Differentially expressed genes SNRPC and PRPF38A are 1 potential biomarkers candidates for osteosarcoma

12 Background: Osteosarcoma (osteogenic sarcoma, OS) is a primary cause of morbidity 13 and mortality and is associated with poor prognosis in the field of orthopedic. Globally, 14 rates of OS are highest among 15 to 25-year-old adolescent. However, the mechanism 15 of gene regulation and signaling pathway is unknown. 16 Material and Methods: GSE9508, including 34 OS samples and 5 non-malignant bone 17 samples, was gained from Gene Expression Omnibus (GEO) database. The 18 differentially expressed genes (DEGs) were picked out by GEO2R online R soft tool. 19 Furthermore, the protein-protein interaction (PPI) network between the DEGs was 20 molded utilizing STRING online software. Afterward, PPI network of DEGs was 21 constructed. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes 22 (KEGG) pathway enrichment analysis of DEGs were carried out on DAVID online tool 23 and visualized via cytoscape software. Subsequently, module analysis of PPI was 24 performed by using MCODE app. What’s more, prognosis-related genes were screened 25 by using online databases including GEPIA, UALCAN and cBioPortal databases. 26 Results: Totally, 671 DEGs were picked out, including 501 up-regulated genes and 170 27 down-regulated genes. Moreover, 22 hub genes were identified to be significantly 28 expressed in PPI network (16 up-regulated and 6 down-regulated). We found that 29 spliceosome signaling pathway may provide a potential target in OS. Furthermore, on 30 the basis of common crucial pathway, PRPF38A and SNRPC were closely associated 31 with spliceosome. 32 Conclusion: This study showed that SNRPC and PRPF38A are potential biomarkers 33 candidates for osteosarcoma.


Introduction
The early detection of OS is limited. Most patients were diagnosed at a middle or 37 advance stage. Therefore, it is very valuable to find a biomarker for evaluation of the 38 of OS(1-4). Recently. High-throughput biochip technology has been prevalent in 39 screening of pre-symptomatic disease (5)(6)(7). Bioinformatics have led to using for data- 40 intensive statistical analysis in various fields of healthcare (8)(9)(10)(11)(12). Development of big- 41 data has attracted attention in recent years (13)(14)(15)(16). It provided new insights toward 42 novel strategies for early testing and diagnosis of cancer (17)(18)(19)(20). However, 43 bioinformatics study of OS has not been catalogued. We tested GSE9508(Platform 44 GPL676) from GEO. The dataset was divided into two part :34 OS sample and 5 non- 45 malignant bone sample. Microarray dataset was downloaded from GEO and analyzed 46 via GEO2R online tool. What' s more, DEGs between OS samples and non-malignant 47 bone samples were screened by GEO2R. Moreover, DAVID version 6.8 (21)was used 48 to analyze KEGG pathway and GO enrichment analysis with 501 up-regulated and 170 49 down-regulated DEGs. Multiple PPI network was calculated by using STRING online 50 tool version 11.0 (22). Furthermore, we establish simulation of PPI network with DEGs 51 in Cytoscape software (version 3.7.2) (23)and then 23 core genes were picked out by 52 MCODE app (24). Next, these genes were imported into (25) cBioPortal 53 (26)databases for significant prognosis information(P<0.05). However, only 8 genes 54 were valid and then re-analyzed these central genes for KEGG pathway enrichment.

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Last but not least, PRPF38A and SNRPC were obtained and enriched in spliceosome 56 pathway. The aim of present research was to provide a potential biomarker in OS. photoreceptor outer segment, ribosome, perinuclear region of cytoplasm (Table Ⅱ).

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KEGG pathway analysis was shown that up-regulated genes mainly play a role in 105 spliceosome(P-value<0.05). While the significance of identified pathway, down-106 regulated related, was ribosome(P-value<0.05) ( Table Ⅲ).

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Protein-protein interaction. The DEGs were mapped to PPI network by using 108 STRING (Fig. Ⅰ). In addition, the network was identified via MCODE plugin in 109 cytoscape software. Additionally, 23 central genes were screened, which include 16 up-110 regulated and 7 down-regulated ( Fig. ⅡA and Fig. ⅡB).

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Overall survival of prognosis-related genes. Analysis of central gene by using 112 cBioPortal database. A total of 8 prognosis-associated genes were identified using 113 overall survival analysis from above database (Table Ⅳand Fig.Ⅲ).  Early diagnosis and timely treatment contribute to improving the prognosis of 120 sarcoma (29)(30)(31)(32). In this study, we analyzed GSE9508 dataset downloaded from GEO  were screened.

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Additionally, re-analysis 8 novel genes by using DAVID shown that SNRPC and 150 PRPF38A as potential biomarkers and spliceosome had a significant, which provided a 151 new effective target to improve the prognosis of OS patients.

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Small nuclear ribonucleoprotein poly peptide C(SNRPC), is also known as U1 153 snRNP, which is important for recognition of pre-mRNA and assembly of 154 spliceosome (35,36). Another study proved that SNRPC is related to splice-site (37). breast cancer (44).

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To date, many studies confirm that SNRPC and PRPF38A were related to 165 numerous types of cancer progression (36,(45)(46)(47)(48). Nevertheless, studies were very few 166 and far between in OS. Thus, these data may provide some functional information from 167 bench to bedside.

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In summary, our study presents SNRPC and PRPF38A were identified as potential  Availability of supporting data 184 We declare that all data supporting our findings are provided in the manuscript.         Table Ⅲ KEGG enrichment analysis of DEGs in osteosarcoma. Table Ⅳ The prognosis-related information of 23 central genes.