Developing a comprehensive molecular subgrouping model for cervical cancer using machine learning.

This study developed a molecular classification model for cervical cancer using machine learning, integrating prognosis related biomarkers with clinical features. Analyzing 281 specimens, 27 biomarkers were identified, associated with recurrence and treatment response. The model identified four molecular subgroups: group 1 (OALO) with Overexpression of ATP5H and LOw risk; group 2 (LASIM) with low expression of ATP5H and SCP, indicating InterMediate risk; group 3 (LASNIM) characterized by Low expression of ATP5H, SCP, and NANOG, also at InterMediate risk; and group 4 (LASONH), with Low expression of ATP5H, and SCP, Over expression of NANOG, indicating High risk, and potentially aggressive disease. This classification correlated with clinical outcomes such as tumor stage, lymph node metastasis, and response to treatment, demonstrating that combining molecular and clinical factors could significantly enhance the prediction of recurrence and aid in personalized treatment strategies for cervical cancer.