一种用于预测乳腺癌预后和免疫治疗疗效的新型糖酵解相关基因特征。

A novel glycolysis-related gene signature for predicting prognosis and immunotherapy efficacy in breast cancer.

作者信息

Huang Rui, Li Yi, Lin Kaige, Zheng Luming, Zhu Xiaoru, Huang Leqiu, Ma Yunhan

机构信息

Clinical Laboratory, Jinan Children's Hospital, Jinan, Shandong, China.

Clinical Laboratory, Children's Hospital Affiliated to Shandong University, Jinan, Shandong, China.

出版信息

Front Immunol. 2025 Feb 19;16:1512859. doi: 10.3389/fimmu.2025.1512859. eCollection 2025.

DOI:10.3389/fimmu.2025.1512859

PMID:40046063

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11880812/

Abstract

BACKGROUND

Previous studies have shown that glycolysis-related genes (GRGs) are associated with the development of breast cancer (BC), and the prognostic significance of GRGs in BC has been reported. Considering the heterogeneity of BC patients, which makes prognosis difficult to predict, and the fact that glycolysis is regulated by multiple genes, it is important to establish and evaluate new glycolysis-related prediction models in BC.

METHODS

In total, 170 GRGs were selected from the GeneCards database. We analyzed data from the Cancer Genome Atlas Breast Invasive Carcinoma (TCGA-BRCA) database as a training set and data from the Gene Expression Omnibus (GEO) database as a validation cohort. Based on the overall survival data and the expression levels of GRGs, Cox regression analyses were applied to develop a glycolysis-related prognostic gene (GRPGs)-based prediction model. Kaplan (KM) survival and ROC analyses were performed to assess the performance of this model. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were used to identify the potential biological functions of GRPGs. cBioPortal database was used to explore the tumor mutation burden (TMB). The tumor immune dysfunction and exclusion indicator (TIDE) was used to estimate the patient response to immune checkpoint blockade (ICB). The levels of tumor-infiltrating immune cells (TICs) and stromal cells were quantitatively analyzed based on gene expression profiles.

RESULTS

We constructed a prediction model of 10 GRPGs (ADPGK, HNRNPA1, PGAM1, PIM2, YWHAZ, PTK2, VDAC1, CS, PGK1, and GAPDHS) to predict the survival outcomes of patients with BC. Patients were divided into low- and high-risk groups based on the gene signature. The AUC values of the ROC curves were 0.700 (1-year OS), 0.714 (3-year OS), 0.681 (5-year OS). TMB and TIDE analyses showed that patients in the high-risk group might respond better to ICB. Additionally, by combining the GRPGs signature and clinical characteristics of patients, a novel nomogram was constructed. The AUC values for this combined prediction model were 0.827 (1-year OS), 0.792 (3-year OS), and 0.783 (5-year OS), indicating an outstanding predictive performance.

CONCLUSION

A new GRPGs based prediction model was built to predict the OS and immunotherapeutic response of patients with BC.

摘要

背景

既往研究表明，糖酵解相关基因（GRGs）与乳腺癌（BC）的发生发展相关，且已报道了GRGs在BC中的预后意义。考虑到BC患者的异质性使得预后难以预测，以及糖酵解受多个基因调控这一事实，建立并评估BC中新的糖酵解相关预测模型具有重要意义。

方法

从GeneCards数据库中总共选取了170个GRGs。我们分析了来自癌症基因组图谱乳腺浸润性癌（TCGA-BRCA）数据库的数据作为训练集，并分析了来自基因表达综合数据库（GEO）的数据作为验证队列。基于总生存数据和GRGs的表达水平，应用Cox回归分析来建立基于糖酵解相关预后基因（GRPGs）的预测模型。进行Kaplan（KM）生存分析和ROC分析以评估该模型的性能。使用基因本体（GO）和京都基因与基因组百科全书（KEGG）分析来鉴定GRPGs的潜在生物学功能。使用cBioPortal数据库探索肿瘤突变负担（TMB）。使用肿瘤免疫功能障碍和排除指标（TIDE）来估计患者对免疫检查点阻断（ICB）的反应。基于基因表达谱对肿瘤浸润免疫细胞（TICs）和基质细胞的水平进行定量分析。

结果

我们构建了一个由10个GRPGs（ADPGK、HNRNPA1、PGAM1、PIM2、YWHAZ、PTK2、VDAC1、CS、PGK1和GAPDHS）组成的预测模型，以预测BC患者的生存结局。根据基因特征将患者分为低风险组和高风险组。ROC曲线的AUC值分别为0.700（1年总生存）、0.714（3年总生存）、0.681（5年总生存）。TMB和TIDE分析表明，高风险组患者可能对ICB反应更好。此外，通过结合GRPGs特征和患者的临床特征，构建了一个新的列线图。该联合预测模型的AUC值分别为0.827（1年总生存）、0.792（3年总生存）和0.783（5年总生存），表明具有出色的预测性能。