生物信息学2025，Vol.23Issue(4)：277-290,14.DOI:10.12113/202407012
基于溶酶体相关基因的膀胱癌预后模型构建
Construction of a lysosome-related gene prognostic model for bladder cancer
HUANG Congying 1LI Yongxing 2GU Xuefeng3
作者信息
- 1. School of Health Sciences and Engineering,University of Shanghai for Science and Technology,Shanghai 200093,China
- 2. School of Pharmacy,Shanghai University of Medicine&Health Sciences,Shanghai 201318,China
- 3. School of Health Sciences and Engineering,University of Shanghai for Science and Technology,Shanghai 200093,China||School of Pharmacy,Shanghai University of Medicine&Health Sciences,Shanghai 201318,China
- 折叠
摘要
Abstract
The high recurrence rate and poor prognosis of bladder cancer(BLCA)are the primary causes of treatment failure.Lysosomes have garnered significant attention as potential therapeutic targets in cancer.However,the role of lysosome-related genes(LRGs)in BLCA remains unclear.This study employs a comprehensive approach involving differential analysis,univariate Cox regression,Lasso,random forest,and multivariate Cox regression to develop an LRG score.BLCA patients are classified into high-and low-score groups to examine associations between the LRG score and multiple outcomes,including prognosis,functional enrichment,immune infiltration,immunotherapy response,and single-cell-level functional characteristics.Screening identifies five genes(COL6A1,CTSV,GPC2,GZMH,and LRP1)for constructing the LRG score.The low LRG score group exhibits a significantly better prognosis and demonstrates superior response to immunotherapy compared to the high-score group.Based on bulk RNA-seq and single-cell RNA-seq analyses,extracellular matrix remodeling-related processes are identified as the primary distinguishing factor between the two groups.In conclusion,this study designs a novel LRG score and confirms its reliability and applicability in future clinical assessment and therapeutic interventions,providing valuable insights for predicting BLCA prognosis.关键词
膀胱癌/溶酶体/预后模型/机器学习Key words
Bladder cancer/Lysosome/Prognostic model/Machine learning分类
生物科学引用本文复制引用
HUANG Congying,LI Yongxing,GU Xuefeng..基于溶酶体相关基因的膀胱癌预后模型构建[J].生物信息学,2025,23(4):277-290,14.
