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Research Paper|Volume 15, Issue 21|pp 12104—12119

Collaborating single-cell and bulk RNA sequencing for comprehensive characterization of the intratumor heterogeneity and prognostic model development for bladder cancer

Jie Wang1,3, Zili Zuo1, Zongze Yu1, Zhigui Chen1, Lisa Jia Tran4, Jing Zhang5, Jinsong Ao2, Fangdie Ye6, Zhou Sun2,3
  • 1Department of Urology, The Second People’s Hospital of Meishan, Meishan, Sichuan 620500, China
  • 2Department of Urology, The First People’s Hospital of Jiangxia, Wuhan 430200, Hubei, China
  • 3Department of Urology, China-Japan Union Hospital of Jilin University, Changchun 130000, Jilin, China
  • 4Department of General, Visceral, And Transplant Surgery, Ludwig-Maximilians-University Munich, Bayern, Munich 81377, Germany
  • 5Division of Basic Biomedical Sciences, The University of South Dakota Sanford School of Medicine, Vermillion, SD 57069, USA
  • 6Department of Urology, Huashan Hospital, Fudan University, Jing’an 200000, Shanghai, China
* Equal contribution
Received: July 31, 2023Accepted: October 2, 2023Published: November 6, 2023
https://doi.org/10.18632/aging.205166

Copyright: © 2023 Wang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Abstract

Introduction: Gaining a deeper insight into the single-cell RNA sequencing (scRNA-seq) results of bladder cancer (BLCA) provides a transcriptomic profiling of individual cancer cells, which may disclose the molecular mechanisms involved in BLCA carcinogenesis.

Methods: scRNA data were obtained from GSE169379 dataset. We used the InferCNV software to determine the copy number variant (CNV) with normal epithelial cells serving as the reference, and performed the pseudo-timing analysis on subsets of epithelial cell using Monocle3 software. Transcription factor analysis was conducted using the Dorothea software. Intercellular communication analysis was performed using the Liana software. Cox analysis and LASSO regression were applied to establish a prognostic model.

Results: We investigated the heterogeneity of tumors in four distinct cell types of BLCA cancer, namely immune cells, endothelial cells, epithelial cells, and fibroblasts. We evaluated the transcription factor activity of different immune cells in BLCA and identified significant enrichment of TCF7 and TBX21 in CD8+ T cells. Additionally, we identified two distinct subtypes of cancer-associated fibroblasts (CAFs), namely iCAFs and myoCAFs, which exhibited distinct communication patterns. Using sub-cluster and cell trajectory analyses, we identified different states of normal-to-malignant cell transformation in epithelial cells. TF analysis further revealed high activation of MYC and SOX2 in tumor cells. Finally, we identified five model genes (SLCO3A1, ANXA1, TENM3, EHBP1, LSAMP) for the development of a prognostic model, which demonstrated high effectiveness in stratifying patients across seven different cohorts.

Conclusions: We have developed a prognostic model that has demonstrated significant efficacy in stratifying patients with BLCA.