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Abstract：
Lung squamous cell carcinoma (LUSC) is one of the subtypes of lung cancer (LC) that contributes to approximately 25%-30% of its prevalence. Cancer-associated fibroblasts (CAFs) are key cellular components of the TME, and the large number of CAFs in tumour tissues creates a favourable environment for tumour development. However, the function of CAFs in the LUSC is complex and uncertain. First, we processed the scRNA-seq data and classified distinct types of CAFs. We also identified prognostic CAFRGs using univariate Cox analysis and conducted survival analysis. Additionally, we assessed immune cell infiltration in CAF clusters using ssGSEA. We developed a model with a significant prognostic correlation and verified the prognostic model. Furthermore, we explored the immune landscape of LUSC and further investigated the correlation between malignant features and LUSC. We identified CAFs and classified them into three categories: iCAFs, mCAFs and apCAFs. The survival analysis showed a significant correlation between apCAFs and iCAFs and LUSC patient prognosis. Kaplan-Meier analysis showed that patients in CAF cluster C showed a better survival probability compared to clusters A and B. In addition, we identified nine significant prognostic CAFRGs (CLDN1, TMX4, ALPL, PTX3, BHLHE40, TNFRSF12A, VKORC1, CST3 and ADD3) and subsequently employed multivariate Cox analysis to develop a signature and validate the model. Lastly, the correlation between CAFRG and malignant features indicates the potential role of CAFRG in promoting tumour angiogenesis, EMT and cell cycle alterations. We constructed a CAF prognostic signature for identifying potential prognostic CAFRGs and predicting the prognosis and immunotherapeutic response for LUSC. Our study may provide a more accurate prognostic assessment and immunotherapy targeting strategies for LUSC.
摘要：
肺鳞状细胞癌（LUSC）是肺癌（LC）的亚型之一，约占其患病率的25％-30％。癌相关成纤维细胞（CAFs）是TME的关键细胞成分，肿瘤组织中大量的CAF为肿瘤发展创造了有利的环境。然而,LUSC中CAF的功能是复杂且不确定的。首先,我们处理了scRNA-seq数据并对不同类型的CAFs进行了分类.我们还使用单变量Cox分析鉴定了预后性CAFRGs，并进行了生存分析。此外,我们使用ssGSEA评估了CAF簇中的免疫细胞浸润。我们开发了一个具有显著预后相关性的模型，并验证了预后模型。此外，我们探索了LUSC的免疫景观,并进一步研究了恶性特征与LUSC之间的相关性.我们确定了CAF，并将它们分为三类：iCAF，mCAFs和apCAFs。生存分析显示apCAFs和iCAFs与LUSC患者预后有显著相关性。Kaplan-Meier分析显示，与A和B组相比，CAFC组中的患者表现出更好的生存概率。我们确定了9个显著的预后CAFRGs(CLDN1，TMX4，ALPL，PTX3,BHLHE40,TNFRSF12A,VKORC1，CST3和ADD3），随后采用多变量Cox分析来开发签名并验证模型。最后,CAFRG与恶性特征之间的相关性表明CAFRG在促进肿瘤血管生成中的潜在作用，EMT和细胞周期改变。我们构建了CAF预后特征，用于识别潜在的预后CAFRGs并预测LUSC的预后和免疫治疗反应。我们的研究可能为LUSC提供更准确的预后评估和免疫治疗靶向策略。
