Endometrial cancer (EC) is a prevalent gynecological malignancy with a complex molecular landscape, contributing to significant global morbidity and mortality. Dysregulated signaling pathways such as PI3K/AKT/mTOR and RAS/RAF/MEK drive EC progression by promoting uncontrolled cell proliferation, survival, angiogenesis, and metastasis. Mutations in genes like PTEN and PIK3CA further underpin tumor aggressiveness. Molecular alterations in these pathways not only serve as biomarkers for prognosis but also guide the formulation of targeted therapies, such as mTOR inhibitors and anti-angiogenic agents. While such therapies show promise, optimizing their efficacy and minimizing adverse effects requires further research. A comprehensive approach integrating early detection (e.g., addressing postmenopausal bleeding), preventive strategies (e.g., managing obesity), increasing diagnostic sensitivity (e.g., transvaginal ultrasound) and advanced molecularly tailored treatments (e.g., AI & ML) is critical to reducing the burden of this disease. By targeting key signaling pathways, leveraging AI-driven methodologies, and addressing treatment resistance, we can enhance patient outcomes, also mitigate the rising global impact of EC.
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