Li Hui, Chen Linyan, Zeng Hao, Liao Qimeng, Ji Jianrui, Ma Xuelei
Department of Biotherapy, State Key Laboratory of Biotherapy, Cancer Center, West China Hospital, Sichuan University, Chengdu, China.
West China Hospital, West China School of Medicine, Sichuan University, Chengdu, China.
Front Oncol. 2021 Sep 27;11:636451. doi: 10.3389/fonc.2021.636451. eCollection 2021.
Colon adenocarcinoma (COAD) is one of the most common malignant tumors in the world. The histopathological features are crucial for the diagnosis, prognosis, and therapy of COAD.
We downloaded 719 whole-slide histopathological images from TCIA, and 459 corresponding HTSeq-counts mRNA expression and clinical data were obtained from TCGA. Histopathological image features were extracted by CellProfiler. Prognostic image features were selected by the least absolute shrinkage and selection operator (LASSO) and support vector machine (SVM) algorithms. The co-expression gene module correlated with prognostic image features was identified by weighted gene co-expression network analysis (WGCNA). Random forest was employed to construct an integrative prognostic model and calculate the histopathological-genomic prognosis factor (HGPF).
There were five prognostic image features and one co-expression gene module involved in the model construction. The time-dependent receiver operating curve showed that the prognostic model had a significant prognostic value. Patients were divided into high-risk group and low-risk group based on the HGPF. Kaplan-Meier analysis indicated that the overall survival of the low-risk group was significantly better than the high-risk group.
These results suggested that the histopathological image features had a certain ability to predict the survival of COAD patients. The integrative prognostic model based on the histopathological images and genomic features could further improve the prognosis prediction in COAD, which may assist the clinical decision in the future.
结肠腺癌（COAD）是世界上最常见的恶性肿瘤之一。组织病理学特征对于COAD的诊断、预后和治疗至关重要。
我们从TCIA下载了719张全切片组织病理学图像，并从TCGA获得了459个相应的HTSeq计数mRNA表达和临床数据。通过CellProfiler提取组织病理学图像特征。通过最小绝对收缩和选择算子（LASSO）和支持向量机（SVM）算法选择预后图像特征。通过加权基因共表达网络分析（WGCNA）确定与预后图像特征相关的共表达基因模块。采用随机森林构建综合预后模型并计算组织病理学-基因组预后因子（HGPF）。
模型构建涉及五个预后图像特征和一个共表达基因模块。时间依赖性受试者工作曲线表明预后模型具有显著的预后价值。根据HGPF将患者分为高风险组和低风险组。Kaplan-Meier分析表明，低风险组的总生存率明显优于高风险组。
这些结果表明组织病理学图像特征具有一定的预测COAD患者生存的能力。基于组织病理学图像和基因组特征的综合预后模型可以进一步改善COAD的预后预测，这可能有助于未来的临床决策。
