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. 2021 May 27:9:e11496.
doi: 10.7717/peerj.11496. eCollection 2021.

Identification of potential gene signatures associated with osteosarcoma by integrated bioinformatics analysis

Yutao Jia  1 , Yang Liu  1 , Zhihua Han  2 , Rong Tian  1
Affiliations

Identification of potential gene signatures associated with osteosarcoma by integrated bioinformatics analysis

Yutao Jia et al. PeerJ. .

Abstract

Background: Osteosarcoma (OS) is the most primary malignant bone cancer in children and adolescents with a high mortality rate. This work aims to screen novel potential gene signatures associated with OS by integrated microarray analysis of the Gene Expression Omnibus (GEO) database.

Material and methods: The OS microarray datasets were searched and downloaded from GEO database to identify differentially expressed genes (DEGs) between OS and normal samples. Afterwards, the functional enrichment analysis, protein-protein interaction (PPI) network analysis and transcription factor (TF)-target gene regulatory network were applied to uncover the biological function of DEGs. Finally, two published OS datasets (GSE39262 and GSE126209) were obtained from GEO database for evaluating the expression level and diagnostic values of key genes.

Results: In total 1,059 DEGs (569 up-regulated DEGs and 490 down-regulated DEGs) between OS and normal samples were screened. Functional analysis showed that these DEGs were markedly enriched in 214 GO terms and 54 KEGG pathways such as pathways in cancer. Five genes (CAMP, METTL7A, TCN1, LTF and CXCL12) acted as hub genes in PPI network. Besides, METTL7A, CYP4F3, TCN1, LTF and NETO2 were key genes in TF-gene network. Moreover, Pax-6 regulated four key genes (TCN1, CYP4F3, NETO2 and CXCL12). The expression levels of four genes (METTL7A, TCN1, CXCL12 and NETO2) in GSE39262 set were consistent with our integration analysis. The expression levels of two genes (CXCL12 and NETO2) in GSE126209 set were consistent with our integration analysis. ROC analysis of GSE39262 set revealed that CYP4F3, CXCL12, METTL7A, TCN1 and NETO2 had good diagnostic values for OS patients. ROC analysis of GSE126209 set revealed that CXCL12, METTL7A, TCN1 and NETO2 had good diagnostic values for OS patients.

Keywords: Activating transcription factor; Bioinformatic; Diagnosis; Genes; Osteosarcoma.

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Conflict of interest statement

The authors declare there are no competing interests.

Figures

Figure 1
Figure 1. Top 20 significantly enriched Gene Ontology terms of differentially expressed genes.
Figure 2
Figure 2. Protein–protein interaction networks of differentially expressed genes.
Red and green ellipses represent up-regulated and down-regulated genes, respectively. The black borders indicate top 20 up-regulated and down-regulated genes.
Figure 3
Figure 3. Transcription factor-top 20 up-regulated and down-regulated genes network.
Diamonds and ellipses represent transcription factors and top 20 up-regulated and down-regulated genes, respectively. Red and green ellipses represent up-regulated and down-regulated genes, respectively.
Figure 4
Figure 4. Box plots of seven differentially expressed genes in the GSE39262 dataset.
The x-axes represent control and case groups while the y-axes represent the relative expression levels of the genes. Seven genes included NETO2, CAMP, METTL7A, TCN1, LTF, CXCL12 and CYP4F3.
Figure 5
Figure 5. (A-G) Box plots of seven differentially expressed genes in GSE126209 dataset.
The x-axes represent control and case groups while the y-axes represent the relative expression levels of the genes. Seven genes included NETO2, CAMP, METTL7A, TCN1, LTF, CXCL12 and CYP4F3.
Figure 6
Figure 6. ROC curves of selected differentially expressed genes in the GSE39262 dataset.
The x-axes and the y-axes show 1-specificity and sensitivity, respectively. ROC, receiver operating characteristic. (A-G) The seven genes included NETO2, CAMP, METTL7A, TCN1, LTF, CXCL12 and CYP4F3.
Figure 7
Figure 7. ROC curves of selected differentially expressed genes in the GSE126209 dataset.
The x-axes and the y-axes show 1-specificity and sensitivity, respectively. ROC, receiver operating characteristic. (A-G) The seven genes included NETO2, CAMP, METTL7A, TCN1, LTF, CXCL12 and CYP4F3.
See this image and copyright information in PMC

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