Colon adenocarcinoma (COAD) is one of the common gastrointestinal malignant diseases, with high mortality rate and poor prognosis due to delayed diagnosis. This study aimed to construct a prognostic prediction model for patients with colon adenocarcinoma (COAD) recurrence. Differently expressed RNAs (DERs) between recurrence and non-recurrence COAD samples were identified based on expression profile data from the NCBI Gene Expression Omnibus (GEO) repository and The Cancer Genome Atlas (TCGA) database. Then, recurrent COAD discriminating classifier was established using SMV-RFE algorithm, and receiver operating characteristic curve was used to assess the predictive power of classifier. Furthermore, the prognostic prediction model was constructed based on univariate and multivariate Cox regression analysis, and Kaplan-Meier survival curve analysis was used to estimate this model. Furthermore, the co-expression network of DElncRNAs and DEmRNAs was constructed followed by GO and KEGG pathway enrichment analysis. A total of 54 optimized signature DElncRNAs were screened and SMV classifier was constructed, which presented a high accuracy to distinguish recurrence and non-recurrence COAD samples. Furthermore, six independent prognostic lncRNAs signatures (LINC00852, ZNF667-AS1, FOXP1-IT1, LINC01560, TAF1A-AS1, and LINC00174) in COAD patients with recurrence were screened, and the prognostic prediction model for recurrent COAD was constructed, which possessed a relative satisfying predicted ability both in the training dataset and validation dataset. Furthermore, the DEmRNAs in the co-expression network were mainly enriched in glycan biosynthesis, cardiac muscle contraction, and colorectal cancer. Our study revealed that six lncRNA signatures acted as an independent prognostic biomarker for patients with COAD recurrence.

中文翻译：

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基于lncRNA预后的结肠腺癌复发的潜在预后模型。

结肠腺癌（COAD）是常见的胃肠道恶性疾病之一，由于诊断延迟，死亡率高，预后差。这项研究旨在为结肠腺癌（COAD）复发患者建立预后预测模型。根据来自NCBI基因表达综合库（GEO）和癌症基因组图谱（TCGA）数据库的表达谱数据，鉴定了复发和非复发COAD样品之间差异表达的RNA（DER）。然后，使用SMV-RFE算法建立了循环COAD判别式分类器，并使用接收机工作特性曲线来评估分类器的预测能力。此外，基于单变量和多变量Cox回归分析构建了预后预测模型，用Kaplan-Meier生存曲线分析来估计该模型。此外，构建了DElncRNA和DEmRNA的共表达网络，然后进行GO和KEGG途径富集分析。总共筛选了54个优化的签名DElncRNA，并构建了SMV分类器，该分类器具有很高的区分复发和非复发COAD样品的准确率。此外，在复发的COAD患者中筛选了六个独立的预后lncRNAs信号（LINC00852，ZNF667-AS1，FOXP1-IT1，LINC01560，TAF1A-AS1和LINC00174），并建立了复发COAD的预后预测模型，该模型具有相对优势在训练数据集和验证数据集中都满足预期的能力。此外，共表达网络中的DEmRNA主要富含聚糖生物合成，心肌收缩和大肠癌。我们的研究表明，六个lncRNA标记可作为COAD复发患者的独立预后生物标志物。