Exploring biomarkers for uterine leiomyosarcoma via combination of iMDM algorithm and pathway enrichment analysis

Objective: To understand the pathogenesis and etiology of uterine leiomyosarcoma (ULMS) at its early age, as well as explore an effective method for the treatment of it. Materials and Methods: First of all, the gene expression profile data of ULMS and the protein-protein interaction (PPI) data were recruited and preprocessed. Then, the inference of multiple differential modules (iMDM) algorithm, which contained differential co-expression network (DCN) construction and identification of multiple differential modules (M-DMs) in DCN was introduced to identify candidate M-DMs. In the following, the M-DMs were identified via statistical analysis was conducted. Finally, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was conducted to disclose the function of these M-DMs. Results: A DCN consisting of 1656 nodes (4182 edges) was built, and 16 seed genes were exacted from the DCN by ranking the z-scores in descending order and setting the threshold value of the top 1%. After refinement, 12 candidate M-DMs were obtained and all of these M-DMs resulted to be M-DMs. The pathway enrichment analysis indicated that five modules were enriched in mRNA Splicing pathway, and three modules were enriched in Gene Expression pathway. The authors predicted that these two pathways and the 12 seed genes might play important roles during the process of the occurrence and development of ULMS. Conclusions: This method that was used in the present study to perform the analysis on ULMS was suitable. The authors predict that the results could offer investigators valuable resources for better understanding the underlying mechanisms ULMS on the gene level, and the results will give great insights to reveal pathological mechanism underlying this disease, or even provide a hand for future study of related disease research.