HMMR Functions as a Prognostic Biomarker in Uterine Carcinosarcoma Identified by Comprehensive Bioinformatics Analysis

Abstract

BackgroundUterine carcinosarcoma (UCS) is a rare aggressive tumor with a high metastasis rate and poor prognosis. Bioinformatics analysis has been widely applied to screen and analyze genes in linkage to various types of cancer progression. This study aims to explore the molecular mechanism of UCS. MethodsFirst, transcriptional different expression data between UCS and normal samples were got from the GEPIA database. Subsequently, differentially expressed genes were analyzed through the Metascape with Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Then, the STRING website and Cytoscape software were applied to construct the protein-protein interaction network. Finally, the top 30 genes obtained through the MCC algorithm were selected as hub genes, which was finally validated in TIMER, and UALCAN databases. ResultsA total of 1894 DEGs (579 up-regulated and 1315 down-regulated) were identified, GO and KEGG functional enrichment analysis were performed for the DEGs. The PPI network was constructed based on DEGs, and four clusters were excavated for further analysis and the top 30 genes were identified as hub genes. Our data showed that the expression of HMMR is significantly higher in UCS tissues compared to the paired normal tissues (p<0.05) and the elevated expression of HMMR is related to poor prognosis in patients with UCS (p= 0.0031). TPX2, AURKA, BRCA1 and BARD1 are essential for the function of HMMR. TPX2 and AURKA were found to be significantly higher in UCS compared to the normal tissue (p<0.05), and there was a statistically significant positive correlation between the expression of HMMR and AURKA, TPX2, BRCA1, BARD1 in UCS (p=1.08e-07, p=1.62e-05, p=2.02e-3, p=6.54e-6). ConclusionsOur study suggested that HMMR may be a potential biomarker for predicting the prognosis of UCS patients.