Bioinformatics integrated analysis to investigate candidate biomarkers and associated metabolites in osteosarcoma

Jun Wang,Zhenggang Xiong,Mingzhi Gong,Yangyang Zhao,Deguo Xing

doi:10.1186/s13018-021-02578-0

Jun Wang, Zhenggang Xiong + Show 3 more

Open Access

https://doi.org/10.1186/s13018-021-02578-0

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Abstract

BackgroundThis study hoped to explore the potential biomarkers and associated metabolites during osteosarcoma (OS) progression based on bioinformatics integrated analysis.MethodsGene expression profiles of GSE28424, including 19 human OS cell lines (OS group) and 4 human normal long bone tissue samples (control group), were downloaded. The differentially expressed genes (DEGs) in OS vs. control were investigated. The enrichment investigation was performed based on DEGs, followed by protein–protein interaction network analysis. Then, the feature genes associated with OS were explored, followed by survival analysis to reveal prognostic genes. The qRT-PCR assay was performed to test the expression of these genes. Finally, the OS-associated metabolites and disease-metabolic network were further investigated.ResultsTotally, 357 DEGs were revealed between the OS vs. control groups. These DEGs, such as CXCL12, were mainly involved in functions like leukocyte migration. Then, totally, 38 feature genes were explored, of which 8 genes showed significant associations with the survival of patients. High expression of CXCL12, CEBPA, SPARCL1, CAT, TUBA1A, and ALDH1A1 was associated with longer survival time, while high expression of CFLAR and STC2 was associated with poor survival. Finally, a disease-metabolic network was constructed with 25 nodes including two disease-associated metabolites cyclophosphamide and bisphenol A (BPA). BPA showed interactions with multiple prognosis-related genes, such as CXCL12 and STC2.ConclusionWe identified 8 prognosis-related genes in OS. CXCL12 might participate in OS progression via leukocyte migration function. BPA might be an important metabolite interacting with multiple prognosis-related genes.

Highlights

This study hoped to explore the potential biomarkers and associated metabolites during osteosarcoma (OS) progression based on bioinformatics integrated analysis
Enrichment analysis and gene set variation analysis (GSVA) investigation The upregulated differentially expressed genes (DEGs) mainly enriched in 14 GOBP functions including leukocyte migration
Downregulated DEGs were mainly involved in Gene ontology-biological process (GO-BP) functions including neutrophil activation (GO:0042119, genes: Fc fragment of IgG receptor IIIb (FCGR3B), etc.) and pathways like phagosome (Fig. 2C, D)

Summary

Introduction

This study hoped to explore the potential biomarkers and associated metabolites during osteosarcoma (OS) progression based on bioinformatics integrated analysis. It has been proved that some genes are differentially expressed between OS samples and normal samples in humans, which can be further used for the prediction of chemotherapy response [6]. A previous study shows that the differentially expressed genes (DEGs) such as SRY-Box transcription factor 2 are taking part in the development of OS via influencing cell stemness and migration [7]. A previous study shows that catecholamines and their receptors can be potential molecular markers for OS [11]. The potential biomarkers and their associated detail molecular mechanism during the OS progression is unknown

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