Identification of Immune-Related Gene Signature Model for Predicting Lung Cancer Survival and Response to Immunotherapy.

IF 2.5 3区医学 Q3 ONCOLOGY

Oncology Pub Date : 2024-10-16 DOI:10.1159/000541990

Wenrong Lin, XiaoJun Cai, YiJin Lin, Weikun Su, Guibin Weng, Lin Chen, Jianming Ding, Yibin Cai

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引用次数: 0

Abstract

Introduction: Studies have shown that immune-related genes play a crucial role in tumor development and treatment. However, the specific roles and potential value of these genes in lung cancer patients are still not fully understood. Therefore, this study aims to establish a novel risk model based on immune-related genes for evaluating the prognostic risk and response to immune therapy in lung cancer patients.

Methods: Gene expression and clinical data of lung cancer patients were retrieved from the Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases, while immune-related genes were obtained from the ImmPort database. A risk signature model was developed using univariate Cox analysis and LASSO regression analysis. The prognostic value of the model and its response to immunotherapy were analyzed by survival analysis, immune infiltration analysis, and immunotherapy response analysis.

Results: We have developed a risk signature model based on eight key immune-related genes, which can classify patients into high-risk and low-risk groups. The prognosis of the high-risk group was significantly lower than that of the low-risk group and was validated in multiple GEO datasets. The mutation frequency was lower in the low-risk group compared to the high-risk group (TP53: 55% vs. 65%; TTN: 52% vs. 60%; CSMD3: 34% vs. 45%). Futhermore, CD274 expression was lower in the low-risk patients, and the high-risk patients in the IMvigor210 cohort had lower survival. Immune infiltration analyses showed that the high-risk group was negatively correlated with the infiltration level of B cells, CD4+ T cells, and NK cells. Importantly, patients in the low-risk group exhibit significantly lower TIDE scores, suggesting that they are more responsive to immunotherapy.

Conclusion: Our study has established a novel and robust immune-related gene risk model that can assist in evaluating the prognostic risk and immune therapy response of lung cancer patients.

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鉴定用于预测肺癌生存率和免疫疗法反应的免疫相关基因特征模型。

背景：研究表明，免疫相关基因在肿瘤发生和治疗中起着至关重要的作用。然而，这些基因在肺癌患者中的具体作用和潜在价值仍未得到充分了解。因此，本研究旨在建立一个基于免疫相关基因的新型风险模型，用于评估肺癌患者的预后风险和对免疫治疗的反应：方法：肺癌患者的基因表达和临床数据来自癌症基因组图谱（TCGA）和基因表达总库（GEO）数据库，免疫相关基因来自ImmPort数据库。利用单变量 Cox 分析和 LASSO 回归分析建立了风险特征模型。通过生存分析、免疫浸润分析和免疫治疗反应分析，对模型的预后价值及其对免疫治疗的反应进行了分析：我们建立了一个基于八个关键免疫相关基因的风险特征模型，该模型可将患者分为高危和低危两组。高危组的预后明显低于低危组，这在多个 GEO 数据集中得到了验证。与高危组相比，低危组的突变频率较低（TP53：55% vs 65%；TTN：52% vs 60%；CSMD3：34% vs 45%）。此外，CD274在低危患者中表达较低，IMvigor210队列中的高危患者生存率较低。免疫浸润分析显示，高风险组与 B 细胞、CD4+ T 细胞和 NK 细胞的浸润水平呈负相关。重要的是，低风险组患者的TIDE评分明显较低，这表明他们对免疫疗法的反应更强：我们的研究建立了一个新颖、稳健的免疫相关基因风险模型，有助于评估肺癌患者的预后风险和免疫治疗反应。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Oncology 医学-肿瘤学

CiteScore

6.00

自引率

2.90%

发文量

审稿时长

6-12 weeks

期刊介绍： Although laboratory and clinical cancer research need to be closely linked, observations at the basic level often remain removed from medical applications. This journal works to accelerate the translation of experimental results into the clinic, and back again into the laboratory for further investigation. The fundamental purpose of this effort is to advance clinically-relevant knowledge of cancer, and improve the outcome of prevention, diagnosis and treatment of malignant disease. The journal publishes significant clinical studies from cancer programs around the world, along with important translational laboratory findings, mini-reviews (invited and submitted) and in-depth discussions of evolving and controversial topics in the oncology arena. A unique feature of the journal is a new section which focuses on rapid peer-review and subsequent publication of short reports of phase 1 and phase 2 clinical cancer trials, with a goal of insuring that high-quality clinical cancer research quickly enters the public domain, regardless of the trial’s ultimate conclusions regarding efficacy or toxicity.