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

IF 2.8 3区生物学 Q2 GENETICS & HEREDITY Frontiers in Genetics Pub Date : 2025-03-04 eCollection Date: 2025-01-01 DOI:10.3389/fgene.2025.1521663

Bin Zhao, Shi Fu, Yuanlong Shi, Jinye Yang, Chengwei Bi, Libo Yang, Yong Yang, Xin Li, Zhiyu Shi, Yuanpeng Duan, Zongyan Luo, Guoying Zhang, Jiansong Wang

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Abstract

Background: 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.

Methods: 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.

Results: 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.

Conclusion: 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.

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利用免疫检查点相关基因的透明细胞肾细胞癌公共数据集的预后和诊断模型的开发和验证。

背景：透明细胞肾细胞癌（ccRCC）是肾癌中最常见的亚型，基于免疫检查点调节因子的免疫疗法已成为晚期肾癌的有效治疗方法。然而，免疫检查点相关基因（ICRGs）在ccRCC中的表达模式、预后意义和诊断价值仍未得到充分探讨。本研究利用来自The Cancer Genome Atlas （TCGA）、Gene Expression Omnibus （GEO）和国际癌症基因组联盟（ICGC）的大规模ccRCC数据集，分析ICRGs并建立预后和诊断模型，并在ccRCC患者的临床样本中使用定量PCR进行验证。方法：从TCGA、ICGC和GEO数据库中检索RNA-seq数据和临床资料。鉴定差异表达基因（deg），并通过将deg与ICRGs交叉筛选免疫检查点相关基因（DICRGs），然后在独立数据集中进行验证。采用单因素和多因素Cox回归分析建立预后模型。利用人类蛋白图谱（HPA）的数据，通过免疫组织化学（IHC）验证关键基因的蛋白表达。qRT-PCR证实了基因在ccRCC和正常肾组织中的表达水平。使用机器学习构建诊断模型，并进行功能富集和免疫浸润分析。结果：共鉴定出14个DICRGs，其中4个（EGFR、TRIB3、ZAP70和CD4）在Cox分析中显示预后意义。IHC显示这些基因在ccRCC组织中高表达，qRT-PCR证实EGFR、TRIB3、CD4表达增加，而ZAP70表达无明显变化。根据基因表达水平制定预后风险评分。功能分析发现了与有机阴离子运输和代谢相关的富集途径，而免疫浸润分析揭示了ZAP70、CD4和风险评分之间的关联。结论：本研究建立了基于4种ICRGs的ccRCC预后模型，为ccRCC预后和诊断的分子机制提供了有价值的见解。

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来源期刊

Frontiers in Genetics Biochemistry, Genetics and Molecular Biology-Molecular Medicine

CiteScore

5.50

自引率

8.10%

发文量

3491

审稿时长

14 weeks

期刊介绍： Frontiers in Genetics publishes rigorously peer-reviewed research on genes and genomes relating to all the domains of life, from humans to plants to livestock and other model organisms. Led by an outstanding Editorial Board of the world’s leading experts, this multidisciplinary, open-access journal is at the forefront of communicating cutting-edge research to researchers, academics, clinicians, policy makers and the public. The study of inheritance and the impact of the genome on various biological processes is well documented. However, the majority of discoveries are still to come. A new era is seeing major developments in the function and variability of the genome, the use of genetic and genomic tools and the analysis of the genetic basis of various biological phenomena.