Integrating Bulk and Single-Cell RNA Sequencing Reveals Heterogeneity, Tumor Microenvironment, and Immunotherapeutic Efficacy Based on Sialylation-Related Genes in Bladder Cancer

Abstract

Zhiyong Tan,1– 3,* Xiaorong Chen,4,* Jieming Zuo,1– 3,* Shi Fu,1– 3,* Jiansong Wang,1– 3 Haifeng Wang1– 3 1Department of Urology, the Second Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, People’s Republic of China; 2Urological Disease Clinical Medical Center of Yunnan Province, the Second Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, People’s Republic of China; 3Scientific and Technological Innovation Team of Basic and Clinical Research of Bladder Cancer in Yunnan Universities, the Second Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, People’s Republic of China; 4Department of Kidney Transplantation, the Third Hospital of Sun Yat-Sen University, Guangzhou, People’s Republic of China*These authors contributed equally to this workCorrespondence: Jiansong Wang; Haifeng Wang, Department of Urology, the Second Affiliated Hospital of Kunming Medical University, Yunnan Institute of Urology, Kunming, 650101, People’s Republic of China, Email [email protected]; [email protected]: As known abnormal sialylation exerts crucial roles in the growth, metastasis, and immune evasion of cancers, but the molecular characteristics and roles in bladder cancer (BLCA) remain unclear. This study intends to establish BLCA risk stratification based on sialylation-related genes and elucidate its role in prognosis, tumor microenvironment, and immunotherapy of BLCA.Methods: Bulk RNA-seq and scRNA-seq data were downloaded from open-access databases. The scRNA-seq data were processed using the R package “Seurat” to identify the core cell types. The tumor sub-typing of BLCA samples was performed by the R package “ConsensusClusterPlus” in the bulk RNA-seq data. Signature genes were identified by the R package “limma” and univariate regression analysis to calculate risk scores using the R package “GSVA” and establish risk stratification of BLCA patients. Finally, the differences in clinicopathological characteristics, tumor microenvironment, and immunotherapy efficacy between the different groups were investigated.Results: 5 core cell types were identified in the scRNA-seq dataset, with monocytes and macrophages presenting the greatest percentage, sialylation-related gene expression, and sialylation scores. The bulk RNA-seq samples were classified into 3 tumor subtypes based on 19 prognosis-related sialylation genes. The 10 differential expressed genes (DEGs) with the smallest p-values were collected as signature genes, and the risk score was calculated, with the samples divided into high and low-risk score groups. The results showed that patients in the high-risk score group exhibited worse survival outcomes, higher tumor grade, more advanced stage, more frequency of gene mutations, higher expression levels of immune checkpoints, and lower immunotherapy response.Conclusion: We established a novel risk stratification of BLCA from a glycomics perspective, which demonstrated good accuracy in determining the prognostic outcome, clinicopathological characteristics, immune microenvironment, and immunotherapy efficacy of patients, and we are proposing to apply it to direct the choice of clinical treatment options for patients.Keywords: bladder cancer, bulk RNA-seq, scRNA-seq, sialylation, tumor microenvironment, immunotherapy

Keywords

• bladder cancer
• bulk rna-seq
• scrna-seq
• sialylation
• tumor microenvironment
• immunotherapy