Literature DB >> 32801781

PKMYT1 Promotes Gastric Cancer Cell Proliferation and Apoptosis Resistance.

Qi-Yong Zhang1, Xiao-Qin Chen1, Xiong-Chang Liu1, De-Ming Wu1.   

Abstract

BACKGROUND: Abnormal expression of protein kinase membrane associated tyrosine/threonine 1 (PKMYT1) is closely associated with multiple types of cancers. In the present study, we examined the roles of PKMYT1 in gastric cancer (GC) progression.
METHODS: We examined the expression status of PKMYT1 in GC tissues and cell lines. Meanwhile, short hairpin RNA (shRNA) was used to inhibit the endogenous expression of PKMYT1 in GC cells. Then we analyzed the effect of PKMYT1 on the malignant biological behavior of GC cells by in vitro and in vivo experiments.
RESULTS: The findings showed high PKMYT1 expressions in GC tissues as well as a positive correlation between PKMYT1 expression and prognosis of patients with GC. Additional findings also revealed that PKMYT1 silencing significantly enhanced apoptosis and inhibited GC cell proliferation. In vivo, the silence of PKMYT1 inhibits tumor growth. Further analysis showed that the increase in PKMYT1 expressions led to malignant biological behavior through activation of the MAPK signaling pathway.
CONCLUSION: Our data suggested that PKMYT1 promotes cell proliferation and apoptosis resistance in GC cells by activating the MAPK signaling pathway, making it a potential therapeutic target for GC.
© 2020 Zhang et al.

Entities:  

Keywords:  MAPK pathway; PKMYT1; apoptosis; gastric cancer; prognosis; proliferation

Year:  2020        PMID: 32801781      PMCID: PMC7414979          DOI: 10.2147/OTT.S255746

Source DB:  PubMed          Journal:  Onco Targets Ther        ISSN: 1178-6930            Impact factor:   4.147


Introduction

Gastric cancer (GC) is the most common digestive cancer and has the third-highest mortality rate among all malignancies. According to the statistics, there were more than 1,000,000 new cases of GC and approximately 780,000 deaths from this disease in 2018.1 The incidence of GC is high in East Asia, including China. Although surgery-based therapeutic approaches can improve the long-term survival of patients with early-stage GC, the 5-year survival rate of patients with advanced-stage GC is low because of the lack of effective treatments.2 Therefore, studies aimed at understanding the molecular mechanism regulating GC and identifying novel and suitable therapeutic targets for the treatment of this disease are urgently needed. The protein kinase membrane associated tyrosine/threonine 1 (PKMYT1) gene, located on 16p13.3 of human chromosome 16, encodes an important protein belonging to the Wee kinase family. PKMYT1 is a membrane-associated bispecific protein kinase that can regulate cell cycle by phosphorylating Thr14 and Tyr15.3–5 In mammalian somatic cells, PKMYT1 affects the assembly of the Golgi apparatus and endoplasmic reticulum during telophase of mitosis by inhibiting cyclin activities.6,7 Abnormal expression of PKMYT1 is closely associated with multiple types of cancers. However, the role and mechanism of PKMYT1 in GC progression remain unclear. A recent study showed that microglobulin 1 (MCRS1) interacts with PKMYT1 in GC.8 MCRS1 has low expression in GC and exerts inhibitory effect on PKMYT1, promoting the proliferation, invasion, migration, and epithelial-mesenchymal transition (EMT) of GC cells. Thus, PKMYT1 may play a tumor-promoting role in the progression of GC. In the present study, we examined the roles of PKMYT1 in GC progression and clarified the effects of PKMYT1 on the malignant biological behaviors of GC cells.

Materials and Methods

Bioinformatics Analysis

The expression of PKMYT1 in common digestive cancers and GC was predicted using a gene expression profiling and interactive analyses tool (GEPIA; ).9 GEPIA is a web-based tool that provides information on differential gene expression in tumors and normal tissues, correlation analysis, and patient survival analysis based on extensive RNA sequencing data. In addition, the Kaplan–Meier Plotter () containing 54,675 genes and 10,461 cancer samples, including 1065 GC samples, was used to assess the effects of PKMYT1 on the survival rate of patients with GC.10

Tissue Samples and Cell Culture

A total of 103 paraffin-embedded GC samples (including cancer tissues and matched adjacent tissues (>3 cm away from cancer tissue)) were collected from patients with GC who underwent treatment at The First People’s Hospital of Lanzhou City between January 2010 and December 2013. All patients were pathologically diagnosed and their complete clinicopathological data and follow-up information were available. Of the 103 patients with GC, there were 56 males and 47 females aged 35–78 years, with a mean age of 56.8 ± 12.3 years. In addition, another 10 pairs of fresh GC tissue and adjacent tissue samples were collected and frozen at −80°C. No patients had been administered chemotherapy, radiotherapy, or targeted therapy. This study was strictly conducted according to the Declaration of Helsinki and was approved by the Ethics Committee of The First People’s Hospital of Lanzhou City. Written informed consent was obtained from all patients with GC involved in this study for the collection of tissue samples. The normal gastric mucosal epithelial cell line GES-1 and the GC cell lines AGS, HGC27, MKN45, and BGC-823 were purchased from the American Type Culture Collection (Manassas, VA, USA). The cells were maintained in Dulbecco’s modified Eagle medium supplemented with 10% fetal bovine serum, 1 × 105 U/L penicillin, and 100 mg/L streptomycin at 37°C in an incubator with 5% CO2. The culture medium was changed every other day. Cell passaging was performed when the cells reached 80–90% confluency. PKMYT1 expression in the cell lines was detected by Western blotting. The cell line showing the highest PKMYT1 protein expression was selected for subsequent functional assays.

Immunohistochemistry (IHC)

The paraffin-embedded GC tissues and adjacent tissues were subjected to IHC. Briefly, the tissue block was cut into 4-μm-thick sections, dewaxed, and hydrated at 70°C in an oven. Next, 3% H2O2 was added to the sections to block endogenous peroxidase activity. The sections were placed in citrate buffer (pH 6) at 95°C for antigen retrieval and were then incubated with primary anti-PKMYT1 antibody (1: 100; Cell Signaling Technology, Danvers, MA, USA) at 4°C overnight. On the next day, the sections were reacted with secondary antibodies and stained with diaminobenzidine (Vector Laboratories, Burlingame, CA, USA). After counterstaining with hematoxylin, the slides were mounted. The immunoreaction score for PKMYT1 was determined as the staining intensity of tumor cells multiplied by the positive staining degree. The staining intensity of tumor cells was scored as follows: 0 (negative), 1 (weak), 2 (moderate), and 3 (strong). The positive staining scores were as follows: 0 (0%), 1 (1–25%), 2 (26–50%), 3 (51–75%), and 4 (76–100%). According to the immunoreaction score, a score of ≥4 was defined as high expression of PKMYT1, whereas a score of <4 was defined as low expression of PKMYT1.

Inhibition of PKMYT1 Expression by shRNA

A short hairpin RNA (shRNA) lentiviral vector was constructed to inhibit the expression of endogenous PKMYT1 in GC cells. The sequence of shRNA targeting PKMYT1 was as follows – shPKMYT1 #1: CCGGTGTCAAGCCTGCCAACATC TTCTCGAGAAGATGTTGGCAGGCTTGACATTTTTG; shPKMYT1 # 2: CCGGG AACCTGGATTCTCCCTCAAGCTCGAGCTTGAGGGAGAATCCAGGTTCTTTTTG. The shRNA sequence of the negative control (shNC) was GGCCTTGGACTCG GACTCCCTACATCCGGTATGCATCGATTCCGCAGGTCGTACCTAG. The above plasmids were transfected into pGLVU6/Puro vector (GenePharma Corporation, Shanghai, China). Lipofectamine 3000 transfection reagent (Thermo Fisher Scientific, Waltham, MA) was used for transfection according to the manufacturer’s instructions. Puromycin was subsequently used to select stable cell lines with suppressed expression of PKMYT1. The rate of suppression of PKMYT1 expression was determined by Western blotting.

Western Blotting

Tissue samples were fully ground and lysed by adding an appropriate amount of liquid nitrogen, followed by protein extraction using a total protein extraction kit (Sigma-Aldrich, Inc., Dorset, UK). For cell samples, a total protein extraction kit (Sigma-Aldrich) was directly used to extract proteins. After protein denaturation and measurement of protein concentration, the protein samples were electrophoresed on sodium dodecyl sulfate-polyacrylamide gel and then transferred onto nitrocellulose membranes, which were incubated with appropriate primary antibodies at 4°C overnight. After washing three times (15 min each time) with TBST, the membranes were reacted with secondary antibody at room temperature for 1 h and then washed again as described above. A chemiluminescence kit (EZ-ECL, Biological Industries, Cromwell, CT, USA) was used according to the manufacturer’s instructions to visualize the results. Finally, a ChemiDoc XRS system (Bio-Rad Laboratories, Hercules, CA, USA) was used to detect the expression of the target protein. GAPDH (Bioworld Technology, Minneapolis, MN, USA) was used as the loading control. The antibodies used for Western blotting were as follows: anti-PKMYT1 antibody (1: 1000), anti-ERK1/2 antibody (1: 1500), anti-Phospho-ERK1/2 antibody (1: 1000), anti-p38 antibody (1: 1000), anti-Phospho-p38 antibody (1: 1200), and anti-Cyclin D1 antibody (1:800). The above antibodies were purchased from Cell Signaling Technology.

Cell Cycle Analysis

The cell cycle of GC cells was analyzed by flow cytometry. Briefly, the cells were digested with trypsin and fixed with 70% ethanol, followed by centrifugation and repeated washing. The cell suspension was added to 50 μg of propidium iodide (PI) (Fermentas) containing RNase. Finally, the samples were subjected to flow cytometry (Beckman, Brea, CA, USA).

Cell Viability Analysis

Cell Counting Kit-8 (CCK-8; Beyotime Biotechnology, Shanghai, China) was used to detect the proliferation of GC cells. Cells were inoculated into 96-well plates at a density of 3 × 103 cells/well in 200 μL of culture medium. Next, 10 μL of CCK-8 reagent was added to each well at the indicated time points for five consecutive days. After incubation at 37°C for an additional 1–2 h, the absorbance of the cells was measured at 450 nm using a microplate reader. The assay was performed in triplicate.

Plate Colony Formation Assay

A plate clone formation assay was performed to assess the survival rate of GC cells. Briefly, the cells were uniformly seeded into 6-well plates at a density of 1000 cells/well and cultured for 2–3 weeks. When macroscopic colonies appeared, the supernatant was discarded. The cells were washed twice, fixed with 4% paraformaldehyde for 30 min, and stained with 1% crystal violet for 30 min. Finally, colony formation was assessed.

Apoptosis Assay

Apoptosis was detected using an Annexin-V/PI apoptosis detection kit (Solarbio Life Sciences, Beijing, China) according to the manufacturer’s instructions. Finally, the percentage of apoptotic cells was assessed by flow cytometry (Beckman).

In vivo Experiments

In vivo experiments were performed to investigate the effect of PKMYT1 on GC. Six-weeks-old female BALB/c nude mice were purchased from Huafukang Biotechnology (Beijing, China). The Animal Ethics Committee of The First People’s Hospital of Lanzhou City approved the use of laboratory animals. The average weight of nude mice was 18–20 g. The GC cells were grown and prepared in suspensions of 5×106 cells/mL. Using a 1 mL syringe, 0.2 mL of the cell suspension was inoculated subcutaneously into the left armpit of the nude mouse. Thereafter, the size of the tumor was measured and recorded every 1 week. The tumor volume was defined as follows: volume (mm3) = (length × width2)/2. The small animal living imaging system was used for imaging (Lumina LT; PerkinElmer, Inc.). Twenty-eight days later, the mice were sacrificed via cervical dislocation and the tumors were removed and weighed. All the animal experiments were carried out in accordance with the principles and procedures of the National Institutes of Health’s Guidelines for the Care and Use of Laboratory Animals.

Statistical Analysis

Continuous data were expressed as mean ± standard deviation. Chi-square test was used to analyze categorical data while t test was used to analyze measurement data. In this study, overall survival (OS) was used as the primary endpoint for patients with GC. Kaplan–Meier survival curves were used to analyze OS. All data were analyzed by GraphPad Prism v7.0 software (GraphPad Software, Inc., La Jolla, CA, USA). A P value of <0.05 indicated statistical significance.

Results

Bioinformatics Prediction Results

We analyzed the mRNA expression of PKMYT1 in common digestive cancers and GC using the online GEPAI tool. The mRNA expression of PKMYT1 was found to be upregulated in esophageal cancer, GC, pancreatic cancer, liver cancer, colon cancer, and rectal cancer tissues compared to that in normal tissues (Figure 1A). As shown in Figure 1B, the mRNA expression of PKMYT1 was significantly higher in GC tissues than in normal gastric tissues. The online tool Kaplan–Meier Plotter was used to assess the effects of PKMYT1 on the survival of patients with GC; the results suggested that patients with GC showing high PKMYT1 expression had worse prognosis (Figure 1C).
Figure 1

Biological analysis results. (A) GEPIA database shows PKMYT1 is up-regulated in most digestive cancers. (B) The expression level of PKMYT1 mRNA in GC samples was significantly higher than that in normal samples, which originates from the GEPIA database. (C) KM Plotter shows GC patients with high PKMYT1 expression own worse prognosis. *P <0.05.

Biological analysis results. (A) GEPIA database shows PKMYT1 is up-regulated in most digestive cancers. (B) The expression level of PKMYT1 mRNA in GC samples was significantly higher than that in normal samples, which originates from the GEPIA database. (C) KM Plotter shows GC patients with high PKMYT1 expression own worse prognosis. *P <0.05.

Expression of PKMYT1 in GC

Bioinformatics analysis revealed that the expression of PKMYT1 was upregulated in GC and was closely associated with the prognosis of patients with GC, indicating that it plays a tumor-promoting role in GC. To confirm the results of the bioinformatics analysis, we determined the expression of PKMYT1 in GC samples and cell lines. First, the expression of PKMYT1 protein was detected in GC tissues and paired adjacent tissues. As shown in Figure 2A, PKMYT1 protein was mainly localized in the cytoplasm. The expression level of PKMYT1 was significantly higher in GC tissues than in adjacent tissues (52.4% vs 31.1%, P = 0.002). Western blotting was performed to quantitatively detect the expression of PKMYT1 protein in 10 pairs of fresh GC and adjacent tissues. PKMYT1 protein expression was significantly higher in GC tissues than in adjacent tissues (Figure 2B). We also compared and analyzed the expression of PKMYT1 protein in GC cell lines. Compared with the normal gastric mucosal epithelial cell line GES-1, PKMYT1 protein expression was significantly upregulated in GC cell lines (Figure 2C), with highest expression levels observed in the MKN45 cell line. Therefore, MKN45 cell line was selected for subsequent in vitro studies. The above experimental results were consistent with the results of the bioinformatics analysis.
Figure 2

The expression of PKMYT1 in GC. (A) Confirm that PKMYT1 protein is located in the cytoplasm of GC and adjacent tissues through IHC. (B) Detection of PKMYT1 protein expression in 10 pairs GC and matched adjacent tissues by Western blotting. (C) PKMYT1 expression was detected and compared between normal gastric mucosal epithelial cell line GES-1 and all GC cell lines by Western blotting. *P <0.001.

The expression of PKMYT1 in GC. (A) Confirm that PKMYT1 protein is located in the cytoplasm of GC and adjacent tissues through IHC. (B) Detection of PKMYT1 protein expression in 10 pairs GC and matched adjacent tissues by Western blotting. (C) PKMYT1 expression was detected and compared between normal gastric mucosal epithelial cell line GES-1 and all GC cell lines by Western blotting. *P <0.001.

Correlation Between PKMYT1 Expression and Patient Prognosis

Based on the IHC scores, 103 patients with GC were divided into low PKMYT1 expression group (N = 49) and high PKMYT1 expression group (N = 54). Chi-square test revealed that high PKMYT1 expression was closely correlated with tumor infiltration and distant metastasis in patients with GC (P < 0.05, Table 1), although it was not significantly correlated with the age, gender, tumor size, tumor differentiation, or lymph node status of the patients.
Table 1

Correlation Between PKMYT1 Expression and Clinicopathological Parameters of 103 GC Patients

CharacteristicsNPKMYT1 Expressionχ2P
Low (n=49)High (n=54)
Age (years)1.1450.285
 <553715 (40.5)22 (59.5)
 ≥556634 (51.5)32 (48.5)
Gender0.2900.590
 Male5628 (50)28 (50)
 Female4721 (44.7)26 (55.3)
Tumor size1.6090.205
 <3cm5322 (41.5)31 (58.5)
 ≥3cm5027 (54)23 (46)
Tumor differentiation0.1420.706
 Well and moderately6530 (46.2)35 (53.8)
 Poorly3819 (50)19 (50)
Tumor infiltration10.4940.001
 T1+T23826 (68.4)12 (31.6)
 T3+T46523 (35.4)42 (64.6)
Local lymph node metastasis0.7640.382
 Negative5423 (42.6)30 (57.4)
 Positive4926 (53.1)24 (46.9)
Distant metastasis8.3530.004
 M08546 (54.1)39 (45.9)
 M1183 (16.7)15 (83.3)

Abbreviations: PKMYT1, protein kinase membrane associated tyrosine/threonine 1; GC, gastric cancer; T, tumor; M, metastasis.

Correlation Between PKMYT1 Expression and Clinicopathological Parameters of 103 GC Patients Abbreviations: PKMYT1, protein kinase membrane associated tyrosine/threonine 1; GC, gastric cancer; T, tumor; M, metastasis. Based on the long-term follow-up data, Kaplan–Meier survival curves were plotted for patients with GC according to their clinicopathological characteristics; these curves showed that patients with GC exhibiting deeper tumor infiltration, positive lymph nodes, distant metastases, and high PKMYT1 expression had worse prognosis (Figure 3A–D). To further evaluate the prognostic effects of PKMYT1 in patients with GC, a Cox proportional hazard model was established. Univariate analysis showed that tumor differentiation, tumor infiltration, local lymph node metastasis, distant metastasis, and PKMYT1 expression were closely related to OS in patients with GC (P < 0.05, Table 2). Multivariate analysis revealed that high PKMYT1 expression was an independent risk factor for OS in patients with GC (P < 0.05, Table 2).
Figure 3

Survival analysis of patients with GC. (A) The survival rate of GC patients in the group of deep tumor infiltration was significantly lower than that of patients in the group of shallow tumor infiltration. (B) The survival rate of GC patients in the group of positive lymph nodes was significantly lower than that of patients in the group of negative lymph nodes. (C) The survival rate of GC patients in the group of distant metastases was significantly lower than that of patients in the group of negative metastases. (D) The survival rate of GC patients in the group of high PKMYT1 expression was significantly lower than that of patients in the group of low PKMYT1 expression.

Table 2

Univariate and Multivariate Analyses of OS of Patients with GC

VariablesUnivariate AnalysisMultivariate Analysis
HR (95% CI)PHR (95% CI)P
Age (≥55y vs <55y)0.904 (0.594–1.376)0.639
Gender (Female vs Male)1.301 (0.864–1.96)0.208
Tumor size (≥3cm vs <3cm)0.699 (0.463–1.056)0.089
Tumor differentiation (Poorly vs Well and moderately)1.593 (1.046–2.424)0.0301.869 (1.156–3.021)0.011
Tumor infiltration (T3+T4 vs T1+T2)1.594 (1.038–2.45)0.0331.724 (1.011–2.939)0.045
Local lymph node metastasis (Positive vs Negative)1.682 (1.116–2.536)0.0132.214 (1.405–3.487)0.001
Distant metastasis (M1 vs M0)2.204 (1.308–3.714)0.0031.869 (1.023–3.415)0.042
PKMYT1 expression (High vs Low)1.882 (1.241–2.855)0.0031.634 (1.014–2.635)0.044

Abbreviations: OS, overall survival; GC, gastric cancer; HR, hazard ratio; CI, confidence interval; T, tumor; M, metastasis; PKMYT1, protein kinase membrane associated tyrosine/threonine 1.

Univariate and Multivariate Analyses of OS of Patients with GC Abbreviations: OS, overall survival; GC, gastric cancer; HR, hazard ratio; CI, confidence interval; T, tumor; M, metastasis; PKMYT1, protein kinase membrane associated tyrosine/threonine 1. Survival analysis of patients with GC. (A) The survival rate of GC patients in the group of deep tumor infiltration was significantly lower than that of patients in the group of shallow tumor infiltration. (B) The survival rate of GC patients in the group of positive lymph nodes was significantly lower than that of patients in the group of negative lymph nodes. (C) The survival rate of GC patients in the group of distant metastases was significantly lower than that of patients in the group of negative metastases. (D) The survival rate of GC patients in the group of high PKMYT1 expression was significantly lower than that of patients in the group of low PKMYT1 expression.

Effects of PKMYT1 on Cell Proliferation and Apoptosis in GC Cells

To investigate the effects of PKMYT1 on GC cell function, shRNA was used to downregulate the expression of PKMYT1 in the MKN45 cell line. After transfection for 48 h, the expression of PKMYT1 protein was significantly inhibited, as indicated by Western blotting (Figure 4A). First, we analyzed the effect of PKMYT1 on the GC cell cycle by flow cytometry. Compared to GC cells in the shNC group, the percentage of GC cells in the G0/G1 phase increased in the shPKMYT1 group, while the GC cells in the G2/M phase decreased. Therefore, knockdown of PKMYT1 caused G0/G1 cell cycle arrest in MKN45 cells (Figure 4B).
Figure 4

Effect of PKMYT1 on the malignant biological behavior of GC cells. (A) Identify the knockdown efficiency of PKMYT1 in GC cells by Western blotting. (B) Analysis of the effect of PKMYT1 on GC cell cycle by flow cytometry. Knockdown of PKMYT1 caused G0/G1 cell cycle arrest in MKN45 cells. (C) Detection of effect of PKMYT1 on the proliferation of GC cells was by the CCK-8 kit. Knockdown of PKMYT1 inhibited the proliferation of MKN45 cells. (D) Analysis of the effect of PKMYT1 on the survival rate of GC cells by plate clone formation. Knockdown of PKMYT1 inhibited the the colony formation ability of MKN45 cells. (E) Analysis of the effect of PKMYT1 on GC cell apoptosis by flow cytometry. Knockdown of PKMYT1 promoted apoptosis of MKN45 cells. *P <0.001.

Effect of PKMYT1 on the malignant biological behavior of GC cells. (A) Identify the knockdown efficiency of PKMYT1 in GC cells by Western blotting. (B) Analysis of the effect of PKMYT1 on GC cell cycle by flow cytometry. Knockdown of PKMYT1 caused G0/G1 cell cycle arrest in MKN45 cells. (C) Detection of effect of PKMYT1 on the proliferation of GC cells was by the CCK-8 kit. Knockdown of PKMYT1 inhibited the proliferation of MKN45 cells. (D) Analysis of the effect of PKMYT1 on the survival rate of GC cells by plate clone formation. Knockdown of PKMYT1 inhibited the the colony formation ability of MKN45 cells. (E) Analysis of the effect of PKMYT1 on GC cell apoptosis by flow cytometry. Knockdown of PKMYT1 promoted apoptosis of MKN45 cells. *P <0.001. The proliferation activity of GC cells was detected using a CCK-8 kit. As expected, shPKMYT1 treatment significantly inhibited the proliferation activity of MKN45 cells (Figure 4C). Moreover, the survival rate of GC cells was evaluated via a plate colony assay. Compared to MKN45 cells in shNC group, the colony formation ability of GC cells was significantly reduced after PKMYT1 knockdown (Figure 4D). These data confirm that PKMYT1 promoted the proliferation ability of GC cells. In addition, Annexin-V/PI apoptosis detection kit was used to analyze the effect of PKMYT1 knockdown on GC cell apoptosis by flow cytometry. Compared to GC cells in the shNC group, those in the shRNA-treated group had significantly increased apoptotic ratio (Figure 4E), indicating that PKMYT1 has an anti-apoptotic effect on GC cells. To examine the role of PKMYT1 in vivo, we established a GC xenograft model in nude mice. Every week after injection, the intensity of luciferase was evaluated using the small animal live imaging system to confirm the occurrence of subcutaneous tumors. The results showed that the knockdown of PKMYT1 suppressed tumorigenesis. After observation, a strong luciferase signal was detected in the control group earlier than in the experimental group (shPKMYT1) of nude mice (shNC). The growth curve of subcutaneous tumors in nude mice showed that the knockdown of PKMYT1 significantly inhibited the tumor growth rate. Moreover, the weights of the tumors removed from the nude mice at the end of the animal experiment were significantly lower in the shPKMYT1 group than in the control group. The results of in vivo experiments are shown in Figure 5.
Figure 5

Knockdown of PKMYT1 inhibit tumor growth in mice xenograft models. (A) The bioluminescence imaging of tumors in shNC group and shPKMYT1 group. (B) Tumor volume was measured every week. Two weeks after the establishment of the xenograft model, the volume of tumors in the shPKMYT1 group was significantly smaller than that of the shNC group. (C) The weight of tumors was measured after twenty-eight days. The weight of tumors in the shPKMYT1 group was significantly lower than that of the shNC group. *P <0.001.

Knockdown of PKMYT1 inhibit tumor growth in mice xenograft models. (A) The bioluminescence imaging of tumors in shNC group and shPKMYT1 group. (B) Tumor volume was measured every week. Two weeks after the establishment of the xenograft model, the volume of tumors in the shPKMYT1 group was significantly smaller than that of the shNC group. (C) The weight of tumors was measured after twenty-eight days. The weight of tumors in the shPKMYT1 group was significantly lower than that of the shNC group. *P <0.001.

PKMYT1 Activated the MAPK Signaling Pathway

Mitogen-activated protein kinase (MAPK) signaling pathways play important roles in multiple cellular processes, such as proliferation, apoptosis, and survival.11,12 Our previous data revealed that PKMYT1 is closely related to the cell proliferation and anti-apoptosis of GC cells. Therefore, we compared and analyzed the changes in the MAPK signaling pathway before and after PKMYT1 knockdown. Compared with the control group, the expression of phosphorylated ERK1/2, p38, and Cyclin D1 was suppressed in the GC cell line when PKMYT1 was effectively knocked down. However, the aggregate levels of ERK1/2, p38, and Cyclin D1 did not change (Figure 6). Consequently, overexpression of PKMYT1 activated the MAPK signaling pathway to promote GC progression.
Figure 6

PKMYT1 activates MAPK signaling pathway. Knockdown of PKMYT1 can significantly inhibit the phosphorylation level of ERK1/2, p38, and Cyclin D1 in GC cells. *P >0.05; ** P <0.001.

PKMYT1 activates MAPK signaling pathway. Knockdown of PKMYT1 can significantly inhibit the phosphorylation level of ERK1/2, p38, and Cyclin D1 in GC cells. *P >0.05; ** P <0.001.

Discussion

Under physiological conditions, PKMYT1 acts as a cyclin-regulating kinase and is mainly responsible for inhibiting the phosphorylation of cyclin-dependent kinase 1 (Cdk1).13,14 PKMYT1 shows high substrate specificity, as it can phosphorylate Cdk1 but not the Cdk2 complex.14 PKMYT1 is mainly localized in the cellular Golgi apparatus and endoplasmic reticulum.15 The binding of PKMYT1 and Cdk1 complexes causes them to be retained in the cytoplasm, preventing their localization into the nucleus and cell cycle progression.14,16-18 In vitro studies revealed that PKMYT1 knockdown increases the kinetics of the G2/M transition, promotes early entry into mitosis, or leads to total checkpoint abrogation, which also increases the level of subsequent cell death.14 Abnormal expression of PKMYT1, which plays a role in tumor progression, has been reported in various solid malignancies. In a study on metastatic renal cell carcinoma, cDNA microarray analysis revealed that PKMYT1 is associated with tumor metastasis.19 The lentivirus-mediated silencing of PKMYT1 has been shown to effectively inhibit the viability of hepatic carcinoma cells mediated by high β-catenin signaling.20 In addition, high expression of PKMYT1 has been predicted in breast cancer, regardless of the molecular subtype, through bioinformatics analysis. Additionally, high expression of PKMYT1 indicates a worse prognosis for patients with breast cancer.21 Similarly, bioinformatics analysis confirmed that PKMYT1 is upregulated in hepatocellular carcinoma and can accurately predict the poor prognosis of patients with hepatic carcinoma.22 Other studies have detected differential expression of PKMYT1 in lung, esophageal, and colorectal cancers.23–25 In this study, we first confirmed that PKMYT1 was upregulated in GC tissues and that high expression of PKMYT1 was an independent risk factor for poor prognosis in patients with GC. In terms of cell function, we found that the knockdown of PKMYT1 affected the GC cell cycle, inhibited the proliferation ability GC cells, and promoted GC cell apoptosis. In vivo data corroborated the above findings. Tumor cell proliferation is a complex process driven by certain signaling pathways. MAPK signaling pathways play important roles in various cellular processes, such as proliferation, apoptosis, and survival.11,12 During carcinogenesis, gene mutation can dysregulate kinase activity and activate the MAPK pathway. In cellular functional studies, we confirmed that PKMYT1 was involved in the proliferation and apoptosis of GC cells. Therefore, we analyzed the correlation between PKMYT1 and the MAPK signaling pathway. In terms of potential mechanisms, overexpression of PKMYT1 promoted tumor cell proliferation and apoptosis resistance by activating the MAPK signaling pathway. According to previous results, PKMYT1 also promotes carcinogenesis through other pathways. For example, PKMYT1 mainly activates the Notch signaling pathway in the progression of non-small cell lung cancer.23 In addition, PKMYT1 can promote the growth and motility of hepatic carcinoma cells by over-activating the β-catenin/TCF pathway.26 Therefore, PKMYT1 may promote the progression of GC through multiple signaling pathways.

Conclusion

In summary, we demonstrated that PKMYT1 promotes cell proliferation and apoptosis resistance in GC cells by activating the MAPK signaling pathway, making it a potential therapeutic target for GC.
  26 in total

1.  Negative regulation of mitosis by wee1+, a gene encoding a protein kinase homolog.

Authors:  P Russell; P Nurse
Journal:  Cell       Date:  1987-05-22       Impact factor: 41.582

2.  Structural Basis of Wee Kinases Functionality and Inactivation by Diverse Small Molecule Inhibitors.

Authors:  Jin-Yi Zhu; Rebecca A Cuellar; Norbert Berndt; Hee Eun Lee; Sanne H Olesen; Mathew P Martin; Jeffrey T Jensen; Gunda I Georg; Ernst Schönbrunn
Journal:  J Med Chem       Date:  2017-09-14       Impact factor: 7.446

Review 3.  Negative regulators of cyclin-dependent kinases and their roles in cancers.

Authors:  M H Lee; H Y Yang
Journal:  Cell Mol Life Sci       Date:  2001-11       Impact factor: 9.261

4.  Computer-aided design, synthesis and biological characterization of novel inhibitors for PKMYT1.

Authors:  Abdulkarim Najjar; Charlott Platzer; Anton Luft; Chris Alexander Aßmann; Nehal H Elghazawy; Frank Erdmann; Wolfgang Sippl; Matthias Schmidt
Journal:  Eur J Med Chem       Date:  2018-10-24       Impact factor: 6.514

5.  Gene expression profiling, pathway analysis and subtype classification reveal molecular heterogeneity in hepatocellular carcinoma and suggest subtype specific therapeutic targets.

Authors:  Rahul Agarwal; Jitendra Narayan; Amitava Bhattacharyya; Mayank Saraswat; Anil Kumar Tomar
Journal:  Cancer Genet       Date:  2017-07-08

6.  GEPIA: a web server for cancer and normal gene expression profiling and interactive analyses.

Authors:  Zefang Tang; Chenwei Li; Boxi Kang; Ge Gao; Cheng Li; Zemin Zhang
Journal:  Nucleic Acids Res       Date:  2017-07-03       Impact factor: 16.971

Review 7.  Nuclear ERK: Mechanism of Translocation, Substrates, and Role in Cancer.

Authors:  Galia Maik-Rachline; Avital Hacohen-Lev-Ran; Rony Seger
Journal:  Int J Mol Sci       Date:  2019-03-08       Impact factor: 5.923

8.  Overexpressed PKMYT1 promotes tumor progression and associates with poor survival in esophageal squamous cell carcinoma.

Authors:  Qingyi Zhang; Xuan Zhao; Chaoqi Zhang; Wei Wang; Feng Li; Donglei Liu; Kai Wu; Dengyan Zhu; Shasha Liu; Chunyi Shen; Xin Yuan; Kai Zhang; Yang Yang; Yi Zhang; Song Zhao
Journal:  Cancer Manag Res       Date:  2019-08-19       Impact factor: 3.989

Review 9.  Regulation of G2/M Transition by Inhibition of WEE1 and PKMYT1 Kinases.

Authors:  Matthias Schmidt; Alexander Rohe; Charlott Platzer; Abdulkarim Najjar; Frank Erdmann; Wolfgang Sippl
Journal:  Molecules       Date:  2017-11-23       Impact factor: 4.411

10.  Systematic expression analysis of WEE family kinases reveals the importance of PKMYT1 in breast carcinogenesis.

Authors:  Yu Liu; Jian Qi; Zhen Dou; Jiliang Hu; Li Lu; Haiming Dai; Hongzhi Wang; Wulin Yang
Journal:  Cell Prolif       Date:  2019-12-14       Impact factor: 6.831

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  6 in total

1.  Accompaniment of Time-Lapse Parameters and Cumulus Cell RNA-Sequencing in Embryo Evaluation.

Authors:  Azam Govahi; Fatemehsadat Amjadi; Mohammad-Hossein Nasr-Esfahani; Ehsan Raoufi; Mehdi Mehdizadeh
Journal:  Reprod Sci       Date:  2021-10-12       Impact factor: 3.060

2.  The DNA damage repair-related gene PKMYT1 is a potential biomarker in various malignancies.

Authors:  Changjian Shao; Yuanyong Wang; Minghong Pan; Kai Guo; Tamas F Molnar; Florian Kocher; Andreas Seeber; Martin P Barr; Alfons Navarro; Jing Han; Zhiqiang Ma; Xiaolong Yan
Journal:  Transl Lung Cancer Res       Date:  2021-12

3.  c-Myb-mediated inhibition of miR-601 in facilitating malignance of osteosarcoma via augmentation of PKMYT1.

Authors:  Peng Luo; Jiarui Fang; Houqing Chen; Feng He; Siying Xiao; He Liu; Shizhuang Zhu; Jianzhou Luo; Changqing Jiang
Journal:  Sci Rep       Date:  2022-04-23       Impact factor: 4.996

4.  Integrated PPI- and WGCNA-retrieval of hub gene signatures for soft substrates inhibition of human fibroblasts proliferation and differentiation.

Authors:  Ziran Xu; Tian Zhou; Yin Wang; Leijie Zhu; Jihao Tu; Zhixiang Xu; Lisha Li; Yulin Li
Journal:  Aging (Albany NY)       Date:  2022-09-02       Impact factor: 5.955

5.  Identification of Survival-Associated Hub Genes in Pancreatic Adenocarcinoma Based on WGCNA.

Authors:  Liya Huang; Ting Ye; Jingjing Wang; Xiaojing Gu; Ruiting Ma; Lulu Sheng; Binwu Ma
Journal:  Front Genet       Date:  2022-01-03       Impact factor: 4.599

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

Authors:  Juan Chen; Xiaoliang Hua; Heying Chen; Xiangmin Qiu; Haibing Xiao; Shengdong Ge; Chaozhao Liang; Qin Zhou
Journal:  Aging (Albany NY)       Date:  2021-12-27       Impact factor: 5.682

  6 in total

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