基于机器学习的肿瘤浸润免疫细胞相关假基因特征可预测卵巢癌的预后和免疫治疗反应。

A tumor-infiltrating immune cells-related pseudogenes signature based on machine-learning predicts outcomes and immunotherapy responses in ovarian cancer.

作者信息

Zhang Yuyuan, Guo Manman, Wang Libo, Weng Siyuan, Xu Hui, Ren Yuqing, Liu Long, Guo Chunguang, Cheng Quan, Luo Peng, Zhang Jian, Han Xinwei

机构信息

Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450052, China; Interventional Institute of Zhengzhou University, Zhengzhou, Henan 450052, China; Interventional Treatment and Clinical Research Center of Henan Province, Zhengzhou, Henan 450052, China.

Reproductive Medical Center, The First Affiliated Hospital of Zhengzhou University, Henan 450052, China.

出版信息

Cell Signal. 2023 Nov;111:110879. doi: 10.1016/j.cellsig.2023.110879. Epub 2023 Sep 1.

DOI:10.1016/j.cellsig.2023.110879

PMID:37659727

Abstract

Previous researches have provided evidence for the significant involvement of pseudogenes in immune-related functions across different types of cancer. However, the mechanisms by which pseudogenes regulate immunity in ovarian cancer (OV) and their potential impact on clinical outcomes remain unclear. To address this gap in knowledge, our study utilized a novel computational framework to analyze a total of 491 samples from three public datasets. We employed a combination of 10 machine-learning algorithms to construct a signature known as the tumor-infiltrating immune cells-related pseudogenes signature (TIICPS). The TIICPS, consisting of 12 pseudogenes, demonstrated independent prognostic value for overall survival, surpassing conventional clinical traits, 62 published signatures, and TP53 and BRCA mutation status in three cohorts. Patients with low TIICPS exhibited heightened immune-related pathways, intricate genomic alterations, substantial immune infiltration, and greater potential for immunotherapy efficacy. Consequently, TIICPS holds promise as a predictive tool for prognosis and immunotherapy response in ovarian cancer.

摘要

先前的研究已经提供了证据，证明假基因在不同类型癌症的免疫相关功能中发挥着重要作用。然而，假基因在卵巢癌（OV）中调节免疫的机制及其对临床结果的潜在影响仍不清楚。为了填补这一知识空白，我们的研究利用了一种新颖的计算框架，对来自三个公共数据集的总共491个样本进行了分析。我们采用了10种机器学习算法的组合，构建了一种名为肿瘤浸润免疫细胞相关假基因特征（TIICPS）的特征。由12个假基因组成的TIICPS在三个队列中显示出对总生存期的独立预后价值，超过了传统临床特征、62个已发表的特征以及TP53和BRCA突变状态。TIICPS低的患者表现出增强的免疫相关通路、复杂的基因组改变、大量的免疫浸润以及更高的免疫治疗疗效潜力。因此，TIICPS有望成为卵巢癌预后和免疫治疗反应的预测工具。