GNPNAT1 promotes cancer cells metastasis through stabilization Slug in lung adenocarcinoma

doi:10.21203/rs.3.rs-4204938/v1

Download PDF

Research Article

GNPNAT1 promotes cancer cells metastasis through stabilization Slug in lung adenocarcinoma

https://doi.org/10.21203/rs.3.rs-4204938/v1

This work is licensed under a CC BY 4.0 License

Version 1

posted

You are reading this latest preprint version

Background

Lung cancer is a common malignant tumor with high morbidity and mortality rate. GNPNAT1 has been identified as a metastasis-associated gene in LUAD. However, the exact role and related mechanism of GNPNAT1 in regulating LUAD metastasis remain largely unknown.

Method

We analyzed the expression of GNPNAT1 in the TIMER2, GEPIA2 and GEO databases and confirmed the results by immunohistochemistry (IHC). The potential biological functions of GNPNAT1 in LUAD was investigated based on TCGA-LUAD database. The correlations between GNPNAT1 and cancer immune characteristics were analyzed via the ESTIMATE and CIBERSORT R package. The underlying mechanisms of altered GNPNAT1 expression on LUAD cell tumorigenesis, proliferation, migration, invasion, and metastasis were explored in vitro and in vivo.

Result

We demonstrated that GNPNAT1 expression was markedly increased in lung adenocarcinoma (LUAD) tissues and negatively correlated with the overall survival (OS) of patients. hsa − miR − 1−3p and hsa − miR − 26a − 5p were the upstream miRNA targets of GNPNAT1. GNPNAT1 was positively correlated with the infiltration levels of CD8 T cells, memory activated CD4 T cells, NK cells resting, Macrophages M0, Macrophages M1, Neutrophils, gamma delta T cells, Eosinophils, and was negatively correlated with memory resting CD4 T cells, regulatory T cells (Tregs), resting NK cells, Monocytes, resting dendritic cells, resting mast cells. GNPNAT1 knockdown significantly inhibited proliferation, migration, invasion, EMT, and metastasis of LUAD cell, while overexpression of GNPNAT1 revealed the opposite effects. Rescue assay showed that Slug knockdown reversed GNPNAT1-induced LUAD cells migration, invasion, and EMT. Mechanistically, GNPNAT1 promoted cancer cells metastasis via repressing ubiquitination degradation of Slug in LUAD.

Conclusion

These data indicated that GNPNAT1 was critical for proliferation, migration, invasion, EMT process, and metastasis of LUAD cells and may be a potential therapeutic target for LUAD.

LUAD

GNPNAT1

Slug

proliferation

metastasis

prognosis

Lung cancer is one of the most common malignant tumors and the primary cause of cancer death worldwide [1, 2]. Lung adenocarcinoma (LUAD) is the most common pathologic type of lung cancer, accounting for approximately more than 50% of all lung cancer cases and almost 40% of death related to lung cancer[3–5]. Although remarkable improvements have been achieved in early diagnosis and clinical treatment of LUAD, and the overall survival (OS) of LUAD remain unsatisfactory. The 5-year survival rate of LUAD patients remains less than 15% and has not increased in the past few years due to its usually invasive and metastatic activity[6–8]. Metastasis are the leading causes of LUAD patients. Although many studies have been conducted on LUAD metastasis, our understanding and the molecular mechanism on LUAD metastasis remains limited and unclear. Therefore, it is of great significance to identify new biomarkers for improving the OS of LUAD patients and overcoming LUAD metastasis.

GNPNAT1, a small dimer protein located in the Golgi matrix and endomembrane, is the rate-limiting enzyme in the second step of the HBP (hexosamine biosynthetic pathway) process and convert glucosamine-6-phosphate to N-acetylglucosamine-6-phosphate[9, 10]. It was reported that GNPNAT1 was abnormally expressed in castrated-resistant prostate cancer (CRPC) and LUAD[11–15]. GNPNAT was down-regulated in CRPC tissues and participated in metastasis of CRPC by regulating PI3K/AKT signal pathway[11]. GNPNAT1 was highly expressed in LUAD tissues and was positively correlated to LUAD with poor prognosis[12–15]. It was also found that GNPNAT1 could promoted the growth, proliferation, migration and invasion of LUAD cells[16–18]. However, the regulation of GNPNAT1 on LUAD and its molecular mechanism remain unclear.

In this study, we confirmed that GNPNAT1 was highly expressed in LUAD through public database and immunohistochemical analysis, and was closely correlated with the poor prognosis of LUAD patients, the infiltration of multiple immune cells and multiple immunomodulator genes. hsa − miR − 1−3p and hsa − miR − 26a − 5p were negatively correlated with GNPNAT1. Functional experiments showed that GNPNAT1 could promote the growth, proliferation, migration, invasion, EMT process and metastasis of LUAD cells. Slug was required in GNPNAT1-mediated LUAD cells EMT process, migration and invasion. Mechanically, GNPNAT1 promoted LUAD metastasis by stabilization Slug. Therefore, GNPNAT1 was regarded as a new prognostic marker and a promising potential therapeutic target for LUAD patients.

Data Collection

TCGA RNA-seq datasets and clinical data for LUAD were downloaded from the TCGA data portal (https://portal.gdc.cancer.gov/). GSE32863, GSE40791, GSE75037, GSE115002, GSE13213, and GSE72094 was download from the GEO database (https://www.ncbi.nlm.nih.gov/). The expression level of GNPNAT1 in LUAD was analyzed in TIMER2, GEPIA2, GSE32863, GSE40791, GSE75037 and GSE115002. Survival analysis for GNPNAT1 in LUAD was conducted in TCGA-LUAD cohort, GSE13213, and GSE72094.

Immune infiltration of GNPNAT1 in LUAD

“Estimation of Stromal and Immune cells in Malignant Tumor tissues using Expression data (ESTIMATE)” R package was applied to assess the levels of the immune cell infiltration, the stromal content, the stromal-immune comprehensive score for each LUAD sample based on TCGA-LUAD cohort. “CIBERSORT” R package was utilized to evaluated infiltration status of 22 distinct immune cells, and the correlation between immune cell infiltration and GNPNAT1 expression was identified using the Spearman correlation test.

Cell lines and cell culture

The human lung adenocarcinoma cell lines PC9, H1299, A549, H1993, HCC827, and H1650 were preserved in the Heilongjiang Cancer Institute (Harbin, China). The H1299, A549, H1993, HCC827, and H1650 were cultivated in Roswell Park Memorial Institute (RPMI)-1640 medium (Gibco, USA) supplemented with 10% fetal bovine serum (PAN, Germany), and antibiotics (100 U/ml streptomycin and 100 mg/ml penicillin) (Invitrogen, USA). PC9 was maintained in Dulbecco’s modified Eagle’s (DMEM) (Gibco, USA) medium with 10% FBS and antibiotics (100 U/ml streptomycin and 100 mg/ml penicillin) (Invitrogen, USA). All cell lines were cultivated in a humidified incubator with 5% CO2 at 37℃.

Cell transfection

Small interference RNAs(siRNAs)for GNPNAT1 (si-GNPNAT#1, si-GNPNAT#2, si-GNPNAT#3) from RiboBio Company (Guangzhou, China) were utilized to transiently knock down the expression of GNPNAT1. GNPNAT1-targeting siRNAs were transfected into PC9 and H1299cells using jetPRIME reagent (Polyplus, France). For overexpression of GNPNAT1, pCMV3-GNPNAT1 from Shen Gong Company (Shanghai, China) was transfected into H1993 cells using jetPRIME reagent (Polyplus, France). For control experiment, cells were transfected with an equal amount of negative control siRNAs or pCMV3-vector. si-Slug#1 and si-Slug#2 were selected to transiently knock down the expression of Slug in GNPNAT1-overexpressing H1993 cells. All procedure were executed in accordance to the manufacture’s protocol. The sequence for siRNAs were listed in Table S1. si-GNPNAT#3 was chosen to produce lentiviruses to constructed stably transfected H1299 cell lines that were screened using puromycin on the basis of these results.

Quantitative real time PCR (qRT-PCR)

Total RNA from cultured LUAD cell lines was isolated using TRK lysis buffer (Omega, USA) in accordance to the manufacture’s protocol. 5xFastKing-RT SuperMix (Tiangen, China) was utilized to synthesized cDNA. FastStart Universal SYBR Green Master (ROX, Germany) was applied to carried out qRT-PCR analysis. The 2^−△△ method was used to calculate the relative expression level of target mRNA that were normalize to that of GAPDH as internal control. The quantitative real time PCR primers were provided by Sheng Gong Company. The primer sequences for qRT-qPCR analysis were listed in Table S2.

Western blot assay

The cultured LUAD cell lines were harvested and suspended in radioimmunoprecipitation assay (RIPA) (Beyotime, China) lysis buffer containing phenylmethysulfony fluoride (PMSF) for protein extraction. The bicinchoninic acid (BCA) protein assay kit (Beyotime, China) was selected to detect protein concentration. Equal amount of protein (30ug) was added to 10% or 12.5% sodium dodecyl sulfate-polyacrylamide gel (SDS-PAGE) for electrophoresis and then were transferred onto polyvinylidene difluoride9 (PVDF) membranes via electroblotting. The membranes were blocked with 5% fat-free milk in phosphate-buffered saline with 0.1% tween (PBST) for 1h at room temperature. The membranes were probed at 4 ℃ overnight with the specific primary antibodies, followed by incubation with horseradish peroxides (HRP)-coupled secondary antibodies for 1h at room temperature. The enhanced chemiluminescene reagent was used to visualize the specific protein bands on the membrane. The antibodies against protein were listed in Table S3.

Co-immunoprecipitation assay

PC9 cells transfected with si-NC or si-GNPNAT1#3 were extracted in RIPA lysis buffer (Thermo Scientific, USA) for protein supernatant. Protein supernatant was incubated with the corresponding antibodies at 4℃ overnight and with protein G magnetic beads for 2–4 hours at 4℃. The protein G magnetic beads were then subjected to be separated with magnetic racket and washed with PBST. Subsequently, the bound proteins were utilized to perform WB analysis.

Wound-healing assay

The H1299, PC9 and H1993 were added to the 6-well plates containing culture medium. After cells growth up to 85%-90% confluence, an artificial wound gap was produced using a sterile micropipette tip. The wound gap was visualized through microscopic examination.

Transwell assay

Tanswell chambers (Corning, USA) were applied to perform tumor cell migration and invasion assays. For migration assay, 2x10⁴ tumor cells suspended in 200ul of medium without 10% FBS were added to the upper chambers, whereas the lower chambers were filled with 600ul of medium containing 10% FBS. For invasion assay, 2x10⁴ tumor cells suspended in 200ul of medium without 10% FBS were seed into the upper chambers with the matrigel-coated insert. The migrated or invasive tumor cells were fixed by 4% paraformaldehyde, stained by 0.1% crystal violet, photographed and counted by a light microscope.

Cell counting kit-8 (CCK8) assay

The proliferative ability for tumor cells was detected using CCK8 (Dojindo Laboratories,

Japan) assay. H1993 (pCMV3 and pCMV3-GNPNAT1), PC9 (si-NC, si-GNPNAT1#1, si-GNPNAT1#2, and si-GNPNAT1#3), and H1299 (si-NC, si-GNPNAT1#1, si-GNPNAT1#2, and si-GNPNAT1#3) were seeded into each well of the 96-well plates(3x10³/well). After incubation for 24, 48, 72h at 37 ℃, respectively, the 10ul of CCK8 reagent was added to the corresponding well. A microplate reader was used to examine the absorbance of the corresponding well at 450nm.

Immunohistochemistry (IHC) staining assay

Tissue microarrays of LUAD patients (HLungA150CS03, Shanghai Outdo Biotech, China) with 75 matched pairs of primary LUAD samples and adjacent normal tissues were purchased for IHC analysis. The sections mounted on slides were incubated at 65℃ for 60min, deparaffinized in xylene, and dehydrated in ethanol of different concentration gradients. 3% H₂0₂ was applied to eliminate endogenous peroxidase. 0.01M citrate buffer (PH 6.0) was used to retrieve antigen. Non-specific antibody binding was blocked using normal goat serum. The sections were incubated with a primary antibody against GNPNAT1(1/500) at 4 ℃ overnight, followed by incubation with a HRP-coupled goat anti-rabbit IgG for 1h at room temperature. Subsequently, the sections were subjected to staining, counterstaining using 3,3 diaminobenzidine (DAB), hematoxylin-eosin, respectively. Finally, the sections were dehydrated in a graded ethanol, sealed using neutral balsam, detected under a light microscope. The IHC staining score was independently evaluated by two experienced pathologists in accordance to the staining intensity and the proportion of positive tumor cell staining[19, 20]. The score for the staining intensity was determined as follow: 0 (negative), 1 (weakly positive), 2 (moderately positive), 3 (significantly positive). The score for the proportion of positive tumor cell staining was defined as follow: 0 (0%), 1 (1–25%), 2 (26–50%), 3 (51–75%), 4 (76–100%). The final score for IHC staining was equal to the sum of the staining intensity and the area of positive staining. Based on the median of the IHC staining scores as cut-off value, the LUAD patients were separated into high- and low-GNPNAT1 groups.

Ubiquitination assay

PC9 cell was transfected with the indicated siRNAs or si-NC. After culture for 48 hours, MG132 was executed to treat cells to protect slug protein from degradation. Protein supernatant from cells was extracted in denature lysis buffer. Finally, the denatured protein supernatant was applied to carry out WB and IP analysis.

Cycloheximide (CHX) pulse-chase assay

PC9 cells transfected with the indicated siRNAs or si-NC were seed into each well of the 12-well plates at a density of 1-2x10⁵/well. After incubation for 48 hours, the protein supernatant from PC9 cells was harvested, denatured, followed by performing WB analysis.

In vivo assay

All animal studies were authorized by the ethic committee of Harbin Medical University cancer hospital. 4 to 6-week-old BALB/c female nude mice were purchased from Charles river Co.,Ltd (Beijing, China). For the in vivo xenograft assays, 3*10⁶ H1299 cells stably transfected with sh-GNPNAT1 (Miaolingbio, China) or the control were separately subcutaneously inoculated into the right subaxillary of the nude mice (4 per group). The tumor volume (V) was measured by calipers and calculated according to the following formula: (length * width * width). After subcutaneous inoculation for 4 weeks, the nude mice were anesthetized and then sacrificed. Tumors from the nude mice were harvested and then fixed in 4% paraformaldehyde solution. For the in vivo metastasis assays, 3*10⁶ H1299 cells stably transfected with sh-GNPNAT1 or the control were separately injected into the tail veins of the nude mice. After injection for 7 weeks, the nude mice were anesthetized and then sacrificed. The lung tissues from nude mice were collected and then fixed in 4% paraformaldehyde solution.

Statistical analysis

All experiments were performed at least three times. R software and GraphPad prism 9 were utilized to perform statistical analysis. All results were presented as the mean + SEM. The difference comparison between two groups was evaluated using student’s t-test. Results with P value less than 0.05 were deemed to be statistically significant.

GNPNAT1 was highly expressed in LUAD

To investigate the expression of GNPNAT1 in LUAD, we performed bioinformatics analysis on GNPNAT1 in TIMER2.0 (http://timer.comp-genomics.org) and GEPIA2 database (http://gepia2.cancer-pku.cn/). It was shown in Fig. 1A and Fig. 1B that GNPNAT1 were highly expressed in multiple malignant tumors, including LUAD and LUSC. In addition, the expression of GNPNAT1 in LUAD was significantly higher than normal lung tissues in multiple datasets, including GSE32863, GSE40791, GSE75037 and GSE115002 (Fig. 1C-F). Immunohistochemistry confirmed that the expression of GNPNAT1 in LUAD were significantly higher than normal lung tissue (Fig. 1G, 1H).

GNPNAT1 was associated with poor prognosis in LUAD

Survival analysis demonstrated that GNPNAT1 high expression was closely correlated to the poor prognosis of LUAD patients in TCGA-LUAD cohort, GSE72094 and GSE13213 (Fig. 2A-C). Wilcoxon test showed that GNPNAT1 was positively correlated with TNM stage, tumor stage and lymph node metastasis stage of LUAD (Fig. 2D). Univariate and multivariate Cox proportional hazard model confirmed that GNPNAT1 was an independent factor for predicting LUAD patient’s OS (Fig. 2E, F). Based on GNPNAT1, the nomogram constructed by integrating clinical variables, including age, gender, TNM stage, T stage, N stage, M stage, could directly show the 1-, 3-, 5-year OS of LUAD patients (Fig. 2G, H).

hsa − miR − 1−3p and hsa − miR − 26a − 5p were the miRNA targets of GNPNAT1

To better investigate the potential biological functions of GNPNAT1 in LUAD in depth, we obtained 288 positively correlated genes and 56 negatively correlated genes with GNPNAT1 by the criteria of r = 0.55 and P = 0.001 in the TCGA-LUAD cohort. Top 11 positively correlated genes and 10 negatively correlated genes with GNPNAT1 were listed in Fig. 3A Subsequently, GO analysis and KEGG analysis with the 344 genes were carried out. The results of Go analysis revealed that the genes were mainly involved in mitotic nuclear division, nuclear division, chromosome segregation, chromosomal region, condensed chromosome, ATP hydrolysis activity (Fig. 3B). The results of KEGG analysis revealed that the genes were mainly associated with cell cycle, oocyte meiosis, DNA replication (Fig. 3C). In order to further dig out the upstream miRNA targets of GNPNAT1, we obtained potential miRNAs that bound with GNPNAT1 from ENCORI website (https://rnasysu.com/encori/index.php). As shown in Fig, there were 51 miRNA targets of GNPNAT1 (Fig. 3D). Correlation analysis demonstrated that hsa − miR − 1−3p, hsa − miR − 26a − 5p, hsa − miR − 664b − 3p, hsa − miR − 135b − 5p, and hsa − miR − 27b − 3p were significantly negative correlation with GNPNAT1 (Fig. 3E-I). hsa − miR − 664b − 3p, hsa − miR − 135b − 5p, and hsa − miR − 27b − 3p expression in LUAD were significantly higher than that in normal lung tissues (Fig. 3L-N), whereas hsa − miR − 1−3p or hsa − miR − 26a − 5p had opposite results (Fig. 3J, K). Further analysis indicated compared with low hsa − miR − 1−3p or hsa − miR − 26a − 5p expression, high hsa − miR − 1−3p or hsa − miR − 26a − 5p expression had a better OS in patients with LUAD (Fig. 3O, P). Unreasonably, hsa − miR − 664b − 3p, hsa − miR − 135b − 5p, and hsa − miR − 27b − 3p were upregulated in tumor samples, but their expression predicted an excellent prognosis (Fig. 3Q-S) and correlation between their expression and GNPNAT1 was markedly negative, so they were excluded. Eventually, We inferred that hsa − miR − 1−3p and hsa − miR − 26a − 5p were the upstream miRNA targets of GNPNAT1.

Correlation of GNPNAT1 expression with immune infiltration in LUAD

The results of ESTIMATE analysis indicated that the presence of immune cells in the low GNPNAT1 expression was more abundant than that in the GNPNAT1 high expression (Fig. 4A). The results of CIBERSORT analysis revealed that the infiltration levels of CD8 T cells, memory activated CD4 T cells, resting NK cells, Macrophages M0, Macrophages M1, Neutrophils were increased in the high GNPNAT1 expression group (Fig. 4B). In contrast, the infiltration of memory resting CD4 T cells, regulatory T cells (Tregs), resting NK cells, Monocytes, resting dendritic cells, resting mast cells were decreased in the high GNPNAT1 expression group (Fig. 4B). Further analysis indicated that GNPNAT1 was positively correlated with the infiltration levels of CD8 T cells, memory activated CD4 T cells, NK cells resting, Macrophages M0, Macrophages M1, Neutrophils, gamma delta T cells, Eosinophils, and was negatively correlated with memory resting CD4 T cells, regulatory T cells (Tregs), resting NK cells, Monocytes, resting dendritic cells, resting mast cells (Fig. 4C).

Construction of a prognostic signature based on GNPNAT1 associated immunomodulator genes

Immune system functions are regulated by immunomodulator genes. 69 immunomodulator genes were collected from TISIDB database (http://cis.hku.hk/TISIDB/) Correlation between GNPNAT1 and immunomodulator genes was presented in Fig. 4D. The 9-GNPNAT1 associated immunomodulator genes were revealed to be correlated with the OS of LUAD patients using univariate Cox proportional hazards model (Fig. 4E). Subsequently, a prognostic signature, including CD276, CD40LG, PVR, REAT1E, was constructed using multivariate Cox proportional hazards model (Fig. 4F). The median risk score was used as the cut off value to classify the patients from TCGA-LUAD cohort into either high-risk or low-risk groups. The risk score, survival status and gene expression heatmap of these prognostic MGs were shown in Fig. 4G. LUAD patients with high risk scores had a poorer OS than LUAD patients with low risk scores (Fig. 4H). The area under curve (AUC) of ROC for the risk score was 0.688, the AUC value of the ROC curve for the combination of risk score and clinical stage was 0.743, which was better than other clinical variables (Fig. 4I). Univariate and multivariate Cox regression suggested that the risk score was an independent factor for predicting OS of LUAD patients (Fig. 4J). Nomogram analysis and the calibration curves further indicated that 4-gene signature reliably predicted the OS of patients with LUAD (Fig. 4K-N).

GNPNAT1 promoted LUAD cell proliferation

In order to explore whether GNPNAT1 is essential for cell proliferation, we first detected the endogenous GNPNAT1 expression in six LUAD cell lines (H1650, HCC827, PC9, H1993, H1299, A549). The results revealed that PC9 and H1299 cell had a higher GNPNAT1 expression than in comparison to A549, H1650, HCC827, H1993 cell, whereas the expression of GNPNAT1 in H1993 was the lowest (Fig. 5A). H1299, PC9 and H1993 were selected to perform subsequent cell function experiments. The results showed that GNPNAT1 was successfully overexpressed or knocked down in H1993 (Fig. 5F, G) or H1299 (Fig. 5B, C) and PC9 (Fig. 5D, E) at transcriptional and protein levels. CCK8, colony formation and EdU assay showed that cell proliferative ability markedly enhanced in GNPNAT1-overexpression LUAD cell (Fig. 6C, F, I). In contrast, knockdown of GNPNAT1 resulted in a significantly lower proliferative rate than observed in the control cells (Fig. 6A, D, G, Fig. 6B, E, H).

GNPNAT1 promoted LUAD cells migration and invasion

Cancer cell metastasis requires the ability of migration and invasion. In order to validated whether GNPNAT1 has the ability to induce the migration and invasion of LUAD cell, the wound healing assay was executed to explore the role of GNPNAT1 transfection in H1299, PC9 and H1993 cells. As shown in Fig. 7A and 7B, the speed with which cells migrated towards the scratch was lower in the cells transfected with si-GNPNAT1 than in the control cells, while the opposite results were observed in H1993 cells (Fig. 7C). In addition, knockdown of GNPNAT1 led to significant decrease of H1299 (Fig. 7D) or PC9 (Fig. 7E) cells invasion ability, while overexpression of GNPNAT1 resulted in remarkable enhancement of H1993 cells invasion, as measured by transwell assay in LUAD cells (Fig. 7F). In addition, animal experiments were carried out to investigate the effect of GNPNAT1 on proliferative metastatic capacity of LUAD cell in vivo. As expected, the tumors from H1299-sh-GNPNAT1 cells grew slower and smaller than those from their control cell (Fig. 7G, H). In vivo metastasis assay showed that the number of metastatic lung nodules from H1299-sh-GNPNAT1 cells was significantly less than those from the control cells (Fig. 7I).

GNPNAT1 induced EMT process of LUAD cell

EMT plays critical and intricate roles in promoting tumor invasion and metastasis in epithelium-derived carcinomas. As the result showed, H1299 or PC9 cells transfected with si-GNPNAT1 underwent the typical MET phenotypes that were featured by up-regulation of E-cadherin, and down-regulation of N-cadherin, Vimentin and Slug (Fig. 8A, B). Interestingly, the typical EMT phenotypes were observed in GNPNAT1-overexpressing H1993 cells, including up-regulation of mesenchymal markers and transcriptional factor, such as N-cadherin, Vimentin, Slug, and the loss of the epithelial marker, such as E-cadherin (Fig. 8C). Together, these results suggested that GNPNAT1 promoted EMT process in LUAD.

Slug was required in GNPNATI-mediated migration, invasion and EMT process

Slug, as a classical transcription factor, can trigger pathological EMT via inhibiting E-cadherin expression. To investigate whether Slug was required for the function of GNPNAT1 in migration, invasion and EMT process, we firstly found that GNPNAT1 was positively associated with Slug in GEPIA2 database (http://gepia2.cancer-pku.cn/) (Fi. 9A). Next, we indicated that the decrease of Slug was verified at protein levels (Fig. 9B). Subsequently, rescue assay showed that Slug knockdown dampened GNPNAT1 overexpression-induced H1993 cells migration and invasion, as measured by wound-healing, migration and invasion assays (Fig. 9C, D). In addition, GNPNAT1-induced E-cadherin down-regulation and N-cadherin up-regulation was reversed by Slug knockdown in GNPNAT1-overexpressing H1993 cells (Fig. 9E). Based on the above results, we suggested that Slug was absolutely necessary for GNPNAT1-mediated migration, invasion, and EMT process.

GNPNAT1 promoted cancer cell metastasis through stabilization of Slug

To further elucidate the potential molecular mechanism of GNPNAT1 in regulating LUAD metastasis, we carried out GSEA analysis. The results showed that cell-cycle, ubiquitin-mediated proteolysis was significantly enriched in LUAD patients with GNPNAT1 high expression (Fig. 10A). Ubiquitin is an important post-translational modification that regulates protein degradation, transportation, activity as well as protein-protein interaction. Disorders of the ubiquitin pathway are associated with many diseases, including cancer. Therefore, we hypothesized that GNPNAT1 promoted LUAD metastasis by stabilization of Slug. CHX or MG132 was utilized to treat PC9 cells to inhibit new protein synthesis or prevent protein degradation. CHX assay showed that compared with si-NC, GNPNAT1 knockdown significantly decreased the expression levels of endogenous Slug in PC9 cells (Fig. 10B). MG132 assay showed that endogenous Slug protein could be degraded by ubiquitin-mediated proteasome pathway (Fig. 10C). Eventually, ubiquitin assay showed that GNPNAT1 could restrain the degradation of endogenous Slug through repressing ubiquitin-proteasome pathway (Fig. 10D).

The lack of key molecular therapy targets is an important cause of failure to control cancer metastasis and cancer death. In this study, we confirmed that GNPNAT1, which was a prognostic marker of LUAD patients and a potential target for LUAD molecular therapy, could promote the proliferation and metastasis of LUAD cells.

In the TIMER2.0 (http://timer.comp-genomics.org), GEPIA2 database (http://gepia2.cancer-pku.cn/) and multiple datasets (GSE32863, GSE40791, GSE75037, GSE115002), GNPNAT1 was highly expressed in LUAD patients. Tissue microarray further confirmed that there was a higher expression for GNPNAT1 in LUAD in comparison to normal tissue. TCGA-LUAD cohort, GSE72094 and GSE13213 validated that GNPNAT1 high expression had a worse OS for LUAD patients. The correlation analysis between GNPNAT1 and clinicopathological features showed that GNPNAT1 was positively correlated with tumor stage, lymph node metastasis and clinical stage (TNM stage). Univariate and multivariate Cox proportional hazard models confirmed that GNPNAT1 was an independent factor in predicting OS of LUAD patients, and the nomogram further demonstrated the accuracy of GNPNAT in predicting LUAD OS (the 1-, 3-and 5-year OS: 91.5%, 71.6% and 46.2%). Our results confirmed that GNPNAT1 was an oncogene in LUAD and was closely correlated to the poor prognosis and the progression of LUAD.

Increasing evidence showed that miRNAs had been reported to participate in different physiological and pathological processes such as cell proliferation, migration, apoptosis, invasion, and angiogenesis by regulating their target genes[21, 22]. In this study, we verified that hsa − miR − 1−3p and hsa − miR − 26a − 5p were the upstream miRNA targets of GNPNAT1 and were negatively correlated with GNPNAT1 in LUAD. More importantly, GNPNAT1 was identified that could promote LUAD cell proliferation, migration, invasion, EMT process, and metastasis. Therefore, we speculated hsa − miR − 1−3p and hsa − miR − 26a − 5p hampered LUAD progress via inhibiting GNPNAT1. Tan et al found that C1GALT1 could promote migratory ability and proliferation of BLCA YTS-1 cells in vitro and in vivo, whereas its carcinogenicity was suppressed by miR-1-3p through binding directly to its 3′-untranslated region (3′-UTR)[23]. Zhou et al reported that miR-1-3p negatively mediated CENPF to hamper gastric cancer progression[24]. Du et al suggested that inhibited colorectal cancer cell proliferation and metastasis through suppressing YWHAZ mediated epithelial–mesenchymal transition (EMT)[25]. Li et al demonstrated miR-1-3p could prevent 22RV1 and Lnca cell growth and cell cycle progression in vitro and in vivo via directly suppressing E2F5 and PFTK1[26]. Exosomal miR-26a-5p loss resulted in tumor cell proliferation, migration, invasion, lymphangiogenesis and lymphatic metastasis in endometrial cancer[27]. miR-26a-5p were significantly downregulated in bladder cancer specimens and significantly inhibited cancer cell migration and invasion by repressing PLOD2 in bladder cancer[28].

Tumor microenvironment consist of immune cells, extracellular matrix, fibroblasts, signal molecules and surrounding blood vessels, which plays an important role in tumor growth, metastasis and clinical survival. It was reported that immune infiltration could influence the prognosis in patients with tumors, and tumor-infiltrating lymphocyte grade was considered as an independent factor for predicting sentinel lymph node status of tumor patients[29]. In this study, we found that GNPNAT1 was closely correlated with the infiltration of multiple immune cells and multiple immunomodulator genes. 4-gene signature was developed based on GNPNAT1 associated immunomodulator genes. Survival analysis, ROC analysis, nomogram analysis, univariated and multivariate Cox regression analysis further indicated the 4-gene signature reliably predicted the OS of patients with LUAD. These results revealed that GNPNAT1 play an essential regulatory role in the tumor immune microenvironment and the development of LUAD.

The proliferative migrative and invasive ability of tumor cells is a prerequisite for cancer progression. Functional experiments were executed to explore the effects of GNPNAT1 on the proliferation, migration and invasion of LUAD. CCK8, EdU and clone formation assay fully illustrated that GNPNAT1 could promote the proliferation of LUAD cell. Wound-healing and Transwell assay demonstrated that GNPNAT1 could promote the migration and invasion of LUAD cells. In vivo assay further suggested that GNPNAT1 could promote LUAD cells growth and metastasis. EMT is an evolutionarily conserved transdifferentiation process that plays a central role in cancer metastasis[30]. During EMT, the expression of epithelial markers (such as E-cadherin and ZO-1) is downregulated, while expression of mesenchymal markers (such as Vimentin, Fibronectin) and EMT-associated transcription factors (such as Snail1, Slug, Zeb1/2, Twist1/2) is upregulated[31–34]. We found that GNPNAT1 upregulated N-cadherin, Vimentin and Slug, and downregulated E-cadherin in LUAD, confirming that GNPNAT1 could trigger EMT process of LUAD. These results demonstrated that GNPNAT1 had the ability to promote occurrence, growth, EMT, and metastasis of LUAD.

The EMT is regulated by different signal pathways, such as PI3K/AKT and Wnt, but also by its transcription factors. Slug, which contains five zinc finger domain in C-termina, is located at the 8q11.21 and consists of 268 amino acids[35]. Slug has been reported to be overexpressed in many cancers[36], such as lung cancer, breast cancer, colorectal cancer, gastric cancer, esophageal cancer, liver cancer, pancreatic cancer, cholangiocarcinoma and prostate cancer. Slug is the most thoroughly investigated EMT-related transcription factor in LUAD[35]. What’s more, it was reported that Slug was regarded as a marker of poor prognosis and may promote cancer cell metastasis in LUAD[35, 37, 38]. Studies have also shown that Slug may be degraded through ubiquitin-mediated proteasome pathway[39]. LncSNHG15 promoted cancer cell growth and metastasis by preventing Slug ubiquitination in colon cancer[40]. In head and neck squamous cell carcinoma, TNF- α may restrain Slug ubiquitination by inhibiting NF- κ B signal pathway, which in turn promoted EMT and metastasis of cancer cells[41]. It was reported that Dub3 could promote cancer cells migration and invasion through interaction with Slug and sustaining the stability of Slug in breast cancer[41]. In lung cancer, Pellino could interact with Slug to prevent ubiquitination of Slug, which leads to cancer cell proliferation, migration and invasion[42]. In this study, Rescue experiments confirmed that Slug, a target gene downstream of GNPNAT1, played a critical role in GNPNAT1-mediated LUAD cells migration, invasion and EMT process. CHX pulse-chase assay, proteasome inhibition and ubiquitin assay indicated that GNPNAT1 was involved in the regulation of ubiquitin-mediated proteasome pathway and repressed ubiquitination of Slug in LUAD. These results revealed that GNPNAT1 could be contribute to cancer cells metastasis via stabilization of Slug in LUAD.

We confirmed that the expression level of GNPNAT1 in LUAD was significantly higher than normal lung tissue. GNPNAT1 high expression indicated that LUAD patients has a poor OS and was an independent factor for LUAD patients with poor OS. GNPNAT1 was closely correlated with the infiltration of multiple immune cells and multiple immunomodulator genes. hsa − miR − 1−3p and hsa − miR − 26a − 5p were the upstream miRNA targets of GNPNAT1. GNPNAT1 could promote LUAD cells growth, proliferation, migration, invasion, EMT process and metastasis. Slug was required in GNPNAT1-mediated LUAD cells migration, invasion and EMT. GNPNAT1 promoted LUAD cell metastasis through preventing Slug from ubiquitination degradation.

Acknowledgements

We are very grateful to TCGA and GEO database for providing their platforms and contributors for uploading their meaningful datasets.

Author contributions

Yan Yu, Jinqi He and Faxiang Li designed this study. Jinqi He and Faxiang Li downloaded and analyzed the multi-omic data. Jinqi He, Zihan Jing, Xingmei Ren and Dexin Jia performed the experiments. Jinqi He wrote the manuscript. Dexin Jia, Yan Yu revised the manuscript. All authors approved the final version of the manuscript.

Funding

No funding was received for conducting this study.

Data availability

RNA data are available at the TCGA database and GEO datasets (accession number: GSE32863, GSE40791, GSE75037, GSE115002, GSE13213, and GSE72094). The data that support the findings of this study are available from the corresponding author upon reasonable request.

Ethics approval and consent to participate

The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). The animal experiments were performed according to the guidelines of the ethic committee of Harbin Medical University cancer hospital and protocols were approved by the ethic committee of Harbin Medical University cancer hospital (No. KY2023-26).

Consent for publication

Not applicable.

Competing interests

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgements

We are very grateful to TCGA and GEO database for providing their platforms and contributors for uploading their meaningful datasets.

Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA-Cancer J Clin. 2021;71(3):209–49.
Ferlay J, Colombet M, Soerjomataram I, Mathers C, Parkin DM, Piñeros M, et al. Estimating the global cancer incidence and mortality in 2018: GLOBOCAN sources and methods. Int J Cancer. 2019;144(8):1941–53.
Chen Z, Fillmore CM, Hammerman PS, Kim CF, Wong KK. Non-small-cell lung cancers: a heterogeneous set of diseases. Nat Rev Cancer. 2014;14(8):535–46.
Spira A, Ettinger DS. Multidisciplinary Management of Lung Cancer. New Engl J Med. 2004;350(4):379–92.
Fennell DA, Summers Y, Cadranel J, Benepal T, Christoph DC, Lal R, et al. Cisplatin in the modern era: The backbone of first-line chemotherapy for non-small cell lung cancer. Cancer Treatt Rev. 2016;44:42–50.
Kasinski AL, Slack FJ. miRNA-34 Prevents Cancer Initiation and Progression in a Therapeutically Resistant K-ras and p53-Induced Mouse Model of Lung Adenocarcinoma. Cancer Res. 2012;72(21):5576–87.
Song YH, Zhang CQ, Chen FF, Lin XY. Upregulation of Neural Precursor Cell Expressed Developmentally Downregulated 4 – 1 is Associated with Poor Prognosis and Chemoresistance in Lung Adenocarcinoma. Chin Med J-Peking. 2018;131(1):16–24.
Copur MS, Crockett D, Gauchan D, Ramaekers R, Mleczko K. Molecular Testing Guideline for the Selection of Patients With Lung Cancer for Targeted Therapy. J Clin Oncol. 2018;36(19):2006–06.
Lam C, Low JY, Tran PT, Wang H. The hexosamine biosynthetic pathway and cancer: Current knowledge and future therapeutic strategies. Cancer Lett. 2021;503:11–8.
Wang J, Liu X, Liang YH, Li LF, Su XD. Acceptor substrate binding revealed by crystal structure of human glucosamine-6-phosphate N-acetyltransferase 1. FEBS Lett. 2008;582(20):2973–78.
Kaushik AK, Shojaie A, Panzitt K, Sonavane R, Venghatakrishnan H, Manikkam M, et al. Inhibition of the hexosamine biosynthetic pathway promotes castration-resistant prostate cancer. Nat Commun. 2016;7(1):11612.
Zhang S, Zhang H, Li H, Guo J, Wang J, Zhang L. Potential role of glucosamine-phosphate N-acetyltransferase 1 in the development of lung adenocarcinoma. Aging. 2021;13(5):7430–53.
Zheng X, Li Y, Ma C, Zhang J, Zhang Y, Fu Z, et al. Independent Prognostic Potential of GNPNAT1 in Lung Adenocarcinoma. Biomed Res Int. 2020;2020:8851437.
Liu W, Jiang K, Wang J, Mei T, Zhao M, Huang D. Upregulation of GNPNAT1 Predicts Poor Prognosis and Correlates With Immune Infiltration in Lung Adenocarcinoma. Front Mol Biosci. 2021;8:605754.
Zhu P, Gu S, Huang H, Zhong C, Liu Z, Zhang X, et al. Upregulation of glucosamine-phosphate N-acetyltransferase 1 is a promising diagnostic and predictive indicator for poor survival in patients with lung adenocarcinoma. Oncol Lett. 2021;21(6):488.
Ding P, Peng B, Li G, Sun X, Wang G. Glucosamine-phosphate N-acetyltransferase 1 and its DNA methylation can be biomarkers for the diagnosis and prognosis of lung cancer. J Clin Lab Anal. 2022;36(9):e24628.
Feng Y, Li N, Ren Y. GNPNAT1 Predicts Poor Prognosis and Cancer Development in Non-Small Cell Lung Cancer. Cancer Manag Res. 2022;14:2419–28.
Zhao M, Li H, Ma Y, Gong H, Yang S, Fang Q, et al. Nanoparticle abraxane possesses impaired proliferation in A549 cells due to the underexpression of glucosamine 6-phosphate N-acetyltransferase 1 (GNPNAT1/GNA1). Int J Nanomed. 2017;12:1685–97.
Xiao C, Wu G, Zhou Z, Zhang X, Wang Y, Song G, et al. RBBP6, a RING finger-domain E3 ubiquitin ligase, induces epithelial–mesenchymal transition and promotes metastasis of colorectal cancer. Cell Death Dis. 2019;10(11):833.
Song G, Xu S, Zhang H, Wang Y, Xiao C, Jiang T, et al. TIMP1 is a prognostic marker for the progression and metastasis of colon cancer through FAK-PI3K/AKT and MAPK pathway. J Exp Clin Cancer Res. 2016;35(1):148.
Bartel DP. MicroRNAs: target recognition and regulatory functions. Cell. 2009;136(2):215–33.
Alipoor SD, Adcock IM, Garssen J, Mortaz E, Varahram M, Mirsaeidi M, et al. The roles of miRNAs as potential biomarkers in lung diseases. Eur J Pharmacol. 2016;791:395–404.
Tan Z, Jiang Y, Liang L, Wu J, Cao L, Zhou X, et al. Dysregulation and prometastatic function of glycosyltransferase C1GALT1 modulated by cHP1BP3/ miR-1-3p axis in bladder cancer. J Exp Clin Cancer Res. 2022;41(1):228.
Zhou S, Han H, Yang L, Lin H. MiR-1-3p targets CENPF to repress tumor-relevant functions of gastric cancer cells. BMC Gastroenterol. 2022;22(1):145.
Du G, Yu X, Chen Y, Cai W. MiR-1-3p Suppresses Colorectal Cancer Cell Proliferation and Metastasis by Inhibiting YWHAZ-Mediated Epithelial–Mesenchymal Transition. Front Oncol. 2021;11.
Li SM, Wu HL, Yu X, Tang K, Wang SG, Ye ZQ, et al. The putative tumour suppressor miR-1-3p modulates prostate cancer cell aggressiveness by repressing E2F5 and PFTK1. J Exp Clin Cancer Res. 2018;37(1):219.
Wang J, Gong X, Yang L, Li L, Gao X, Ni T, et al. Loss of exosomal miR-26a-5p contributes to endometrial cancer lymphangiogenesis and lymphatic metastasis. Clin Transl Med. 2022;12(5):e846.
Miyamoto K, Seki N, Matsushita R, Yonemori M, Yoshino H, Nakagawa M, et al. Tumour-suppressive miRNA-26a-5p and miR-26b-5p inhibit cell aggressiveness by regulating PLOD2 in bladder cancer. Br J Cancer. 2016;115(3):354–63.
Slack FJ, Chinnaiyan AM. The Role of Non-coding RNAs in Oncology. Cell. 2019;179(5):1033–55.
Valastyan S, Weinberg Robert A. Tumor Metastasis: Molecular Insights and Evolving Paradigms. Cell. 2011;147(2):275–92.
Cano A, Pérez-Moreno MA, Rodrigo I, Locascio A, Blanco MJ, del Barrio MG, et al. The transcription factor Snail controls epithelial–mesenchymal transitions by repressing E-cadherin expression. Nat Cell Biol. 2000;2(2):76–83.
Rosivatz E, Becker I, Specht K, Fricke E, Luber B, Busch R, et al. Differential Expression of the Epithelial-Mesenchymal Transition Regulators Snail, SIP1, and Twist in Gastric Cancer. Am J Pathol. 2002;161(5):1881–91.
Vandewalle C, Comijn J, De Craene B, Vermassen P, Bruyneel E, Andersen H, et al. SIP1/ZEB2 induces EMT by repressing genes of different epithelial cell–cell junctions. Nucleic Acids Res. 2005;33(20):6566–78.
Graham TR, Zhau HE, Odero-Marah VA, Osunkoya AO, Kimbro KS, Tighiouart M, et al. Insulin-like growth factor-I–dependent up-regulation of ZEB1 drives epithelial-to-mesenchymal transition in human prostate cancer cells. Cancer Res. 2008;68(7):2479–88.
Shih JY, Yang PC. The EMT regulator slug and lung carcinogenesis. Carcinogenesis. 2011;32(9):1299–304.
Alves CC, Carneiro F, Hoefler H, Becker KF. Role of the epithelial-mesenchymal transition regulator Slug in primary human cancers. Front Biosci-Landmrk. 2009;14(8):3035–50.
Atmaca A, Wirtz RW, Werner D, Steinmetz K, Claas S, Brueckl WM, et al. SNAI2/SLUG and estrogen receptor mRNA expression are inversely correlated and prognostic of patient outcome in metastatic non-small cell lung cancer. BMC Cancer. 2015;15(1):300.
Hu Y, Zheng Y, Dai M, Wu J, Yu B, Zhang H, et al. Snail2 induced E-cadherin suppression and metastasis in lung carcinoma facilitated by G9a and HDACs. Cell Adhes Migr. 2019;13(1):284–91.
Wang SP, Wang WL, Chang YL, Wu CT, Chao YC, Kao SH, et al. P53 controls cancer cell invasion by inducing the MDM2-mediated degradation of Slug. Nat Cell Biol. 2009;11(6):694–704.
Jiang H, Li T, Qu Y, Wang X, Li B, Song J, et al. Long non-coding RNA SNHG15 interacts with and stabilizes transcription factor Slug and promotes colon cancer progression. Cancer Lett. 2018;425:78–87.
Liu S, Shi L, Wang Y, Ye D, Ju H, Ma H, et al. Stabilization of Slug by NF-κB is Essential for TNF-α -Induced Migration and Epithelial-Mesenchymal Transition in Head and Neck Squamous Cell Carcinoma Cells. Cell Physiol Biochem. 2018;47(2):567–78.
Jeon YK, Kim CK, Hwang KR, Park HY, Koh J, Chung DH, et al. Pellino-1 promotes lung carcinogenesis via the stabilization of Slug and Snail through K63-mediated polyubiquitination. Cell Death Differ. 2017;24(3):469–80.

No competing interests reported.

Supplementarymaterial.pdf

Download PDF

Version 1

posted

You are reading this latest preprint version

GNPNAT1 promotes cancer cells metastasis through stabilization Slug in lung adenocarcinoma

Status:

Version 1

Abstract

Background

Method

Result

Conclusion

Figures

Introduction

Materials and Methods

Data Collection

Immune infiltration of GNPNAT1 in LUAD

Cell lines and cell culture

Cell transfection

Quantitative real time PCR (qRT-PCR)

Western blot assay

Co-immunoprecipitation assay

Wound-healing assay

Transwell assay

Cell counting kit-8 (CCK8) assay

Immunohistochemistry (IHC) staining assay

Ubiquitination assay

Cycloheximide (CHX) pulse-chase assay

In vivo assay

Statistical analysis

Results

GNPNAT1 was highly expressed in LUAD

GNPNAT1 was associated with poor prognosis in LUAD

hsa − miR − 1−3p and hsa − miR − 26a − 5p were the miRNA targets of GNPNAT1

Correlation of GNPNAT1 expression with immune infiltration in LUAD

Construction of a prognostic signature based on GNPNAT1 associated immunomodulator genes

GNPNAT1 promoted LUAD cell proliferation

GNPNAT1 promoted LUAD cells migration and invasion

GNPNAT1 induced EMT process of LUAD cell

Slug was required in GNPNATI-mediated migration, invasion and EMT process

GNPNAT1 promoted cancer cell metastasis through stabilization of Slug

Discussion

Conclusion

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1