Department of Thoracic Surgery, Chongqing General Hospital, Chongqing 401120, China.
Department of Respiratory Medicine, Second Affiliated Hospital of Chongqing Medical University, Chongqing 400000, China.
Genet Res (Camb). 2022 Aug 16;2022:3483498. doi: 10.1155/2022/3483498. eCollection 2022.
To screen the cell differentiation trajectory-related genes and build a cell differentiation trajectory-related signature for predicting the prognosis of lung adenocarcinoma (LUAD).
LUAD single cell mRNA expression profile, TCGA-LUAD transcriptome data were obtained from GEO and TCGA databases. Single-cell RNA-seq data were used for cell clustering and pseudotime analysis after dimensionality reduction analysis, and the cell differentiation trajectory-related genes were acquired after differential expression analysis conducted between the main branches. Then, the consensus clustering analysis was carried out on TCGA-LUAD samples, and the GSEA analysis was performed, then the differences on the expression levels of immune checkpoint genes and immunotherapy response were compared among clusters. The prognostic model was constructed, and the GSE42127 dataset was used to validate. A nomogram evaluation model was used to predict prognosis.
Two subsets with distinct differentiation states were found after cell differentiation trajectory analysis. TCGA-LUAD samples were divided into two cell differentiation trajectory-related gene-based clusters, GSEA found that cluster 1 was significantly related to 20 pathways, cluster 2 was significantly enriched in three pathways, and it was also shown that clusters could better predict immune checkpoint gene expression and immunotherapy response. A six cell differentiation-related genes-based prognostic signature was constructed, and the patients in the high-risk group had poorer prognosis than those in the low-risk group. Moreover, a nomogram was constructed based on the prognostic signature and clinicopathological features, and this nomogram had strong predictive performance and high accuracy.
The cell differentiation-related signature and the prognostic nomogram could accurately predict survival.
筛选细胞分化轨迹相关基因，构建细胞分化轨迹相关特征，用于预测肺腺癌（LUAD）的预后。
从 GEO 和 TCGA 数据库中获取 LUAD 单细胞 mRNA 表达谱和 TCGA-LUAD 转录组数据。对单细胞 RNA-seq 数据进行降维分析后进行细胞聚类和伪时间分析，在主分支间进行差异表达分析后获取细胞分化轨迹相关基因。然后对 TCGA-LUAD 样本进行共识聚类分析，并进行 GSEA 分析，比较聚类间免疫检查点基因和免疫治疗反应的表达水平差异。构建预后模型，并使用 GSE42127 数据集进行验证。使用列线图评估模型进行预后预测。
细胞分化轨迹分析后发现两种具有明显分化状态的亚群。TCGA-LUAD 样本分为两个基于细胞分化轨迹相关基因的聚类，GSEA 发现簇 1 与 20 条通路显著相关，簇 2 显著富集于 3 条通路，且簇可以更好地预测免疫检查点基因表达和免疫治疗反应。构建了一个基于 6 个细胞分化相关基因的预后特征，高危组患者的预后明显差于低危组患者。此外，还基于预后特征和临床病理特征构建了列线图，该列线图具有较强的预测性能和较高的准确性。
细胞分化相关特征和预后列线图可准确预测生存。
