| 杨莉,黄璐璐,陆诗芬,等.基于坏死性凋亡相关长链非编码 RNA在肾透明细胞癌中的预后模型构建和分析[J].安徽医药,2026,30(6):1149-1155. |
| 基于坏死性凋亡相关长链非编码 RNA在肾透明细胞癌中的预后模型构建和分析 |
| Construction and analysis of a prognostic model based on necroptosis-associated long non-coding RNAs in kidney renal clear cell carcinoma |
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| DOI：10.3969/j.issn.1009-6469.2026.06.018 |
| 中文关键词: 癌，肾细胞 坏死性凋亡 RNA，长链非编码 预后 肿瘤微环境 |
| 英文关键词: Carcinoma, renal cell Necroptosis RNA, long noncoding Prognosis Tumor microenvironment |
| 基金项目: |
| 作者 | 单位 | E-mail | | 杨莉 | 广东医科大学附属医院, 消化肿瘤专科,广东湛江 524000 | | | 黄璐璐 | 广东医科大学,研究生学院,广东湛江 524023 | | | 陆诗芬 | 广东医科大学,研究生学院,广东湛江 524023 | | | 廖玉琳 | 广东医科大学,第一临床学院,广东湛江 524023 | | | 容绍焯 | 广东医科大学,第一临床学院,广东湛江 524023 | | | 麦婵清 | 广东医科大学,第一临床学院,广东湛江 524023 | | | 李海文 | 广东医科大学附属医院,头颈部肿瘤专科,广东湛江 524000 | 505147279@qq.com |
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| 中文摘要: |
| 目的筛选肾透明细胞癌( KIRC)预后坏死性凋亡相关长链非编码 RNA(NRL)并构建基于 NRL的预后模型，以预测 KIRC病人的预后和筛选其预后生物标志物。方法该研究的起止时间为 2024年 5—12月，。从癌症基因组图谱( TCGA)数据库获取 KIRC病人 RNA测序数据、临床及预后信息，共纳入 532例具有完整生存时间的病人。通过 GeneCards筛选坏死性凋亡相关基因，利用 “limma”包筛选差异表达基因并进行功能富集分析；通过 Pearson相关性分析、 LASSO和 Cox回归分析筛选 NRLs构建预后模型；采用 Kaplan-Meier生存分析、 Cox回归、临床病理特征相关性研究和受试者操作特征曲线( ROC曲线)评价预后模型对 KIRC生存率的预测能力；使用药物敏感性分析检验风险评分和抗癌药物敏感性之间的相关性；最后，采用逆转录实时定量聚合酶链式反应( RT-qPCR)检测于 2024年 9—10月从广东医科大学附属医院收集的 6例癌组织和癌旁组织 NRLs的表达水平。结果共筛选出 50个与坏死性凋亡相关的差异表达基因，包括 37个上调基因和 13个下调基因，这些基因在坏死性凋亡过程和免疫相关通路中显著富集；进一步分析筛选出 5个与预后相关的 NRLs(AC084876.1、AC093797.1、DLGAP1-AS2、 LINC01605、AC093895.1)并构建预后风险模型，模型的风险评分为:风险评分 =(0.392×AC084876.1表达值) +(0.353×DLGAP1-AS2表达值) +(0.151×LINC01605表达值) +(0.194×AC093895.1表达值) +(.0.284×AC093797.1表达值)；根据风险评分中位数将病人分为高风险组( 264例)和低风险组( 268例)，Kaplan-Meier生存分析显示低风险组的总生存率 81.9%显著优于高风险组的45.8%(P<0.001)，预后风险模型的曲线下面积(AUC)值为 0.85；Cox回归和分层生存分析提示风险评分是 KIRC病人的独立预测因子。此外，高风险组在免疫细胞浸润和免疫检查点表达水平上显著高于低风险组，且对 A-443654和 ABT-888更为敏感(P<0.05)； AC084876.1(1.58±0.20)、 DLGAP1-AS2(1.41±0.07)、 LINC01605(1.43±0.09)和 AC093895.1(1.60±0.08)在癌组织相对表达量均高于癌旁组织(均为 1.00±0.00)(均 P<0.05)相反 AC093797.1(1.00±0.00)在癌旁组织相对表达量高于癌组织( 0.32±0.06)(P<0.05)。结论基于 AC093797.1、AC084876.1LGAP1-AS2、LINC01605、AC093895.1构建的预后模型可有效预测 KIRC病、D，人的预后，为临床诊断和个性化治疗提供了新的生物标志物和潜在治疗靶点。 |
| 英文摘要: |
| Objective To screen for necroptosis-related long non-coding RNAs (NRLs) associated with the prognosis of kidney renalclear cell carcinoma (KIRC) and construct a prognostic model based on NRLs to predict the prognosis of KIRC patients and screen forprognostic biomarkers.Methods The study period was from May to December 2024. RNA sequencing data, clinical information, andprognostic data of patients with KIRC were obtained from the Cancer Genome Atlas (TCGA) database, and a total of 532 patients withcomplete survival information were included. Necroptosis-related genes were obtained from GeneCards, and differentially expressedgenes were identified using the "limma" package and subjected to functional enrichment analysis. NRLs were selected for constructing the prognostic model through Pearson correlation analysis, LASSO, and Cox regression analysis. The predictive ability of the prognosticmodel for KIRC overall survival rates was evaluated using Kaplan-Meier analysis, Cox regression, correlation studies with clinical path.ological characteristics, and receiver operating characteristic curve (ROC curve) analysis. In addition, the correlation between riskscores and immune cell infiltration and anti-cancer drug sensitivity was examined. Finally, the expression levels of NRLs in six pairs ofcancerous tissues and adjacent normal tissues collected from the Affiliated Hospital of Guangdong Medical University between Septem.ber and October 2024 were detected by reverse transcription quantitative real-time polymerase chain reaction (RT-qPCR).Results A total of 50 differentially expressed genes related to necroptosis were identified, including 37 up-regulated and 13 down-regulated genes, which were significantly enriched in necroptosis processes and immune-related pathways. Further analysis identified 5 NRLs (AC084876.1, AC093797.1, DLGAP1-AS2, LINC01605, AC093895.1), and a prognostic risk model was constructed. The risk score ofthe model was calculated as follows: risk score = (0.392×expression of AC084876.1)+(0.353×expression of DLGAP1-AS2)+(0.151×ex.pression of LINC01605)+(0.194×expression of AC093895.1)+(.0.284×expression of AC093797.1). Patients were divided into a high-risk group (264 cases) and a low-risk group (268 cases) based on the median risk score. Kaplan-Meier analysis showed that the low-risk group had a significantly better overall survival rate of 81.9% compared to 45.8% in the high-risk group (P<0.001), and the AUC of therisk score was 0.85. Cox regression and stratified survival analysis indicated that the risk score is an independent predictor for KIRCpatients. Additionally, the high-risk group had significantly higher levels of immune cell infiltration and immune checkpoint expressionlevels and was more sensitive to A-443654 and ABT-888 (P<0.05). The expression levels of AC084876.1 (1.58±0.20), DLGAP1-AS2 (1.41±0.07), LINC01605 (1.43±0.09) and AC093895.1 (1.60±0.08) in cancerous tissues were all higher than those in adjacent normaltissues (1.00±0.00) (all P<0.05). Conversely, the expression levels of AC093797.1 in adjacent normal tissues (1.00±0.00) were higherthan those in cancerous tissues (0.32±0.06) (P<0.05). Conclusion The prognostic model constructed based on AC093797.1, AC084876.1, DLGAP1-AS2, LINC01605, and AC093895.1 can effectively predict the prognosis of KIRC patients, providing new bio.markers and potential therapeutic traget for clinical diagnosis and personalized treatment. |
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