Integrating Multi-dimensional RNA Sequencing to Construct a Prognostic Risk Model for Bladder Cancer

<title>Abstract</title> <p>Background Bladder cancer (BLCA) is a highly heterogeneous malignancy, and accurate prognostic assessment remains challenging in clinical practice. Recent advances in multidimensional RNA sequencing, including bulk RNA-seq and single-cell RNA sequencing (scRNA-seq), have provided new opportunities to identify prognostic biomarkers. In this study, we integrated bulk RNA-seq and scRNA-seq data from BLCA to construct an immune microenvironment-related prognostic model for more accurate survival prediction. Methods Bulk RNA-seq and scRNA-seq datasets for BLCA were obtained from the GEO and TCGA databases to identify key genes and cell populations. After quality control of the scRNA-seq data, principal component analysis (PCA) and uniform manifold approximation and projection (UMAP) were used for dimensionality reduction and cell clustering. Cell types were annotated using previously reported markers and the CellMarker database. Core cell populations were identified by marker-gene analysis using Seurat and the MAST test, followed by single-sample gene set enrichment analysis (ssGSEA). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed using clusterProfiler. Differentially expressed genes (DEGs) were identified using DESeq2 and limma, and weighted gene co-expression network analysis (WGCNA) was used to identify BLCA-associated gene modules. Intersecting candidate genes were then evaluated by univariate Cox and LASSO-Cox regression analyses to establish a multigene prognostic risk model. Finally, gene set enrichment analysis (GSEA) and immune microenvironment analyses were performed to explore the model's potential association with immunotherapy response. Results scRNA-seq analysis identified 12 cell clusters and seven major cell types, of which five showed reduced enrichment in BLCA and were defined as core cell populations. Marker genes of these core cells were closely associated with immune responses, cell adhesion, and signal transduction. Differential analysis of bulk RNA-seq datasets identified 3,100 overlapping DEGs enriched in pathways related to the cell cycle, signal transduction, and immune regulation. WGCNA further identified key gene modules associated with BLCA progression. A five-gene prognostic model showed stable predictive performance for patient survival and was significantly associated with clinical features, including T stage, N stage, and M stage. GSEA and immune microenvironment analyses indicated that the high-risk group had a higher mutation frequency and was enriched for pathways related to immune cell infiltration and immune escape, suggesting a more immunosuppressive phenotype. Conclusions By integrating bulk RNA-seq and scRNA-seq data, we developed an immune microenvironment-related prognostic risk score model for BLCA. This model may serve as a useful tool for prognosis prediction and may help identify patients with BLCA who are more likely to benefit from immunotherapy.</p>