A New Prognostic Risk Model Based on PPAR Pathway-Related Genes in Kidney Renal Clear Cell Carcinoma

Abstract

Objective. This study is aimed at using genes related to the peroxisome proliferator-activated receptor (PPAR) pathway to establish a prognostic risk model in kidney renal clear cell carcinoma (KIRC). Methods. For this study, we first found the PPAR pathway-related genes on the gene set enrichment analysis (GSEA) website and found the KIRC mRNA expression data and clinical data through TCGA database. Subsequently, we used R language and multiple R language expansion packages to analyze the expression, hazard ratio analysis, and coexpression analysis of PPAR pathway-related genes in KIRC. Afterward, using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) website, we established the protein-protein interaction (PPI) network of genes related to the PPAR pathway. After that, we used LASSO regression curve analysis to establish a prognostic survival model in KIRC. Finally, based on the model, we conducted correlation analysis of the clinicopathological characteristics, univariate analysis, and multivariate analysis. Results. We found that most of the genes related to the PPAR pathway had different degrees of expression differences in KIRC. Among them, the high expression of 27 genes is related to low survival rate of KIRC patients, and the high expression of 13 other genes is related to their high survival rate. Most importantly, we used 13 of these genes successfully to establish a risk model that could accurately predict patients’ prognosis. There is a clear correlation between this model and metastasis, tumor, stage, grade, and fustat. Conclusions. To the best of our knowledge, this is the first study to analyze the entire PPAR pathway in KIRC in detail and successfully establish a risk model for patient prognosis. We believe that our research can provide valuable data for future researchers and clinicians.

Identification of Hypoxia–Immune-Related Gene Signatures and Construction of a Prognostic Model in Kidney Renal Clear Cell Carcinoma

“…The 5-year AUC score was 0.746, and the 10-year AUC score was 0.825, which indicated that this risk model could accurately predict the 5- and 10-year survival rates of KIRC patients. However, the PPAR-related model involved 13 gene signatures, and their research lacked external verification ( Xu et al, 2020 ). In addition, we also carried out a DCA to determine the clinical usefulness of the gene risk model by quantifying the net benefits at different threshold probabilities.…”

Identification of a Prognostic Risk Signature of Kidney Renal Clear Cell Carcinoma Based on Regulating the Immune Response Pathway Exploration

“…Bioinformatics analysis methods have been increasingly used in the medical field [ 14 , 15 ]. In this study, we used various bioinformatics methods to deeply study the potential role of pathways regulating immune responses in KIRC.…”

Multi-Omics Data Analysis of Gene Expressions and Alterations, Cancer-Associated Fibroblast and Immune Infiltrations, Reveals the Onco-Immune Prognostic Relevance of STAT3/CDK2/4/6 in Human Malignancies

“…The use of bioinformatics for the identification of important cancer biomarkers is increasingly becoming a reliable and profitable method [ 26 , 27 ], owing to the availability of multi-omics clinical data including differentially expressed genes, mutation profile, therapeutic response, and survival profile of cancer patients in public databases providing a reliable guideline for the development of appropriate therapeutic intervention [ 12 ]. In addition, network analysis of multi-omics data has also helped our understanding of the epigenetic mechanism of cancer development and facilitated the discovery of epigenetic-based prognostic biomarkers and therapies [ 28 , 29 , 30 , 31 , 32 ].…”