Development and validation of an oxidative stress‑related prognostic signature in osteosarcoma: A combination of molecular experiments and bioinformatics
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- Published online on: May 12, 2023 https://doi.org/10.3892/ol.2023.13865
- Article Number: 279
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Copyright: © Xie et al. This is an open access article distributed under the terms of Creative Commons Attribution License.
Abstract
Introduction
Osteosarcoma (OS) is one of the most commonly encountered malignancies in the world, with an occurrence rate of one to four in a million (1,2). It is more commonly noted in the epiphysis of young bones. OS is a highly invasive disease. It easily spreads and metastasizes through the bloodstream during the initial stages of its development and the resulting metastases to the lungs are the primary reasons of patient mortality (3). Despite significant progress in the diagnosis and treatment of OS, the survival rate is low and the prognosis is poor. Currently, it is considered that the majority of cases with OS are developed by interactions between the environment and the cytogenetic material, with oxidative stress possibly being one of the main attributing factors (4,5).
Oxidative stress is a physiological disorder induced by an unbalance between the production of free radicals during oxidation and the ability to scavenge them; it is characterized by increased levels of reactive oxygen species (ROS). Increased ROS production has been found in a variety of tumors to date (6,7). Elevated levels of ROS have long been considered to be a carcinogenic factor as increased ROS can damage cellular genetic material (8,9). However, recent studies have reported that ROS can act as a signal in tumors, blocking cancer differentiation (10,11). The importance of ROS in the regulation of cellular function cannot be overstated.
It has been shown that the generation of ROS is critical in regulating osteocyte function; in addition, the pathophysiology of mineralized tissues is affected by oxidative stress (12). The role of oxidative stress in OS has rarely been reported in the literature. It has been shown that OS cell proliferation and migration are regulated in part by redox-activated sodium-hydrogen antiporter 1 (13). Oxidative stress induced by low-dose doxorubicin promotes the invasiveness of OS (14). Therefore, the role of oxidative stress in OS remains to be further explored. It is important to build a prognostic signature for OS management by screening for oxidative stress-related genes (OXSRGs) that are associated with OS prognosis.
In the present study, an OXSRG signature was identified to be associated with OS prognosis following the analysis of data from The Cancer Genome Atlas (TCGA). The interaction between this signature and the tumor microenvironment (TME) was subsequently investigated. In addition, molecular analysis was performed to further explore the role of OXSRGs in OS.
Materials and methods
Acquisition of hub OXSRGs associated with OS prognosis
The RNA sequencing (RNA-seq) data and the clinical characteristics of OS were obtained from the TARGET program of the TCGA database (https://portal.gdc.cancer.gov/). The keywords used for the searching data were ‘transcriptome profiling’, ‘gene expression quantification’, ‘RNA-seq’ and ‘bones, joints, and articular cartilage of limbs’. Following exclusion of the patients with unknown survival status, data from 84 cases were included. This dataset was used as the training cohort. GSE21257, which was used as an external validation set, was a dataset of 53 OS samples with prognostic information obtained from the Gene Expression Omnibus database.
The GeneCards website was searched with the keyword ‘oxidative stress’ and the pertinent genes were obtained. The genes with a score ≥7 were selected for the next step of the univariate COX regression analysis. The least absolute shrinkage and selection operator regression (LASSO) with default parameters was used for subsequent screening of the hub OXSRGs.
Functional enrichment analysis of hub OXSRGs
The Spearman test was used to calculate the correlation between the expression levels of the hub OXSRGs. Furthermore, a network of hub OXSRGs and their 20 co-expression genes was analyzed via GeneMANIA (15). In addition, enrichment analyses of the hub OXSRGs in Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were performed using the clusterProfiler package (16).
Construction and validation of the prognostic signature
The risk score was calculated as follows: Risk score=(Exp=expression level; Coe=regression coefficient). To assess the probability of survival in OS, a nomogram was constructed, combining clinical characteristics and the prognostic signature. The calibration curves were used for assessing the nomogram.
The patients were separately classified into a low-risk and a high-risk group according to the median value of the risk score. Kaplan-Meier (KM) survival analysis was used to estimate the overall survival of the two groups. Receiver operating characteristic (ROC) curves were performed to calculate the sensitivity and specificity. These analyses were repeated in the validation dataset. Finally, COX regression was used to determine whether the signature was an independent prognostic factor.
In addition, KM survival analysis and ROC curves were used for each of the hub genes to assess the significance of each single gene.
Analysis of TME
Estimate package was applied to calculate the TME scores and tumor purity (17). The Wilcoxon rank sum test was used to analyze the differences between two groups. In addition, the correlation between the risk score and the tumor purity was assessed by the Spearman test.
Single sample gene set enrichment analysis was applied to explore the immune infiltration levels in OS. Pearson correlation was used to assess the correlation between risk score and immune infiltration.
Identification and functional enrichment analysis of differentially expressed genes (DEGs)
The comparison of low- and high-risk groups with the limma package resulted in the identification of DEGs in the high-risk group (18). When the DEGs were obtained, false discovery rate (FDR)<0.05 and |log2FC|>2 was considered to indicate a statistically significant difference. The Protein-Protein Interaction (PPI) network, which was based on DEGs, was constructed by the STRING database. The confidence level was set as a combined score >0.9. Finally, a functional enrichment analysis of DEGs was performed in KEGG and GO.
Cell culture and transfection
Three OS cell lines (MG63, U2OS, and HOS) and one osteoblast cell line (hFOB1.19) were used in this study. The OS cell lines were purchased from Zhong Qiao Xin Zhou Biotechnology Co. The osteoblast cell line (hFOB1.19) was purchased from Procell Life Science & Technology Co., Ltd. Three human OS cell lines (HOS, MG63, and U2OS) were maintained in a humidified incubator at 37°C in the presence of 5% CO2, while the human osteoblast cell line (hFOB1.19) was maintained at 34°C. MG63, U2OS, and HOS cells were cultured in MEM complete medium and hFOB1.19 cells were cultured in DMEM/F-12 complete medium. All complete media contained 10% FBS (Gibco; Thermo Fisher Scientific, Inc.) and 1% antibiotics (100 units/ml penicillin and 100 units/ml streptomycin).
A mitogen-activated protein kinase kinase kinase 5 (MAP3K5) overexpression plasmid was purchased from Shanghai GeneChem Co., Ltd. and used to infect HOS cells according to the manufacturer's protocol. The name of the plasmid backbone for the overexpression vector was CMV enhancer-MCS-SV40-puromycin. The control was empty plasmid, which was constructed using the same method but without the target gene inserted.
Total RNA was extracted from cells using TRIzol® reagent (Thermo Fisher Scientific, Inc.) according to the instructions of the manufacturer. PCR was subsequently performed to determine the mRNA expression levels of the genes according to previous studies (19). β-actin was set as an internal control and the relative gene expression was calculated by the 2−ΔΔCq method. The sequences of primers were included in Table SI.
Detection of the effect of MAP3K5 on OS cells
The cells were seeded at a density of 1×103 cells/well in a 96-well plate. A total of 10 µl Cell Counting Kit-8 (CCK-8) reagent was added at specific timepoints. Following incubation at 37°C for 1 h, the optical density was measured at 450 nm.
The EdU Detection Kit (Guangzhou RiboBio Co., Ltd.) was used to assess the fraction of DNA-replicating cells, which represented the cell proliferative status.
The wound healing assays were performed by using a sterile pipette tip to scratch and form a gap when cells were completely confluent in the 6-well plate. Subsequently, serum-free medium was added. Following incubation for 24 h, the gap was imaged.
Transwell assays were performed according to a previous study (20). Initially, 1×104 cells per group were inoculated in the upper chamber. Following 24 h of cell incubation, the cells in the bottom chamber were fixed, stained, and photographed.
The cell suspensions were made in a phosphate-buffered saline solution. Annexin V-FITC Apoptosis Detection Kit (Vazyme Biotech Co., Ltd.) and flow cytometry were used to detect apoptosis.
According to the manufacturer's protocol, ROS generation was detected via a Reactive Oxygen Species Assay Kit (Beyotime Institute of Biotechnology). The incremental production of ROS was expressed as a percentage of control.
Statistical analysis
The present study was based on the R software (version 4.1.2) to analyze the data and generate graphs. All experiments were performed at least three times independently. Continuous variables were expressed using the mean ± standard deviation. Flexible statistical methods were used in the statistical analysis. P<0.05 was considered to indicate a statistically significant difference.
Results
Identification of hub OXSRGs and functional enrichment analysis
The data were collated to obtain a gene expression matrix containing 84 patients, of whom 55 survived and 29 did not. The pertinent information is presented in Table I. Following screening, 807 genes associated with oxidative stress were obtained from the GeneCards website. Subsequently, 17 genes associated with prognosis of OS were obtained by univariate COX regression (Fig. 1A). The five most powerful prognostic genes were identified by LASSO regression (Fig. 1B and C).
The data indicated a significant correlation in the expression levels between the five hub OXSRGs (Fig. 2A). GeneMANIA revealed that mitogen activated protein kinase 1 (MAPK1) and MAP3K5 were involved in several biological functions related to oxidative stress, such as response to oxidative stress and ROS and cellular response to oxidative radicals (Fig. 2B). The GO and KEGG enrichment results also indicated that hub OXSRGs were involved in a variety of oxidative stress-related pathways and functions including the forkhead box signaling pathway. Notably, certain hub genes were also associated with inflammatory and immune-related pathways, such as the IL-17 and the TNF signaling pathways (Fig. 2C-F).
Establishment of the prognostic signature based on five hub OXSRGs
Regression coefficients were assigned to the formula to calculate a combined risk score for the five genes to assess the prognosis of patients. Patients were grouped according to the median risk score (Fig. 3A). The risk score was higher in patients with poor survival as shown in the scatter plot (Fig. 3B). The heat map indicated the expression profiles of the five hub genes in the training cohort (Fig. 3C). Finally, a nomogram including age, sex, and the risk score based on the signature was successfully constructed (Fig. 3D). The calibration plots demonstrated that the predictive results of the nomogram were highly consistent with the actual observations (Fig. 3E).
A significant difference was noted in the survival probability between the two groups (P<0.001; Fig. 3F). The time-dependent ROC curves suggested optimal specificity and sensitivity for the prognostic evaluation of the signature in patients with OS (Fig. 3G). The data from the validation set indicated that the prognostic signature remained highly stable (Fig. 3H and I).
Finally, the univariate and multivariate COX regression analyses were used to investigate the predictive power of the signature. The results of both analyses indicated that the risk score was an independent prognostic factor for patients with OS (P<0.001; Fig. 3J and K). The coefficients of five hub OXSRGs are depicted (Fig. 3L).
Correlation analysis between risk score and TME
The results indicated that the high-risk group exhibited lower stromal scores, immune scores, and higher tumor purity, compared with those of the low-risk group (Fig. 4A and B). Scatter plot analysis indicated a positive correlation between the risk score and the tumor purity (R=0.558; Fig. 4C).
The butterfly diagram illustrated the correlation between the risk score and the immune infiltration phenotype (Fig. 4D). The risk score was negatively correlated with the level of infiltration of almost all immune cells and immune-related functions (P<0.05). The aforementioned results indicated that the prognostic signature could assess the immune status of patients with OS to a certain extent.
Functional enrichment analysis of DEGs
The data indicated that in the high-risk group, the majority of the genes were downregulated (Fig. 5A). The PPI network demonstrated the interactions between these DEGs (Fig. 5B).
GO and KEGG enrichment analysis revealed that these DEGs were significantly enriched in a large number of pathways. For instance, in KEGG, DEGs were involved in the NF-κB signaling pathway (Fig. 5C). This pathway has been demonstrated to regulate the amount of intracellular ROS and to be involved in a variety of immune processes (21,22). GO enrichment analysis indicated regulation of T cell activation and regulation of the inflammatory response, and other immune-related pathways (Fig. 5D-F). Table II describes the detailed information related to the five hub genes.
Analysis of single gene
In Fig. 6A and B, single-gene analysis indicated that all hub OXSRGs with the exception of MAPK1 (P=0.13) were significantly associated with patient prognosis. ROC curves indicated the satisfactory predictive value of hub OXSRGs.
In Fig. 6C, compared with hFOB 1.19, the mRNA levels of chemokine ligand 2 (CCL2), catalase (CAT), MAPK1 and glucose-6-phospate dehydrogenase (G6PD) were significantly elevated in the OS cell line, whereas the expression levels of MAP3K5 were significantly decreased (P<0.05). In the five hub genes, the absolute values of the regression coefficients of MAP3K5 were the highest. In addition, MAP3K5 was the only gene that was downregulated in the OS cell lines. Therefore, the role of MAP3K5 was assessed in OS.
MAP3K5 inhibited the OS cell and induced ROS generation
MAP3K5 expression was successfully upregulated in HOS cells (Fig. 7A). CCK-8 assays and EdU staining revealed that overexpression of MAP3K5 inhibited cell proliferation (Fig. 7B and C). By using wound healing assays, it was shown that MAP3K5 attenuated cell motility (Fig. 7D). Moreover, transwell assays further validated these findings (Fig. 7D). The data from flow cytometry analysis revealed that MAP3K5 caused increased apoptosis in the cells (Fig. 7F). Finally, higher ROS generation was detected in HOS cells following MAP3K5 overexpression (Fig. 7G). The aforementioned data indicated that MAP3K5 exhibited inhibitory effects on OS cells, which may be mediated via ROS production.
Discussion
OS is a frequent type of primary orthopedic malignancy. As the clinical significance of specific molecular markers is continuously explored, accumulating studies have concluded that molecular markers can predict tumor prognosis. A high number of predictors and therapeutic targets are being identified. The usage of risk models to predict tumor prognosis is becoming increasingly accepted. However, no studies have been conducted to construct an effective prognostic signature based on OXSRG in OS. In the present study, OXSRGs were studied using a bioinformatics approach and their prognostic power for OS was demonstrated. Ultimately, a five-gene signature (CAT, MAPK1, G6PD, MAP3K5, and CCL2) was constructed. The combination of clinical characteristics and the signature created a nomogram with optimal applicable value.
Regulation of oxidative stress is an important factor in tumor development and in the response to antitumor therapy (23). One of the characteristics of tumors is the Warburg effect and the high levels of oxidative stress (24,25). High ROS levels may inhibit tumorigenesis. However, ROS can also promote tumor formation through the induction of DNA mutations and pro-oncogenic signaling pathways (26). This paradox has important implications for developing therapies against cancer by regulating ROS levels. Therefore, the advantage of the present study was that it improved the understanding of oxidative stress in OS. In addition, the present study also provided suggestions for treating OS via regulation of oxidative stress.
A study by Babior et al reported that human phagocytes (mainly neutrophils and macrophages) utilized membrane NADPH oxidase to produce large amounts of O2, which was subsequently decomposed by superoxide dismutase into H2O2 and oxygen (27). Subsequently, the study examining the balance between oxidative and antioxidant systems in physiological and pathophysiological conditions has had a significant influence on the scientific field of immunology. It is now becoming more and more apparent that ROS has an important role in the immune system and that it is closely associated with various aspects of the immune response, such as immune cell interactions and activation, immunosuppression, and host defense (28). The interaction between immune function and tumors has received increasing attention and a large number of studies have been reported in the literature. Mifamurtide is an immune adjuvant, which has been at the forefront of OS treatment; however, there is a lack of large-scale clinical trials to demonstrate its efficacy and safety. Therefore, Kansara et al have suggested that the understanding of the relationship between bone tumors and immune function is still in its infancy (1). The data of the present study indicated a significant difference in the immune infiltration between the high-risk and low-risk groups. In the highly risk scores, both immune scores and levels of immune infiltration were significantly lower (P<0.05). This may explain the poor outcomes noted in the high-risk group of patients with OS.
Some of these five genes involved in the signature have already been studied by a number of studies. MAPK1 encodes a member of the MAP kinase family, which serves as an integration point for various biochemical signals involved in several cellular processes, such as proliferation, differentiation, transcriptional regulation, and development. The study by Wu et al indicated that microRNA (miR)-511 mimics inhibited OS cell proliferation and invasion, and reduced tumor load in metastatic OS in nude mice by targeting MAPK1 (29). Although G6PD is a relatively well-known gene, its role in the development of OS remains understudied. Wang et al demonstrated that long non-coding RNA OR3A4 regulated the growth of OS cells by regulating miR-1207-5p/G6PD signaling (30). Chemokines are a family of secreted proteins involved in immune regulation and inflammatory processes. It has been shown that CCL2 is highly expressed in OS and acts by promoting the proliferation and invasion of OS cells via the receptor activator of nuclear factor kappa-Β ligand signaling pathway (31).
Several studies have been published reporting the role of MAP3K5 in various human diseases (32,33). Currently, MAP3K5 has been identified as one of the key components in the regulation of ROS (34). Activation of MAP3K5 can produce pro-inflammatory cytokines, which are critical to the innate immune response (35). However, a limited number of studies have focused on the association of MAP3K5 with OS. OS. Gao et al demonstrated that miR-494 repressed OS development by modulating the formation of MAP3K5-related apoptosis complexes (36). In the present study, molecular experiments were performed to further investigate the role of MAP3K5 in OS. The data revealed that MAP3K5 was a negative factor to OS. Patients with high expression of MAP35 had higher survival rates. MAP3K5 exhibited inhibitory effects on OS cells. In addition, MAP3K5 induced ROS generation in OS cells.
However, certain limitations are evident in the present study. Firstly, the total number of patients with OS in the present study was limited and future studies require a larger dataset to further validate this prediction model. Secondly, biological experiments are required to investigate the molecular mechanisms by which OXSRGs affect OS prognosis. Importantly, bioinformatic methods were applied in interpreting these results in the data and presenting the new findings of the current study. These results are, to some extent, enlightening for subsequent mechanistic studies.
In the current study, an OXSRG signature was developed and validated to predict the prognosis of patients with OS, which provided a novel idea for the treatment of OS. It was also discovered that MAP3K5 may be a therapeutic target of OS. The detection of MAP3K5 expression in the osteosarcoma tissue of patients is a limitation of our study and an aim of future experiments.
Supplementary Material
Supporting Data
Acknowledgements
Not applicable.
Funding
Funding: No funding was received.
Availability of data and materials
The data generated during and/or analyzed during the current study are available from the corresponding author upon reasonable request.
Authors' contributions
JL planned the study and supervised the analyses. BX collected data, performed the experiments and analyses. ST and CL drafted the manuscript and have made significant contributions to the analysis and interpretation of the data. JL and BX confirm the authenticity of all the raw data. All authors read and approved the final version of the manuscript.
Ethics approval and consent to participate
Not applicable.
Patient consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
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