癌细胞的增殖、转移和耐药性对肺鳞状细胞癌（LUSC）的治疗提出了重大挑战。然而，目前缺乏能够准确预测患者预后并指导靶向治疗选择的最佳预测模型。从多层次分子生物学获得的广泛多组学数据为理解癌症的潜在生物学特征提供了独特的视角，为LUSC患者提供潜在的预后指标和药物敏感性生物标志物。我们整合了不同的数据集，包括来自 LUSC 患者的基因表达、DNA 甲基化、基因组突变和临床数据，以使用一套 10 种多组学整合算法实现共识聚类。随后，我们采用了 10 种常用的机器学习算法，将它们组合成 101 种独特的配置，以设计最佳性能模型。然后我们探讨了高危和低危LUSC患者群体在肿瘤微环境和免疫治疗反应方面的特征，最终通过体外实验验证了模型基因的功能作用。通过应用 10 种聚类算法，我们确定了两种与预后相关的亚型，其中 CS1 表现出更良好的预后。然后，我们构建了一个亚型特异性机器学习模型，即基于七个关键枢纽基因的 LUSC 多组学特征 (LMS)。与之前发布的 LUSC 生物标志物相比，我们的 LMS 评分表现出卓越的预测性能。 LMS 评分较低的患者总体生存率较高，对免疫治疗的反应也较好。值得注意的是，高 LMS 组更倾向于“冷”肿瘤，其特点是免疫抑制和排斥，但达沙替尼等药物可能为这些患者提供有前途的治疗选择。值得注意的是，我们还通过细胞实验验证了模型基因 SERPINB13，证实了其作为影响 LUSC 进展的潜在癌基因的作用以及作为有希望的治疗靶点的作用。我们的研究为完善 LUSC 的分子分类和进一步优化免疫治疗策略提供了新的见解。© 2024 国际生物化学与分子生物学联合会。

The proliferation, metastasis, and drug resistance of cancer cells pose significant challenges to the treatment of lung squamous cell carcinoma (LUSC). However, there is a lack of optimal predictive models that can accurately forecast patient prognosis and guide the selection of targeted therapies. The extensive multi-omic data obtained from multi-level molecular biology provides a unique perspective for understanding the underlying biological characteristics of cancer, offering potential prognostic indicators and drug sensitivity biomarkers for LUSC patients. We integrated diverse datasets encompassing gene expression, DNA methylation, genomic mutations, and clinical data from LUSC patients to achieve consensus clustering using a suite of 10 multi-omics integration algorithms. Subsequently, we employed 10 commonly used machine learning algorithms, combining them into 101 unique configurations to design an optimal performing model. We then explored the characteristics of high- and low-risk LUSC patient groups in terms of the tumor microenvironment and response to immunotherapy, ultimately validating the functional roles of the model genes through in vitro experiments. Through the application of 10 clustering algorithms, we identified two prognostically relevant subtypes, with CS1 exhibiting a more favorable prognosis. We then constructed a subtype-specific machine learning model, LUSC multi-omics signature (LMS) based on seven key hub genes. Compared to previously published LUSC biomarkers, our LMS score demonstrated superior predictive performance. Patients with lower LMS scores had higher overall survival rates and better responses to immunotherapy. Notably, the high LMS group was more inclined toward "cold" tumors, characterized by immune suppression and exclusion, but drugs like dasatinib may represent promising therapeutic options for these patients. Notably, we also validated the model gene SERPINB13 through cell experiments, confirming its role as a potential oncogene influencing the progression of LUSC and as a promising therapeutic target. Our research provides new insights into refining the molecular classification of LUSC and further optimizing immunotherapy strategies.© 2024 International Union of Biochemistry and Molecular Biology.