School of Software, East China Jiaotong University, Nanchang, 330013, China.
College of Computer Science and Electronic Engineering, Hunan University, Changsha, China.
NPJ Syst Biol Appl. 2023 Feb 10;9(1):4. doi: 10.1038/s41540-023-00267-8.
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.
深入了解肺腺癌（LUAD）肿瘤微环境（TME）中各种细胞成分之间的复杂相互作用机制，是理解其耐药性、复发和转移的前提。在这项研究中，我们提出了两种互补的计算框架，用于整合多源和多组学数据，即 ImmuCycReg 框架（单样本水平）和 L0Reg 框架（群体或亚群水平），以对正常人群和不同 LUAD 亚型之间进行差异分析。然后，我们旨在确定不同 LUAD 亚型患者可能采用的免疫逃逸途径，导致可能发生在免疫周期不同阶段的免疫缺陷。更重要的是，将单样本水平和群体水平的研究结果相结合，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 方法确定的风险因素建立了一个预后评分模型，以预测 LUAD 患者的生存情况。实验结果表明，我们的框架可以可靠地识别调节免疫相关基因的转录因子（TF），并可以分析每个 LUAD 亚型甚至单个样本所采用的主要免疫逃逸途径。值得注意的是，所提出的计算框架也可能适用于泛癌的免疫逃逸机制分析。
