Identification of Differentially Expressed Genes under the Regulation of Transcription Factors in Osteosarcoma

Identification and prognostic evaluation of differentially expressed long noncoding RNAs associated with immune infiltration in osteosarcoma

Osteosarcoma is a malignant bone cancer that originates from the bone with the strongest invasiveness. Tumor formation strongly correlates with immune cell infiltration into the tumor immune microenvironment (TIME). Therefore, we aimed to identify TIME-related biomarkers as potential prognostic markers of osteosarcoma. The mRNA and long noncoding RNA (lncRNA) transcriptome data of 88 patients with osteosarcoma and the expression profile of GSE99671 were downloaded from The Cancer Genome Atlas and Gene Expression Omnibus, respectively. Immune infiltration scores and types were evaluated using ESTIMATE and CIBERSORT. A linear model was established to identify the differentially expressed genes (DEGs) and lncRNAs (DElncRNAs). Functional enrichment analysis of DEGs was conducted by Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, gene set enrichment analysis, and gene set variation analysis. DElncRNAs were analyzed using a weighted gene co-expression network. Least absolute shrinkage and selection operator regression was applied to screen for prognostic markers. Patient survival was predicted by the risk score and analyzed by receiver operating characteristic curve. Clinical features affecting patient survival were assessed. Immune infiltration positively correlated with osteosarcoma patient survival. Different immune cell infiltrates in patients with osteosarcma may serve as prognostic indicators and targets for immunotherapy. In total, 1125 DEGs, 80 DElncRNAs, and 11 pairs of co-expressed lncRNA-mRNAs were identified. DEGs in the three modules were associated with immune infiltration into the TIME. Four DElncRNAs, namely AC015819.1, AC015911.3, AL365361.1, and USP30-AS1, showed good prognostic ability for osteosarcoma and were positively correlated with the immune score. Tumor metastasis and risk scores alone were good prognostic indicators, and a combination of the two variables can better predict the prognosis of osteosarcoma. We identified four lncRNAs, AC015819.1, AC015911.3, AL365361.1, and USP30-AS1, as potential biomarkers for osteosarcoma prognosis.

Evaluation of cytotoxic activity and anticancer potential of indigenous Rosemary (Rosmarinus officinalis L.) and Oregano (Origanum vulgare L.) dry extracts on MG-63 bone osteosarcoma human cell line

We aimed to investigate the cytotoxic activity of indigenous Rosemary and Oregano freeze-dried extracts upon MG-63 osteosarcoma human cell line. We have determined the influence of analyzed dry extracts on cell morphology, cell survival and cell proliferation. The evaluation of dry extracts effect upon cell proliferation and viability was assessed by means of 3-[4,5-dimethylthiazol-2-yl]-2,5-diphenyl tetrazolium bromide (MTT) method. For cytotoxicity evaluation, Live & Dead and lactate dehydrogenase assays have been used. To further investigate the potential anticancer effect, we have studied the influence of dry extracts upon cells, by means of caspase-3/7 assay and proliferation cell nuclear antigen (PCNA) expression. Cells were incubated with extracts in the following concentration range (100–700 μg/mL) for 24 hours. According to our results, both dry extracts have shown cytotoxic effects by means of all used methods. Bone osteosarcoma cells viability significantly decreased with increasing concentration of analyzed extracts (beyond 300 μg/mL for Rosemary dry extract and only at 700 μg/mL for Oregano dry extract). According to our results, apoptosis is one of the main mechanisms involved in the cytotoxic properties of analyzed extracts. Moreover, Rosemary extract has also shown decreased expression of PCNA, when compared to control (untreated cells). Both extracts were standardized in phenolic compounds (being a rich source of flavones and phenolcarboxylic acids), so we assume that these are the main constituents involved in the cytotoxic effect. Still, further preclinical studies are needed to confirm the antitumor properties and to go deeply in the molecular mechanisms involved.

Development of a novel immune-related genes prognostic signature for osteosarcoma

Immune-related genes (IRGs) are responsible for osteosarcoma (OS) initiation and development. We aimed to develop an optimal IRGs-based signature to assess of OS prognosis. Sample gene expression profiles and clinical information were downloaded from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) and Genotype-Tissue Expression (GTEx) databases. IRGs were obtained from the ImmPort database. R software was used to screen differentially expressed IRGs (DEIRGs) and functional correlation analysis. DEIRGs were analyzed by univariate Cox regression and iterative LASSO Cox regression analysis to develop an optimal prognostic signature, and the signature was further verified by independent cohort (GSE39055) and clinical correlation analysis. The analyses yielded 604 DEIRGs and 10 hub IRGs. A prognostic signature consisting of 13 IRGs was constructed, which strikingly correlated with OS overall survival and distant metastasis (p < 0.05, p < 0.01), and clinical subgroup showed that the signature’s prognostic ability was independent of clinicopathological factors. Univariate and multivariate Cox regression analyses also supported its prognostic value. In conclusion, we developed an IRGs signature that is a prognostic indicator in OS patients, and the signature might serve as potential prognostic indicator to identify outcome of OS and facilitate personalized management of the high-risk patients.

Identification of the Novel Target Genes for Osteosarcoma Therapy Based on Comprehensive Bioinformatic Analysis

Osteosarcoma is one of the most common primary malignant tumors of the bone and tends to develop in teenage years. Although multitreatments for the diagnosis and therapy of osteosarcoma have been developed, there are still needs of new methods to prevent and treat the osteosarcoma. Here, we performed bioinformatic analysis to screen for the key genes, molecules, and pathways involved in osteosarcoma survival. Four microarray data sets (GSE99671, GSE87624, GSE65071, and GSE28423), which include data from human bone and osteosarcoma samples, were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed mRNAs and miRNAs were identified. Kyoto Encyclopedia of Genes and Genomes enriched pathways, miRNA-mRNA target, gene/disease relationship, and overall survival was elucidated using related websites and software according to bioinformatic analysis protocols. We found three critical genes miR-29c, blood vessel epicardial substance (BVES), and proteasome 20S subunit beta 2 (PSMB2) through the GEO database and predicting miRNA-mRNA target. Among these genes, BVES and PSMB2 presented a high expression level in osteosarcoma based on GSE99671 and GSE87624 data sets, while miR-29c showed a low expression level in osteosarcoma based on GSE65071 and GSE28423 data sets. Furthermore, we found that the high expression level of miR-29c and BVES associated with better prognosis, while highly expressed PSMB2 associated with poor prognosis. The abnormally expressed mRNAs and miRNAs, which were identified by integrated bioinformatic analysis, provided insights into the molecular mechanisms of osteosarcoma. Notably, we found three critical genes that could be used as novel therapeutic targets for preventing or diagnosing osteosarcoma. Finally, PSMB2 may be the target of miR-29c.