Screening of candidate key genes associated with human osteosarcoma using bioinformatics analysis

Abstract

The aim of the present study was to identify the key genes associated with osteosarcoma (OS) using a bioinformatics approach. Microarray data (GSE36004) was downloaded from the Gene Expression Omnibus database, including 19 OS cell lines and 6 normal controls. Differentially expressed genes (DEGs) in the OS cell lines were identified using the Limma package, and differentially methylated regions were screened with methyAnalysis in R. Copy number analysis was performed and genes with copy number gains/losses were further screened using DNAcopy and cghMCR packages. Functional enrichment analyses were performed using the Database for Annotation, Visualization and Integrated Discovery online tool, and protein-protein interactions were identified based on information obtained from the Search Tool for the Retrieval of Interacting Genes database. A total of 47 downregulated genes were screened in hyper-methylated regions, including the fragment crystallizable (Fc) region of immunoglobulin E, high affinity I, receptor for; γ polypeptide (FCER1G), leptin (LEP) and feline Gardner-Rasheed sarcoma viral oncogene homolog (FGR). In addition, a total of 17 upregulated genes, including the TPase family, AAA domain containing 2 (ATAD2) and cyclin-dependent kinase 4 (CDK4), exhibited copy number gains, while 5 downregulated genes, including Rho GTPase activating protein 9 (ARHGAP9) and major histocompatibility complex, class II, DO α (HLA-DOA), exhibited copy number losses. These results indicate that hyper-methylation of FCER1G, LEP, and FGR may serve a crucial function in the development of OS. In addition, copy number alterations of these DEGs, including ATAD2, CDK4, ARHGAP9 and HLA-DOA, may also contribute to OS progression. These DEGs may be candidate targets for the diagnosis and treatment of this disease.

Comprehensive Genomic and Epigenomic Analyses on Transcriptomic Regulation in Stomach Adenocarcinoma

“…Thereafter, we utilized the R package circular binary segmentation algorithm in "DNAcopy." library for mapping DNA CNV data at the genetic level for all samples based on the segmented CNV data (Zhang et al, 2017). With regard to the DNA MET profiles, the β-values of the probe were filtered for eliminating probes positioned on the sex chromosomes.…”

Immune Microenvironment in Osteosarcoma: Components, Therapeutic Strategies and Clinical Applications

“…Zhang et al. ( 101 ) conducted bioinformatic analysis on 19 osteosarcoma cases and six normal samples obtained from the Gene Expression Omnibus database and compared the differentially expressed genes, differentially methylated regions, copy number, and functional enrichment of the two groups. The results showed that hypermethylation in the fragment crystallizable region of immunoglobulin (Ig) E, high-affinity I, receptor for γ polypeptide was significantly associated with osteosarcoma development.…”

Identification of Potential Therapeutic Targets and Immune Cell Infiltration Characteristics in Osteosarcoma Using Bioinformatics Strategy

“…In the previous studies, the limited number of samples and inappropriate analysis methods of multiple datasets led to deviation of the results. Zhang et al ( 9 ) and Diao et al ( 10 ) described the role of gene copy number alterations and methylation changes in the malignant progression of osteosarcoma using bioinformatics analyses, respectively. However, these studies were only based on single datasets and had a limited sample size, which may have biased the final results.…”