Department of Urology, Fudan University Shanghai Cancer Center, Shanghai, PR China.
Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, PR China.
Ann Med. 2021 Dec;53(1):596-610. doi: 10.1080/07853890.2021.1908588.
This study aims to identify potential prognostic biomarkers of bladder cancer (BCa) based on large-scale multi-omics data and investigate the role of SRC in improving predictive outcomes for BCa patients and those receiving immune checkpoint therapies (ICTs).
Large-scale multi-comic data were enrolled from the Cancer Proteome Atlas, the Cancer Genome Atlas and gene expression omnibus based on machining-learning methods. Immune infiltration, survival and other statistical analyses were implemented using R software in cancers ( = 12,452). The predictive value of SRC was performed in 81 BCa patients receiving ICT from aa validation cohort ( = 81).
Landscape of novel candidate prognostic protein signatures of BCa patients was identified. Differential BECLIN, EGFR, PKCALPHA, ANNEXIN1, AXL and SRC expression significantly correlated with the outcomes for BCa patients from multiply cohorts ( = 906). Notably, risk score of the integrated prognosis-related proteins (IPRPs) model exhibited high diagnostic accuracy and consistent predictive ability (AUC = 0.714). Besides, we tested the clinical relevance of baseline SRC protein and mRNA expression in two independent confirmatory cohorts ( = 566) and the prognostic value in pan-cancers. Then, we found that elevated SRC expression contributed to immunosuppressive microenvironment mediated by immune checkpoint molecules of BCa and other cancers. Next, we validated SRC expression as a potential biomarker in predicting response to ICT in 81 BCa patient from FUSCC cohort, and found that expression of SRC in the baseline tumour tissues correlated with improved survival benefits, but predicts worse ICT response.
This study first performed the large-scale multi-omics analysis, distinguished the IPRPs ( and ) and revealed novel prediction model, outperforming the currently traditional prognostic indicators for anticipating BCa progression and better clinical strategies. Additionally, this study provided insight into the importance of biomarker SRC for better prognosis, which may inversely improve predictive outcomes for patients receiving ICT and enable patient selection for future clinical treatment.
本研究旨在基于大规模多组学数据,确定膀胱癌(BCa)的潜在预后生物标志物,并探讨 SRC 在提高 BCa 患者和接受免疫检查点治疗(ICTs)患者的预测结果方面的作用。
采用机器学习方法,从癌症蛋白组图谱(Cancer Proteome Atlas)、癌症基因组图谱(Cancer Genome Atlas)和基因表达综合数据库(gene expression omnibus)中纳入大规模多组学数据。使用 R 软件对免疫浸润、生存等进行统计分析。在一个验证队列(81 例接受 ICT 的 BCa 患者,验证队列)中,对 SRC 的预测价值进行了评估。
确定了 BCa 患者新型候选预后蛋白标志物的全景。来自多个队列的(906 例)BCa 患者的差异 BECLIN、EGFR、PKCALPHA、ANNEXIN1、AXL 和 SRC 表达与患者的结局显著相关。值得注意的是,综合预后相关蛋白(IPRPs)模型的风险评分显示出较高的诊断准确性和一致的预测能力(AUC=0.714)。此外,我们在两个独立的验证队列(566 例)中测试了基线 SRC 蛋白和 mRNA 表达的临床相关性,以及在泛癌中的预后价值。然后,我们发现升高的 SRC 表达有助于免疫检查点分子介导的 BCa 和其他癌症的免疫抑制微环境。接下来,我们在 FUSCC 队列中验证了 SRC 表达作为预测 ICT 反应的潜在生物标志物,发现基线肿瘤组织中 SRC 的表达与改善的生存获益相关,但预测 ICT 反应较差。
本研究首次进行了大规模多组学分析,区分了 IPRPs(和),并揭示了新的预测模型,优于目前用于预测 BCa 进展和更好临床策略的传统预后指标。此外,本研究深入了解了生物标志物 SRC 对更好预后的重要性,这可能会反转为接受 ICT 的患者改善预测结果,并为未来的临床治疗选择患者。