DataSheet_3_A Machine-Learning Approach to Developing a Predictive Signature Based on Transcriptome Profiling of Ground-Glass Opacities for Accurate Classification and Exploring the Immune Microenvironment of Early-Stage LUAD.xls

Screening for early-stage lung cancer with low-dose computed tomography is recommended for high-risk populations; consequently, the incidence of pure ground-glass opacity (pGGO) is increasing. Ground-glass opacity (GGO) is considered the appearance of early lung cancer, and there remains an unmet clinical need to understand the pathology of small GGO (<1 cm in diameter). The objective of this study was to use the transcriptome profiling of pGGO specimens <1 cm in diameter to construct a pGGO-related gene risk signature to predict the prognosis of early-stage lung adenocarcinoma (LUAD) and explore the immune microenvironment of GGO. pGGO-related differentially expressed genes (DEGs) were screened to identify prognostic marker genes with two machine learning algorithms. A 15-gene risk signature was constructed from the DEGs that were shared between the algorithms. Risk scores were calculated using the regression coefficients for the pGGO-related DEGs. Patients with Stage I/II LUAD or Stage IA LUAD and high-risk scores had a worse prognosis than patients with low-risk scores. The prognosis of high-risk patients with Stage IA LUAD was almost identical to that of patients with Stage II LUAD, suggesting that treatment strategies for patients with Stage II LUAD may be beneficial in high-risk patients with Stage IA LUAD. pGGO-related DEGs were mainly enriched in immune-related pathways. Patients with high-risk scores and high tumor mutation burden had a worse prognosis and may benefit from immunotherapy. A nomogram was constructed to facilitate the clinical application of the 15-gene risk signature. Receiver operating characteristic curves and decision curve analysis validated the predictive ability of the nomogram in patients with Stage I LUAD in the TCGA-LUAD cohort and GEO datasets.

针对高危人群，临床推荐采用低剂量计算机断层扫描（low-dose computed tomography, LDCT）进行早期肺癌筛查；由此，纯磨玻璃影（pure ground-glass opacity, pGGO）的发病率呈上升趋势。磨玻璃影（ground-glass opacity, GGO）被视为早期肺癌的典型影像学表现，目前临床仍亟需阐明直径小于1cm的小型GGO的病理机制。本研究旨在通过对直径小于1cm的pGGO标本进行转录组谱分析，构建pGGO相关基因风险特征模型，以预测早期肺腺癌（lung adenocarcinoma, LUAD）患者的预后，并探究GGO的免疫微环境。研究通过两种机器学习算法筛选pGGO相关差异表达基因（differentially expressed genes, DEGs），以鉴定预后标志物基因。选取两种算法共同筛选得到的DEGs，构建了包含15个基因的风险评分模型；基于pGGO相关DEGs的回归系数计算患者的风险评分。在I/II期LUAD患者以及IA期LUAD患者中，高风险评分人群的预后均显著差于低风险评分人群。IA期LUAD高风险患者的预后与II期LUAD患者几乎一致，这提示II期LUAD的治疗策略或可使IA期高风险LUAD患者获益。pGGO相关DEGs主要富集于免疫相关通路。同时具备高风险评分与高肿瘤突变负荷的患者预后更差，但可能从免疫治疗中获益。为便于该15基因风险模型的临床应用，本研究构建了列线图（nomogram）。在TCGA-LUAD队列与GEO数据集的I期LUAD患者中，受试者工作特征曲线（receiver operating characteristic curve, ROC）与决策曲线分析（decision curve analysis, DCA）均验证了该列线图的预测效能。