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Archive > Volume 33 Issue 5 > 2026,33(5):510-520. DOI:10.3872/j.issn.1007-385X.2026.05.005 Prev Next
Basic Research
Identification of stemness molecular subtypes in esophageal squamous cell carcinoma via integrated bioinformatics and machine learning algorithms
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Abstract:
[Abstract] Objective: Esophageal squamous cell carcinoma (ESCC) exhibits pronounced inter- and intra-tumoral heterogeneity, posing substantial challenges for prognostic prediction and therapeutic response evaluation. Cancer stem cells (CSCs) function as pivotal drivers underlying oncogenesis, metastasis, and therapy resistance. In this study, we aim to identify stemness-related molecular subtypes, and construct a subtype classifier to further assist in predicting ESCC patients' therapeutic response. Methods: Twenty-six stem-associated gene sets were retrieved from the StemChecker database. The transcriptome data of 259 ESCC samples from the TCGA-ESCC and GSE53625 databases were quantified using single-sample gene set enrichment analysis. Consensus clustering was then employed to identify stemness subtypes. Subtype-specific differences in prognosis, tumor microenvironment, stemness-related biological process, and therapeutic responses were predicted and analyzed. By applying weighted gene co-expression network analysis (WGCNA), the stemness subtype-related hub genes were identified. Subsequently, the ESCC stemness subtype classifier was constructed and evaluated using three machine learning algorithms: random forest, XGBoost, and the neural network. Results: Based on the single-sample gene set enrichment analysis (ssGSEA) of 26 stemness gene sets, two stemness subtypes (named C1 and C2) were determined, each exhibiting distinct stem-related molecular, immunological and therapeutic characteristics. C2 subtype exhibited adverse prognosis, stronger stemness, higher levels of M2 macrophages and cancer-associated fibroblasts, as well as higher stromal scores. In terms of therapeutic options, immunotherapy might be more appropriate for C1 ESCC patients, while cisplatin, gemcitabine · · 510 郑航, 等. 生物信息学整合机器学习算法鉴定食管鳞状细胞癌干性分子亚型 and docetaxel might be more suitable for C2 ESCC patients. WGCNA identified the blue module in TCGA-ESCC cohort and the black module in GSE53625 cohort as hub gene modules most relevant to the stemness C2 subtype. By integrating the gene sets screened from these two modules, a total of 7 genes were obtained as the final hub genes of the stemness C2 subtype. From the results of the confusion matrix, the validity of the model for predicting stemness subtype using the 7-gene transcriptome data and the machine learning model proposed in this study was proven. The random forest-based stemness classification model achieved higher accuracy, precision and AUC values compared with XGBoost and neural network model. Conclusion: This study delineates valuable perspectives on ESCC stemness heterogeneity, and the developed random forest-based stemness subtype classifier could aid in molecular stratification and personalized therapeutic regimen selection for ESCC patients.
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