Osteosarcoma transcriptome data exploration reveals STC2 as a novel risk indicator in disease progression

BACKGROUND

Osteosarcoma has been the most common primary bone malignant tumor in children and adolescents. Despite the considerable improvement in the understanding of genetic events attributing to the rapid development of molecular pathology, the current information is still lacking, partly due to the comprehensive and highly heterogeneous nature of osteosarcoma. The study is to identify more potential responsible genes during the development of osteosarcoma, thus identifying promising gene indicators and aiding more precise interpretation of the disease.

METHODS

Firstly, from GEO database, osteosarcoma transcriptome microarrays were used to screen the differential expression genes (DEGS) in cancer comparing to normal bone samples, followed by GO/KEGG interpretation, risk score assessment and survival analysis of the genes, for the purpose of selecting a credible key gene. Further, the basic physicochemical properties, predicted cellular location, gene expression in human cancers, the association with clinical pathological features and potential signaling pathways involved in the key gene's regulation on osteosarcoma development were in succession explored.

RESULTS

Based on the selected GEO osteosarcoma expression profiles, we identified the differential expression genes in osteosarcoma versus normal bone samples, and the genes were classified into four groups based on the difference level, further genes interpretation indicated that the high differently level (> 8 fold) genes were mainly located extracellular and related to matrix structural constituent regulation. Meanwhile, module function analysis of the 67 high differential level (> 8 fold) DEGS revealed a 22-gene containing extracellular matrix regulation associated hub gene cluster. Further survival analysis of the 22 genes revealed that STC2 was an independent prognosis indicator in osteosarcoma. Moreover, after validating the differential expression of STC2 in cancer vs. normal tissues using local hospital osteosarcoma samples by IHC and qRT-PCR experiment, the gene's physicochemical property revealed STC2 as a cellular stable and hydrophilic protein, and the gene's association with osteosarcoma clinical pathological parameters, expression in pan-cancers and the probable biological functions and signaling pathways it involved were explored.

CONCLUSION

Using multiple bioinformatic analysis and local hospital samples validation, we revealed the gain of expression of STC2 in osteosarcoma, which associated statistical significantly with patients survival, and the gene's clinical features and potential biological functions were also explored. Although the results shall provide inspiring insights into further understanding of the disease, further experiments and detailed rigorous clinical trials are needed to reveal its potential drug-target role in clinical medical use.

[Bioinformatics analysis of differently expressed genes in osteoblastic sarcoma and screening of key genes]

Objective: To screen the different expressed genes between osteosarcoma and normal osteoblasts, and find the key genes for the occurrence and development of osteosarcoma. Methods: The gene expression dataset GSE33382 of normal osteoblasts and osteosarcoma was obtained from Gene Expression Omnibus (GEO) database. The different expressed genes between normal osteoblasts and osteosarcoma were screened by limma package of R language, and the different expressed genes were analyzed by Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analysis. The protein interaction network was constructed by the String database, and the network modules in the interaction network were screened by the molecular complex detection (MCODE) plug-in of Cytoscape software. The different expressed genes contained in the first three main modules screened by MCODE were analyzed by gene ontology (GO) using the BiNGO module of Cytoscape software. The MCC algorithm was used to screen the top 10 key genes in the protein interaction network. The gene expression and survival dataset GSE39055 of osteosarcoma was obtained from GEO database, and the survival analysis was performed by Kaplan-Meier method. The data of 48 patients with osteosarcoma treated in the First Affiliated Hospital of Fujian Medical University from January 2005 to December 2015 were selected for verification. The expression of STC2 protein in osteosarcoma was detected by immunohistochemical method, and the survival analysis was carried out combined with the clinical data of the patients. Results: A total of 874 different expressed genes were identified from GSE33382 dataset, including 402 down-regulated genes and 472 up-regulated genes. KEGG enrichment analysis showed that different expressed genes were mainly related to p53 signal pathway, glutathione metabolism, extracellular matrix receptor interaction, cell adhesion molecules, folate tolerance, and cell senescence. The top 10 key genes in the interaction network were GAS6, IL6, RCN1, MXRA8, STC2, EVA1A, PNPLA2, CYR61, SPARCL1 and FSTL3. STC2 was related to the survival rate of patients with osteosarcoma (P<0.05). The results showed that the expression of STC2 protein was related to tumor size and Enneking stage in 48 cases of osteosarcoma. The median survival time of 25 cases with STC2 high expression was 21.4 months, and that of 23 cases with STC2 low expression was 65.4 months. The survival rate of patients with high expression of STC2 was lower than that of patients with low expression of STC2 (P<0.05). Conclusions: Bioinformatics analysis can effectively screen the different expressed genes between osteosarcoma and normal osteoblasts. STC2 is one of the important predictors for the prognosis of osteosarcoma.