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Updated: Jul 9, 2026
Predictive Immune Modeling of Solid Tumors
Published on: February 25, 2020
Glycolytic Genes Predict Immune Status and Prognosis Non-Small-Cell Lung Cancer Patients with Radiotherapy and
Tianye Zhao1,2, Jingjing Shao1,3, Jia Liu1,2
1Nantong University Medical College, 226006, China.
Background:
Non-small-cell lung cancer (NSCLC) is a major health problem that endangers human health. The prognosis of radiotherapy or chemotherapy is still unsatisfactory. This study is aimed at investigating the predictive value of glycolysis-related genes (GRGs) on the prognosis of NSCLC patients with radiotherapy or chemotherapy.
Methods:
Download the clinical information and RNA data of NSCLC patients receiving radiotherapy or chemotherapy from TCGA and geo databases and obtain GRGs from MsigDB. The two clusters were identified by consistent cluster analysis, the potential mechanism was explored by KEGG and GO enrichment analyses, and the immune status was evaluated by estimate, TIMER, and quanTIseq algorithms. Lasso algorithm is used to build the corresponding prognostic risk model.
Results:
Two clusters with different GRG expression were identified. The high-expression subgroup had poor overall survival. The results of KEGG and GO enrichment analyses suggest that the differential genes of the two clusters are mainly reflected in metabolic and immune-related pathways. The risk model constructed with GRGs can effectively predict the prognosis. The nomogram combined with the model and clinical characteristics has good clinical application potential.
Conclusion:
In this study, we found that GRGs are associated with tumor immune status and can assess the prognosis of NSCLC patients receiving radiotherapy or chemotherapy.
