文献检索，用中文搜 PubMed

BACKGROUND

Clear cell renal cell carcinoma (ccRCC) is a prevalent urogenital malignancy characterized by heterogeneous patterns. Stemness is a pivotal factor in tumor progression, recurrence, and metastasis. Nevertheless, the impact of stemness-related long non-coding RNAs (SRlncRNAs) on the prognosis of ccRCC remains elusive. In this study, we aimed to delve into the SRlncRNAs of ccRCC and develop a signature for risk stratification and prognosis prediction.

METHOD

Gene-expression and clinical data were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. We calculated RNA stemness scores (RNAss) for the samples to evaluate their stemness. SRlncRNAs and stemness-related mRNAs (SRmRNAs) in ccRCC were identified through weighted correlation network analysis (WGCNA), which employed sophisticated statistical methodologies to identify interconnected modules of related genes. Enrichment analysis was performed to explore the potential functions of SRmRNAs. Multiple machine learning algorithms were employed to construct a prognostic signature. Samples from TCGA-KIRC and GSE29609 cohorts were designated as the training and validation cohorts, respectively. Based on their risk scores, samples were stratified into low- and high-risk groups. Prognosis analysis, immune infiltration assessment, drug sensitivity prediction, mutation landscape, and gene set enrichment analysis (GSEA) were conducted to investigate the distinct characteristics of the low- and high-risk groups. Additionally, a web-based calculator was developed to facilitate clinical application. Expression and effects of SRlncRNAs in ccRCC were further corroborated through the utilization of single-cell RNA-seq (scRNA-seq), as well as in vitro and in vivo experiments.

RESULTS

SRlncRNAs and SRmRNAs were identified based on RNAss and WGCNA. The least absolute shrinkage and selection operator (LASSO) in combination with multivariate Cox regression was selected as the optimal approach. Six SRlncRNAs were used to construct the prognostic signature. Samples in the low- and high-risk groups exhibited distinct characteristics in terms of prognosis, GSEA pathways, immune infiltration profiles, drug sensitivity, and mutation status. A nomogram and a web-based calculator were developed to facilitate the clinical application of the model. ScRNA-seq and RT-qPCR demonstrated the differential expression of SRlncRNAs between ccRCC tumors and normal tissues. In vitro and in vivo experiments demonstrated that downregulation of EMX2OS and LINC00944 affected the proliferation, migration, invasion, apoptosis, and metastasis of ccRCC cells.

CONCLUSION

We uncovered the crucial associations between SRlncRNAs and the prognosis of ccRCC. By leveraging these findings, we developed a novel SRlncRNA-related signature and a user-friendly web calculator. This signature holds great potential in facilitating risk stratification and guiding tailored treatment strategies for ccRCC patients. Both in vitro and in vivo experiments confirmed the role of SRlncRNAs in the progression of ccRCC.

BACKGROUND

METHOD

RESULTS

CONCLUSION

背景

透明细胞肾细胞癌（ccRCC）是一种常见的泌尿生殖系统恶性肿瘤，具有异质性模式。干性是肿瘤进展、复发和转移的关键因素。然而，干性相关长链非编码RNA（SRlncRNAs）对ccRCC预后的影响仍不明确。在本研究中，我们旨在深入研究ccRCC的SRlncRNAs，并开发一种用于风险分层和预后预测的特征。

方法

从癌症基因组图谱（TCGA）和基因表达综合数据库（GEO）下载基因表达和临床数据。我们计算样本的RNA干性评分（RNAss）以评估其干性。通过加权基因共表达网络分析（WGCNA）确定ccRCC中的SRlncRNAs和干性相关mRNA（SRmRNAs），该分析采用复杂的统计方法来识别相关基因的相互连接模块。进行富集分析以探索SRmRNAs的潜在功能。采用多种机器学习算法构建预后特征。将来自TCGA-KIRC和GSE29609队列的样本分别指定为训练队列和验证队列。根据风险评分，将样本分为低风险和高风险组。进行预后分析、免疫浸润评估、药物敏感性预测、突变图谱分析和基因集富集分析（GSEA），以研究低风险和高风险组的不同特征。此外，开发了一个基于网络的计算器以促进临床应用。通过单细胞RNA测序（scRNA-seq）以及体外和体内实验进一步证实了SRlncRNAs在ccRCC中的表达和作用。

结果

基于RNAss和WGCNA确定了SRlncRNAs和SRmRNAs。选择最小绝对收缩和选择算子（LASSO）结合多变量Cox回归作为最佳方法。使用六个SRlncRNAs构建预后特征。低风险和高风险组的样本在预后、GSEA途径、免疫浸润谱、药物敏感性和突变状态方面表现出不同的特征。开发了列线图和基于网络的计算器以促进该模型的临床应用。scRNA-seq和RT-qPCR证明了ccRCC肿瘤与正常组织之间SRlncRNAs的差异表达。体外和体内实验表明，EMX2OS和LINC00944的下调影响了ccRCC细胞的增殖、迁移、侵袭、凋亡和转移。