Cui Yankang, Shen Tianyi, Xu Feng, Zhang Jing, Wang Yuhao, Wu Jiajin, Bu Hengtao, Fu Dian, Fang Bo, Lv Huichen, Wang Suchun, Shi Changjie, Liu Bianjiang, He Haowei, Tang Hao, Ge Jingping
Department of Urology, Clinical School of Medical College, Jinling Hospital, Nanjing University, Nanjing, China.
School of Chemistry and Chemical Engineering, Jiangsu Province Hi-Tech Key Laboratory for Biomedical Research, Southeast University, Nanjing, China.
Cancer Cell Int. 2022 Jun 10;22(1):211. doi: 10.1186/s12935-022-02626-7.
BACKGROUND: Studies over the past decade have shown that competitive endogenous RNA (ceRNA) plays an essential role in the tumorigenesis and progression of clear cell renal cell carcinoma (ccRCC). Meanwhile, immune checkpoint blocker is gradually moving towards the first-line treatment of ccRCC. Hence, it's urgent to develop a new prediction model for the efficiency of immunotherapy. At present, there is no study to reveal the effect of ceRNA network on the efficiency of immunotherapy for ccRCC. METHODS: To systematically analyze the effect of ceRNA hub genes in ccRCCon immune response, we constructed prognosis models based on ceRNAs and immune cells, respectively. We constructed ceRNA network using hypergeometric distribution test and correlation analysis with R script based on The Cancer Genome Atlas (TCGA) database. We then applied the Cibersort algorithm to simulate the infiltration overview of immune cells in kidney renal clear carcinoma (KIRC) samples. Prognosis-related immune cells were screened and a predictive model of these cells was constructed. Prognosis-related immune cells and ceRNA hub genes were performed with co-expression analysis. Finally, qRT-PCR and immunofluorescence assays were performed to validate the results. RESULTS: The construction of ceRNA related prognosis model contained 8 hub genes, including RELT, MYO9B, KCNN4, SIX1, OTOGL, MALAT1, hsa-miR-130b-3p, and hsa-miR-21-5p. The area under the receiver operating characteristic curve (AUC) was 0.77 at 5 years. For the construction of immune cells prognosis model, 3 immune cells (T cells regulatory, Macrophages, Mast cells resting) were adopted, and the AUC was 0.65 at 5 years. We then merged the two models by correlation analysis and co-expression analysis. Finally, we found that KCNN4 positively correlates with T cells regulatory (Tregs) and negatively correlates with mast cells resting significantly. Furthermore, higher expression of KCNN4 may lead to a higher potential for immune evasion and lower efficiency for immune checkpoint inhibitors (ICIs). CONCLUSIONS: Generally, this is the first study to assess the prognostic value of immune related ceRNA hub genes in ccRCC, and KCNN4 was finally demonstrated to be a key regulatory factor with strong correlation with Tregs and mast cells resting.
背景：过去十年的研究表明，竞争性内源性RNA（ceRNA）在透明细胞肾细胞癌（ccRCC）的肿瘤发生和进展中起重要作用。与此同时，免疫检查点阻断剂正逐渐走向ccRCC的一线治疗。因此，迫切需要开发一种新的免疫治疗疗效预测模型。目前，尚无研究揭示ceRNA网络对ccRCC免疫治疗疗效的影响。 方法：为了系统分析ccRCC中ceRNA枢纽基因对免疫反应的影响，我们分别基于ceRNAs和免疫细胞构建了预后模型。我们使用超几何分布检验和基于R脚本的相关性分析，基于癌症基因组图谱（TCGA）数据库构建ceRNA网络。然后，我们应用Cibersort算法模拟肾透明细胞癌（KIRC）样本中免疫细胞的浸润概况。筛选出与预后相关的免疫细胞，并构建这些细胞的预测模型。对与预后相关的免疫细胞和ceRNA枢纽基因进行共表达分析。最后，进行qRT-PCR和免疫荧光检测以验证结果。 结果：ceRNA相关预后模型的构建包含8个枢纽基因，包括RELT、MYO9B、KCNN4、SIX1、OTOG1、MALAT1、hsa-miR-130b-3p和hsa-miR-21-5p。5年时受试者操作特征曲线（AUC）下面积为0.77。对于免疫细胞预后模型的构建，采用了3种免疫细胞（调节性T细胞、巨噬细胞、静息肥大细胞），5年时AUC为0.65。然后，我们通过相关性分析和共表达分析将这两个模型合并。最后，我们发现KCNN4与调节性T细胞（Tregs）呈正相关，与静息肥大细胞呈显著负相关。此外，KCNN4的高表达可能导致更高的免疫逃逸潜力和更低的免疫检查点抑制剂（ICIs）效率。 结论：总体而言，这是第一项评估免疫相关ceRNA枢纽基因在ccRCC中的预后价值的研究，最终证明KCNN4是与Tregs和静息肥大细胞密切相关的关键调节因子。
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