Artificial intelligence‐driven consensus gene signatures for improving bladder cancer clinical outcomes identified by multi‐center integration analysis. Issue 22 (22nd September 2022)

• Record Type: Journal Article
• Authors: Xu, Hui; Liu, Zaoqu; Weng, Siyuan; Dang, Qin; Ge, Xiaoyong; Zhang, Yuyuan; Ren, Yuqing; Xing, Zhe; Chen, Shuang; Zhou, Yifang; Ren, Jianzhuang; Han, Xinwei
• Is Part Of: Molecular oncology. Volume 16:Issue 22(2022)
• Page Start: 4023
• Page End: 4042
• Publication Date: 2022-09-22
• Subjects: biomarker -- bladder cancer -- immunotherapy -- multi‐omics -- prognosis; Cancer -- Molecular aspects -- Periodicals; 616.994005
• DOI: 10.1002/1878-0261.13313
• Languages: English
• ISSNs: 1574-7891

Abstract

Abstract : To accurately predict the prognosis and further improve the clinical outcomes of bladder cancer (BLCA), we leveraged large‐scale data to develop and validate a robust signature consisting of small gene sets. Ten machine‐learning algorithms were enrolled and subsequently transformed into 76 combinations, which were further performed on eight independent cohorts ( n = 1218). We ultimately determined a consensus artificial intelligence‐derived gene signature (AIGS) with the best performance among 76 model types. In this model, patients with high AIGS showed a higher risk of mortality, recurrence, and disease progression. AIGS is not only independent of traditional clinical traits [(e.g., American Joint Committee on Cancer (AJCC) stage)] and molecular features (e.g., TP53 mutation) but also demonstrated superior performance to these variables. Comparisons with 58 published signatures also indicated that AIGS possessed the best performance. Additionally, the combination of AIGS and AJCC stage could achieve better performance. Patients with low AIGS scores were sensitive to immunotherapy, whereas patients with high AIGS scores might benefit from seven potential therapeutics: BRD‐K45681478, 1S, 3R‐RSL‐3, RITA, U‐0126, temsirolimus, MRS‐1220, and LY2784544. Additionally, some mutations ( TP53 and RB1 ), copy number variations (7p11.2), and a methylation‐driven target were characterized by AIGS‐related multi‐omics alterations. Overall, AIGS provides an attractive platform to optimize decision‐making and surveillance protocol for individual BLCA patients. Abstract : Based on multiple bioinformatics and machine‐learning algorithms, we developed a robust and powerful consensus artificial intelligence gene signature (AIGS) that can accurately predict the prognosis, recurrence, and immune response for bladder cancer. In addition, AIGS is also a promising biomarker for predicting chemotherapy response, and the identification of potential compounds demonstrates dramatic implications of precise treatment for high‐risk patients.