Prediction of TNFRSF9 expression and molecular pathological features in thyroid cancer using machine learning to construct Pathomics models.

IF 3 3区医学 Q2 ENDOCRINOLOGY & METABOLISM

Endocrine Pub Date : 2024-10-01 Epub Date: 2024-05-16 DOI:10.1007/s12020-024-03862-9

Ying Liu, Junping Zhang, Shanshan Li, Wen Chen, Rongqian Wu, Zejin Hao, Jixiong Xu

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引用次数: 0

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.

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利用机器学习构建病理组学模型，预测甲状腺癌中 TNFRSF9 的表达和分子病理特征。

背景：TNFRSF9分子在甲状腺癌（THCA）的发生发展中起着关键作用。本研究利用病理组学技术预测TNFRSF9在THCA组织中的表达，并探索其分子机制：方法：分析癌症基因组图谱（TCGA）中的转录组数据、病理图像和临床信息。使用 OTSU 算法和 Pyradiomics 软件包进行图像分割和特征提取。数据集分为训练集和验证集。使用最大相关性最小冗余递归特征消除（mRMR_RFE）选择特征，并使用梯度提升机（GBM）算法进行建模。模型评估包括接收者操作特征曲线（ROC）分析。病理组学模型为基因表达预测输出概率病理组学评分（PS），并在 TNFRSF9 表达组中评估其预后价值。随后的分析包括基因组变异分析（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，是一个独立的风险因素[危险比（HR）=2.178，95%置信区间（CI）1.045-4.538，P=0.038]。确定了九种病理组织学特征。GBM 病理组学模型显示出良好的预测效果[曲线下面积 (AUC) 0.819 和 0.769]和临床效益。高 PS 是 PFI 风险因素（HR = 2.156，95% CI 1.047-4.440，P = 0.037）。高PS患者可能表现出丰富的通路、TIGIT基因表达增加、Tregs浸润（P 结论：高PS患者可能表现出丰富的通路、TIGIT基因表达增加、Tregs浸润）：根据H&E染色切片的病理组织学特征构建的GBM病理组学模型很好地预测了THCA中TNFRSF9分子的表达水平。

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来源期刊

Endocrine ENDOCRINOLOGY & METABOLISM-

CiteScore

6.50

自引率

5.40%

发文量

295

审稿时长

1.5 months

期刊介绍： Well-established as a major journal in today’s rapidly advancing experimental and clinical research areas, Endocrine publishes original articles devoted to basic (including molecular, cellular and physiological studies), translational and clinical research in all the different fields of endocrinology and metabolism. Articles will be accepted based on peer-reviews, priority, and editorial decision. Invited reviews, mini-reviews and viewpoints on relevant pathophysiological and clinical topics, as well as Editorials on articles appearing in the Journal, are published. Unsolicited Editorials will be evaluated by the editorial team. Outcomes of scientific meetings, as well as guidelines and position statements, may be submitted. The Journal also considers special feature articles in the field of endocrine genetics and epigenetics, as well as articles devoted to novel methods and techniques in endocrinology. Endocrine covers controversial, clinical endocrine issues. Meta-analyses on endocrine and metabolic topics are also accepted. Descriptions of single clinical cases and/or small patients studies are not published unless of exceptional interest. However, reports of novel imaging studies and endocrine side effects in single patients may be considered. Research letters and letters to the editor related or unrelated to recently published articles can be submitted. Endocrine covers leading topics in endocrinology such as neuroendocrinology, pituitary and hypothalamic peptides, thyroid physiological and clinical aspects, bone and mineral metabolism and osteoporosis, obesity, lipid and energy metabolism and food intake control, insulin, Type 1 and Type 2 diabetes, hormones of male and female reproduction, adrenal diseases pediatric and geriatric endocrinology, endocrine hypertension and endocrine oncology.