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Abstract：
UNASSIGNED: Clear cell renal cell carcinoma (ccRCC) predominates among kidney cancer cases and is influenced by mutations in cancer driver genes (CDGs). However, significant obstacles persist in the early diagnosis and treatment of ccRCC. While various genetic models offer new hopes for improving ccRCC management, the relationship between CDG-related long non-coding RNAs (CDG-RlncRNAs) and ccRCC remains poorly understood. Therefore, this study aims to construct prognostic molecular features based on CDG-RlncRNAs to predict the prognosis of ccRCC patients, and aims to provide a new strategy to enhance clinical management of ccRCC patients.
UNASSIGNED: This study employed Cox and Least Absolute Shrinkage and Selection Operator (LASSO) regression analyses to comprehensively investigate the association between lncRNAs and CDGs in ccRCC. Leveraging The Cancer Genome Atlas (TCGA) dataset, we identified 97 prognostically significant CDG-RlncRNAs and developed a robust prognostic model based on these CDG-RlncRNAs. The performance of the model was rigorously validated using the TCGA dataset for training and the International Cancer Genome Consortium (ICGC) dataset for validation. Functional enrichment analysis elucidated the biological relevance of CDG-RlncRNA features in the model, particularly in tumor immunity. Experimental validation further confirmed the functional role of representative CDG-RlncRNA SNHG3 in ccRCC progression.
UNASSIGNED: Our analysis revealed that 97 CDG-RlncRNAs are significantly associated with ccRCC prognosis, enabling patient stratification into different risk groups. Development of a prognostic model incorporating key lncRNAs such as HOXA11-AS, AP002807.1, APCDD1L-DT, AC124067.2, and SNHG3 demonstrated robust predictive accuracy in both training and validation datasets. Importantly, risk stratification based on the model revealed distinct immune-related gene expression patterns. Notably, SNHG3 emerged as a key regulator of the ccRCC cell cycle, highlighting its potential as a therapeutic target.
UNASSIGNED: Our study established a concise CDG-RlncRNA signature and underscored the pivotal role of SNHG3 in ccRCC progression. It emphasizes the clinical relevance of CDG-RlncRNAs in prognostic prediction and targeted therapy, offering potential avenues for personalized intervention in ccRCC.
UNASSIGNED: This study employed Cox and Least Absolute Shrinkage and Selection Operator (LASSO) regression analyses to comprehensively investigate the association between lncRNAs and CDGs in ccRCC. Leveraging The Cancer Genome Atlas (TCGA) dataset, we identified 97 prognostically significant CDG-RlncRNAs and developed a robust prognostic model based on these CDG-RlncRNAs. The performance of the model was rigorously validated using the TCGA dataset for training and the International Cancer Genome Consortium (ICGC) dataset for validation. Functional enrichment analysis elucidated the biological relevance of CDG-RlncRNA features in the model, particularly in tumor immunity. Experimental validation further confirmed the functional role of representative CDG-RlncRNA SNHG3 in ccRCC progression.
UNASSIGNED: Our analysis revealed that 97 CDG-RlncRNAs are significantly associated with ccRCC prognosis, enabling patient stratification into different risk groups. Development of a prognostic model incorporating key lncRNAs such as HOXA11-AS, AP002807.1, APCDD1L-DT, AC124067.2, and SNHG3 demonstrated robust predictive accuracy in both training and validation datasets. Importantly, risk stratification based on the model revealed distinct immune-related gene expression patterns. Notably, SNHG3 emerged as a key regulator of the ccRCC cell cycle, highlighting its potential as a therapeutic target.
UNASSIGNED: Our study established a concise CDG-RlncRNA signature and underscored the pivotal role of SNHG3 in ccRCC progression. It emphasizes the clinical relevance of CDG-RlncRNAs in prognostic prediction and targeted therapy, offering potential avenues for personalized intervention in ccRCC.
摘要：
■透明细胞肾细胞癌（ccRCC）在肾癌病例中占主导地位，并受到癌症驱动基因（CDG）突变的影响。然而,ccRCC的早期诊断和治疗仍然存在重大障碍。虽然各种遗传模型为改善ccRCC管理提供了新的希望，CDG相关的长链非编码RNA(CDG-RlncRNA)和ccRCC之间的关系仍然知之甚少.因此,本研究旨在构建基于CDG-RlncRNAs的预后分子特征来预测ccRCC患者的预后,旨在为加强ccRCC患者的临床管理提供新的策略。
■本研究采用Cox和最小绝对收缩和选择算子(LASSO)回归分析来全面调查ccRCC中lncRNAs和CDGs之间的关联。利用癌症基因组图谱（TCGA）数据集，我们鉴定了97个预后显著的CDG-RlncRNAs,并基于这些CDG-RlncRNAs建立了一个稳健的预后模型.使用用于训练的TCGA数据集和用于验证的国际癌症基因组联盟(ICGC)数据集严格验证模型的性能。功能富集分析阐明了模型中CDG-RlncRNA特征的生物学相关性，特别是在肿瘤免疫方面。实验验证进一步证实了代表性CDG-RlncRNASNHG3在ccRCC进展中的功能作用。
■我们的分析显示97个CDG-RlncRNAs与ccRCC预后显著相关，能够将患者分层为不同的风险组。结合关键lncRNAs如HOXA11-AS的预后模型的开发，AP002807.1,APCDD1L-DT,AC124067.2和SNHG3在训练和验证数据集中都显示出强大的预测准确性。重要的是,基于该模型的风险分层揭示了不同的免疫相关基因表达模式。值得注意的是,SNHG3成为ccRCC细胞周期的关键调节因子，强调其作为治疗靶点的潜力。
■我们的研究建立了一个简洁的CDG-RlncRNA签名，并强调了SNHG3在ccRCC进展中的关键作用。它强调了CDG-RlncRNAs在预后预测和靶向治疗中的临床相关性，为ccRCC的个性化干预提供了潜在的途径。
■本研究采用Cox和最小绝对收缩和选择算子(LASSO)回归分析来全面调查ccRCC中lncRNAs和CDGs之间的关联。利用癌症基因组图谱（TCGA）数据集，我们鉴定了97个预后显著的CDG-RlncRNAs,并基于这些CDG-RlncRNAs建立了一个稳健的预后模型.使用用于训练的TCGA数据集和用于验证的国际癌症基因组联盟(ICGC)数据集严格验证模型的性能。功能富集分析阐明了模型中CDG-RlncRNA特征的生物学相关性，特别是在肿瘤免疫方面。实验验证进一步证实了代表性CDG-RlncRNASNHG3在ccRCC进展中的功能作用。
■我们的分析显示97个CDG-RlncRNAs与ccRCC预后显著相关，能够将患者分层为不同的风险组。结合关键lncRNAs如HOXA11-AS的预后模型的开发，AP002807.1,APCDD1L-DT,AC124067.2和SNHG3在训练和验证数据集中都显示出强大的预测准确性。重要的是,基于该模型的风险分层揭示了不同的免疫相关基因表达模式。值得注意的是,SNHG3成为ccRCC细胞周期的关键调节因子，强调其作为治疗靶点的潜力。
■我们的研究建立了一个简洁的CDG-RlncRNA签名，并强调了SNHG3在ccRCC进展中的关键作用。它强调了CDG-RlncRNAs在预后预测和靶向治疗中的临床相关性，为ccRCC的个性化干预提供了潜在的途径。
