Identification of Prognostic Genes in Leiomyosarcoma by Gene Co-Expression Network Analysis

Background/Aims: Leiomyosarcoma (LMS) is a tumor derived from malignant mesenchymal tissue associated with poor prognosis. Determining potential prognostic markers for LMS can provide clues for early diagnosis, recurrence, and treatment. Methods: RNA sequence data and clinical features of 103 LMS were obtained from the Cancer Genome Atlas (TCGA) database. Application Weighted Gene Co-Expression Network Analysis (WGCNA) was used to construct a free-scale gene co-expression network, to study the interrelationship between its potential modules and clinical features, and to identify hub genes in the module. The hub gene function was verified by an external database. Results: Twenty-four co-expression modules were constructed using WGCNA. A dark red co-expression module was found to be significantly associated with disease recurrence. Functional enrichment analysis and GEPIA and ONCOMINE database analyses demonstrated that hub genes CDK4, CCT2, and MGAT1 may play an important role in LMS recurrence. Conclusion: Our study constructed an LMS co-expressing gene module and identified prognostic markers for LMS recurrence detection and treatment.