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

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.

Methods

LMS and LM differentially expressed genes (DEGs) were screened by analyzing GEO datasets;

SUBMITTER: Yang Z 

PROVIDER: S-EPMC12170143 | biostudies-literature | 2025 May

REPOSITORIES: biostudies-literature