Combining Single-Cell and Bulk RNA Sequencing Data to Create a Reliable Prognostic Model for Predicting Clear-cell Renal Cell Carcinoma Progression

Introduction: Clear-cell renal cell carcinoma (ccRCC) is the most common pathological type of kidney cancer and is characterized by a low survival rate. Accurate prediction ofthe occurrence and progression of ccRCC is crucial for diagnosis and treatment. This study aimed to integrate multiple publicly available bulk sequencing and single-cell datasets onccRCC to establish a novel prognostic model for reliable and precise predictions of ccRCCdevelopment. Methods: We used data from fiveccRCC samples from the Gene Expression Omnibus (GEO) database to identify 1,303 overlapping differentially expressed genes (DEGs). Through pseudotime analysis of single-cell ccRCC data sourced from the GEO database, we identified 4,002 genes that were highly associated with cancer progression. Subsequently,The Cancer Genome Atlas - Kidney Renal Clear-Cell Carcinoma Collection (TCGA-KIRC) dataset was merged with the ccRCC GEO dataset to establish a co-expression network. The combined dataset was screened for 1,608 renal cancer genes known to be associated with cancer prognosis. Using these genes, we constructed a prognostic model for renal cancer in TCGA-KIRC and validated its effectiveness using a novel GEO renal cancer dataset. Finally, we explored the expression of these prognostic genes. Results: The five ccRCC sequencing datasets exhibited significant heterogeneity. Therefore, we screened 211 DEGs that were highly associated with the development and prognosis of renal cancer. By exploring the biological functions of these genes, we found that they closely influenced the prognosis of patients with cancer and thatmost genes were enriched in cancer metabolic pathways. Immune-cell infiltration analysis revealed that the DEGs weresignificantly correlated with the functions of immune-cell subtypes within the tumors. Upon screening, a reliable set of renal cancer-related DEGs was obtained from multiple samples. The prognostic model accurately identified renal cancer stages and predicted outcomes. Discussion: We developed a valuable predictive tool for ccRCC progression, which can help estimate the survival period of patients with renal cancer and aid in the clinical diagnosis and targeted therapy of tumors.