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Designing a predictive Framework: Immune-Related Gene-Based nomogram and prognostic model for kidney renal papillary
Adrian Lim1, Mouad Edderkaoui2, Yi Zhang1
1Departments of Medicine and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California.
Background:
Kidney renal papillary cell carcinoma (KIRP) is frequently associated with an unfavorable prognosis for affected individuals. Unfortunately, there has been insufficient exploration in search for a reliable prognosis signature and predictive indicators to forecast outcomes for KIRP patients.
Aim:
The aim of this study is to employ a comprehensive analysis of data for the identification of prognosis genes, leading to the development of a nomogram with strong predictive capabilities. The objective is to provide a valuable statistical tool that, when implemented in a clinical setting, can offer patients an early opportunity for treatment and enhance their chances of ultimate recovery from this life-threatening disease.
Methods:
Different packages in R were used to analyze RNA-seq data from the TCGA data portal. Multivariate Cox regression analysis and Kaplan-Meier analysis were also used to investigate the prognostic values of immune-related genes and construct the predictive model and nomogram. A p-value < 0.05 was considered to be significant.
Results:
A total of 368 immune-related genes and 60 TFs were identified as differentially expressed in KIRP tissues compared with normal tissues. Of the 368, 23 were found to be related to overall survival. GO and KEGG analysis suggested that these prognostic immune-related genes mainly participated in the ERK1 and ERK2 cascades, Rap1 signaling pathway, and the PI3K-Akt signaling pathway. 9 genes were identified from Cox regression to be statistically significant prognostic-related genes. Survival analysis showed that a model based on these 9 prognostic-related genes has high predictive performance. Immunohistochemistry results show that APOH, BIRC5, CCL19, and GRN were significantly increased in kidney cancer. B cells and CD4 + T cells were positively correlated with risk score model.
Conclusion:
A prognostic model was successfully created based on 9 immune-related genes correlated with overall survival in KIRP. This work aims to provide some insight into therapeutic approaches and prognostic predictors of KIRP.
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