Predicting novel genes and pathways associated with osteosarcoma by using bioinformatics analysis

This aim of this study was to explore novel biomarkers related to osteosarcoma. The mRNA expression profile GSE41293 dataset was downloaded from the Gene Expression Omnibus (GEO) database, which included seven osteosarcoma and six control samples. After preprocessing, the FASTQ format reads of 13 samples were mapped to the reference sequences to screen for unique mapping reads. Differentially expressed genes (DEGs) were selected, which were then used for pathway and protein-protein interaction (PPI) network analyses. Moreover, the microarray data GSE63631 were downloaded from GEO database to verify our results. The percentages of unique mapping reads for osteosarcomas and control samples were both > 85%. A total of 6157 DEGs were identified between the two groups. DEGs that were upregulated were significantly enriched in 19 pathways, and those that were downregulated were enriched in 14 pathways. In the PPI network, DEGs such as SRC, ERBB2, and CAV3 in cluster 1 were enriched in the pathway responsible for focal adhesions. The DEGs in cluster 2, such as CDK4 and CDK6, were enriched in the cell cycle pathway. In GSE63631, DEGs were significantly enriched in focal adhesion pathway, which was in accordance with the result in GSE41293. Thus, the focal adhesion and cell cycle pathways may play important roles in osteosarcoma progression, and SRC, ERBB2, CAV3, CDK4, and CDK6 may be used as critical biomarkers of osteosarcoma.