Introduction
Osteosarcoma (OS) is one of the most common malignancies in the skeletal system. It originates in the mesenchymal tissue and is most likely to occur in the long diaphyseal region with abundant blood supply, such as the distal femur and proximal tibia [
1]. Most osteosarcomas are primary and a few are secondary [
2]. Osteosarcoma mainly occurs in children and adolescents with strong bone growth and development. Due to the highly invasive nature of osteosarcoma, 75% of patients with osteosarcoma have distant metastases, mainly to the lung, and the prognosis is poor, with an overall survival rate of less than 20% [
3‐
5]. At present, the basic treatment techniques for osteosarcoma are neo-adjuvant chemotherapy and surgical resection, which can enhance the survival percentage of patients with an early diagnosis of osteosarcoma by 60–70% [
6]. With the development of molecular biology and tissue bioengineering, great progress has been made in treating osteosarcoma, which has significantly improved the postoperative quality of life and the 5-year survival rate of patients with malignant tumors [
2]. However, those with metastatic tumors who cannot be treated with surgery have a very poor prognosis, with a 5-year survival rate of less than 20% [
7]. Due to the high heterogeneity and low incidence of osteosarcoma, it is difficult to identify a specific driver gene, so we currently cannot predict the prognosis of patients based on the changes of a single molecule in their body [
8]. Therefore, there is an urgent need for some biomarkers to predict the survival status of patients with clinical osteosarcoma to better assess the risk and personalized management of patients.
Rho family is a member of the Ras supergene family of guanosine triphosphatase (
GTPase), which is involved in cell morphology, gene transcription, cell cycle, cell apoptosis, cell carcinogenesis, cell migration and infiltration, and other processes [
9,
10]. However, the
Rho GTPase-activating proteins (
RHOGAPs) family is a negative regulatory factor of the
Rho family proteins [
11]. We have found
ARHGAP proteins to be altered in expression in many diseases, including cancer, so they may be a potential target for treating diseases [
12,
13]. They have reported that the high expression of
ARHGAP protein can inhibit the proliferation and migration of tumor cells, and has a good prognosis for mice, so it may become a target for tumor therapy [
14]. However, there have been no systematic studies on whether
ARHGAP protein affects the prognosis of patients with osteosarcoma and its clinical significance.
In this study, we have studied the ARHGAP family genes systematically. We identified the genes associated with the prognosis of osteosarcoma in ARHGAP and selected five genes to construct a unique prognostic risk model for predicting the survival time of patients with osteosarcoma. Then we validated the prognostic results in the training cohort, testing cohort, entire cohort, and GSE39055 cohort, respectively. Finally, we studied the relationship between our constructed prognostic model and the immune microenvironment. Our study is more helpful in evaluating the prognosis of patients with osteosarcoma and conducting individualized treatment.
Materials and methods
Data acquisition
We collected RNA sequencing data (RNA-seq) in fragment per kilobase method (FPKM) format and matching clinical information from The Cancer Genome Atlas (
https://portal.gdc.cancer.gov/) for 88 patients with osteosarcoma. Clinical data of these patients should include age, sex, survival status, follow-up time, diagnosis time, etc. To make the results more reliable, we downloaded the GSE39055 dataset from the Gene Expression Omnibus database (
https://www.ncbi.nlm.nih.gov/geo/) for external validation. This dataset contains patient RNA-seq data and corresponding clinical information. For the convenience of follow-up analysis, we excluded those patients with no follow-up information or unknown survival status and finally included a total of 123 patients for the follow-up study, including 86 patients in the TARGET database and 37 patients in the GSE39055 dataset. Then, we used the “sva” package in R to process the two data sets to eliminate the batch effect.
Identification and differential expression analysis of prognostic-related ARHGAP family genes
We got the
ARHGAP family gene from the GeneCards database (
https://www.genecards.org/) and previous research [
11]. We then performed a univariate Cox regression analysis based on the expression of these genes and patient clinical information, and those genes with
p < 0.05 were considered to be prognostic-related. According to the expression amount of these genes, we drew the expression heatmap of each gene in each sample and the expression correlation between them.
Construction of prognostic risk profiles based on the ARHGAP family and subsequent validation
To construct a prognostic risk model and for further verification, we randomly divided patients in the TARGET cohort 1: 1 into a training cohort and a testing cohort. We then performed multivariate Cox regression and the Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis for prognostic-related genes in the ARHGAP family in the training cohort. Based on this result, we identified the ARHGAP family genes and their corresponding regression coefficients that ultimately participated in the construction of the prognosis model. We used the following formula to calculate the risk score for each osteosarcoma patient in the cohort: Risk score = \({\sum }_{k=1}^{n}( Coefficient\left(i\right)*Expr(i))\), where Coefficient is the regression coefficient, Expr is the expression of ARHGAPs and n is the number of genes we included in the prognostic model. Using the same formula, we can determine the respective risk coefficients for the testing cohort, the entire internal cohort, and the external GSE39055 cohort. We divided osteosarcoma patients in each cohort into high—and low-risk groups based on their risk scores. We did a survival analysis of the training cohort, the testing cohort, the entire internal cohort, and the external GSE39055 cohort to assess survival differences between high- and low-risk groups in each cohort. To confirm the sensitivity, specificity, and accuracy of the risk-prognosis model, we simulated the time-dependent receiver operating characteristic (ROC) curve.
Construct a nomogram to verify and predict the prognosis of osteosarcoma patients
A nomogram was created by combining risk score and other two clinicopathological characteristics including gender, and age in the TCGA cohort. We were then using the nomogram to predict 2-year, 3-year, and 5-year survival for osteosarcoma. Time-dependent ROC curves and calibration curves were simulated in the TCGA and GSE39055 datasets, respectively, to verify the efficacy of the nomogram in predicting the overall survival of patients with osteosarcoma.
Screening of differential genes between high- and low-risk groups
We have divided patients in the TCGA entire cohort into high- and low-risk groups. We secured the differentially expressed genes (DEGs) between the two groups, and the screening standard was | log2FC |> 0.5 and p-value < 0.05. Then we made a heatmap based on the expression of different differential genes in each sample. We generated the hub genes by simulating the PPI network of differential genes between the two groups using The Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) web-based database (string-interaction.org).
Functional enrichment analysis between differentially expressed genes
Gene enrichment analysis can help us identify which biological functions and pathways are primarily responsible for prognostic risk scores between the two groups. Next, we carried out the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis and Gene Ontology (GO) enrichment analyses to analyze these differential genes [
15‐
17]. To have a more intuitive and in-depth understanding of the mechanism of differential genes, we then conducted a GSEA enrichment analysis on the differential genes between the high- and low-risk groups to determine whether they play a role in a biological process or pathway. We analyzed the standard genetic set "c2.cp.kegg.v7.0.symbols.gmt" by using GSEA software.
Analysis of immune function between high- and low-risk group
To understand whether the prognostic risk score, we constructed works in the tumor microenvironment (TME), we calculated the immunological scores, estimate scores, stromal scores, and tumor purity in the immune microenvironment of osteosarcoma patients between the two risk groups. We used the CIBERSORT algorithm to analyze the expression data of each sample and calculate the relative abundance of 22 types of immune cells in them [
18,
19], and then used R packets to visualize the abundance of immune cells between the two risk groups. We also analyzed the correlation between each immune infiltrating cell in the osteosarcoma sample. Then we performed single-sample gene set enrichment analysis (ssGSEA) in the TCGA and GSE39055 datasets respectively and obtained the differences in immune function scores and expression of 22 kinds of infiltrated cells between high- and low-risk groups.
Cell line culture and transfection
We obtained the 143B and U2OS human osteosarcoma cell lines from the China Center for Type Culture Collection (Wuhan, China). To culture these cell lines, we used RPMI-1640 medium (Invitrogen, USA) supplemented with 10% fetal bovine serum (FBS; Tian Hang, China) and 1% Penicillin–Streptomycin and incubated them at 37 °C with 5% CO2. The ARHGAP28 expression plasmids were purchased from Sangon Biotech Co., Ltd (Shanghai, China), and we cultured OS cells in 6-well and 96-well plates for subsequent experiments. We conducted a series of assays, including western blot analysis, CCK-8 assay, transwell invasion assay, and wound healing assay, using ARHGAP28-overexpressing cells.
Western blot analysis
We extracted total proteins from osteosarcoma cells in good growth condition using RIPA buffer (Servicebio Technology, Wuhan, China) and followed the instructions. The protein concentration was quantified using a BCA kit (Servicebio Technology, Wuhan, China). Next, the total protein was separated by electrophoresis and transferred to a membrane. After blocking and washing the membrane three times with tris-buffered saline with tween (TBST), we cut the membrane and incubated primary antibodies overnight at 4 ℃. Following that, the membrane was washed and incubated with secondary antibodies. Finally, we used the ECL kit (Thermo Fisher Scientific) for display.
CCK-8 assay
We inoculated osteosarcoma cells in good growth condition on 96-well plates, with three multiple wells in each group. We replaced the complete medium, which contained 10 μL CCK-8 reagent, at 0, 24, 48, 72, 96, and 120 h after planting the plates and incubated them for 2 h each time. We then used a microplate reader (Bio-Rad Laboratories, Inc.) to measure the absorbance of each well at OD 450 nm (optical density), which indicates the viability of each cell line.
Transwell invasion assay
Transwell chambers (Corning, USA) and Matrigel (Corning, USA) were used for conducting invasion experiments. The upper chamber was filled with the medium at a 1:6 ratio. After coagulation, 200 µL of medium containing 1 × 105 cells was added to the upper layer of each chamber, and 600µL of complete medium (containing 10%FBS) was added to the lower layer of each chamber. After 48 h of cell culture, the cells were removed and fixed with 4% paraformaldehyde and stained with 1% crystal violet. Take pictures under an inverted microscope (Olympus, Japan) and count the number of cells.
Wound healing assay
We inoculated osteosarcoma cell lines 143B and U2OS into 6-well plates with 2 mL complete medium added to each well and 3 multiple Wells in each group. When cell density reached 95%, a 100µL gun tip was used to mark the bottom of each well of the culture plate, and serum-free medium was replaced, and the culture was continued after photographing with an inverted microscope (Olympus, Japan). After 24 and 48 h of culture, the area of intermediate scratches was observed with an inverted microscope and photographed. The wound area was measured using the ImageJ software and wound healing percentage was calculated to evaluate the migrate ability. The calculation formula is below: Migration rate = (area of 0 h—area of 24 h or 48 h) / (area of 0 h) × 100.
Animal studies
The Renmin Hospital of Wuhan University Ethics Committee authorized the experiments. Female BALB/c nude mice (4–6 weeks old) were obtained from Beijing HFK Experiment Animal Center (Beijing, China) and randomly divided into two groups of six mice each (NC, ARHGAP28 OE). These mice were subcutaneously injected with stably transfected 143B osteosarcoma cells and kept in a standard environment with food and water. Tumor size and volume were monitored weekly. After four weeks, all mice were euthanized with 2% pentobarbital sodium (150 mg/kg), and tumors were excised and weighed. The tumors were then either preserved in liquid nitrogen or fixed in 4% paraformaldehyde. The care of the laboratory animals and animal experiments were performed following the animal ethics guidelines and approved protocols of Renmin Hospital of Wuhan University.
Immunohistochemistry (IHC) staining
The tumor tissue was fixed with 4% paraformaldehyde for 24 h and cut into 4-µm slices, which were then blocked with 1% bovine serum albumin at room temperature for 1 h. After that, the slices were incubated with corresponding primary antibodies at 4 ℃ overnight, followed by 1-h incubation with secondary antibodies at room temperature. Finally, chromogenic detection was performed using a DAB kit (CST, USA) and observed under an inverted microscope (Olympus).
Statistical analysis
All our data processing and picture drawing was carried out by using R software (version 4.2.1). We used the log-rank test for the Kaplan–Meier survival difference analysis of the high—and low-risk groups. We used the Wilcoxon rank-sum test and the two-tailed Student’s t-test to compare the high- and low-risk groups. Multivariate Cox regression analysis was used to identify factors that could independently predict the prognosis of patients with osteosarcoma. We defined p < 0.05 as a significant difference. “*” is equal to “p < 0.05”, “**” is equal to “p < 0.01” and “***” is equal to “p < 0.001”.
Discussion
Osteosarcoma is the most common primary malignant bone tumor in orthopedics, with two peaks in adolescents and the elderly. The peak age of adolescent-onset is about 15 years old, mainly primary OS, and the second peak age is about 75 years old, mainly secondary OS [
21]. OS mainly occurs in the epiphyseal region of the long diaphysis, where blood transport is abundant [
1]. In the early stage of osteosarcoma, blood transport to the lung is most common and develops rapidly, which greatly reduces the survival of patients with osteosarcoma [
22]. With the increase and improvement of treatment methods, the survival rate of patients with osteosarcoma has been greatly improved, but how to further improve the prognosis of patients is a major clinical challenge, especially for patients with pulmonary metastatic osteosarcoma, which occurs earlier and has a worse prognosis [
23]. Hence, on the one hand, we should be looking for more effective treatments; on the other hand, we should also develop some new ideas. We can use high-throughput sequencing technology and existing sequencing results to screen genes, and then predict the prognosis of patients so that we can carry out more personalized treatment for patients.
For nearly 20 years, we have considered the
Rho family as an anti-tumor target, especially for
RAS-driven tumors.
Rho family proteins transition between active GTP-binding states and inactive GDP-binding states, which are regulated by the
ARHGAP family and can increase the intrinsic GTPase activity of
Rho GTPase to convert it to inactive GDP-binding states [
11]. Nowadays, it is believed that most
ARHGAP genes have multiple functional domains except the
RHOGAP functional domain, which integrates signal factors in many signaling pathways and may mediate the interaction between the
Rho family and other signaling pathways. However, the number of
ARHGAP proteins is much larger than that of their substrate
Rho proteins, and
ARHGAP proteins have diverse biological functions. Therefore, the deep regulatory mechanism of
ARHGAP on the
Rho family is still far from clear. The
ARHGAP family is involved in many biological activities, such as exocytosis, endocytosis, cytokinesis, cell differentiation, cell migration, neuronal morphogenesis, angiogenesis, and tumor suppression [
11]. In recent years, the relationship between
ARHGAP family genes and tumor development, invasion, and metastasis has attracted more and more attention [
24]. In this study, we screened the
ARHGAP family genes and finally screened 5 genes to construct a risk model for evaluating the prognosis of patients with osteosarcoma, and to provide certain ideas and help for clinical treatment.
In this study, we first collected gene expression information and clinical information of patients with osteosarcoma in the TARGET cohort and the GSE39055 cohort and then removed patients without follow-up information and survival status to prepare the data for our next analysis. For better analysis, we then obtained the genetic information of the
ARHGAP family from the online website, and uniformly named it in the two datasets. We then performed univariate COX analysis in the TCGA cohort and screened out 9 genes associated with the prognosis of patients with osteosarcoma. For subsequent analysis and verification, we randomly divided patients in the TCGA cohort into two groups at a ratio of 1: 1, namely the training cohort and the testing cohort. We performed LASSO regression analysis and multivariate COX regression analysis on the patients in the training cohort and finally obtained five genes:
ARHGAP1,
ARHGAP8,
ARHGAP10,
ARHGAP25, and
ARHGAP28, and then we constructed a risk prediction model for patients with osteosarcoma based on these five genes. We calculated each patient's risk score and divided them into high- and low-risk groups, and then analyzed the relationship between survival status and survival time and risk score in the training cohort, testing cohort, entire internal cohort, and GSE39055 cohort respectively, finally, Kaplan–Meier survival analysis and ROC curve were performed for verification. The results show that the high-risk group had a lower survival time than the low-risk group. This suggests that our risk model can well predict the prognosis of patients with osteosarcoma. We can tell that these genes are protective factors for patients with osteosarcoma. Among them,
ARHGAP1,
ARHGAP8, and
ARHGAP10 play different roles in different tumors or different pathways.
ARHGAP1 was the first gene discovered in this family, and its content in cervical cancer cells and Ewing Sarcoma (ES) cells was lower than that in the matching normal tissue, which proved that it could inhibit the cell vitality, cell migration, and invasion of these two cancer cells in a time-dependent manner to a certain extent [
25,
26]. However, in breast cancer (BC),
ARHGAP1 is a carcinogenic factor, and its expression level in BC samples is higher than that in normal tissues, its overexpression can promote the proliferation and invasion of BC cells while inhibiting its expression can significantly inhibit the growth of tumors [
27‐
29].
ARHGAP1 may also regulate the bone microenvironment by inhibiting the
RhoA/ROCK pathway, which stimulates osteogenic differentiation of mesenchymal stem cells [
30]. However, the role of
ARHGAP1 in osteosarcoma has not been reported, which can be further studied in the future. Similarly,
ARHGAP8 is overexpressed in most colorectal cancers compared to normal tissues, but we observe relatively low expression in Bladder cancer, suggesting that
ARHGAP8 may play different roles in different tumors, but its role in osteosarcoma is unknown [
31,
32].
ARHGAP10 is well known as a tumor suppressor and has been demonstrated in a variety of cancers, such as Uterine leiomyomas (ULs), prostate cancer, ovarian cancer (OC), lung cancer, colon carcinoma (CRC) and BC [
33‐
38].
Cdc42, a key protein that cancer cells need to metastasize, helps them spread through the bloodstream to other parts of the body. In ovarian cancer,
RHGAP10 inhibits
Cdc42 activity in cells, in turn, it can inhibit the growth and invasion of tumors, thus playing a role in cancer suppression [
37]. However,
ARHGAP10 manifested as an oncogene in gastric tumors and non-small cell lung cancer (NSCLC) [
39‐
41]. The expression level of
ARHGAP10 in NSCLC is higher than that in normal tissues. When its expression is decreased, the expression of
GLUT1 is also decreased, which inhibits the glucose metabolism process of cells and thus the progression of cancer [
40]. Therefore, the role of
ARHGAP10 in different tumors may be related to its participation in different pathways or different regulatory molecules upstream and downstream of the same pathway. However, its specific role in osteosarcoma is still unclear, so we can conduct further research.
ARHGAP25 has also been widely studied as a tumor suppressor gene in cancer, including Pancreatic adenocarcinoma (PAAD), NSCLC, Lung cancer, and CRC [
14,
42‐
46]. Epithelial-mesenchymal transition (EMT) is a common mechanism of tumor metastasis, which can reduce the adhesion between cells so that tumor cells can be separated from the original site to metastasize [
47]. The
Wnt/β-catenin pathway can increase the viability and invasion ability of cancer cells by activating EMT [
48]. However,
ARHGAP25 exerts its anticancer effects by negatively regulating EMT and
Wnt/β-catenin pathways [
44]. Of course,
ARHGAP25 may regulate different pathways in different tumors to play a role in cancer inhibition. However, whether
ARHGAP25 can inhibit the metastasis of osteosarcoma has not been studied, and it can become the object of our subsequent research [
49]. Finally, the expression level of
ARHGAP28 in osteosarcoma is significantly related to the prognosis and survival time of patients, but it has not been studied in osteosarcoma. Therefore, we speculate that
ARHGAP28 is a tumor suppressor gene for osteosarcoma, and we plan to further study it in the next step.
We then constructed a nomogram based on the TCGA cohort to incorporate age, gender, and risk scores and simulated 2-, 3-, and 5-year time-dependent AUC curves for osteosarcoma patients in the TCGA and GSE39055 cohorts, respectively. The results showed that the risk score was an independent predictor of the prognosis of patients with osteosarcoma, and the simulation results of the AUC curve were good, which could better prove the accuracy and applicability of the model. To find out which functional pathway the molecular differences between the high- and low-risk groups are enriched in, to better screen the differential genes that can be used as targets for clinical diagnosis and treatment, we screened the differential genes between the high- and low-risk groups and conducted functional enrichment analysis. KEGG results show that it mainly enriched them in Protein digestion and absorption and Cytokine-cytokine receptor interaction, which also confirms our above analysis. They interact with different upstream and downstream molecules in different tumors to show different functions, and the process is very complex [
11].
Nowadays, immunotherapy has been emphasized in the treatment of patients with osteosarcoma [
50]. Therefore, we further studied whether they relate the risk model to the immune microenvironment, which can provide some ideas for the immunotherapy of osteosarcoma. GSEA analysis showed that the low-risk group had higher enrichment of immune function than the high-risk group [
51], such as the B cell receptor signaling pathway, natural killer cell-mediated cytotoxicity, T cell receptor signaling pathway, and antigen processing and presentation. They have reported that Macrophages
M2 can promote the generation of tumors [
52‐
54]. In our study, the content of Macrophages
M2 is relatively high in osteosarcoma. ssGSEA showed that the content of immune cells in the high-risk group was much lower than that in the low-risk group, indicating that the occurrence and development of osteosarcoma are closely related to the immune environment, which will provide new ideas for us to find new therapeutic targets and methods for osteosarcoma in the future.
Since the role of ARHGAP28 in osteosarcoma remains unclear, we confirmed the role of ARHGAP28 through in vitro and in vivo biological experiments. Overexpression of ARHGAP28 had significant effects on the viability, proliferation, migration, and invasion of OS cells. We found that overexpression of ARHGAP28 can inhibit the proliferation, migration, and invasion of osteosarcoma cells. In vivo experiments have shown that overexpression of ARHGAP28 can inhibit tumor growth in mice, and IHC has shown that the reduced level of Ki-67 in the ARHGAP28 overexpression group can inhibit the proliferation of tumor cells. In summary, ARHGAP28 may play a positive role in inhibiting the growth and progression of osteosarcoma.
However, inevitably, our research also has some shortcomings. First, we only used an external GSE39055 cohort for verification, which may have some discrepancies in some data sets. Second, the expression levels of ARHGAP1, ARHGAP8, and ARHGAP10 in our model showed the same trend with the prognosis and survival time of patients with osteosarcoma, but there was no significant correlation. Whether the model constructed by combining these five genes is also applicable to other cohorts needs further verification. Third, we lack clinical samples to verify the accuracy of the model we constructed, so we can only test our hypothesis with cell experiments. Finally, we did not investigate ARHGAP28 further, such as its relationship to human immunity.
The study categorized OS invalids into risk groups based on the ARHGAP family. The high-OS group displayed abnormal immune function, such as the B cell receptor signaling pathway, natural killer cell-mediated cytotoxicity, T cell receptor signaling pathway, and antigen processing and presentation. The results show that ARHGAP family genes are likely to play a role in the immune function of the human body, inhibiting the occurrence and progression of tumors, and these gene targets may also be promising personalized drug targets.
In summary, we constructed a five-gene (ARHGAP1, ARHGAP8, ARHGAP10, ARHGAP25, and ARHGAP28) risk prognostic model based on the ARHGAP family. It can predict the prognosis of patients with osteosarcoma, and verify its accuracy and universality. Finally, we also analyzed the relationship between it and the immune system of patients, which provided ideas and directions for our follow-up research and the management and treatment of clinical patients.