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开发和验证免疫相关基因特征,以预测低级别脑胶质瘤的放射敏感性。

Development and validation of an immune-related gene signature for predicting the radiosensitivity of lower-grade gliomas.

机构信息

Department of Biostatistics, School of Public Health, Medical College of Soochow University, Suzhou, 215123, Jiangsu, China.

Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow University, Suzhou, China.

出版信息

Sci Rep. 2022 Apr 23;12(1):6698. doi: 10.1038/s41598-022-10601-5.

DOI:10.1038/s41598-022-10601-5
PMID:35461367
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9035187/
Abstract

Radiotherapy is an important treatment modality for lower-grade gliomas (LGGs) patients. This analysis was conducted to develop an immune-related radiosensitivity gene signature to predict the survival of LGGs patients who received radiotherapy. The clinical and RNA sequencing data of LGGs were obtained from The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA). Lasso regression analyses were used to construct a 21-gene signature to identify the LGGs patients who could benefit from radiotherapy. Based on this radiosensitivity signature, patients were classified into a radiosensitive (RS) group and a radioresistant (RR) group. According to the Kaplan-Meier analysis results of the TCGA dataset and the two CGGA validation datasets, the RS group had a higher overall survival rate than that of the RR group. This gene signature was RT-specific and an independent prognostic indicator. The nomogram model performed well in predicting 3-, and 5-year survival of LGGs patients after radiotherapy by this gene signature and other clinical factors (age, sex, grade, IDH mutations, 1p/19q codeletion). In summary, this signature is a powerful supplement to the prognostic factors of LGGs patients with radiotherapy and may provide an opportunity to incorporate individual tumor biology into clinical decision making in radiation oncology.

摘要

放射治疗是低级别胶质瘤(LGG)患者的重要治疗方式。本分析旨在构建一个与免疫相关的放射敏感性基因特征,以预测接受放射治疗的 LGG 患者的生存情况。从癌症基因组图谱(TCGA)和中国脑胶质瘤基因组图谱(CGGA)获取 LGG 的临床和 RNA 测序数据。使用 Lasso 回归分析构建 21 个基因特征,以确定能从放射治疗中获益的 LGG 患者。基于此放射敏感性特征,将患者分为放射敏感(RS)组和放射抵抗(RR)组。根据 TCGA 数据集和两个 CGGA 验证数据集的 Kaplan-Meier 分析结果,RS 组的总生存率高于 RR 组。该基因特征是 RT 特异性的,是独立的预后指标。列线图模型通过该基因特征和其他临床因素(年龄、性别、分级、IDH 突变、1p/19q 共缺失)很好地预测了 LGG 患者接受放射治疗后 3 年和 5 年的生存率。总之,该特征是放射治疗的 LGG 患者预后因素的有力补充,可能为将个体肿瘤生物学纳入放射肿瘤学的临床决策提供机会。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e4c1/9035187/f0b1390ddb2f/41598_2022_10601_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e4c1/9035187/18fc8ae3a5a1/41598_2022_10601_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e4c1/9035187/e9cd1a29b2e7/41598_2022_10601_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e4c1/9035187/8835186c2a7a/41598_2022_10601_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e4c1/9035187/7bdb42ce015b/41598_2022_10601_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e4c1/9035187/79a9806d0cb6/41598_2022_10601_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e4c1/9035187/ec7ea97e2a7a/41598_2022_10601_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e4c1/9035187/87bd3a847f37/41598_2022_10601_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e4c1/9035187/f0b1390ddb2f/41598_2022_10601_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e4c1/9035187/18fc8ae3a5a1/41598_2022_10601_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e4c1/9035187/e9cd1a29b2e7/41598_2022_10601_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e4c1/9035187/8835186c2a7a/41598_2022_10601_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e4c1/9035187/7bdb42ce015b/41598_2022_10601_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e4c1/9035187/79a9806d0cb6/41598_2022_10601_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e4c1/9035187/ec7ea97e2a7a/41598_2022_10601_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e4c1/9035187/87bd3a847f37/41598_2022_10601_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e4c1/9035187/f0b1390ddb2f/41598_2022_10601_Fig8_HTML.jpg

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