# Development and validation of prognostic and diagnostic models utilizing immune checkpoint-related genes in public datasets for clear cell renal cell carcinoma

## TL;DR

This study identifies immune checkpoint-related genes that predict outcomes and help diagnose clear cell kidney cancer using large datasets and lab validation.

## Contribution

The study introduces a novel prognostic and diagnostic model for ccRCC based on four immune checkpoint-related genes validated through multiple datasets and clinical samples.

## Key findings

- Fourteen immune checkpoint-related genes were identified, with four (EGFR, TRIB3, ZAP70, CD4) showing prognostic significance.
- EGFR, TRIB3, and CD4 showed increased expression in ccRCC tissues confirmed by qRT-PCR.
- A prognostic risk score was developed based on gene expression levels and validated with immune infiltration analysis.

## Abstract

Clear cell renal cell carcinoma (ccRCC) is the most prevalent subtype of renal cell carcinoma, and immune checkpoint regulator-based immunotherapy has emerged as an effective treatment for advanced stages of the disease. However, the expression patterns, prognostic significance, and diagnostic value of immune checkpoint-related genes (ICRGs) in ccRCC remain underexplored. This study utilized large-scale ccRCC datasets from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), and the International Cancer Genome Consortium (ICGC) to analyze ICRGs and develop a prognostic and diagnostic model, which was validated using quantitative PCR in clinical samples from ccRCC patients.

RNA-seq data and clinical information were retrieved from TCGA, ICGC, and GEO databases. Differentially expressed genes (DEGs) were identified, and immune checkpoint-related genes (DICRGs) were selected by intersecting DEGs with ICRGs, followed by validation in independent datasets. Univariate and multivariate Cox regression analyses were used to develop the prognostic model. Protein expression of key genes was validated through immunohistochemistry (IHC) using data from the Human Protein Atlas (HPA). qRT-PCR confirmed gene expression levels in ccRCC and normal kidney tissues. Diagnostic models were constructed using machine learning, and functional enrichment and immune infiltration analyses were performed.

Fourteen DICRGs were identified, with four (EGFR, TRIB3, ZAP70, and CD4) showing prognostic significance in Cox analyses. IHC revealed high expression of these genes in ccRCC tissues, and qRT-PCR confirmed increased expression of EGFR, TRIB3, and CD4, while ZAP70 expression showed no significant change. A prognostic risk score was developed based on gene expression levels. Functional analysis identified enriched pathways related to organic anion transport and metabolism, while immune infiltration analysis revealed associations between ZAP70, CD4, and risk scores.

This study establishes a prognostic model for ccRCC based on four ICRGs, providing valuable insights into the molecular mechanisms underlying prognosis and diagnosis in ccRCC.