详细理解肺腺癌（LUAD）的肿瘤微环境（TME）中各种细胞成分之间复杂的相互作用机制，是理解其药物耐受性、复发和转移的先决条件。本研究提出了两种互补计算框架，即ImmuCycReg框架（单样本水平）和L0Reg框架（人群或亚型水平），用于在正常人群和不同LUAD亚型之间进行差异分析，集成多来源和多组学数据。然后，我们旨在确定不同LUAD亚型患者采用的可能免疫逃逸通路，导致免疫周期的不同阶段出现免疫缺陷。更重要的是，结合单个样本水平和人口水平的研究结果，可以提高调控网络分析结果的可信度。此外，我们还基于Lasso-Cox方法识别的风险因素建立了预后评分模型，以预测LUAD患者的生存。实验结果表明，我们的框架能够可靠地识别调控免疫相关基因的转录因子（TF），并能够分析每个LUAD亚型或甚至单个样本采用的主导免疫逃逸通路。注意，所提出的计算框架也适用于全癌症免疫逃逸机制的分析。© 2023.作者（们）。

A deep understanding of the complex interaction mechanism between the various cellular components in tumor microenvironment (TME) of lung adenocarcinoma (LUAD) is a prerequisite for understanding its drug resistance, recurrence, and metastasis. In this study, we proposed two complementary computational frameworks for integrating multi-source and multi-omics data, namely ImmuCycReg framework (single sample level) and L0Reg framework (population or subtype level), to carry out difference analysis between the normal population and different LUAD subtypes. Then, we aimed to identify the possible immune escape pathways adopted by patients with different LUAD subtypes, resulting in immune deficiency which may occur at different stages of the immune cycle. More importantly, combining the research results of the single sample level and population level can improve the credibility of the regulatory network analysis results. In addition, we also established a prognostic scoring model based on the risk factors identified by Lasso-Cox method to predict survival of LUAD patients. The experimental results showed that our frameworks could reliably identify transcription factor (TF) regulating immune-related genes and could analyze the dominant immune escape pathways adopted by each LUAD subtype or even a single sample. Note that the proposed computational framework may be also applicable to the immune escape mechanism analysis of pan-cancer.© 2023. The Author(s).