背景：头颈部鳞状细胞癌（HNSCC）肿瘤微环境的典型特征是癌相关成纤维细胞（CAFs）的丰富浸润，这有助于HNSCC的进展。然而，一些临床试验显示，有针对性的CAFs治疗以失败告终，甚至加速了癌症的进展。因此，对CAFs进行全面的探索应该解决这个缺点，并促进针对HNSCC的CAFs治疗。方法：在本研究中，我们确定了两种CAFs基因表达模式，并进行单样本基因集富集分析（ssGSEA）来量化表达和构建评分系统。我们使用多种方法来揭示CAFs致癌进展的潜在机制。最后，我们集成了10种机器学习算法和107种算法组合，构建了最准确和稳定的风险模型。机器学习算法包括随机生存森林（RSF）、弹性网络（Enet）、Lasso、Ridge、逐步Cox、CoxBoost、用于Cox的偏最小二乘回归（plsRcox）、监督主成分（SuperPC）、广义提升回归建模（GBM）和生存支持向量机（survival-SVM）。结果：存在两个簇，具有不同的CAFs基因模式。与低CafS组相比，高CafS组与显著的免疫抑制、不良预后和HPV阴性的增加前景相关。高CafS患者还经历了致癌信号通路的丰富富集，如血管生成、上皮间充质转化和凝血。癌相关成纤维细胞和其他细胞簇之间的MDK和NAMPT配体-受体细胞交互可能在机制上导致免疫逃逸。此外，从107种机器学习算法组合中开发的随机生存森林预后模型可以最准确地分类HNSCC患者。结论：我们揭示了CAFs会导致一些致癌途径的活化，如血管生成、上皮间充质转化和凝血，并揭示了通过靶向糖酵解途径增强针对CAFs的治疗的独特可能性。我们开发了一个前所未有的稳定和强大的风险评分，用于评估预后。我们的研究有助于理解患有头颈鳞癌细胞癌的患者的CAFs微环境复杂性，为未来深入研究CAFs基因临床探索奠定基础。 版权所有©2023 Wang、Zhao、Wang和Tan。

Background: A hallmark signature of the tumor microenvironment in head and neck squamous cell carcinoma (HNSCC) is abundantly infiltration of cancer-associated fibroblasts (CAFs), which facilitate HNSCC progression. However, some clinical trials showed targeted CAFs ended in failure, even accelerated cancer progression. Therefore, comprehensive exploration of CAFs should solve the shortcoming and facilitate the CAFs targeted therapies for HNSCC. Methods: In this study, we identified two CAFs gene expression patterns and performed the single-sample gene set enrichment analysis (ssGSEA) to quantify the expression and construct score system. We used multi-methods to reveal the potential mechanisms of CAFs carcinogenesis progression. Finally, we integrated 10 machine learning algorithms and 107 algorithm combinations to construct most accurate and stable risk model. The machine learning algorithms contained random survival forest (RSF), elastic network (Enet), Lasso, Ridge, stepwise Cox, CoxBoost, partial least squares regression for Cox (plsRcox), supervised principal components (SuperPC), generalised boosted regression modelling (GBM), and survival support vector machine (survival-SVM). Results: There are two clusters present with distinct CAFs genes pattern. Compared to the low CafS group, the high CafS group was associated with significant immunosuppression, poor prognosis, and increased prospect of HPV negative. Patients with high CafS also underwent the abundant enrichment of carcinogenic signaling pathways such as angiogenesis, epithelial mesenchymal transition, and coagulation. The MDK and NAMPT ligand-receptor cellular crosstalk between the cancer associated fibroblasts and other cell clusters may mechanistically cause immune escape. Moreover, the random survival forest prognostic model that was developed from 107 machine learning algorithm combinations could most accurately classify HNSCC patients. Conclusion: We revealed that CAFs would cause the activation of some carcinogenesis pathways such as angiogenesis, epithelial mesenchymal transition, and coagulation and revealed unique possibilities to target glycolysis pathways to enhance CAFs targeted therapy. We developed an unprecedentedly stable and powerful risk score for assessing the prognosis. Our study contributes to the understanding of the CAFs microenvironment complexity in patients with head and neck squamous cell carcinoma and serves as a basis for future in-depth CAFs gene clinical exploration.Copyright © 2023 Wang, Zhao, Wang and Tan.