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

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; GSE764, GSE68312 and GSE64763; and TCGA data. A protein-protein interaction (PPI) network was constructed, and hub genes were identified utilizing the CytoHubba plug-in from Cytoscape software. In addition, weighted gene co-expression network analysis (WGCNA) was performed to identify hub genes. We took the intersection of the hub genes generated from the PPI network and WGCNA. Subsequently, random forest (RF) and support vector machine (SVM) algorithms were used to screen for key genes as predictive biomarkers. Finally, we constructed a nomogram with these genes.

Results: A total of 37 hub genes were selected using WGCNA. A total of 245 DEGs were identified; 63 DEGs were upregulated, and 182 DEGs were downregulated. Functional enrichment analysis revealed that these genes were mainly associated with the cell cycle, extracellular matrix receptor interactions and oocyte meiosis. The final hub genes were CENPA, KIF2C, TTK, MELK and CDC20. Gene set enrichment analysis (GSEA) revealed that these genes were mostly enriched in the cell cycle, mismatch repair and amino sugar and nucleotide sugar metabolism. Tumor-infiltrating immune cell analysis indicated that these genes did not have an obvious correlation with immune cells.

Conclusions: CENPA, KIF2C, TTK, MELK and CDC20 were key genes significantly associated with LMS and LM. Functional enrichment analysis and tumor-infiltrating immune cell analysis indicated that these genes might be correlated with tumor proliferation, which might shed light on the possible pathogenesis and predictive biomarkers of LMS.