Chen Rui, Liu Yuzhen, Xie Junping
Department of Respiratory and Critical Care Medicine, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, No.1, Minde Road, Donghu District, Nanchang, Jiangxi, 330006, China.
Department of Oncology, Jiangxi Provincial People's Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, China.
Heliyon. 2024 Aug 29;10(17):e37100. doi: 10.1016/j.heliyon.2024.e37100. eCollection 2024 Sep 15.
This study aimed to predict the level of stemness index (mRNAsi) and survival prognosis of lung adenocarcinoma (LUAD) using pathomics model.
From The Cancer Genome Atlas (TCGA) database, 327 LUAD patients were randomly assigned to a training set (n = 229) and a validation set (n = 98) for pathomics model development and evaluation. PyRadiomics was used to extract pathomics features, followed by feature selection using the mRMR-RFE algorithm. In the training set, Gradient Boosting Machine (GBM) was utilized to establish a model for predicting mRNAsi in LUAD. The model's predictive performance was evaluated using ROC curves, calibration curves, and decision curve analysis (DCA). Prognostic analysis was conducted using Kaplan-Meier curves and cox regression. Additionally, gene enrichment analysis, tumor microenvironment analysis, and tumor mutational burden (TMB) analysis were performed to explore the biological mechanisms underlying the pathomics prediction model.
Multivariable cox analysis (HR = 1.488, 95 % CI 1.012-2.187, P = 0.043) identified mRNAsi as a prognostic risk factor for LUAD. A total of 465 pathomics features were extracted from TCGA-LUAD histopathological images, and ultimately, the most representative 8 features were selected to construct the predictive model. ROC curves demonstrated the significant predictive value of the model for mRNAsi in both the training set (AUC = 0.769) and the validation set (AUC = 0.757). Calibration curves and Hosmer-Lemeshow goodness-of-fit test showed good consistency between the model's prediction of mRNAsi levels and the actual values. DCA indicated a good net benefit of the model. The prediction of mRNAsi levels by the pathomics model is represented using the pathomics score (PS). PS was strongly associated with the prognosis of LUAD (HR = 1.496, 95 % CI 1.008-2.222, P = 0.046). Signaling pathways related to DNA replication and damage repair were significantly enriched in the high PS group. Prediction of immune therapy response indicated significantly reduced Dysfunction in the high PS group (P < 0.001). The high PS group exhibited higher TMB values (P < 0.001).
The predictive model constructed based on pathomics features can forecast the mRNAsi and survival risk of LUAD. This model holds promise to aid clinical practitioners in identifying high-risk patients and devising more optimized treatment plans for patients by jointly employing therapeutic strategies targeting cancer stem cells (CSCs).
本研究旨在利用病理组学模型预测肺腺癌（LUAD）的干性指数（mRNAsi）水平和生存预后。
从癌症基因组图谱（TCGA）数据库中，随机将327例LUAD患者分为训练集（n = 229）和验证集（n = 98），用于病理组学模型的开发和评估。使用PyRadiomics提取病理组学特征，随后使用mRMR-RFE算法进行特征选择。在训练集中，利用梯度提升机（GBM）建立预测LUAD中mRNAsi的模型。使用ROC曲线、校准曲线和决策曲线分析（DCA）评估模型的预测性能。使用Kaplan-Meier曲线和Cox回归进行预后分析。此外，进行基因富集分析、肿瘤微环境分析和肿瘤突变负荷（TMB）分析，以探索病理组学预测模型背后的生物学机制。
多变量Cox分析（HR = 1.488，95%CI 1.012 - 2.187，P = 0.043）确定mRNAsi为LUAD的预后危险因素。从TCGA-LUAD组织病理学图像中总共提取了465个病理组学特征，最终选择最具代表性的8个特征构建预测模型。ROC曲线显示该模型在训练集（AUC = 0.769）和验证集（AUC = 0.757）中对mRNAsi均具有显著的预测价值。校准曲线和Hosmer-Lemeshow拟合优度检验表明模型对mRNAsi水平的预测与实际值之间具有良好的一致性。DCA表明该模型具有良好的净效益。病理组学模型对mRNAsi水平的预测用病理组学评分（PS）表示。PS与LUAD的预后密切相关（HR = 1.496，95%CI 1.008 - 2.222，P = 0.046）。与DNA复制和损伤修复相关的信号通路在高PS组中显著富集。免疫治疗反应预测表明高PS组的功能障碍显著降低（P < 0.001）。高PS组表现出更高的TMB值（P < 0.001）。
基于病理组学特征构建的预测模型可以预测LUAD的mRNAsi和生存风险。该模型有望帮助临床医生识别高危患者，并通过联合采用针对癌症干细胞（CSC）的治疗策略为患者制定更优化的治疗方案。
