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The prognosis of radiotherapy or chemotherapy is still unsatisfactory. This study is aimed at investigating the predictive value of glycolysis\u2010related genes (GRGs) on the prognosis of NSCLC patients with radiotherapy or chemotherapy. <jats:italic>Methods<\/jats:italic>. 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. <jats:italic>Results<\/jats:italic>. Two clusters with different GRG expression were identified. The high\u2010expression 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\u2010related 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. <jats:italic>Conclusion<\/jats:italic>. 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.<\/jats:p>","DOI":"10.1155\/2023\/4019091","type":"journal-article","created":{"date-parts":[[2023,4,17]],"date-time":"2023-04-17T22:10:16Z","timestamp":1681769416000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Glycolytic Genes Predict Immune Status and Prognosis Non\u2010Small\u2010Cell Lung Cancer Patients with Radiotherapy and 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