Abstract
Bladder cancer (BCa) is a heterogeneous malignancy characterized by distinct immune subtypes, primarily due to differences in tumor-infiltrating immune cells and their functional characteristics. Therefore, understanding the tumor immune microenvironment (TIME) landscape in BCa is crucial for prognostic prediction and guiding precision therapy. In this study, we integrated 10 machine learning algorithms to develop an immune-related machine learning signature (IRMLS) and subsequently created a deep learning model to detect the IRMLS subtype based on pathological images. The IRMLS proved to be an independent prognostic factor for overall survival (OS) and demonstrated robust and stable performance (p < 0.01). The high-risk group exhibited an immune-inflamed phenotype, associated with poorer prognosis and higher levels of immune cell infiltration. We further investigated the cancer immune cycle and mutation landscape within the IRMLS model, confirming that the high-risk group is more sensitive to immune checkpoint immunotherapy (ICI) and adjuvant chemotherapy with cisplatin (p = 2.8e-10), docetaxel (p = 8.8e-13), etoposide (p = 1.8e-07), and paclitaxel (p = 6.2e-13). In conclusion, we identified and validated a machine learning-based molecular characteristic, IRMLS, which reflects various aspects of the BCa biological process and offers new insights into personalized precision therapy for BCa patients.