的关联。
研究结果：本研究构建了可将患者划分为两个风险组的5基因特征标记（FKBP4、HMMR、B4GALT1、SLC2A1、STC1）。高风险组与低风险组患者的总生存期（overall survival, OS）存在显著差异（p < 0.001），低风险组患者的生存结局更佳。通过多因素Cox分析，证实风险评分是独立的预后影响因子（风险比HR=2.709，95%置信区间CI=1.981–3.705，p < 0.001），且ROC曲线与列线图均表现出良好的预测效能。GEO数据库数据集的验证结果与之一致。此外，高低风险组对免疫治疗、顺铂、紫杉醇、吉西他滨、多西他赛、吉非替尼与厄洛替尼的敏感性均存在显著差异。
研究结论：本研究结果表明，糖酵解相关基因可作为LUAD患者生存状况与治疗反应的有效预测因子。