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Updated: Jan 6, 2026
Microfluidics-based High-throughput Circulating Tumor Cell Sorting and Single-cell Sequencing Technology
Published on: November 14, 2025
Combining single-cell and bulk RNA sequencing data to create a reliable prognostic model for predicting clear-cell
Yongchen Li1,2, Jingwen Liu2,3, Diman Mai2,3
1Department of Urology, The First Affiliated Hospital of Guangxi Medical University, Nanning, China.
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 in vivo and in vitro experiments.
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 in vivo and in vitro experimental results, we found that intervening in the expression of these prognostic genes can significantly slow down the progression of ccRCC.
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.
