| 引用本文: | 黄聪颖,李永星,顾雪锋.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. |
| HUANG Congying,LI Yongxing,GU Xuefeng.Construction of a lysosome-related gene prognostic model for bladder cancer[J].Chinese Journal of Bioinformatics,2025,23(4):277-290. |
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| 摘要: |
| 膀胱癌(BLCA)的高复发率和预后不良是其治疗失败的主要原因。溶酶体作为癌症治疗的潜在靶点一直受到广泛关注。然而,溶酶体相关基因(LRG)在BLCA中的作用仍然不明确。本研究采用综合方法,利用差异分析、单因素Cox回归、Lasso、随机森林和多因素Cox回归,来开发LRG评分。然后,将BLCA患者分为高分组和低分组,以检查LRG评分与各种结果之间的关联,包括预后、功能富集、免疫浸润、免疫治疗和单细胞水平功能。筛选结果得到了5个基因(COL6A1、CTSV、GPC2、GZMH和LRP1)用于构建LRG评分。发现LRG评分低的患者预后显著更好,并且对免疫治疗的反应优于LRG评分高的患者。基于从bulk RNA-seq和单细胞RNA-seq中获得的结果,确定细胞外基质重塑相关过程是高分组和低分组之间的主要区别因素。总之,本研究设计了一个新型LRG评分,并证实了LRG评分在未来临床评估和治疗干预中具有可靠性和适用性,从而为预测BLCA预后提供了有价值的见解。 |
| 关键词: 膀胱癌 溶酶体 预后模型 机器学习 |
| DOI：10.12113/202407012 |
| 分类号:Q343.1+2 |
| 文献标识码:A |
| 基金项目: |
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| Construction of a lysosome-related gene prognostic model for bladder cancer |
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HUANG Congying1, LI Yongxing2, GU Xuefeng1,2
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(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 )
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| 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. |
| Key words: Bladder cancer Lysosome Prognostic model Machine learning |
