肿瘤微环境(TME)在发病过程中起着举足轻重的作用,programming,和癌症的治疗反应。在TME的各种组件中,癌症相关成纤维细胞(CAFs)是免疫和非免疫细胞功能的关键调节因子。利用单细胞RNA测序(scRNA)数据,我们已经发现了这个特定的CAF子组中以前隐藏和有希望的角色,为其临床应用铺平了道路。然而,几个关键问题仍然存在,主要源于CAFs的异质性和在各种样品分析中使用不同的成纤维细胞标记,在临床实施中造成混乱和阻碍。在这项开创性的研究中,我们系统地筛选了多个数据库,以确定区分肺癌CAFs的最可靠标记,特别关注它们在早期诊断中的潜在用途,分期,和治疗反应评估。我们的调查显示COL1A1,COL1A2,FAP,和PDGFRA是表征大多数肺腺癌数据集中CAF亚组的有效标志物。通过对治疗反应的综合分析,我们确定COL1A1是所有CAF标记中最有效的指标.COL1A1不仅破译与CAF相关的TME特征,而且还显示出与治疗反应和多种生存结果的高度敏感性和特异性相关性。第一次,我们揭示了CAF标记簇在区分不同TME组中所发挥的独特作用.我们的发现证实了CAFs对多种肺癌治疗反应的敏感和独特贡献。这些见解极大地增强了我们对TME功能的理解,并推动了广泛scRNA序列结果的翻译应用。COL1A1是scRNA分析中定义CAF亚组的最敏感和特异性标记。由COL1A1代表的CAF比率可能作为临床实践中治疗反应的可靠预测指标。从而为TME组件的影响作用提供有价值的见解。这项研究标志着我们在彻底改变癌症诊断和治疗方法方面迈出了关键的一步。
The tumor microenvironment (TME) plays a pivotal role in the onset, progression, and treatment response of cancer. Among the various components of the TME, cancer-associated fibroblasts (CAFs) are key regulators of both immune and non-immune cellular functions. Leveraging single-cell RNA sequencing (scRNA) data, we have uncovered previously hidden and promising roles within this specific CAF subgroup, paving the way for its clinical application. However, several critical questions persist, primarily stemming from the heterogeneous nature of CAFs and the use of different fibroblast markers in various sample analyses, causing confusion and hindrance in their clinical implementation. In this groundbreaking study, we have systematically screened multiple databases to identify the most robust marker for distinguishing CAFs in lung cancer, with a particular focus on their potential use in early diagnosis, staging, and treatment response evaluation. Our investigation revealed that COL1A1, COL1A2, FAP, and PDGFRA are effective markers for characterizing CAF subgroups in most lung adenocarcinoma datasets. Through comprehensive analysis of treatment responses, we determined that COL1A1 stands out as the most effective indicator among all CAF markers. COL1A1 not only deciphers the TME signatures related to CAFs but also demonstrates a highly sensitive and specific correlation with treatment responses and multiple survival outcomes. For the first time, we have unveiled the distinct roles played by clusters of CAF markers in differentiating various TME groups. Our findings confirm the sensitive and unique contributions of CAFs to the responses of multiple lung cancer therapies. These insights significantly enhance our understanding of TME functions and drive the translational application of extensive scRNA sequence results. COL1A1 emerges as the most sensitive and specific marker for defining CAF subgroups in scRNA analysis. The CAF ratios represented by COL1A1 can potentially serve as a reliable predictor of treatment responses in clinical practice, thus providing valuable insights into the influential roles of TME components. This research marks a crucial step forward in revolutionizing our approach to cancer diagnosis and treatment.