Exploring serum metabolic markers for the discrimination of ccRCC from renal angiomyolipoma by metabolomics

Does the Urinary Proteome Reflect ccRCC Stage and Grade Progression?

Due its ability to provide a global snapshot of kidney physiology, urine has emerged as a highly promising, non-invasive source in the search for new molecular indicators of disease diagnosis, prognosis, and surveillance. In particular, proteomics represents an ideal strategy for the identification of urinary protein markers; thus, a urinomic approach could also represent a powerful tool in the investigation of the most common kidney cancer, which is clear cell Renal Cell Carcinoma (ccRCC). Currently, these tumors are classified after surgical removal using the TNM and nuclear grading systems and prognosis is usually predicted based upon staging. However, the aggressiveness and clinical outcomes of ccRCC remain heterogeneous within each stratified group, highlighting the need for novel molecular indicators that can predict the progression of these tumors. In our study, we explored the association between the urinary proteome and the ccRCC staging and grading classification. The urine proteome of 44 ccRCC patients with lesions of varying severity was analyzed via label-free proteomics. MS data revealed several proteins with altered abundance according to clinicopathological stratification. Specifically, we determined a panel of dysregulated proteins strictly related to stage and grade, suggesting the potential utility of MS-based urinomics as a complementary tool in the staging process of ccRCC.

Identification of Prognostic Metabolism-Related Genes in Clear Cell Renal Cell Carcinoma

Background

Clear cell renal cell carcinoma (ccRCC) is a cancer with abnormal metabolism. The purpose of this study was to investigate the effect of metabolism-related genes on the prognosis of ccRCC patients.

Methods

The data of ccRCC patients were downloaded from the TCGA and the GEO databases and clustered using the nonnegative matrix factorization method. The limma software package was used to analyze differences in gene expression. A random forest model was used to screen for important genes. A novel Riskscore model was established using multivariate regression. The model was evaluated based on the metabolic pathway, immune infiltration, immune checkpoint, and clinical characteristics.

Results

According to metabolism-related genes, kidney clear cell carcinoma (KIRC) datasets downloaded from TCGA were clustered into two groups and showed significant differences in prognosis and immune infiltration. There were 667 differentially expressed genes between the two clusters, of which 408 were screened by univariate analysis. Finally, 12 differentially expressed genes (MDK, SLC1A1, SGCB, C4orf3, MALAT1, PILRB, IGHG1, FZD1, IFITM1, MUC20, KRT80, and SALL1) were filtered out using the random forest model. The model of Riskscore was obtained by multiplying the expression levels of these 12 genes with the corresponding coefficients of the multivariate regression. We found that the Riskscore correlated with the expression of these 12 genes; the high Riskscore matched the low survival rate verified in the verification set. The analysis found that the Riskscore model was associated with most of the metabolic processes, immune infiltration of cells such as plasma cells, immune checkpoints such as PD-1, and clinical characteristics such as M stage.

Conclusion

We established a new Riskscore model for the prognosis of ccRCC based on metabolism. The genes in the model provided several novel targets for the study of ccRCC.