个人中心| | 帮助
高级检索
当前位置： 首页 > 详情页

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

| 认领 | 导出 | |

文献详情

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[2024]版：
无
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

相关文献

[1]Construction of an immune-related gene prognostic model with experimental validation and analysis of immune cell infiltration in lung adenocarcinoma [2]Establishment and validation of a carbohydrate metabolism-related gene signature for prognostic model and immune response in acute myeloid leukemia [3]A novel prognostic and therapeutic target biomarker based on necroptosis-related gene signature and immune microenvironment infiltration in gastric cancer [4]Artificial intelligence in brachytherapy [5]Artificial intelligence applications in pathological diagnosis of gastric cancer [6]Applying post-neoadjuvant pathologic stage as prognostic tool in esophageal squamous cell carcinoma [7]A Prognostic Scoring Model for the Utility of Induction Chemotherapy Prior to Neoadjuvant Chemoradiotherapy in Esophageal Cancer [8]Application of Artificial Intelligence in Oncology [9]Artificial intelligence aids in development of nanomedicines for cancer management [10]Screening Cancer Immunotherapy: When Engineering Approaches Meet Artificial Intelligence.

资源点击量：52808 今日访问量：0 总访问量：4561 更新日期：2025-01-01 建议使用谷歌、火狐浏览器 常见问题

版权所有©2020 四川省肿瘤医院 技术支持：重庆聚合科技有限公司 地址：成都市人民南路四段55号