Identification Of 7-gene Signature Prognostic Biomarkers Based On Gene Expression Profile Of Osteosarcoma

Background and objective: Osteosarcoma is a malignant tumor developed from interstitial cells,which mostly occurs in people under 20 years old.The incidence rate of osteosarcoma is about 5% in tumors of children.Osteosarcoma forms tumor bone like tissue and bone tissue rapidly from cartilage stage.A large number of studies have shown that tumor suppressor and oncogenes and their upstream and downstream molecules are involved in the occurrence and development of osteosarcoma.Therefore,exploring osteosarcoma related genes is helpful to the diagnosis,treatment and prevention of osteosarcoma.Similar to other tumors,the prognosis of osteosarcoma varies greatly among individuals.Therefore,the search for prognostic markers is very important for the treatment,judgment and improvement of prognosis.Therefore,in this study,genes related to the occurrence and development of osteosarcoma cancer were screened through gene expression profile,and potential cancer-related genes were obtained through protein interaction network.The key genes affecting the prognosis of patients were screened,and the expression level of the selected key genes was verified,and their regulatory role on the progress of osteosarcoma was studied,so as to provide reference for clinicians and biologist provides targets and references.Materials and Methods: Firstly,three sets of datasets related to osteosarcoma were selected from the GEO database.The three sets of datasets all contain cancer samples and normal samples.Limma package was used to analyze the gene expression difference of each dataset separately,and then the differential genes were divided into up-regulated genes and down-regulated genes.The genes with differences in at least two sets of data were taken as the final candidate genes.Next,we integrate the human protein interaction data from five databases to construct a human protein interaction network,through which we can further screen the potential genes related to osteosarcoma and analyze the functions of these genes.Finally,using the osteosarcoma dataset of TARGET database,through single factor screening prognosis related genes,and then through lasso dimensionality reduction,multivariate COX proportional hazard regression analysis to construct the gene model of osteosarcoma patients prognosis.These genes were used to construct prognostic index,and the patients were divided into two groups to analyze the difference of survival between the two groups.The selected genes were verified by two sets of GEO datasets.Furthermore,q RT-PCR and Western blot were used to detect gene m RNA and protein expression.CCK-8 assay and colony formation assay were used to detect the viability and colony formation ability of osteosarcoma cells.Transwell invasion and migration assay was used to detect the invasion and migration ability of osteosarcoma cells.Results: The difference genes were selected from each GEO dataset,and at least242 genes were extracted from the two sets of datasets with the criterion of p-value less than 0.05,fold change greater than 1.2.Next,242 genes were put into the human protein interaction network as seed genes,and the next neighbor nodes were extracted to construct a subnet,which contains 1240 genes.A total of 690 unique genes,including 380 genes and 242 differentially expressed genes,were extracted from the subnet with a degree greater than 15 as candidate genes for further research.The g:Profiler database was used to annotate candidate genes,which showed that these genes were related to bone development,osteocyte development and other biological processes,and were also annotated into the human protein map of osteoclast differentiation pathway,bone marrow and hematopoietic cells.Furthermore,a univariate Cox proportional hazard regression model was used to analyze 690 gene expression data and survival data,and 43 genes related to the prognosis of osteosarcoma were obtained.The 43 candidate genes were further dimensionally reduced by lasso regression analysis,and 12 target genes that are most closely related to the clinical manifestations of osteosarcoma patients were screened.Finally,the 12 candidate genes were analyzed by multivariate regression analysis to construct the model with 7 genes most closely related to the survival time and survival status of OS patients.The expression of two genes(MYO6 and RPL37A)also increased with the increase of risk value,indicating that the high expression of these two genes is related to high risk and they are the risk factor.Five genes(RP2,PHB,MLH1,CSNK2 B and CEBPA)decreased with the increase of risk value,indicating that the high expression of these five genes is related to low risk,which are the protective factor.The same conclusion was also verified in the other two sets of GEO datasets.QRT-PCR and Western blot showed that the high-risk genes MYO6 and RPL37 A were highly expressed in osteosarcoma cells,while the low-risk genes RP2,PHB,MLH1,CSNK2 B and CEBPA were decreased in osteosarcoma cells.In addition,knockdown of MYO6 and RPL37 A inhibited the proliferation,migration,invasion and epithelial to mesenchymal transition(EMT)of osteosarcoma cells.Overexpression of RP2,PHB,MLH1,CSNK2 B and CEBPA also inhibited the proliferation,migration,invasion and EMT of osteosarcoma cells.Conclusion: In this experiment,seven genes related to the risk of osteosarcoma were screened by bioinformatics analysis.Among them,the high expression of MYO6 and RPL37 A are related to the high risk.They are risk factors.They are highly expressed in osteosarcoma cells and promote the proliferation,migration,invasion and epithelial to mesenchymal transformation of osteosarcoma cells;high expression of RP2,PHB,MLH1,csnk2 b and CEBPA is associated with low risk.As protective factors,there are low expression in osteosarcoma cells and inhibit the proliferation,migration,invasion and epithelial to mesenchymal transformation of osteosarcoma cells.MYO6,RPL37 A,RP2 and CSNK2 B were first reported in osteosarcoma.These prognostic markers are helpful to explore the pathogenesis and new therapeutic targets of osteosarcoma.