Department of Radiology, Shandong Cancer Hospital and Institute, Shandong First Medical University, Shandong Academy of Medical Sciences, Jinan, Shandong, China.
Department of Oncology, Binzhou Medical University Hospital, Binzhou, Shandong, China.
Sci Rep. 2024 Oct 6;14(1):23243. doi: 10.1038/s41598-024-74336-1.
Angiogenesis, metastasis, and resistance to therapy are all facilitated by cancer-associated fibroblasts (CAFs). A CAF-based risk signature can be used to predict patients' prognoses for Lung adenocarcinoma (LUAD) based on CAF characteristics. The Gene Expression Omnibus (GEO) database was used to gather signal-cell RNA sequencing (scRNA-seq) data for this investigation. The GEO and TCGA databases were used to gather bulk RNA-seq and microarray data for LUAD. The scRNA-seq data were analyzed using the Seurat R program based on the CAF markers. Our goal was to use differential expression analysis to discover differentially expressed genes (DEGs) across normal and tumor samples in the TCGA dataset. Pearson correlation analysis was utilized to discover prognostic genes related with CAF, followed by univariate Cox regression analysis. Using Lasso regression, a risk signature based on CAF-related prognostic genes was created. A nomogram model was created based on the clinical and pathological aspects. 5 CAF clusters were identified in LUAD, 4 of which were associated with prognosis. From 2811 DEGs, 1002 genes were found to be significantly correlated with CAF clusters, which led to the creation of a risk signature with 8 genes. These 8 genes were primarily connected with 41 pathways, such as antigen paocessing and presentation, apoptosis, and cell cycle. Meanwhile, the risk signature was significantly associated with stromal and immune scores, as well as some immune cells. Multivariate analysis revealed that risk signature was an independent prognostic factor for LUAD, and its value in predicting immunotherapeutic outcomes was confirmed. A novel nomogram integrating the stage and CAF-based risk signature was constructed, which exhibited favorable predictability and reliability in the prognosis prediction of LUAD. CAF-based risk signatures can be effective in predicting the prognosis of LUAD, and they may provide new strategies for cancer treatments by interpreting the response of LUAD to immunotherapy.
癌症相关成纤维细胞 (CAF) 促进血管生成、转移和对治疗的抵抗。基于 CAF 特征的 CAF 风险特征可以用于预测肺腺癌 (LUAD) 患者的预后。本研究使用基因表达综合 (GEO) 数据库收集信号细胞 RNA 测序 (scRNA-seq) 数据。使用 GEO 和 TCGA 数据库收集 LUAD 的批量 RNA-seq 和微阵列数据。使用 Seurat R 程序基于 CAF 标记对 scRNA-seq 数据进行分析。我们的目标是使用差异表达分析在 TCGA 数据集的正常和肿瘤样本中发现差异表达基因 (DEGs)。使用 Pearson 相关分析发现与 CAF 相关的预后基因，然后进行单因素 Cox 回归分析。使用 Lasso 回归构建基于 CAF 相关预后基因的风险特征。根据临床和病理方面创建了列线图模型。在 LUAD 中鉴定出 5 个 CAF 簇，其中 4 个与预后相关。从 2811 个 DEGs 中发现了 1002 个与 CAF 簇显著相关的基因，由此创建了一个具有 8 个基因的风险特征。这 8 个基因主要与 41 个途径相关，如抗原处理和呈递、细胞凋亡和细胞周期。同时，风险特征与基质和免疫评分以及一些免疫细胞显著相关。多变量分析显示风险特征是 LUAD 的独立预后因素，其在预测免疫治疗结果方面的价值得到了验证。构建了一个新的列线图，将分期和基于 CAF 的风险特征整合在一起，在 LUAD 预后预测中表现出良好的预测能力和可靠性。基于 CAF 的风险特征可有效预测 LUAD 的预后，并通过解释 LUAD 对免疫治疗的反应，为癌症治疗提供新策略。
