The Gene Expression Omnibus (GEO, http://www.ncbi.nlm.nih.gov/geo/) was used in this study. GEO, which is established and managed by the National Center for Biotechnology Information, National Library of Medicine, is an invaluable international resource that preserves and freely accesses comprehensive sets of microarray data submitted by researchers globally12, 13. The database includes data from a wide variety of technologies, such as Serial Analysis of Gene Expression, DNA microarray, protein array, high-throughput nucleic acid sequencing, and Real-Time PCR14, 15.

The search strategy we employed to identify OS-related datasets in the GEO database was as follows: “(osteosarcoma OR OS) AND Expression profiling by array[DataSet Type] AND Homo sapiens[Organism]”. Datasets GSE2842416, GSE4235217 and GSE2125718 were eventually included and extracted after a thorough search.

The platform for GSE28424 is GPL13376 — Illumina HumanWG-6 v2.0 expression beadchip, which contains nineteen OS cell lines and four normal bones. The platform for GSE42352 is GPL10295 — Illumina human-6 v2.0 expression beadchip, which comprises nineteen OS samples and twelve mesenchymal stem cell (MSC) samples used as controls. The GSE21257 dataset, which is also based on the GPL10295 platform, has fifty-three OS samples with complete clinicopathological characteristics.

The R software (version 4.0.5) was used to acquire and process the expression profiling data. First, the raw data of all datasets was obtained using the GEOquery package (v2.58.0) and normalized and standardized using the limma package (v3.46.0). Then, the Wilcoxon rank-sum test was performed to evaluate the differential expression of SKP2 gene between the OS and the control groups in the GSE28424 and GSE42352 datasets. In the GSE21257 dataset, the samples were divided into a high expression group and low expression group based on the median value of SKP2 expression, and survival analysis was performed using the Kaplan–Meier method and log-rank test.

UALCAN (http://ualcan.path.uab.edu) is a web-portal with user-friendly interactive features for conducting in-depth analysis of gene expression data from The Cancer Genome Atlas (TCGA)19. The mRNA expression of SKP2 in sarcoma tissues and adjacent normal tissues were explored. In addition, we also investigated the effect of SKP2 expression level on sarcoma patient survival through the UALCAN database by comparing the overall survival between the SKP2 high expression group and the SKP2 low/medium expression group.

The STRING database, which is a tool for retrieving and displaying the genes that a query gene appears within clusters on the genome20, was employed to establish the PPI network of SKP2 protein and other proteins. The PPI network was then visualized using the Cytoscape program21. Furthermore, Cytohubba, a Cytoscape plug-in, was performed to detect the hub genes that are significantly related to SKP2.

Gene Ontology (GO) analysis aims to standardize the representation of gene and gene product properties across species, comprising cell components (CC), biological process (BP) and molecular function (MF)22. Kyoto Encyclopaedia of Genes and Genomes (KEGG) analysis serves as a combined database resource for biologically interpreting totally sequenced genomes, the process to map genes in the genome to generated pathway maps23.

GO and KEGG were utilized to probe the molecular mechanisms and pathways related to SKP2 for the development and prognosis of OS, and the visualization of the enrichment analyses was conducted through the cluster-Profiler package (v3.18.1), the GOplot package (v1.0.2), and the enrichplot package (v1.10.2) in the R language software.

Figure 1

The expression of SKP2 in OS cells and control cells was compared. In GSE28424 (Figure 1 A), the expression level of SKP2 in OS cell lines was significantly higher than that in normal bones (P < 0.001). We also found that SKP2 expressed highly in OS samples compared to MSC samples in GSE42352 (P = 0.0014), as shown in Figure 1 B. Besides, the expression difference of SKP2 between the tumour tissues of sarcoma and the corresponding normal tissues in the TCGA project was additionally evaluated through the UALCAN web portal (Figure 1 C), and it was statistically significant (P < 0.001). These findings indicate that SKP2 is expressed at an obviously high level in OS cells.

Figure 2

The Huvos grading system is widely used in the chemotherapy assessment of OS, and it is an important parameter for predicting long-term prognosis. Grades I and II are considered to be poor in response to chemotherapy, while grades III and IV are regarded to have a favourable response. We used the GSE21257 dataset to perform a correlation analysis between SKP2 expression and Huvos grade. The level of SKP2 expression was observed to decrease as Huvos grade increased (P < 0.05) (Figure 2), indicating that the expression level of SKP2 falls as the rate of tumour necrosis tissue increases the following chemotherapy. This suggests that SKP2 might be used to predict the effectiveness of OS chemotherapy.

Figure 3

Figure 4

Table 1

GO/KEGG	ID	Description	BgRatio	P value	Adjusted P value	q value	Count
BP	GO:0044843	cell cycle G1/S phase transition	310/18866	1.98E-20	1.11E-17	5.05E-18	11
BP	GO:0000082	G1/S transition of mitotic cell cycle	287/18866	6.16E-18	1.73E-15	7.88E-16	10
BP	GO:0000079	regulation of cyclin-dependent protein serine/threonine kinase activity	102/18866	3.55E-14	6.56E-12	2.99E-12	7
BP	GO:1904029	regulation of cyclin-dependent protein kinase activity	106/18866	4.68E-14	6.56E-12	2.99E-12	7
BP	GO:0000086	G2/M transition of mitotic cell cycle	254/18866	2.33E-11	2.61E-09	1.19E-09	7
CC	GO:0000307	cyclin-dependent protein kinase holoenzyme complex	43/19559	9.35E-27	2.99E-25	1.08E-25	10
CC	GO:1902554	serine/threonine protein kinase complex	89/19559	2.47E-23	3.96E-22	1.43E-22	10
CC	GO:1902911	protein kinase complex	104/19559	1.27E-22	1.35E-21	4.90E-22	10
CC	GO:0061695	transferase complex, transferring phosphorus-containing groups	253/19559	1.19E-18	9.55E-18	3.46E-18	10
CC	GO:0016592	mediator complex	41/19559	0.000233	0.001491	0.00054	2
MF	GO:0016538	cyclin-dependent protein serine/threonine kinase regulator activity	50/18352	2.35E-16	7.29E-15	3.22E-15	7
MF	GO:0019887	protein kinase regulator activity	185/18352	3.01E-12	3.90E-11	1.72E-11	7
MF	GO:0030332	cyclin binding	30/18352	3.77E-12	3.90E-11	1.72E-11	5
MF	GO:0019207	kinase regulator activity	216/18352	9.00E-12	6.98E-11	3.08E-11	7
MF	GO:0004693	cyclin-dependent protein serine/threonine kinase activity	29/18352	1.65E-09	8.51E-09	3.75E-09	4
KEGG	hsa04110	Cell cycle	126/8095	8.35E-21	5.18E-19	1.76E-19	11
KEGG	hsa05203	Viral carcinogenesis	204/8095	1.99E-18	6.18E-17	2.10E-17	11
KEGG	hsa04218	Cellular senescence	156/8095	5.73E-17	1.18E-15	4.02E-16	10
KEGG	hsa05169	Epstein-Barr virus infection	202/8095	8.08E-16	1.25E-14	4.25E-15	10
KEGG	hsa04115	p53 signaling pathway	73/8095	4.76E-15	5.91E-14	2.01E-14	8

Figure 5

The PPI network created by STRING, which consisted of 51 nodes and 709 edges, was visualized using Cytoscape to explore the functional connections between SKP2 and the other proteins related to it (Figure 3 A). Subsequently, as shown in Figure 3 B, the top ten related genes identified using the Maximal Clique Centrality method in the Cytohubba plug-in were CDKs (1, 2, 4, and 6), Cyclin A1-2, E1-2, D1, and CDKN1A.

Enrichment analyses of GO and KEGG pathway for SKP2 and its top ten associated genes were performed and displayed using the R software packages clusterProfiler, GOplot, and enrichplot (Figure 4, Table 1 and Table S1). According to the GO enrichment results, regarding BP, the genes were significantly enriched in cell cycle G1/S phase transition, G1/S transition of mitotic cell cycle, regulation of cyclin−dependent protein serine/threonine kinase activity, regulation of cyclin−dependent protein kinase activity, and G2/M transition of mitotic cell cycle (Figure 4 A). For CC, the genes were significantly enriched in cyclin−dependent protein kinase holoenzyme complex, serine/threonine protein kinase complex, protein kinase complex, and transferase complex, transferring phosphorus−containing groups (Figure 4 B). Regarding MF, the genes were significantly enriched in cyclin−dependent protein serine/threonine kinase regulator activity, protein kinase regulator activity, cyclin binding, and kinase regulator activity (Figure 4 C). The findings of the KEGG pathway analysis, as shown in Figure 4 D, revealed that the genes were mainly engaged in the cell cycle, viral carcinogenesis, and cellular senescence.

We further studied the role of SKP2 in the prognosis of OS patients. In the GSE21257 dataset, based on the cut-off value, patients were split into two groups: low expression group (26 patients) and high expression group (27 patients). The Kaplan-Meier curve and log-rank test shown that the elevated SKP2 expression level was significantly related to the overall survival (P < 0.01) of OS patients (Figure 5 A). Moreover, the dataset in UALCAN also indicated that there was a significant difference in survival time between the SKP2 high expression group (65 patients) and the SKP2 medium/low expression group (194 patients) (P = 0.017), as shown in Figure 5 B. Taken together, OS patients with a high level of SKP2 expression were predicted to have a poor prognosis.