A polyamine metabolism risk signature for predicting the prognosis and immune therapeutic response of kidney cancer

Abstract

Background Polyamine metabolism is critically involved in the proliferation and metastasis of tumor cells, including in kidney renal clear cell (KIRC) cancer. However, the molecular mechanisms underlying the effect of polyamines in KIRC cancer remain largely unknown. Methods The messenger RNA (mRNA) expression profile of KIRC was downloaded from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), and ArrayExpress database. Differential expression analysis was performed with the “limma” package in R. Univariate Cox regression and multivariable Cox regression were used to estimate correlation between variables and prognosis. Least absolute shrinkage and selection operator (LASSO) Cox regression analysis was employed to screen variables and construct a risk signature. A nomogram model was established using the risk signature and clinical variables. Receiver operating characteristic (ROC), calibration curve, and decision curve analysis (DCA) were used to assess the predicted accuracy and clinical benefit of the model. Results We identified nine differentially expressed polyamine metabolism-related genes (PMRGs) in TCGA-KIRC. Of these, six were closely associated with patients’ outcomes. These six genes participated in different pathways and originated from different cell types within the tumor microenvironment (TME). Using the mRNA expression values of these genes, we constructed a 4-gene PMRG risk signature. Patients with high PMRG risk exhibited worse outcomes, and our analysis showed that the PMRG risk signature was an independent prognostic factor when clinical information was used as a covariate. We also found that multiple immune- or metabolism-related pathways were differentially enriched in high or low PMRG risk groups, suggesting that altering these pathways could lead to different clinical outcomes. Finally, in two external datasets, we found that the PMRG risk signature could predict the response of patients to immune therapy. Conclusions In summary, our study identified several potentially important PMRGs in KIRC and constructed a practical risk signature, which could serve as a foundation for further development of polyamine metabolism–based targeted therapies for KIRC.

Construction of exosome-related genes risk model in kidney cell carcinoma predicts prognosis and immune therapy response

“…The same analyses were performed in another two external validation cohorts, and the results proved to be the same trends (Figure 6B, 6C; Supplementary Figure 6B, 6C). We then compared our PRERG signature with nine other risk signatures [24][25][26][27][28][29][30][31][32]. PRERGs risk signature showed the top 3 of AUC for 3-/5-/7-year prognosis of the KIRC patients (Supplementary Figure 7A-7C), indicating that our risk signature has a higher predicted accuracy.…”