Table_2_Novel macrophage-related gene prognostic index for glioblastoma associated with M2 macrophages and T cell dysfunction.xlsx

This study aims to construct a Macrophage-Related Gene Prognostic Index (MRGPI) for glioblastoma (GBM) and explore the underlying molecular, metabolic, and immunological features. Based on the GBM dataset from The Cancer Genome Atlas (n = 156), 13 macrophage-related hub genes were identified by weighted gene co-expression network (WGCNA) analysis. 5 prognostic genes screened by Kaplan-Meire (K-M) analysis and Cox regression model were used to construct the MRGPI, including GPR84, NCF2, HK3, LILRB2, and CCL18. Multivariate Cox regression analysis found that the MRGPI was an independent risk factor (HR = 2.81, CI95: 1.13-6.98, p = 0.026), leading to an unfavorable outcome for the MRGPI-high group, which was further validated by 4 validation GBM cohorts (n = 728). Thereafter, the molecular, metabolic, and immune features and the clinical implications of the MRGPI-based groups were comprehensively characterized. Gene set enrichment analysis (GSEA) found that immune-related pathways, including inflammatory and adaptive immune response, and activated eicosanoid metabolic pathways were enriched in the MRGPI-high group. Besides, genes constituting the MRGPI was primarily expressed by monocytes and macrophages at single-cell scope and was associated with the alternative activation of macrophages. Moreover, correlation analysis and receiver operating characteristic (ROC) curves revealed the relevance between the MRGPI with the expression of immune checkpoints and T cell dysfunction. Thus, the responsiveness of samples in the MRGPI-high group to immune checkpoint inhibitors (ICI) was detected by algorithms, including Tumor Immune Dysfunction and Exclusion (TIDE) and Submap. In contrast, the MRGPI-low group had favorable outcome, was less immune active and insensitive to ICI. Together, we have developed a promising biomarker to classify the prognosis, metabolic and immune features for GBM, and provide references for facilitating the personalized application of ICI in GBM.

本研究旨在构建一种针对胶质母细胞瘤（GBM）的巨噬细胞相关基因预后指数（MRGPI），并探究其背后的分子、代谢和免疫学特征。基于来自癌症基因组图谱（TCGA）的GBM数据集（n = 156），通过加权基因共表达网络分析（WGCNA）确定了13个与巨噬细胞相关的枢纽基因。通过Kaplan-Meier（K-M）分析和Cox回归模型筛选出的5个预后基因，包括GPR84、NCF2、HK3、LILRB2和CCL18，被用于构建MRGPI。多变量Cox回归分析发现，MRGPI是一个独立的危险因素（HR = 2.81，CI95%: 1.13-6.98，p = 0.026），导致MRGPI高表达组的预后不良，这一发现通过4个验证GBM队列（n = 728）得到了进一步验证。随后，对基于MRGPI的组的分子、代谢和免疫学特征及其临床意义进行了全面表征。基因集富集分析（GSEA）发现，包括炎症和适应性免疫反应在内的免疫相关通路，以及活化的二十碳代谢途径在MRGPI高表达组中富集。此外，构成MRGPI的基因主要在单细胞水平上由单核细胞和巨噬细胞表达，并与巨噬细胞的替代激活相关。此外，相关性分析和受试者工作特征（ROC）曲线揭示了MRGPI与免疫检查点表达和T细胞功能障碍之间的相关性。因此，通过TIDE和Submap等算法检测了MRGPI高表达组样本对免疫检查点抑制剂（ICI）的敏感性。相反，MRGPI低表达组预后良好，免疫活性较低，对ICI不敏感。综上所述，本研究开发了一种有前景的生物标志物，用于对GBM的预后、代谢和免疫学特征进行分类，并为促进ICI在GBM中的个性化应用提供参考。