通过预测TIGIT表达预测肺腺癌预后：一种病理组学模型
Predicting prognosis in lung adenocarcinoma by predicting TIGIT expression: a pathomics model.
作者信息
Hu Peihong, Tian Bo, Gu Hang, Liu Haoran, Li Qiang
机构信息
Department of Thoracic Surgery, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China.
School of Medicine, University of Electronic Science and Technology of China, Chengdu, China.
出版信息
J Thorac Dis. 2024 Nov 30;16(11):7617-7629. doi: 10.21037/jtd-24-978. Epub 2024 Nov 29.
BACKGROUND
Traditional diagnostic methods have limited efficacy in predicting the prognosis of lung adenocarcinoma (LUAD), T cell immunoreceptor with immunoglobulin and immunoreceptor tyrosine-based inhibitory motif domain (TIGIT) is a new biomarker. This study aimed to evaluate TIGIT expression as a LUAD biomarker and predict patient prognosis using a pathological feature model.
METHODS
Clinical data and pathological images from The Cancer Genome Atlas (TCGA) were analyzed. The prognostic value of TIGIT was verified by genetic prognostic analysis and gene set enrichment analysis (GSEA). The OTSU algorithm was used to segment LUAD pathological images, and features were extracted using the PyRadiomics package and standardized with z-scores. Feature selection was performed using min-redundancy, recursive feature elimination (RFE) and stepwise regression algorithms, and a logistic regression algorithm was used to establish the pathomics model. Receiver operating characteristics, calibration, and decision curves were used for model evaluation. The pathomics score (PS) was used to predict TIGIT gene expression and analyze prognostic value and pathological mechanisms through Spearman correlation.
RESULTS
The study included 443 clinical samples and 327 pathological images. Prognostic analysis showed significantly higher TIGIT expression in tumor tissues (P<0.001), with TIGIT being a protective factor for overall survival (OS) in LUAD [hazard ratio (HR) =0.65; 95% confidence interval (CI): 0.44-0.95; P=0.03]. GSEA revealed significant enrichment of differentially expressed genes in the TGF-β and MAPK signaling pathways. From 465 pathological features, the four best features were selected to construct a pathomics model with good predictive performance. Higher PS values were observed in the TIGIT high-expression group, correlating with improved OS (P=0.009). PS was positively correlated with the epithelial-mesenchymal transition related (EMT-related) genes (, , ) and immune checkpoints (, , ) (P<0.001). Increased abundance of G2/M checkpoint-related genes (, ) and infiltration of CD8 T cells and M2 macrophages were noted in the high PS group (P<0.05).
CONCLUSIONS
TIGIT expression is significantly correlated with LUAD prognosis and can effectively predict patient outcomes.
背景
传统诊断方法在预测肺腺癌（LUAD）预后方面疗效有限，具有免疫球蛋白和基于免疫受体酪氨酸的抑制基序结构域的T细胞免疫受体（TIGIT）是一种新的生物标志物。本研究旨在评估TIGIT表达作为LUAD生物标志物，并使用病理特征模型预测患者预后。
方法
分析来自癌症基因组图谱（TCGA）的临床数据和病理图像。通过基因预后分析和基因集富集分析（GSEA）验证TIGIT的预后价值。使用大津算法分割LUAD病理图像，并使用PyRadiomics软件包提取特征并用z分数进行标准化。使用最小冗余、递归特征消除（RFE）和逐步回归算法进行特征选择，并使用逻辑回归算法建立病理组学模型。使用受试者工作特征、校准和决策曲线进行模型评估。病理组学评分（PS）用于预测TIGIT基因表达，并通过Spearman相关性分析预后价值和病理机制。
结果
该研究纳入443个临床样本和327幅病理图像。预后分析显示肿瘤组织中TIGIT表达显著更高（P<0.001），TIGIT是LUAD总生存期（OS）的保护因素[风险比（HR）=0.65；95%置信区间（CI）：0.44 - 0.95；P = 0.03]。GSEA显示转化生长因子-β（TGF-β）和丝裂原活化蛋白激酶（MAPK）信号通路中差异表达基因显著富集。从465个病理特征中，选择四个最佳特征构建具有良好预测性能的病理组学模型。在TIGIT高表达组中观察到更高的PS值，与改善的OS相关（P = 0.009）。PS与上皮-间质转化相关（EMT相关）基因（、、）和免疫检查点（、、）呈正相关（P<0.001）。在高PS组中，G2/M检查点相关基因（、）丰度增加，CD8 T细胞和M2巨噬细胞浸润增加（P<0.05）。
结论
TIGIT表达与LUAD预后显著相关，可有效预测患者结局。
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