成纤维细胞相关特征可预测食管鳞癌的预后和免疫治疗反应。
A fibroblast-associated signature predicts prognosis and immunotherapy in esophageal squamous cell cancer.
机构信息
Department of Thoracic Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
Department of Thoracic Surgery, Nanjing Gaochun People's Hospital, Nanjing, China.
出版信息
Front Immunol. 2023 May 29;14:1199040. doi: 10.3389/fimmu.2023.1199040. eCollection 2023.
BACKGROUND
Current paradigms of anti-tumor therapies are not qualified to evacuate the malignancy ascribing to cancer stroma's functions in accelerating tumor relapse and therapeutic resistance. Cancer-associated fibroblasts (CAFs) has been identified significantly correlated with tumor progression and therapy resistance. Thus, we aimed to probe into the CAFs characteristics in esophageal squamous cancer (ESCC) and construct a risk signature based on CAFs to predict the prognosis of ESCC patients.
METHODS
The GEO database provided the single-cell RNA sequencing (scRNA-seq) data. The GEO and TCGA databases were used to obtain bulk RNA-seq data and microarray data of ESCC, respectively. CAF clusters were identified from the scRNA-seq data using the Seurat R package. CAF-related prognostic genes were subsequently identified using univariate Cox regression analysis. A risk signature based on CAF-related prognostic genes was constructed using Lasso regression. Then, a nomogram model based on clinicopathological characteristics and the risk signature was developed. Consensus clustering was conducted to explore the heterogeneity of ESCC. Finally, PCR was utilized to validate the functions that hub genes play on ESCC.
RESULTS
Six CAF clusters were identified in ESCC based on scRNA-seq data, three of which had prognostic associations. A total of 642 genes were found to be significantly correlated with CAF clusters from a pool of 17080 DEGs, and 9 genes were selected to generate a risk signature, which were mainly involved in 10 pathways such as NRF1, MYC, and TGF-Beta. The risk signature was significantly correlated with stromal and immune scores, as well as some immune cells. Multivariate analysis demonstrated that the risk signature was an independent prognostic factor for ESCC, and its potential in predicting immunotherapeutic outcomes was confirmed. A novel nomogram integrating the CAF-based risk signature and clinical stage was developed, which exhibited favorable predictability and reliability for ESCC prognosis prediction. The consensus clustering analysis further confirmed the heterogeneity of ESCC.
CONCLUSION
The prognosis of ESCC can be effectively predicted by CAF-based risk signatures, and a comprehensive characterization of the CAF signature of ESCC may aid in interpreting the response of ESCC to immunotherapy and offer new strategies for cancer treatment.
背景
目前的抗肿瘤疗法模式无法消除肿瘤的恶性特征，因为肿瘤基质在加速肿瘤复发和治疗抵抗方面具有重要功能。癌相关成纤维细胞（CAFs）已被确定与肿瘤进展和治疗抵抗密切相关。因此，我们旨在探讨食管鳞状细胞癌（ESCC）中 CAFs 的特征，并构建基于 CAFs 的风险特征来预测 ESCC 患者的预后。
方法
GEO 数据库提供了单细胞 RNA 测序（scRNA-seq）数据。GEO 和 TCGA 数据库分别用于获取 ESCC 的批量 RNA-seq 数据和微阵列数据。使用 Seurat R 包从 scRNA-seq 数据中识别 CAF 簇。随后使用单因素 Cox 回归分析鉴定 CAF 相关预后基因。使用 Lasso 回归构建基于 CAF 相关预后基因的风险特征。然后，基于临床病理特征和风险特征构建了一个列线图模型。进行共识聚类以探索 ESCC 的异质性。最后，通过 PCR 验证了关键基因在 ESCC 中的作用。
结果
根据 scRNA-seq 数据，在 ESCC 中鉴定出 6 个 CAF 簇，其中 3 个与预后相关。在 17080 个差异表达基因中，共发现 642 个基因与 CAF 簇显著相关，其中 9 个基因被选择用于生成风险特征，这些基因主要涉及 NRF1、MYC 和 TGF-β等 10 个通路。风险特征与基质和免疫评分以及一些免疫细胞显著相关。多因素分析表明，风险特征是 ESCC 的独立预后因素，其预测免疫治疗疗效的潜力得到了验证。开发了一种新的列线图，该列线图整合了基于 CAF 的风险特征和临床分期，对 ESCC 预后预测具有良好的预测能力和可靠性。共识聚类分析进一步证实了 ESCC 的异质性。
结论
基于 CAF 的风险特征可以有效预测 ESCC 的预后，对 ESCC 的 CAF 特征进行全面表征可能有助于解释 ESCC 对免疫治疗的反应，并为癌症治疗提供新的策略。
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