Liu Xiangrong, Wang Dan, Han Shiyu, Wang Fang, Zang Jinfeng, Xu Caifeng, Dong Xue
College of Nursing, Changchun University of Chinese Medicine, Changchun, China.
Department of Endocrinology, Metabolism and Gastroenterology, The Third Affiliated Clinical Hospital of Changchun University of Chinese Medicine, Changchun, China.
J Oncol. 2022 Feb 15;2022:7467797. doi: 10.1155/2022/7467797. eCollection 2022.
Pancreatic cancer (PC) has a high mortality and dismal prognosis, predicting to be the second most lethal malignancy. 5-Methylcytosine (m5C) and long noncoding RNAs (lncRNAs) are both crucial in the prognostic outcome and immunotherapeutic effect for PC patients. Therefore, we aimed to create an m5C-related lncRNA signature (m5C-LS) for PC patients' prognosis and treatment.
Clinicopathological information and RNAseq data were acquired from The Cancer Genome Atlas (TCGA) database. Pearson's correlation analysis was used to extract m5C-related lncRNAs in PC. Univariate, least absolute shrinkage and selection operator (LASSO), and multivariate Cox analyses were adopted to build an m5C-LS. Kaplan-Meier (K-M), principal component analysis (PCA), and nomogram were utilized to assess model accuracy. In addition, we explored the model's possible immunotherapeutic responses and drug sensitivity targets.
Three m5C-related lncRNAs were finally established to construct the risk signature, which has a good and independent predictive ability for PC patients. Based on the m5C-LS, patients were classified into the low- and high-m5C-LS group, with the latter having a worse prognosis. Furthermore, the m5C-LS allowed us to better discriminate the immunotherapeutic responses of PC patients in different subgroups.
Our study constructed an m5C-LS and established a nomogram model that accurately predicted the prognosis of PC patients, as well as provides promising immunotherapeutic strategies in the future.
胰腺癌（PC）死亡率高，预后不佳，预计将成为第二大致命性恶性肿瘤。5-甲基胞嘧啶（m5C）和长链非编码RNA（lncRNA）在PC患者的预后结果和免疫治疗效果中都至关重要。因此，我们旨在创建一个用于PC患者预后和治疗的m5C相关lncRNA特征（m5C-LS）。
从癌症基因组图谱（TCGA）数据库获取临床病理信息和RNA测序数据。采用Pearson相关性分析在PC中提取与m5C相关的lncRNA。采用单因素、最小绝对收缩和选择算子（LASSO）以及多因素Cox分析构建m5C-LS。利用Kaplan-Meier（K-M）法、主成分分析（PCA）和列线图评估模型准确性。此外，我们还探索了该模型可能的免疫治疗反应和药物敏感性靶点。
最终确定了三个与m5C相关的lncRNA来构建风险特征，该特征对PC患者具有良好且独立的预测能力。基于m5C-LS，患者被分为低m5C-LS组和高m5C-LS组，后者预后较差。此外，m5C-LS使我们能够更好地区分不同亚组PC患者的免疫治疗反应。
我们的研究构建了m5C-LS并建立了列线图模型，该模型准确预测了PC患者的预后，并为未来提供了有前景的免疫治疗策略。
