摘要
出身背景
细胞凋亡相关基因（ARGs）被用于开发一种新的特征来预测总生存率（OS），并检查其与膀胱癌症（BC）免疫浸润的关系。
方法
从在线数据集中获取BC样本的基因表达矩阵以及相关临床数据。根据从正常膀胱组织和癌症样本中获得的差异表达的ARG，进行功能富集分析。在LASSO和Cox回归分析的帮助下，成功地建立了一个新的模型，并通过外部和内部验证进行了评估。
后果
最终，使用17个ARG（SLC5A6、GULP1、TAP1、MMP9、P4HB、FOXL2、CIDEC、EN2、NES、EPHA7、SUSD2、TMPRSS3、HOXB7、SATB1、MEST、PCDHGC3、ASPM）来构建签名。我们构建的特征通过内部和外部验证显著区分了OS的高风险和低风险BC患者，也被证明能够作为独立的预后生物标志物（均P<0.05）。此外，还基于TCGA数据集构建了预后列线图，以预测BC患者的OS预后。此外，这种基于ARG的模型与肿瘤分期（P=3.98e−06）、种族（P=8.255e−06。
结论
我们根据不列颠哥伦比亚省的17个ARG，通过外部和内部验证，成功开发了预后标志，使临床医生能够预测不列颠哥伦比亚省患者的OS，并促进患者护理的特定个性化。
Abstract
Background
Apoptosis-related genes (ARGs) were used to develop a novel signature for forecasting overall survival (OS) and examining their relationships with immune infiltrates in bladder cancer (BC).

Methods
Gene expression matrices as well as related clinical data were acquired for BC samples from online datasets. According to differentially expressed ARGs acquired from normal bladder tissues and cancer samples, functional enrichment analyses were conducted. With the assistance of LASSO and Cox regression analysis, a novel model was successfully established and evaluated by external and internal validations.

Results
Eventually, 17 ARGs (SLC5A6, GULP1, TAP1, MMP9, P4HB, FOXL2, CIDEC, EN2, NES, EPHA7, SUSD2, TMPRSS3, HOXB7, SATB1, MEST, PCDHGC3, ASPM) were utilized to construct the signature. Our constructed signature significantly distinguished high-risk from low-risk BC patients of OS by internal and external validations and was also proven to be able to serve as an independent prognostic biomarker (all P < 0.05). Furthermore, a prognostic nomogram was also constructed based on TCGA dataset to predict OS prognosis in BC suffers. Besides, this ARG based model was markedly associated with clinical characteristics like tumor stage (P = 3.98e−06), race (P = 8.255e−06), N stage (P = 0.002), T stage (P = 3.679e−05) and M stage (P = 0.002). As for immune infiltration, our established model was significantly associated with seven tumor-infiltrating immune cells.

Conclusions
A prognostic signature was successfully developed by us according to 17 ARGs in BC using external and internal verifications, enabling clinicians to predict BC suffers' OS and promote specific individualization of patient care.

https://www.sciencedirect.com/science/article/pii/S2405844022026317