Research Paper Volume 13, Issue 5 pp 7361—7381

Artificial intelligence prediction model for overall survival of clear cell renal cell carcinoma based on a 21-gene molecular prognostic score system

Study pipeline. In this study we first used the TCGA clear cell renal cell carcinoma (ccRCC) cohort to identify a list of 275 genes, which were associated with the prognosis of ccRCC patients according to survival analysis and were also differentially expressed between ccRCC patients and healthy controls. Then, artificial intelligence (AI) methods including random forest and neural network were applied to establish the mPS system based on 21 prognosis-related genes. Then, we validated the mPS system by using the ICGC cohort. Next, we found that the mPS system could be applied to ccRCC subsets. Moreover, we evaluated the mPS system by conducting univariate and multivariate Cox regression analysis of the TCGA dataset and built a nomogram comprising the mPS score and several independent variables to predict ccRCC patient prognosis. Finally, we explored the potential mechanisms underlying the mPS system by performing gene set enrichment analysis (GSEA), mutations, copy number variations (CNVs) and immune cell infiltration analysis.

Figure 1. Study pipeline. In this study we first used the TCGA clear cell renal cell carcinoma (ccRCC) cohort to identify a list of 275 genes, which were associated with the prognosis of ccRCC patients according to survival analysis and were also differentially expressed between ccRCC patients and healthy controls. Then, artificial intelligence (AI) methods including random forest and neural network were applied to establish the mPS system based on 21 prognosis-related genes. Then, we validated the mPS system by using the ICGC cohort. Next, we found that the mPS system could be applied to ccRCC subsets. Moreover, we evaluated the mPS system by conducting univariate and multivariate Cox regression analysis of the TCGA dataset and built a nomogram comprising the mPS score and several independent variables to predict ccRCC patient prognosis. Finally, we explored the potential mechanisms underlying the mPS system by performing gene set enrichment analysis (GSEA), mutations, copy number variations (CNVs) and immune cell infiltration analysis.