FOXM1 promote the growth and metastasis of uveal melanoma cells by regulating CDK2 expression

The gene expression profile matrix file from GSE22138 based on the platform GPL570 (including transcription profile data of 63 UVM tissue samples and matching clinically relevant information) was acquired from the gene expression omnibus (GEO) database (https://​www.​ncbi.​nlm.​nih.​gov/​geo/​). The grouping was performed based on whether the tumors had metastasis, and the 63 patients were divided into the “No” group (28 cases) and “Yes” group (35 cases).

The limma R package (Version 3.38.3; http://​www.​bioconductor.​org/​packages/​release/​bioc/​html/​limma.​html) was utilized to explore the differentially expressed genes between tumor tissues of “No” group (28 cases) and the “Yes” (35 cases), the threshold was set to: |log2FC|> 0 and P value < 0.05. The discrepant expression of genes is shown in the form of a heat map and volcano map. Then, the R software package WGCNA was utilized to conduct the weighted analysis of the correlative network of the above-mentioned differential genes. Based on this, the associations among the genes were acquired, and the topological overlap matrix and correlative matrix between the genes were established to estimate the gene network connectivity and identify the soft threshold value. Genes with similar expression levels were divided into one gene module and linked hierarchical clustering was performed. Simultaneously, each module weight in the dataset was assessed, and the dataset with the highest weight was screened for subsequent analysis. The R package of WGCNA, its source code, and supplementary materials are available at http://​www.​genetics.​ucla.​edu/​labs/​horvath/​CoexpressionNetw​ork/​Rpackages/​WGCNA [13]. Subsequently, the modules that were most relevant to metastasis were selected, and gene ontology analysis was performed to make functional annotations for these module genes.

A protein–protein interaction (PPI) network was constructed by importing the obtained genes into the significant modules into STRING database (https://​cn.​string-db.​org/​), and Cyctoscape software (https://​cytoscape.​org/​) was used for visualization. In this setting, the lowest interaction score of proteins > 0.9 was selected as the reliability basis for the interaction between proteins, and out-of-network nodes were hidden. Finally, a network diagram of protein interactions was obtained. Then, PPI Network-related topology analysis was performed using the CytoNCA plugin, taking “Betweenness”, “Closeness”, “Degree”, “Eigenvector”, “LAC,” and “network” as reference standards. The candidate targets were chosen from the genes whose criterion scores were larger than their matching standard mean, and later suffered from the foregoing topological analysis for 2 times to get candidate genes. The relationship between these genes and overall survival was further explored, and genes that were significantly related to prognosis were regarded as core genes.

The relationship between candidate genes (FOXM1 or MET) and the prognosis of UVM patients was analyzed through the online databases GEPIA (http://​gepia.​cancer-pku.​cn/​index.​html) and UALCAN (https://​ualcan.​path.​uab.​edu/​analysis.​html). 39 UVM patients with high FOXM1 or MET expression and 39 UVM patients with low FOXM1 or MET expression were included in GEPIA database; and 20 UVM patients with high FOXM1 or MET expression and 60 UVM patients with low/medium FOXM1 or MET expression were included in UALCAN database. The Cancer Genome Atlas (TCGA) is a landmark cancer genomics program that sequenced and molecularly characterized over 11,000 cases of primary cancer samples [14]. Here, TCGA database was used to show the expression of FOXM1 or MET in UVM samples based on individual cancer stages (n = 39 for stage2, n = 36 for stage3 and n = 4 for stage4).

The immortalized human normal epidermal melanocyte cell line (PIG1) and choroidal melanoma cell lines (M619 and MUM-2B) were acquired from Otwo Biotech (Shenzhen, China) and BNCC (Beijing, China). PIG1 and MUM-2B cells were cultured in 90% DMEM (Procell) and M619 cells were propagated in RPMI-1640 medium (Procell, Wuhan, China) in an incubator with 5% CO₂ at 37 °C. All media were supplemented with 10% FBS (Procell) and 1% antibiotics (Procell).

For FOXM1 silencing, FOXM1 small interference RNA (si-FOXM1#1, si-FOXM1#2, and si-FOXM1#3) were generated, and si-NC served as homologous contrasts. RiboFECTTM CP Reagent Ribobio (Guangzhou, China) and the designated oligonucleotides or plasmids from RiboBio were individually transduced into UVM cells.

Total RNA segregation was performed using Triquick Reagent (Solarbio, Beijing, China). Subsequently, the cDNA of FOXM1 was amplified from RNA using the SweScript RT I First Strand cDNA Synthesis Kit (Servicebio, Wuhan, China), and the synthesized cDNA was used for qRT-PCR with 2 × SYBR Green qPCR Master Mix (Low ROX) (Servicebio) and specific primers on a PCR system. The RNA abundance of FOXM1 was computed using the 2^−ΔΔCt strategy and normalized to the level of inner contrast GAPDH. The primer sequences were as follows: FOXM1-F, TCTGCCAATGGCAAGGTCTCCT; FOXM1-R, CTGGATTCGGTCGTTTCTGCTG; GAPDH-F, GTCTCCTCTGACTTCAACAGCG; GAPDH-R, ACCACCCTGTTGC TGTAGCCAA.

The extractive protein was obtained employing RIPA buffer (Solarbio). The protein was segregated using SDS-PAGE gel (10%; Beyotime, Shanghai, China) and subsequently received the transference to PVDF membrane (Beyotime). Following sealing with 5% slim milk in indoor environment for 1 h, the membrane was reacted with primary antibodies against FOXM1 (13147-1-AP, 1:1000, Proteintech, Wuhan, China), CDK2 (10122-1-AP, 1:2000, Proteintech), and internal protein standard GAPDH (1:2000; 10494-1-AP; Proteintech) overnight at 4 °C. Subsequently, Goat Anti-Rabbit IgG H&L secondary antibody (Alexa Fluor® 680) (ab175773, 1:20,000, Abcam, Cambridge, UK) was utilized to interact with the membrane for 2 h in indoor environment. Later, BeyoECL Star Kit (Beyotime) was utilized for visualizing the immunoblots.

To analyze viability, the transduced MUM-2B and M619 cells were cultivated in 96-well plates for specified times (0, 24, 48, or 72 h), and then every well was supplemented with CCK-8 solution (Servicebio) for 4 h at 37 °C. Finally, the OD value was recorded using a microplate reader at a wavelength of 450 nm.

MUM-2B and M619 cells were maintained in 6-well plates to analyze cell migration 24 h after transfection. Later, scarification was performed using aseptic pipette tips (200 μL) on the cell monolayers, and the floating cells were removed with PBS. After 0 or 24 h of cultivation, the images were acquired using a microscope, followed by the analysis of the wound widths (migrated distance) at 40 × magnification using ImageJ software.

The invasive abilities of MUM-2B and M619 cells were tested using a transwell chamber (8 μm, Corning, Cambridge, MA, USA), in which the insert membrane was covered with Matrigel (Solarbio), the upper compartment was added to transfected cells in non-serum medium, and the bottom compartment was replenished with 600 μL complete medium containing FBS. After 24 h, the metastasizing cells in the bottom compartment were immobilized and stained with 4% paraformaldehyde and 0.1% crystal violet (Solarbio) for 1 h. Finally, a microscope was used at 100 × magnification to observe and take a picture of the invasive cells from five randomly chosen areas.

MUM-2B and M619 cells were seeded in 6-well plates prior to siRNA transfection and maintained in complete medium for 2 weeks at 37 °C. Next, the cells were rinsed with PBS, fixed, stained using 4% paraformaldehyde and 0.1% crystal violet (all from Solarbio), and photographed using a camera.

All data from the lowest three repeats are displayed as the mean ± standard deviation (SD) and charted using GraphPad Prism 7 software. Differences were compared using Student’s t-test or ANOVA. Statistical significance was defined as P < 0.05.

First, differential expression analysis was performed on the transcription profiles of two groups (metastasis, yes; no metastasis, no) of samples in the GSE22138 database, and 5362 genes with differential expression were obtained, among which 2659 genes were up-regulated in the non-metastasis group and 2703 genes were up-regulated in the metastasis group (Fig. 1A). WGCNA analysis was performed based on this differential gene. To dislodge sample outliers, UVM samples were clustered according to the gene expression matrix, and a clustering tree was established (Fig. 1B), in which the horizontal axis corresponds to each sample, and vertical coordinates represent clustering distances. None of these samples showed a significant deviation, and no samples were removed (Fig. 1C). The selection criterion for the soft threshold was set as signed R² > 0.8, and a set of alternative thresholds was selected to export the matching network arguments. As shown in Fig. 1D, when the soft threshold was 7, the gene network exhibited both high internal connectivity and high gene similarity. The gene co-expression network was established with a threshold value of 7, hierarchical clustering of genes with differential expression was performed based on the heterogeneity matrix, and a clustering tree was established (Fig. 1E). The network modules were set to cover a minimum of 50 genes, different gene modules were authenticated by the dynamic cutting method, modules with high similarity were merged, finally, 6 different gene modules were obtained. These different gene modules are represented by different colors, and genes in the same color modules have high similarity. To filter the modules with high associations with UVM metastasis, we first analyzed the principal components of genes in each module; the value of the first principal component was extracted as the module eigenvalue (ME), and then the correlation coefficient between the module eigenvalue and glycolytic type or glutamine decomposition type was calculated. The relevant heat map is shown in Fig. 1F. We found that gray modules were the most relevant to UVM metastasis. The significance and module membership of genes in turquoise modules are shown in Fig. 1E F, and the values of these variables exhibited a strong positive association (cor = 0.43, P = 5.3e-41).

In view of the above significance, GO enrichment analysis was carried out for the gray modules. A total of 61 items were enriched in GO analysis, including 12 molecular functions, 30 biological processes, and 19 cellular components. Figure 2A shows the top 10 biological processes, molecular functions, and cellular components in which the core target genes are involved. Among them, the biological processes are mainly concentrated in regulating GTPase activity, DNA-binding transcription factor activity, and organ growth; the cell components are mainly concentrated in the cell-substrate junction, focal adhesion, cell leading edge, etc. and are mainly concentrated in Ras GTPase binding, small GTPase binding, nucleoside-triphosphatase regulator activity, etc. To screen out the core genes connected with the UVM further, the aforementioned gray module genes were input into the STRING database, and human genes were selected for protein interaction network analysis (relevant protein nodes were obtained by screening the interactional score ≥ 0.9). To obtain a PPI network diagram, the TSV file of the PPI network was imported into Cytoscape 3.9.0 (Fig. 2B). The CytoNCA plugin was used to topologically analyze the PPI network topologically for 2 times. Seven candidate genes (VEGFA, KRAS, MET, SRC, EZR, FOXM1, and CCNB1) were identified.

Next, the relationship between each of the above-mentioned seven genes and the prognosis of UVM patients (OS) was further explored. Through the analysis of 5-year survival rate of patients with UVM in GEPIA (http://​gepia.​cancer-pku.​cn/​index.​html) (Fig. 3A) and UALCAN (https://​ualcan.​path.​uab.​edu/​analysis.​html) (Fig. 3B) online databases, it was demonstrated that patients with high FOXM1 or MET expression had poorer overall survival rates, as reflected by Kaplan–Meier analysis (Fig. 3A, B). These results suggest that only two genes (FOXM1 and MET) are associated with the prognosis of UVM patients. Therefore, FOXM1 and MET were identified as core genes associated with UVM metastasis. The TCGA database (https://​ualcan.​path.​uab.​edu/​cgi-bin/​TCGAExResultNew2​.​pl?​genenam=​MET&​ctype=​UVM) analyzed in UALCAN also suggested that individual cancer stages were related to the expression of FOXM1 and MET, in which stage 4 had higher FOXM1 and MET levels than other stages (Fig. 3C). Here, we selected FOXM1 for further studies on UVM pathogenesis. To probe the function of FOXM1 in UVM, the expression of FOXM1 in normal PIG1 and UVM cells was tested. As shown in Fig. 3D, E, the abundance of FOXM1 was notably elevated in UVM cells (MUM-2B and M619) compared to that in normal PIG1 cells. Based on this, we deduced that aberrantly increased levels of FOXM1 might be associated with the advancement and poor prognosis of UVM.

To elucidate the biological function of FOXM1 in UVM cell development, si-NC, si-FOXM1#1, si-FOXM1#2, and si-FOXM1#3 were transduced into MUM-2B and M619 cells. As expected, FOXM1 levels were dramatically reduced in UVM cells after si-FOXM1#1, si-FOXM1#2, or si-FOXM1#3 transfection relative to the control si-NC and non-transfection groups (blank) (Fig. 4A), demonstrating the successful knockdown of FOXM1. Therefore, si-FOXM1#2, with a higher transfection efficiency, was chosen for subsequent experiments. Loss-of-function assays revealed that FOXM1 interference led to an evident impediment in cell viability and clonal numbers of MUM-2B and M619 cells (Fig. 4B, C). Wound healing and Transwell assays confirmed that FOXM1 deficiency overtly restrained MUM-2B and M619 the migratory and invasive abilities (Fig. 4D, E). To summarize, FOXM1 silencing represses the development of UVM cells.

To probe the regulatory mechanism of FOXM1 in UVM cell development, the potential interacting proteins of FOXM1 were probed. As shown in Fig. 5A, the STRING online network (https://​cn.​string-db.​org/​cgi/​network?​taskId=​b450Od5yPz9x&​sessionId=​bCcb7CZ3idrj) suggested that 10 genes were predicted to interact with FOXM1, some of which were connected to the cell cycle. In addition, through GeneCards analysis (https://​www.​genecards.​org/​), FOXM1 was found to participate in several biological processes, including cell cycle, DNA repair, and transcription regulation (Supplementary Table 1). KEGG analysis revealed that FOXM1 participates in cellular senescence (map04218, Supplementary Fig. 1), whereas CDK2 participates in FOXM1-mediated cell cycle arrest. In a subsequent study, we explored the relationship between FOXM1 and CDK2 in UVM cells. As exhibited in Fig. 5B, the expression of CDK2 was declined in MUM-2B and M619 cells transfected with si-FOXM1#2. Therefore, we speculated that FOXM1 may regulate UVM cell cycle arrest by interacting with CDK2.