基于肥大细胞相关基因的膀胱癌预后模型构建

中华养生保健 ›› 2024, Vol. 42 ›› Issue (12): 1-5.

• 论著 • 下一篇

基于肥大细胞相关基因的膀胱癌预后模型构建

米俊豪^1,2, 周荣斌^1,2, 杨日荣^1,3,*

1.广西医科大学基因组与个体化医学研究中心,广西基因组与个体化医学研究重点实验室,广西基因组与个体化医学协同创新中心,广西南宁,530021;
2.广西医科大学再生医学与医用生物资源开发应用省部共建协同创新中心,广西南宁,530021;
3.广西医科大学基础医学院免疫学教研室,广西南宁,530021

出版日期:2024-06-16 发布日期:2024-06-14
通讯作者: ^*杨日荣,E-mail：yangrirong@sr.gxmu.edu.cn。
作者简介:米俊豪（1996—）,男,汉族,籍贯：山西省运城市,在读硕士研究生,研究方向：肿瘤免疫。
基金资助:
国家自然科学基金（82260575）

Construction of Bladder Cancer Prognostic Model Based on Mast Cell-related Genes

MI Jun-hao^1,2, ZHOU Rong-bin^1,2, YANG Ri-rong^1,3,*

1. Center for Genomic and Personalized Medicine, Guangxi key Laboratory for Genomic and Personalized Medicine, Guangxi Collaborative Innovation Center for Genomic and Personalized Medicine, Guangxi Medical University, Nanning Guangxi 530021, China;
2. Collaborative Innovation Centre of Regenerative Medicine and Medical BioResource Development and Application Co-constructed by the Province and Ministry, Guangxi Medical University, Nanning Guangxi 530021, China;
3. Department of Immunology, School of Basic Medical Sciences, Guangxi Medical University, Nanning Guangxi 530021, China

Online:2024-06-16 Published:2024-06-14

摘要/Abstract

摘要： 目的分析肥大细胞相关基因与膀胱癌预后间的关系,筛选预后关键基因,构建膀胱癌预后风险模型。方法利用肥大细胞批量RNA测序（bulk RNA-seq）数据提取差异基因,并进行KEGG、GO分析与基因集富集分析（GSEA）。通过查阅文献获得膀胱癌单细胞测序内的肥大细胞特征基因。选取共有基因,使用Lasso回归与多因素COX回归筛选关键预后基因,基于风险评分,将患者分为高风险组和低风险组。最后通过单因素与多因素COX回归分析,结合风险评分与多个独立预后因素共同开发列线图以预测膀胱癌患者的生存率。结果构建预后模型的五个基因是WDR45B、EI24、NCOR1、VEGFA和RNF19A,五个基因成功将患者分为高风险组和低风险组。相较于低风险组,高风险组患者生存预后显著变差。同时成功在GSE31864数据验证模型效能。此外,在整合T/N分期、风险评分和年龄而构建的列线图,在膀胱癌患者生存预后方面具有良好的预测性。结论本研究提出一种基于膀胱癌患者肥大细胞五个基因的预后风险模型,可帮助临床医生评估预后情况,为膀胱癌患者提供个性化治疗建议。

关键词: 肥大细胞, RNA测序, 膀胱癌, LPS, 预后风险模型

Abstract: Objective Analyzing the relationship between mast cell-related genes and bladder cancer prognosis, screening prognostic key genes, and constructing a bladder cancer prognosis model. Methods Utilizing bulk RNA-seq data from mast cells, differential gene expression analysis was conducted followed by KEGG and GO enrichment analyses as well as Gene Set Enrichment Analysis (GSEA). Mast cell characteristic genes within bladder cancer single cells RNA sequencing were obtained from the literature. After identifying overlapping genes, key prognostic genes were selected using Lasso regression and multivariable COX regression. Based on the risk score, patients were stratified into high-risk and low-risk groups. Finally, through univariable and multivariable COX regression analyses, in conjunction with risk scores and multiple independent prognostic factors, a nomogram was developed to predict the survival rate of bladder cancer patients. Results The prognostic model comprises five genes: WDR45B, EI24, NCOR1, VEGFA, and RNF19A, effectively stratifying patients into high-risk and low-risk groups. Patients in the high-risk group exhibit significantly poorer survival prognosis compared to those in the low-risk group. The model's efficacy was successfully validated using the GSE31864 dataset. Additionally, the forest plot, constructed by integrating T/N staging, risk score, and age, demonstrates excellent predictive performance for bladder cancer patient survival prognosis. Conclusion This study proposes a prognostic risk model based on five mast cell-related genes in bladder cancer patients. It can assist clinicians in evaluating prognosis and providing personalized treatment recommendations for bladder cancer patients.

Key words: mast cells, RNA sequencing, bladder cancer, LPS, prognostic risk model

中图分类号:

R392.3

米俊豪, 周荣斌, 杨日荣. 基于肥大细胞相关基因的膀胱癌预后模型构建[J]. 中华养生保健, 2024, 42(12): 1-5.

MI Jun-hao, ZHOU Rong-bin, YANG Ri-rong. Construction of Bladder Cancer Prognostic Model Based on Mast Cell-related Genes[J]. ZHONGHUA YANGSHENG BAOJIAN, 2024, 42(12): 1-5.

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基于肥大细胞相关基因的膀胱癌预后模型构建

Construction of Bladder Cancer Prognostic Model Based on Mast Cell-related Genes

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