Clear cell renal cell carcinoma (ccRCC) is the most common renal cancer. Despite the rapid evolution of targeted therapies, immunotherapy with checkpoint inhibition (ICI) as well as combination therapies, the cure of metastatic ccRCC (mccRCC) is infrequent, while the optimal use of the various novel agents has not been fully clarified. With the different treatment options, there is an essential need to identify biomarkers to predict therapeutic efficacy and thus optimize therapeutic approaches. This study seeks to explore the diversity in mRNA expression profiles of inflammation and immunity-related circulating genes for the development of biomarkers that could predict the effectiveness of immunotherapy-based treatments using ICIs for individuals with mccRCC. Gene mRNA expression was tested by the RT2 profiler PCR Array on a human cancer inflammation and immunity crosstalk kit and analyzed for differential gene expression along with a machine learning approach for sample classification. A number of mRNAs were found to be differentially expressed in mccRCC with a clinical benefit from treatment compared to those who progressed. Our results indicate that gene expression can classify these samples with high accuracy and specificity.
透明细胞肾细胞癌（ccRCC）是最常见的肾脏恶性肿瘤。尽管靶向治疗、免疫检查点抑制剂（ICI）免疫疗法及联合治疗方案快速发展，转移性透明细胞肾细胞癌（mccRCC）的治愈仍属罕见，且各类新型药物的最佳应用方案尚未完全明确。面对多样化的治疗选择，亟需寻找能够预测疗效的生物标志物以优化治疗策略。本研究旨在探索炎症与免疫相关循环基因mRNA表达谱的多样性，以开发能够预测mccRCC患者接受ICI免疫疗法疗效的生物标志物。通过人类癌症炎症与免疫交互作用检测试剂盒中的RT2 profiler PCR阵列检测基因mRNA表达，并采用机器学习方法进行样本分类与差异基因表达分析。研究发现，与疾病进展患者相比，从治疗中获得临床获益的mccRCC患者存在多个差异表达的mRNA。我们的结果表明，基因表达谱能够以高准确度和特异性对这些样本进行分类。
肿瘤(癌症)患者之家