English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Papillary renal cell carcinoma (PRCC) is one of the most common histological subtypes of renal cell carcinoma. Type 1 and type 2 PRCC are reported to be clinically and biologically distinct. However, little is known about immune infiltration and the expression patterns of immune-related genes in these two histologic subtypes, thereby limiting the development of immunotherapy for PRCC. Thus, we analyzed the expression of 22 immune cells in type 1 and type 2 PRCC tissues by combining The Cancer Genome Atlas (TCGA) database with the ESTIMATE and CIBERSORT algorithms. Subsequently, we extracted a list of differentially expressed genes associated with the immune microenvironment. Multichip mRNA microarray data sets for PRCC were downloaded from the Gene Expression Omnibus (GEO) to further validate our findings. We found that the immune scores and stromal scores were associated with overall survival in patients with type 2 PRCC rather than type 1 PRCC. Tumor-infiltrating M1 and M2 macrophages could predict the clinical outcome by reflecting the host's immune capacity against type 2 PRCC. Furthermore, CCL19/CCR7, CXCL12/CXCR4, and CCL20/CCR6 were shown to be potential new targets for tumor gene therapy in type 2 PRCC. Our findings provide valuable resources for improving immunotherapy for PRCC.

Immune Cell Infiltration and Identifying Genes of Prognostic Value in the Papillary Renal Cell Carcinoma Microenvironment by Bioinformatics Analysis