Xie Zhenwei, Feng Cheng, Hong Yude, Chen Libo, Li Mingyong, Deng Weiming
Department of Kidney Transplantation, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
Department of Thyroid and Galactophore Surgery, People's Hospital of Longhua, Shenzhen, China.
Front Mol Biosci. 2025 May 6;12:1587196. doi: 10.3389/fmolb.2025.1587196. eCollection 2025.
Clear Cell Renal Cell Carcinoma (ccRCC) is a malignant tumor with high mortality and recurrence rates and the molecular mechanism of ccRCC genesis remains unclear. In this study, we identified several key genes associated with the prognosis of ccRCC by using integrated bioinformatics.
Two ccRCC expression profiles were downloaded from Gene Expression Omnibus and one dataset was gained from The Cancer Genome Atlas The Robust Rank Aggregation method was used to analyze the three datasets to gain integrated differentially expressed genes The Gene Ontology and KEGG analysis were performed to explore the potential functions of DEGs. The Search Tool for the Retreival of Interacting Genes/Proteins (STRING) and Cytoscape software were used to construct protein-protein interaction network and module analyses to screen the hub genes. Spearman's correlation analysis was conducted to evaluate the interrelationships among the hub genes. The prognostic value was evaluated through K-M survival analysis, Cox regression analysis, and receiver operating characteristic curve analysis to determine their potential as prognostic biomarkers in ccRCC. The expression of hub genes between ccRCC and adjacent normal tissues was analyzed by RT-qPCR, Western blotting, and immunohistochemical (IHC).
125 DEGs were identified using the limma package and RRA method, including 62 up-expressed genes and 63 down-expressed genes. GO and KEGG analysis showed some associated pathways. Spearman's correlation analysis revealed that the hub genes are not only interrelated but also closely associated with immune cell infiltration. Gene expression analysis of the hub genes based on the TCGA-KIRC cohort, along with K-M survival analysis, Cox regression, and ROC curve analysis, consistently demonstrated that CCL5, LOX, and C3 are significantly upregulated in ccRCC and are associated with poor clinical outcomes. In contrast, PLG showed opposite result. These results were further validated at the mRNA and protein levels.
Our findings indicate that CCL5, LOX, C3, and PLG are significantly associated with the progression and prognosis of ccRCC, highlighting their potential as prognostic biomarkers. These results provide a foundation for future research aimed at uncovering the underlying mechanisms and identifying potential therapeutic targets for ccRCC.
透明细胞肾细胞癌（ccRCC）是一种死亡率和复发率都很高的恶性肿瘤，其发病的分子机制尚不清楚。在本研究中，我们通过整合生物信息学方法鉴定了几个与ccRCC预后相关的关键基因。
从基因表达综合数据库（Gene Expression Omnibus）下载了两个ccRCC表达谱，并从癌症基因组图谱（The Cancer Genome Atlas）获得了一个数据集。使用稳健秩聚合（Robust Rank Aggregation）方法分析这三个数据集，以获得整合的差异表达基因。进行基因本体论（Gene Ontology）和京都基因与基因组百科全书（KEGG）分析，以探索差异表达基因的潜在功能。使用搜索相互作用基因/蛋白质的工具（STRING）和Cytoscape软件构建蛋白质-蛋白质相互作用网络并进行模块分析，以筛选枢纽基因。进行Spearman相关性分析，以评估枢纽基因之间的相互关系。通过K-M生存分析、Cox回归分析和受试者工作特征曲线分析评估预后价值，以确定它们作为ccRCC预后生物标志物的潜力。通过实时定量PCR（RT-qPCR）、蛋白质免疫印迹法和免疫组织化学（IHC）分析ccRCC与相邻正常组织之间枢纽基因的表达。
使用limma软件包和RRA方法鉴定出125个差异表达基因，其中包括62个上调基因和63个下调基因。基因本体论和KEGG分析显示了一些相关途径。Spearman相关性分析表明，枢纽基因不仅相互关联，而且与免疫细胞浸润密切相关。基于TCGA-KIRC队列对枢纽基因进行基因表达分析，并结合K-M生存分析、Cox回归和ROC曲线分析，一致表明CCL5、LOX和C3在ccRCC中显著上调，并与不良临床结果相关。相比之下，PLG则呈现相反的结果。这些结果在mRNA和蛋白质水平上得到了进一步验证。
我们的研究结果表明，CCL5、LOX、C3和PLG与ccRCC的进展和预后显著相关，突出了它们作为预后生物标志物的潜力。这些结果为未来旨在揭示潜在机制和确定ccRCC潜在治疗靶点的研究奠定了基础。
