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.

Overexpression of glucocorticoid receptor promotes the poor progression and induces cisplatin resistance through p38 MAP kinase in cervical cancer patients.

Glucocorticoid receptor (GR) is activated by synthetic glucocorticoid or endogenous cortisol which were released by the physical and psychosocial stress, and recent studies reported that it is involved in tumor initiation and metastasis in various solid cancers. However, role of GR in cervical cancer has not been elucidated yet. Therefore, here we aim to unveil the role of GR in cervical cancer with cervical cancer clinical specimen and cervical cancer cell lines.