Machine Learning-based Macrophage Signature for Predicting Prognosis and Immunotherapy Benefits in Cholangiocarcinoma.

IF 3.5 4区 医学 Q2 BIOCHEMISTRY & MOLECULAR BIOLOGY Current medicinal chemistry Pub Date : 2024-10-24 DOI:10.2174/0109298673342462241010072026
Junkai Huang, Yu Chen, Zhiguo Tan, Yinghui Song, Kang Chen, Sulai Liu, Chuang Peng, Xu Chen
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Abstract

Aims: We aimed to develop a macrophage signature for predicting clinical outcomes and immunotherapy benefits in cholangiocarcinoma.

Background: Macrophages are potent immune effector cells that can change phenotype in different environments to exert anti-tumor and anti-tumor functions. The role of macrophages in the prognosis and therapy benefits of cholangiocarcinoma was not fully clarified.

Objective: The objective of this study is to develop a prognostic model for cholangiocarcinoma.

Methods: The macrophage-related signature (MRS) was developed using 10 machine learning methods with TCGA, GSE89748 and GSE107943 datasets. Several indicators (TIDE score, TMB score and MATH score) and two immunotherapy datasets (IMvigor210 and GSE91061) were used to investigate the performance of MRS in predicting the benefits of immunotherapy.

Results: The Lasso + CoxBoost method's MRS was considered a robust and stable model that demonstrated good accuracy in predicting the clinical outcome of patients with cholangiocarcinoma; the AUC of the 2-, 3-, and 4-year ROC curves in the TCGA dataset were 0.965, 0.957, and 1.000. Moreover, MRS acted as an independent risk factor for the clinical outcome of cholangiocarcinoma cases. Cholangiocarcinoma cases with higher MRS scores are correlated with a higher TIDE score, higher tumor escape score, higher MATH score, and lower TMB score. Further analysis suggested high MRS score indicated a higher gene set score correlated with cancer-related hallmarks.

Conclusion: With regard to cholangiocarcinoma, the current study created a machine learning-based MRS that served as an indication for forecasting the prognosis and therapeutic advantages of individual cases.

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基于机器学习的巨噬细胞特征用于预测胆管癌的预后和免疫疗法疗效
目的:我们旨在开发一种巨噬细胞特征,用于预测胆管癌的临床预后和免疫疗法的益处:巨噬细胞是一种强效免疫效应细胞,可在不同环境中改变表型,发挥抗肿瘤和抗癌功能。巨噬细胞在胆管癌的预后和治疗中的作用尚未完全明确:本研究旨在建立胆管癌的预后模型:方法:采用10种机器学习方法,利用TCGA、GSE89748和GSE107943数据集开发了巨噬细胞相关特征(MRS)。结果:Lasso + CoxButton、CoxButton和CoxButton均能预测免疫治疗的疗效,而Lasso + CoxButton则不能预测免疫治疗的疗效:Lasso+CoxBoost方法的MRS被认为是一种稳健而稳定的模型,在预测胆管癌患者的临床结局方面表现出良好的准确性;在TCGA数据集中,2年、3年和4年ROC曲线的AUC分别为0.965、0.957和1.000。此外,MRS还是胆管癌病例临床结局的独立风险因素。MRS评分较高的胆管癌病例与较高的TIDE评分、较高的肿瘤逃逸评分、较高的MATH评分和较低的TMB评分相关。进一步分析表明,MRS得分越高,表明与癌症相关特征相关的基因组得分越高:结论:针对胆管癌,本研究创建了基于机器学习的 MRS,可作为预测个别病例预后和治疗优势的指标。
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来源期刊
Current medicinal chemistry
Current medicinal chemistry 医学-生化与分子生物学
CiteScore
8.60
自引率
2.40%
发文量
468
审稿时长
3 months
期刊介绍: Aims & Scope Current Medicinal Chemistry covers all the latest and outstanding developments in medicinal chemistry and rational drug design. Each issue contains a series of timely in-depth reviews and guest edited thematic issues written by leaders in the field covering a range of the current topics in medicinal chemistry. The journal also publishes reviews on recent patents. Current Medicinal Chemistry is an essential journal for every medicinal chemist who wishes to be kept informed and up-to-date with the latest and most important developments.
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