ArticleBJC reports2025
Transcriptome profiling of uterine leiomyosarcomas identifies a leiomyoma-like expression pattern that indicates better survival.
Article in BJC reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed.
- A Review of Proteomic Studies in Uterine Leiomyosarcoma: Biomarkers, Pathways, and Clinical Potential.Current issues in molecular biology · 2026Review
- SUMOylation inhibitor TAK-981 suppresses proliferation and induces apoptosis in SK-UT-1B uterine leiomyosarcoma cells.Oncology letters · 2026Article
- Rescuing a refractory Leiomyosarcoma: ALK-DCTN1 fusion as a gateway to long-term control with Ceritinib.Gynecologic oncology reports · 2026Article
- Exploratory Preoperative Risk Stratification of Uterine Mesenchymal Tumors in Postmenopausal Women with Complementary Transcriptomic Analysis.Diagnostics (Basel, Switzerland) · 2026Article
- Precision oncology in the age of AI: lessons from AI-driven drug discovery and clinical translation.BJC reports · 2026Review
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundUterine leiomyosarcomas are rare and aggressive cancers with poor survival. Their non-cancerous counterparts, uterine leiomyomas, are common tumors affecting many women during reproductive years. Distinguishing leiomyosarcomas from leiomyomas remains a diagnostic challenge. This study aimed to identify molecular biomarkers that differentiate leiomyosarcomas from leiomyomas and to assess their prognostic value.
methodsWe analyzed 3'RNA sequencing data from 51 leiomyosarcomas and 44 leiomyomas with differential gene expression analysis and machine learning to identify diagnostic biomarkers. We utilized immunohistochemistry to validate the findings. We used Kaplan-Meier and Cox regression models to assess disease-specific survival in leiomyosarcoma patients.
resultsLeiomyosarcomas exhibited significant dysregulation of the retinoblastoma and cell cycle pathways. Clustering based on retinoblastoma pathway genes identified a subset of leiomyosarcomas with a leiomyoma-like expression profile that associated with better survival. Machine learning-based classification identified genes with high predictive accuracy, including genes from the retinoblastoma pathway. Immunohistochemistry validated TOP2A and CDK1 as potential diagnostic biomarkers, and their higher expression was associated with worse survival. Leiomyoma driver alterations were found in 27% of leiomyosarcomas, but these showed no association with survival.
conclusionOur results reveal molecular heterogeneity in uterine leiomyosarcomas and provide potential diagnostic and prognostic biomarkers for improved patient management.
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Registered trials
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