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An immune-related gene prognostic prediction risk model for neoadjuvant chemoradiotherapy in rectal cancer using artificial intelligence

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资源类型：
Pubmed体系：
作者：
Shu Pei[1,2,3] ; Liu Ning[4] ; Luo Xu[5,6] ; Tang Yuanling[1,2] ; Chen Zhebin[5,6] ; Li Dandan[1,2] ; Miao Dong[5,6] ; Duan Jiayu[1,2] ; Yan Ouying[1,2] ; Sheng Leiming[1,2] ; Ouyang Ganlu[1,2] ; Wang Sen[5,6] ; Jiang Dan[7,8] ; Deng Xiangbing[9] ; Wang Ziqiang[8,9] ; Li Qingyun[10] ; Wang Xin[1,2,8] ;
机构： [1]Department of Radiation Oncology, Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China. [2]Department of Abdominal Tumor Multimodality Treatment, Cancer Center, West China Hospital, Sichuan University, Chengdu, China. [3]Clinical Trial Center, National Medical Products Administration Key Laboratory for Clinical Research and Evaluation of Innovative Drugs, West China Hospital, Sichuan University, Chengdu, Sichuan, China. [4]Division of Thoracic Tumor Multimodality Treatment, Cancer Center, West China Hospital, Sichuan University, Chengdu, China. [5]Chengdu Institute of Computer Application, Chinese Academy of Sciences, Chengdu, China. [6]School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing, China. [7]Department of Pathology, West China Hospital, Sichuan University, Chengdu, China. [8]Sichuan University-University of Oxford Huaxi Joint Center for Gastrointestinal Cancer, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Chengdu, Sichuan, China. [9]Division of Gastrointestinal Surgery, Department of General Surgery, West China Hospital, Sichuan University, Chengdu, China. [10]Genecast Biotechnology Co., Ltd., Xishan District, Wuxi, Jiangsu, China.
出处：
DOI： 10.3389/fonc.2024.1294440
ISSN：

关键词： artificial intelligence prognostic model immune related gene rectal carcinoma neoadjuvant chemoradiotherapy

摘要：
This study aimed to establish and validate a prognostic model based on immune-related genes (IRGPM) for predicting disease-free survival (DFS) in patients with locally advanced rectal cancer (LARC) undergoing neoadjuvant chemoradiotherapy, and to elucidate the immune profiles associated with different prognostic outcomes.Transcriptomic and clinical data were sourced from the Gene Expression Omnibus (GEO) database and the West China Hospital database. We focused on genes from the RNA immune-oncology panel. The elastic net approach was employed to pinpoint immune-related genes significantly impacting DFS. We developed the IRGPM for rectal cancer using the random forest technique. Based on the IRGPM, we calculated prognostic risk scores to categorize patients into high-risk and low-risk groups. Comparative analysis of immune characteristics between these groups was conducted.In this study, 407 LARC samples were analyzed. The elastic net identified a signature of 20 immune-related genes, forming the basis of the IRGPM. Kaplan-Meier survival analysis revealed a lower 5-year DFS in the high-risk group compared to the low-risk group. The receiver operating characteristic (ROC) curve affirmed the model's robust predictive capability. Validation of the model was performed in the GSE190826 cohort and our institution's cohort. Gene expression differences between high-risk and low-risk groups predominantly related to cytokine-cytokine receptor interactions. Notably, the low-risk group exhibited higher immune scores. Further analysis indicated a greater presence of activated B cells, activated CD8 T cells, central memory CD8 T cells, macrophages, T follicular helper cells, and type 2 helper cells in the low-risk group. Additionally, immune checkpoint analysis revealed elevated PDCD1 expression in the low-risk group.The IRGPM, developed through random forest and elastic net methodologies, demonstrates potential in distinguishing DFS among LARC patients receiving standard treatment. Notably, the low-risk group, as defined by the IRGPM, showed enhanced activation of adaptive immune responses within the tumor microenvironment.Copyright © 2024 Shu, Liu, Luo, Tang, Chen, Li, Miao, Duan, Yan, Sheng, Ouyang, Wang, Jiang, Deng, Wang, Li and Wang.

基金：
This work was supported by National Natural Science Foundation of China, No. 82073338; Sichuan Science and Technology Support Project, No. 2023YFS0319 and No. 2022YFS0217; The 1·3·5 Project for Disciplines of Excellence-Clinical Research Incubation Project West China Hospital, Sichuan University, No. 2020HXFH002; and The 1.3.5 Project for Disciplines of Excellence, West China Hospital, Sichuan University, No. JCJS2022034.
语种：
外文
PubmedID：
中科院(CAS)分区：
出版当年[2023]版：
大类 | 3 区 医学
小类 | 3 区 肿瘤学
最新[2023]版：
大类 | 3 区 医学
小类 | 3 区 肿瘤学
第一作者：
第一作者机构： [1]Department of Radiation Oncology, Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China. [2]Department of Abdominal Tumor Multimodality Treatment, Cancer Center, West China Hospital, Sichuan University, Chengdu, China. [3]Clinical Trial Center, National Medical Products Administration Key Laboratory for Clinical Research and Evaluation of Innovative Drugs, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
共同第一作者：
通讯作者：
通讯机构： [1]Department of Radiation Oncology, Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China. [2]Department of Abdominal Tumor Multimodality Treatment, Cancer Center, West China Hospital, Sichuan University, Chengdu, China. [8]Sichuan University-University of Oxford Huaxi Joint Center for Gastrointestinal Cancer, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
推荐引用方式(GB/T 7714)：
Shu Pei,Liu Ning,Luo Xu,et al.An immune-related gene prognostic prediction risk model for neoadjuvant chemoradiotherapy in rectal cancer using artificial intelligence[J].Frontiers In Oncology.2024,14:1294440.doi:10.3389/fonc.2024.1294440.
APA：
Shu Pei,Liu Ning,Luo Xu,Tang Yuanling,Chen Zhebin...&Wang Xin.(2024).An immune-related gene prognostic prediction risk model for neoadjuvant chemoradiotherapy in rectal cancer using artificial intelligence.Frontiers In Oncology,14,
MLA：
Shu Pei,et al."An immune-related gene prognostic prediction risk model for neoadjuvant chemoradiotherapy in rectal cancer using artificial intelligence".Frontiers In Oncology 14.(2024):1294440

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