Machine Learning-enhanced Signature of Metastasis-related T Cell Marker Genes for Predicting Overall Survival in Malignant Melanoma.

IF 3.2 4区 医学 Q3 IMMUNOLOGY
Chaoxin Fan, Yimeng Li, Aimin Jiang, Rui Zhao
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引用次数: 0

Abstract

In this study, we aimed to investigate disparities in the tumor immune microenvironment (TME) between primary and metastatic malignant melanoma (MM) using single-cell RNA sequencing (scRNA-seq) and to identify metastasis-related T cell marker genes (MRTMGs) for predicting patient survival using machine learning techniques. We identified 6 distinct T cell clusters in 10×scRNA-seq data utilizing the Uniform Manifold Approximation and Projection (UMAP) algorithm. Four machine learning algorithms highlighted SRGN, PMEL, GPR143, EIF4A2, and DSP as pivotal MRTMGs, forming the foundation of the MRTMGs signature. A high MRTMGs signature was found to be correlated with poorer overall survival (OS) and suppression of antitumor immunity in MM patients. We developed a nomogram that combines the MRTMGs signature with the T stage and N stage, which accurately predicts 1-year, 3-year, and 5-year OS probabilities. Furthermore, in an immunotherapy cohort, a high MRTMG signature was associated with an unfavorable response to anti-programmed death 1 (PD-1) therapy. In conclusion, primary and metastatic MM display distinct TME landscapes with different T cell subsets playing crucial roles in metastasis. The MRTMGs signature, established through machine learning, holds potential as a valuable biomarker for predicting the survival of MM patients and their response to anti-PD-1 therapy.

机器学习增强型转移相关 T 细胞标记基因特征用于预测恶性黑色素瘤的总生存期
在这项研究中,我们旨在利用单细胞 RNA 测序(scRNA-seq)研究原发性和转移性恶性黑色素瘤(MM)之间肿瘤免疫微环境(TME)的差异,并利用机器学习技术识别转移相关的 T 细胞标记基因(MRTMGs)以预测患者的生存期。我们利用统一表层逼近和投影(UMAP)算法在10×scRNA-seq数据中确定了6个不同的T细胞群。四种机器学习算法强调 SRGN、PMEL、GPR143、EIF4A2 和 DSP 为关键的 MRTMGs,构成了 MRTMGs 特征的基础。研究发现,高MRTMGs特征与MM患者较差的总生存期(OS)和抗肿瘤免疫力抑制相关。我们开发了一种将MRTMGs特征与T分期和N分期相结合的提名图,可准确预测1年、3年和5年的OS概率。此外,在免疫疗法队列中,高MRTMGs特征与抗程序性死亡1(PD-1)疗法的不利反应相关。总之,原发性和转移性MM显示出不同的TME景观,不同的T细胞亚群在转移中发挥着关键作用。通过机器学习建立的MRTMGs特征有望成为预测MM患者生存率及其对抗PD-1疗法反应的重要生物标志物。
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
Journal of Immunotherapy
Journal of Immunotherapy 医学-免疫学
CiteScore
6.90
自引率
0.00%
发文量
79
审稿时长
6-12 weeks
期刊介绍: Journal of Immunotherapy features rapid publication of articles on immunomodulators, lymphokines, antibodies, cells, and cell products in cancer biology and therapy. Laboratory and preclinical studies, as well as investigative clinical reports, are presented. The journal emphasizes basic mechanisms and methods for the rapid transfer of technology from the laboratory to the clinic. JIT contains full-length articles, review articles, and short communications.
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