Identification of Prognostic Genes in Leiomyosarcoma by Gene Co-Expression Network Analysis - Yang, Jun; Li, Cuili; Zhou, Jiaying; Liu, Xiaoquan; Wang, Shaohua (Frontiers in Genetics, 2020)
Abstract. Background/Aims: Leiomyosarcoma (LMS) is a tumor derived from malignant mesenchymal tissue with a poor prognosis. Determining potential prognostic markers for LMS can provide clues for early diagnosis of 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 and to study the interrelationship between its potential modules and clinical features and to identify hub genes in the module. The Hub gene function is verified by an external database. Results: Twenty-four co-expression modules were constructed using WGCNA. A darkred co-expression module was found to be significantly associated with disease recurrence. Functional enrichment analysis and GEPIA and ONCOMINE database analysis found that hub genes CDK4, CCT2 and MGAT1 may play an important role in LMS recurrence. Conclusion: Our study constructed a LMS co-expressing gene module and identified prognostic markers for LMS recurrence detection and treatment.