Background: Immunotherapeutic approaches have recently emerged as effective treatment regimens against various types of cancer. However, the immune-mediated mechanisms surrounding papillary renal cell carcinoma (pRCC) remain unclear. This study aimed to investigate the tumor microenvironment (TME) and identify the potential immune-related biomarkers for pRCC.
Methods: The CIBERSORT algorithm was used to calculate the abundance ratio of immune cells in each pRCC samples. Univariate Cox analysis was used to select the prognostic-related tumor-infiltrating immune cells (TIICs). Multivariate Cox regression analysis was performed to develop a signature based on the selected prognostic-related TIICs. Then, these pRCC samples were divided into low- and high-risk groups according to the obtained signature. Analyses using Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA) were performed to investigate the biological function of the DEGs (differentially expressed genes) between the high- and low-risk groups. The hub genes were identified using a weighted gene co-expression network analysis (WGCNA) and a protein-protein interaction (PPI) analysis. The hub genes were subsequently validated by multiple clinical traits and databases.
Results: According to our analyses, nine immune cells play a vital role in the TME of pRCC. Our analyses also obtained nine potential immune-related biomarkers for pRCC, including TOP2A, BUB1B, BUB1, TPX2, PBK, CEP55, ASPM, RRM2, and CENPF.
Conclusion: In this study, our data revealed the crucial TIICs and potential immune-related biomarkers for pRCC and provided compelling insights into the pathogenesis and potential therapeutic targets for pRCC.
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| http://dx.doi.org/10.1002/jcla.24022 | DOI Listing |
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