Identification of molecular subtypes based on liquid–liquid phase separation and cross-talk with immunological phenotype in bladder cancer - Sun, Le; Liu, Xiao-Ping; Yan, Xin; Wu, Shaojie; Tang, Xiaoyu; Chen, Chen; Li, Gang; Hu, Hankun; Wang, Du; Li, Sheng (Frontiers in Immunology, 2022)
Background: Mounting evidence has demonstrated that an imbalance in liquid-liquid phase separation (LLPS) can induce alteration in the spatiotemporal coordination of biomolecular condensates, which plays a role in carcinogenesis and cachexia. However, the role of LLPS in the occurrence and progression of bladder cancer (BLCA). Identifying the role of LLPS in carcinogenesis may aid in cancer therapeutics. Methods: A total of 1,351 BLCA samples from six cohorts were retrieved from publicly available databases like the Cancer Genome Atlas, Gene Expression Omnibus, and ArrayExpress. The samples were divided into three distinct clusters, and their multi-dimensional heterogeneities were explored. The LLPS patterns of all patients were determined based on the LLPS-related risk score (LLPSRS), and its multifaceted landscape was depicted and experimentally validated at the multi-omics level. Finally, a cytotoxicity-related and LLPSRS-based classifier was established to predict the patient’s response to immune checkpoint blockade (ICB) treatment. Results: We identified and validated three LLPS-related subtypes. The differences in prognosis, tumor microenvironment (TME) features, cancer hallmarks, and certain signatures of the three LLPS-related subtypes were validated. LLPSRS was calculated, which could be used as a prognostic biomarker. A close correlation was observed between clinicopathological features, genomic variations, biological mechanisms, immune infiltration in TME, chemosensitivity, and LLPSRS. Furthermore, our classifier could effectively predict immunotherapy response in patients with BLCA. Conclusions: Our study identified a novel categorization of BLCA patients based on LLPS. The LLPSRS could predict the prognosis of patients and aid in designing personalized medicine. Further, our binary classifier could effectively predict patients’ sensitivity to immunotherapy.