Integrated analysis of uterine leiomyosarcoma and leiomyoma utilizing TCGA and GEO data: a WGCNA and machine learning approach.

Journal: Translational cancer research
Published Date: May 30, 2025

Abstract

BACKGROUND: Uterine sarcoma is a gynecological mesenchymal tumor with an elusive pathogenesis. The uterine leiomyosarcoma (LMS) is the most common subtype of uterine sarcoma. LMS is a highly aggressive tumor with a poor prognosis. The genomic landscape of LMS remains unclear. Rare cases of LMS are observed to arise from leiomyoma (LM). We conducted a study to explore the genomic relationship between LMS and LM using public microarray data from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA). Using bioinformatics analysis tools, we would like to provide molecular insight into the pathogenesis of LMS and to discover novel predictive biomarkers for this disease.

Authors

  • Zixin Yang
    Chester F. Carlson Center for Imaging Science, Rochester Institute of Technology, Rochester, NY 14623, USA.
  • Fan Yang
    School of Electrical Engineering and Automation, Jiangsu Normal University, Xuzhou, China.
  • Fanlin Li
    Department of Obstetrics and Gynaecology, West China Second University Hospital, Sichuan University, Chengdu, China.
  • Ying Zheng
    Department of Ultrasound, Beijing Youan Hospital, Capital Medical University, Beijing 100000, China. Electronic address: xl2264@126.com.

Keywords

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