结合免疫检查点基因预测肺腺癌免疫疗法疗效的预后模型:一项整合机器学习算法的队列研究。

IF 4.3 3区 材料科学 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
ACS Applied Electronic Materials Pub Date : 2024-08-01 Epub Date: 2024-05-16 DOI:10.1007/s12026-024-09492-7
Xi-Lin Yang, Zheng Zeng, Chen Wang, Guang-Yu Wang, Fu-Quan Zhang
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引用次数: 0

摘要

本研究旨在开发和验证一种基于免疫检查点基因(ICGs)的提名图,用于预测肺腺癌(LUAD)患者的预后和免疫检查点阻断疗法(ICB)的疗效。TCGA数据库中的385名LUAD患者和合并数据集(GSE41272 + GSE50081)中的269名LUAD患者分别被分为训练组和验证组。采用三种不同的机器学习算法,包括随机森林(RF)、最小绝对收缩和选择算子(LASSO)逻辑回归分析和支持向量机(SVM),从82个ICGs中选择预测标志物,构建预后提名图。根据提名图得出的风险评分,使用X-tile软件将患者分为高风险亚组和低风险亚组。利用基因组变异分析(GSVA)和各种算法评估了两个亚组在功能富集和免疫浸润方面的差异。此外,还利用接受 ICB 治疗的三个肺癌队列来评估该模型在现实世界中预测 ICB 疗效的能力。在所有三种机器学习算法中,有五个 ICG 被确定为预测标志物,从而构建了一个提名图,该提名图在训练队列和验证队列中都具有很强的预后预测潜力(所有 AUC 值均接近 0.800)。患者被分为高风险亚组(风险评分≥ 185.0)和低风险亚组(风险评分≥ 185.0)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Prognostic model incorporating immune checkpoint genes to predict the immunotherapy efficacy for lung adenocarcinoma: a cohort study integrating machine learning algorithms.

Prognostic model incorporating immune checkpoint genes to predict the immunotherapy efficacy for lung adenocarcinoma: a cohort study integrating machine learning algorithms.

This study aimed to develop and validate a nomogram based on immune checkpoint genes (ICGs) for predicting prognosis and immune checkpoint blockade (ICB) efficacy in lung adenocarcinoma (LUAD) patients. A total of 385 LUAD patients from the TCGA database and 269 LUAD patients in the combined dataset (GSE41272 + GSE50081) were divided into training and validation cohorts, respectively. Three different machine learning algorithms including random forest (RF), least absolute shrinkage and selection operator (LASSO) logistic regression analysis, and support vector machine (SVM) were employed to select the predictive markers from 82 ICGs to construct the prognostic nomogram. The X-tile software was used to stratify patients into high- and low-risk subgroups based on the nomogram-derived risk scores. Differences in functional enrichment and immune infiltration between the two subgroups were assessed using gene set variation analysis (GSVA) and various algorithms. Additionally, three lung cancer cohorts receiving ICB therapy were utilized to evaluate the ability of the model to predict ICB efficacy in the real world. Five ICGs were identified as predictive markers across all three machine learning algorithms, leading to the construction of a nomogram with strong potential for prognosis prediction in both the training and validation cohorts (all AUC values close to 0.800). The patients were divided into high- (risk score ≥ 185.0) and low-risk subgroups (risk score < 185.0). Compared to the high-risk subgroup, the low-risk subgroup exhibited enrichment in immune activation pathways and increased infiltration of activated immune cells, such as CD8 + T cells and M1 macrophages (P < 0.05). Furthermore, the low-risk subgroup had a greater likelihood of benefiting from ICB therapy and longer progression-free survival (PFS) than did the high-risk subgroup (P < 0.05) in the two cohorts receiving ICB therapy. A nomogram based on ICGs was constructed and validated to aid in predicting prognosis and ICB treatment efficacy in LUAD patients.

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CiteScore
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