Soft tissue sarcomas (STS) are rare and heterogeneous cancers with limited biomarkers. Comprehending their underlying mechanisms is crucial for identifying biomarkers that improve diagnosis and facilitate targeted therapy. This paper applies two independent strategies—ne co-expression network (GCN) analysis and biclustering—identify biomarkers in uterine leiomyosarcoma (ULMS), using one RNA-Seq and one microarray dataset. GCN analysis uncovered globally co-expressed hub genes like FOXM1, E2F1, MYBL2, and PITX1, linked to mitosis and DNA replication. In contrast, biclustering identified local co-expression patterns, including FOXM1 and HLF as transcriptional regulators, and other genes such as CCNB1, POLQ, and TRIP13 involved in genomic stability. Notably, FOXM1 was detected by both methods, reinforcing its relevance. While GCN highlights global regulatory roles, biclustering captures condition-specific signals. Their complementarity enhances biomarker discovery and contributes to understanding ULMS transcriptional architecture.