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基于单细胞和 bulk-RNA 测序的机器学习和联合分析,确定了一个 DC 基因特征,用于预测肺腺癌患者的预后和免疫治疗反应。

Machine-learning and combined analysis of single-cell and bulk-RNA sequencing identified a DC gene signature to predict prognosis and immunotherapy response for patients with lung adenocarcinoma.

机构信息

Department of Thoracic Surgery, The First Affiliated Hospital, Fujian Medical University, Fuzhou, 350005, China.

Department of Thoracic Surgery, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, 350212, China.

出版信息

J Cancer Res Clin Oncol. 2023 Nov;149(15):13553-13574. doi: 10.1007/s00432-023-05151-w. Epub 2023 Jul 28.


DOI:10.1007/s00432-023-05151-w
PMID:37507593
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10590321/
Abstract

BACKGROUND: Innate immune effectors, dendritic cells (DCs), influence cancer prognosis and immunotherapy significantly. As such, dendritic cells are important in killing tumors and influencing tumor microenvironment, whereas their roles in lung adenocarcinoma (LUAD) are largely unknown. METHODS: In this study, 1658 LUAD patients from different cohorts were included. In addition, 724 cancer patients who received immunotherapy were also included. To identify DC marker genes in LUAD, we used single-cell RNAsequencing data for analysis and determined 83 genes as DC marker genes. Following that, integrative machine learning procedure was developed to construct a signature for DC marker genes. RESULTS: Using TCGA bulk-RNA sequencing data as the training set, we developed a signature consisting of seven genes and classified patients by their risk status. Another six independent cohorts demonstrated the signature' s prognostic power, and multivariate analysis demonstrated it was an independent prognostic factor. LUAD patients in the high-risk group displayed more advanced features, discriminatory immune-cell infiltrations and immunosuppressive states. Cell-cell communication analysis indicates that tumor cells with lower risk scores communicate more actively with the tumor microenvironment. Eight independent immunotherapy cohorts revealed that patients with low-risk had better immunotherapy responses. Drug sensitivity analysis indicated that targeted therapy agents exhibited greater sensitivity to low-risk patients, while chemotherapy agents displayed greater sensitivity to high-risk patients. In vitro experiments confirmed that CTSH is a novel protective factor for LUAD. CONCLUSIONS: An unique signature based on DC marker genes that is highly predictive of LUAD patients' prognosis and response to immunotherapy. CTSH is a new biomarker for LUAD.

摘要

背景:先天免疫效应细胞,树突状细胞(DC),对癌症预后和免疫治疗有显著影响。因此,树突状细胞在杀伤肿瘤和影响肿瘤微环境方面发挥着重要作用,而它们在肺腺癌(LUAD)中的作用在很大程度上是未知的。

方法:本研究纳入了来自不同队列的 1658 例 LUAD 患者。此外,还纳入了 724 例接受免疫治疗的癌症患者。为了鉴定 LUAD 中的 DC 标记基因,我们使用单细胞 RNA 测序数据进行分析,并确定了 83 个基因作为 DC 标记基因。随后,我们采用集成机器学习程序构建了 DC 标记基因的特征。

结果:我们使用 TCGA 批量 RNA 测序数据作为训练集,开发了一个由七个基因组成的特征,并根据风险状况对患者进行分类。另外六个独立的队列验证了该特征的预后能力,多变量分析表明它是一个独立的预后因素。高风险组的 LUAD 患者表现出更晚期的特征、更具区分性的免疫细胞浸润和免疫抑制状态。细胞间通讯分析表明,风险评分较低的肿瘤细胞与肿瘤微环境的通讯更为活跃。八个独立的免疫治疗队列表明,低风险患者的免疫治疗反应更好。药物敏感性分析表明,靶向治疗药物对低风险患者更敏感,而化疗药物对高风险患者更敏感。体外实验证实 CTSH 是 LUAD 的一种新的保护因子。

结论:基于 DC 标记基因的独特特征,能够高度预测 LUAD 患者的预后和对免疫治疗的反应。CTSH 是 LUAD 的一个新的生物标志物。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/140e/10590321/0d1b2d1261bd/432_2023_5151_Fig10_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/140e/10590321/7b6716857c23/432_2023_5151_Fig1_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/140e/10590321/ee4aa67eb65e/432_2023_5151_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/140e/10590321/0a2aa45c7f53/432_2023_5151_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/140e/10590321/2fd78a5cc79d/432_2023_5151_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/140e/10590321/0d1b2d1261bd/432_2023_5151_Fig10_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/140e/10590321/7b6716857c23/432_2023_5151_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/140e/10590321/e0f0065e9b37/432_2023_5151_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/140e/10590321/c1a798be0603/432_2023_5151_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/140e/10590321/e81a9f68f6cd/432_2023_5151_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/140e/10590321/8a6c33575e33/432_2023_5151_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/140e/10590321/5994f6365846/432_2023_5151_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/140e/10590321/ee4aa67eb65e/432_2023_5151_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/140e/10590321/0a2aa45c7f53/432_2023_5151_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/140e/10590321/2fd78a5cc79d/432_2023_5151_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/140e/10590321/0d1b2d1261bd/432_2023_5151_Fig10_HTML.jpg

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[5]
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[6]
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[7]
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Identification and Validation of a Novel Signature Based on NK Cell Marker Genes to Predict Prognosis and Immunotherapy Response in Lung Adenocarcinoma by Integrated Analysis of Single-Cell and Bulk RNA-Sequencing.

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