Identification of DEGs in UCS
We used p < 0.01 and |LFC| ≥ 2 as the cutoff criteria and screened 1894 DEGs, including 579 up-regulated genes and 1315 down-regulated genes. The position of the DEGs on the chromosome is shown in Figure 1.
Functional Enrichment Analysis
Metascape was utilized to perform enrichment analysis for DEGs. Figure 2 showed the top 20 most highly enriched items of GO and KEGG in up-regulated and down-regulated DEGs. The up-regulated DEGs were mainly enriched in BP, including cell division, positive regulation of cell cycle, attachment of spindle microtubules to kinetochore, DNA conformation change, metaphase plate congression, epithelial cell differentiation, regulation of cyclin-dependent protein serine/threonine kinase activity and DNA replication. MF analysis showed that the DEGs were significantly enriched in kinase binding. For the CC, the DEGs were enriched in spindle, microtubule organizing center and extracellular matrix (Fig. 2a). Moreover, the result of KEGG pathway analysis showed that DEGs were mainly enriched in the cell cycle, p53 signaling pathway, ECM-receptor interaction, pathways in cancer and cell adhesion molecules (CAMs) (Fig. 2c). These BP terms and pathways are related to tumorigenesis and pathogenesis in multiple tumors.
In down-regulated DEGs analysis, BP analysis showed that the DEGs were mostly enriched in muscle structure development, blood vessel development, actin filament-based process, regulation of ion transport, extracellular structure organization, positive regulation of cellular component movement and cell-matrix adhesion. For the MF, the DEGs were highly enriched in glycosaminoglycan binding. For the CC, the DEGs were enriched in contractile fiber, collagen-containing extracellular matrix, actin cytoskeleton, sarcolemma, stress fiber and adherens junction (Fig. 2b). The result of KEGG pathway analysis showed that down-regulated DEGs were mainly enriched in focal adhesion, vascular smooth muscle contraction, complement and coagulation cascades, calcium signaling pathway and ras signaling pathway (Fig. 2d). The above enrichment analysis can help us further study the role of DEGs in UCS.
PPI network and analysis on clusters
To better understand the protein interactions among the DEGs, we performed the PPI network analysis. Via the STRING website, 1894 DEGs were screened into the PPI network complex, which contained 1510 nodes and 6586 edges (Fig. 3). Then, MCODE was used to identify modules for the network. 42 clusters were found according to the criterion described previously and the top four significant clusters were selected (Fig. 4). Cluster 1 contained 76 nodes and 2437 edges, which got the highest score in these clusters (Fig. 4a). Cluster 2 contained 21 nodes and 210 edges (Fig. 4d). Cluster 3 contained 15 nodes and 105 edges (Fig. 4g). Cluster 4 contained 12 nodes and 66 edges (Fig. 4j). GO analysis and KEGG analysis were independently applied to each cluster.
The DEGs of cluster 1 were mostly enriched in cell division (BP), regulation of chromosome segregation (BP), positive regulation of cell cycle (BP), meiotic cell cycle (BP), kinase binding (MF), chromosomal region (CC), spindle (CC), centrosome (CC), cell cycle p53 signaling pathway (KEGG) and DNA replication (KEGG) (Fig. 4b). The DEGs of cluster 2 were highly enriched in adenylate cyclase-modulating G protein-coupled receptor signaling pathway (BP), cellular calcium ion homeostasis (BP), positive regulation of response to external stimulus (BP), G protein-coupled receptor binding (MF), G protein-coupled peptide receptor activity (MF), chemokine signaling pathway (KEGG) and neuroactive ligand-receptor interaction (KEGG) (Fig. 4e). The DEGs of cluster 3 were mostly enriched in regulation of growth (BP), negative regulation of canonical Wnt signaling pathway (BP), endoplasmic reticulum lumen (CC) and collagen-containing extracellular matrix (CC) (Fig. 4h). The DEGs of cluster 4 were highly enriched in protein polyubiquitination (BP) and ubiquitin ligase complex (CC) (Fig. 4k). Genes in cluster 3 and 4 did not enrich any KEGG pathway. Networks of GO enriched terms of the four clusters were also shown in Figure 4, where terms containing more genes tend to have a more significant p-value. The top 30 genes evaluated by the MCC algorithm of CytoHubba were identified as hug genes (Fig. 5).
Hub gene validation
All of the 30 hub genes were validated in UALCAN and we found the mRNA expression of HMMR was significantly associated with UCS patients’ prognosis, whose tissues displayed a higher expression of HMMR had significantly shorter survival compared to those with lower expression (p= 0.0031) (Fig. 6a). HMMR not only has higher expression in UCS than in normal tissues (p<0.05) (Fig. 6b), but also has higher expression in multiple cancer types compared to paired normal tissues (Fig. 6c).
Predicted functions and pathways of HMMR and HMMR related genes
We explored the interaction network based on HMMR through GeneMANIA and STRING databases (Fig. 7a, b), and then the functions of HMMR and related genes were analyzed by GO and KEGG through Metascape. As were shown in Figure 7c, HMMR related genes were mainly involved in cell cycle and mitosis, which is consistent with a review on HMMR published by Christopher Alan Maxwell . In the review, it described that RHAMM–centrosome–mitotic-spindle associations have the potential to affect cell transformation and tumor progression by promoting genomic instability in the cell, and TPX2, AURKA, BRCA1 and BARD1 are essential collaborators for HMMR in the process of accomplishing intracellular functions . Together with the bioinformatics analyses, TPX2 and AURKA were found to be significantlyhigherinUCS tissues compared to the normal tissues (p<0.05) (Fig. 7d) and there were statistically significantpositivecorrelations 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) (Fig. 7e).