Introduction

Osteosarcoma, a malignant bone tumor, poses a significant threat to the lives of teenagers, children, and individuals over 60 years old1. Substantial advancements have been made in the diagnosis and management of osteosarcoma in recent decades2. The implementation of multi-agent chemotherapy several decades ago resulted in a significant improvement in the 5-year event-free survival rate for localized high-grade osteosarcoma, increasing it from less than 20% to approximately 60%3. However, chemotherapy has altered the timing, distribution, and incidence of metastases, yet it has not yielded a substantial impact on patient survival, as only 20 to 30% of patients with synchronous metastases and 30–40% of those with metachronous metastases achieve a cure4,5,6,7. Furthermore, the high degree of heterogeneity observed in osteosarcoma renders therapy even more problematic, which poses challenges to investigators8. Despite the identification of certain prognostic biomarkers, accurately predicting the prognosis of osteosarcoma patients remains challenging. Hence, the identification of novel diagnostic and prognostic biomarkers, along with the investigation of potential therapeutic targets, is imperative for the efficacious management of osteosarcoma.

The human genome contains a vast family of regulatory proteins, known as zinc finger (ZNF) protein genes, which are characterized by their zinc ion–binding finger-like ___domain9. These proteins exhibit a multitude of functions in diverse cellular processes, encompassing transcriptional regulation, mRNA stability, and protein degradation, through their interactions with RNA, DNA, or proteins 10,11. Recent research has shown that ZNF proteins are also involved in oncogenesis and tumor progression12,13,14,15, and abnormal expressions of ZNF proteins or genes have been found in malignant tumors, making them potential diagnostic or prognostic biomarkers. For instance, the expression level of ZNF233 exhibited correlations with the grade, stage, and prognosis of hepatocellular carcinoma (HCC) patients. This suggests that ZNF233 has the potential to be developed as a novel biomarker and therapeutic target for HCC16. ZNF655 demonstrated a notable role in promoting the interaction between E2F transcription factor 1 (E2F1) and the promoter region of cyclin-dependent kinase 1 (CDK1). Furthermore, the upregulation of ZNF655 expression in pancreatic cancer cells was attenuated upon suppression of CDK1. These discoveries elucidate the contributory function of ZNF655 in pancreatic cancer via its association with CDK1, thereby sparking interest in its prospective utilization as a promising therapeutic target in clinical settings17. ZNF331 serves crucial roles as a newly identified functional tumor-suppressor gene in inhibiting the development of gastric cancer. Suppression of ZNF331 resulted in an elevation of cell viability and enhanced colony formation ability in the gastric cancer cell line MKN4518.

A study found that mutations observed in ZNF560 demonstrated a notable positive correlation with the survival of patients diagnosed with lung adenocarcinoma (LUAD)19. An alternative study indicated that alterations in ZNF560 expression resulting from chronic hypoxia play a crucial role in the progression of prostate cancer20. Furthermore, ZNF560 was identified as a high-risk gene associated with unfavorable outcomes in individuals diagnosed with acute myeloid leukemia (AML)21. These findings indicate a potential connection between ZNF560 and the development of tumors. Through an analysis of the GEO and TCGA databases, we identified ZNF560 as a gene associated with osteosarcoma, exhibiting distinct expression patterns in osteosarcoma tissues compared to normal tissues. The objective of this study was to comprehend the functional role of ZNF560 in osteosarcoma and assess its diagnostic and prognostic significance.

Materials and methods

In silico data collection and preprocessing

UALCAN (http://ualcan.path.uab.edu/, version 08/16/2022), a comprehensive platform designed to facilitate gene expression and survival analyses across various cancers within the TCGA database, was employed to scrutinize the expression levels of ZNF560 in multiple cancer types, with a particular focus on sarcoma. The abbreviation of various cancers included in TCGA was shown in Table 1. Gene expression profiling datasets (GSE99671) were procured from the GEO database (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE99671), consisting of total RNA sequencing data from 36 fresh-frozen samples (18 tumoral bone samples and 18 non-tumoral paired samples) matching in pairs for each osteosarcoma patient [PMID: 29250102] Stringent statistical criteria, including a p-value < 0.05 and |fold change (FC)| ≥ 1.5, were applied along with Benjamini and Hochberg false discovery rate (FDR) adjustments to balance the identification of statistically significant genes while mitigating false positives.The ZNF560-low and ZNF560‐high groups were stratified using median values of the ZNF560 transcript in the RNAseq data from TCGA. Stringent thresholds (p < 0.01 and |fold change (FC)| ≥ 2) were employed to define differentially expressed genes (DEGs). Data visualization encompassed the creation of volcano plots and heatmaps utilizing R 4.3.1. Kaplan-Meier survival plots were generated, displaying hazard ratios, 95% confidence intervals, and log‐rank p-values, with statistical significance set at p < 0.05. The GSEA (Gene Set Enrichment Analysis) methodology, providing a computational framework to investigate changes in particular biological processes and pathways within a given dataset, was employed with the software (v4.3.2 for Windows). Gene expression profiles of sarcoma patients were subjected to analysis using the “c2.cp.kegg.v2023.1.Hs.symbols.gmt” gene set from the Molecular Signatures Database (MSigDB 2023.1), with a significance threshold set at P < 0.05.

Table 1 Abbreviations of various cancers in TCGA databse.
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Osteosarcoma tissues collection

Totally 49 human osteosarcoma tissue samples were procured from patients undergoing primary total knee arthroplasty and orthopedic surgery for osteosarcoma at The Second Affiliated Hospital of Nantong University, after obtaining approval from the local Institutional Review Board (P20230224-025). All methods were performed in accordance with the relevant guidelines. Prior to enrollment, all participants provided written informed consent.

Immunohistochemistry (IHC) staining

Immunohistochemical (IHC) analysis was conducted on formalin-fixed tissues. The samples were deparaffinized in xylene, rehydrated in ethanol, and subjected to 3% hydrogen peroxide treatment to inhibit endogenous peroxidase activity and minimize nonspecific binding. Tissues were then incubated overnight at 4 °C with primary antibodies targeting ZNF560 (1:200 dilution, Biorbyt, USA, orb26621), followed by incubation with a secondary antibody, HRP-conjugated goat anti-rabbit IgG (1:200 dilution, Beyotime, A0208), at room temperature for 1 h. After incubation with peroxidase-conjugated streptavidin and diaminobenzidine, hematoxylin was used for counterstaining. The proportion of positively stained cells, categorized by score for both nuclear and cytoplasmic areas, was evaluated to the nearest 5% within a standardized field of five photomicrographs per sample (across 26 OSA samples) for each antibody. H-scores were determined with the formula: H-score = [1 × (% cells 1+) + 2 × (% cells 2+) + 3 × (% cells 3+)]. Each specimen’s cytoplasmic, nuclear, and total H-scores (ranging from 0 to 300) were computed for each relevant marker. An initial double-blinded researcher performed the H-score calculations and set the scoring criteria. Subsequently, a second researcher reviewed a random 10% of the samples to verify consistency and accuracy, achieving an intraclass correlation coefficient (ICC) exceeding 90% for all evaluated proteins. Tissue samples with staining intensity scores equal to or higher than the median values of immunohistochemistry (IHC) were classified as having high expression of ZNF560, while those with lower scores were categorized as exhibiting low expression.

Cell lines and cell culture conditions

Human OS cells lines including HOS and MG63 were procured from the Type Culture Collection of the Chinese Academy of Sciences(China). Cells were cultured in DMEM medium (GIBCO, USA), supplemented with 10% fetal bovine serum (FBS, GIBCO, USA), and maintained in a humidified atmosphere at 37 °C with 5% CO2.

Lentivirus transfection and cell infection

To suppress the activity of ZNF560, cells were infected with lentiviruses carrying short hairpin RNAs (shRNAs) targeting the human ZNF560 gene (Gene ID: 147741), along with pGCSIL-green fluorescent protein (GFP) for assessing transduction efficiency. Three shRNAs were synthesized to target specific interference sequences within ZNF560: shZNF560-1 (5’- GCTCATATCTTACCAAACATT-3’), shZNF560-2 (5’- CCGTCAGGCTTTCTTGAACAT-3’), and shZNF560-3 (5’- GTACATCCTCAGGTGTTATTG − 3’). The shRNA exhibiting the most potent interference effect on ZNF560 expression was individually selected. Lentivirus lacking the shRNA insert served as a control. HOS and MG63 cells were both seeded in 6-well plates at a density of 2 × 105 cells per well and transduced with shRNA-ZNF560 (5 × 108 TU/mL) or shRNA-NC lentivirus (7 × 108 TU/mL). After 72 h of transduction, cells were visualized under a fluorescence microscope and subsequently subjected to puromycin selection. Five days after infection, the downregulation of ZNF560 was confirmed via qRT-PCR analysis.

RNA extraction and quantitative real-time PCR(qRT-PCR)

Total RNA was extracted from HOS and MG63 cells using TRIzol total RNA reagent (Pufei Biotech, China). Subsequently, cDNA was synthesized according to the instructions of M-MLV reverse transcriptase (Promega, USA). The primers targeting ZNF560 were synthesized by Gene Chem Co.Ltd. (China), with GAPDH serving as a reference gene. The primer sequences were as follows: GAPDH forward, 5’-GAAGGTGAAGGTCGGAGTC-3’, and reverse, 5’-GAAGATGGTGATGGGATTTC-3’; ZNF560 forward, 5’- CCTGAAATGGTTGAATGGGTTAG-3’, and reverse, 5’- CCATCAGAGTCAGAAGGGAAAGT-3’;PCNA forward, 5’- GCGTGAACCTCACCAGTATGT-3’, and reverse, 5’- TCTTCGGCCCTTAGTGTAATGAT-3’; UBE2C forward, 5’- GACCTGAGGTATAAGCTCTCGC-3’, and reverse, 5’- CAGGGCAGACCACTTTTCCTT-3’; CDCA5 forward, 5’- AAAGCCCATCGTCTTAAAGAGG-3’, and reverse, 5’- GGGACGCTGTGTGTCTTGA-3’; CDK4 forward, 5’- TCAGCACAGTTCGTGAGGTG-3’, and reverse, 5’- GTCCATCAGCCGGACAACAT-3’; CDK6 forward, 5’- TCTTCATTCACACCGAGTAGTGC-3’, and reverse, 5’-TGAGGTTAGAGCCATCTGGAAA-3’.The reactions were performed using SYBR premix Ex Taq II (Takara Biomedical Technology Co., Ltd., Japan). Relative expression levels of ZNF560 were analyzed by normalizing to GAPDH. The amplification process was conducted with the following thermocycling protocol: an initial reverse transcription at 50˚C for 30 min, followed by a denaturation step at 95˚C for 15 min. This was succeeded by 45 cycles consisting of denaturation at 94˚C for 30 s, annealing at 55˚C for 30 s, and extension at 72˚C for 30 s. The relative mRNA expression of ZNF560 was calculated using the comparative threshold cycle (2−△△Ct and 10000/2△Ct) method.

Western blotting (WB) assay

HOS and MG63 cells were treated with RIPA buffer (Beyotime) to extract proteins, and the protein concentration was quantified using the BCA Protein Assay Kit (Beyotime). A total of 20 µg of protein per well was loaded onto a 10% SDS-PAGE gel and transferred onto a PVDF membrane (Millipore). The membrane was then incubated overnight at 4 °C with the respective primary antibodies (1:500, Invitrogen, USA, PA5-41224), followed by a 2-hour incubation with secondary antibodies at room temperature. Protein bands were visualized using a chemiluminescence ECL kit (Thermo Fisher Scientific), with GAPDH utilized as the loading control.

3-4,5-dimethylthiahiazo (-z-y1)-3,5-di- phenytetrazoliumromide (MTT) assay and cell counting assay

HOS and MG63 cells were plated in 96-well plates at a density of 2000 cells per well. The MTT assay was utilized to assess cell viability for a duration of 5 days, and a growth curve was constructed to determine the cell proliferation rate. The GFP-expressing cells were detected using Celigo, and images were acquired to obtain cell counts, enabling the generation of a growth curve spanning 5 days.

Analysis of cell cycle and apoptosis

HOS and MG63 cells were cultured in 6-well plates at a volume of 2 mL per well for a period of 5 days. To analyze apoptosis, the cells were first washed using pre-cooled D-hanks (pH = 7.2 ~ 7.4) followed by 1×binding buffer (eBioscience). After harvesting, the cellular pellets were reconstituted in 200 µL of 1× binding buffer. Subsequently, they were subjected to staining with 10 µL of Annexin V-APC (eBioscience) at room temperature under dark conditions for a duration of 15 min. Flow cytometry analysis (Millipore) was then performed to assess the samples.

For cell cycle analysis, the cells were centrifuged for 5 min, followed by washing of the resulting cell pellets with pre-cooled PBS (pH = 7.2 ~ 7.4). To induce fixation, the cells were immersed in 70% ethanol for at least 1 h. They were later stained with propidium iodide (PI) (Sigma) and subsequently analyzed using flow cytometry.

Wound-healing assay

HOS and MG63 cells were seeded in 6-well plates at a density of 4000 cells per well with a volume of 100 µL. The experimental protocol was conducted following the procedures described in a referenced literature. In order to mitigate the influence of cell proliferation on migration, the cells were cultured in a serum-deprived medium and subjected to a 1-hour treatment with mitomycin prior to the initiation of the wound-healing experiments. A straight-line incision was then generated across the cellular monolayer using a pipette tip. Subsequent to this, the cells underwent a PBS wash, followed by fixation in 3.7% paraformaldehyde (Corning) for 15 min and staining with 1% crystal violet (Corning) for 10 min. Microscopic imaging was employed to capture cell visuals, and the measurement of the distance (in µm) between the incisions at 0 h and 24 h was conducted using Image J software (National Institutes of Health).

Transwell assay

HOS and MG63 cell lines were cultured at a density of 80,000 cells per well in Transwell chambers (24-well format, 8-mm pore size, Corning) and maintained for a period of 24 h at a temperature of 37 °C. The inner compartment of the Transwell chambers contained 100 µL of cell suspension, while the outer compartment was supplemented with 500 µL of DMEM medium containing 30% fetal bovine serum (FBS). After the 24-hour incubation period, non-invasive cells residing in the upper chamber were carefully eliminated, and the cells adhering to the polycarbonate membrane were fixed using 4% pre-cooled paraformaldehyde for a duration of 30 min. Following fixation, the cells were subjected to staining with 0.1% crystal violet solution for 20 min at ambient room temperature. Subsequently, high-resolution images of the cells were acquired from five randomly selected microscopic fields using a microscope with a magnification of 200×.

Clone formation experiment

Following the transfection of cells, the cell suspensions were transferred to 1.5mL Eppendorf tubes, thoroughly mixed and diluted, and subsequently seeded into 6-well plates at a density of 20,000 cells per well. The culture medium was refreshed every three days, and the cells were allowed to form clones over a period of approximately ten days. Subsequently, the culture medium was aspirated, and the cells were gently washed and fixed in 4% paraformaldehyde for a duration of 15 min, followed by staining with 0.1% crystal violet for an additional 15 min. Finally, the cells were washed, air-dried, and subjected to photographic documentation to enumerate clonal cell clusters, followed by subsequent statistical analysis.

Statistical analysis

The data processing was performed with GraphPad 8.0.2 and figure arrangement was carried out with Adobe Illustrator 2022. Continuous variables were reported as the means ± standard deviation (SD). Statistical analysis for comparison among multiple groups was performed using one-way analysis of variance (ANOVA) followed by a post-hoc test. Each experiment was replicated at least three times to ensure reliability and reproducibility of the results. The significance level for all analyses was set at a P-value less than 0.05, indicating statistical significance.

Results

ZNF560 is highly expressed and is a poor prognostic factor in osteosarcoma

Utilizing UALCAN for data mining of the TCGA database, we characterized the mRNA expression of ZNF560 across 24 tumor types in comparison with their respective normal controls. As depicted in Fig. 1A, ZNF560 mRNA expression exhibited a significant elevation in various human solid tumors, notably in PRAD (Prostate adenocarcinoma), SARC (Sarcoma), THCA (Thyroid carcinoma), and THYM (Thymoma). Given the restricted availability of normal control samples within the SARC datasets, as 2 normal control and 260 sarcoma samples were included, the statistical significance of ZNF560 expression levels in comparison to both cancer and adjacent non-cancer samples is constrained. To address this limitation, we gathered data from the GSE99671 dataset, encompassing 18 tumoral osteosarcoma samples and 18 non-tumoral paired samples. Through differential gene analysis, we confirmed a total of 127 differentially expressed genes (DEGs), with 110 being upregulated and 17 downregulated (Fig. 1B and C, and supplementary Table 1). Notably, ZNF560 exhibited significant upregulation in osteosarcoma samples. Concurrently, integrating the prognostic data of sarcoma patients from TCGA, we conducted survival analysis, revealing a noteworthy association between elevated ZNF560 expression levels and unfavorable prognosis in individuals afflicted with osteosarcoma, particularly concerning overall survival (OS; p = 0.012; Fig. 1D).

Fig. 1
figure 1

ZNF560 was highly expressed and was a prognostic factor for poor outcomes in osteosarcoma. (A). ZNF560 mRNA levels across TCGA tumors compared with normal tissue samples, determined using the UALCAN database. (B). Volcano plot of differentially expressed genes (DEGs) from GSE99671 dataset. The horizontal axis represents the log2 (fold change) and the vertical axis represents the-log10 (P-value). Red dot indicates genes with high levels of expression (110), blue indicates genes with low levels of expression17, and gray indicates genes with no differential expression based on the criteria of P value < 0.05 and |log2 (fold change)|> 1.5. (C). Heat map of top differentially expressed genes from GSE99671 dataset. Gene expression levels were indicated by colors as shown by the row, red represents high expression level and blue represents low expression level. (D). OS survival curves showing the relationship of ZNF560 expression with the survival probabilities of sarcoma patients in the TCGA database.

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To delve deeper into the association between the distinct expression patterns of ZNF560 and other genes, we categorized sarcoma patients from TCGA into high and low expression cohorts using the median ZNF560 expression as the threshold. Following this stratification, we proceeded with a comparative gene analysis between these two cohorts, revealing a total of 348 upregulated genes and 209 downregulated genes (Fig. 2A, and supplementary Table 2). A heatmap illustrating the top 30 highest expressed genes is depicted in Fig. 2B, revealing that ZNF229, ZNF728, among others, exhibit similar expression patterns to ZNF560. Furthermore, employing Gene Set Enrichment Analysis (GSEA) on the differential gene expression between these two groups, we discovered significant enrichments in signaling pathways related to cell cycle, cell cycle mitotic, cell cycle checkpoints, and DNA replication (Fig. 2C). These findings suggest a potential involvement of ZNF560 in cellular processes associated with the cell cycle and mitosis.

Fig. 2
figure 2

Biological processes involved in ZNF560 gene signaling. Volcano plots (A). and heat maps (B). showing the level of differentially expressed genes with 348 genes upregulated and 209 genes downregulated for the ZNF560 highly expressed group. Red and blue dots indicate differentially expressed genes; gray dots indicate genes that are not differentially expressed. (C). KEGG enrichment result by gene set enrichment analysis (GSEA) of coregulated gene expression changes for ZNF560 highly expressed groups. Genes in the KEGG cell cycle mitotic, cell cycle checkpoints and DNA replication signaling pathway revealed a significant enrichment in higher expression of ZNF560 group compared with lower ZNF560 expression group. The top portion of the figure plots the enrichment scores for each gene and the bottom portion of the plot shows the value of the ranking, moving down the list of ranked genes. KEGG, Kyoto Encyclopedia of Genes and Genomes.

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Simultaneously, we gathered 49 osteosarcoma tissue specimens, which were utilized to construct tissue microarrays. ZNF560 was subjected to immunohistochemical staining, and positive scoring was conducted (Fig. 3A). Based on the scoring results, the specimens were categorized into a high-expression group (24 cases) and a low-expression group (25 cases). We conducted a Kaplan-Meier survival analysis to examine the relationship between the expression levels of ZNF560 (high and low) and the overall survival of osteosarcoma patients. The findings demonstrated a significant association, whereby an elevation in ZNF560 expression was correlated with a notable reduction in patient survival time. This observation suggests that a higher expression level of ZNF560 is indicative of a worse prognosis in individuals with osteosarcoma (Fig. 3B). In summary, the elevated expression of ZNF560 is correlated with unfavorable outcomes in osteosarcoma.

Fig. 3
figure 3

Immunohistochemistry (IHC) staining of ZNF560 and Kaplan–Meier survival plots for overall survival by ZNF560 status. (A). ZNF560 high score tissues and ZNF560 low score tissues were prepared for IHC staining with ZNF560 antibody. Two representative images from each group are shown (magnification, left×200, right×400). (B). The clinical relevance between ZNF560 and Overall survival of osteosarcoma patients was analyzed by Kaplan–Meier method.

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ZNF560 downregulation inhibits cell viability and the clone formation of osteosarcoma cells

Following the transfection of siRNA, we initially assessed the expression levels of ZNF560 in both HOS and MG63 cells. As depicted in Fig. 4A, B and a noticeable decrease in ZNF560 content was observed in cells transfected with si-ZNF560 compared to those transfected with scramble RNA, confirming the successful knockdown of ZNF560 expression. Furthermore, the results obtained from the MTT assay demonstrated a significant reduction in sarcoma cell viability upon downregulation of ZNF560(Fig. 4C). Next, we detected the effect of ZNF560 downregulation on clone formation capability of sarcoma cells HOS and MG63. As is shown in Fig. 4D and E, upon downregulation of ZNF560, the number of colonies decreased markedly compared to the untreated control cells. Moreover, expression of PCNA, UBE2C, CDCA5, CDK4, and CDK6 mRNA were downregulated after silencing of ZNF560 (Fig. 4F), supporting the inhibitory effects of ZNF560 on cell proliferation.

Fig. 4
figure 4

ZNF560 downregulation inhibits cell viability and the clone formation of both MG63 and HOS cells. (A). Real-time PCR analysis of the mRNA expression with shRNA-mediated silencing of ZNF560 gene in MG63 cells and in HOS cells. (B). Western blot analysis of bands for relative ZNF560 expression in MG63 and HOS cells. (C). Effects of ZNF560 downregulation on sarcoma cell viability assay by MTT assay. Both MG63 and HOS cells were treated for 24 h,48 h,72 h,96 h, respectively. (D, E). The effects of ZNF560 downregulation on the colony forming ability of both MG63 and HOS cells. Images are representative of three independent experiments. Data was presented as mean ± SD for three independent experiments. *** P < 0.001.

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Knockdown of ZNF560 has a potent effect to trigger cell cycle arrest at the G2/M and S phases and to induce apoptosis of osteosarcoma cells

Recent studies focusing on cell cycle regulation have revealed the intricate and precise control mechanisms that govern cell cycle progression in normal cells. Any deviations from these well-defined checkpoints can result in aberrant cell proliferation and contribute to the development of cancer. Cancer cells are particularly prone to acquiring defects in these checkpoints, which ultimately leads to deregulation of the cell cycle and uncontrolled cell proliferation. In order to evaluate whether the knockdown of ZNF560 can inhibit cell cycle progression, cell cycle distribution of MG63 and HOS cells with/without ZNF560 knockdown was tested by flow cytometry(FCM). The flow cytometric analysis provided compelling evidence that the downregulation of ZNF560 induced cell cycle arrest at the G2/M and S phases. This arrest was manifested by a notable accumulation of cells in the G2/M and S phases, accompanied by a concomitant decrease in the proportion of cells in the G0/G1 phases. These findings unequivocally establish that the knockdown of ZNF560 elicits cell cycle arrest at the G2/M and S phases in both HOS and MG63 cells(Fig. 5A and B). To estimate the percentage of apoptotic cell death upon the downregulation of ZNF560, the flow cytometric analyses were conducted in both HOS and MG63 cells for 48 h. As is shown in Fig. 5C and D, downregulation of ZNF560 caused an increase in apoptotic population as compared to the untreated control cells. Collectively, these results demonstrated that the knockdown of ZNF560 has a potent effect to trigger cell cycle arrest at the G2/M and S phases and to induce apoptosis of osteosarcoma cells.

Fig 5
figure 5

Flow cytometry analysis of cell cycle distribution and apoptosis of both HOS and MG63 cells with modified expression of ZNF560. (A, B). The cell cycle distribution was measured by flow cytometry, and the number of cells in each cycle was counted by PI for nuclear staining and the percentage of cell cycle distribution was shown in the bar chart. (C, D) Apoptosis was assessed by the Annexin V-FITC/PI double staining analysis, and the proportion of apoptotic cells were shown in the bar chart. Data was presented as mean ± SD for three independent experiments. *** P < 0.001.

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ZNF560 was deemed indispensable in the migration of human osteosarcoma cells

Both HOS and MG63 cells were subjected to transduction with ZNF560-targeting siRNA. Subsequent wound healing and Transwell assays were repeatedly performed to assess the impact of ZNF560 knockdown on cell migration in both cell lines. As depicted in Fig. 6A and B, the effect of ZNF560 on cell migration was evaluated using a wound healing assay. Remarkably, the inhibition of ZNF560 resulted in a profound impairment of cell migration in both HOS and MG63 cells (Fig. 6A and B). Furthermore, the invasiveness of both HOS and MG63 cells was assessed through the Transwell assay to investigate the influence of ZNF560. It was observed that the proportion of ZNF560 knockdown cells that migrated was significantly lower compared to cells transfected with scramble RNA (Fig. 6C and D). Taken together, these findings unequivocally demonstrate that ZNF560 plays a crucial role in facilitating the motility of human osteosarcoma cells, specifically HOS and MG63 cells, and its knockdown substantially attenuates cell migration. These results strongly suggest the potential involvement of ZNF560 in the metastatic process of osteosarcoma.

Fig. 6
figure 6

Knockdown of ZNF560 inhibits cell migration and invasion in both HOS and MG63 cells. (A, B) Wound healing assay was performed to determine the cell migration in both HOS and MG63 cells. Cell migration was determined by the rate of cells moving towards the scratched area after 24 h incubation using ImageJ ™ software. (C, D) Transwell assay was employed to evaluate the invasion of these cells. The quantitative results for each group are shown as the number of invaded cells 48 h after incubation. Data represented mean ± SD of three separate experiments. ∗∗∗P < 0.001.

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Discussion

Osteosarcoma, an extensively heterogeneous malignant neoplasm predominantly localized within the metaphyseal regions of long bones, exhibits varying clinical outcomes even with standard treatment protocols22. Multiple investigations have highlighted the constraints of the conventional AJCC staging system when it comes to accurately prognosticating outcomes for osteosarcoma patients23. The TNM staging system primarily focuses on tumor size, regional lymph node involvement, and distant metastasis as key determinants, while disregarding individualized parameters such as age24. Lately, the reevaluation of public database datasets through bioinformatic techniques has surfaced as a promising approach for uncovering prognostic biomarkers in malignancies. For instance, Wu et al. (2020) created an eight-gene prognostic model and nomogram to precisely forecast clinical outcomes for patients diagnosed with osteosarcoma25. In this investigation, information pertaining to osteosarcoma patients was retrieved from the TCGA and GSE databases, and the differentially expressed genes as well as ZNF560 data were acquired and scrutinized to develop a prognostic risk model.

An ideal diagnostic biomarker is anticipated to demonstrate exceptional sensitivity, enabling its detection in the majority of tumors, along with remarkable specificity, ensuring its absence in normal control samples. Nevertheless, it is uncommon for biomarkers to fulfill these criteria individually, necessitating the combination of molecular panels to attain a reliable and robust test26. The newly discovered ZNF560, as presented in this paper, exhibits remarkable specificity and satisfactory sensitivity specifically in osteosarcoma, making it a valuable candidate as a cancer biomarker. Integrating ZNF560 with other biomarkers unique to osteosarcoma would enhance its effectiveness in the detection of cancer. In a recent investigation conducted by Xing Y et al., ZNF692 was unveiled as a novel oncogene with significant implications as a potential therapeutic target in colon adenocarcinoma27.

Limited research has been conducted on the potential oncogenic properties of ZNF560. Nevertheless, it is worth noting that the ZNF family of regulatory proteins are widely distributed and recognized as one of the most prevalent families in this category28. Examples include ZNF233, which exhibited associations with tumor grade, tumor stage, and prognosis in patients with hepatocellular carcinoma (HCC)16 and ZNF331 serves crucial roles as a newly discovered functional tumor-suppressor gene in suppressing gastric carcinogenesis18. Considering the roles played by various zinc finger proteins, ZNF560 could potentially serve as a suitable subject for further examination. In this research, decreased expression of ZNF560 resulted in decreased cell viability, reduced colony numbers, and induced apoptosis of osteosarcoma cells. Additionally, ZNF560 was necessary for HOS and MG63 human osteosarcoma cell migration. In aggregate, these findings suggest that the transcription factor ZNF560 demonstrates considerable potential as a promising prognostic biomarker and a prospective therapeutic target for individuals afflicted with osteosarcoma.

Existing research has demonstrated that ZNFs facilitate tumorigenicity through the inhibition of cell cycle arrest. For instance, the aberrant overexpression of ZNF671 resulted in the imposition of S phase arrest in NPC cells through the upregulation of p21 and concurrent downregulation of cyclin D1 and c-myc, thereby manifesting its regulatory influence on cell cycle progression29. As another illustration, the ectopic upregulation of ZNF23 led to a substantial elevation in p27kip-1 expression, exerting a suppressive effect on cellular proliferation and prompting cell cycle arrest specifically within the G1 phase30. The study conducted by L He and colleagues definitively established that the depletion of ZNF384 expression significantly impedes the proliferative capacity of hepatocellular carcinoma (HCC) cells by inducing cell cycle arrest precisely at the critical checkpoint of G1/S phase transition31. In this investigation, we demonstrated that the expression level of ZNF560 correlated with patient prognosis. Upon downregulating ZNF560 expression using shRNA in osteosarcoma cell lines, we observed cell cycle arrest at the G2/S transition in ZNF560 knockdown cells. Preliminary indications propose that ZNF560 might be involved in the transcriptional regulation of the cyclin family31; nevertheless, additional elucidation is warranted to enhance our comprehension of this intricate association.

In conclusion, ZNF560 appears to hold promise as a potential prognostic biomarker in osteosarcoma. Nonetheless, it is crucial to address certain limitations within this study. Firstly, the evaluation of the ZNF protein gene-based signature predominantly relied on publicly available datasets, necessitating validation at both the mRNA and protein levels using our own cohort of osteosarcoma patients. Secondly, the utilization of certain bioinformatics tools, such as the Kaplan-Meier plotter, may possess restricted functionality without the incorporation of multivariable Cox regression analysis. Lastly, there is a lack of direct in vivo evidence to verify the mechanism of ZNF560 in the tumorigenicity of osteosarcoma. To address this, we aim to establish a patient-derived xenograft (PDX) mice model to further elucidate the role of ZNF560 in tumor formation.