Expression And Identification Of Key Genes In Osteosarcoma By Bioinformatics Methods

Objective and BackgroundOsteosarcoma(Osteosarcoma,OS)is a primary malignant tumor.Children and adolescents are the main affected population.The most common site is the proximal or distal femur,which has the characteristics of malignant metastasis.At present,research on the treatment of osteosarcoma has made great progress.The treatment can adopt various strategies such as chemotherapy,radiotherapy and tumor resection,due to it behaves high malignancy,with rapid growth,and strong invasion ability,and it is prone to metastasis,resulting in poor prognosis and mortality.Currently,surgery and chemotherapy,as well as extensive resection of the tumor are applied during the chemotherapy cycle,but about half of the patients still have recurrence and distant metastasis after surgery.In addition,the 5-year survival rate for clinical patients with osteosarcoma is relatively low.Previous studies have reported some potential molecular therapeutic targets,and experiments have shown that these targets are closely related to the development of osteosarcoma.However,the pathogenic mechanism of osteosarcoma still needs to be further studied.In this study,bioinformatics methods were applied to analyze the key genes expressed in osteosarcoma tissue and normal tissue in the GEO database.Functional analysis by visual software and programming language was also carried on these differential expression genes,analyzing the possible mechanism accordingly and validating the results,which lays the solid foundation for osteosarcoma.1.Retrieve data sets in the GEO online database of NCBI(National Center for Biotechnology Information)using keywords such as "osteosarcoma","tissue","normal" and "Homo sapiens".The data set should be based on the dataset of osteosarcoma tissue greater than 10 cases,while the selected data set should be a data set containing osteosarcoma tissue and normal tissue with a number of cases not less than 3 cases,samples that do not meet the requirements in the dataset are excluded,and finally the three sequencing data sets GSE87624(containing 44 cases of osteosarcoma and 3 cases of normal bone tissue),GSE126209(including 12 cases of osteosarcoma and 11 cases of normal tissue),GSE99671(including 18 cases of osteosarcoma and 18 cases of normal tissue)conformed and were included in this study,with platform comments for GPL11154 Illumina Hi Seq 2000(Homo sapiens),GPL20301 Illumina Hi Seq4000(Homo sapiens),GPL20148 AB5500xl? Filter the results and download a chip sequencing data and platform annotation file with osteosarcoma and normal tissue.2.Use the R language limma package to analyze the differential gene expression of GSE87624 and GSE126209.GSE99671 to as validation data set,first of all,the gene expression matrix sequence is normalized,and then the platform annotation file for gene name conversion,osteosarcoma compared to normal tissue for gene difference expression analysis,limma package screening difference gene standard is set to log2(fold chang)> 1,P.Value < 0.05,that is,gene expression difference of more than2 times and P.Value < 0.05?3.Apply the online database DAVID(The Database for Annotation,Visualization and Integrated Discovery)to do GO(Gene ontology)and KEGG(Kyoto Encyclopedia of Genes and Genomes)pathway enrichment analysis;Protein-Protein Interactive interoperability analysis tool STRING(Search Tool for The Searchal of Interacting Genes Database)and desktop software Cyto Scape simulates the network of different genes,using plug-ins MCODE and Cyto Hubba to filter and extract core modules and key genes in network interrelationships.Using the Cluster Profiler package of R language,all differential expression genes are enriched and analyzed to understand their expression in a particular functional gene set and whether there is some statistical significance of this expression.Methods4.Analysis of expression level differences in validated data sets of key target genes obtained through enrichment analysis.5.15 patients with pathological type osteosarcoma fresh osteosarcoma and normal bone tissue next to the tumor are collected from March 2018 to December 2018 at Henan Cancer Hospital,and confirmed by clinical pathology histology,keeping in liquid nitrogen preservation spare.Use q RT-PCR to measure the amount of gene expression of the target gene in collected osteosarcoma and normal bone tissue.6.The data is statistically analyzed using spss22 software,expressed as the average ± standard deviation.The t-test is used for comparison between the two independent groups.P value of less than 0.05 is considered statistically significant.Results1.Normalize the data for GSE87624 and GSE126209 to remove inter-batch differences and eliminate non-experimental differences between measurement batches.Using the limma package to import the difference analysis in the R language environment with the converted final expression matrix files containing the gene name,the difference analysis in the R language environment using the R language limma package to the converted final expression matrix files containing the gene name,the results show that GSE87624 has 21884 gene differences compared to 44 samples of osteosarcoma and 3 normal tissue samples.The results showed that 1732 differential expression genes met the screening criteria,of which 1216 were significantly downregulated,516 genes were significantly upregulated,GSE126209 was expressed with 37,568 gene differences compared to 12 samples of osteosarcoma and 11 normal tissue samples,and 3,172 differential expression genes met screening criteria,of which1799 were significantly reduced,1373 genes were significantly increased.The result of two data sets were intersected with different genes,with 195 up expressed genes and82 down expressed genes.2.Importing common differential genes into DAVID to analyze GO functions,GO enrichment analyzes three directions,namely molecular function MF,biological process BP,and cell component CC,where the molecular functions are mainly enriched to DNA catalytic activity,DNA helicase activity,DNA replication binding,and DNA depended ATP enzyme activity,biological processes are mainly enriched in DNA replication,cell nuclear division,organelle generation and chromosomal separation,the main components of the cells are chromosomes,spindles and silk particles.The results of the cluster profile packet enrichment analysis using the R language are mainly concentrated in some pathways associated with tumor occurrence and invasion metastasis,such as "Cell cycle" and "DNA replication" and "Amplification of signal from the kinetochores".Build protein mutual network on STRING,parameter settings:evidence shows the existence of mutual,trust value: 0.4,generate network mutual mapping for the next visual display,protein mutual network results show a total of 196 nodes and 1,423 edges.Import the result file into Cyto Scape to build the network,extract the core intergenerational genes in the network diagram with MCODE,get a set sub-region with a high score,with 41 nodes,766 edges,and use the Cyto Hubba plugin to screen the top 6 key genes.Suggesting that they play a key role in the pathogenic process of osteosarcoma,we chose them for further study of the target genes.3.An analysis of the expression level of the target gene in dataset GSE99671 found that in 18 osteosarcomas and normal tissues,CDC6,MCM3,and PBK had higher expression levels than control,and the differences were statistically significant,while the difference in expression levels of KIF2 C,AURKB and DTL is not significant.4.The collected clinical specimens testing the expression of the target gene found that the expression of CDC6,MCM3 and PBK increased significantly compared to normal tissue,and the difference was statistically significant.Conclusions1.Using GSE87624 and GSE 126209 data,the core gene regions and key genes of differential expression genes were excavated,which were initially proved to play an important role in the progression of osteosarcoma.2.Through bioinformatics method meta-analysis,6 key genes CDC6,MCM3,PBK,KIF2 C,AURKB and DTL were analyzed,and dataset validation and clinical specimen testing showed that the key genes CDC6,MCM3 and PBK were identified and expressed significantly in osteosarcoma tissue,providing a basis for further research.