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A Novel KIF4A-Related Model for Predicting Immunotherapy Response and Prognosis in Kidney Renal Clear Cell Carcinoma.
Yang, Guang Hua; Ma, Xu Dong; Wei, Xi Feng; Liu, Ran Lu; Wang, Chao.
Afiliación
  • Yang GH; Department of Urology, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Third Hospital of Shanxi Medical University, Taiyuan, People's Republic of China.
  • Ma XD; Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, People's Republic of China.
  • Wei XF; Department of Urology, Baotou Central Hospital, Inner Mongolia Medical University, Baotou, China.
  • Liu RL; Department of Urology, People's Hospital of Ningxia Hui Autonomous Region, Yinchuan, China.
  • Wang C; Department of Urology, Tianjin Institute of Urology, The Second Hospital of Tianjin Medical University, Tianjin, China.
Comb Chem High Throughput Screen ; 2024 Feb 14.
Article en En | MEDLINE | ID: mdl-38357945
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 lowexpression 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.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Comb Chem High Throughput Screen Asunto de la revista: BIOLOGIA MOLECULAR / QUIMICA Año: 2024 Tipo del documento: Article Pais de publicación: Emiratos Árabes Unidos
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Comb Chem High Throughput Screen Asunto de la revista: BIOLOGIA MOLECULAR / QUIMICA Año: 2024 Tipo del documento: Article Pais de publicación: Emiratos Árabes Unidos