Identification of abnormally high expression of POGZ as a new biomarker associated with a poor prognosis in osteosarcoma.

Osteosarcoma (OS) is the most prevalent malignant bone tumor in children and young adults. There is an urgent need for a novel biomarker related to the prognosis of OS. We performed a meta-analysis incorporating six independent datasets and performed a survival analysis with one independent dataset GSE21257 in the GEO database for gene screening. The results revealed that one potential biomarker related to OS survival, POGZ was the most significantly upregulated gene. We also verified that the POGZ was overexpressed in clinical samples. The survival analysis revealed that POGZ is associated with a poor prognosis in OS. Moreover, flow cytometry analysis of isolated OS cells demonstrated that OS cells were arrested in the G1 phase after POGZ knockdown. The RNA-seq results indicated that POGZ was co-expressed with CCNE1 and CCNB1. Pathway analysis showed that genes associated with high expression levels of POGZ were related to the cell cycle pathway. A cell model was constructed to detect the effects of POGZ. After POGZ knockdown, OS cell proliferation, invasion and migration were all decreased. Therefore, POGZ is an important gene for evaluating the prognosis of OS patients and is a potential therapeutic target.

Introduction

Osteosarcoma (OS) accounts for approximately 60% of malignant bone tumors in children and young people, making it the most prevalent tumor of this type; it seriously affects patients’ quality of life and results in death due to rapid development of the disease.[1,2] Treatment options for OS include surgery, chemotherapy and radiotherapy. Despite the fact that advances in treatment methods have increased the five-year survival rate of OS patients to approximately 60-70%, approximately 30%-40% of patients still develop lung metastases or die.[3,4] It is worth noting that the prognosis of patients with metastatic OS is extremely poor.[5] Prognostic evaluation and early diagnosis are crucial and can effectively improve the survival of OS patients.[6] However, currently available biomarkers cannot effectively predict the prognosis of OS patients.[7] Therefore, there is a pressing clinical need to discover new biomarkers correlated with the prognosis of OS.[6-8] In medical oncology, highthroughput sequencing and gene chip technology are promising tools used to identify potential molecular targets. They provide new methods to explore OS-related genes and predict new tumor molecular interactions, key regulatory molecules, and therapeutic drug targets.[9] Recently, these technologies have been successfully utilized to discover new biomarkers in tumors, including the MYC[10,11] and RAS,[12] which both promote OS metastasis. In addition, VEGF[13] was found to be positively associated with metastasis and a poor prognosis of in OS patients. Nonetheless, these aforementioned methods do not offer many potential biomarkers related to the prognosis of OS.[14] Hence, it is of tremendous clinical significance to discover new biomarkers related to the prognosis of OS and explore their molecular mechanisms. To expand the research on molecules related to the prognosis of OS, we identified a gene known as POGZ that is considerably linked to OS survival using bioinformatics. To further evaluate the relationship between the expression level of POGZ and OS, we constructed a cytological model verifying the effects of POGZ inhibition on OS cell proliferation and metastasis. POGZ might be regarded as a potential therapeutic target for OS.

Materials and Methods

Clinical specimens

The tissue microarray chip was provided by Typos Biotechnology Company (Xi’an, China) and included 41 OS samples and 19 normal control samples. Table 1 displays the clinicopathological characteristics of the corresponding OS patients.

Table 1.

Clinicopathological characteristics of OS patients.

	Character	Number of case (%)
Grade	G1~G2	17 (0.41)
	G3~G4	24 (0.59)
Sex	F	14 (0.34)
	M	27 (0.66)
Stage	I~II	38 (0.93)
	III~IV	3 (0.07)
Age	<20	13 (0.32)
	>20	28 (0.68)
Location	Lower limb bone	28 (0.68)
	Upper limb bone	13 (0.32)

Data extraction and analysis of screened genes

In the Gene Expression Omnibus (GEO) database (http://www.ncbi.nlm.nih.gov/geo/), we selected 6 expression profiles and downloaded the original (.CEL file) and platform files. Background correction, quantile normalization, professional summary, log2 conversion and missing values supplementation of the matrix data for each GEO dataset were performed using the “affy” software package and R/Bioconductor software (version 3.5.3) “limma” package. Subsequently, the “mama” package was used to group OS patients and normal controls, perform a meta-analysis, and obtain the z-score value. We used the “meta” package in the R software to download the pan genome project containing multitumor expression data to draw a forest map.

The Kaplan-Meier survival curve was plotted using the GEO expression profile and the survival data through the R software “survival” package for single factor Cox survival analysis.

Immunohistochemical staining

After deparaffinization, the tissue microarray chip was subjected to antigen retrieval and endogenous peroxidase blocking. After serum blocking, polyclonal rabbit anti-human POGZ (1:100, Absin, abs132798) was added and incubated overnight at 4°C. Next, the secondary antibody (1:100, Absin, abs20044) of the corresponding species was added, and the DAB staining kit was used to (Vector Laboratories, USA) to detect the signal. The histochemical score was calculated using the Quant Center analysis tool: H score= Σ(PI×I) = (percentage of weak intensity cells×1) + (percentage of medium intensity cells×2) + (percentage of high intensity cells×3).[23]

Receiver operating characteristic (ROC) curve and logistic regression analysis

ROC curve analysis was performed to evaluate the sensitivity (true positive rate) and specificity (true negative rate) of POGZ for OS diagnosis. We also investigated the size of the area under the curve (AUC) by using the “pROC” package of R software.

Biological function and pathway enrichment analysis

Gene set enrichment analysis (GSEA) is a computational method that determines whether an a priori defined set of genes shows statistically significant, concordant differences between two biological states. It can be used to evaluate microarray data at the gene set level. The Kyoto Encyclopedia of Genes and Genomes (KEGG) is a bioinformatics resource. We can use it to study the relationship between genes. Samples from the GSE42352 datasets were divided into two groups based on the expression levels of POGZ (median value) and the gene set enrichment analysis (GSEA) software (http://software.broadinstitute.org/gsea/index.jsp) was applied for both groups.

Cell culture and transient transfection

OS cell lines including Saos-2 and U-2OS were supplied by the China Center Type Culture Collection (CCTCC, Shanghai, China). Saos-2 and U-2OS cells were cultured in a DMEM media (Gibco, Waltham, MA) supplemented with 10% FBS (Gibco, Waltham, MA). All the cells were grown at 37°C in 5% CO2. Following the manufacturer’s recommendations, Turbofect transfection reagent was used (Thermo Fisher Scientific Inc, Shanghai, China) to transfect OS cells with negative control (NC) and POGZ siRNA (GenePharma, Shanghai, China) into OS cells. The siRNA sequences targeting POGZ were as follows: siPOGZ-1: 5’– GCCACGAACUGUUCCUGUATT–3’, 5’–UACAGGAACAGUUCGUGGCTT– 3’; siPOGZ-2: 5’–CCUAAUCAUUUCCCUACUUTT– 3’, 5’ –AAGUAGGGAAAUGAUUAGGTT–3’; and siPOGZ-3: 5’–CCACAUGAUCAACAAUCAUTT–3’, 5’–AUGAUUGUUGAUCAUGUGGTT–3’. The negative control siRNA sequence was as follows: 5’–UUCUCCGAACGUGUCACGUTT– 3’, 5’–ACGUGACACGUUCGGAGAATT–3’.

RNA extraction and real-time quantitative RT-PCR

The total amount of RNA from the OS cells was extracted with the AxyPrep Multisource Total RNA Miniprep Kit (Axygen Scientific, Union City, CA, USA) according to the manufacturer’s protocol. A cDNA volume of 20 μl was synthesized utilizing the Takara PrimeScriptTM RT Reagent Kit and gDNA Eraser (Cat# RR047A, Lot# AK2802) using 1 μg total RNA following the measurements of total RNA concentration using the software Quantity One software (PDI Inc., New York, New York). Afterwards, the quantitative real-time PCR(RT-qPCR) primers were synthesized by Xiangyin Biotechnology China (Hangzhou China). RT-qPCR was performed using TB GreenTM Premix Ex TaqTM II (TakaRa Code: DRR820A) following the standard protocol with the 7900HT Fast real-time System (Applied Biosystems, Foster City, CA, USA). The thermal cycling program consisted of 1 cycle at 95°C for 1 min, followed by 40 cycles at 95°C for 5 s, and then 60°C for 30 s. GAPDH was used as the reference gene. The relative gene expression levels were determined according to the critical threshold (Ct) number and calculated using the 2-ΔΔCt method. The primers for POGZ used for RT-qPCR were as follows: POGZ F1 ACCCAGTTTGTTAAGCCGACA, POGZ R1 CTGGAGACTGAACAGCTAGTTG, POGZ F2 GTGAAGCGACCTGGTGTTACA and POGZ R2 ACATCGTGGACATATTTTCCGTC. The primers for the housekeeping gene were as follows: 5’-TGACTTCAACAGCGACACCCA-3’ (GAPDH forward primer) and 5’- CACCCTGTTGCTGTAGCCAAA- 3’ (GAPDH reverse primer).

Sample collection and high-throughput sequencing

After the U-2OS and Saos-2 cells were transfected with siPOGZ-2 and siPOGZ-3 and NC was used as a control. Next, RNA was extracted separately for high-throughput sequencing 48 h later. We used high-throughput sequencing technology to purify mRNA from total RNA and amplified it with PCR technology to build our RNA database. The concentration of the RNA library was detected and diluted to 1 ng/μL. After accurate quantification, the insertion size was qualified and used to cluster with the coded samples to form clusters. Finally, these clusters were sequenced to determine the expression of the desired gene expression.

Western blotting

Cells were lysed with RAPI buffer 48 h following siRNA transfection, and then centrifuged at 12,000 g for 10 min, and total protein samples were collected. Protein concentration was determined in each sample using the PierceTM BCA Protein Assay Kit (Thermo Fisher Scientific). Subsequently, 25 μg of protein was separated by SDS-PAGE, and then transferred to polyvinylidene difluoride membranes. After being blocked in Tris-buffered saline (TBS) containing 5% non-fat milk for 1 h to saturate additional protein binding sites, the blots were incubated with the following primary antibodies: anti-POGZ (1:1,000, Absin, abs132798), anti- E-cadherin (1:5,000, Proteintech, cat20874-1-AP), anti-N-cadherin (1:2000, Proteintech, cat22018-1-AP), anti-vimentin (1:2,000, Proteintech, cat10366-AP), anti-cyclinE1 (1:1,000, Proteintech, cat11554-1-AP), and cyclinB1 (1:1,000, Proteintech, cat55004-1-AP). The antibodies were maintained at 4°C for 12 h, followed by incubation with horseradish peroxidase-conjugated secondary (anti-mouse or anti-rabbit) IgGs at room temperature for 2 h or 4 h. The proteins were visualized by using a BM Chemiluminescence Western blotting kit (Roche Diagnostics GmbH). To ensure the equal loading and the accuracy of changes in protein abundance, the level of each protein was normalized to that of GAPDH as a housekeeping control.

Colony formation assay

Cells were plated on six-well tissue culture plates at a density of 50 cells/cm². Fourteen days later, the colonies were fixed with ethanol and stained with 2% crystal violet, then washed with water to remove the excess dye, and imaged using a scanner. Changes in clonogenicity were quantified by counting the number of colonies, using the ImageJ software.

Cell proliferation by EdU and Cell Counting Kit-8 (CCK8) assay

To assess the degree of cell proliferation, 5x103 U-2OS and Saos-2 cells were plated on 24-well plates, and then the cells were incubated under standard conditions in a complete media. After 48 h following siRNA transfection, cell proliferation was detected based on the incorporation of 5-ethynyl-2′-deoxyuridine (EdU) with the EdU Cell Proliferation Assay Kit. Images were captured using a fluorescence microscope (OLYMPUS, BX53, China). The level of cell proliferation was evaluated using the CCK-8 assay (DOJINDO, Kumamoto, Japan, Cat# CK04). For this purpose, approximately 3,000 cells in 100 μL medium were seeded in each well of a 96-well plate, and three independent parallel experiments were set up. The cells were incubated at 37°C in 5% CO[2], and 10 μL CCK-8 reagent was added to the wells at 1, 2, 3, 4, 5 and 6 days, and incubated for 2 h. Finally, the absorbance was measured at a wavelength of 450 nm.

Cell migration and invasion assay

OS cells in 200 μL of serum-free DMEM were added to upper chamber of prepared Transwell plates for the migration and invasion assays, and media containing 10% FBS was added to the lower chamber. The plates were incubated in 5% CO2 at 37°C overnight. The cells on the upper surface were removed using a cotton bud. The remaining invading cells were fixed and stained with 2% crystal violet. Five representative fields of view for each membrane were selected, then images were taken via microscopy, and the number of migrating cells was counted using the ImageJ software.

Cell cycle analysis by flow cytometry

Fixed cells were stained with propidium iodide (PI) (50 μg/mL, Sigma) 48 h after transfection with siRNA targeting POGZ. The tests were performed in triplicates. To guarantee the accuracy of the cell cycle analysis, the cells that interfered after 48 hours were centrifuged at 1,500 rpm for 5 min and then resuspended in 500 μl of PBS, and 1.5 mL of a 95% ethanol solution (-20°C pre-cooled) was added to fix the cells at -20°C for 10 min. After centrifugation of the cells centrifuged at 1,500 rpm for 10 min, the supernatant was discarded, and 500 μl PBS was added to resuspend the hydrated cells for 10 min. Subsequently, RNase A was added at 37°C for 10 min. Ultimately, we added PI (50 μg/ml) to DNA and the content was stained for 15 min. Then, the EdU test was performed. EdU analysis and cell cycle determination were performed using BD CellQuest Pro™ on a BD FACSCalibur flow cytometer (BD Biosciences, New Jersey, USA) by obtaining at least 20,000 mononuclear cells.

Statistical analyses

Statistical analyses were performed using the SPSS software version 22.0 and GraphPad Prism 7 software. All the data are expressed as the mean ± standard deviation. One-way analysis of variance (ANOVA) was conducted to compare the data from multiple groups simultaneously. Student’s t-test was used for data comparison between two groups. For all analyses, two-tailed pvalues below 0.05 were considered statistically significant.

Results

Identification of genes that are significantly related to the prognosis of OS

After excluding 244 studies involving cell lines and 31 nonconforming studies in the GEO, we selected 6 studies, whose data were included in the GSE11414,[15] GSE12865,[16] GSE14359,[17] GSE16102,[18] GSE42352,[19,20] GSE42572[21] datasets, for inclusion in the meta-analysis (Supplementary Figure 1). We selected 10,390 genes from the 6 datasets for meta-analysis and obtained the corresponding negative Z score. There were no survival data in the six datasets for meta-analysis, so the GSE21257 dataset was used for survival analysis to obtain the log2 HR value corresponding to each gene. Based on the cut-off criteria of a negative Z score greater than 4.5 and a log 2 hazard ratio (HR) value greater than 1, we screened out 4 genes that met the criteria (POGZ, CTSE, GALNT14, HSD11B2, Figure 1a). Among these 4 genes, we selected the most significantly upregulated gene (with the most negative Z score), POGZ (negative Z score=5.06, log2 HR=1.26, p=0.04). Additionally, the forest plot revealed that within the 6 datasets, POGZ was steadily upregulated and had no heterogeneity among the datasets (I2 = 25%, T2 = 0.1338, p=0.25, Figure 1b).

Figure 1.

Identify genes that are unfavorable upregulated and associated with a poor prognosis of OS. A) Identifying candidate genes according to the criteria: negative Z score >4.5 and log 2 HR >1; among these 4 genes, POGZ had the most significantly upregulated gene (the most negative Z score). b) Forest plot of POGZ expression across meta-analysis; the plot revealed that within the 6 datasets, POGZ was steadily upregulated and had no heterogeneity (I2 = 25%, T2=0.1338, p=0.25). c) KM analysis of overall survival was performed to indicate higher expression of POGZ were correlated with the poor survival of OS patients in GSE21257. d) Forest plot of POGZ expression in different types of tumors; BRCA, breast invasive carcinoma; COAD, Colon adenocarcinoma; ESCA, esophageal carcinoma; HNSC, head and neck squamous cell carcinoma; kidney chromophobe-primary tumor; KIRC kidney renal clear cell carcinoma; KIRP, kidney renal papillary cell carcinoma; LIHC, liver hepatocellular carcinoma; LUAD, lung adenocarcinoma; LUSC, lung squamous cell carcinoma; PAAD, pancreatic adenocarcinoma; PRAD, prostate adenocarcinoma; READ, rectum adenocarcinoma; THCA, thyroid carcinoma; UCEC, uterine corpus endometrial carcinoma. e-f ) The relationship between high expression of POGZ and prognosis of multiple tumors was drawn.

As illustrated in Figure 1c, the survival analysis of 53 OS patients from the GSE21257 dataset suggested that the high expression level of POGZ was significantly related to the poor prognosis of OS (p<0.05, HR = 2.387). In addition, we performed the survival analysis using the TARGET_OS and GSE39055 datasets. The results also showed a consistent trend (Supplementary Figures 2 and 3). The data of GSE21257, GSE39055 and TARGET_OS data were used for meta-analysis. The results showed that POGZ was steadily upregulated without heterogeneity among the datasets (I2 = 0%, Z=2.66, p=0.008, Supplementary Figure 4).

Figure 2.

POGZ expression is upregulated in clinical OS samples and related to metastasis. a) Representative images of IHC staining for POGZ in normal bone tissues and OS tissues. b) Quantification of POGZ IHC staining in OS (n=41) and normal tissues (n=19). c,d) The ROC curve from GSE42352 and shows that POGZ is a potential marker enabling to distinguish OS tissues from normal tissues. e,f ) High expression of POGZ is associated with metastasis of osteosarcoma; *p<0.05, **p<0.01, ***p<0.001.

Figure 3.

Cell proliferation assay in POGZ inhibited OS cell lines. a) The capability of different siRNAs in terms of downregulating POGZ expression. b) The protein levels of POGZ by western blot after different siRNAs knock down. c) Representative clonogenic assay of U-2OS cells and Saos-2 cells expressing the indicated plasmids. Quantitative analysis was performed using the ImageJ software. d,e) EdU assays for proliferation rates. f,g) CCK8 assays for proliferation rates; *p<0.05, **p<0.01, ***p<0.001.

Figure 4.

POGZ inhibitions prevent the metastasis and invasion of OS cells. a,b) Transwell assays showed that POGZ inhibitions significantly reduced the migration (upper) and invasion (lower) of OS cells. c,d) EMT markers were detected by Western blot; *p<0.05, **p<0.01, ***p<0.001.

To explore the effects of POGZ on a broad range of tumors, we analyzed tumor tissue versus adjacent normal tissue gene expression in 15 tumor types (a total of 7,316 samples), based on the data containing the tumor and controls in The Cancer Genome Atlas (TCGA) database. The results indicated that the expression level of POGZ in each tumor was unstable (Figure 1d). Compared with that in the control group, POGZ expression was upregulated in liver cancer (P<0.05) and downregulated in renal chromophobe cell carcinoma (p<0.05). Moreover, we analyzed the correlation between POGZ expression levels and multi-tumor overall survival and disease- free survival, and the results confirmed that the upregulation of POGZ in liver cancer was not related to the prognosis (p>0.05, Figure 1 e-f). Generally, we believe that POGZ is an unfavorable risk factor for OS.

Verification that POGZ is upregulated and related to OS metastasis

In order to verify the upregulation of POGZ in OS tissue, we performed IHC staining on 41 OS tissues and 19 control tissues. POGZ was overexpressed in OS tissues versus control tissues (Figure 2a). Subsequently, we conducted ROC curve analysis to evaluate the sensitivity and specificity of POGZ for the diagnosis of OS. The sensitivity and specificity values of the ROC curve were 0.927 and 0.739, respectively based on the GSE42352 and GSE99671 datasets (Figure 2 b,c).

Moreover, based on 6 datasets including metastatic and nonmetastatic OS samples, we developed a forest map of POGZ expression level analysis (Supplementary Figure 5). After excluding 4 datasets with small sample sizes, we selected the gene expression profiles and corresponding clinical information in the GSE42352 and GSE14359 datasets for the further independent analysis. Next, we utilized POGZ expression levels to perform a ttest and found that POGZ expression levels were increased in the metastatic groups within the GSE42352 (t = 1.90 p=0.031) and GSE14359 cohorts (t = 2.29 p=0.018) were all increased (Figure 2 d,e). Ultimately, high expression levels of POGZ play a vital role in the progression of OS.

Figure 5.

Genes associated with POGZ were enriched in cell cycle related processes and POGZ controls the cell cycle progression in vitro. a) KEGG pathway enrichment analysis of genes positively and negatively associated with POGZ. b) POGZ is associated with cell cycle progression. c) Heat map of core-DEGs related to cell cycle process in POGZ inhibited U-2OS and Saos-2 cells; the heat map reveals that CCNE1 was upregulated and CCNB1 was downregulated in OS cells after POGZ inhibition. d) Representative images (left) and quantification (right) of negative control (siPOGZ-3-transfected U-2OS and Saos-2 cells) were analyzed in the cell cycle assay. e) Cell cycle related makers were detected by Western blot; *p<0.05, **p<0.01, ***p<0.001.

POGZ knockdown inhibited OS cell proliferation

To determine the impact of POGZ on OS proliferation, two siRNA sequences were used to knock down the expression of POGZ in U-2OS and Saos-2 cells. RT-qPCR and Western blotting were carried out to gauge the knockout efficiency (Figure 3 a,b). A clonogenic assay proved that compared with that in the NC group, the number of colonies formed by OS cells transfected with siPOGZ-2 and siPOGZ-3 was greatly reduced (Figure 3c). The EdU detection method was implemented to evaluate the cell proliferation. This method is an immunochemical detection method that measures the incorporation of nucleotide analogs into newly copied DNA. In U-2OS and Saos-2 cells, the percentage of EdUpositive cells treated with siPOGZ-2 and siPOGZ-3 was significantly reduced (Figure 3 d,e). Additionally, we assessed the proliferation of OS cells using the CCK-8 assay. The growth rate of OS cells with POGZ knockdown was decreased compared to that of control cells (Figure 3 f,g). These results indicate that interference with POGZ expression inhibits OS cell proliferation.

Interference with POGZ inhibits the migration and invasion of OS cells

Migration and invasion are key steps in the development of tumors and metastasis of tumors. In vitro transwell analysis was used to study the effect of POGZ on the migration and invasion of OS cells. Compared with NC cells, U-2OS and Saos-2 cells in the siPOGZ-2 and siPOGZ-3 groups exhibited remarkably reduced migration and invasion (Figure 4 a,b). Furthermore, Western blotting was performed to assess the levels of epithelial-mesenchymal transition (EMT)-related proteins, and the results indicated that vimentin and N-cadherin were both downregulated, while E-cadherin was more upregulated in the U-2OS and Saos-2 cells of the siPOGZ groups than in those of the NC group (Figure 4c). These findings suggest that interference with POGZ expression inhibits OS cell metastasis.

Genes associated with POGZ are enriched in cell cycle related processes

Based on the RNA-seq, the KEGG pathway analysis results revealed that the following pathways were positively related to POGZ expression: the DNA replication, homologous recombination, the cell cycle, the Fanconi anemia, RNA transport, spliceosomes pathways. Most of these pathway’ terms (cell cycle, DNA replication, homologous recombination, and Fanconi anemia) are related to the cell cycle (Figure 5 a,b). To determine which genes in the POGZ and cell cycle pathways were co-expressed to affect the growth of OS cells, we used the high-throughput sequencing results to perform a differential analysis, which demonstrated that CCNE1 was upregulated and CCNB1 was downregulated (Figure 5c). The above results confirm that POGZ has a regulatory effect on cell cycle pathways and also affects the expression of downstream CCNE1 and CCNB1.

POGZ controls the cell cycle progression in vitro

To verify the results of our biological analysis and highthroughput sequencing, we performed a flow cytometric analysis of isolated OS cell cell cycle. OS cells with POGZ knockdown exhibited cell cycle arrest in the G[1] phase, confirming the role of POGZ in regulating cell cycle progression (Figure 5d). Western blot detection of cycle-related proteins revealed that CCNE1 was upregulated and CCNB1 was downregulated in knockdown cells compared to control cells (Figure 5e). Overall, POGZ was shown to affect the cell cycle of OS cells by participating in the regulation of the G1 phase.

Discussion

POGZ is an important gene for evaluating the prognosis of OS patients and is highly expressed in OS cells. Inhibiting the expression of POGZ can reduce OS cell proliferation and tumor metastasis. POGZ regulates OS cell cycle-related processes, but the mechanism by which OS cells become arrested in the G1 phase after POGZ inhibition requires more in-depth investigation.

In this study, four candidate genes (POGZ, CTSE, GALNT14, HSD11B2) met our inclusion criteria: highly expressed in OS and related to a poor prognosis. POGZ contains at least one short region called the zinc finger domain and also contains a centromere protein (CENP)-B-like DNA binding domain and a DDE domain derived from a transposase encoded by a pogo-like DNA transposon. POGZ is involved in normal kinetochore assembly, cohesion of mitotic sister chromatids, and separation of mitotic chromosomes.[26] Previous genetic findings have shown that POGZ is responsible for various neurodevelopmental disorders, including autism spectrum disorders and intellectual disabilities, and is also considered to be related to ‘synaptic disorders’.[27] Since POGZ is the most upregulated of the four target genes, as well as the most harmful to the survival of OS patients, and given that there is no literature report revealing its impact on tumorigenesis, we decided to conduct an in-depth research on it.

Based on the TCGA database, we used Gene Expression Profiling Interactive Analysis (GEPIA) to analyze the relationship between POGZ expression and the prognosis of multiple tumors. Supplementary Figures 6 and 7 show that POGZ acts as a survival protective factor in other solid tumors (disease-free survival HR = 0.91, p=0.017; overall survival HR = 0.9, p=0.0065). Interestingly, these results indicate that high expression of POGZ is a specific risk factor in OS. Furthermore, based on the gene expression profiles and clinical information of the two datasets, we divided the dataset samples into metastatic and non-metastatic OS groups, and found that the expression of POGZ was significantly higher in the metastatic OS group.

In terms of molecular phenotype, we established a cell model with POGZ downregulation to explore the role of POGZ in OS. The proliferation and colony formation of OS cells decreased after POGZ knockdown. Transwell experiments demonstrated that the migration and invasion abilities of OS cells were weakened after POGZ knockdown. EMT is a process involving the invasion and metastasis of various tumors.[28] In this study, we found that the expression of EMT-related proteins vimentin, N-cadherin and Ecadherin in OS cells was positively correlated with the expression of POGZ. This finding indicates that POGZ could induce the metastasis of OS via EMT.

The function of many oncogenes is to either stimulate cell division or counteract cell cycle arrest.[29] Western blot analysis of cell cycle-related gene levels by Tanak et al. showed that OS cells that were not treated with a gamma-secretase inhibitor (GSI) had higher expression levels of cyclin E1 and a higher number of cells in the G[1] phase than those treated with a GSI treatment.[30] In an experiment to study the effect of TCTP expression on the growth of OS cells, Shen et al. found that cyclinB1 protein levels in Saos-2 and U2O-S cells decreased after Lv-shTCTP infection and the proportion of cells in the G[2]/M phase was significantly reduced.[31] In our study, the KEGG pathway analysis in Figure 5a indicates that the OS cell cycle pathway is activated, and the first four pathways with the most significant enrichment are all cell cycle related. According to POGZ and OS cell cycle pathway gene co-expression annotation analysis, we found that after knocking down POGZ, CCNE1 was upregulated and CCNB1 was downregulated. Western blotting was used to verify the above results. CCNB1 (cyclin B) is necessary for adequate control of the G2/M transition phase of the cell cycle.[32] CCNE1 (cyclin E) is a positive regulator of the cell cycle. It promotes the G[1]/S phase transition by binding to CDK2 and activating CDK2.[33] Thus, we believe that POGZ regulates the OS cells, thereby affecting cell proliferation. However, the limitation of our research was the underlying molecular mechanism was not investigated.