Bioinformatics analysis of differentially expressed pathways related to the metastatic characteristics of osteosarcoma

Identification of key genes as predictive biomarkers for osteosarcoma metastasis using translational bioinformatics

Background

Osteosarcoma (OS) metastasis is the most common cause of cancer-related mortality, however, no sufficient clinical biomarkers have been identified. In this study, we identified five genes to help predict metastasis at diagnosis.

Methods

We performed weighted gene co-expression network analysis (WGCNA) to identify the most relevant gene modules associated with OS metastasis. An important machine learning algorithm, the support vector machine (SVM), was employed to predict key genes for classifying the OS metastasis phenotype. Finally, we investigated the clinical significance of key genes and their enriched pathways.

Results

Eighteen modules were identified in WGCNA, among which the pink, red, brown, blue, and turquoise modules demonstrated good preservation. In the five modules, the brown and red modules were highly correlated with OS metastasis. Genes in the two modules closely interacted in protein–protein interaction networks and were therefore chosen for further analysis. Genes in the two modules were primarily enriched in the biological processes associated with tumorigenesis and development. Furthermore, 65 differentially expressed genes were identified as common hub genes in both WGCNA and protein–protein interaction networks. SVM classifiers with the maximum area under the curve were based on 30 and 15 genes in the brown and red modules, respectively. The clinical significance of the 45 hub genes was analyzed. Of the 45 genes, 17 were found to be significantly correlated with survival time. Finally, 5/17 genes, including ADAP2 (P = 0.0094), LCP2 (P = 0.013), ARHGAP25 (P = 0.0049), CD53 (P = 0.016), and TLR7 (P = 0.04) were significantly correlated with the metastatic phenotype. In vitro verification, western blotting, wound healing analyses, transwell invasion assays, proliferation assays, and colony formation assays indicated that ARHGAP25 promoted OS cell migration, invasion, proliferation, and epithelial–mesenchymal transition.

Conclusion

We identified five genes, namely ADAP2, LCP2, ARHGAP25, CD53, and TLR7, as candidate biomarkers for the prediction of OS metastasis; ARHGAP25 inhibits MG63 OS cell growth, migration, and invasion in vitro, indicating that ARHGAP25 can serve as a promising specific and prognostic biomarker for OS metastasis.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-021-02308-w.

Identification of hub genes specific to pulmonary metastasis in osteosarcoma through integrated bioinformatics analysis

BACKGROUND

Pulmonary metastasis is the most frequent cause of death in osteosarcoma (OS) patients. Recently, several bioinformatics studies specific to pulmonary metastatic osteosarcoma (PMOS) have been applied to identify genetic alterations. However, the interpretation and reliability of the results obtained were limited for the independent database analysis.

OBJECTIVE

The expression profiles and key pathways specific to PMOS remain to be comprehensively explored. Therefore, in our study, three original datasets of GEO database were selected.

METHODS

Initially, three microarray datasets (GSE14359, GSE14827, and GSE85537) were downloaded from the GEO database. Differentially expressed genes (DEGs) between PMOS and nonmetastatic osteosarcoma (NMOS) were identified and mined using DAVID. Subsequently, GO and KEGG pathway analyses were carried out for DEGs. Corresponding PPI network of DEGs was constructed based on the data collected from STRING datasets. The network was visualized with Cytoscape software, and ten hub genes were selected from the network. Finally, survival analysis of these hub genes also used the TARGET database.

RESULTS

In total, 569 upregulated and 1238 downregulated genes were filtered as DEGs between PMOS and NMOS. Based on the GO analysis result, these DEGs were significantly enriched in the anatomical structure development, extracellular matrix, biological adhesion, and cell adhesion terms. Based on the KEGG pathway analysis result, these DEGs were mainly enriched in the pathways in cancer, PI3K-Akt signaling, MAPK signaling, focal adhesion, cytokine-cytokine receptor interaction, and IL-17 signaling. Hub genes (ANXA1 and CXCL12) were significantly associated with overall survival time in OS patient.

CONCLUSION

Our results may provide new insight into pulmonary metastasis of OS. However, experimental studies remain necessary to elucidate the biological function and mechanism underlying PMOS.

Serglycin promotes proliferation, migration, and invasion via the JAK/STAT signaling pathway in osteosarcoma

BACKGROUND

Osteosarcoma (OS) is a common disease in the world, and its pathogenesis is still unclear. This study aims to identify the key genes that promote the proliferation, invasion, and metastasis of osteosarcoma cells.

METHOD

GSE124768 and GSE126209 were downloaded from the Gene Expression Omnibus (GEO) database. The gene ontology and enrichment pathway were analyzed by FunRich software. qPCR and Western blot were used to detect the gene expression. After gene knockdown, Transwell and wound healing assays were conducted on osteosarcoma cells to detect whether the genes were defined before enhancing the invasion of osteosarcoma.

RESULTS

Totally, 341 mRNAs were found to be regulated differentially in osteosarcoma cells compared to osteoblasts. In addition, the expression level of Serglycin (SRGN) in osteosarcoma cells was higher than that in human osteoblasts. The invasion and proliferation ability of osteosarcoma cells with upregulated Serglycin was significantly increased, and on the contrary, decreased after Serglycin knockdown. Moreover, we preliminarily found that Serglycin may associate with the JAK/STAT signaling pathway.

CONCLUSIONS

By using microarray and bioinformatics analyses, differently expressed mRNAs were identified and a complete gene network was constructed. To our knowledge, we describe for the first time Serglycin as a potential biomarker.

Integrated bioinformatic analysis and experiment confirmation of the antagonistic effect and molecular mechanism of ginsenoside Rh2 in metastatic osteosarcoma

This study aimed to compare the gene expression variation of clinical primary osteosarcoma (OS) and metastatic OS, identify expression profiles and signal pathways related to disease classification, and systematically evaluate the potential anticancer effect and molecular mechanism of ginsenoside Rh2 on OS. A raw dataset (GSE14359), which excluded GSM359137 and GSM359138, was downloaded from the Gene Expression Omnibus. Differentially expressed genes (DEGs) and principal component analysis (PCA) were obtained with limma. Pathways enrichment analysis was understood by GSEA app. Rh2-associated targets were harvested and mapped through PharmMapper and Cytoscape 3.4.0. The toxicity of Rh2 was determined using crystal staining and MTT assay on 143B and MG63 cell lines. The relative protein expression was confirmed through Western blot analysis. The mitochondrial membrane potential (△Ψm) was evaluated by JC-1 fluorescence staining. The cell mobility was measured via wound healing and transwell assays. A total of 752 genes were upregulated, while 161 genes were downregulated. GSEA and PCA displayed significant function enrichment and classification. Through PharmMapper and Cytoscape 3.4.0, Rh2 was found to target the mitogen activated protein kinase (MAPK) and PI3K signaling pathways, which are the key pathways in the metastasis of OS. Furthermore, Rh2 induced a concentration-dependent decrease in cell viability and early apoptosis associated with ΔΨm decline, while a non-lethal dose of Rh2 weakened the metastatic capability. Moreover, systematic evaluation showed that promoting the MAPK signaling pathway and inhibiting PI3K/Akt/mTOR were correlated with the anticancer effects of Rh2 on metastatic OS. In conclusion, transcriptome-derived approaches may be beneficial in diagnosing early metastases, and Rh2, a multi-targeting agent, shows promising application potential in suppressing metastatic OS in an MAPK- and PI3K/Akt/mTOR-dependent manner.

High Expression of N-Acetylgalactosaminyl-transferase 1 (GALNT1) Associated with Invasion, Metastasis, and Proliferation in Osteosarcoma

BACKGROUND Osteosarcoma (OS) is very common worldwide, and the mechanisms underlying its development remain unclear. This study aims to identify key genes promoting the reproduction, invasion, and transfer of osteosarcoma cells. MATERIAL AND METHODS Gene expression profile data (GSE42352 and GSE42572) were downloaded from the Gene Expression Omnibus database. Differentially expressed genes were calculated using R software. Gene ontology and enriched pathway analysis of mRNAs were analyzed by using FunRich. Verification of the genes was conducted by using quantitative real-time polymerase chain reaction and western blot analyses to measure gene expression. Transwell and wound-healing assays were performed on osteosarcoma cells after knockdown to detect whether the genes enhanced the aggressiveness of osteosarcoma. RESULTS In total, 34 genes were selected after filtering. Kyoto Encyclopedia of Genes and Genomes enrichment analysis demonstrated that the genes were enriched in multiple tumor pathways. N-acetylgalactosaminyltransferase 1 (GALNT1) was identified for further study, and its expression was higher in osteosarcoma cells than in human osteoblasts. The invasion ability of cells was significantly decreased after gene knockdown. CONCLUSIONS Through the use of microarray and bioinformatics analysis, differentially expressed genes were selected and a complete gene network was constructed. Our findings provide new biomarkers for the treatment and prognosis of osteosarcoma. These biomarkers may contribute to the discovery of new therapeutic targets for clinical application.

Development of a prognostic gene signature based on an immunogenomic infiltration analysis of osteosarcoma

Osteosarcoma is the most common primary malignant bone tumour predominantly occurring in children and adolescents with a high tendency of local invasion and early metastases. Currently, tumour immune microenvironment (TME) is becoming the focus of studying of malignant tumours.. However, no sound evidence shows a specific immune molecular target in osteosarcoma. We downloaded the gene expression profile and clinical data of osteosarcoma from the TARGET portal, and extracted and normalized via R software. Then, the immune cell infiltration assessed by CIBERSORT and ESTIMATE algorithms. Three survival‐related immune cells and immune score were obtained via Kaplan‐Meier survival analysis, and 232 immune‐related genes were obtained as candidate genes. Enrichment and protein‐protein interaction co‐expression analyses were performed to identify 13 hub genes. Lastly, a seven gene prognostic signature was identified by univariate and multivariate Cox regression analyses. More importantly, our validations and TIMER algorithm suggested this immune‐related prognostic signature a good predictive tool. Our findings have provided novel insights that could demonstrate new targets of immunotherapy in osteosarcoma.

Identification of Osteosarcoma Metastasis-Associated Gene Biomarkers and Potentially Targeted Drugs Based on Bioinformatic and Experimental Analysis

BACKGROUND

Metastasis is the leading cause of death for patients with osteosarcoma (OS). In the present study, we explore the biomarkers for metastatic OS and provide potential therapeutic approaches.

MATERIALS AND METHODS

RNA-Seq data and clinical follow-up information were downloaded from TARGET and GEO databases. A Cox regression model was used to analyze metastatic events. L1000FWD, DGIdb, and CMap databases were used to identify potential drugs related to metastasis. Invasion and migration transwell assays and an adhesion assay were used to identify biological functions of genes.

RESULTS

A total of 15 metastasis-related signatures (MRSs) were associated with the prognosis based on the TARGET or GSE21257 cohorts, among which IL10RA and TLR7 genes were especially significant. In the DGIdb drug-gene interaction database, TLR7 and IFNGR1 were found to have potential interactions with drugs. After inhibiting the expression of TLR7, the migration, invasion, and adhesion ability of OS cells were significantly enhanced, which further promoted metastasis.

CONCLUSION

We identified a set of MRS that may be related to OS metastases. Among them, TLR7 plays a vital role and may be a potential target for OS metastasis treatment.

Identification of potential crucial genes and key pathways in osteosarcoma

Background

The aim of this study is to identify the potential pathogenic and metastasis-related differentially expressed genes (DEGs) in osteosarcoma through bioinformatic analysis of Gene Expression Omnibus (GEO) database.

Results

Gene expression profiles of GSE14359, GSE16088, and GSE33383, in total 112 osteosarcoma tissue samples and 7 osteoblasts, were analyzed. Seventy-four normal-primary DEGs (NPDEGs) and 764 primary-metastatic DEGs (PMDEGs) were screened. VAMP8, A2M, HLA-DRA, SPARCL1, HLA-DQA1, APOC1 and AQP1 were identified continuously upregulating during the oncogenesis and metastasis of osteosarcoma. The enriched functions and pathways of NPDEGs include procession and presentation of antigens, activation of MHC class II receptors and phagocytosis. The enriched functions and pathways of PMDEGs include mitotic nuclear division, cell adhesion molecules (CAMs) and focal adhesion. With protein-protein interaction (PPI) network analyzed by Molecular Complex Detection (MCODE) plug-in of Cytoscape software, one hub NPDEG (HLA-DRA) and 7 hub PMDEGs (CDK1, CDK20, CCNB1, MTIF2, MRPS7, VEGFA and EGF) were eventually selected, and the most significant pathways in NPDEGs module and PMDEGs module were enriched in the procession and presentation of exogenous peptide antigen via MHC class II and the nuclear division, respectively.

Conclusions

By integrated bioinformatic analysis, numerous DEGs related to osteosarcoma were screened, and the hub DEGs identified in this study are possibly part of the potential biomarkers for osteosarcoma. However, further experimental studies are still necessary to elucidate the biological function and mechanism of these genes.

Identification of a Two-Gene (PML-EPB41) Signature With Independent Prognostic Value in Osteosarcoma

Background: Osteosarcoma (OSA) is the most prevalent form of malignant bone cancer and it occurs predominantly in children and adolescents. OSA is associated with a poor prognosis and highest cause of cancer-related death. However, there are a few biomarkers that can serve as reasonable assessments of prognosis.

Methods: Gene expression profiling data were downloaded from dataset GSE39058 and GSE21257 from the Gene Expression Omnibus database as well as TARGET database. Bioinformatic analysis with data integration was conducted to discover the significant biomarkers for predicting prognosis. Verification was conducted by qPCR and western blot to measure the expression of genes.

Results: 733 seed genes were selected by combining the results of the expression profiling data with hub nodes in a human protein-protein interaction network with their gene functional enrichment categories identified. Following by Cox proportional risk regression modeling, a 2-gene (PML-EPB41) signature was developed for prognostic prediction of patients with OSA. Patients in the high-risk group had significantly poorer survival outcomes than in the low-risk group. Finally, the signature was validated and analyzed by the external dataset along with Kaplan–Meier survival analysis as well as biological experiment. A molecular gene model was built to serve as an innovative predictor of prognosis for patients with OSA.

Conclusion: Our findings define novel biomarkers for OSA prognosis, which will possibly aid in the discovery of novel therapeutic targets with clinical applications.

Identification of differentially expressed genes in pancreatic ductal adenocarcinoma and normal pancreatic tissues based on microarray datasets

Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive malignant tumor with rapid progression and poor prognosis. In the present study, 11 high‑quality microarray datasets, comprising 334 tumor samples and 151 non‑tumor samples from the Gene Expression Omnibus, were screened, and integrative meta‑analysis of expression data was used to identify gene signatures that differentiate between PDAC and normal pancreatic tissues. Following the identification of differentially expressed genes (DEGs), two‑way hierarchical clustering analysis was performed for all DEGs using the gplots package in R software. Hub genes were then determined through protein‑protein interaction network analysis using NetworkAnalyst. In addition, functional annotation and pathway enrichment analyses of all DEGs were conducted in the Database for Annotation, Visualization, and Integrated Discovery. The expression levels and Kaplan‑Meier analysis of the top 10 upregulated and downregulated genes were verified in The Cancer Genome Atlas. A total of 1,587 DEGs, including 1,004 upregulated and 583 downregulated genes, were obtained by comparing PDAC with normal tissues. Of these, hematological and neurological expressed 1, integrin subunit α2 (ITGA2) and S100 calcium‑binding protein A6 (S100A6) were the top upregulated genes, and kinesin family member 1A, Dymeclin and β‑secretase 1 were the top downregulated genes. Reverse transcription‑quantitative PCR was performed to examine the expression levels of S100A6, KRT19 and GNG7, and the results suggested that S100A6 was significantly upregulated in PDAC compared with normal pancreatic tissues. ITGA2 overexpression was significantly associated with shorter overall survival times, whereas family with sequence similarity 46 member C overexpression was strongly associated with longer overall survival times. In addition, network‑based meta‑analysis confirmed growth factor receptor‑bound protein 2 and histone deacetylase 5 as pivotal hub genes in PDAC compared with normal tissue. In conclusion, the results of the present meta‑analysis identified PDAC‑related gene signatures, providing new perspectives and potential targets for PDAC diagnosis and treatment.

Anticancer Effects of Constituents of Herbs Targeting Osteosarcoma

Osteosarcoma is a rare primary malignancy of bone that is prone to early metastasis. Resection surgery and chemotherapeutic regimens are current standard treatments for osteosarcoma. However, the long-term survival rate of patients with osteosarcoma is low due to a high risk of metastasis. Hence, a new approach is urgently needed to improve the treatment of osteosarcoma. Compared with chemotherapy, natural active constituents isolated from herbs exhibit less adverse effects and better anti-tumor effects. This study aimed to summarize the anticancer effects of constituents of herbs on the progression and metastasis of osteosarcoma cells. It showed that many constituents of herbs inhibited osteosarcoma by targeting proliferation, matrix metalloproteinases, integrin and cadherin, and angiogenesis. The findings might be beneficial for the development of new drugs and treatment strategies.

A risk score model for the prediction of osteosarcoma metastasis

Osteosarcoma is the most common primary solid malignancy of the bone, and its high mortality usually correlates with early metastasis. In this study, we developed a risk score model to help predict metastasis at the time of diagnosis. We downloaded and mined four expression profile datasets associated with osteosarcoma metastasis from the Gene Expression Omnibus. After data normalization, we performed LASSO logistic regression analysis together with 10-fold cross validation using the GSE21257 dataset. A combination of eight genes (RAB1,CLEC3B,FCGBP,RNASE3,MDL1,ALOX5AP,VMO1 and ALPK3) were identified as being associated with osteosarcoma metastasis. These genes were put into a gene risk score model, and the prediction efficiency of the model was then validated using three independent datasets (GSE33383, GSE66673, and GSE49003) by plotting receiver operating characteristic curves. The expression levels of the eight genes in all datasets were shown as heatmaps, and gene ontology gene annotation and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis were performed. These eight genes play a role in cancer-related biological processes, such as apoptosis and biosynthetic processes. Our results may aid in elucidating the possible mechanisms of osteosarcoma metastasis, and may help to facilitate the individual management of patients with osteosarcoma after treatment.