利用单细胞测序鉴定膀胱癌患者的巨噬细胞相关基因并构建预后模型

IF 1.4 Q4 IMMUNOLOGY American journal of clinical and experimental immunology Pub Date : 2024-06-25 eCollection Date: 2024-01-01 DOI:10.62347/VLDZ7581
Weizhuo Wang, Junheng Shen, Dalong Song, Kai Fu, Xu Fu
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引用次数: 0

摘要

单细胞测序是一种新兴技术,能有效识别肿瘤中的细胞类型。在膀胱癌的肿瘤微环境中,巨噬细胞对肿瘤的侵袭和免疫逃逸起着至关重要的作用。本研究旨在评估膀胱癌患者肿瘤微环境中巨噬细胞相关基因(MRGs)的表达情况,并利用生物信息学方法构建基于MRGs的预后模型:方法:从 GEO 下载膀胱癌患者的单细胞测序数据。方法:从 GEO 下载膀胱癌患者的单细胞测序数据,经过质量控制和细胞类型鉴定后,提取样本中的巨噬细胞进行重新聚类。然后确定特征基因,评估MRGs。此外,还从 TCGA 数据库下载并整理了膀胱癌患者的基因数据。确定MRGs与TCGA基因集的交集。临床信息与该交集相连,数据被分为训练集和验证集。训练集用于构建模型,验证集用于验证模型。利用 LASSO 算法和 Cox 回归建立了基于 MRGs 的预后模型。根据预后特征将患者分为高风险组和低风险组,并观察训练集和验证集的生存信息。使用 ROC 曲线评估了模型的预测能力,随后使用校准图预测了 1 年、3 年和 5 年的生存率:结果:确定了四种细胞类型,提取巨噬细胞后,对三个细胞亚群进行了聚类,得出了 1,078 个特征基因。从每个巨噬细胞亚群中提取前100个特征基因,并与TCGA表达基因交叉构建模型。得到了一个由CD74、METRN、PTPRR和CDC42EP5组成的风险预测模型。生存曲线和 ROC 曲线显示,该模型具有良好的预测能力。校准曲线也证明了该模型对患者具有良好的预后能力:本研究基于单细胞数据、TCGA 数据和临床信息,利用多组学方法构建了基于 MRG 的膀胱癌预后模型。该模型在预测膀胱癌患者的生存期和预后方面具有良好的准确性和可靠性,为了解MRGs与膀胱癌之间的相互作用提供了参考。
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Identification of macrophage-related genes in bladder cancer patients using single-cell sequencing and construction of a prognostic model.

Single-cell sequencing is an emerging technology that can effectively identify cell types in tumors. In the tumor microenvironment of bladder cancer, macrophages play a crucial role in invasion and immune escape. This study aimed to assess the expression of macrophage-related genes (MRGs) in the tumor microenvironment of bladder cancer patients and construct a prognostic model based on MRGs using bioinformatics methods.

Methods: Single-cell sequencing data from bladder cancer patients was downloaded from the GEO. After quality control and cell type identification, macrophages in the samples were extracted for re-clustering. Feature genes were then identified, and MRGs were assessed. Genetic data from TCGA database bladder cancer patients was also downloaded and organized. The intersection of MRGs and the TCGA gene set was determined. Clinical information was connected with this intersection, and the data was divided into training and validation sets. The training set was used for model construction and the validation set for model verification. A prognostic model based on MRGs was built using the LASSO algorithm and Cox regression. Patients were divided into high-risk and low-risk groups based on their prognostic features, and survival information in the training and validation sets was observed. The predictive ability of the model was assessed using a ROC curve, followed by a calibration plot to predict 1-, 3-, and 5-year survival rates.

Results: Four cell types were identified, and after extracting macrophages, three cell subgroups were clustered, resulting in 1,078 feature genes. The top 100 feature genes from each macrophage subgroup were extracted and intersected with TCGA expressed genes to construct the model. A risk prediction model composed of CD74, METRN, PTPRR, and CDC42EP5 was obtained. The survival and ROC curves showed that this model had good predictive ability. A calibration curve also demonstrated good prognostic ability for patients.

Conclusion: This study, based on single-cell data, TCGA data, and clinical information, constructed an MRG-based prognostic model for bladder cancer using multi-omics methods. This model has good accuracy and reliability in predicting the survival and prognosis of patients with bladder cancer, providing a reference for understanding the interaction between MRGs and bladder cancer.

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