A stemness-based signature with inspiring indications in discriminating the prognosis, immune response, and somatic mutation of endometrial cancer patients revealed by machine learning.

Abstract excerpt

Endometrial cancer (EC) is a fatal gynecologic tumor. Bioinformatic tools are increasingly developed to screen out molecular targets related to EC. Our study aimed to identify stemness-related prognostic biomarkers for new therapeutic strategies in EC. In this study, we explored the prognostic value of cancer stem cells (CSCs), characterized by self-renewal and unlimited proliferation, and its correlation with...

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• Biomarkers, Tumor
• Endometrial Neoplasms
• Gene Expression Profiling
• Gene Expression Regulation, Neoplastic
• Machine Learning