| 肖智良,董洋,史振铎,韩从辉.染色质调节因子相关的lncRNA肾透明细胞癌预后模型的构建和验证[J].中国医药导报,2024,21(2):4-14 |
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| 染色质调节因子相关的lncRNA肾透明细胞癌预后模型的构建和验证 |
| Construction and validation of a chromatin regulator-related lncRNA prognostic model for clear cell renal carcinoma |
| 收稿日期：2023-04-25 |
| DOI：10.20047/j.issn1673-7210.2024.02.01 |
| 关键词: 肾透明细胞癌 染色质调节因子 长链非编码RNA 预后标志物 |
| Key Words: |
| 基金项目:国家自然科学基金青年科学基金资助项目（82204866）；国家自然科学基金面上项目（12271467）；江苏省徐州市卫生健康委员会科技项目面上项目（XWKYHT 20210545） |
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| 摘要:目的 构建染色质调节因子（CR）相关长链非编码RNA（lncRNA）的肾透明细胞癌（ccRCC）预后模型，提高ccRCC的预后管理。 方法 从TCGA数据库下载ccRCC的转录组数据和临床数据，并通过“caret”R包将ccRCC样本以7∶3的比例分为训练集（166例）和验证集（71例）。基于“DESeq2”R包筛选出差异表达的CR及相关的lncRNA。构建CR与lncRNA的共表达网络，并筛选出相关系数|rs|＞0.3且P＜0.05的差异lncRNA。利用单因素Cox、Lasso和多因素逐步Cox回归分析，构建CR相关的lncRNA预后模型。计算训练集和验证集样本的风险评分，根据风险评分的中位数将ccRCC患者分为高、低风险组。K-M曲线和受试者操作特征（ROC）曲线评价模型，单因素和多因素Cox回归分析评估风险评分的独立预测性能。“DynNom”和“shiny”R包开发在线预测工具。单样本基因集富集分析探索风险评分与免疫微环境及免疫检查点的关系。 结果 本研究最终纳入237个肿瘤样本和72个癌旁正常组织样本，鉴定了1 025个CR相关的lncRNA，最终筛选得到7个CR相关lncRNA（DUXAP8、AC026462.3、LINC01460、AL592494.1、AL353804.2、AC012462.1、AC009518.1）构建预后模型，并将其开发成在线工具（https://xzlmodelshiny.shinyapps.io/DynNomapp/）。高危组患者的生存率低于低危组（P＜0.05），ROC曲线表明模型预测效能较好，单因素分析和多因素分析中风险评分的HR值分别为4.058（95%CI：2.530~6.508，P＜0.001）和3.096（95%CI：1.887~5.080，P＜0.001）。高风险组MDSC和Tregs细胞等免疫抑制细胞的浸润比例高于低风险组，趋化因子、免疫检查点及副炎症等通路的富集水平高于低风险组（P＜0.05）。此外，风险评分与免疫检查点TNFRSF25和TNFSF14呈正相关（r＞0.3，P＜0.05）。 结论 本研究构建的CR相关的lncRNA风险模型可独立有效地预测ccRCC患者的预后。 |
| Abstract:Objective To construct a prognostic model for chromatin regulator (CR)-related long non-coding RNA (lncRNA) in clear cell renal carcinoma (ccRCC), and improve the prognosis management of ccRCC. Methods Transcriptome data and clinical data of ccRCC were downloaded from TCGA database, and ccRCC samples were divided into training set (166 cases) and validation set (71 cases) at a ratio of 7∶3 using the “caret” R package. The differentially expressed CR and related lncRNA were screened based on the “DESeq2” R package. A co-expression network between CR and lncRNA was constructed, and differentially expressed lncRNA with correlation coefficients |rs|＞0.3 and P＜0.05 were selected. Using univariate Cox, Lasso, and multivariate stepwise Cox regression analysis, a prognostic model for CR-related lncRNA was constructed. The risk scores of the training set and validation set were calculated, and ccRCC patients were divided into high-risk group and low-risk group based on the median risk score. The K-M curve and receiver operating characteristic (ROC) curve were used to evaluate the model, and univariate and multivariate Cox regression analyses were used to assess the independent prognostic performance of the risk score. An online prediction tool was developed using the “DynNom” and “shiny” R packages. Single-sample gene set enrichment analysis was used to explore the relationship between the risk score and the immune microenvironment and immune checkpoints. Results In total, 237 tumor samples and 72 adjacent normal tissue samples were included in this study. A total of 1 025 CR-related lncRNA were identified, and finally, seven CR-related lncRNA (DUXAP8, AC026462.3, LINC01460, AL592494.1, AL353804.2, AC012462.1, and AC009518.1) were selected to construct the prognostic model, which was developed into an online tool (https://xzlmodelshiny.shinyapps.io/DynNomapp/). The survival rate of the high-risk group was lower than that of the low-risk group (P＜0.05). The ROC curve showed that the prognostic performance of the model was good. The HR values of the risk score in the univariate analyse and multivariate analyse were 4.058 (95%CI: 2.530-6.508, P＜0.001) and 3.096 (95%CI: 1.887-5.080, P＜0.001), respectively. The proportion of immune-inhibitory cells such as MDSCs and Tregs in the high-risk group was higher than that in the low-risk group, and the enrichment levels of chemokines, immune checkpoints, and pro-inflammatory pathways were higher than those in the high-risk group (P＜0.05). In addition, the risk score was significantly positively correlated with immune checkpoint genes TNFRSF25 and TNFSF14（r＞0.3, P＜0.05）. Conclusion The CR-related lncRNA risk model constructed in this study can effectively predict the prognosis of ccRCC patients independently. |
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