Table 5_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 microenvironment, TMEs）是由癌细胞、浸润免疫细胞及多种异质性细胞共同构成的复杂生态系统。肿瘤微环境内的细胞间与细胞内信号通路可显著影响癌症进展进程与治疗结局。尽管已有诸多计算工具可用于解析肿瘤微环境的细胞互作机制，但针对不同癌症类型构建肿瘤进展的显式模型仍是一项挑战。

方法
本研究提出一套综合分析框架，基于多层网络模型整合单细胞RNA测序（single-cell RNA sequencing, scRNA-seq）数据，以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能够复现生物系统的层级组织结构，覆盖从遗传基本元件到细胞功能乃至表型表现的全维度信息。

结果
将该框架应用于胶质瘤单细胞RNA测序数据后，可针对不同癌症阶段开展复杂网络分析，鉴定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）调控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，明确了参与互作重编程过程的核心节点与连接边。通路富集分析筛选出4个核心功能基因：配体PDGFA、受体PDGFRA、转录因子CREB1以及靶基因PLAT，上述基因均参与受体酪氨酸激酶（Receptor Tyrosine Kinases, RTK）信号通路，该通路在III级胶质瘤向IV级进展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的核心特征，用于胶质瘤进展阶段的预测；通过卡普兰-迈耶（Kaplan-Meier）分析开展3年生存期预测时，该模型实现了87%的预测准确率与93%的AUC值。本研究提出的分析框架可深化对胶质瘤细胞调控机制的理解，揭示了可指导临床预后评估与治疗策略开发的关键分子关联。