基底膜相关lncRNA模型预测肾透明细胞癌患者预后

The lncRNA Signature Associated with Basement Membrane as a Novel Biomarker of Prognostic Significance and Identification for Clear Cell Renal Cell Carcinoma

摘要: 因基底膜(BM)在癌症转移和侵袭中的关键作用，本研究旨在构建一个与肾透明细胞癌(ccRCC)患者基底膜(BM)相关的长非编码RNA (lncRNA)风险模型。为了实现这一目标，我们从癌症基因组图谱(TCGA)和BM-BASE数据库中检索了ccRCC患者的转录和临床数据。通过比较肿瘤和邻近正常组织，统计出差异表达的BM基因。随后，在肿瘤和正常组织之间鉴定出差异表达的BM相关lncRNA (DEBM lncRNA)。使用多变量和单变量Cox回归分析评估这些lncRNA的风险，并使用LASSO分析构建预后模型。所得模型由8个DEBM lncRNA组成，即AL161457.2、ITGA9-AS1、LINC01725、AP001021.1、KDM4A-AS1、AC020891.1、AP000919.2和AF230666.2。预后风险模型在预测ccRCC患者的预后方面表现出高度的准确性，揭示了较高的风险评分与远处转移和癌症侵袭性的存在之间的正相关关系。该模型准确预测了1年、3年和5年的预后，曲线下面积(AUC)超过0.70。值得注意的是，与肿瘤细胞相关的基因的变化通常先于形态异常，这使得我们的模型成为评估肾癌预后的传统方法的有价值补充。此外，我们的模型在预测ccRCC分期方面表现出高度的准确性。该模型为临床评估和治疗提供了新的视角。

Abstract: The objective of this study was to construct a risk model for long non-coding RNAs (lncRNAs) asso-ciated with the basement membrane (BM) in clear cell renal cell carcinoma (ccRCC) patients, taking into account the pivotal role of the BM in cancer metastasis and invasion. To achieve this, we re-trieved transcriptional and clinical data from ccRCC patients from the Cancer Genome Atlas (TCGA) and BM-BASE databases. Statistically different BM genes were identified by comparing tumors and adjacent normal tissues. Subsequently, differentially expressed BM-related lncRNAs (DEBM lncRNAs) were identified between tumors and normal tissues. The risk assessment of these lncRNAs was evaluated using multivariate/univariate Cox regression analyses, and a prognostic model was constructed using LASSO analysis. The resulting model consisted of 8 DEBM lncRNAs, namely AL161457.2, ITGA9-AS1, LINC01725, AP001021.1, KDM4A-AS1, AC020891.1, AP000919.2, and AF230666.2. The prognostic risk models demonstrated a high degree of accuracy in predicting the prognosis of ccRCC patients, revealing a positive correlation between a higher risk score and the presence of distant metastasis and cancer aggressiveness. The model accurately predicted progno-sis at 1, 3, and 5 years, with an Area Under the Curve (AUC) exceeding 0.70. Notably, the identifica-tion of genes associated with tumor cells often precedes morphological abnormalities, rendering our model a valuable complement to traditional methods for evaluating renal cancer prognosis. Furthermore, our model exhibited a high level of accuracy in predicting the stage of ccRCC. The model provides a novel viewpoint for clinical evaluation and therapy.