PURPOSE: Osteosarcoma is characterized by features of rapid growth and early metastasis with a poor prognosis. The aim of our research is to investigate the potential transcription factor (TF)-miRNA-mRNA regulatory mechanism in osteosarcoma utilizing bioinformatics methods and validate by qRT-PCR. METHODS: The microRNA (miRNA) expression profiling datasets (GSE28423 and GSE65071) and mRNA expression profiling dataset GSE33382 were collected from the Gene Expression Omnibus (GEO) database. Differentially expressed miRNAs (DEMs) and differentially expressed genes (DEGs) were screened using the limma package. Then, the TransmiR v2.0, miRDB, and Targetscan 7.2 database were applied for the acquisition of TF-miRNA and miRNA-mRNA interaction relationships, respectively. Finally, we built a TF-miRNA-mRNA interactive network. Furthermore, survival analysis was performed to identify sub-network with prognostic value and validate through qRT-PCR. RESULTS: Eight overlapping DEMs and 682 DEGs were identified. Based on bioinformatics methods, 30 TF-miRNA interaction pairs and 25 miRNA-mRNA interaction pairs were screened. Finally, we constructed a TF-miRNA-mRNA regulatory network. Furthermore, laminin subunit gamma 1 (LAMC1) and thrombospondin-1 (THBS1), which involved in the network, were determined to have prognostic value and the corresponding subnetwork was identified. qRT-PCR results showed that LAMC1 mRNA expression was higher in osteosarcoma cells. CONCLUSION: Based on the survival analysis, a TF-miRNA-mRNA sub-network, that is TFs (SPI1, HEY1, and CEBPB)-hsa-miR-338-3p-target genes (LAMC1 and THBS1) was established. In conclusion, the construction of a potential TF-related regulatory network will help elucidate the underlying pathological mechanisms of osteosarcoma, and may provide novel insights for the diagnosis and treatment of osteosarcoma.
PURPOSE: Osteosarcoma is characterized by features of rapid growth and early metastasis with a poor prognosis. The aim of our research is to investigate the potential transcription factor (TF)-miRNA-mRNA regulatory mechanism in osteosarcoma utilizing bioinformatics methods and validate by qRT-PCR. METHODS: The microRNA (miRNA) expression profiling datasets (GSE28423 and GSE65071) and mRNA expression profiling dataset GSE33382 were collected from the Gene Expression Omnibus (GEO) database. Differentially expressed miRNAs (DEMs) and differentially expressed genes (DEGs) were screened using the limma package. Then, the TransmiR v2.0, miRDB, and Targetscan 7.2 database were applied for the acquisition of TF-miRNA and miRNA-mRNA interaction relationships, respectively. Finally, we built a TF-miRNA-mRNA interactive network. Furthermore, survival analysis was performed to identify sub-network with prognostic value and validate through qRT-PCR. RESULTS: Eight overlapping DEMs and 682 DEGs were identified. Based on bioinformatics methods, 30 TF-miRNA interaction pairs and 25 miRNA-mRNA interaction pairs were screened. Finally, we constructed a TF-miRNA-mRNA regulatory network. Furthermore, laminin subunit gamma 1 (LAMC1) and thrombospondin-1 (THBS1), which involved in the network, were determined to have prognostic value and the corresponding subnetwork was identified. qRT-PCR results showed that LAMC1 mRNA expression was higher in osteosarcoma cells. CONCLUSION: Based on the survival analysis, a TF-miRNA-mRNA sub-network, that is TFs (SPI1, HEY1, and CEBPB)-hsa-miR-338-3p-target genes (LAMC1 and THBS1) was established. In conclusion, the construction of a potential TF-related regulatory network will help elucidate the underlying pathological mechanisms of osteosarcoma, and may provide novel insights for the diagnosis and treatment of osteosarcoma.