Exploring the key genes and pathways of osteosarcoma with pulmonary metastasis using a gene expression microarray

Abstract

Osteosarcoma is a common and highly malignant tumour in children and teenagers that is characterized by drug resistance and high metastatic potential. Patients often develop pulmonary metastasis and have a low survival rate. However, the mechanistic basis for pulmonary metastasis remains unclear. To identify key gene and pathways associated with pulmonary metastasis of osteosarcoma, the authors downloaded the gene expression dataset GSE85537 and obtained the differentially expressed genes (DEGs) by analyzing high-throughput gene expression in primary tumours and lung metastases. Subsequently, the authors performed gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) enrichment analyses and a protein-protein interaction (PPI) network was constructed and analyzed by Cytoscape software. In total, 2,493 genes were identified as DEGs. Of these, 485 genes (19.45%) were upregulated, and the remaining 2,008 genes (80.55%) were downregulated. The authors identified the predominant GO categories and KEGG pathways that were significantly over-represented in the metastatic OS samples compared with the non-metastatic OS samples. A PPI network was constructed, and the results indicated that ALB, EGFR, INS, IL6, CDH1, FYN, ERBB2, IL8, CXCL12 and RAC2 were the top 10 core genes. The enrichment analyses of the genes involved in the top three significant modules demonstrated that the DEGs were principally related to neuroactive ligand-receptor interaction, the Rap1 signaling pathway, and protein digestion and absorption. Together, these data elucidated the molecular mechanisms of OS patients with pulmonary metastasis and provide potential therapeutic targets. However, further experimental studies are needed to confirm these results.

Gene Prioritization through Consensus Strategy, Enrichment Methodologies Analysis, and Networking for Osteosarcoma Pathogenesis

“…Additionally, more specific biological processes were described, for instance, fibroblast proliferation, osteoblast differentiation and development, and mesenchymal cell proliferation and transition. In accordance with our results, previous studies have identified similar biological processes related to OS, where the following are considered OS-associated terms: cell cycle regulation (mainly mediated by RB1 and TP53), osteoblast differentiation (mediated by RUNX2), DNA damage, stress response, epigenetic processes, mitosis, cell motility functions, and members involved in OS cell proliferation (weighting NFKB signaling, NFKBIE, and RELA members) [3,[35][36][37]. Taken together, these processes suggest that the consensus list is evidence of the genes associated with osteogenesis, cell differentiation, and transition to bone cell types.…”

Down-regulation of microRNA-144-3p and its clinical value in non-small cell lung cancer: a comprehensive analysis based on microarray, miRNA-sequencing, and quantitative real-time PCR data

“…The protein digestion and absorption pathway is a key pathway in several human cancers. A study by Shi et al showed that this pathway might contribute to the pulmonary metastasis of osteosarcoma patients [24]. It might be involved in the up-regulation of differentially expressed genes in breast cancer [25], and it has already been associated with the down-regulated differentially expressed genes in pancreatic neuroendocrine tumors [26].…”

Identification of key biomarkers and functional pathways in osteosarcomas with lung metastasis: Evidence from bioinformatics analysis

“…In recent years, with the constant development of high-throughput technologies and improvements in data analysis techniques, microarray techniques combined with bioinformatics analyses have been extensively applied to identify genetic alterations at the genome level of tumors, particularly in seeking the differentially expressed genes (DEGs). At present, Shi et al have analyzed the enrichment pathway and key genes of lung metastasis of osteosarcoma using a single microarray [8] . However, due to the inherent differences in experimental samples, detection platforms, and experimental methods, falsepositive rates in independent microarray analysis reduce the reliability of the results obtained.…”