Multi-cohort validation of Ascore: an anoikis-based prognostic signature for predicting disease progression and immunotherapy response in bladder cancer

被引:0
|
作者
Tianlei Xie
Shan Peng
Shujun Liu
Minghao Zheng
Wenli Diao
Meng Ding
Yao Fu
Hongqian Guo
Wei Zhao
Junlong Zhuang
机构
[1] Nanjing Drum Tower Hospital,Department of Urology
[2] The Affiliated Hospital of Nanjing University Medical School,Department of Urology
[3] Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University,Department of Pathology
[4] Affiliated Drum Tower Hospital,Department of Urology, Jiangsu Province Hospital of Chinese Medicine
[5] Medical School of Nanjing University,Department of Clinical Biochemistry School of Laboratory Medicine/Sichuan Provincial Engineering Laboratory for Prevention and Control Technology of Veterinary Drug Residue in Animal
[6] Affiliated Hospital of Nanjing University of Chinese Medicine,Origin Food
[7] Chengdu Medical College,undefined
来源
关键词
Bladder cancer; Prognostic signature; Immunotherapy; Anoikis; Tumor immune microenvironment;
D O I
暂无
中图分类号
学科分类号
摘要
Bladder cancer ranks as the 10th most common cancer worldwide, with deteriorating prognosis as the disease advances. While immune checkpoint inhibitors (ICIs) have shown promise in clinical therapy in both operable and advanced bladder cancer, identifying patients who will respond is challenging. Anoikis, a specialized form of cell death that occurs when cells detach from the extracellular matrix, is closely linked to tumor progression. Here, we aimed to explore the anoikis-based biomarkers for bladder cancer prognosis and immunotherapeutic decisions. Through consensus clustering, we categorized patients from the TCGA-BLCA cohort into two clusters based on anoikis-related genes (ARGs). Significant differences in survival outcome, clinical features, tumor immune environment (TIME), and potential ICIs response were observed between clusters. We then formulated a four-gene signature, termed "Ascore", to encapsulate this gene expression pattern. The Ascore was found to be closely associated with survival outcome and served as an independent prognosticator in both the TCGA-BLCA cohort and the IMvigor210 cohort. It also demonstrated superior predictive capacity (AUC = 0.717) for bladder cancer immunotherapy response compared to biomarkers like TMB and PD-L1. Finally, we evaluated Ascore’s independent prognostic performance as a non-invasive biomarker in our clinical cohort (Gulou-Cohort1) using circulating tumor cells detection, achieving an AUC of 0.803. Another clinical cohort (Gulou-Cohort2) consisted of 40 patients undergoing neoadjuvant anti-PD-1 treatment was also examined. Immunohistochemistry of Ascore in these patients revealed its correlation with the pathological response to bladder cancer immunotherapy (P = 0.004). Impressively, Ascore (AUC = 0.913) surpassed PD-L1 (AUC = 0.662) in forecasting immunotherapy response and indicated better net benefit. In conclusion, our study introduces Ascore as a novel, robust prognostic biomarker for bladder cancer, offering a new tool for enhancing immunotherapy decisions and contributing to the tailored treatment approaches in this field.
引用
收藏
相关论文
共 50 条
  • [1] Multi-cohort validation of Ascore: an anoikis-based prognostic signature for predicting disease progression and immunotherapy response in bladder cancer
    Xie, Tianlei
    Peng, Shan
    Liu, Shujun
    Zheng, Minghao
    Diao, Wenli
    Ding, Meng
    Fu, Yao
    Guo, Hongqian
    Zhao, Wei
    Zhuang, Junlong
    MOLECULAR CANCER, 2024, 23 (01)
  • [2] Development of an anoikis-related gene signature and prognostic model for predicting the tumor microenvironment and response to immunotherapy in colorectal cancer
    Li, Chuanchang
    Weng, Junyong
    Yang, Le
    Gong, Hangjun
    Liu, Zhaolong
    FRONTIERS IN IMMUNOLOGY, 2024, 15
  • [3] Prognostic potential of m7G-associated lncRNA signature in predicting bladder cancer response to immunotherapy and chemotherapy
    Li, Deng-xiong
    Wu, Rui-cheng
    Wang, Jie
    Feng, De-chao
    Deng, Shi
    ONCOLOGIE, 2023, 25 (06) : 729 - 742
  • [4] The inflammatory response-related robust machine learning signature in endometrial cancer: Based on multi-cohort studies
    Zhou, Chufan
    Xiang, Pan
    Xu, Xindi
    Yue, Chaomin
    Gao, Kefei
    JOURNAL OF GENE MEDICINE, 2024, 26 (01):
  • [5] Integration of Machine Learning and Experimental Validation to Identify Anoikis-Related Prognostic Signature for Predicting the Breast Cancer Tumor Microenvironment and Treatment Response
    Li, Longpeng
    Li, Longhui
    Wang, Yaxin
    Wu, Baoai
    Guan, Yue
    Chen, Yinghua
    Zhao, Jinfeng
    GENES, 2024, 15 (11)
  • [6] Prognostic Value of a Novel Multi-mRNA Signature for Predicting Disease Progression of Hepatoblastoma
    Wang, Bingrui
    Han, Qiucheng
    Guo, Han
    Qu, Xiaoye
    Wang, Fang
    Xia, Qiang
    Kong, Xiaoni
    INDIAN JOURNAL OF PHARMACEUTICAL SCIENCES, 2021, 83 : 141 - 147
  • [7] Identification of a rank-based radiomic signature with individualized prognostic value for lung adenocarcinoma in a multi-cohort study
    Liu, Yixin
    Wang, Zhihui
    Yang, Liping
    Zhang, Meng
    Li, Mengyue
    Zhang, Juxuan
    Tang, Lefan
    Jiang, Zhiyun
    Li, Xin
    Deng, Jiaxing
    Meng, Qingwei
    Liu, Shilong
    Wang, Kezheng
    Qi, Lishuang
    EUROPEAN JOURNAL OF RADIOLOGY, 2024, 181
  • [8] Multi-cohort study in gastric cancer to develop CT-based radiomic models to predict pathological response to neoadjuvant immunotherapy
    Huang, Ze-Ning
    Zhang, Hao-Xiang
    Sun, Yu-Qin
    Zhang, Xing-Qi
    Lin, Yi-Fen
    Weng, Cai-Ming
    Zheng, Chao-Hui
    Wang, Jia-Bin
    Chen, Qi-Yue
    Cao, Long-Long
    Lin, Mi
    Tu, Ru-Hong
    Huang, Chang-Ming
    Lin, Jian-Xian
    Xie, Jian-Wei
    JOURNAL OF TRANSLATIONAL MEDICINE, 2025, 23 (01)
  • [9] Integrative analysis of cuproptosis-associated genes for predicting immunotherapy response in single-cell and multi-cohort studies
    Li, Hua
    Wang, Yichen
    Li, Guangxiao
    Xiong, Jian
    Qin, Lingshan
    Wen, Qirong
    Yue, Chaomin
    JOURNAL OF GENE MEDICINE, 2024, 26 (01):
  • [10] Multi-cohort validation study of a four-gene signature for risk stratification and treatment response prediction in hepatocellular carcinoma
    Liu, Cuicui
    Xiao, Zhijun
    Wu, Shenghong
    Yang, Zhen
    Ji, Guowen
    Duan, Jingjing
    Zhou, Ting
    Cao, Jinming
    Liu, Xiufeng
    Xu, Feng
    COMPUTERS IN BIOLOGY AND MEDICINE, 2023, 167