School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China.
Clinical Research Center, Jiading District Central Hospital Affiliated Shanghai University of Medicine & Health Sciences, Shanghai, 201800, China.
BMC Bioinformatics. 2022 Oct 18;23(1):435. doi: 10.1186/s12859-022-04969-4.
The aim of this study was to identify and screen long non-coding RNA (lncRNA) associated with immune genes in colon cancer, construct immune-related lncRNA pairs, establish a prognostic risk assessment model for colon adenocarcinoma (COAD), and explore prognostic factors and drug sensitivity.
Our method was based on data from The Cancer Genome Atlas (TCGA). To begin, we obtained all pertinent demographic and clinical information on 385 patients with COAD. All lncRNAs significantly related to immune genes and with differential expression were identified to construct immune lncRNA pairs. Subsequently, least absolute shrinkage and selection operator and Cox models were used to screen out prognostic-related immune lncRNAs for the establishment of a prognostic risk scoring formula. Finally, We analysed the functional differences between subgroups and screened the drugs, and establish an individual prediction nomogram model.
Our final analysis confirmed eight lncRNA pairs to construct prognostic risk assessment model. Results showed that the high-risk and low-risk groups had significant differences (training (n = 249): p < 0.001, validation (n = 114): p = 0.022). The prognostic model was certified as an independent prognosis model. Compared with the common clinicopathological indicators, the prognostic model had better predictive efficiency (area under the curve (AUC) = 0.805). Finally, We have analysed highly differentiated cellular pathways such as mucosal immune response, identified 9 differential immune cells, 10 sensitive drugs, and establish an individual prediction nomogram model (C-index = 0.820).
Our study verified that the eight lncRNA pairs mentioned can be used as biomarkers to predict the prognosis of COAD patients. Identified cells, drugs may have an positive effect on colon cancer prognosis.
本研究旨在鉴定和筛选与结肠癌免疫基因相关的长链非编码 RNA（lncRNA），构建免疫相关 lncRNA 对，建立结肠癌（COAD）的预后风险评估模型，并探讨预后因素和药物敏感性。
我们的方法基于来自癌症基因组图谱（TCGA）的数据。首先，我们获得了 385 例 COAD 患者的所有相关人口统计学和临床信息。鉴定所有与免疫基因显著相关且表达差异的 lncRNA，构建免疫 lncRNA 对。随后，使用最小绝对收缩和选择算子和 Cox 模型筛选出与预后相关的免疫 lncRNA，以建立预后风险评分公式。最后，我们分析了亚组之间的功能差异，并筛选了药物，建立了个体预测列线图模型。
我们的最终分析确定了 8 个 lncRNA 对构建预后风险评估模型。结果表明，高低风险组之间存在显著差异（训练（n=249）：p<0.001，验证（n=114）：p=0.022）。该预后模型被证明是一个独立的预后模型。与常见的临床病理指标相比，该预后模型具有更好的预测效率（曲线下面积（AUC）=0.805）。最后，我们分析了高度分化的细胞途径，如黏膜免疫反应，鉴定了 9 种差异免疫细胞、10 种敏感药物，并建立了个体预测列线图模型（C 指数=0.820）。
本研究验证了上述 8 个 lncRNA 对可作为预测 COAD 患者预后的生物标志物。鉴定的细胞、药物可能对结肠癌的预后有积极影响。
