Glycolysis affects tumor growth, invasion, chemotherapy resistance, and the tumor microenvironment. In this study, we aimed to construct a glycolysis-related prognostic model for ovarian cancer and analyze its relationship with the tumor microenvironment’s immune cell infiltration. We obtained six glycolysis-related gene sets for gene set enrichment analysis (GSEA). Ovarian cancer data from The Cancer Genome Atlas (TCGA) database and two Gene Expression Omnibus (GEO) datasets were divided into two groups after removing batch effects. We compared the tumor environments' immune components in high-risk and low-risk groups and analyzed the correlation between glycolysis- and immune-related genes. Then, we generated and validated a predictive model for the prognosis of ovarian cancer using the glycolysis-related genes. Overall, 27/329 glycolytic genes were associated with survival in ovarian cancer, 8 of which showed predictive value. The tumor cell components in the tumor microenvironment did not differ between the high-risk and low-risk groups; however, the immune score differed significantly between groups. In total, 13/24 immune cell types differed between groups, including 10 T cell types and three other immune cell types. Eight glycolysis-related prognostic genes were related to the expression of multiple immune-related genes at varying degrees, suggesting a relationship between glycolysis and immune response. We identified eight glycolysis-related prognostic genes that effectively predicted survival in ovarian cancer. To a certain extent, the newly identified gene signature was related to the tumor microenvironment, especially immune cell infiltration and immune-related gene expression. These findings provide potential biomarkers and therapeutic targets for ovarian cancer.

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卵巢癌新型糖酵解相关预后基因特征的建立及其与肿瘤微环境免疫浸润的关系

糖酵解影响肿瘤生长、侵袭、化疗耐药性和肿瘤微环境。本研究旨在构建与糖酵解相关的卵巢癌预后模型，并分析其与肿瘤微环境免疫细胞浸润的关系。我们获得了六个糖酵解相关基因集用于基因集富集分析（GSEA）。去除批次效应后，来自癌症基因组图谱 (TCGA) 数据库和两个基因表达综合 (GEO) 数据集的卵巢癌数据分为两组。我们比较了高危组和低危组肿瘤环境的免疫成分，并分析了糖酵解和免疫相关基因之间的相关性。然后，我们使用糖酵解相关基因生成并验证了卵巢癌预后的预测模型。总体，27/329 糖酵解基因与卵巢癌的生存相关，其中 8 个显示出预测价值。肿瘤微环境中的肿瘤细胞成分在高危组和低危组之间没有差异；然而，免疫评分在各组之间存在显着差异。总共有 13/24 种免疫细胞类型在各组之间存在差异，包括 10 种 T 细胞类型和三种其他免疫细胞类型。8个糖酵解相关预后基因与多个免疫相关基因的表达不同程度相关，提示糖酵解与免疫反应之间存在关系。我们确定了八种与糖酵解相关的预后基因，可有效预测卵巢癌的存活率。在一定程度上，新发现的基因特征与肿瘤微环境有关，尤其是免疫细胞浸润和免疫相关基因表达。这些发现为卵巢癌提供了潜在的生物标志物和治疗靶点。