Feng Kunlun, Li Jingxiang, Li Jianye, Li Zhichao, Li Yahui
Shandong University of Traditional Chinese Medicine, Jinan, 250013, Shandong, China.
The Second Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, Shandong, China.
Discov Oncol. 2025 Feb 22;16(1):225. doi: 10.1007/s12672-025-01870-3.
INTRODUCTION/BACKGROUND: The specific role of efferocytosis-related long noncoding RNAs (ERLncRNAs) in Clear Cell Renal Cell Carcinoma (ccRCC) has not been thoroughly examined. This study aims to identify and validate a signature of ERLncRNAs for prognostic prediction and characterization of the immune landscape in individuals with ccRCC.
Analysis of ccRCC samples was conducted by utilizing clinical and RNA sequencing information obtained from The Cancer Genome Atlas (TCGA). Pearson correlation analysis was utilized to identify lncRNAs associated with efferocytosis, which was then used to create a new prognostic model through univariate Cox regression, Least Absolute Shrinkage and Selection Operator (LASSO) regression, and stepwise multivariate Cox analysis. In order to investigate the biological significance, we performed a functional enrichment analysis to assess how well the model predicts outcomes. Differences in the immune landscape were observed through a comparison of immune cell infiltration, tumor mutational burden (TMB), and tumor microenvironment (TME) characteristics. Following this, drug sensitivity analysis was conducted.
This led to the identification of a unique signature consisting of seven ERLncRNAs (LINC01615, RUNX3-AS1, FOXD2-AS1, AC002070.1, LINC02747, LINC00944, and AC092296.1). Model performance was measured by Kaplan-Meier curves and receiver operating characteristic (ROC) curves. The nomogram and C-index provided additional validation of the strong correlation between the risk signature and clinical decision-making.
On the whole, our innovative signature exhibits potential for prognostic prediction and assessment of immunotherapeutic response in patients with ccRCC.
引言/背景：与胞葬作用相关的长链非编码RNA（ERLncRNAs）在透明细胞肾细胞癌（ccRCC）中的具体作用尚未得到充分研究。本研究旨在识别和验证一种ERLncRNAs特征，用于ccRCC患者的预后预测和免疫微环境特征分析。
利用从癌症基因组图谱（TCGA）获得的临床和RNA测序信息，对ccRCC样本进行分析。采用Pearson相关性分析来识别与胞葬作用相关的lncRNAs，然后通过单因素Cox回归、最小绝对收缩和选择算子（LASSO）回归以及逐步多因素Cox分析，创建一个新的预后模型。为了研究其生物学意义，我们进行了功能富集分析，以评估该模型对结果的预测能力。通过比较免疫细胞浸润、肿瘤突变负担（TMB）和肿瘤微环境（TME）特征，观察免疫微环境的差异。随后，进行了药物敏感性分析。
由此确定了一个由7个ERLncRNAs（LINC01615、RUNX3-AS1、FOXD2-AS1、AC002070.1、LINC02747、LINC00944和AC092296.1）组成的独特特征。通过Kaplan-Meier曲线和受试者工作特征（ROC）曲线来衡量模型性能。列线图和C指数进一步验证了风险特征与临床决策之间的强相关性。
总体而言，我们创新的特征在ccRCC患者的预后预测和免疫治疗反应评估方面具有潜力。
