Duan Xiaoyang, Du Huazhen, Yuan Meng, Liu Lie, Liu Rongfeng, Shi Jian
Department of Medical Oncology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei 050000, P.R. China.
Department of Emergency, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei 050000, P.R. China.
Exp Ther Med. 2023 May 19;26(1):331. doi: 10.3892/etm.2023.12030. eCollection 2023 Jul.
Esophageal carcinoma (ESCA) is one of the most common malignancies in the world, and has high morbidity and mortality rates. Necrosis and long noncoding RNAs (lncRNAs) are involved in the progression of ESCA; however, the specific mechanism has not been clarified. The aim of the present study was to investigate the role of necrosis-related lncRNAs (nrlncRNAs) in patients with ESCA by bioinformatics analysis, and to establish a nrlncRNA model to predict ESCA immune infiltration and prognosis. To form synthetic matrices, ESCA transcriptome data and related information were obtained from The Cancer Genome Atlas. A nrlncRNA model was established by coexpression, univariate Cox (Uni-Cox), and least absolute shrinkage and selection operator analyses. The predictive ability of this model was evaluated by Kaplan-Meier, receiver operating characteristic (ROC) curve, Uni-Cox, multivariate Cox regression, nomogram and calibration curve analyses. A model containing eight nrlncRNAs was generated. The areas under the ROC curves for 1-, 3- and 5-year overall survival were 0.746, 0.671 and 0.812, respectively. A high-risk score according to this model could be used as an indicator for systemic therapy use, since the half-maximum inhibitory concentration values varied significantly between the high-risk and low-risk groups. Based on the expression of eight prognosis-related nrlncRNAs, the patients with ESCA were regrouped using the 'ConsensusClusterPlus' package to explore potential molecular subgroups responding to immunotherapy. The patients with ESCA were divided into three clusters based on the eight nrlncRNAs that constituted the risk model: The most low-risk group patients were classified into cluster 1, and the high-risk group patients were mainly concentrated in clusters 2 and 3. Survival analysis showed that Cluster 1 had a better survival than the other groups (P=0.016). This classification system could contribute to precision treatment. Furthermore, two nrlncRNAs (LINC02811 and LINC00299) were assessed in the esophageal epithelial cell line HET-1A, and in the human esophageal cancer cell lines KYSE150 and TE1. There were significant differences in the expression levels of these lncRNAs between tumor and normal cells. In conclusion, the present study suggested that nrlncRNA models may predict the prognosis of patients with ESCA, and provide guidance for immunotherapy and chemotherapy decision making. Furthermore, the present study provided strategies to promote the development of individualized and precise treatment for patients with ESCA.
食管癌（ESCA）是世界上最常见的恶性肿瘤之一，发病率和死亡率都很高。坏死与长链非编码RNA（lncRNA）参与了食管癌的进展；然而，具体机制尚未阐明。本研究的目的是通过生物信息学分析研究坏死相关lncRNA（nrlncRNA）在食管癌患者中的作用，并建立一个nrlncRNA模型来预测食管癌的免疫浸润和预后。为了形成合成矩阵，从癌症基因组图谱中获取了食管癌转录组数据及相关信息。通过共表达、单因素Cox（Uni-Cox）和最小绝对收缩和选择算子分析建立了一个nrlncRNA模型。通过Kaplan-Meier、受试者工作特征（ROC）曲线、Uni-Cox、多因素Cox回归、列线图和校准曲线分析评估了该模型的预测能力。生成了一个包含8个nrlncRNA的模型。1年、3年和5年总生存的ROC曲线下面积分别为0.746、0.671和0.812。根据该模型得出的高风险评分可作为全身治疗使用的指标，因为高风险组和低风险组之间的半数最大抑制浓度值有显著差异。基于8个与预后相关的nrlncRNA的表达，使用“ConsensusClusterPlus”软件包对食管癌患者进行重新分组，以探索对免疫治疗有反应的潜在分子亚组。根据构成风险模型的8个nrlncRNA，将食管癌患者分为三个聚类：最低风险组患者被归类为聚类1，高风险组患者主要集中在聚类2和聚类3。生存分析表明，聚类1的生存率高于其他组（P=0.016）。这种分类系统有助于精准治疗。此外，在食管上皮细胞系HET-1A以及人食管癌细胞系KYSE150和TE1中评估了两个nrlncRNA（LINC02811和LINC-00299）。这些lncRNA在肿瘤细胞和正常细胞中的表达水平存在显著差异。总之，本研究表明nrlncRNA模型可能预测食管癌患者的预后，并为免疫治疗和化疗决策提供指导。此外，本研究为促进食管癌患者个体化精准治疗的发展提供了策略。
