综合单细胞和机器学习方法表征LUAD免疫原性细胞死亡和肿瘤微环境。

IF 7.5 2区 医学 Q1 MEDICINE, RESEARCH & EXPERIMENTAL
Han Zhang, Qiuqiao Mu, Yuhang Jiang, Xiaojiang Zhao, Xiaoteng Jia, Kai Wang, Xin Li, Daqiang Sun
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引用次数: 0

摘要

背景:免疫原性细胞死亡(Immunogenic cell death, ICD)触发抗肿瘤免疫反应,在肿瘤微环境(tumor microenvironment, TME)形成中起关键作用。然而,其在肺腺癌(LUAD)进展和免疫治疗反应中的具体作用仍未得到充分探讨。方法:我们将单细胞RNA测序与机器学习相结合,以表征LUAD中icd相关的转录特征。使用五种评分算法对不同细胞类型的ICD活性进行量化。为了建立稳健的预测模型,我们评估了超过100种机器学习算法组合,并根据最高的一致性指数(C-index)选择了CoxBoost + SuperPC方法。得到的icd相关基因标记(ICDRS)在6个外部队列中得到验证。下游分析包括免疫浸润、突变谱、药物敏感性和免疫治疗反应。选择SLC2A1进行qRT-PCR、CCK-8、Transwell、集落形成和异种移植试验的功能验证。结果:单细胞分析显示,巨噬细胞表现出最高的ICD活性,并对细胞间通讯起重要作用。基于icd相关基因,构建了由11个核心基因组成的ICDRS模型,并在多个队列中对112个已发表的LUAD特征显示出优越的预后性能。ICDRS将患者分层为不同的风险组,在总生存率、免疫浸润模式和免疫治疗获益方面存在显著差异。低风险患者表现出更高水平的CD8 + T细胞、树突状细胞和免疫功能评分,同时对标准化疗药物和免疫检查点抑制剂更敏感。功能实验证实SLC2A1在LUAD组织和细胞系中表达上调。沉默SLC2A1抑制体外增殖和侵袭,抑制异种移植物模型中的肿瘤生长,支持其致癌作用。结论:本研究强调了ICD在LUAD免疫调节和预后中的重要作用。ICDRS可作为风险分层和免疫治疗指导的强大生物标志物,而SLC2A1可作为增强免疫治疗疗效的潜在治疗靶点。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Integrative single-cell and machine learning approach to characterize immunogenic cell death and tumor microenvironment in LUAD.

Background: Immunogenic cell death (ICD) triggers antitumor immune responses and plays a critical role in shaping the tumor microenvironment (TME). However, its specific contribution to lung adenocarcinoma (LUAD) progression and immunotherapy response remains insufficiently explored.

Method: We integrated single-cell RNA sequencing with machine learning to characterize ICD-related transcriptional features in LUAD. ICD activity was quantified across cell types using five scoring algorithms. To develop a robust prognostic model, we evaluated over 100 machine learning algorithm combinations and selected the CoxBoost + SuperPC approach based on the highest concordance index (C-index). The resulting ICD-related gene signature (ICDRS) was validated in six external cohorts. Downstream analyses included immune infiltration, mutation profiling, drug sensitivity, and immunotherapy response. SLC2A1 was selected for functional validation using qRT-PCR, CCK-8, Transwell, colony formation, and xenograft assays.

Results: Single-cell analysis revealed that macrophages exhibited the highest ICD activity and contributed significantly to intercellular communication. Based on ICD-associated genes, the ICDRS model consisting of 11 core genes was constructed and showed superior prognostic performance over 112 published LUAD signatures across multiple cohorts. The ICDRS stratified patients into distinct risk groups with significant differences in overall survival, immune infiltration patterns, and immunotherapy benefit. Low-risk patients exhibited higher levels of CD8⁺ T cells, dendritic cells, and immune function scores, along with greater sensitivity to standard chemotherapeutics and immune checkpoint inhibitors. Functional experiments confirmed that SLC2A1 was upregulated in LUAD tissues and cell lines. Silencing SLC2A1 suppressed proliferation and invasion in vitro and inhibited tumor growth in xenograft models, supporting its oncogenic role.

Conclusion: This study highlights the crucial role of ICD in LUAD immune regulation and prognosis. The ICDRS serves as a robust biomarker for risk stratification and immunotherapy guidance, while SLC2A1 emerges as a potential therapeutic target to augment immunotherapeutic efficacy.

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来源期刊
Journal of Translational Medicine
Journal of Translational Medicine 医学-医学:研究与实验
CiteScore
10.00
自引率
1.40%
发文量
537
审稿时长
1 months
期刊介绍: The Journal of Translational Medicine is an open-access journal that publishes articles focusing on information derived from human experimentation to enhance communication between basic and clinical science. It covers all areas of translational medicine.
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