Developing angiogenesis-related prognostic biomarkers and therapeutic strategies in bladder cancer using deep learning and machine learning.

Bladder cancer (BLCA) is a prevalent urological malignancy that exhibits a high degree of tumor heterogeneity and morbidity. Tumor angiogenesis, a vital hallmark of cancer, greatly influences the tumor microenvironment (TME). The emergence of anti-angiogenic drugs has provided a new turning point in cancer treatment. An integrated machine learning system was constructed to build the angiogenesis-related gene signatures (ARGS). ARGS was used to assess TME status in BLCA. Pharmacophore construction was employed to construct pharmacophore features of highly cytotoxic drug payload combinations for antibody-drug conjugates (ADCs). In addition, we developed a natural compound using artificial intelligence-driven drug design technology. This compound exhibits anti-angiogenic effects in BLCA and serves as a highly cytotoxic drug payload for ADCs. Multi-dimensional machine learning was used to screen biomarkers for evaluating the post-treatment effects of drug therapy in BLCA. The ARGS consists of 12 angiogenesis-related genes associated with prognostic risk in BLCA. The ARGS divides BLCA patients into high-risk and low-risk groups. Significant TME remodeling was identified in the high-risk BLCA cohort and demonstrated a strong association with tumor angiogenesis. Expression levels of key immune checkpoint markers significantly differed between BLCA risk groups. Saikosaponin D (SSD) shows promising potential as a novel ADC drug for anti-angiogenic treatment in BLCA. Multi-dimensional machine learning results indicate that MYH11 is the most likely biomarker for evaluating the post-treatment effects of SSD therapy. SSD may potentially treat tumors by regulating angiogenesis in BLCA. The detection of MYH11 can be used to assess the therapeutic effectiveness of SSD in BLCA.