Identification of prognostic immune-related genes in rhabdoid tumor of kidney based on TARGET database analysis.

Background: Malignant rhabdoid tumor of the kidney (RTK) is a rare and highly aggressive pediatric malignancy. Immune system dysfunction is significantly correlated with tumor initiation and progression.

Methods: We integrated and analyzed the expression profiles of immune-related genes (IRGs) in 65 RTK patients based on the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database. Prognostic related IRGs in RTK patients were analyzed using univariate and multivariate analysis, based on which a prognostic model with IRGs was constructed. Correlation analysis between the risk score of our model and tumor-infiltrating cell were also investigated.

Results: Twenty two IRGs were significantly associated with the clinical outcomes of RTK patients. Gene ontology (GO) analysis revealed that inflammatory pathways were most frequently implicated in RTK. A prognostic model was constructed using 7 IRGs (), which were independent prognostic indices that could differentiate patients based on their survival outcomes. Furthermore, the risk scores from our prognostic model was positively associated with cancer-associated fibroblasts (CAFs).

Conclusions: We screened seven IRGs of clinical significance to distinguish patients with different survival outcomes. This may enhance our understanding of the immune microenvironment of RTK, and could use to design individualized treatments for RTK patients.

Background: Malignant rhabdoid tumor of the kidney (RTK) is a rare and highly aggressive pediatric malignancy. Immune system dysfunction is significantly correlated with tumor initiation and progression.

Methods: We integrated and analyzed the expression profiles of immune-related genes (IRGs) in 65 RTK patients based on the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database. Prognostic related IRGs in RTK patients were analyzed using univariate and multivariate analysis, based on which a prognostic model with IRGs was constructed. Correlation analysis between the risk score of our model and tumor-infiltrating cell were also investigated.

Results: Twenty two IRGs were significantly associated with the clinical outcomes of RTK patients. Gene ontology (GO) analysis revealed that inflammatory pathways were most frequently implicated in RTK. A prognostic model was constructed using 7 IRGs (), which were independent prognostic indices that could differentiate patients based on their survival outcomes. Furthermore, the risk scores from our prognostic model was positively associated with cancer-associated fibroblasts (CAFs).

Conclusions: We screened seven IRGs of clinical significance to distinguish patients with different survival outcomes. This may enhance our understanding of the immune microenvironment of RTK and could use to design individualized treatments for RTK patients.