单细胞测序与Bulk RNA测序联合分析构建膀胱癌风险预后模型

Construction of a Novel Bladder Cancer Prognostic Risk Assessment Model Through Combined Single-Cell and Bulk RNA Sequencing Analysis

目的本研究致力于开发一种基于膀胱癌核心细胞差异性表达基因的预后风险评估模型。通过在多个数据集中验证其效力,旨在为膀胱癌患者提供一个新的临床应用工具,用于预后风险评估。方法本研究综合利用了单细胞和Bulk RNA测序数据。我们首先从GEO数据库下载并分析了相关的膀胱癌单细胞和芯片RNA数据集。通过生物信息学方法,我们鉴定了核心细胞的差异性表达基因,并对其进行了功能及通路富集分析。基于这些分析,我们使用单变量和多变量Cox回归方法筛选出与膀胱癌预后显著相关的关键基因,并据此构建了一个预后风险评估模型。该模型在TCGA-BLCA数据集中进一步进行了效力验证。结果经过全面的生物信息学分析,我们鉴定出了223个核心细胞的差异性表达基因。这些基因在细胞外基质的结构和功能方面发挥着重要作用。构建的预后风险评估模型包括5个独立的预后相关基因(MFAP5、PDE4D、ISG15、ADAMTS1和FGL2)。在GEO和TCGA-BLCA数据集中的验证结果表明,该模型具有良好的预测效力,为膀胱癌患者的预后评估提供新型生物学标志工具。结论本研究成功开发了一个基于5个关键基因标志物的膀胱癌预后风险评估模型并具有良好的预测效力。此模型的开发为膀胱癌的生物学研究和临床预后评估提供了一个新的工具,有助于更好地理解膀胱癌的生物学特性并指导患者的个性化治疗。

Objective The aim of this study was to develop a prognostic risk assessment model based on differential expression of core cell genes in bladder cancer.By validating its efficacy across multiple datasets,it aims to provide a new tool for clinical application in bladder cancer patients for prognostic risk assessment.Methods This research utilized a combination of single-cell and Bulk RNA sequencing data.We began by downloading and analyzing bladder cancer single-cell and microarray RNA datasets from the GEO database.Using bioinformatics methods,we identified differential gene expressions in core cells and conducted functional and pathway enrichment analyses.Based on these analyses,key genes significantly related to bladder cancer prognosis were selected using univariate and multivariate Cox regression methods,leading to the development of a prognostic risk assessment model.This model was further validated in the TCGA-BLCA dataset.Results Through comprehensive bioinformatics analysis,we identified 223 differentially expressed genes in core cells.These genes play significant roles in the structure and function of the extracellular matrix.The constructed prognostic risk assessment model includes five independent prognostic-related genes(MFAP5,PDE4D,ISG15,ADAMTS1,and FGL2).Validation in the GEO and TCGA-BLCA datasets demonstrated the model′s robust predictive power,offering a novel biological marker tool for the prognosis assessment of bladder cancer patients.Conclusion This study successfully developed a bladder cancer prognostic risk assessment model based on five key gene markers,demonstrating strong predictive efficacy.The development of this model provides a new tool for biological research and clinical prognostic assessment of bladder cancer,aiding in a better understanding of the disease′s biological characteristics and guiding personalized treatment for patients.