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目的 探讨基于癌症基因组图谱（TCGA）数据库，构建子宫内膜癌（EC）乳酸代谢相关基因预后风险模型，筛选关键生物标志物并验证其临床意义。方法 从TCGA项目中的子宫体子宫内膜癌（TCGA-UCEC）获取了35例正常子宫内膜组织和546例EC组织的转录组、临床及生存数据。将这546例癌症组织样本按1∶1比例随机分为训练集和验证集各273例。通过比较正常组织与训练队列中癌组织的转录组数据，鉴定出差异表达基因，并将其与分子信号数据库中的344个乳酸代谢相关基因取交集，将共有基因定义为后续分析的候选基因集。在此基础上，先通过单变量Cox回归分析初步筛选与预后相关的基因，再采用最小绝对收缩与选择算子（LASSO）-Cox回归进一步降维，构建出最终的风险评分模型。根据风险评分中位数将患者分为高危组与低危组，采用Wilcoxon秩和检验与卡方检验比较高危组与低危组间临床特征的差异，并通过Kaplan-Meier生存分析和Log-rank检验评估组间生存差异。此外，利用oncoPredict R包预测并比较高、低危组间对常用化疗药物的敏感性（以半数抑制浓度值衡量）。结果 从TCGA-UCEC数据库中筛选出7 008个差异表达基因。进一步聚焦于乳酸代谢相关基因，通过单变量及LASSO-Cox回归分析，构建了一个包含溶质载体家族16成员1(SLC16A1)、GATA结合蛋白2(GATA2)和间变性淋巴瘤激酶（ALK）3个基因的预后风险模型。结果显示高危组患者年龄更大、国际妇产科联盟分期更晚（均P<0.05）。生存分析证实，与低危组患者相比，高危组患者的总生存期在训练集（P<0.001）和验证集（P=0.034）中均显著缩短。药物敏感性分析进一步提示，低危组对紫杉醇、多西他赛和环磷酰胺的敏感性更高（均P<0.05），而两组对铂类药物及拓扑替康敏感性的差异无统计学意义（P>0.05）。结论 基于乳酸代谢相关基因构建的3基因（SLC16A1、GATA2和ALK）风险模型可有效预测EC预后，并为个性化治疗提供理论依据。
Abstract：Objective To construct a prognostic risk model based on lactate metabolism-related genes using The Cancer Genome Atlas(TCGA) database,and to identify key biomarkers while validating their clinical significance in endometrial carcinoma(EC).Methods Transcriptionic,clinical,and survival data from 35 normal endometrial tissues and 546 EC tissues were obtained from the Uterine Corpus Endometrial Carcinoma(UCEC) project of TCGA.The 546 cancer tissue samples were randomly divided into a training set and a validation set at a 1:1 ratio.By comparing transcriptionic data between normal tissues and the cancer tissues in the training cohort,differentially expressed genes were identified and intersected with 344 lactate metabolismrelated genes from the Molecular Signatures Database to derive a candidate gene set.Subsequently,univariate Cox regression analysis was initially used to screen for prognosis-related genes,followed by Least Absolute Shrinkage and Selection Operator(LASSO)-Cox regression for further dimensionality reduction,ultimately constructing a final risk score model.Patients were stratified into high-risk and low-risk groups based on the median risk score from the model.Differences in clinical characteristics between the high-risk and low-risk groups were compared using the Wilcoxon rank-sum test and the chi-square test,while survival differences between the groups were assessed via Kaplan-Meier survival analysis and the log-rank test.Additionally,the oncoPredict R package was employed to predict and compare the sensitivity to commonly used anticancer drugs,measured by the half-maximal inhibitory concentration value,between the high-risk and low-risk groups.Results A total of 7 008 differentially expressed genes were identified from the TCGA-UCEC database.Focusing on lactate metabolism-related genes,a prognostic risk model comprising three genes,solute carrier family 16 member 1(SLC16A1),GATA binding protein 2(GATA2),and anaplastic lymphoma kinase(ALK),was constructed using univariate and LASSO-Cox regression analyses.The results showed that patients in the high-risk group were older and had more advanced FIGO stages(all P<0.05).Survival analysis confirmed that,compared to the low-risk group,patients in the high-risk group had significantly shorter overall survival in both the training set(P<0.001) and the validation set(P=0.034).Drug sensitivity analysis further revealed that the low-risk group exhibited higher sensitivity to paclitaxel,docetaxel,and cyclophosphamide(all P<0.05),whereas no statistically significant differences in sensitivity to platinum-based drugs or topotecan were observed between the two groups(P>0.05).Conclusion The threegene risk model,comprising SLC16A1,GATA2,and ALK,establishes lactate metabolism as a key predictor of prognosis in endometrial carcinoma and lays the groundwork for personalized treatment strategies.
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基本信息:
中图分类号:R737.33
引用信息:
[1]张午洋,韦炜.基于乳酸代谢基因的子宫内膜癌预后模型的构建与验证[J].临床肿瘤学杂志,2025,30(10):964-970.
基金信息:
国家自然科学基金(82271991)