兰州大学学报（医学版）2025，Vol.51Issue(3)：21-29,9.DOI:10.13885/j.issn.1000-2812.2025.03.004
基于胰腺癌糖酵解异常构建LASSO-Cox预后模型
Constructing a LASSO-Cox prognostic model based on aberrant glycolysis in pancreatic cancer
摘要
Abstract
Objectives A prognostic model for pancreatic cancer based on glycolysis-related genes will be con-structed to aid in clinical risk stratification and personalized treatment.Methods Based on the TCGA and GTEx databases,the intersection of the upregulated genes in pancreatic cancer and the genes in the glycolysis pathway was taken to obtain the glycolysis gene set.The 178 patients in the TCGA-PAAD dataset were divid-ed into a training set and a validation set.The former was used to construct a Cox risk model for the glycolysis gene set.LASSO regression was applied to screen genes to prevent overfitting,and a multivariate Cox regres-sion was used to determine the final prognostic model.Risk curves,survival curves,and receiver operating characteristic curves were plotted to evaluate the prognostic prediction performance of the model.Functional enrichment analysis,as well as Kyoto Encyclopedia of Genes and Genomes and Gene Ontology analyses,were conducted to explore the tumor biological mechanisms involved in the model.Results Differential anal-ysis showed that 5 542 genes were significantly upregulated in pancreatic cancer tissues.The correlation analysis indicated that 7 752 genes were closely related to glycolytic metabolism.By taking the intersection,a glycolytic gene set containing 3 092 genes was obtained.LASSO regression analysis determined that 12 characteristic genes contributed the most to the prognosis of patients.A prognostic model containing 8 glycol-ysis-related genes was fitted,and the patients were grouped according to the median risk score in the training set.The risk curve showed that the higher the risk score,the more frequent the outcome events.The Kaplan-Meier survival curve demonstrated that the prognosis of the high-risk group was worse.The receiver operating characteristic curve confirmed that the model had a good ability in predicting the long-term survival of patients.Enrichment analysis showed that the classical tumor signaling and immune-related pathways were significantly enriched in the high-risk group.Mutation analysis revealed that the risk score was related to the frequency of gene mutations.Conclusion The prognostic model constructed in this study based on glycolysis-related genes can be effectively applied for assessing long-term survival outcomes in pancreatic cancer patients,demonstrating good predictive efficacy.关键词
胰腺癌/糖酵解/代谢重编程/预后/预测模型Key words
pancreatic cancer/glycolysis/metabolic reprogramming/prognosis/forecasting model分类
临床医学引用本文复制引用
李文嘉,杜岩,李昕,周文策..基于胰腺癌糖酵解异常构建LASSO-Cox预后模型[J].兰州大学学报（医学版）,2025,51(3):21-29,9.基金项目
国家自然科学基金资助项目(82260555) （82260555）
甘肃省科技重大专项资助项目(22ZD6FA021-4) （22ZD6FA021-4）
甘肃省拔尖领军人才资助项目[(2023)9] （2023）
甘肃省青年科技基金资助项目(24JRRA374) （24JRRA374）
