Zheng Pengdou, Zhang Huojun, Jiang Weiling, Wang Lingling, Liu Lu, Zhou Yuhao, Zhou Ling, Liu Huiguo
Department of Respiratory and Critical Care Medicine, Key Laboratory of Pulmonary Diseases of Health Ministry, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Front Mol Biosci. 2022 Apr 11;9:807497. doi: 10.3389/fmolb.2022.807497. eCollection 2022.
Lung cancer is one of the main cancer types due to its persistently high incidence and mortality, yet a simple and effective prognostic model is still lacking. This study aimed to identify independent prognostic genes related to the heterogeneity of lung adenocarcinoma (LUAD), generate a prognostic risk score model, and construct a nomogram in combination with other pathological characteristics to predict patients' overall survival (OS). A significant amount of data pertaining to single-cell RNA sequencing (scRNA-seq), RNA sequencing (RNA-seq), and somatic mutation were used for data mining. After statistical analyses, a risk scoring model was established based on eight independent prognostic genes, and the OS of high-risk patients was significantly lower than that of low-risk patients. Interestingly, high-risk patients were more sensitive and effective to immune checkpoint blocking therapy. In addition, it was noteworthy that CCL20 not only affected prognosis and differentiation of LUAD but also led to poor histologic grade of tumor cells. Ultimately, combining risk score, clinicopathological information, and CCL20 mutation status, a nomogram with good predictive performance and high accuracy was established. In short, our research established a prognostic model that could be used to guide clinical practice based on the constantly updated big multi-omics data. Finally, this analysis revealed that CCL20 may become a potential therapeutic target for LUAD.
肺癌因其持续高发的发病率和死亡率，成为主要的癌症类型之一，但仍缺乏简单有效的预后模型。本研究旨在识别与肺腺癌（LUAD）异质性相关的独立预后基因，生成预后风险评分模型，并结合其他病理特征构建列线图，以预测患者的总生存期（OS）。大量与单细胞RNA测序（scRNA-seq）、RNA测序（RNA-seq）和体细胞突变相关的数据被用于数据挖掘。经过统计分析，基于八个独立预后基因建立了风险评分模型，高危患者的OS显著低于低危患者。有趣的是，高危患者对免疫检查点阻断治疗更敏感且有效。此外，值得注意的是，CCL20不仅影响LUAD的预后和分化，还导致肿瘤细胞的组织学分级较差。最终，结合风险评分、临床病理信息和CCL20突变状态，建立了具有良好预测性能和高准确性的列线图。简而言之，我们的研究基于不断更新的大量多组学数据建立了一个可用于指导临床实践的预后模型。最后，该分析表明CCL20可能成为LUAD的潜在治疗靶点。
