Overexpression of PPM1G promotes cell metabolism and activates the NOTCH signaling pathway in lung adenocarcinoma
Highlight box
Key findings
• Protein phosphatase, Mg2+/Mn2+-dependent 1G (PPM1G) acted as a prognostic factor for lung adenocarcinoma (LUAD) and promoted LUAD cell growth, metastasis, and glycolysis. Mechanistically, PPM1G activated the NOTCH pathway to promote LUAD development, indicating its potential as a novel therapeutic target for treating LUAD.
What is known and what is new?
• PPM1G has been reported to affect the progression of various human cancers, including pancreatic cancer and hepatocellular carcinoma.
• Our study demonstrated the role and possible molecular mechanism of PPM1G in LUAD progression.
What is the implication, and what should change now?
• These findings demonstrated PPM1G acts as a key oncogene during LUAD occurrence and development. One of the mechanisms of the tumor-promoting effects of PPM1G might be its regulation on glycolysis and NOTCH pathway. Further studies based on clinic data are needed to confirm the potential of PPM1G as a treatment target.
Introduction
Lung cancer is among of the most prevalent malignant cancers worldwide (1,2). According to Global Cancer Statistics 2020 (GLOBOCAN 2020), lung cancer is the second most commonly diagnosed cancer and is the primary cause of cancer-related death worldwide (3). Lung adenocarcinoma (LUAD) accounts for the majority of new cancer cases (4-6). Despite the advancements made in LUAD therapy, including surgical resection, targeted therapy, radiotherapy, and immunotherapy (7), over the past decades, the prognosis of this disease remains unsatisfactory, with a 5-year overall survival rate of <20% (8,9). Therefore, determining the underlying molecular mechanisms is necessary to develop effective treatment strategies for LUAD.
Protein phosphatase, Mg2+/Mn2+-dependent 1 G (PPM1G) is a phosphatase belonging to the PP2C family of serine/threonine protein phosphatases (10,11). Several studies have suggested the involvement of PPM1G in cancer regulation processes, such as messenger RNA (mRNA) splicing, stimulator of interferon genes (STING) signaling in immunosuppression, and cell adhesion, by modulating the phosphorylation of various regulatory factors (12-15). In innate immune pathways, PPM1G acts as a negative regulator by inhibiting STING phosphorylation and the subsequent immune response (14). Previous studies have indicated that PPM1G expression is correlated with the prognosis of hepatocellular carcinoma, and its depletion suppresses the in vitro and in vivo growth of hepatocellular carcinoma cells (13,16). However, the roles of PPM1G in LUAD carcinogenesis have not yet been elucidated.
In this study, we examined the correlation between PPM1G expression and LUAD via bioinformatics analysis and performed in vitro and in vivo verification of the related mechanisms. We found that PPM1G is a potential prognostic factor for poor outcomes among patients with LUAD. PPM1G activated the glycolysis and NOTCH signaling pathway to promote LUAD cell growth and metastasis. Therefore, PPM1G may be used as a prognostic factor and therapeutic target for LUAD. We present this article in accordance with the ARRIVE and MDAR reporting checklists (available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2025-659/rc).
Methods
Bioinformatics analysis
The expression profile of PPM1G in several cancer types was analyzed using the online tool, Gene Expression Profiling Interactive Analysis (GEPIA, http://gepia2.cancer-pku.cn/#index). RNA-sequencing data and clinical information of patients with LUAD were obtained from the public database, The Cancer Genome Atlas (TCGA; https://portal.gdc.cancer.gov/). Based on Gene Expression Omnibus (GEO) database (GSE11969, https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE11969), PPM1G expression was analyzed in epidermal growth factor receptor (EGFR)-mutant and EGFR-wild type LUAD. We performed survival analysis of PPM1G available in the “survival” R package (The R Foundation for Statistical Computing) using the Kaplan-Meier (K-M) curve method. Comparison of PPM1G expression level between tumor and non-tumor tissues or different stages [pathology and tumor-node-metastasis (TNM) stages] were performed using R software version 4.0.1. To evaluate the association between PPM1G expression levels and overall survival, we performed Cox proportional hazards regression analysis with adjustment for age, gender, and stage using the R software “survival” and “survminer” packages. Furthermore, we also conducted Gene Ontology (GO) analysis using R software to determine the altered cellular processes correlated with high PPM1G expression in LUAD. The GO analysis was performed using the Biological Process database as a reference gene set (17). Gene names were converted to Entrez IDs using the “org.Hs.eg.db” package in R. The enrichGO function in the “clusterProfiler” package was used to perform GO enrichment analysis with a significance threshold of P<0.05. Gene set enrichment analysis (GSEA) was conducted to determine the enriched gene sets and signaling pathways between the high- and low-expression groups of PPM1G. GSEA was conducted using R with c2.cp.kegg.v6.1.symbols.gmt as a reference gene set. Significance was set at P<0.05. The c2.cp.kegg.v6.1.symbols.gmt gene set used in our GSEA was obtained from the Molecular Signatures Database (MSigDB, https://www.gsea-msigdb.org/gsea/msigdb). The gene sets in MSigDB are derived from various sources, including the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway database. This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.
Patient specimens
Tissue microarray paraffin blocks of LUAD tissues were purchased from Shanghai Outdo Biotech Co., Ltd. (Shanghai, China). Thirty pairs of cancerous and paracancerous tissue samples were subjected to immunohistochemistry (IHC) staining.
Cell culture and treatment
Human normal bronchial epithelioid (16HBE) and LUAD (NCI-H1975, H1299, H23, and A549) cell lines were purchased from Cobioer Biosciences (Nanjing, China). 16HBE cells were maintained in keratinocyte serum-free medium (Gibco, Thermo Fisher Scientific, Waltham, MA, USA) supplemented with 0.005 mg/mL of insulin and 500 ng/mL of hydrocortisone. NCI-H1975, H1299, and H23 cells were maintained in RPMI-1640 medium (HyClone Laboratories, Logan, UT, USA) supplemented with 10% fetal bovine serum (FBS; HyClone). A549 cells were maintained in F12K medium (Gibco) supplemented with 10% FBS. All cells were cultured at 37 ℃ in a humidified atmosphere of 5% CO2. DAPT, a γ-secretase inhibitor that blocks the Notch pathway, was added to the culture medium to evaluate its effects on cell proliferation, apoptosis, migration, invasion, and glycolysis. All cell lines used in the study passed cell line authentication via short-tandem repeats (STR) profiling.
Cell transfection
PPM1G and notch receptor 1 (NOTCH1)-amplified products were ligated into the pc-DNA3.1 expression vector (Tsingke Biotechnology, Beijing, China). Small interfering RNA (siRNA) against PPM1G (si1-PPM1G and si2-PPM1G) and their negative controls (si-NC) were purchased from RiboBio (Guangzhou, China). Cell transfection was performed using Lipofectamine 2000 (Thermo Fisher Scientific, Waltham, MA, USA), according to the manufacturer’s protocol. After 48 hours, the transfection efficiency was measured using reverse transcription quantitative polymerase chain reaction (RT-qPCR) and Western blotting. The specific sequence was as follows: si-NC (5'-GAGCCGTATGACGACTAAGTT-3'), si1-PPM1G (5'-GGAGGATGCTCACAACTGTAT-3'), and si2-PPM1G (5'-GGGAGGAAGTTGCCTTGTACT-3').
RNA extraction and real-time quantitative polymerase chain reaction (RT-qPCR) analysis
All cancer cell lines were treated with the TRIzol reagent (Invitrogen, Thermo Fisher Scientific) to isolate the total RNA. Next, complement DNA (cDNA) was synthesized using the HiScript First Strand cDNA Synthesis Kit (R111-01; Vazyme, Nanjing, China). RT-qPCR was performed via the SYBR Green System (Vazyme), with glyceraldehyde-3-phosphate dehydrogenase (GAPDH) serving as the internal control. The specific sequence was as follows: PPM1G (forward: 5'-TTGTAGCCAACGCAGGAGAC-3'; reverse: 5'-ATAGAAGTGGTCCCCAATGGC-3') and GAPDH (forward: 5'-AATGGGCAGCCGTTAGGAAA-3'; reverse: 5'-GCCCAATACGACCAAATCAGAG-3').
Western blotting
Protein samples were extracted using the radio immunoprecipitation assay (RIPA) lysis buffer (Beyotime, Nantong, China), separated via sodium dodecyl sulfate-polyacrylamide gel electrophoresis, and then transferred onto polyvinylidene fluoride membranes. After being blocked with 5% skim milk for 1 hour at room temperature, membranes were incubated with primary antibodies against PPM1G (15532-1-AP; Proteintech, Wuhan, China), Bcl-2 (ab32124; Abcam, Cambridge, UK), BCL-2-associated X (Bax; ab32503; Abcam), cleaved caspase-3 (ab32042; Abcam), cyclin dependent kinase 4 (CDK4; ab108357; Abcam), cyclin E1 (CCNE1; ab33911; Abcam), cyclin D1 (CCND1; ab16663; Abcam), glucose transporter 1 (GLUT1; ab115730; Abcam), hexokinase 2 (HK2; ab209847; Abcam), phosphofructokinase (PFKP; 13389-1-AP; Proteintech), pyruvate kinase M (PKM2; ab150377; Abcam), lactate dehydrogenase (LDHA; 19987-1-AP; Proteintech), NOTCH1 (ab52627; Abcam), NOTCH3 (ab23426; Abcam), HES family bHLH transcription factor 1 (HES1; ab108937; Abcam), and GAPDH (ab9485; Abcam) at 4 ℃ overnight; this was followed by incubation with secondary antibody (ab7090; Abcam) at room temperature for 1 hour. Membranes were then reacted with the enhanced chemiluminescence (ECL) solution (P0018; Beyotime) and observed using a Tanon 4800 image analysis system (Tanon, Shanghai, China).
Cell viability assay
Cell viability was assessed using the Cell Counting Kit-8 (CCK-8; Beyotime). For the CCK-8 assay, H1299 and A549 cells were seeded into a 96-well plate at 5,000 cells/well and incubated for the indicated time period (0, 24, 48, 72, and 96 hours). Subsequently, 10 µL of CCK-8 reagent was added to each well and incubated for 1.5 hours. Optical density (OD) at 450 nm was recorded using a microplate detector (Thermo Fisher Scientific).
Colony formation assay
Cell proliferation was assessed using the colony formation assay. Briefly, 1,000 H1299 and A549 cells were suspended as single cells, seeded into a six-well plate, and incubated for 15 days. The cell culture medium was changed every 3 days. Visible colonies were fixed, stained with crystal violet reagent (Solarbio, Beijing, China) in methanol, and counted under a microscope (Leica, Wetzlar, Germany).
5-ethynyl-2'-deoxyuridine (EdU) assay
EdU assay was performed using an EdU assay kit (Beyotime), following the manufacturer’s protocol. Briefly, 5,000 cells were seeded into a 96-well plate for 24 hours, EdU reagent (50 µM) was added to each well, and cells were incubated for 2 hours. Cells were washed with phosphate-buffered saline (PBS), fixed in 4% paraformaldehyde (PFA), permeabilized with 0.5% TritonX-100, and treated with click reaction solution. Cell nuclei were labelled with 4’,6-diamidino-2-phenylindole (DAPI, Beyotime). Images were captured using a fluorescence microscope (Leica).
Flow cytometry
Cell apoptosis and cell cycle progression were assessed using flow cytometry. For cell cycle detection, H1299 and A549 cells were collected and fixed in ice-cold 70% ethanol at 4 ℃ for 24 hours, washed with PBS, incubated with a staining solution containing propidium iodide (PI; Sigma-Aldrich, St. Louis, MO, USA) and RNase A (Sigma-Aldrich) at 4 ℃ for 30 min. Samples were loaded and assessed using a FACSCalibur flow cytometer (BD Biosciences, Franklin Lakes, NJ, USA).
Cell apoptosis was measured via annexin V-FITC/PI staining (Beyotime). Briefly, cells were harvested, suspended in binding buffer, and stained with Annexin V-FITC/PI at room temperature for 15 min in the dark. The samples were then immediately analyzed using FACSCalibur flow cytometer.
Transwell assay
To assess cell migration, H1299 and A549 cells were seeded into the top chambers of Transwell plates (Corning, NY, USA) with serum-free medium, and the bottom chambers were filled with the normal culture medium. After incubation for 24 hours, the migrated or invasive cells were fixed with 4% PFA and stained with crystal violet. Images of five random areas were captured using a microscope (Leica). Cell invasion was analyzed using chambers coated with Matrigel (BD Biosciences).
Wound healing assay
Cell migration was analyzed by measuring the movement of H1299 and A549 cells in a scratched area. H1299 and A549 cells were seeded into a six-well plate and incubated overnight until confluent. The scratched area was created vertically in the middle of each well with a sterilized 200-µL pipette tip and washed with PBS to remove the debris. Images of the scratched areas were obtained at 0 and 48 hours after scratching.
Determination of the levels of glycolysis biomarkers
According to the manufacturers’ instructions, the changes of glucose uptake, lactate production, and the ATP/ADP ratio were measured using a Glucose Uptake Assay Kit (ab136955; Abcam), L-Lactate Assay Kit with WST-8 (S0208S; Beyotime), and ADP/ATP Ratio Assay Kit (K255-200, BioVision, Milpitas, CA, USA), respectively.
Xenograft mouse model
All animal experiments in this study were approved by the Ethics Committee of Qilu Normal University (approval number: 2024-059), in compliance with the Guide for the Care and Use of Laboratory Animals published by the US National Institutes of Health. A protocol was prepared before the study without registration. Male BALB/c nude mice (6–8 weeks old; 20–22 g) were obtained from GemPharmatech (Nanjing, China) and maintained in a specific pathogen-free environment. All mice were randomly divided into two groups: sh-NC (n=4) and sh-PPM1G (n=4). A549 cells were transfected with short hairpin RNA (shRNA) and then inoculated into the left flanks of each mouse with 5×106 cells in 100 µL of PBS. Lentiviral vectors encoding scrambled shRNA (sh-NC, 5'-ACCTCGCCGTAGTAGGATTCGTACAATCAAGAGTTGTACGAATCCTACTACGGCTT-3') and shRNA against PPM1G (sh-PPM1G, 5'-ACCTCGGAGGATGCTCACAACTGTATTCAAGAGATACAGTTGTGAGCATCCTCCTT-3') were purchased from GeneChem (Shanghai, China). From the 10th day of inoculation, the length and width of each tumor were measured to calculate the tumor size (volume = 0.5 × length × width2) every 5 days. On the 30th day of inoculation, mice were killed and the tumors were isolated and weighed.
IHC staining
Tissues collected from the patients with LUAD and xenograft mouse models were cut into 5-µm paraffin-embedded sections and subjected to deparaffinization, rehydration, antigen retrieval, and endogenous peroxidase blockade. The samples were blocked with goat serum and incubated with primary antibodies against PPM1G (15532-1-AP; Proteintech) and Ki-67 (ab15580; Abcam) overnight at 4 ℃, followed by incubation with secondary antibody (ab205718; Abcam) at room temperature for 1 hour. Positive staining was visualized using 3, 3'-diaminobenzidine (Solarbio).
Terminal deoxynucleotidyl transferase-mediated dUTP nick end-labeling (TUNEL) assay
Apoptosis of tumor cells was assessed using a TUNEL assay kit (Beyotime), according to the manufacturer’s instructions. Briefly, paraffin-embedded tissue samples were subjected to deparaffinization, rehydration, antigen retrieval, and blocking of endogenous peroxidase. Tissues were incubated with proteinase K at 37 ℃ for 20 min, washed with PBS, incubated with 3% H2O2, and reacted with the working solution at 37 ℃ for 30 min. Positive staining was observed under a fluorescence microscope (Leica).
Statistical analysis
Experiments were repeated three times independently. Data are presented as the mean ± standard deviation. Statistical analysis was conducted with SPSS version 20.0 (IBM Corp., Armonk, NY, USA). Differences were analyzed with the Student t-test or one-way analysts of variance. K-M curves were plotted to estimate the overall and disease-free survival via the log-rank test. Statistical significance was set at P<0.05.
Results
PPM1G was overexpressed in human LUAD tissues
As shown in Figure 1A, PPM1G was significantly overexpressed in several different types of tumor tissues as compared to in normal tissues. By analyzing TCGA database, we found that PPM1G was overexpressed in patients with LUAD and lung squamous cell carcinoma (LUSC) (Figure 1B,1C and Figure S1A). Compared with EGFR-mutant LUAD, PPM1G was found to be upregulated in EGFR-wild type LUAD (Figure 1D). Compared with normal tissues, the elevated levels of PPM1G were observed in different tumor stage, pathological stage, and lymph node metastasis (Figure 1E-1G). Moreover, among patients with LUAD, high levels of PPM1G were notably associated with overall survival (P<0.001; Figure 1H) and progress free interval (P=0.003; Figure 1I). IHC staining also confirmed the elevated levels of PPM1G in LUAD tissues (Figure 1J). These findings suggest the critical role of PPM1G in LUAD progression.
In addition, we conducted a multivariable Cox regression analysis on the overall risk of death in TCGA-LUAD (Figure S1B). The Cox proportional hazards regression analysis revealed that low expression of PPM1G [hazard ratio (HR) =0.67, 95% confidence interval (CI): 0.49–0.91; P=0.01], stage III–IV (HR =2.03, 95% CI: 1.01–4.09; P=0.047), and T3–T4 (HR =1.76, 95% CI: 1.03–3.02; P=0.04) were associated with overall survival. These findings suggest that PPM1G expression and tumor stage may serve as useful prognostic indicators for patients with LUAD.
PPM1G promoted LUAD proliferation and inhibited cell apoptosis
To further determine the role of PPM1G in LUAD development, we induced ectopic expression and knockdown of PPM1G in LUAD cells and evaluated cell proliferation and apoptosis. We first observed the basic level of PPM1G in several LUAD cell lines and found notably elevated expression in all LUAD cell lines as compared with the normal cell line 16HBE (Figure 2A and Figure S1C). Among these LUAD cell lines, PPM1G expression in H1299 and A549 cells was between H23 and NCI-H1975 cells. H1299 and A549 cells with medium expression of PPM1G are more likely to reflect the biological characteristics of most lung cancer cells and enhance the transformability of the results. Therefore, we selected H1299 and A549 cells for subsequent experiments. As shown in Figure 2B,2C and Figure S1D, the expression of PPM1G was notably elevated or suppressed by transfection with PPM1G-overexpressing vectors or siRNAs (si1-PPM1G and si2-PPM1G). Next, the potential effects of PPM1G on LUAD cell viability, proliferation, and apoptosis were investigated. Cell viability and proliferation were elevated in the PPM1G-overexpression group and suppressed in the si-PPM1G group (Figure 2D-2H). Moreover, cell apoptosis was elevated by knockdown of PPM1G (Figure 2I,2J), and there was a decrease in the expression of antiapoptotic protein Bcl-2 and an increase in that of proapoptotic proteins Bax and cleaved caspase-3 (Figure 2K; Figure S2A). These data indicate the pro-proliferative role of PPM1G in LUAD progression.
PPM1G promoted metastasis and blocked cell cycle in LUAD cells
To investigate the potential effects of PPM1G on LUAD cell metastasis and growth, we also assessed the function of PPM1G in LUAD cell metastasis and cell cycle. Wound healing assay revealed that PPM1G overexpression accelerated H1299 and A549 cell migration, while knockdown of PPM1G suppressed cell migration (Figure 3A,3B). Transwell assay also demonstrated that PPM1G accelerated LUAD cell migration and invasion (Figure 3C-3F). We also found that knockdown of PPM1G caused cell cycle arrest at the G1 phase (Figure 3G,3H). Cell cycle-related proteins, CDK4, CCNE1, and CCND1, are involved in cell’s passage from the G1 to the S-phase of the cell cycle (18-20). Here, we found that the levels of CDK4, CCNE1, and CCND1 were decreased via knockdown of PPM1G (Figure 3I; Figure S2B).
PPM1G activated glycolysis in LUAD cells
We additionally sought to characterize the biological processes correlated with PPM1G. We conducted GO analysis and found that PPM1G was correlated with canonical glycolysis (Figure 4A). Knockdown of PPM1G impeded the glucose uptake, suppressed the accumulation of lactate, and decreased the ATP/ADP ratio in LUAD cells (Figure 4B-4D), while PPM1G overexpression had the opposite effect. In addition, the levels of glycolysis regulatory proteins, GLUT1, HK2, PFKP, PKM2, and LDHA, were decreased by the knockdown of PPM1G and upregulated by the overexpression of PPM1G (Figure 4E; Figure S2C). These results suggest that PPM1G can accelerate glycolysis in LUAD cells.
PPM1G modulated LUAD cell functions via the NOTCH signaling pathway
We conducted GSEA to identify the signaling pathways that were regulated by PPM1G (Figure S2D). We found that PPM1G overexpression was positively correlated with the expression of the regulatory genes of the NOTCH signaling pathway (Figure 5A,5B). Western blotting revealed that PPM1G overexpression promoted NOTCH1, NOTCH3, and HES1 protein expression, while knockdown of PPM1G had an opposite effect (Figure 5C; Figure S2E). To determine whether PPM1G promotes LUAD progression via NOTCH signaling, si2-PPM1G and pc-DNA3.1 NOTCH1 was cotransfected into LUAD cells (Figure S3A). We found that NOTCH1 overexpression reversed the suppression of cell proliferation, migration, invasion, cell cycle, and glycolysis and the promotion of cell apoptosis caused by PPM1G knockdown (Figure 5D-5P). Conversely, treatment with the DAPT reversed the promotion of cell proliferation, migration, invasion, and glycolysis and the suppression of cell apoptosis induced by PPM1G overexpression (Figure 6A-6K). These findings suggest that the NOTCH signaling pathway plays an important role in PPM1G -mediated cancer cell proliferation, invasion, and glycolysis.
Knockdown of PPM1G inhibited LUAD tumor growth in vivo
We used an in vivo xenograft mouse model to confirm the regulatory role of PPM1G in LUAD. Figure S3B revealed that PPM1G protein expression was downregulated in sh-PPM1G group compared with sh-NC group. As shown in Figure 7A-7C, PPM1G knockdown notably reduced the volume and weight of subcutaneous tumors in the xenograft mouse model. In IHC staining, there were lower levels of the proliferative biomarker, Ki-67, in the sh-PPM1G group (Figure 7D,7E). In addition, knockdown of PPM1G promoted cell apoptosis (Figure 7F,7G).
Discussion
LUAD is the most prevalent subtype of lung cancer, and effective diagnostic and therapeutic strategies urgently need to be developed to treat its malignant phenotype (21). In this study, we analyzed the LUAD dataset from TCGA database and found that PPM1G was significantly overexpressed in the tumor tissues of patients with LUAD and was correlated with elevated pathological and clinical staging in these patients. Survival analysis also revealed that high PPM1G expression was significantly correlated with poor overall survival in patients with LUAD.
PPM1G is a protein phosphatase that modulates the phosphorylation of serine and threonine, regulating protein stability and function (22,23). In a previous study, bioinformatics analysis revealed a correlation between PPM1G expression and hepatocellular carcinoma development (16). In liver cancer, PPM1G expression is modulated by methylation and kinases and is involved in immune infiltration, cell cycle, and mRNA splicing (13). Moreover, PPM1G promotes the progression of hepatocellular carcinoma via the phosphorylation regulation of alternative splicing protein SRSF3 (20). PPM1G also downregulates the expression of ubiquitin-specific peptidase 7, destabilizes Mdm2, and elevates p53 levels, leading to a modulated DNA damage response (12). PPM1G dephosphorylates p27 and delays the cell cycle progression from G1 to S phase (24). In this study on LUAD, we found that knockdown of PPM1G suppressed cell proliferation, migration, and invasion; blocked cell cycle at the G1 phase; and promoted cell apoptosis.
GO analysis revealed a correlation between PPM1G expression and glycolysis in LUAD. Rapid progression of glycolysis in energy metabolism under aerobic conditions, known as the Warburg effect, is a major characteristic of malignant cancers (25-27). Targeting glycolysis may be an effective means to treating cancer (28-30). During glycolysis, cancer cells rapidly uptake glucose and produce lactic acid, which is commonly used to evaluate the level of glycolysis (31). Several enzymes, including HK2, PFKP, PKM2, and LDHA, are important for the control of glycolysis (32-34). GLUT1, a member of the glucose transporter family, modulates the transportation of glucose across the cell membrane, which is the first rate-limiting step of glycolysis (35-37). Our data revealed that knockdown of PPM1G suppressed glucose uptake and production of lactate and ATP while downregulating the protein expression of glycolysis regulators, GLUT1, HK2, PFKP, PKM2, and LDHA. Meanwhile, PPM1G overexpression had the opposite effect. These findings indicate that PPM1G may activate glucose metabolism to promote LUAD malignancy.
The NOTCH signaling pathway is a highly conserved pathway that modulates intracellular communication and affects various lung functions, including the survival, differentiation, and malignancy of cells (38-40). Normal NOTCH signaling maintains homeostasis in lungs and is necessary for tissue repair and plasticity (41,42). However, the aberrant activity of NOTCH has been implicated in the initiation and progression of lung cancer, and studies have showed that activation of the NOTCH pathway is linked to poor prognosis among those with LUAD (43,44). In this study, we identified an activated NOTCH signaling pathway associated with high PPM1G expression in LUAD. NOTCH signaling is a widely studied pathway in various cancers and figures prominently in cancer cell growth and tissue repair (45,46). NOTCH may participate in glycolysis during tissue repair, osteoblast differentiation, and cancer cell metabolism (47-50). In mouse and human liver fibrosis cell models, blocking WW domain-containing protein 2-PPM1G interaction with costunolide has been reported to promote NOTCH3 degradation and to inhibit the NOTCH3/HES1 pathway (51). Our data verified that the activation of NOTCH with the transfection of pc-DNA3.1 NOTCH1 could abolish the si-PPM1G-induced suppression of LUAD cell growth, metastasis, and glycolysis. Moreover, the inhibition of the NOTCH signaling pathway reversed the promotion of cell growth, metastasis, and glycolysis induced by PPM1G overexpression. These results suggest that NOTCH participates in PPM1G-regulated LUAD progression.
However, there were still several limitations in this study. Firstly, functional experiments were performed only in H1299 and A549 cells. Addressing the comparisons between cells exhibiting high and low PPM1G expression will provide deeper mechanistic insights. Secondly, since CCND1 and CCNE1 exert their functions through interactions with CDK4/6 and CDK2, respectively (19,52). Therefore, assessment of the protein levels of CDK6 and CDK2 may further confirm the mechanistic roles of PPM1G in regulation of cell cycle. Thirdly, GLUT1-mediated glucose uptake affects not only glycolysis but also mitochondrial oxidative phosphorylation (OXPHOS) (53). Assessment of PPM1G’s influences on OXPHOS activity is valuable to fully dissect the metabolic impact of PPM1G. Fourthly, we only focused our rescue experiments on functional phenotypes (proliferation, apoptosis, metastasis, and glycolysis). Assessment of cyclins, CDKs, glycolytic enzymes in rescue conditions would provide additional mechanistic detail. Finally, we found that PPM1G directly regulated the expression of CCND1, CCNE1, CDK4, and glycolytic enzymes through in vitro. Further assessment of the correlation between PPM1G and CCND1, CCNE1, CDK4, and glycolytic enzymes in patient-derived samples would enhance the translational relevance of our findings. In future studies, we plan to include these analyses to further dissect the molecular mechanisms of PPM1G.
Conclusions
In this study, we found that PPM1G activated glycolysis to provide energy to cancer cells and simultaneously stimulated the NOTCH signaling pathway to promote the progression of LUAD. Therefore, PPM1G may be a potential prognostic factor and therapeutic target for LUAD.
Acknowledgments
None.
Footnote
Reporting Checklist: The authors have completed the ARRIVE and MDAR reporting checklists. Available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2025-659/rc
Data Sharing Statement: Available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2025-659/dss
Peer Review File: Available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2025-659/prf
Funding: This research was funded by
Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2025-659/coif). G.W. reports grants from the National Natural Science Foundation of China (No. 31700787) and the Key Laboratory of Animal Stress and Disease of Qilu Normal University (No. 2017ZDSYS02). The other authors have no conflicts of interest to declare.
Ethical Statement: 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. This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The animal study was approved by the Ethics Committee of Qilu Normal University (approval number: 2024-059), in compliance with the Guide for the Care and Use of Laboratory Animals published by the US National Institutes of Health.
Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.
References
- Brody H. Lung cancer. Nature 2020;587:S7. [Crossref] [PubMed]
- Neal RD, Sun F, Emery JD, et al. Lung cancer. BMJ 2019;365:l1725. [Crossref] [PubMed]
- Sung H, Ferlay J, Siegel RL, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin 2021;71:209-49. [Crossref] [PubMed]
- Schabath MB, Cote ML. Cancer Progress and Priorities: Lung Cancer. Cancer Epidemiol Biomarkers Prev 2019;28:1563-79. [Crossref] [PubMed]
- Herbst RS, Morgensztern D, Boshoff C. The biology and management of non-small cell lung cancer. Nature 2018;553:446-54. [Crossref] [PubMed]
- Liang J, Bi G, Huang Y, et al. MAFF confers vulnerability to cisplatin-based and ionizing radiation treatments by modulating ferroptosis and cell cycle progression in lung adenocarcinoma. Drug Resist Updat 2024;73:101057. [Crossref] [PubMed]
- Hirsch FR, Scagliotti GV, Mulshine JL, et al. Lung cancer: current therapies and new targeted treatments. Lancet 2017;389:299-311. [Crossref] [PubMed]
- de Sousa VML, Carvalho L. Heterogeneity in Lung Cancer. Pathobiology 2018;85:96-107. [Crossref] [PubMed]
- Duruisseaux M, Esteller M. Lung cancer epigenetics: From knowledge to applications. Semin Cancer Biol 2018;51:116-28. [Crossref] [PubMed]
- Foster WH, Langenbacher A, Gao C, et al. Nuclear phosphatase PPM1G in cellular survival and neural development. Dev Dyn 2013;242:1101-9. [Crossref] [PubMed]
- Park CI, Kim HW, Hwang SS, et al. Association of PPM1G methylation with risk-taking in alcohol use disorder. Sci Rep 2020;10:5490. [Crossref] [PubMed]
- Khoronenkova SV, Dianova II, Ternette N, et al. ATM-dependent downregulation of USP7/HAUSP by PPM1G activates p53 response to DNA damage. Mol Cell 2012;45:801-13. [Crossref] [PubMed]
- Lin YR, Yang WJ, Yang GW. Prognostic and immunological potential of PPM1G in hepatocellular carcinoma. Aging (Albany NY) 2021;13:12929-54. [Crossref] [PubMed]
- Yu K, Tian H, Deng H. PPM1G restricts innate immune signaling mediated by STING and MAVS and is hijacked by KSHV for immune evasion. Sci Adv 2020;6:eabd0276. [Crossref] [PubMed]
- Kumar P, Tathe P, Chaudhary N, et al. PPM1G forms a PPP-type phosphatase holoenzyme with B56δ that maintains adherens junction integrity. EMBO Rep 2019;20:e46965. [Crossref] [PubMed]
- Xiong DL, Li Q, Wang H, et al. High expression of PPM1G is associated with the progression and poor prognosis of hepatocellular carcinoma. Cancer Biomark 2022;34:13-22. [Crossref] [PubMed]
- Gene Ontology Consortium. The Gene Ontology knowledgebase in 2023. Genetics 2023;224:iyad031. [Crossref] [PubMed]
- Wang Q, He G, Hou M, et al. Cell Cycle Regulation by Alternative Polyadenylation of CCND1. Sci Rep 2018;8:6824. [Crossref] [PubMed]
- Dietrich C, Trub A, Ahn A, et al. INX-315, a Selective CDK2 Inhibitor, Induces Cell Cycle Arrest and Senescence in Solid Tumors. Cancer Discov 2024;14:446-67. [Crossref] [PubMed]
- Lei Y, Xu X, Liu H, et al. HBx induces hepatocellular carcinogenesis through ARRB1-mediated autophagy to drive the G(1)/S cycle. Autophagy 2021;17:4423-41. [Crossref] [PubMed]
- Thakur SK, Singh DP, Choudhary J. Lung cancer identification: a review on detection and classification. Cancer Metastasis Rev 2020;39:989-98. [Crossref] [PubMed]
- Hyder U, McCann JL, Wang J, et al. The ARF tumor suppressor targets PPM1G/PP2Cγ to counteract NF-κB transcription tuning cell survival and the inflammatory response. Proc Natl Acad Sci U S A 2020;117:32594-605. [Crossref] [PubMed]
- Kamada R, Kudoh F, Ito S, et al. Metal-dependent Ser/Thr protein phosphatase PPM family: Evolution, structures, diseases and inhibitors. Pharmacol Ther 2020;215:107622. [Crossref] [PubMed]
- Sun C, Wang G, Wrighton KH, et al. Regulation of p27(Kip1) phosphorylation and G1 cell cycle progression by protein phosphatase PPM1G. Am J Cancer Res 2016;6:2207-20.
- DeBerardinis RJ, Chandel NS. We need to talk about the Warburg effect. Nat Metab 2020;2:127-9. [Crossref] [PubMed]
- Poff A, Koutnik AP, Egan KM, et al. Targeting the Warburg effect for cancer treatment: Ketogenic diets for management of glioma. Semin Cancer Biol 2019;56:135-48. [Crossref] [PubMed]
- Rathee M, Umar SM, Dev AJR, et al. Canonical WNT/β-catenin signaling upregulates aerobic glycolysis in diverse cancer types. Mol Biol Rep 2024;51:788. [Crossref] [PubMed]
- Icard P, Shulman S, Farhat D, et al. How the Warburg effect supports aggressiveness and drug resistance of cancer cells? Drug Resist Updat 2018;38:1-11. [Crossref] [PubMed]
- Vaupel P, Schmidberger H, Mayer A. The Warburg effect: essential part of metabolic reprogramming and central contributor to cancer progression. Int J Radiat Biol 2019;95:912-9. [Crossref] [PubMed]
- Yan F, Teng Y, Li X, et al. Hypoxia promotes non-small cell lung cancer cell stemness, migration, and invasion via promoting glycolysis by lactylation of SOX9. Cancer Biol Ther 2024;25:2304161. [Crossref] [PubMed]
- Vaupel P, Multhoff G. Revisiting the Warburg effect: historical dogma versus current understanding. J Physiol 2021;599:1745-57. [Crossref] [PubMed]
- Wu Z, Wu J, Zhao Q, et al. Emerging roles of aerobic glycolysis in breast cancer. Clin Transl Oncol 2020;22:631-46. [Crossref] [PubMed]
- Abbaszadeh Z, Çeşmeli S, Biray Avcı Ç. Crucial players in glycolysis: Cancer progress. Gene 2020;726:144158. [Crossref] [PubMed]
- Upadhyay S, Khan S, Hassan MI. Exploring the diverse role of pyruvate kinase M2 in cancer: Navigating beyond glycolysis and the Warburg effect. Biochim Biophys Acta Rev Cancer 2024;1879:189089. [Crossref] [PubMed]
- Ancey PB, Contat C, Boivin G, et al. GLUT1 Expression in Tumor-Associated Neutrophils Promotes Lung Cancer Growth and Resistance to Radiotherapy. Cancer Res 2021;81:2345-57. [Crossref] [PubMed]
- Wu Q, Ba-Alawi W, Deblois G, et al. GLUT1 inhibition blocks growth of RB1-positive triple negative breast cancer. Nat Commun 2020;11:4205. [Crossref] [PubMed]
- Xiao H, Wang J, Yan W, et al. GLUT1 regulates cell glycolysis and proliferation in prostate cancer. Prostate 2018;78:86-94. [Crossref] [PubMed]
- Sun J, Dong M, Xiang X, et al. Notch signaling and targeted therapy in non-small cell lung cancer. Cancer Lett 2024;585:216647. [Crossref] [PubMed]
- Lim JS, Ibaseta A, Fischer MM, et al. Intratumoural heterogeneity generated by Notch signalling promotes small-cell lung cancer. Nature 2017;545:360-4. [Crossref] [PubMed]
- Antar SA, ElMahdy MK, Darwish AG. Examining the contribution of Notch signaling to lung disease development. Naunyn Schmiedebergs Arch Pharmacol 2024;397:6337-49. [Crossref] [PubMed]
- Augert A, Eastwood E, Ibrahim AH, et al. Targeting NOTCH activation in small cell lung cancer through LSD1 inhibition. Sci Signal 2019;12:eaau2922. [Crossref] [PubMed]
- Katoh M, Katoh M. Precision medicine for human cancers with Notch signaling dysregulation Int J Mol Med 2020;45:279-97. (Review). [Crossref] [PubMed]
- Leonetti A, Facchinetti F, Minari R, et al. Notch pathway in small-cell lung cancer: from preclinical evidence to therapeutic challenges. Cell Oncol (Dordr) 2019;42:261-73. [Crossref] [PubMed]
- Sharif A, Shaji A, Chammaa M, et al. Notch Transduction in Non-Small Cell Lung Cancer. Int J Mol Sci 2020;21:5691. [Crossref] [PubMed]
- Roper N, Velez MJ, Chiappori A, et al. Notch signaling and efficacy of PD-1/PD-L1 blockade in relapsed small cell lung cancer. Nat Commun 2021;12:3880. [Crossref] [PubMed]
- Sosa Iglesias V, Giuranno L, Dubois LJ, et al. Drug Resistance in Non-Small Cell Lung Cancer: A Potential for NOTCH Targeting? Front Oncol 2018;8:267. [Crossref] [PubMed]
- Bayin NS, Frenster JD, Sen R, et al. Notch signaling regulates metabolic heterogeneity in glioblastoma stem cells. Oncotarget 2017;8:64932-53. [Crossref] [PubMed]
- Kuwabara S, Yamaki M, Yu H, et al. Notch signaling regulates the expression of glycolysis-related genes in a context-dependent manner during embryonic development. Biochem Biophys Res Commun 2018;503:803-8.
- Lee SY, Long F. Notch signaling suppresses glucose metabolism in mesenchymal progenitors to restrict osteoblast differentiation. J Clin Invest 2018;128:5573-86.
- Jabs M, Rose AJ, Lehmann LH, et al. Inhibition of Endothelial Notch Signaling Impairs Fatty Acid Transport and Leads to Metabolic and Vascular Remodeling of the Adult Heart. Circulation 2018;137:2592-608.
- Ge MX, Liu HT, Zhang N, et al. Costunolide represses hepatic fibrosis through WW domain-containing protein 2-mediated Notch3 degradation. Br J Pharmacol 2020;177:372-87. [Crossref] [PubMed]
- Kato S, Okamura R, Adashek JJ, et al. Targeting G1/S phase cell-cycle genomic alterations and accompanying co-alterations with individualized CDK4/6 inhibitor-based regimens. JCI Insight 2021;6:e142547. [Crossref] [PubMed]
- Shi Y, Kotchetkov IS, Dobrin A, et al. GLUT1 overexpression enhances CAR T cell metabolic fitness and anti-tumor efficacy. Mol Ther 2024;32:2393-405. [Crossref] [PubMed]
(English Language Editor: J. Gray)

