Identification of key biomarkers and functional pathways in osteosarcomas with lung metastasis: Evidence from bioinformatics analysis

doi:10.21203/rs.3.rs-56834/v1

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Identification of key biomarkers and functional pathways in osteosarcomas with lung metastasis: Evidence from bioinformatics analysis

https://doi.org/10.21203/rs.3.rs-56834/v1

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published 12 Feb, 2021

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Background: In osteosarcoma, the lung is the most common metastatic organ. Intensive work has been made to illuminate the pathogeny, but the specific metastatic mechanism remains unclear. Thus, we conducted the study to seek to find the key genes and critical functional pathways associated with progression and treatment in osteosarcoma with lung metastasis.

Methods: Two independent datasets (GSE14359 and GSE85537) were screened out from the Gene Expression Omnibus (GEO) database and the overlapping differentially expressed genes (DEGs) were identified using GEO2R online platform. Subsequently, the Gene Ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways enrichment analysis of DEGs were conducted using DAVID. Meanwhile, the protein-protein interaction (PPI) network constructed by STRING was visualized using Cytoscape. Afterwards, the key module and hub genes were extracted from the PPI network using the MCODE and cytoHubba plugin. Moreover, the raw data obtained from GSE73166 and GSE21257 was applied to verify the expression differences and conduct the survival analyses of hub genes, respectively. Finally, the interaction network of miRNAs and hub genes constructed by ENCORI was visualized using Cytoscape.

Results: A total of 364 DEGs were identified, comprising 96 downregulated genes and 268 upregulated genes, which were mainly involved in cancer-associated pathways, adherens junction, ECM-receptor interaction, focal adhesion, MAPK signaling pathway. Subsequently, 10 hub genes were obtained and survival analysis demonstrated SKP2 and ASPM were closely related to poor prognosis of patients with osteosarcoma. Finally, hsa-miR-340-5p were found to be most closely associated with these hub genes according to the interaction network of miRNAs and hub genes.

Conclusion: The key genes and functional pathways identified in the study may contribute to understanding the molecular mechanisms involved in the carcinogenesis and progression of osteosarcoma with lung metastasis, and provide potential diagnostic and therapeutic targets.

Oncology

General Surgery

osteosarcoma

lung metastasis

differentially expressed genes

functional pathways

bioinformatics analysis

Osteosarcoma is the most common malignant bone tumor that mainly originates from the tubular long bones with uneven crowd distribution and high prevalence in children and adolescents^[1–3]. As reported, osteosarcoma is prone to develop local infiltration and distant metastasis^[4]. The most common metastatic organ is lung, with an incidence of approximately 40%-50%^[5]. In recent years, due to the development of surgical techniques, multidrug system chemotherapy, precise radiotherapy and immunotherapy, significant progress has been made in the treatment of osteosarcoma. However, even with aggressive treatment, the prognosis of patients with lung metastasis remains poor, with a 5-year survival rate of only 20–30%^{[3, 6–7]}. Therefore, it is essential to understand the precise molecular mechanisms involved in lung metastasis of osteosarcoma and thus develop effective diagnostic and therapeutic strategies.

In recent years, with the constant development of high-throughput technologies and improvements in data analysis techniques, microarray techniques combined with bioinformatics analyses have been extensively applied to identify genetic alterations at the genome level of tumors, particularly in seeking the differentially expressed genes (DEGs). At present, Shi et al. have analyzed the enrichment pathway and key genes of lung metastasis of osteosarcoma using a single microarray^[8]. However, due to the inherent differences in experimental samples, detection platforms, and experimental methods, false-positive rates in independent microarray analysis reduce the reliability of the results obtained. Therefore, in the present study, several original microarray datasets of the Gene Expression Omnibus (GEO) database were downloaded and analyzed to obtain the overlapping DEGs between lung metastatic and non-metastatic osteosarcoma samples. Subsequently, we performed Gene Ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses to further investigate the potential functions of these DEGs. Then, the hub genes were identified by the cytoHubba and the other important bioinformatics approaches including the protein-protein interactions (PPI) network analysis, co-expression network construction, survival analysis and miRNA-hub genes network construction were also conducted, which may contribute to seeking the key gene signatures and functional pathways involved in lung metastasis of osteosarcomas.

Microarray data

The Gene Expression Omnibus (GEO, http://www.ncbi.nlm.nih.gov/geo/) database, containing high-throughput gene expression data, chips and microarrays, is an international public repository^[9]. After rigorous screening of all relevant datasets that analyze gene expression differences between osteosarcomas with lung metastasis group and non-metastasis group, two independent datasets GSE14359^[10] and GSE85537 were identified from the GEO database. Among them, the GSE14359 dataset was based on the GPL96 platform([HG-U133A] Affymetrix Human Genome U133A Array) containing 8 lung metastasis samples and 10 non-metastasis osteosarcoma samples. The other dataset utilized the GPL570 platform ([HGU133_Plus_2] consisting of 3 lung metastasis samples and 3 non-metastasis osteosarcoma samples.

Identification of DEGs

GEO2R (http://www.ncbi.nlm.nih.gov/geo/geo2r), a web-based application, contributes to filtrating DEGs through the Bioconductor’s GEO Query and limma R software^[11]. Therefore, in the present study, the DEGs between lung metastasis group and non-metastasis group were screened out by the GEO2R online platform with cut-off criteria of P-value < 0.05 and |log fold-change| (|logFC|) > 1.

GO and KEGG enrichment analysis

The Database for Annotation, Visualization and Integrated Discovery (DAVID, http://david.ncifcrf.gov; version 6.8)^[12] is an online bioinformatics resource website that provides a series of functional annotation tools to study the biological function of genes or proteins. GO, including biological process (BP), cytological component (CC), and molecular function (MF), is a standard semantic vocabulary to define and describe the function of genes and proteins^[13]. While KEGG is an integrated database resource containing biological interpretation of genome sequences and other high-throughput data^[14]. Hence, DAVID was utilized to conduct GO annotation and KEGG pathway enrichment analyses to further analyze the characteristic biological functions and potential pathways of DEGs in the present study. P-value < 0.05 was considered statistically significant.

PPI Networks Construction and Module Analysis

The protein-protein interactions, existing in the life activities of each cell of the body, regulate many biological phenomena such as replication, transcription, translation, splicing, secretion, cell cycle regulation, signal transduction and intermediate metabolism. Therefore, it may provide important insights into the carcinogenesis and progression of tumors. In this study, the PPI network of DEGs was constructed using the Search Tool for the Retrieval of Interacting Gene (STRING, http://string-db.org; version 11.0)^[15] with interaction score ≥ 0.4. Afterwards, the PPI network was visualized using Cytoscape 3.7.2 software^[16], which is a public software platform for visualizing, integrating and analyzing molecular interaction networks. And the most key module in the PPI networks was identified using the MCODE plugin of Cytoscape with MCODE score༞5, degree cutoff = 2, node score cutoff = 0.2, k-core = 2, and max. depth = 100. Finally, in order to further analyze the functions of genes in the most key module, GO annotation and KEGG enrichment analysis were performed using the DAVID online website.

Hub Genes selection and analysis

The top 10 genes of DEGs were defined as the hub genes based on the Maximum Correlation Criteria of the cytoHubba plugin of Cytoscape^[17]. And the co-expression network of hub genes was constructed by STRING. Meanwhile, in order to validate the expression differences of hub genes between lung metastasis group and non-metastatic group, the expression levels of hub genes obtained from GSE73166 datasets were analyzed and shown by box-plots. In addition, we obtained the online GSE21257 dataset^[18] which provided survival data of patients with metastasis and non-metastasis osteosarcoma. According to the expression level of hub genes, these patients were divided into high expression groups(≥ 50%) and low expression groups. And the prognostic value of the hub genes was confirmed by Kaplan-Meier method using SPSS version 22.0 (SPSS Inc, Chicago, Illinois, USA). P-value < 0.05 was considered statistically significant.

Construction of the miRNA-hub gene regulatory network

miRNAs are a type of endogenous non-coding RNA with regulatory functions, which can bind to target genes through base complementary pairing to inhibit translation or induce degradation of target mRNA. The Encyclopedia of RNA Interactomes (ENCORI, http://starbase.sysu.edu.cn/index.php) is a public platform that provides a series of miRNA-hub genes prediction databases to explore miRNA–target gene interactions. In the present study, the interaction relationship between miRNAs and hub genes is established based on the positive results of at least two databases in the following miRNA-hub genes prediction databases: miRanda, PITA, PicTar and TargetScan. Finally, Cytoscape was applied to visualize the miRNA-mRNA regulatory network^[19–20].

Identification of DEGs

The raw data of two gene expression microarray datasets (GSE14359, GSE85537) were obtained from the GEO database and then subjected to differential expression analysis by the GEO2R online platform. Based on the predefined cut-off values, 1,742 DEGs were screened from the GSE14359 dataset and 3,133 DEGs were identified from the GSE85537 dataset, respectively (Fig. 1a-b). The overlap among the two datasets were identified by drawing a Venn diagram (Fig. 1c), consisting of 96 downregulated genes and 268 upregulated genes.

KEGG and GO enrichment analyses of DEGs

In order to explore the biological functions of DEGs, DAVID was utilized to perform GO annotation and KEGG pathways enrichment analysis. With regard to the biological processes, the DEGs were mainly involved in the regulation of apoptosis, lymphocyte mediated immunity, adaptive immune response, gland development, cell proliferation, enzyme linked receptor protein signaling pathways, and DNA-dependent transcription(Fig. 2a). With respect to cell components, the DEGs were significantly enriched in the extracellular region, extracellular matrix, extracellular space, plasma membrane part, and cell surface(Fig. 2b). As for the molecular functions, the DEGs were mainly enriched in transcription activator activity, identical protein binding, sequence-specific DNA binding, polysaccharide binding, and RNA polymerase II transcription factor activity(Fig. 2c). Moreover, the results of KEGG pathway enrichment analyses demonstrated that the DEGs were significantly enriched in cancer-associated pathways, adherens junction, ECM-receptor interaction, focal adhesion, and MAPK signaling pathway(Fig. 2d).

PPI network construction and module analysis

To analyze the functional interactions between DEGs, the PPI network constructed by STRING was visualized using Cytoscape, consisting of 282 nodes and 673 edges(Fig. 3). Subsequently, a total of 8 modules were obtained from the PPI network through the MCODE plugin and the most key module was identified based on MCODE score༞5(Fig. 4a). And the functional analyses of genes in the most key module were conducted by DAVID. As shown in Fig. 4b, these genes were significantly enriched in the modification-dependent macromolecule catabolic process, cell cycle, DNA replication, DNA metabolic process, proteolysis, cell division, protein ubiquitination, regulation of programmed cell death, cell proliferation, and protein modification.

Hub gene selection and analysis

The top 10 genes (SKP2, SMC2, CDC25C, SOCS3, PARPBP, DTL, ASPM, ECT2, CENPN, TOP2A) were defined as hub genes after ranking by the Maximum Correlation Criteria of the cytoHubba plugin. The abbreviations, full names and functions of hub genes were shown in Table 1. Meanwhile, the co-expression network of these hub genes showed that ASPM was more closely related to other genes (Fig. 5a). Subsequently, we utilized the microarray profile GSE73166 to verify the expression differences of hub genes between lung metastasis group and non-metastatic group. As shown in Fig. 5b, the expression levels of hub genes in the lung metastasis group were significantly elevated compared to the non-metastatic group, which was consistent with our results. Next, the original survival data obtained from GSE21257 was used to analyze the prognostic value of hub genes in osteosarcoma by Kaplan-Meier method. And the results demonstrated SKP2 and ASPM were closely related to poor prognosis of patients with osteosarcoma (Fig. 6). However, it is regrettable that the prognostic data of PARPBP, CENPN and SMC2 can not be obtained from the GSE21257 datasets.

Table 1

The detailed information of hub genes
Gene symbol	Full name	Function
SKP2	S-Phase kinase associated protein 2	SKP2 encodes a substrate recognition component of a SCF (SKP1-CUL1-F-box protein) E3 ubiquitin-protein ligase complex which mediates the ubiquitination and subsequent proteasomal degradation of target proteins involved in cell cycle progression, signal transduction and transcription.
ASPM	Abnormal spindle microtubule assembly	ASPM encodes a centrosomal protein that plays an essential part in maintaining the normal function of the mitotic spindle and regulating neurogenesis.
TOP2A	Topoisomerase(DNA) II alpha	TOP2A encodes a DNA topoisomerase that is essential in the regulation of DNA structure and transcription, and it is the direct molecular target of anthacyclines.
SMC2	Structural maintenance of chromosomes 2	SMC2 encodes the central component of the condensin complex which is required for conversion of interphase chromatin into mitotic-like condense chromosomes.
CDC25C	Cell division cycle 25C	CDC25C encoded a tyrosine protein phosphatase that participated in regulating G2/M progression and mediating DNA damage repair
SOCS3	Suppressor of cytokine signaling 3	SOCS family proteins form part of a classical negative feedback system that mainly regulates cytokine signal transduction.
DTL	Denticleless E3 ubiquitin protein ligase homolog	DTL encodes a substrate-specific adapter of a DCX (DDB1-CUL4-X-box) E3 ubiquitin-protein ligase complex required for cell cycle control, DNA damage response and translesion DNA synthesis
ECT2	Epithelial cell transforming 2	ECT2 is a guanine nucleotide exchange factor (GEF) of the Rho family members of small GTPases and essential for signal transduction pathways involved in the regulation of cytokinesis.
CENPN	Centromere protein N	CENPN is the vital component of the CENPA-NAC (nucleosome-associated) complex which plays a central role in assembly of kinetochore proteins, mitotic progression and chromosome segregation
PARPBP	PARP1 binding protein	PARPBP contributes to suppressing inappropriate homologous recombination, thereby playing a central role in DNA repair and the maintenance of genomic stability.

MiRNA-hub gene regulatory network

To illustrate the regulatory relationships between miRNA and hub genes, the interaction network of miRNAs and hub genes constructed by ENCORI was visualized using Cytoscape. As illustrated in Fig. 7, the interaction network consists of 10 hub genes and 227 miRNAs. Through the interactive network degree analysis, ECT2, SOCS3 and PARPBP were apparently the interactive hub genes targeted by most miRNAs. While, has-miR-495-3p, hsa-miR-340-5p and hsa-miR-96-5p were the miRNAs that interacted with the most target genes.

Due to its malignant properties and invasion behavior, approximately 20%-30% of osteosarcoma patients present with lung metastasis at the initial diagnosis^{[21, 22]}. Even with aggressive treatment, the prognosis of these patients remains poor^[23]. Thus, exploring the specific molecular mechanisms involved in the progression of osteosarcoma, and then guiding the development of promising therapeutic strategies targeting the molecular pathway, will contribute to ameliorating the prognosis of these patients.

In recent years, owing to the wide application of gene chips and high-throughput sequencing technology, it paves the way for our biological research. In the current study, raw data of two gene expression datasets were obtained to conduct bioinformatics analysis. As a result, a total of 364 DEGs were identified between osteosarcoma with lung metastasis and non-metastatic osteosarcoma, consisting of 268 upregulated DEGs and 96 downregulated DEGs. Subsequently, GO annotation, KEGG pathways enrichment analysis and PPI network construction were conducted to explore interactions between the DEGs. And the results suggest that the DEGs mainly involved in some important signaling pathways including MAPK signaling pathway, adherens junction, ECM-receptor interaction, focal adhesion. MAPK signal pathway, consisting of RAF1 (MAP kinase-kinase-kinase), MEK (MAP kinase-kinase), and ERK (extracellular signal-related kinase), plays a critical role in promoting tumor proliferation, migration, invasion and angiogenesis^[24–27]. Wang et al.^[28] previously reported that the gene mutations associated with lung metastasis of osteosarcoma are mainly concentrated in the MAPK signaling pathway and another study also demonstrated macrophage migration inhibitory factor can promote osteosarcoma growth and lung metastasis through activating the MAPK signaling pathway^[29]. The extracellular matrix (ECM), composed of polysaccharides and fibrous proteins, is an important structural component of the tumor microenvironment. Previous studies have shown that dysregulation of the extracellular matrix-receptor interaction signaling pathway plays a vital role in regulating tumor invasion and metastasis^[30]. Not only that, focal adhesion (FA) promotes cell traction by associating the ECM bound to the transmembrane integrin molecule with the actin cytoskeleton, which also plays an important role in cell migration dynamics^{[31, 32]}. Furthermore, we extracted the most key module from the PPI network based on MCODE score༞5 and found its significantly related to DNA replication, cell cycle, cell proliferation by DAVID analysis.

To conduct deeper research, the top 10 genes (SKP2, SMC2, CDC25C, SOCS3, PARPBP, DTL, ASPM, ECT2, CENPN, TOP2A) of DEGs were regarded as hub genes based on the predefined criteria. And the expression levels of hub genes were verified to be significantly elevated in the patients with lung metastasis from osteosarcoma. Meanwhile, the results of survival analysis indicated that SKP2 and ASPM were closely associated with poor prognosis of patients with osteosarcoma, which means SKP2 and ASPM may play vital roles in the carcinogenesis and progression of patients with osteosarcoma. SKP2 encodes a substrate recognition component of a SCF (SKP1-CUL1-F-box protein) E3 ubiquitin-protein ligase complex which mainly mediates the ubiquitination and subsequent proteasomal degradation of target proteins involved in cell cycle progression and signal transduction^{[33, 34]}. At present, accumulating evidence demonstrates that SKP2 plays a vital role in promoting tumor invasion and metastasis. One hand, SKP2 could take part in the ubiquitination and degradation of E-cadherin to positively regulate cell migration^[35]. On the other hand, Skp2 could combine with Myc, Miz1 and p300 to form a transcriptional complex mediating the transcription of RhoA to participate in the invasion and metastasis of tumors^[36]. Furthermore, SKP2 has also been proven to enhance tumor invasion ability by promoting epithelial-mesenchymal transition (EMT) process and matrix metalloproteinase (MMP) expression^{[37, 38]}. Based on these encouraging results, Zhang et al. found knockdown of Skp2 could result in RhoA and MMP-9 inhibition, thereby significantly reducing tumor cell invasion and lung metastasis in an orthotopic mouse model of osteosarcoma^[39]. Not only that, SKP2 has also been reported to exert an effect in DNA damage response, DNA repair, tumor angiogenesis and drug resistance^{[40, 41]}. For example, Ding et al have demonstrated that SKP2 overexpression was also closely related to the acquisition of methotrexate-resistant and EMT properties in osteosarcoma^[42]. Due to the extensive carcinogenic effects of SKP2, research on the anti-tumor activity of SKP2 small molecule inhibitors is in full swing. Among them, flavokawain A (FKA), an effective apoptotic inducer and antiproliferative agent in the tumor, which could lead to down-regulation of SKP2, result in osteosarcoma growth retardation and metastasis inhibition^[39]. However, more preclinical and clinical trials are necessary to explore the application of the specific SKP2 inhibitors in the treatment of osteosarcoma with lung metastasis.

ASPM encodes a centrosomal protein that plays an essential part in maintaining the normal function of the mitotic spindle and regulating neurogenesis^[43]. Previous studies have prevailingly focused on the relationship between ASPM gene mutations and autosomal recessive primary microcephaly and abnormal neuronal differentiation^[44]. Nevertheless, except for its role in normal physiological regulatory functions, recent studies have also found that the upregulation of ASPM promotes the metastasis of tumors, including prostate cancer, endometrial cancer, melanomas, bladder cancer, clear cell renal cell carcinoma^[45–49]. Pai et al.^[50] previously pointed out that overexpression of ASPM has energetically influence on prostate cancer cell proliferation and invasion, as it could enhance the Wnt-induced β-catenin transcriptional activity through interacting with disheveled-3, which is a cardinal upstream regulator of Wnt signaling. Likewise, Zhou et al. found that knockout of ASPM could inhibit tumor proliferation, migration and metastasis by restraining the Wnt/β-catenin pathway in endometrial cancer^[46]. Interestingly, ASPM was situated on chromosome 1q31, an enhancement area that was significantly correlated with invasion and metastasis of tumors^{[51, 52]}. The above studies suggested that ASPM could give tumor an impetus to metastasize, but the role of ASPM in lung metastasis of osteosarcoma needs to be further explored.

Although no significant correlation was found between the other hub genes and prognosis in our study, they are still proven to be associated with the invasion and metastasis of tumors. TOP2A gene encodes a DNA topoisomerase that is essential in the regulation of DNA structure and transcription, and it is the direct molecular target of anthacyclines drugs. Previous investigations have demonstrated that TOP2A overexpression was involved in tumor proliferation, invasion, and angiogenesis in multiple malignancies including prostate cancer, pancreatic cancer, soft tissue sarcoma and osteosarcoma^{[53, 54]}. And Nguyen et al^[55]. found that TOP2A overexpression was inclined to poor prognosis of osteosarcoma patients without ERBB2 co-amplification. Meanwhile, the therapeutic effect of pirarubicin for osteosarcoma patients with lung metastasis is also closely related to the expression level of TOP2A^[56]. The oncogene ECT2 is a guanine nucleotide exchange factor (GEF)of the Rho family members of small GTPases and essential for signal transduction pathways involved in the regulation of cytokinesis. Chen et al^[57]. showed that overexpression of ECT2 promoted the metastasis of osteosarcoma by regulating the EMT process. And the downregulation of ECT2 by siRNA and miR-223 could suppress osteosarcoma cell migration and invasion^{[58, 59]}. Moreover, Qiu et al^[60]. found that Calycosin could inhibit osteosarcoma cell migration through restraining metastasis-associated IκBα/ECT2 signal pathway, but further research is needed to confirm the results. SOCS family proteins form part of a classical negative feedback system that regulates cytokine signal transduction through the JAK/STAT signaling pathway. Recent studies suggested that Circ_ANKIB1 inhibited the expression of SOCS3 and activated the JAK/STAT signaling pathway by regulating miR-19b, thereby promoting osteosarcoma growth and invasion^[61]. CDC25C encoded cell cycle regulatory protein that participated in regulating G2/M progression and mediating DNA damage repair. It has been reported that Evodiamine, Flavokawain B and Ludartin could inhibit proliferation, migration, and apoptosis of osteosarcoma cells by down-regulating the expression level of CDC25C^[62–64]. At present, the relationship between the rest of hub genes (DTL, CENPN, PARPBP, SMC2) and osteosarcoma remains unclear and the specific role in lung metastasis deserves further exploration.

Based on the established miRNA-hub genes interaction network, we found that hsa-miR-340-5p was most closely associated with these hub genes. And the role of miR-340-5p in the carcinogenesis and progression of osteosarcoma has been reported. For example, miR-340-5p could negatively regulate Wnt/β-catenin signaling pathway through targeting STAT3 gene, thereby inhibiting the development of osteosarcoma^[65]. Meanwhile, overexpression of miR-340-5p could down-regulate the CTNNB1-mediated Notch signaling pathway, thereby suppressing osteosarcoma migration and invasion^[66]. However, the regulatory relationships between miR-340-5p and these hub genes in patients with lung metastasis of osteosarcoma need to be confirmed by further studies.

In the present study, key genes and critical signaling pathways which may be involved in the development of lung metastasis of osteosarcoma were identified via a comprehensive analysis of the two gene expression datasets, which may serve as diagnostic and therapeutic targets. Moreover, SKP2 and ASPM were found to be closely related to the poor prognosis of patients with osteosarcoma, which may present as potential prognostic biomarkers in the future. Meanwhile, the interaction network of miRNAs and hub genes illustrates the regulatory relationships of the hub genes and miRNA, and miR-340-5p which was most closely associated with these hub genes is reported to be involved in the progression of osteosarcoma. These results provide important ideas for a comprehensive understanding of cancer characteristics, however, further studies are needed to validate the current findings and elucidate the specific molecular mechanisms of these genes in osteosarcoma with lung metastasis.

GEO: Gene expression omnibus; DEG: Differentially expressed gene; GO: Gene ontology; KEGG: Kyoto encyclopedia of genes and genomes; PPI: protein-protein interaction; DAVID: Database for annotation, visualization and integrated discovery; BP: Biological process; CC: Cytological component; MF: Molecular function; STRING: Search tool for the retrieval of interacting gene; ENCORI: Encyclopedia of RNA interactomes; ECM: Extracellular matrix; SKP2: S-Phase kinase associated protein 2; ASPM: Abnormal spindle microtubule assembly; TOP2A: Topoisomerase(DNA) II alpha; SMC2: Structural maintenance of chromosomes 2; CDC25C: Cell division cycle 25C; SOCS3: Suppressor of cytokine signaling 3; DTL: Denticleless E3 ubiquitin protein ligase homolog; ECT2: Epithelial cell transforming 2; CENPN: Centromere protein N; PARPBP: PARP1 binding protein.

Availability of data and materials

The datasets generated and/or analyzed during the current study are available in the Gene Expression Omnibus (GEO) repository ( http://www.ncbi.nlm.nih.gov/geo/).

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors certify that there are no conflicts of interest regarding the manuscript.

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Authors’ contributions

The conception and design of the study: Denghua Liu and Rui Zhou. Acquisition of data, analysis and interpretation of data: Denghua Liu and Rui Zhou. Drafting the article and revising it critically for important intellectual content: Denghua Liu and Aiguo Zhou. All authors read and approved the final manuscript.

Acknowledgments

This study was supported by The National Natural Science Foundation of China (grant number 81803569) without any involvement in writing this manuscript.

Mirabello L. Troisi RJ, Savage SA. Osteosarcoma incidence and survival rates from 1973 to 2004: data from the Surveillance, Epidemiology, and End Results Program. Cancer. 2009;115: 1531–43.
Iannaci G, Luise R, Sapere P, Costanzo RMA. Extraskeletal osteosarcoma: a very rare case report of primary tumor of the colon-rectum and review of the literature. Pathol Res Pract. 2013;209:393-6.
Kansara M. Teng MW, Smyth MJ. Thomas DM. Translational biology of osteosarcoma. Nat Rev Cancer. 2014;14(11):722–35.
Sadykova LR, Ntekim AI, Muyangwa-Semenova M, Rutland CS, Jeyapalan JN, Blatt N, et al. Epidemiology and Risk Factors of Osteosarcoma. Cancer Invest. 2020;38:259–69.
Wada T, Isu K, Takeda N, Usui M, Ishii S, Yamawaki S. A preliminary report of neoadjuvant chemotherapy NSH-7 study in osteosarcoma: preoperative salvage chemotherapy based on clinical tumor response and the use of granulocyte colony-stimulating factor. Oncology. 1996;53:221-7.
Huang X, Zhao J, Bai J, Shen H, Zhang B, Deng L, et al. Risk and clinicopathological features of osteosarcoma metastasis to the lung: A population-based study. J Bone Oncol. 2019;16:100230.
Duchman KR. Gao Y, Miller BJ. Prognostic factors for survival in patients with high-grade osteosarcoma using the Surveillance, Epidemiology, and End Results (SEER) Program database. Cancer Epidemiol. 2015;39:593-9.
Shi Z, Zhou H, Pan B, Lu L, Wei Z, Shi L, et al. Exploring the key genes and pathways of osteosarcoma with pulmonary metastasis using a gene expression microarray. Mol Med Rep. 2017;16:7423–31.
Edgar R. Domrachev M, Lash AE. Gene Expression Omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res. 2002;30(1):207–10.
Fritsche-Guenther R, Noske A, Ungethüm U, Kuban R, Schlag PM, Tunn P, et al. De novo expression of EphA2 in osteosarcoma modulates activation of the mitogenic signalling pathway. Histopathology. 2010;57(6):836–50.
Barrett T, Wilhite SE, Ledoux P, Evangelista C, Kim IF, Tomashevsky M, et al. NCBI GEO: archive for functional genomics data sets–update. Nucleic Acids Res. 2013;41:D991–5.
Huang DW. Sherman BT, Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc. 2009;4;44–57.
The Gene Ontology Consortium. The Gene Ontology Resource: 20 years and still GOing strong. Nucleic Acids Res. 2019;47(D1):D330–8.
Kanehisa M. Sato Y. KEGG Mapper for inferring cellular functions from protein sequences. Protein Sci. 2020;29(1):28–35.
Szklarczyk D, Gable AL, Lyon D, Junge A, Wyder S, Huerta-Cepas J. STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res. 2019;47(D1):D607–13.
Smoot ME, Ono K, Ruscheinski J, Wang P, Ideker T. Cytoscape 2.8: new features for data integration and network visualization. Bioinformatics. 2011;27(3):431–2.
Chin CH, Chen SH, Wu HH, Ho CW, Ko MT, Lin CY. cytoHubba: identifying hub objects and sub-networks from complex interactome. BMC Syst Biol. 2014;8:11.
Buddingh EP, Kuijjer ML, Duim RAJ, Bürger H, Agelopoulos K, Myklebost O. Tumor-infiltrating macrophages are associated with metastasis suppression in high-grade osteosarcoma: a rationale for treatment with macrophage activating agents. Clin Cancer Res. 2011;17(8):2110–9.
Yang W, Zhao X, Han Y, Duan L, Lu X, Wang X. Identification of hub genes and therapeutic drugs in esophageal squamous cell carcinoma based on integrated bioinformatics strategy. Cancer Cell Int. 2019;19:142.
Yang D, He Y, Wu B, Deng Y, Wang N, Li M. Integrated bioinformatics analysis for the screening of hub genes and therapeutic drugs in ovarian cancer. J Ovarian Res. 2020;13(1):10.
Buddingh EP, Anninga JK, Versteegh MIM, Taminiau AHM, Egeler RM, Rijswijk CSP. Prognostic factors in pulmonary metastasized high-grade osteosarcoma. Pediatr Blood Cancer. 2010;54(2):216–21.
Zhang C, Guo X, Xu Y, Han X, Cai J, Wang X, et al. Lung metastases at the initial diagnosis of high-grade osteosarcoma: prevalence, risk factors and prognostic factors. A large population-based cohort study. Sao Paulo Med J. 2019;137(5):423–9.
Meazza C. Scanagatta P. Metastatic osteosarcoma: a challenging multidisciplinary treatment. Expert Rev Anticancer Ther. 2016;16(5):543–56.
Ding Y, Boguslawski EA, Berghuis BD, Young JJ, Zhang Z, Hardy K, et al. Mitogen-activated protein kinase kinase signaling promotes growth and vascularization of fibrosarcoma. Mol Cancer Ther. 2008;7(3):648–58.
Sasaki K, Hitora T, Nakamura O, Kono R, Yamamoto T. The role of MAPK pathway in bone and soft tissue tumors. Anticancer Res. 2011;31(2):549–53.
Li L, Liang S, Wasylishen AR, Zhang Y, Yang X, Zhou B, et al. PLA2G16 promotes osteosarcoma metastasis and drug resistance via the MAPK pathway. Oncotarget. 2016;7:18021-35.
Ren T, Guo W, Peng C, Lu X, Yang Y. Arsenic trioxide inhibits osteosarcoma cell invasiveness via MAPK signaling pathway. Cancer Biology Therapy. 2010;10:251-7.
Wang D, Niu X, Wang Z, Song C, Huang Z, Chen K, et al. Multiregion Sequencing Reveals the Genetic Heterogeneity and Evolutionary History of Osteosarcoma and Matched Pulmonary Metastases. Cancer Res. 2019;79:7–20.
Wang C, Zhou X, Li W, Li M, Tu T, Ba X, et al. Macrophage migration inhibitory factor promotes osteosarcoma growth and lung metastasis through activating the RAS/MAPK pathway. Cancer Lett. 2017;403:271–9.
Yeh MH, Tzeng YJ, Fu TY, You JJ, Chang HT, Ger LP, et al. Extracellular Matrix-receptor Interaction Signaling Genes Associated with Inferior Breast Cancer Survival. Anticancer Res. 2018;38:4593–605.
Yang Z, Chen Y, Fu Y, Yang Y, Zhang Y, Chen Y, et al. Meta-analysis of differentially expressed genes in osteosarcoma based on gene expression data. BMC Med Genet. 2014;15:80.
Shibue T, Brooks MW, Inan MF, Reinhardt F, Weinberg RA. The outgrowth of micrometastases is enabled by the formation of filopodium-like protrusions. Cancer Discov. 2012;2(8):706–21.
Cai Z. Moten A, Peng D, et al The Skp2 Pathway: A Critical Target for Cancer Therapy. Semin Cancer Biol. 2020;S1044-579 × (20):30012-2.
Cai Z, Moten A, Peng D, Hsu CC, Pan BS, Manne R, et al. Skp2 in the ubiquitin-proteasome system: A comprehensive review. Med Res Rev. doi:10.1002/med.21675.
Inuzuka H, Gao D, Finley LWS, Yang W, Wan L, Fukushima H, et al. Acetylation-dependent regulation of Skp2 function. Cell. 2012;150(1):179–93.
Chan CH, Lee SW, Li CF, Wang J, Yang WL, Wu CY, et al. Deciphering the transcriptional complex critical for RhoA gene expression and cancer metastasis. Nat Cell Biol. 2010;12:457 – 67.
Ruan D, He J, Li CF, Lee HJ, Liu J, Lin HK, et al. Skp2 deficiency restricts the progression and stem cell features of castration-resistant prostate cancer by destabilizing Twist. Oncogene. 2017;36(30):4299–310.
Ding L, Li R, Sun R, Zhou Y, Zhou Y, Han X, et al. S-phase kinase-associated protein 2 promotes cell growth and motility in osteosarcoma cells. Cell Cycle. 2017;16(16):1547–55.
Zhang Y, Zvi YS, Batko B, Zaphiros N, Donnell EF, Wang J, et al. Down-regulation of Skp2 expression inhibits invasion and lung metastasis in osteosarcoma. Sci Rep. 2018;8(1):14294.
Wu J, Zhang X, Zhang L, Wu CY, Rezaeian AH, Chan CH, et al. Skp2 E3 ligase integrates ATM activation and homologous recombination repair by ubiquitinating NBS1. Mol Cell. 2012;46(3):351–61.
Tian YF, Chen TJ, Lin CY, Chen LT, Lin LC, Hsing CH, et al. SKP2 overexpression is associated with a poor prognosis of rectal cancer treated with chemoradiotherapy and represents a therapeutic target with high potential. Tumour Biol. 2013;34(2):1107–17.
Ding L, Wang C, Cui Y, Han X, Zhou Y, Bai J, et al. S-phase kinase-associated protein 2 is involved in epithelial-mesenchymal transition in methotrexate-resistant osteosarcoma cells. Int J Oncol. 2018;52:1841–52.
Kouprina N, Pavlicek A, Collins NK, Nakano M, Noskov VN, Ohzeki JI, et al. The microcephaly ASPM gene is expressed in proliferating tissues and encodes for a mitotic spindle protein. Hum Mol Genet. 2005;14(15):2155–65.
Bhargav DS, Sreedevi N, Swapna N, Vivek S, Kovvali S. Whole exome sequencing identifies a novel homozygous frameshift mutation in the ASPM gene, which causes microcephaly 5, primary, autosomal recessive. F1000Res. 2017;6:2163.
Xie JJ, Zhuo YJ, Zheng Y, Mo RJ, Liu ZZ, Li BW, et al. High expression of ASPM correlates with tumor progression and predicts poor outcome in patients with prostate cancer. Int Urol Nephrol. 2017;49:817–23.
Zhou JW, Wang H, Sun W, Han NN, Chen L. ASPM is a predictor of overall survival and has therapeutic potential in endometrial cancer. Am J Transl Res. 2020;12(5):1942–53.
Vulcani-Freitas TM, Saba-Silva N, Cappellano A, Cavalheiro S, Marie SKN, Oba-Shinjo SM, et al. ASPM gene expression in medulloblastoma. Childs Nerv Syst. 2011;27(1):71–4.
Xu Z, Zhang Q, Luh F, Jin B, Liu X. Overexpression of the ASPM gene is associated with aggressiveness and poor outcome in bladder cancer. Oncol Lett. 2019;17(2):1865–76.
Wei W, Lv Y, Gan Z, Zhang Y, Han X, Xu Z. Identification of key genes involved in the metastasis of clear cell renal cell carcinoma. Oncol Lett. 2019;17(5):4321–8.
Pai VC, Hsu CC, Chan TS, Liao WY, Chuu CP, Chen WY, et al. ASPM promotes prostate cancer stemness and progression by augmenting Wnt-Dvl-3-β-catenin signaling. Oncogene. 2019;38:1340–53.
Gronwald J, Störkel S, Holtgreve-Grez H, Hadaczek P, Brinkschmidt C, Jauch A, et al. Comparison of DNA gains and losses in primary renal clear cell carcinomas and metastatic sites: importance of 1q and 3p copy number changes in metastatic events. Cancer Res. 1997;57(3):481–7.
Kjellman P, Lagercrantz S, Höög A, Wallin G, Larsson C, Zedenius J. Gain of 1q and loss of 9q21.3-q32 are associated with a less favorable prognosis in papillary thyroid carcinoma. Genes Chromosomes Cancer. 2001;32(1):43–9.
Li X, Liu Y, Chen W, Fang Y, Xu H, Zhu HH, et al. TOP2A^high is the phenotype of recurrence and metastasis whereas TOP2A^neg cells represent cancer stem cells in prostate cancer. Oncotarget. 2014;5(19):9498–513.
Pei YF. Yin XM, Liu XQ. TOP2A induces malignant character of pancreatic cancer through activating β-catenin signaling pathway. Biochim Biophys Acta Mol Basis Dis. 2018;1864(1):197–207.
Nguyen A, Lasthaus C, Guerin E, Marcellin L, Pencreach E, Gaub MP, et al. Role of Topoisomerases in Pediatric High Grade Osteosarcomas: TOP2A Gene Is One of the Unique Molecular Biomarkers of Chemoresponse. Cancers (Basel). 2013;5(2):662–75.
Zhao H, Yao Y, Wang Z, Lin F, Sun Y, Chen P. Therapeutic effect of pirarubicin-based chemotherapy for osteosarcoma patients with lung metastasis. J Chemother. 2010;22:119 – 24.
Chen Z. Liu J, Zhang Y. Role of Epithelial Cell Transforming Sequence 2 (ECT2) in Predicting Prognosis of Osteosarcoma. Med Sci Monit. 2017;23:3861–8.
Xie J. Lei P, Hu Y. Small interfering RNA-induced inhibition of epithelial cell transforming sequence 2 suppresses the proliferation, migration and invasion of osteosarcoma cells. Exp Ther Med. 2015;9(5):1881–6.
Zhang H, Yin Z, Ning K, Wang L, Guo R, Ji Z. Prognostic value of microRNA-223/epithelial cell transforming sequence 2 signaling in patients with osteosarcoma. Hum Pathol. 2014;45(7):1430–6.
Qiu R, Li X, Qin K, Chen X, Wang R, Dai Y, et al. Antimetastatic effects of calycosin on osteosarcoma and the underlying mechanism. Biofactors. 2019;45(6):975–82.
Du YX, Guo LX, Pan HS, Liang YM, Li X. Circ_ANKIB1 stabilizes the regulation of miR-19b on SOCS3/STAT3 pathway to promote osteosarcoma cell growth and invasion. Hum Cell. 2020;33(1):252–60.
Zhou Y. Hu J. Evodiamine Induces Apoptosis, G2/M Cell Cycle Arrest, and Inhibition of Cell Migration and Invasion in Human Osteosarcoma Cells via Raf/MEK/ERK Signalling Pathway. Med Sci Monit. 2018;24:5874–80.
Ji T, Lin C, Krill LS, Eskander R, Guo Y, Zi X, et al. Flavokawain B, a kava chalcone, inhibits growth of human osteosarcoma cells through G2/M cell cycle arrest and apoptosis. Mol Cancer. 2013;12:55.
Zhang SL, Li BL, Li W, Lu M, Ni LY, Ma HL, et al. The Effects of Ludartin on Cell Proliferation, Cell Migration, Cell Cycle Arrest and Apoptosis Are Associated with Upregulation of p21WAF1 in Saos-2 Osteosarcoma Cells In Vitro. Med Sci Monit. 2018;24:4926–33.
Rongxin S, Pengfei L, Li S, Xiaochen J, Yihe H. MicroRNA-340-5p suppresses osteosarcoma development by down-regulating the Wnt/β-catenin signaling pathway via targeting the STAT3 gene. Eur Rev Med Pharmacol Sci. 2019;23(3):982–91.
Pan B, Wu L, Pan L, Yang YX, Li HH, Dai YJ, et al. Up-regulation of microRNA-340 promotes osteosarcoma cell apoptosis while suppressing proliferation, migration, and invasion by inactivating the CTNNB1-mediated Notch signaling pathway. Biosci Rep. 2018;38(4):BSR20171615.

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Identification of key biomarkers and functional pathways in osteosarcomas with lung metastasis: Evidence from bioinformatics analysis

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Abstract

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Introduction

Materials And Methods

Microarray data

Identification of DEGs

GO and KEGG enrichment analysis

PPI Networks Construction and Module Analysis

Hub Genes selection and analysis

Construction of the miRNA-hub gene regulatory network

Results

Identification of DEGs

KEGG and GO enrichment analyses of DEGs

PPI network construction and module analysis

Hub gene selection and analysis

MiRNA-hub gene regulatory network

Discussion

Abbreviations

Declarations

Availability of data and materials

Ethics approval and consent to participate

Consent for publication

Competing interests

Funding

Authors’ contributions

Acknowledgments

References

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