# Integration of Single-Cell and Bulk Transcriptome to Reveal an Endothelial Transition Signature Predicting Bladder Cancer Prognosis

## TL;DR

This study identifies a new endothelial cell transition signature that predicts bladder cancer prognosis and develops a more accurate prognostic model using single-cell RNA sequencing data.

## Contribution

The novel contribution is the discovery of a tip-to-capillary endothelial cell transition signature with prognostic value in bladder cancer.

## Key findings

- A novel EC transition signature was identified, associated with permeability, migration, and vascular maturation.
- The EC transition signature showed strong prognostic significance across multiple bladder cancer datasets.
- The new model outperformed 31 existing models in predicting patient outcomes.

## Abstract

Bladder cancer is a common and serious disease, and finding better ways to predict how it will progress is crucial for improving treatment. In this study, we focused on a particular group of cells in the blood vessels, called endothelial cells, which play an important role in tumour growth. These cells change in specific ways as cancer progresses, and understanding these changes can help us predict the development of cancer. By studying these changes in cells from cancer tissue, we developed a prognostic prediction model that can predict the outcome for bladder cancer patients. Our results show that the model can identify patients who are at higher risk to predict prognosis and provide more information to help doctors and patients choose the treatments.

Endothelial cells (ECs) are critical drivers of tumour progression, and their angiogenic process has been widely studied. However, the post-angiogenic transition of tip endothelial cells after sprouting remains insufficiently characterised. In this study, we utilised single-cell RNA sequencing analyses to identify a novel EC transition signature associated with endothelial permeability, migration, metabolism, and vascular maturation. Within the transition pathway, we discovered a critical EC subpopulation, termed tip-to-capillary ECs (TC-ECs), that was enriched in tumour tissues. Comparative analyses of TC-ECs with tip and capillary ECs revealed distinct differences in pathway activity, cellular communication, and transcription factor activity. The EC transition signature demonstrated substantial prognostic significance, validated across multiple cancer cohorts from TCGA data, particularly in bladder cancer. Subsequently, we constructed a robust prognostic model for bladder cancer by integrating the EC transition signature with multiple machine-learning techniques. Compared with 31 existing models across the TCGA-BLCA, GSE32894, GSE32548, and GSE70691 cohorts, our model exhibited superior predictive performance. Stratification analysis identified significant differences between different risk groups regarding pathway activity, cellular infiltration, and therapeutic sensitivity. In conclusion, our comprehensive investigation identified a novel EC transition signature and developed a prognostic model for patient stratification, offering new insights into endothelial heterogeneity, angiogenesis regulation, and precision medicine.