Zhao Qin-Yu, Liu Le-Ping, Lu Lu, Gui Rong, Luo Yan-Wei
Department of Blood Transfusion, The Third Xiangya Hospital of Central South University, Changsha, China.
College of Engineering and Computer Science, Australian National University, Canberra, ACT, Australia.
Front Genet. 2021 Aug 23;12:702424. doi: 10.3389/fgene.2021.702424. eCollection 2021.
Lung cancer remains the leading cause of cancer death globally, with lung adenocarcinoma (LUAD) being its most prevalent subtype. This study aimed to identify the key intercellular communication-associated genes (ICAGs) in LUAD.
Eight publicly available datasets were downloaded from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. The prognosis-related ICAGs were identified and a risk score was developed by using survival analysis. Machine learning models were trained to predict LUAD recurrence based on the selected ICAGs and clinical information. Comprehensive analyses on ICAGs and tumor microenvironment were performed. A single-cell RNA-sequencing dataset was assessed to further elucidate aberrant changes in intercellular communication.
Eight ICAGs with prognostic potential were identified in the present study, and a risk score was derived accordingly. The best machine-learning model to predict relapse was developed based on clinical information and the expression levels of these eight ICAGs. This model achieved a remarkable area under receiver operator characteristic curves of 0.841. Patients were divided into high- and low-risk groups according to their risk scores. DNA replication and cell cycle were significantly enriched by the differentially expressed genes between the high- and the low-risk groups. Infiltrating immune cells, immune functions were significantly related to ICAGs expressions and risk scores. Additionally, the changes of intercellular communication were modeled by analyzing the single-cell sequencing dataset.
The present study identified eight key ICAGs in LUAD, which could contribute to patient stratification and act as novel therapeutic targets.
肺癌仍然是全球癌症死亡的主要原因，肺腺癌（LUAD）是其最常见的亚型。本研究旨在鉴定LUAD中关键的细胞间通讯相关基因（ICAG）。
从基因表达综合数据库（GEO）和癌症基因组图谱（TCGA）数据库下载了八个公开可用的数据集。鉴定出与预后相关的ICAG，并通过生存分析建立风险评分。基于所选的ICAG和临床信息训练机器学习模型以预测LUAD复发。对ICAG和肿瘤微环境进行综合分析。评估单细胞RNA测序数据集以进一步阐明细胞间通讯的异常变化。
本研究鉴定出八个具有预后潜力的ICAG，并据此得出风险评分。基于临床信息和这八个ICAG的表达水平开发了预测复发的最佳机器学习模型。该模型在受试者工作特征曲线下的面积达到了显著的0.841。根据风险评分将患者分为高风险组和低风险组。高风险组和低风险组之间的差异表达基因显著富集于DNA复制和细胞周期。浸润性免疫细胞、免疫功能与ICAG表达和风险评分显著相关。此外，通过分析单细胞测序数据集对细胞间通讯的变化进行了建模。
本研究在LUAD中鉴定出八个关键的ICAG，它们有助于患者分层并可作为新的治疗靶点。
