通过基因聚类发现跨癌种免疫特征，助力精准免疫治疗决策。
Pan-Cancer Mapping of the Tumor Immune Landscape through Metagene Clustering and Predictive Modeling
- 基于批量RNA测序数据，用统计与聚类方法挖掘48个免疫相关基因簇（metagenes）
- metagenes预测肿瘤免疫亚型准确率达87%，且多数与生存率显著相关
- 揭示了免疫激活、细胞周期与免疫逃逸等关键机制，适合癌症研究者参考
随着免疫疗法成为标准治疗手段，识别患者的免疫特征（包括肿瘤微环境中免疫细胞的活性及特定生物标志物的存在）变得日益重要。然而，驱动免疫表型的机制仍不明确。本研究利用涵盖多种癌种的批量RNA测序数据（来自TCGA），构建了免疫亚型，并通过ANOVA与高斯混合模型（GMM）对基因进行表达筛选与聚类，识别出48个独特的代谢基因簇（metagenes）。这些metagenes在预测已知免疫亚型上达到87%的准确率。SHAP分析揭示了各亚型最具预测性的metagenes，功能富集分析确定了其关联通路。差异表达分析进一步发现，免疫激活与调节因子共表达、细胞周期调控与免疫逃逸存在关联，以及微环境动态重塑的信号。生存分析显示，多个metagenes具有整体生存预后价值。结果表明，metagenes代表了多种癌种中协调一致的生物学程序，为开发超越单基因的广泛适用免疫肿瘤生物标志物奠定了基础，具备指导免疫治疗决策的潜力。
原文摘要 · Abstract (English)
As immunotherapies become standard cancer treatments, it is increasingly important to identify a patient's immune profile, which encompasses the activity of immune cells within the tumor microenvironment and the presence of specific biomarkers. However, we lack mechanistic explanations drivers of immune phenotypes. Despite advances in immune profiling with high-throughput sequencing, the mechanisms driving them remain unclear. This study aimed to identify novel, robust immune-related gene clusters (metagenes) and evaluate their prognostic significance and functional relevance across various pan-cancer types using a comprehensive computational pipeline. We acquired pan-cancer bulk RNA-seq and established immune subtypes from The Cancer Genome Atlas (TCGA). Using expression-based filtering and clustering of genes with ANOVA and Gaussian Mixture Model (GMM), we identified 48 unique metagenes. These metagenes achieved 87% accuracy in predicting the established subtypes. SHAP analysis revealed the most predictive metagenes per subtype, while functional enrichment analysis identified their associated pathways. Genes were ranked by differential expression between high- and low-expression groups. The metagenes revealed insights, including co-expression of immune activation and regulatory factors, links between cell cycle regulation and immune evasion, and dynamic microenvironment remodeling signatures. Kaplan-Meier survival analysis and multivariate Cox Regression revealed that many metagenes had prognostic value for overall survival. Overall, the metagenes represent coordinated biological programs across diverse cancer types, providing a foundation for developing robust, broadly applicable immuno-oncology biomarkers that extend beyond single-gene markers. They demonstrate prognostic value across cancer types and hold potential to guide immunotherapy treatment decisions.
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