Ou Junyong, Yin Haoming, Shu Fan, Wu Zonglong, Liu Shuai, Ye Jianfei, Zhang Shudong
Department of Urology, Peking University Third Hospital, Peking University Health Science Center, 49 North Garden Road, Beijing, 100191, China.
Heliyon. 2024 Aug 14;10(16):e36235. doi: 10.1016/j.heliyon.2024.e36235. eCollection 2024 Aug 30.
Clear cell renal cell carcinoma (ccRCC) is a highly aggressive cancer associated with higher death rates. However, traditional anti-angiogenic therapies have limited effectiveness due to drug resistance. Vascular mimicry (VM) provides a different way for tumors to develop blood vessels without relying on endothelial cells or angiogenesis. However, the intricate mechanisms and interplay between it and the immune microenvironment in ccRCC remain unclear.
A PubMed and GeneCards literature review was conducted to identify VM-related genes (VMRGs). VMRGs expression profiles were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO), developing a novel VM risk score model and nomogram for ccRCC. The EBI ArrayExpress database (the validation set) was obtained to validate the prognostic model. The relationship between VMRGs risk score clinical characteristics and immune infiltration was investigated. Finally, the expression of six model VMRGs was validated using single-cell analysis, GEPIA, Human Protein Atlas (HPA), and quantitative Real-time PCR (qRT-PCR).
Cox regression analysis and nomogram identified L1CAM, TEK, CLDN4, EFNA1, SERPINF1, and MALAT1 as independent prognostic risk factors, which could be used to stratify the ccRCC population into two risk groups with distinct immune profiles and responsiveness to immunotherapy. The results of single-cell analysis, GEPIA, HPA, and qRT-PCR validated the model genes' expression.
Our novel findings constructed a convenient and reliable 6 gene signatures as potential immunologic and prognostic biomarkers of VM in ccRCC.
透明细胞肾细胞癌（ccRCC）是一种侵袭性很强的癌症，死亡率较高。然而，由于耐药性，传统的抗血管生成疗法效果有限。血管生成拟态（VM）为肿瘤提供了一种不依赖内皮细胞或血管生成来形成血管的不同方式。然而，ccRCC中其与免疫微环境之间复杂的机制和相互作用仍不清楚。
进行PubMed和GeneCards文献综述以确定与VM相关的基因（VMRGs）。从癌症基因组图谱（TCGA）和基因表达综合数据库（GEO）获得VMRGs表达谱，为ccRCC开发一种新的VM风险评分模型和列线图。获取EBI ArrayExpress数据库（验证集）以验证预后模型。研究VMRGs风险评分与临床特征和免疫浸润之间的关系。最后，使用单细胞分析、GEPIA、人类蛋白质图谱（HPA）和定量实时聚合酶链反应（qRT-PCR）验证六个模型VMRGs的表达。
Cox回归分析和列线图确定L1CAM、TEK、CLDN4、EFNA1、SERPINF1和MALAT1为独立的预后风险因素，可用于将ccRCC人群分为两个风险组，这两个组具有不同的免疫特征和对免疫治疗的反应性。单细胞分析、GEPIA、HPA和qRT-PCR的结果验证了模型基因的表达。
我们的新发现构建了一个方便可靠的6基因特征，作为ccRCC中VM潜在的免疫和预后生物标志物。
