Wang Biao, Wei Zhuo, Xu Man, Shu Hui, Fan Zheqi
Department of Urology, The Central Hospital of Xiaogan, Xiaogan, 432000, Hubei, China.
Affiliated Eye Hospital of Nanchang University, Nanchang, 330000, Jiangxi, China.
Discov Oncol. 2024 Sep 27;15(1):492. doi: 10.1007/s12672-024-01363-9.
Tumour immunity is highly important for the occurrence and development of tumours, and many cancers are resistant to ferroptosis. This study aims to explore the relationship between ferroptosis-related genes (FRGs) and the immunological characteristics of kidney renal clear cell carcinoma (KIRC). We obtained RNA-seq profiles and clinical data of KIRC patients from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases and identified CD44 and GLRX5 as the key FRGs involved in KIRC immune infiltration through Spearman's correlation analysis. Based on the expression of CD44 and GLRX5, the consensus clustering algorithm was used to classify the TCGA-KIRC samples into two clusters. A nomogram was constructed to evaluate the prognosis of KIRC patients. ESTIMATE, CIBERSORT, and single-sample gene set enrichment analysis (ssGSEA) were performed to evaluate immune infiltration between the two clusters. A weighted gene co-expression network analysis (WGCNA) was used to identify the most relevant genes to the clusters and immunity. Then, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed. The external dataset GSE53757 was used to validate the immunological features between the two clusters. Cluster 2 patients had more active immune infiltration and might be more sensitive to immunotherapy; Cluster 2 patients also had a worse prognosis and might be at a more advanced stage of KIRC. We identified key ferroptosis-related genes and subgroups involved in the immune infiltration of KIRC, which is highly important for exploring the molecular mechanisms and treatments of KIRC.
肿瘤免疫对肿瘤的发生和发展至关重要，许多癌症对铁死亡具有抗性。本研究旨在探讨铁死亡相关基因（FRGs）与肾透明细胞癌（KIRC）免疫特征之间的关系。我们从癌症基因组图谱（TCGA）和基因表达综合数据库（GEO）获取了KIRC患者的RNA测序谱和临床数据，并通过Spearman相关性分析确定CD44和GLRX5为参与KIRC免疫浸润的关键FRGs。基于CD44和GLRX5的表达，采用共识聚类算法将TCGA-KIRC样本分为两个簇。构建列线图以评估KIRC患者的预后。进行ESTIMATE、CIBERSORT和单样本基因集富集分析（ssGSEA）以评估两个簇之间的免疫浸润。使用加权基因共表达网络分析（WGCNA）来识别与簇和免疫最相关的基因。然后，进行基因本体论（GO）和京都基因与基因组百科全书（KEGG）分析。使用外部数据集GSE53757验证两个簇之间的免疫特征。簇2患者的免疫浸润更活跃，可能对免疫治疗更敏感；簇2患者的预后也更差，可能处于KIRC的更晚期阶段。我们确定了参与KIRC免疫浸润的关键铁死亡相关基因和亚组，这对探索KIRC的分子机制和治疗方法非常重要。
