Comprehensive Review of Uterine Leiomyosarcoma: Pathogenesis, Diagnosis, Prognosis, and Targeted Therapy.
Qiwei YangObianuju Sandra Madueke-LaveauxHan CunMarta WlodarczykNatalia GarciaKatia Candido CarvalhoAyman Al-HendyPublished in: Cells (2024)
Uterine leiomyosarcoma (uLMS) is the most common subtype of uterine sarcomas. They have a poor prognosis with high rates of recurrence and metastasis. The five-year survival for uLMS patients is between 25 and 76%, with survival rates approaching 10-15% for patients with metastatic disease at the initial diagnosis. Accumulating evidence suggests that several biological pathways are involved in uLMS pathogenesis. Notably, drugs that block abnormal functions of these pathways remarkably improve survival in uLMS patients. However, due to chemotherapy resistance, there remains a need for novel drugs that can target these pathways effectively. In this review article, we provide an overview of the recent progress in ascertaining the biological functions and regulatory mechanisms in uLMS from the perspective of aberrant biological pathways, including DNA repair, immune checkpoint blockade, protein kinase and intracellular signaling pathways, and the hedgehog pathway. We review the emerging role of epigenetics and epitranscriptome in the pathogenesis of uLMS. In addition, we discuss serum markers, artificial intelligence (AI) combined with machine learning, shear wave elastography, current management and medical treatment options, and ongoing clinical trials for patients with uLMS. Comprehensive, integrated, and deeper insights into the pathobiology and underlying molecular mechanisms of uLMS will help develop novel strategies to treat patients with this aggressive tumor.
Keyphrases
- artificial intelligence
- machine learning
- poor prognosis
- dna repair
- end stage renal disease
- clinical trial
- newly diagnosed
- chronic kidney disease
- prognostic factors
- healthcare
- long non coding rna
- free survival
- deep learning
- signaling pathway
- randomized controlled trial
- big data
- dna damage
- transcription factor
- oxidative stress
- epithelial mesenchymal transition
- drug induced
- reactive oxygen species
- locally advanced
- endoplasmic reticulum stress
- chemotherapy induced