Frontiers in oncology · 2026
Integrative analysis of scRNA-seq and bulk RNA-seq with machine learning develops a nucleotide metabolism-based prognostic model for ccRCC and reveals the function of IFI30.
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Lyu Qiao, Li Ping, Ye ZhenXiong, Chen JiaHui, Luo Feng, Zhong QiYu, Wu GuoHao, Zheng HanDa, Xiao JianSheng, Ye DongMing, Qu LiJun
Аннотация
OBJECTIVES: Nucleotide metabolism in clear cell renal cell carcinoma (ccRCC) remains understudied. Elucidating its heterogeneous characteristics and key genes may provide new insights for prognostic assessment and targeted therapy.
METHODS: By integrating scRNA-seq with bulk RNA-seq data, we first screened for nucleotide metabolism-related genes (NMRGs) using five machine learning algorithms. NMRGs signature was then constructed using 101 machine learning algorithms to optimize clinical prognosis assessment. The selected NMRGs were subjected to GO functional annotation and KEGG pathway enrichment analysis. Finally, the functional role of the key gene IFI30 was validated through both in vitro and in vivo experiments.
RESULTS: The NMRG signature constructed using 101 machine learning algorithms outperformed traditional clinical parameters and 122 published models across multiple independent cohorts. High-risk patients exhibited significantly worse overall survival. Enrichment analysis showed that IFI30 and its associated genes were significantly enriched in nucleotide metabolism pathways. Immunohistochemistry confirmed high IFI30 expression in ccRCC tumor tissues. Functional assays demonstrated that IFI30 knockdown suppressed ccRCC cell proliferation, migration, invasion, and tumorigenicity, while inducing apoptosis. In addition, IFI30 knockdown downregulated PRPS1/2, a key rate-limiting enzyme in nucleotide synthesis, and nucleotide rescue experiments reversed the phenotypic suppression, confirming that IFI30 promotes ccRCC progression through nucleotide metabolism.
CONCLUSION: This study developed an NMRGs signature that outperforms existing models and, for the first time, reveals that IFI30 promotes ccRCC malignant progression by regulating nucleotide metabolism. These findings provide a new theoretical basis for prognostic assessment and metabolism-targeted therapy in ccRCC.
METHODS: By integrating scRNA-seq with bulk RNA-seq data, we first screened for nucleotide metabolism-related genes (NMRGs) using five machine learning algorithms. NMRGs signature was then constructed using 101 machine learning algorithms to optimize clinical prognosis assessment. The selected NMRGs were subjected to GO functional annotation and KEGG pathway enrichment analysis. Finally, the functional role of the key gene IFI30 was validated through both in vitro and in vivo experiments.
RESULTS: The NMRG signature constructed using 101 machine learning algorithms outperformed traditional clinical parameters and 122 published models across multiple independent cohorts. High-risk patients exhibited significantly worse overall survival. Enrichment analysis showed that IFI30 and its associated genes were significantly enriched in nucleotide metabolism pathways. Immunohistochemistry confirmed high IFI30 expression in ccRCC tumor tissues. Functional assays demonstrated that IFI30 knockdown suppressed ccRCC cell proliferation, migration, invasion, and tumorigenicity, while inducing apoptosis. In addition, IFI30 knockdown downregulated PRPS1/2, a key rate-limiting enzyme in nucleotide synthesis, and nucleotide rescue experiments reversed the phenotypic suppression, confirming that IFI30 promotes ccRCC progression through nucleotide metabolism.
CONCLUSION: This study developed an NMRGs signature that outperforms existing models and, for the first time, reveals that IFI30 promotes ccRCC malignant progression by regulating nucleotide metabolism. These findings provide a new theoretical basis for prognostic assessment and metabolism-targeted therapy in ccRCC.