Zhang Xiao, Zhang Pengpeng, Ren Qianhe, Li Jun, Lin Haoran, Huang Yuming, Wang Wei
Department of Thoracic Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
Department of Lung Cancer, Tianjin Lung Cancer Center, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China.
Biofactors. 2025 Jan-Feb;51(1):e2128. doi: 10.1002/biof.2128. Epub 2024 Oct 11.
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.
癌细胞的增殖、转移和耐药性给肺鳞状细胞癌（LUSC）的治疗带来了重大挑战。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大量多组学数据为理解癌症的潜在生物学特征提供了独特视角，为LUSC患者提供了潜在的预后指标和药物敏感性生物标志物。我们整合了来自LUSC患者的包括基因表达、DNA甲基化、基因组突变和临床数据在内的多种数据集，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10种常用的机器学习算法，将它们组合成101种独特配置，以设计一个性能最优的模型。然后，我们从肿瘤微环境和对免疫治疗的反应方面探索了高风险和低风险LUSC患者组的特征，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表现出更有利的预后。然后，我们基于7个关键枢纽基因构建了一个亚型特异性机器学习模型，即LUSC多组学特征（LMS）。与先前发表的LUSC生物标志物相比，我们的LMS评分显示出卓越的预测性能。LMS评分较低的患者总生存率较高，对免疫治疗的反应较好。值得注意的是，高LMS组更倾向于“冷”肿瘤，其特征为免疫抑制和免疫排斥，但达沙替尼等药物可能是这些患者有前景的治疗选择。值得注意的是，我们还通过细胞实验验证了模型基因SERPINB13，证实其作为影响LUSC进展的潜在癌基因以及有前景的治疗靶点的作用。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。
