A Predictive Model Based on Pyroptosis-Related Gene Features Can Effectively Predict Clear Cell Renal Cell Carcinoma Prognosis and May Be an Underlying Target for Immunotherapy

Abstract

Background and Objective. Recent studies show that pyroptosis-related gene affects tumor cell proliferation, invasion, and migration. But the diagnostic and prognostic value of pyroptosis-related gene in clear cell renal cell carcinoma (ccRCC) is unknown. In the present research, a prognosis model according to pyroptosis-related gene was established to forecast prognosis among patients with ccRCC. Methods. The clinical information and RNA-seq data of ccRCC patients were collected from the TCGA dataset to first explore differential pyroptosis-related genes (PRGs). Univariate Cox regression and consensus clustering were applied to identify ccRCC subtypes. The prognostic PRGs were subjected to LASSO regression analysis to establish a prognostic model and to investigate its value and function. Finally, the relationship of the model immunity checkpoints and immunity infiltration was assessed. Results. The receiver operating characteristic (ROC) showed that the 1-year, 3-year, and 5-year prediction rates of the prognostic model were 0.715, 0.693, and 0.732, respectively. The high-risk group had lower overall survival and higher stage than the low-risk group. Functional enrichment analysis showed that PRGs were significantly enriched mainly in the PPAR pathway, inflammatory pathway, and immune activity. ccRCC patient prognosis correlates with immune components in the microenvironment, and immune checkpoint molecules are significantly expressed in the high-risk group. Immunotherapy may be effective in the high-risk group. Conclusion. Pyroptosis-related gene has an important impact on the progression of ccRCC and can be used as an independent predictor of patient prognosis. In addition, immune checkpoint molecules are significantly upregulated in high-risk populations, which may be a potential target for immunotherapy.

Identification of prognostic models for glycosylation-related subtypes and tumor microenvironment infiltration characteristics in clear cell renal cell cancer

“…We thoroughly searched the published signatures of ccRCC to compare the performance of GRGs with other signatures. There were six registered signatures [ [12] , [13] , [14] , [15] , [16] , [17] ](Supplementary table 1). The biological processes of pyroptosis, iron death, RNA-binding proteins, necroptosis, WNT, and drug sensitivity are all linked to these characteristics.…”

Identification of clear cell renal cell carcinoma subtypes by integrating radiomics and transcriptomics

“…Both have shown promising efficacy in evaluating the clinical and prognostic characteristics of ccRCC. Researchers have constructed models based on transcriptomic information, such as mRNA and miRNAs, to predict the staging and OS of ccRCC patients [ [21] , [22] , [23] , [24] ]. Similarly, scholars have integrated CT radiomics and clinical information to predict the tumor grade and prognosis of RCC patients [ [25] , [26] , [27] , [28] ].…”

LASSO regression and WGCNA-based telomerase-associated lncRNA signaling predicts clear cell renal cell carcinoma prognosis and immunotherapy response

“…To compare performance with other signatures, we thoroughly searched ccRCCreleased signatures. Six signatures have been registered [25][26][27][28][29][30] (Supplementary Table 5). These characteristics include pyroptosis, iron death, RNA-binding protein, necroptosis, WNT, and drug-sensitive biological processes.…”