ArticleTranslational andrology and urology2025
Combining single-cell and bulk RNA sequencing data to create a reliable prognostic model for predicting clear-cell renal cell carcinoma progression.
Article in Translational andrology and urology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: 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 of the 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 on ccRCC to establish a novel prognostic model for reliable and precise predictions of ccRCC development. Methods: We used data from five ccRCC 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. By using machine learning to screen reliable prognostic genes, we constructed a prognostic model for renal cancer with The Cancer Genome Atlas-Kidney Renal Clear-Cell Carcinoma Collection (TCGA-KIRC) dataset and validated its effectiveness using a novel GEO renal cancer dataset. Finally, we explored the role of these prognostic genes in the progression of ccRCC through 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. 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. In both Conclusions: 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.
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