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Volume 28, Issue 4
  • ISSN: 1386-2073
  • E-ISSN: 1875-5402

Abstract

Background

The efficacy of chemotherapy in treating Kidney Renal Clear Cell Carcinoma (KIRC) is limited, whereas immunotherapy has shown some promising clinical outcomes. In this context, KIF4A is considered a potential therapeutic target for various cancers. Therefore, identifying the mechanism of KIF4A that can predict the prognosis and immunotherapy response of KIRC would be of significant importance.

Methods

Based on the TCGA Pan-Cancer dataset, the prognostic significance of the KIF4A expression across 33 cancer types was analyzed by univariate Cox algorithm. Furthermore, overlapping differentially expressed genes (DEGs1) between the KIF4A high- and low-expression groups and DEGs2 between the KIRC and normal groups were also analyzed. Machine learning and Cox regression algorithms were performed to obtain biomarkers and construct a prognostic model. Finally, the role of KIF4A in KIRC was analyzed using quantitative real-time PCR, transwell assay, and EdU experiment.

Results

Our analysis revealed that KIF4A was significant for the prognosis of 13 cancer types. The highest correlation with KIF4A was found for KICH among the tumour mutation burden (TMB) indicators. Subsequently, a prognostic model developed with UBE2C, OTX1, PPP2R2C, and RFLNA was obtained and verified with the Renal Cell Cancer-EU/FR dataset. There was a positive correlation between risk score and immunotherapy. Furthermore, the experiment results indicated that KIF4A expression was considerably increased in the KIRC group. Besides, the proliferation, migration, and invasion abilities of KIRC tumor cells were significantly weakened after KIF4A was knocked out.

Conclusion

We identified four KIF4A-related biomarkers that hold potential for prognostic assessment in KIRC. Specifically, early implementation of immunotherapy targeting these biomarkers may yield improved outcomes for patients with KIRC.

© 2025 Bentham Science Publishers
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2025-03-29

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/content/journals/cchts/10.2174/0113862073296897240212114403
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A Novel KIF4A-related Model for Predicting Immunotherapy Response and Prognosis in Kidney Renal Clear Cell Carcinoma
Comb Chem High Throughput Screen 28, 691 (2025); https://doi.org/10.2174/0113862073296897240212114403
/content/journals/cchts/10.2174/0113862073296897240212114403
/content/journals/cchts/10.2174/0113862073296897240212114403
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Supplementary material is available on the publisher’s website along with the published article. Lists of 65 differentially expressed genes (DEGs1) between the high- and low-expression groups according to the median values of KIF4A expression. Lists of 252 Gene Ontology (GO) functions enriched to 65 DEGs1. Results of Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways enriched to 65 DEGs1. Lists of 4718 DEGs2 between the KIRC and normal groups. Results of multivariate Cox analyses for four biomarkers. Lists of Tumor Immune Dysfunction and Exclusion (TIDE) scores of each patient within the TCGA-KIRC dataset.

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  • Article Type:     Research Article
Keyword(s): Kidney renal clear cell carcinoma; KIF4A; Pan-Cancer; prognosis; risk score; tumour mutation burden

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