PKMYT1, exacerbating the progression of clear cell renal cell carcinoma, is implied as a biomarker for the diagnosis and prognosis

Relative expression of WEE family kinases in ccRCC

To explore the roles of WEE family kinases in ccRCC, we first investigated mRNA expression levels of the three members of WEE family kinases in multiple datasets. A heat map was used to illustrate the overall expression levels of the three members of WEE family kinases between normal and tumor samples in the TCGA dataset (Figure 1). Then, we compared expression levels of the three members between control and tumor samples. We observed down-regulated levels of WEE1 and WEE2 and up-regulated levels of PKMYT1 in tumors compared to normal samples for all samples in the TCGA dataset (Figure 2A–2C). We also compared expression levels of paired normal and tumor samples in the TCGA dataset, and found down-regulated levels of WEE1 and WEE2 and up-regulated levels of PKMYT1 in tumors (Figure 2D–2F). Consistently, we also compared mRNA expression levels of the three members in the GSE36895 (Figure 2G–2I) and GSE76351 datasets (Figure 2J–2L). In the validated GEO datasets, we obtained similar analytical results, in which we found up-regulated levels of PKMYT1 in tumors compared with normal samples. While, expression levels of WEE1 and WEE2 showed no significant differences between tumor and normal samples, but all results displayed overexpression of PKMYT1 in tumors.

Prognostic values of WEE family kinases in ccRCC

Prognostic significance assessment of WEE family kinases in ccRCC using Kaplan–Meier curves analysis. Based on the median values of mRNA expression, we divided the patients into high and low expression groups respectively. The Kaplan–Meier curves demonstrated the overall survival of ccRCC patients, and the results reflect the poor prognosis of the PKMYT1 high expression group (Figure 3E).

However, there exhibited no significant difference for survival rates of WEE1 (Figure 3A) and WEE2 (Figure 3C) in high and low expression groups. In addition, we evaluated the disease-free survival profile of the high and low expression groups. We found that disease free survival was lower in the high PKMYT1 expression group compared to the low expression group (Figure 3F). However, the disease-free survival rate showed no significant difference for WEE1 (Figure 3B) or WEE2 (Figure 3D). Subsequently, we performed survival analysis for overall survival in different subgroups of ccRCC patients (Supplementary Figure 1). The results revealed that the high expression group presented poorer overall survival rate for the male, female, stage I + II, stage III + IV, G3+G4, higher age (>60 years) and lower age (≤60 years) subgroups. However, the survival analysis showed no significant difference in G1+G2 subgroup. Based on the above results, we hypothesized that PKMYT1 may be a prospective prognostic biomarker for ccRCC.

PKMYT1’s association with clinical variables

The relationships between clinical variables and the expression levels among WEE family kinases were evaluated in the TCGA dataset. Expression levels of WEE1 were negatively correlated with pathological grade (Figure 4B). However, expression levels of WEE1 showed no significant differences between groups for pathological stage (Figure 4A), pathological T stage (Figure 4C), lymphatic metastasis (Figure 4D), distant metastasis (Figure 4E) or survival status (Figure 4F).

In the lymphatic metastasis (Figure 4J) and distant metastasis groups (Figure 4K), we observed a downregulation of WEE2 expression levels. However, expression levels of WEE2 showed no significant differences between groups for pathological stage (Figure 4G), pathological grade (Figure 4H) pathological T stage (Figure 4I) or survival status (Figure 4L). For PKMYT1 gene, we found significantly positive correlations between expression levels and pathological stage (Figure 4M), pathological grade (Figure 4N) and pathological T stage (Figure 4O). Additionally, we found high expression levels of PKMYT1 in lymphatic metastasis (Figure 4P), distant metastasis (Figure 4Q) and dead status (Figure 4R). These findings suggested that expression levels of PKMYT1 are highly associated with the tumor malignancy.

Taking into consideration the above results, we selected the PKMYT1 gene for further study.

Patients were classified into high and low expression groups based on the median expression value of PKMYT1, and associations with the distributions of clinical phenotypes were investigated. The results showed that high proportions of higher T stage, distant metastasis, advanced stage, lymphatic metastasis, and higher grade in the high expression group of PKMYT1 (Table 1). However, the distributions of age and gender showed no significant difference. Furthermore, we evaluated overall survival and disease-free survival in ccRCC patients by univariate and multivariate Cox regression analysis. The results showed that PKMYT1 was a risk factor for overall survival (Figure 5A) and disease-free survival (Figure 5C) via univariate Cox regression analysis. The expression levels of PKMYT1 were a risk factor for overall survival in ccRCC patients after integration with multiple clinical factors (Figure 5B) through multivariate Cox regression analysis. However, expression levels of PKMYT1 with respect to disease-free survival showed no significant difference (Figure 5D). Based on the aforementioned findings, we confirmed that PKMYT1 is an autonomous risk factor affecting overall survival of ccRCC patients, which might provide a supplement for clinical factors.

Diagnostic value of PKMYT1 mRNA expression in ccRCC

To investigate whether the expression of PKMYT1 has diagnostic value in ccRCC patients, we performed receiver operating characteristic (ROC) investigation and calculated the area under the curve (AUC) to evaluate the diagnostic efficiency. We found that PKMYT1 sufficiently differentiated ccRCC patients from normal controls when the area under the AUC curve was 0.955 (Figure 6A). In addition, the diagnostic value of PKMYT1 expression levels were also evaluated for stage I + II vs. stage III + IV (AUC, 0.673; Figure 6B), (G1 + G2) vs. (G3 + G4) stage (AUC, 0.643; Figure 6C), (T1 + T2) vs. (T3 + T4) stage (AUC, 0.672; Figure 6D), N0 vs. N1 (AUC, 0.815; Figure 6E) and M0 vs. M1 (AUC, 0.711; Figure 6F). These findings suggest that PKMYT1 may serve as a potential diagnostic biomarker for ccRCC patients with excellent performance.

Biological pathogenesis of PKMYT1 in ccRCC

To explore the functional mechanism of PKMYT1 in ccRCC, we employed GSEA analysis to uncover the statistically significant biological pathways between the high expression and low expression groups of PKMYT1 (divided by median expression level). Our findings displayed that PKMYT1expression level was connected with the biological pathways of G2/M checkpoint (Figure 7A) and EMT processes (Figure 7B). We also used the WGCNA (weighted gene co-expression network analysis) method to construct co-expression network where similarly expressed genes were merged into the same module (Figure 7C). The correlations between modules are shown in Figure 7D. We found that PKMYT1 merged into the green module, and these genes were selected for functional enrichment analysis (Supplementary Table 1). The terms, “G2/M transition of mitotic cell cycle”, “regulation of cell cycle”, “G1/S transition of mitotic cell cycle”, “regulation of cyclin-dependent protein serine/threonine kinase activity” and “cell proliferation” were enriched for biological process (Figure 7E). The terms “chromosome, centromere region” and “condensed chromosome kinetochore” were enriched for cellular component (Figure 7F). The terms, including “protein binding”, “protein kinase binding” and “ATP binding” were enriched for molecular function (Figure 7H). The cell cycle term was enriched according to KEGG analysis (Figure 7G). Based on these results, we deduced that the cell cycle related pathway and EMT might be the potential mechanisms of PKMYT1 in ccRCC tumorigenesis.

High expression of PKMYT1 in ccRCC cells and tumor tissues

To detect PKMYT1 expression levels in ccRCC cells and tumor tissues, qRT–PCR and western blotting assays were executed. Compared to HK-2, mRNA expression levels (Figure 8A) and protein levels (Figure 8B) of PKMYT1 were upregulated in ccRCC cells, including ACHN, Caki-1, A498, OSRC-2, and 786-O cell lines. The expression of PKMYT1 in paired tissues of ccRCC patients was also evaluated. The results showed that higher PKMYT1 expression in comparison with the paired normal neighboring tissues (Figure 8C). In contrast, our western blotting assay (Figure 8D) and immunohistochemical analysis (Figure 8E) both confirmed the increased protein expression level of PKMYT1 in ccRCC tissues.

Knockdown of PKMYT1 significantly inhibits the cell migration, proliferation, and invasion of ccRCC

To examine the effect of PKMYT1 in the biological medicine of ccRCC, we transfected si-PKMYT1 into RCC cell lines (ACHN and Caki-1) by down-regulated its expression levels. The down-regulated mRNA levels of PKMYT1 were confirmed using qRT-PCR for ACHN cells (Figure 9A) and Caki-1 cells (Figure 9B) compared to the corresponding negative control. Down-regulated protein levels of PKMYT1 were confirmed using western blotting assays for ACHN cells (Figure 9C) and Caki-1 cells (Figure 9D) compared to the corresponding negative control. Subsequently, whether PKMYT1 gene has an effect on cell migration, proliferation and invasion, a series of assays were performed.

The results of cell proliferation analysis manifested that knockdown of PKMYT1 uncommonly inhibited cell proliferation ability of cells in both ACHN cell lines (Figure 9E) and Caki-1 cell lines (Figure 9F) compared to the negative control. Next, we wanted to know whether knocking down PKMYT1 has any effect on the invasion and migration ability of ccRCC cells? Therefore, we used Transwell’s experiment to further evaluate. Through cell migration and invasion assays, we found that knockdown of PKMYT1 gene expression significantly debilitated the migration and invasion ability of ACHN cells (Figure 9G, 9I) and Caki-1 cells (Figure 9H, 9J). Overall, the multifaceted experiments demonstrated that PKMYT1 contributes to the migration, proliferation and invasion of ccRCC cells.

To further shed light on the molecular mechanism how PKMYT1 affects the metastatic capability of ccRCC cells, we assessed the EMT phenotype, which plays crucial roles in tumor progression and is intently related to cell invasion and migration. Therefore, we examined the expression of some important protein markers during EMT, such as Vimentin, N-cadherin, E-cadherin and Snail by western blot assays. As shown in Figure 10A, knockdown of the PKMYT1 reduced the expression of mesenchymal-associated protein markers (N-cadherin, Vimentin and Snail) and conversely upregulated the protein levels of epithelial markers (E-cadherin) in ccRCC cells. We carried out the same experiments in the Caki-1 cell line following PKMYT1 knockdown and obtained similar experimental findings (Figure 10B). These results all indicated that PKMYT1 accelerates ccRCC cells migration and invasion through EMT.

Implication of PKMYT1 knockdown on the cell apoptosis and cell cycle

Based on the functional enrichment analysis, we speculated that the cell cycle-related pathway might underlie the potential functional mechanism of PKMYT1. Therefore, we designed two different siRNAs and subsequently transfected into ccRCC cells to knock down the expression of PKMYT1 gene. Based on flow cytometry experiments to detect the cell cycle, our results exhibited that the proportion of cells in G2/M phase was reduced in ACHN and Caki-1 cell lines when transfected with PKMYT1 siRNA (Figure 10C, 10D). The previous GSEA analysis revealed that PKMYT1-interplaying proteins are closely associated with cell cycle progression. Then we explored the protein expression of indispensable cyclin genes such as CCNB1 and CCNE1 in ccRCC cells. The results showed that all of these cell cycle-associated proteins were reduced in the PKMYT1 knockdown groups (Figure 10E, 10F). Taken together, these findings indicated that PKMYT1 promotes cell proliferation by affecting the cell cycle. Additionally, to investigate the influence of PKMYT1 on ccRCC cell apoptosis, flow cytometry analysis was performed. We observed that the apoptosis rate of the siRNA groups in ACHN and Caki-1 cell lines was higher than negative control group (Figure 10G).

ccRCC patient tissue samples

45 pairs surgical specimens of ccRCC and adjacent normal renal tissues were collected from the Department of Urology, The First Affiliated Hospital of Anhui Medical University (Hefei, China) between May 2019 and March 2021. Adjacent normal control samples were acquired ≥2 cm away from the tumor site. Samples were freshly frozen in liquid nitrogen and then placed in ultra-low temperature refrigerator (−80°C). Two pathologists uniformly confirmed the histological diagnosis type of ccRCC in these surgical specimens. Informed consent forms were signed by all patients. The present study was approved by the Ethics Committee of Human Research of The First Affiliated Hospital of Anhui Medical University (No. PJ2019-14-22).

RNA extraction and qRT-PCR

Tissue total RNA was obtained with the TRIzol Reagent BD (Invitrogen, Carlsbad, CA). The concentration as well as purity of the RNA solution were measured using a NanoDrop 2000 spectrophotometer (NanoDrop Technologies, Wilmington, DE, USA). Based on the manufacturer’s instructions, 2 μg RNA was utilized for reverse transcription by applying a PrimeScriptTM RT reagent kit (Takara, Kusatsu, Japan). Then, the cDNA was subjected to qPCR with the SYBR Green Mix (Takara, Kusatsu, Japan). We performed the amplification reaction on an ABI7500 platform (Thermo, Massachusetts, USA). GAPDH as an internal reference gene to normalize the relative expression of PKMYT1using the 2^−ΔΔCT formula. The primers were chemically synthesized by Sangon Biotech (Sangon, Shanghai, China). The gene primers were presented as follows: PKMYT1, (forward) 5′-CATGGCTCCTACGGAGAGGT-3′ and (reverse) 5′-ACATGGAACGCTTTACCGCAT-3′; GAPDH, (forward) 5′-GGGAGCCAAAAGGGTCAT-3′ and (reverse) 5′-GAGTCCTTCCACGATACCAA-3′.

Western blotting assay

For western blot analysis, cells were harvested by using radio-immunoprecipitation assay (RIPA) buffer (Beyotime Biotech, Jiangsu, China) with 1 mM inhibitor cocktail (eg: protease and phosphatase) and 1 mM PMSF. PVDF membrane (Millipore) and SDS-PAGE gel were prepared in advance for subsequent experiments. The antibody reagents used in this study were as follows: anti-E-cadherin (Santa Cruz), anti-N-cadherin (Santa Cruz), Snail (Proteintech), Vimentin (Proteintech), PKMYT1 (CST), CCNB1 (Boster), CCNE1 (Proteintech) and GAPDH (CST). GAPDH was detected as a loading control. A Fujifilm LAS4000 mini luminescent image analyzer was performed to photograph the blots.

Immunohistochemistry

The clinically collected renal clear cell carcinoma tissues were embedded in paraffin and made into tissue sections. Subsequently, the tissue sections were dewaxed, antigen repaired, blocked, incubated with primary antibodies, incubated with secondary antibodies, DAB color development, and blocked.

Briefly, 4 μm thickness paraffin-embedded tissue sections were subjected to high-temperature antigen retrieval for 10 minutes in 0.01M citric acid buffer (pH 6.0) and then naturally cooled to room temperature. Endogenous peroxidase was removed with 3% H2O2, 0.3% Triton permeabilization, followed by blocking with 10% bovine serum albumin for 30 min. Then slides were incubated overnight at 4°C with primary antibodies targeting PKMYT1 at a 1:50 dilution. The primary antibody was then washed off with PBS, following the secondary antibody was added dropwise and incubated for 1 h at room temperature. Finally, DAB (daiminobenezidine) (ZSBio) was used for sections visualization and nuclei were re-stained with Mayer-modified hematoxylin.

Cell culture and transfection

The 786-O, A498, OSRC-2, ACHN, Caki-1, and HK-2 cells were acquired from the cell culture center of the Chinese Academy of Medical Sciences (Shanghai, China). Except for the ACHN cell lines were cultured in MEM medium (Gibco), the remaining cell lines were maintained in DMEM/high glucose medium (Gibco) supplemented with 10% fetal bovine serum and 1% penicillin-streptomycin solution. All cell lines were cultured in a cell culture incubator at 37°C with 5% CO₂. The medium was changed daily with fresh medium and cell passages were performed when cells reached 70% growth. Two interference sequences that specifically target the PKMYT1 gene (siRNA#1: 5′-CTGGAAGTGGCATGCAACA-3′ and siRNA#2: 5′-GCGGTAAAGCGTTCCATGT-3′) and a negative control (si-NC) siRNA (Contract number: P202101170036) were synthesized by RiboBio Co., Ltd (Guangzhou, China). According to the manufacturer’s protocol, renal cancer cell lines were transfected with si-PKMYT1 (60 nM) or si-NC (60 nM) via Lipofectamine 2000 (Invitrogen), and then added to the culture medium at a final concentration of 100 nM.

Cell proliferation assay

For the cell proliferation assay, the 96-well plates were incubated at 37°C with 1000 cells per well, and five replicate wells and blank control wells (200 μl of FBS-free medium only) were set up for each group. Take out the 96-well plate every day, discard the old medium and add fresh medium to each well, and then add 10 μl of CCK8 (Dojindo Molecular Technology) reagent to each well in the dark.

The OD450 values of each well were measured every 24 hours for a total of 4 days using an enzyme-linked immunosorbent assay (ELISA). We finally displayed the number of cells per day in the form of a line graph to reflect the rate of cell proliferation.

Cell cycle analysis

After cell transfection, cells were washed with PBS (centrifuged at 2000 rpm, 5 min) to collect and adjust the cell concentration to 1 × 10⁶/ml, and 1 ml of single cell suspension was taken for subsequent analysis. According to the manual instructions of the cell cycle detection kit (KeyGEN BioTECH, KGA512), we detected the cell cycle by flow cytometry. All data were analyzed by FlowJo software, V7.6.

Apoptosis assay

Cells grown in the logarithmic phase were seeded in 24-well plates with 100 × 103 cells per well, and the cells are collected after 24 hours of cultivation until the cell concentration was about 80%–90%. Add 5 μL Annexin V-FITC (DOJINDO), gently pipette evenly and incubate at room temperature for 10 min; then add 10 μL PI dye solution (20 μg/ml) with incubating at room temperature for 5 min in the dark. Finally, flow cytometer was used to detect the cells and Flowjo software (Tree Star Software, San Carlos) was employed to visualize the results.

Transwell cell migration and invasion assays

Cell migration and invasion assays were executed in a 24-well Transwell chamber with 8 μm pores (Costar, Bodenheim, Germany). PKMYT1 siRNAs were transfected into ACHN and Caki-1 cells before subsequent experimentation. ACHN and Caki-1 cells were incubated in serum free medium for 8 h. For migration assays, lower chamber (i.e. bottom of 24-well plate) were added 600 μ1 medium containing 10% FBS and 200 μ1 cell suspension to the upper chamber and continue incubation in the incubator for 24 h. For invasion assays, the upper wells were coated in Matrigel (diluted in serum-free medium 1:8; BD Biosciences), and incubated at 37°C for 8 h. Subsequently, add 600 μ1 of medium containing 10% FBS to the lower chamber of the 24-well plate, then place the Transwell chamber in the 24-well plate with tweezers, add 200 μ1 of the cell suspension to the upper chamber, and finally place it in the incubator for 24 h. After 24 hours of incubation, take out the cell, aspirate the medium, and gently wipe the cells in Matrigel and the upper chamber with a cotton swab. Then fixed with 4% paraformaldehyde for 15 minutes, and stained with 0.05% crystal violet for 5–10 minutes.

Bioinformatics analysis

The mRNA expression data and clinical information (stage, grade, overall survival, status and T, N, M categories) of three members of WEE family kinases in the TCGA dataset were obtained from UCSC Xena (https://xenabrowser.net/). All expression data uses RSEM algorithm to normalize the expression of different genes. Information on the disease-free survival of ccRCC was acquired from the cBio Cancer Genomics Portal (http://cbioportal.org). The normalized independent microarray datasets, namely, GSE36895 (normal = 23; tumor = 29) and GSE76351 (normal = 12; tumor = 12), were obtained from the Gene Expression Omnibus (GEO; https://www.ncbi.nlm.nih.gov/geo/) database. GEO dataset (GSE36895) was sequenced applying the Affymetrix Human Genome U133 Plus 2.0 Array platform (GPL570), and the probes were annotated employing the corresponding “hgu133plus2.db” R package. The GSE76351 dataset was sequenced by the Affymetrix Human Gene 1.1 ST Array platform (GPL11532). For the annotation of the probes, we use the corresponding "hugene11sttranscriptcluster.db" R package to carry out. Normalization of expression data in the GSE36895 and GSE76351 datasets according to robust multi-array averaging (RMA) algorithm. Kaplan–Meier curves were used to evaluate the prognostic significance, and ROC curves were generated to assess the diagnostic efficiency [31]. Univariate and multivariate Cox Proportional Regression Hazard Models were prosecuted to assess the effects of diverse variables on overall survival and disease free survival. Subsequently, we downloaded the h.all.v7.0.symbols.gmt file as the reference target sets and performed gene set enrichment analysis (GSEA) to identify the important signaling pathways that PKMYT1 may be involved in ccRCC [32]. Gene sets with normalized P-value <0.05 and FDR <0.25 were considered as cutoff values.

To obtained genes highly correlated with the expression of PKMYT1, weighted gene co-expression network analysis (WGCNA) model was adopted to evaluate closely connected modules [33]. We first selected genes according to coefficient of variation values greater than 0.1. Then, we constructed a scale-free co-expression network for selected genes using the “WGCNA” R package [34]. All genes in the module in which PKMYT1 was located were selected. GO (Gene ontology) and KEGG (Kyoto Encyclopedia of Genes and Genomes) enrichment analyses were performed to visualize and annotate these genes highly associated with PKMYT1. Only terms with FDR-value of <0.05 were regarded markedly enriched.

Statistical analysis

The RNA levels of wee family members were analyzed with paired student’s t-test. Prognostic values of PKMYT1 was evaluated by univariate and multivariate Cox regression analyses in ccRCC. The Kaplan–Meier (KM) curve combined with log-rank test were evaluated survival rate difference and expression level of PKMYT1. Pearson’s χ² test was performed to analyze the relevance between PKMYT1 mRNA level and clinical data in ccRCC patients. Other statistical analysis and pictures drafting were performed with some packages (WGCNA, ggplot2, survival, pheatmap, rms and pROC) by R (version 4.0.3). For all statistical tests, P-value <0.05 was considered as considerable discrepancy.