Table 2_Integrative analysis of single-cell transcriptomic and multilayer signaling networks in glioma reveal tumor progression stage.xlsx

IntroductionTumor microenvironments (TMEs) encompass complex ecosystems of cancer cells, infiltrating immune cells, and diverse cell types. Intercellular and intracellular signals within the TME significantly influence cancer progression and therapeutic outcomes. Although computational tools are available to study TME interactions, explicitly modeling tumor progression across different cancer types remains a challenge.
MethodsThis study introduces a comprehensive framework utilizing single-cell RNA sequencing (scRNA-seq) data within a multilayer network model, designed to investigate molecular changes across glioma progression stages. The heterogeneous, multilayered network model replicates the hierarchical structure of biological systems, from genetic building blocks to cellular functions and phenotypic manifestations.
ResultsApplying this framework to glioma scRNA-seq data allowed complex network analysis of different cancer stages, revealing significant ligand‒receptor interactions and key ligand‒receptor-transcription factor (TF) axes, along with their associated biological pathways. Differential network analysis between grade III and grade IV glioma highlighted the most critical nodes and edges involved in interaction rewiring. Pathway enrichment analysis identified four essential genes—PDGFA (ligand), PDGFRA (receptor), CREB1 (TF), and PLAT (target gene)—involved in the Receptor Tyrosine Kinases (RTK) signaling pathway, which plays a pivotal role in glioma progression from grade III to grade IV.
DiscussionThese genes emerged as significant features for machine learning in predicting glioma progression stages, achieving 87% accuracy and 93% AUC in a 3-year survival prediction through Kaplan-Meier analysis. This framework provides deeper insights into the cellular machinery of glioma, revealing key molecular relationships that may inform prognosis and therapeutic strategies.

引言
肿瘤微环境（Tumor microenvironments, TMEs）是由癌细胞、浸润免疫细胞及多种其他细胞类型构成的复杂生态系统。TME内的细胞间与细胞内信号显著影响癌症进展与治疗结局。尽管已有可用于研究TME互作的计算工具，但跨不同癌症类型的肿瘤进展显式建模仍是一项挑战。

研究方法
本研究构建了一套综合分析框架，将单细胞RNA测序（single-cell RNA sequencing, scRNA-seq）数据整合至多层网络模型中，用于解析胶质瘤进展各阶段的分子变化。该异质性多层网络模型可复现生物系统的层级结构，涵盖从遗传基本组成单元到细胞功能及表型表征的各个层面。

研究结果
将该框架应用于胶质瘤scRNA-seq数据后，可对不同疾病阶段开展复杂网络分析，进而识别出具有统计学意义的配体-受体互作对、关键的配体-受体-转录因子（transcription factor, TF）轴及其关联的生物学通路。对Ⅲ级与Ⅳ级胶质瘤开展的差异网络分析，明确了参与互作重连的核心节点与边。通过通路富集分析，筛选出4个关键基因——PDGFA（配体）、PDGFRA（受体）、CREB1（TF）与PLAT（靶基因），它们均参与受体酪氨酸激酶（Receptor Tyrosine Kinases, RTK）信号通路，该通路在Ⅲ级胶质瘤进展至Ⅳ级的过程中发挥关键调控作用。

讨论
上述基因可作为机器学习模型预测胶质瘤进展阶段的重要特征，经卡普兰-迈耶（Kaplan-Meier）分析验证，其在3年生存率预测任务中达到87%的准确率与93%的AUC值。本分析框架可深化对胶质瘤细胞调控机制的理解，揭示关键的分子关联，有望为胶质瘤预后评估与治疗策略开发提供理论依据。