PO.BCS01.10 · 生物信息与计算
肺癌与结核病转录组数据集的异质图神经网络荟萃分析揭示趋同的宿主反应网络
Heterogeneous graph neural network meta-analysis of lung cancer and tuberculosis transcriptomic datasets reveals convergent host response networks
作者与单位 Authors & Affiliations
摘要 Abstract
中文摘要
Background（背景）：肉芽肿作为结核病（TB）的标志性细胞结构，与肺肿瘤具有相似性，在感染与恶性肿瘤之间形成了诊断与机制上的交界面。然而，肺癌与TB肉芽肿之间的比较性、全基因组转录景观仍缺乏充分刻画。我们假设肺结核与癌症的病灶可能编码趋同的分子程序，调控病灶内细胞的结构和/或功能。
Methods（方法）：我们对来自肺癌患者、TB患者及健康对照的450个RNA-seq样本进行了大规模整合荟萃分析。原始FASTQ文件经处理生成稳健的计数矩阵。使用DESeq2评估差异基因表达（DGE），并通过带批次校正的WGCNA构建共表达网络。为捕捉基因、通路与临床元数据之间复杂的非线性关系，我们开发了异质图转换器（HGT），一种对多类型节点与边进行建模的图神经网络，从而能够发现共享的与疾病特异性的调控枢纽。
Results（结果）：前100个差异表达基因的热图分析揭示了具有混合模块的不同簇，表明肺癌与TB肉芽肿之间存在趋同生物学。KEGG富集分析揭示了免疫与致癌信号通路之间的重叠，包括NF-κB、PI3K-Akt、MAPK、Toll样受体及PD-1/PD-L1检查点通路。染色质重塑成为一个共同主题，反复出现的组蛋白变体（H2AC、H3C、H4C）提示表观遗传可塑性。这些共享枢纽基因将慢性炎症与免疫模拟牵涉为肉芽肿与癌症之间的机制桥梁，使其成为区分TB与肺癌和/或治疗靶向的潜在生物标志物。
Conclusions（结论）：这种系统水平的方法揭示了一个根植于共同机制通路的独特TB肉芽肿-肺癌交界面，为全球两种负担最重的肺部疾病识别出候选生物标志物与治疗靶点。
查看英文原文 English abstract
Background: Granulomas, hallmark cellular structures of tuberculosis (TB), have similarities to lung tumors, creating a diagnostic and mechanistic interface between infection and malignancy. However, the comparative, genomewide transcriptional landscape between lung cancer and TB granulomas remains poorly characterized. We hypothesize that the lesions in pulmonary TB and cancer may encode convergent molecular programs that regulate the structure and/or function of cells within the lesions.
Methods: We performed a large-scale integrative meta-analysis of 450 RNA-seq samples from patients with lung cancer, TB patients, and healthy controls. Raw FASTQ files were processed to generate robust count matrices. Differential gene expression (DGE) was assessed using DESeq2, and co-expression networks were constructed via WGCNA with batch correction. To capture complex, non-linear relationships between genes, pathways, and clinical metadata, we developed a Heterogeneous Graph Transformer (HGT), a graph neural network that models multi-type nodes and edges, enabling the discovery of shared and disease-specific regulatory hubs.
Results: Heatmap analysis of the top 100 differentially expressed genes revealed distinct clusters with mixed modules indicative of convergent biology between lung cancer and TB granulomas. KEGG enrichment analysis revealed an overlap between immune and oncogenic signaling pathways, including NF-κB, PI3K-Akt, MAPK, Toll-like receptor, and PD-1/PD-L1 checkpoint pathways. Chromatin remodeling emerged as a common theme, with recurrent histone variants (H2AC, H3C, H4C) suggesting epigenetic plasticity. These shared hub genes implicate chronic inflammation and immune mimicry as mechanistic bridges between granulomas and cancer, making them potential biomarkers to distinguish TB vs lung cancer and/or therapeutic targeting.
Conclusions: This systems-level approach reveals a unique TB granuloma-lung cancer interface rooted in common mechanistic pathways, identifying candidate biomarkers and therapeutic targets for two of the world's most burdensome pulmonary diseases.
利益披露 Disclosure
A. Triska, None..
S. Subbian, None..
N. Ganesan, None.