European journal of pharmacology · 2026
Clinical and immunological significance of FOXO1 as a biomarker of improved response to immune checkpoint plus tyrosine kinase inhibitor therapy in metastatic renal cell carcinoma.
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Du Lingzhi, Wang Jiahao, Wang Ying, Wang Hang, Xu Xianglai, Wang Jiajun
Аннотация
BACKGROUND: Predicting response to immune checkpoint inhibitor plus tyrosine kinase inhibitor (IO + TKI) therapy in metastatic renal cell carcinoma (mRCC) remains challenging. Forkhead box protein O1 (FOXO1) is a key transcription factor regulating tumor suppression and T cell immunity, but its clinical and immunological significance in RCC is unclear.
METHODS: FOXO1 expression was analyzed in TCGA-KIRC, ZS-MRCC, ZS-HRRCC, and JAVELIN Renal 101 cohorts. Associations with tumor grade, stage, survival, and IO + TKI response were evaluated. Immune infiltration and T cell functional states were assessed using immunohistochemistry and flow cytometry. An RF-based machine learning model incorporating FOXO1 and immune-related gene features was developed to predict therapeutic benefit.
RESULTS: FOXO1 expression was markedly reduced in RCC tissues and declined with increasing tumor grade and stage. Higher FOXO1 levels were associated with improved overall survival and better IO + TKI response. FOXO1-high tumors exhibited an immune-supportive microenvironment characterized by a trend toward increased cytotoxic CD8+ T cells, decreased CD4+ T cells, reduced exhausted T cells, lower macrophage infiltration, and favorable cytokine profiles. High FOXO1 expression also correlated with fewer BAP1 and BRAF mutations. FOXO1 independently predicted greater IO + TKI benefit (p for interaction = 0.019), and the RF risk score integrating multiple immune gene features achieved a more robust treatment interaction (p for interaction = 0.002).
CONCLUSION: FOXO1 carries both prognostic and predictive relevance in mRCC and independently predicts greater benefit from IO + TKI therapy. Integration of FOXO1 with complementary immune gene features into an RF-based model provides a promising framework for precision immunotherapy in advanced RCC.
METHODS: FOXO1 expression was analyzed in TCGA-KIRC, ZS-MRCC, ZS-HRRCC, and JAVELIN Renal 101 cohorts. Associations with tumor grade, stage, survival, and IO + TKI response were evaluated. Immune infiltration and T cell functional states were assessed using immunohistochemistry and flow cytometry. An RF-based machine learning model incorporating FOXO1 and immune-related gene features was developed to predict therapeutic benefit.
RESULTS: FOXO1 expression was markedly reduced in RCC tissues and declined with increasing tumor grade and stage. Higher FOXO1 levels were associated with improved overall survival and better IO + TKI response. FOXO1-high tumors exhibited an immune-supportive microenvironment characterized by a trend toward increased cytotoxic CD8+ T cells, decreased CD4+ T cells, reduced exhausted T cells, lower macrophage infiltration, and favorable cytokine profiles. High FOXO1 expression also correlated with fewer BAP1 and BRAF mutations. FOXO1 independently predicted greater IO + TKI benefit (p for interaction = 0.019), and the RF risk score integrating multiple immune gene features achieved a more robust treatment interaction (p for interaction = 0.002).
CONCLUSION: FOXO1 carries both prognostic and predictive relevance in mRCC and independently predicts greater benefit from IO + TKI therapy. Integration of FOXO1 with complementary immune gene features into an RF-based model provides a promising framework for precision immunotherapy in advanced RCC.