Integrative machine learning model for subtype identification and prognostic prediction in lung squamous cell carcinoma

作者：Guangliang Duan, Qi Huo, Wei Ni, Fei Ding, Yuefang Ye, Tingting Tang, Huiping Dai · 发表于：Discover Oncology · 年份：2025 · DOI：10.1007/s12672-025-02560-w · 被引用次数：3 · 研究领域：Ferroptosis and cancer prognosis、Radiomics and Machine Learning in Medical Imaging、Lung Cancer Diagnosis and Treatment

BACKGROUND: Lung squamous cell carcinoma (LUSC) is a leading cause of cancer-related mortality, and tumor heterogeneity could result in diverse prognostic subtypes. Traditional prognostic factors, like tumor, node, and metastasis (TNM) staging, offer limited predictive accuracy. This study aims to identify LUSC subtypes and develop predictive models that have the potential to improve prognosis prediction accuracy and support personalized treatment. METHODS: Expression and clinical data were collected from three datasets. One dataset (TCGA-LUSC) was used as a training set, while the others (GSE30219 and GSE73403) were independent testing sets. Unsupervised clustering was applied to the training set to identify LUSC subtypes. The relationship between survival outcomes and these identified subtypes was validated in the testing sets using binary machine learning models and survival curve analysis. The impact of chemotherapy on the prognosis for subtypes was also presented. Subsequently, four survival machine learning models were developed to predict LUSC prognosis. These models were validated in the testing sets and integrated into an online tool to assist in survival prediction. RESULTS: Two subtypes, C1 and C2, were identified in the training set. The C1 subtype was associated with poorer survival outcomes and was enriched in cancer-associated fibroblasts and macrophages. In contrast, the C2 subtype correlated with better outcomes and was enriched in CD8 + T cells. Regarding ch...