Abstract：
BACKGROUND: The TNFRSF9 molecule is pivotal in thyroid carcinoma (THCA) development. This study utilizes Pathomics techniques to predict TNFRSF9 expression in THCA tissue and explore its molecular mechanisms.
METHODS: Transcriptome data, pathology images, and clinical information from the cancer genome atlas (TCGA) were analyzed. Image segmentation and feature extraction were performed using the OTSU\'s algorithm and pyradiomics package. The dataset was split for training and validation. Features were selected using maximum relevance minimum redundancy recursive feature elimination (mRMR_RFE) and modeling conducted with the gradient boosting machine (GBM) algorithm. Model evaluation included receiver operating characteristic curve (ROC) analysis. The Pathomics model output a probabilistic pathomics score (PS) for gene expression prediction, with its prognostic value assessed in TNFRSF9 expression groups. Subsequent analysis involved gene set variation analysis (GSVA), immune gene expression, cell abundance, immunotherapy susceptibility, and gene mutation analysis.
RESULTS: High TNFRSF9 expression correlated with worsened progression-free interval (PFI) and acted as an independent risk factor [hazard ratio (HR) = 2.178, 95% confidence interval (CI) 1.045-4.538, P = 0.038]. Nine pathohistological features were identified. The GBM Pathomics model demonstrated good prediction efficacy [area under the curve (AUC) 0.819 and 0.769] and clinical benefits. High PS was a PFI risk factor (HR = 2.156, 95% CI 1.047-4.440, P = 0.037). Patients with high PS potentially exhibited enriched pathways, increased TIGIT gene expression, Tregs infiltration (P < 0.0001), and higher rates of gene mutations (BRAF, TTN, TG).
CONCLUSIONS: The GBM Pathomics model constructed based on the pathohistological features of H&E-stained sections well predicted the expression level of TNFRSF9 molecules in THCA.
METHODS: Transcriptome data, pathology images, and clinical information from the cancer genome atlas (TCGA) were analyzed. Image segmentation and feature extraction were performed using the OTSU\'s algorithm and pyradiomics package. The dataset was split for training and validation. Features were selected using maximum relevance minimum redundancy recursive feature elimination (mRMR_RFE) and modeling conducted with the gradient boosting machine (GBM) algorithm. Model evaluation included receiver operating characteristic curve (ROC) analysis. The Pathomics model output a probabilistic pathomics score (PS) for gene expression prediction, with its prognostic value assessed in TNFRSF9 expression groups. Subsequent analysis involved gene set variation analysis (GSVA), immune gene expression, cell abundance, immunotherapy susceptibility, and gene mutation analysis.
RESULTS: High TNFRSF9 expression correlated with worsened progression-free interval (PFI) and acted as an independent risk factor [hazard ratio (HR) = 2.178, 95% confidence interval (CI) 1.045-4.538, P = 0.038]. Nine pathohistological features were identified. The GBM Pathomics model demonstrated good prediction efficacy [area under the curve (AUC) 0.819 and 0.769] and clinical benefits. High PS was a PFI risk factor (HR = 2.156, 95% CI 1.047-4.440, P = 0.037). Patients with high PS potentially exhibited enriched pathways, increased TIGIT gene expression, Tregs infiltration (P < 0.0001), and higher rates of gene mutations (BRAF, TTN, TG).
CONCLUSIONS: The GBM Pathomics model constructed based on the pathohistological features of H&E-stained sections well predicted the expression level of TNFRSF9 molecules in THCA.
摘要：
背景：TNFRSF9分子在甲状腺癌（THCA）的发展中至关重要。本研究利用病理组学技术预测TNFRSF9在THCA组织中的表达并探讨其分子机制。
方法：转录组数据，病理图像,和来自癌症基因组图谱（TCGA）的临床信息进行了分析。使用OTSU算法和pyradiomics软件包进行图像分割和特征提取。数据集被分割用于训练和验证。使用最大相关性最小冗余递归特征消除（mRMR_RFE）选择特征，并用梯度增强机（GBM）算法进行建模。模型评估包括受试者工作特性曲线（ROC）分析。Pathomics模型输出用于基因表达预测的概率病理组学评分(PS),在TNFRSF9表达组中评估其预后价值。随后的分析涉及基因集变异分析（GSVA），免疫基因表达，细胞丰度,免疫疗法易感性，和基因突变分析。
结果：TNFRSF9高表达与无进展间期（PFI）恶化相关，并作为独立危险因素[风险比（HR）=2.178，95％置信区间（CI）1.045-4.538，P=0.038]。确定了9个病理组织学特征。GBMPathomics模型显示出良好的预测功效[曲线下面积(AUC)0.819和0.769]和临床益处。高PS是PFI的危险因素(HR=2.156,95%CI1.047-4.440,P=0.037)。高PS患者可能表现出富集的途径，增加TIGIT基因表达，Tregs入渗(P<0.0001),和更高的基因突变率(BRAF，TTN,TG）。
结论：基于H&E染色切片病理组织学特征构建的GBM病理组学模型可以很好地预测TNFRSF9分子在THCA中的表达水平。
方法：转录组数据，病理图像,和来自癌症基因组图谱（TCGA）的临床信息进行了分析。使用OTSU算法和pyradiomics软件包进行图像分割和特征提取。数据集被分割用于训练和验证。使用最大相关性最小冗余递归特征消除（mRMR_RFE）选择特征，并用梯度增强机（GBM）算法进行建模。模型评估包括受试者工作特性曲线（ROC）分析。Pathomics模型输出用于基因表达预测的概率病理组学评分(PS),在TNFRSF9表达组中评估其预后价值。随后的分析涉及基因集变异分析（GSVA），免疫基因表达，细胞丰度,免疫疗法易感性，和基因突变分析。
结果：TNFRSF9高表达与无进展间期（PFI）恶化相关，并作为独立危险因素[风险比（HR）=2.178，95％置信区间（CI）1.045-4.538，P=0.038]。确定了9个病理组织学特征。GBMPathomics模型显示出良好的预测功效[曲线下面积(AUC)0.819和0.769]和临床益处。高PS是PFI的危险因素(HR=2.156,95%CI1.047-4.440,P=0.037)。高PS患者可能表现出富集的途径，增加TIGIT基因表达，Tregs入渗(P<0.0001),和更高的基因突变率(BRAF，TTN,TG）。
结论：基于H&E染色切片病理组织学特征构建的GBM病理组学模型可以很好地预测TNFRSF9分子在THCA中的表达水平。
