Identification of differentially expressed genes in MG63 osteosarcoma cells with drug‑resistance by microarray analysis

Identifying the best reference gene for RT-qPCR analyses of the three-dimensional osteogenic differentiation of human induced pluripotent stem cells

Reverse transcription quantitative real-time polymerase chain reaction (RT-qPCR) is an essential tool for gene expression analysis; choosing appropriate reference genes for normalization is crucial to ensure data reliability. However, most studies on osteogenic differentiation have had limited success in identifying optimal reference genes. To the best of our knowledge, no optimal reference genes in three-dimensional (3D) osteogenic differentiation culture experiments using human induced pluripotent stem cells (hiPSCs) have been identified. In this study, we aimed to identify stable reference genes that could be used for normalization in gene expression analyses during the 3D osteogenic differentiation of hiPSCs using an atelocollagen sponge as a scaffold. Four algorithms-ΔCt, BestKeeper, NormFinder, and geNorm-were used to evaluate the stability of 14 candidate reference genes. Genes encoding TATA box-binding protein, hypoxanthine phosphoribosyltransferase 1, and 14-3-3 protein zeta polypeptide were identified as the most stable reference genes. In comparison, conventionally used reference genes (beta-2 microglobulin and beta-actin genes) ranked among those with low stability. We also demonstrated the successful 3D osteogenic differentiation of hiPSCs on atelocollagen sponge. Our findings provide valuable insights for reference gene selection and bone tissue regeneration from hiPSCs, which could improve the treatment prospects for bone defects and other similar conditions in regenerative medicine.

Identification of therapeutic targets for osteosarcoma by integrating single-cell RNA sequencing and network pharmacology

Background: Osteosarcoma (OS) is a common primary tumor with extensive heterogeneity. In this study, we used single-cell RNA sequencing (scRNA-seq) and network pharmacology to analyze effective targets for Osteosarcoma treatment. Methods: The cell heterogeneity of the Osteosarcoma single-cell dataset GSE162454 was analyzed using the Seurat package. The bulk-RNA transcriptome dataset GSE36001 was downloaded and analyzed using the CIBERSORT algorithm. The key targets for OS therapy were determined using Pearson's correlation analysis. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed on key targets. The DeepDR algorithm was used to predict potential drugs for Osteosarcoma treatment. Molecular docking analysis was performed to verify the binding abilities of the predicted drugs and key targets. qRT-PCR assay was used to detect the expression of key targets in osteoblasts and OS cells. Results: A total of 21 cell clusters were obtained based on the GSE162454 dataset, which were labeled as eight cell types by marker gene tagging. Four cell types (B cells, cancer-associated fibroblasts (CAFs), endothelial cells, and plasmocytes) were identified in Osteosarcoma and normal tissues, based on differences in cell abundance. In total, 17 key targets were identified by Pearson's correlation analysis. GO and KEGG analysis showed that these 17 genes were associated with immune regulation pathways. Molecular docking analysis showed that RUNX2, OMD, and CD4 all bound well to vincristine, dexamethasone, and vinblastine. The expression of CD4, OMD, and JUN was decreased in Osteosarcoma cells compared with osteoblasts, whereas RUNX2 and COL9A3 expression was increased. Conclusion: We identified five key targets (CD4, RUNX2, OMD, COL9A3, and JUN) that are associated with Osteosarcoma progression. Vincristine, dexamethasone, and vinblastine may form a promising drug-target pair with RUNX2, OMD, and CD4 for Osteosarcoma treatment.

TBP, PPIA, YWHAZ and EF1A1 Are the Most Stably Expressed Genes during Osteogenic Differentiation

RT-qPCR is the gold standard and the most commonly used method for measuring gene expression. Selection of appropriate reference gene(s) for normalization is a crucial part of RT-qPCR experimental design, which allows accurate quantification and reliability of the results. Because there is no universal reference gene and even commonly used housekeeping genes’ expression can vary under certain conditions, careful selection of an appropriate internal control must be performed for each cell type or tissue and experimental design. The aim of this study was to identify the most stable reference genes during osteogenic differentiation of the human osteosarcoma cell lines MG-63, HOS, and SaOS-2 using the geNorm, NormFinder, and BestKeeper statistical algorithms. Our results show that TBP, PPIA, YWHAZ, and EF1A1 are the most stably expressed genes, while ACTB, and 18S rRNA expressions are most variable. These data provide a basis for future RT-qPCR normalizations when studying gene expression during osteogenic differentiation, for example, in studies of osteoporosis and other bone diseases.

Analysis of microRNA expression in CD133 positive cancer stem‑like cells of human osteosarcoma cell line MG-63

Osteosarcoma (OS) is a primary malignant tumor of bone occurring in young adults. OS stem cells (OSCs) play an important role in the occurrence, growth, metastasis, drug resistance and recurrence of OS. CD133 is an integral membrane glycoprotein, which has been identified as an OSC marker. However, the mechanisms of metastasis, chemoresistance, and progression in CD133(+) OSCs need to be further explored. In this study, we aim to explore differences in miRNA levels between CD133(+) and CD133(-) cells from the MG-63 cell line. We found 20 differentially expressed miRNAs (DEmiRNAs) (16 upregulated and 4 downregulated) in CD133(+) cells compared with CD133(-) cells. Hsa-miR-4485-3p, hsa-miR-4284 and hsa-miR-3656 were the top three upregulated DEmiRNAs, while hsa-miR-487b-3p, hsa-miR-493-5p and hsa-miR-431-5p were the top three downregulated DEmiRNAs. In addition, RT-PCR analysis confirmed that the expression levels of hsa-miR-4284, hsa-miR-4485-3p and hsa-miR-3656 were significantly increased, while the expression levels of hsa-miR-487b-3p, hsa-miR-493-5p, and hsa-miR-431-5p were significantly decreased in CD133(+) cells compared with CD133(-) cells. Moreover, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis revealed that predicted or validated target genes for all 20 DEmiRNAs or the selected 6 DEmiRNAs participated in the "PI3K-Akt signaling pathway," "Wnt signaling pathway," "Rap1 signaling pathway," "Cell cycle" and "MAPK signaling pathway". Among the selected six DEmiRNAs, miR-4284 was especially interesting. MiR-4284 knockdown significantly reduced the sphere forming capacity of CD133(+) OS cells. The number of invasive CD133(+) OS cells was markedly decreased after miR-4284 knockdown. In addition, miR-4284 knockdown increased the p-β-catenin levels in CD133(+) OS cells. In conclusion, RNA-seq analysis revealed DEmiRNAs between CD133(+) and CD133(-) cells. MiRNAs might play significant roles in the function of OSCs and could serve as targets for OS treatment. MiR-4284 prompted the self-renewal and invasion of OSCs. The function of miR-4284 might be associated with the Wnt signaling pathway.

Differential expression of AURKA/PLK4 in quiescence and senescence of osteosarcoma U2OS cells

This study aimed to identify co-expressed differentially expressed genes (DEGs) in quiescence and senescence of osteosarcoma (OS) U2OS cells and investigate their biological functions. GSE94805 from Gene Expression Omnibus database was extracted, involving 12 samples of OS U2OS cells (4 quiescence, 4 senescence, and 4 control samples). After analysis of DEGs by limma package, VENN analysis was performed to identify co-expressed DEGs in quiescence and senescent. The Cytoscape software was used to construct an interactive network of co-expressed DEGs. Finally, box-plot was drawn for the co-expressed DEGs in sub-network. Besides, the relation literatures were selected in GenCLiP database for the co-expressed DEGs. Seven hundred and forty-three DEGs (255 up-regulated genes, 488 down-regulated genes) were obtained in quiescence and 2135 DEGs (1189 up-regulated genes, 946 down-regulated genes) in senescence. Through VENN analysis, 448 DEGs (131 up-regulated genes, 317 down-regulated genes) were co-expressed in quiescent and senescence. In the co-expressed DEGs network, 896 nodes (448 nodes in quiescent, 448 nodes in senescent) were obtained. Finally, 16 co-expressed DEGs were obtained in the sub-network analysis, in which Aurora kinase A (AURKA) and polo-like kinase (PLK4) had been reported in OS. AURKA and PLK4 might be the key genes in quiescence and senescence of OS U2OS cells.

Knockdown of Ski decreases osteosarcoma cell proliferation and migration by suppressing the PI3K/Akt signaling pathway

Ski, an evolutionary conserved protein, is involved in the development of a number of tumors, such as Barrett's esophagus, leukemia, colorectal cancer, gastric cancer, pancreatic cancer, hemangiomas and melanoma. However, studies on the functions of Ski in osteosarcoma (OS) are limited. In this study, firstly the differential expression of Ski in OS tissues and osteochondroma tissues was detected, and the expression of Ski in both human OS cell lines (MG63 and U2OS) and normal osteoblasts (hFoB1.19) was then detected. The results demonstrated that Ski expression was significantly upregulated in both human OS tissues and cell lines. The results led us to hypothesize that Ski may play an essential role in the pathological process of OS. Thus, Ski specific small interfere RNA (Ski-siRNA) was used. The results revealed that OS cell proliferation was markedly inhibited following the knockdown of Ski, which was identified by CCK8 assay, EdU staining and cell cycle analysis. In addition, OS cell migration was significantly suppressed following Ski knockdown, which was identified by wound healing assay. Moreover, the protein levels of p-PI3K and p-Akt in OS cells declined prominently following Ski knockdown. On the whole, the findings of this study revealed that Ski expression was significantly upregulated in OS tissue and OS cells. The knockdown of Ski decreased OS cell proliferation and migration, which was mediated by blocking the PI3K/Akt signaling pathway. Thus, Ski may act as a tumor promoter gene in tumorigenesis, and Ski may prove to be a potential therapeutic target for the treatment of OS.

Screening of disorders associated with osteosarcoma by integrated network analysis

Osteosarcoma is a common malignant bone tumor in children and adolescents under the age of 20. However, research on the pathogenesis and treatment of osteosarcoma is still insufficient. In the present study, based on gene-phenotype correlation network, an analysis was performed to screen disorders related to osteosarcoma. First, we analyzed the differential expression of osteosarcoma in two groups according to different types of osteosarcoma and screened the differentially expressed genes (DEGs) related to osteosarcoma. Further, these DEG coexpression modules were obtained. Finally, we identified a series of regulatory factors, such as endogenous genes, transcription factors (TFs), and ncRNAs, which have potential regulatory effects on osteosarcoma, based on the prediction analysis of related network of gene phenotypes. A total of 3767 DEGs of osteosarcoma were identified and clustered them into 20 osteosarcoma-related dysfunction modules. And there were 38 endogenous genes (including ARF1, HSP90AB1, and TUBA1B), 53 TFs (including E2F1, NFKB1, and EGR1), and 858 ncRNAs (including MALAT1, miR-590-3p, and TUG1) were considered as key regulators of osteosarcoma through a series of function enrichment analysis and network analysis. Based on the results of the present study, we can show a new way for biologists and pharmacists to reveal the potential molecular mechanism of osteosarcoma typing, and provide valuable reference for different follow-up treatment options.