Cuproptosis-Associated lncRNA Establishes New Prognostic Profile and Predicts Immunotherapy Response in Clear Cell Renal Cell Carcinoma.
Cuproptosis-Associated lncRNA Establishes New Prognostic Profile and Predicts Immunotherapy Response in Clear Cell Renal Cell Carcinoma.
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与库菌病相关的LNCRNA建立了新的预后特征，并预测了透明细胞肾细胞癌中的免疫疗法反应。
DOI：
10.3389/fgene.2022.938259
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发表时间：
2022
影响因子：
3.7
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中科院分区：
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背景：透明细胞肾细胞癌（ccRCC）占所有肾癌的80%，预后不良。最近的研究表明，铜依赖性的受调节的细胞死亡不同于先前已知的死亡机制（细胞凋亡、铁凋亡和坏死凋亡），并且依赖于线粒体呼吸（Tsvetkov等人，Science，2022，375（6586），1254-1261）。研究还表明，靶向铜中毒可能是癌症治疗的一种新的治疗策略。在ccRCC中，铜中毒和lncRNA都是关键的，但其机制尚未完全了解。本研究的目的是构建一个基于铜中毒相关lncRNA的预后谱，以预测ccRCC的预后，并研究肾透明细胞癌（ccRCC）的免疫谱。 方法：我们从癌症基因组图谱（TCGA）下载ccRCC的转录谱和临床信息。采用共表达网络分析、考克斯回归分析和最小绝对收缩选择算子（LASSO）方法对铜中毒相关lncRNA进行识别，并构建风险预测模型。此外，该模型的预测性能进行了验证和认可的综合方法。然后，我们还构建了一个诺模图来预测ccRCC患者的预后。通过GO、KEGG和免疫测定研究生物学功能的差异。使用肿瘤突变负荷（TMB）和肿瘤免疫功能障碍和排斥（TIDE）评分测量免疫治疗应答。 结果如下：我们构建了一组10种铜中毒相关lncRNA（HHLA 3、H1-10-AS 1、PICSAR、LINC 02027、SNHG 15、SNHG 8、LINC 00471、EIF 1B-AS 1、LINC 02154和MINCR），以构建预后预测模型。Kaplan-Meier和ROC曲线表明，该特征在TCGA训练组、测试组和完全组中具有可接受的预测有效性。铜中毒相关lncRNA模型与其他临床特征相比具有更高的诊断效率。免疫细胞浸润和ssGSEA的分析进一步证实了预测特征与ccRCC患者的免疫状态显著相关。值得注意的是，高风险组和高TMB患者的叠加效应导致生存期缩短。此外，高风险组的TIDE评分较高，表明这些患者的免疫检查点阻断反应结果较差。 结论：lncRNA的10个铜中毒相关风险特征可能有助于评估ccRCC患者的预后和分子特征，并改善治疗方案，可进一步应用于临床。
Background: Clear cell renal cell carcinoma (ccRCC) accounts for 80% of all kidney cancers and has a poor prognosis. Recent studies have shown that copper-dependent, regulated cell death differs from previously known death mechanisms (apoptosis, ferroptosis, and necroptosis) and is dependent on mitochondrial respiration (Tsvetkov et al., Science, 2022, 375 (6586), 1254–1261). Studies also suggested that targeting cuproptosis may be a novel therapeutic strategy for cancer therapy. In ccRCC, both cuproptosis and lncRNA were critical, but the mechanisms were not fully understood. The aim of our study was to construct a prognostic profile based on cuproptosis-associated lncRNAs to predict the prognosis of ccRCC and to study the immune profile of clear cell renal cell carcinoma (ccRCC). Methods: We downloaded the transcriptional profile and clinical information of ccRCC from The Cancer Genome Atlas (TCGA). Co-expression network analysis, Cox regression method, and least absolute shrinkage and selection operator (LASSO) method were used to identify cuproptosis-associated lncRNAs and to construct a risk prognostic model. In addition, the predictive performance of the model was validated and recognized by an integrated approach. We then also constructed a nomogram to predict the prognosis of ccRCC patients. Differences in biological function were investigated by GO, KEGG, and immunoassay. Immunotherapy response was measured using tumor mutational burden (TMB) and tumor immune dysfunction and rejection (TIDE) scores. Results: We constructed a panel of 10 cuproptosis-associated lncRNAs (HHLA3, H1-10-AS1, PICSAR, LINC02027, SNHG15, SNHG8, LINC00471, EIF1B-AS1, LINC02154, and MINCR) to construct a prognostic prediction model. The Kaplan–Meier and ROC curves showed that the feature had acceptable predictive validity in the TCGA training, test, and complete groups. The cuproptosis-associated lncRNA model had higher diagnostic efficiency compared to other clinical features. The analysis of Immune cell infiltration and ssGSEA further confirmed that predictive features were significantly associated with the immune status of ccRCC patients. Notably, the superimposed effect of patients in the high-risk group and high TMB resulted in shorter survival. In addition, the higher TIDE scores in the high-risk group suggested a poorer outcome for immune checkpoint blockade response in these patients. Conclusion: The ten cuproptosis-related risk profiles for lncRNA may help assess the prognosis and molecular profile of ccRCC patients and improve treatment options, which can be further applied in the clinic.