PDF(Pubmed)
Abstract：
UNASSIGNED: This study introduces a novel prognostic tool, the Disulfidoptosis-Related lncRNA Index (DRLI), integrating the molecular signatures of disulfidoptosis and long non-coding RNAs (lncRNAs) with the cellular heterogeneity of the tumor microenvironment, to predict clinical outcomes in patients with clear cell renal cell carcinoma (ccRCC).
UNASSIGNED: We analyzed 530 tumor and 72 normal samples from The Cancer Genome Atlas (TCGA), employing k-means clustering based on disulfidoptosis-associated gene expression to stratify ccRCC samples into prognostic groups. lncRNAs correlated with disulfidoptosis were identified and used to construct the DRLI, which was validated by Kaplan-Meier and receiver operating characteristic curves. We utilized single-cell deconvolution analysis to estimate the proportion of immune cell types within the tumor microenvironment, while the ESTIMATE and TIDE algorithms were employed to assess immune infiltration and potential response to immunotherapy.
UNASSIGNED: The Disulfidoptosis-Related lncRNA Index (DRLI) effectively stratified ccRCC patients into high and low-risk groups, significantly impacting survival outcomes (P < 0.001). High-risk patients, marked by a unique lncRNA profile associated with disulfidoptosis, faced worse prognoses. Single-cell analysis revealed marked tumor microenvironment heterogeneity, especially in immune cell makeup, correlating with patient risk levels. In prognostic predictions, DRLI outperformed traditional clinical indicators, achieving AUC values of 0.779, 0.757, and 0.779 for 1-year, 3-year, and 5-year survival in the training set, and 0.746, 0.734, and 0.750 in the validation set. Notably, while the constructed nomogram showed exceptional predictive capability for short-term prognosis (AUC = 0.877), the DRLI displayed remarkable long-term predictive accuracy, with its AUC value reaching 0.823 for 10-year survival, closely approaching the nomogram\'s performance.
UNASSIGNED: The study introduces the DRLI as a groundbreaking molecular stratification tool for ccRCC, enhancing prognostic precision and potentially guiding personalized treatment strategies. This advancement is particularly significant in the context of long-term survival predictions. Our findings also elucidate the complex interplay between disulfidoptosis, lncRNAs, and the immune microenvironment in ccRCC, offering a comprehensive perspective on its pathogenesis and progression. The DRLI and the nomogram together represent significant strides in ccRCC research, highlighting the importance of molecular-based assessments in predicting patient outcomes.
UNASSIGNED: We analyzed 530 tumor and 72 normal samples from The Cancer Genome Atlas (TCGA), employing k-means clustering based on disulfidoptosis-associated gene expression to stratify ccRCC samples into prognostic groups. lncRNAs correlated with disulfidoptosis were identified and used to construct the DRLI, which was validated by Kaplan-Meier and receiver operating characteristic curves. We utilized single-cell deconvolution analysis to estimate the proportion of immune cell types within the tumor microenvironment, while the ESTIMATE and TIDE algorithms were employed to assess immune infiltration and potential response to immunotherapy.
UNASSIGNED: The Disulfidoptosis-Related lncRNA Index (DRLI) effectively stratified ccRCC patients into high and low-risk groups, significantly impacting survival outcomes (P < 0.001). High-risk patients, marked by a unique lncRNA profile associated with disulfidoptosis, faced worse prognoses. Single-cell analysis revealed marked tumor microenvironment heterogeneity, especially in immune cell makeup, correlating with patient risk levels. In prognostic predictions, DRLI outperformed traditional clinical indicators, achieving AUC values of 0.779, 0.757, and 0.779 for 1-year, 3-year, and 5-year survival in the training set, and 0.746, 0.734, and 0.750 in the validation set. Notably, while the constructed nomogram showed exceptional predictive capability for short-term prognosis (AUC = 0.877), the DRLI displayed remarkable long-term predictive accuracy, with its AUC value reaching 0.823 for 10-year survival, closely approaching the nomogram\'s performance.
UNASSIGNED: The study introduces the DRLI as a groundbreaking molecular stratification tool for ccRCC, enhancing prognostic precision and potentially guiding personalized treatment strategies. This advancement is particularly significant in the context of long-term survival predictions. Our findings also elucidate the complex interplay between disulfidoptosis, lncRNAs, and the immune microenvironment in ccRCC, offering a comprehensive perspective on its pathogenesis and progression. The DRLI and the nomogram together represent significant strides in ccRCC research, highlighting the importance of molecular-based assessments in predicting patient outcomes.
摘要：
■这项研究引入了一种新的预后工具，二硫化物掺杂相关lncRNA指数（DRLI），整合二硫化物掺杂和长链非编码RNA（lncRNAs）的分子特征与肿瘤微环境的细胞异质性，预测透明细胞肾细胞癌（ccRCC）患者的临床结局。
■我们分析了来自癌症基因组图谱（TCGA）的530个肿瘤和72个正常样本，采用基于二硫化物相关基因表达的k-means聚类将ccRCC样本分为预后组。与二硫化物掺杂相关的lncRNAs被鉴定并用于构建DRLI，通过Kaplan-Meier和受试者工作特性曲线进行了验证。我们利用单细胞去卷积分析来估计肿瘤微环境中免疫细胞类型的比例。而ESTIMATE和TIDE算法用于评估免疫浸润和对免疫疗法的潜在反应。
■二硫化物掺杂剂相关的lncRNA指数（DRLI）有效地将ccRCC患者分为高危组和低危组，显着影响生存结局（P<0.001）。高危患者,以与二硫化物掺杂相关的独特lncRNA谱为标志，面临更糟糕的预后。单细胞分析显示明显的肿瘤微环境异质性，尤其是在免疫细胞组成中，与患者风险水平相关。在预后预测中，DRLI优于传统临床指标，在1年内实现0.779、0.757和0.779的AUC值，3年,和训练中的5年生存率，以及验证集中的0.746、0.734和0.750。值得注意的是,而构建的列线图显示出对短期预后的出色预测能力（AUC=0.877），DRLI显示出显著的长期预测准确性，其10年生存率的AUC值达到0.823，紧密接近列线图的表现。
■该研究介绍了DRLI作为ccRCC的开创性分子分层工具，提高预后的准确性和潜在的指导个性化治疗策略。这种进步在长期生存预测的背景下尤其重要。我们的发现还阐明了二硫化物之间复杂的相互作用，lncRNAs,和ccRCC中的免疫微环境，对其发病机制和进展提供了全面的视角。DRLI和列线图共同代表了ccRCC研究的重大进展，强调基于分子的评估在预测患者预后中的重要性。
■我们分析了来自癌症基因组图谱（TCGA）的530个肿瘤和72个正常样本，采用基于二硫化物相关基因表达的k-means聚类将ccRCC样本分为预后组。与二硫化物掺杂相关的lncRNAs被鉴定并用于构建DRLI，通过Kaplan-Meier和受试者工作特性曲线进行了验证。我们利用单细胞去卷积分析来估计肿瘤微环境中免疫细胞类型的比例。而ESTIMATE和TIDE算法用于评估免疫浸润和对免疫疗法的潜在反应。
■二硫化物掺杂剂相关的lncRNA指数（DRLI）有效地将ccRCC患者分为高危组和低危组，显着影响生存结局（P<0.001）。高危患者,以与二硫化物掺杂相关的独特lncRNA谱为标志，面临更糟糕的预后。单细胞分析显示明显的肿瘤微环境异质性，尤其是在免疫细胞组成中，与患者风险水平相关。在预后预测中，DRLI优于传统临床指标，在1年内实现0.779、0.757和0.779的AUC值，3年,和训练中的5年生存率，以及验证集中的0.746、0.734和0.750。值得注意的是,而构建的列线图显示出对短期预后的出色预测能力（AUC=0.877），DRLI显示出显著的长期预测准确性，其10年生存率的AUC值达到0.823，紧密接近列线图的表现。
■该研究介绍了DRLI作为ccRCC的开创性分子分层工具，提高预后的准确性和潜在的指导个性化治疗策略。这种进步在长期生存预测的背景下尤其重要。我们的发现还阐明了二硫化物之间复杂的相互作用，lncRNAs,和ccRCC中的免疫微环境，对其发病机制和进展提供了全面的视角。DRLI和列线图共同代表了ccRCC研究的重大进展，强调基于分子的评估在预测患者预后中的重要性。
