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Overexpression of metalloproteinase PAPPA accelerates cancer progression and correlates with immune cell infiltration in gastric cancer: insights from bioinformatics and in vitro investigations

Cancer Cell International volume 25, Article number: 38 (2025) Cite this article

Abstract

Background

Gastric cancer (GC) is one of the most common malignant tumors in the digestive system. However, the development of its targeted therapies has been slow. Therefore, exploring the mechanisms of malignant behavior of GC is key to developing their treatment methods. Pregnancy-associated plasma protein-A(PAPPA) is thought to play an important role in the occurrence and progression of cancer, yet its significance in the development of GC has not been reported.

Methods

Bioinformatics analysis elucidated PAPPA's expression in GC and its prognostic significance. The study correlated PAPPA expression with immune infiltration and signaling pathways. Cellular assays, including CCK-8, Western blotting, and flow cytometry, were utilized to examine PAPPA's role in gastric cancer cell apoptosis, migration, and invasion.

Results

Bioinformatics analysis has demonstrated that the expression of PAPPA is upregulated in GC and correlates with poor prognosis. Correlation and Cox regression analyses have revealed that TNM staging, pathological staging, age, outcome assessment, postoperative tumor residue, and PAPPA expression are prognostic determinants in GC. Further analysis indicates that PAPPA is associated with the infiltration of various immune cells and pathways related to GC. Cellular experiments have shown that PAPPA promotes cell proliferation, and its deficiency can inhibit the proliferation of GC cells, inducing cell cycle arrest at the G1/S phase.

Conclusions

The findings of this investigation suggest that PAPPA serves as a crucial modulator of GC, underscoring its potential as a GC treatment target.

Introduction

Gastric cancer (GC) is a common malignant tumor of the digestive system and poses a significant threat to human health, being the leading cause of cancer-related deaths worldwide [1, 2]. Particularly in East Asia, including China, Japan, and Korea, there is a higher risk of GC [3]. The late clinical presentation of GC often leads to a diagnosis at an advanced stage, characterized by tumor infiltration and metastasis, which greatly hinders effective treatment and prognosis. Despite multimodal therapies involving surgery, chemotherapy, radiotherapy, and emerging targeted and immunotherapies, the overall 5-year survival rate remains below 30% [4]. Therefore, discovering new biomarkers and therapeutic targets is extremely important. Pregnancy-associated plasma protein-A (PAPPA) has been implicated as a key regulator in the insulin-like growth factor (IGF) pathways and has been identified as a critical factor in the progression of various malignancies [5,6,7,8,9]. The interaction between IGF-binding proteins (IGFBPs), extracellular matrix proteins, and proteolytic enzymes constitutes the IGF-IGFR-IGFBP axis, a regulatory process that is crucial for tumor progression [6]. Notably, IGFBP7 has been reported to be upregulated in stomach adenocarcinoma (STAD) and is associated with patient prognosis [6]. Recent studies have highlighted the role of PAPPA in promoting tumorigenesis and metastasis across different types of cancer [10,11,12,13].

For example, Tanaka et al. showed that reducing PAPPA mRNA levels with antisense strategies effectively decreased IGF signaling and cellular proliferation in vitro, while its overexpression enhanced metastatic potential in ovarian cancer in vivo [10]. Additionally, Kuhajda and Eggleston identified strong PAPPA immunostaining as an independent predictor of early recurrence in stage I breast cancer [11], The serum concentration of PAPPA has also been found to be slightly increased in patients with lung cancer, further implicating PAPPA in cancer progression [11]. These findings underscore the potential of PAPPA as a prognostic biomarker and therapeutic target [13]. Building upon these insights, the recent literature offers additional perspectives on the role of PAPPA in cancer biology. For instance, the manuscript by Smith et al. discusses the potential of CAR T cell therapy targeting cancer-specific antigens, which could provide a novel therapeutic approach for GC [14]. Furthermore, the work by Mei et al. presents a bispecific CAR-T cell therapy targeting BCMA and CD38 in relapsed or refractory multiple myeloma, showcasing the versatility of this therapeutic strategy [15]. These studies, along with the cited manuscripts enrich our understanding of PAPPA's role in the tumor microenvironment and its potential as a target for GC treatment [16,17,18,19].

In this study, we aimed to explore the role of PAPPA in GC progression. Our analysis of GC tissues from the TCGA database revealed a significant tumor-promoting effect of PAPPA in GC pathogenesis. Functional assays in GC cell lines, with modulated PAPPA expression levels, further validated its role in tumorigenesis and metastasis. These findings, in conjunction with the current literature, highlight the potential of PAPPA as a therapeutic target in GC and warrant further investigation into its underlying mechanisms and clinical applications.

Materials and methods

Gathering and analyzing data

Patients with stomach adenocarcinoma (STAD) have their gene expression data obtained from TCGA database, which may be accessed at https://portal.gdc.cancer.gov/. The dataset, which used the workflow type HTSeq-FPKM (fragments per kilobase per million), included 375 GC samples and 32 normal samples. For additional analysis, the Level 3 HTSeq-FPKM data was transformed to TPM (transcripts per million reads). The clinical characteristics of the gastric cancer patients, such as gender, age, TNM stage, pathological stage, primary therapy outcome, residual tumor, and other relevant factors were included. Any incomplete or ambiguous clinical information was not considered in the analysis. Statistical analyses were conducted with the R software (version 3.6.3), utilizing the TCGA database as the main source of data for the study.

Time-dependent ROC and logistic regression analyses

ROC curves were utilized to assess the diagnostic precision of PAPPA. The Wilcoxon signed-rank sum test, logistic regression, and multivariate Cox analysis utilized examine association PAPPA levels and clinicopathological characteristics. The impact of PAPPA on survival was evaluated through Cox regression and Kaplan–Meier analysis.

Cox risk regression analyses

Cox regression analyses were employed to assess the risk factors affecting patient overall survival (OS). The variables examined in the univariate analyses encompassed pathological stage, T stage, N stage, primary therapy outcome, residual tumor, and PAPPA expression. These factors were evaluated to determine their independent influence on patient OS.

Construction of PPI network and immune infiltration analyses

The PAPPA-related PPI network was established via the STRING database (https://string-db.org/). In addition to STRING, we also utilized GeneMANIA (http://genemania.org/) to further analyze the PPI network and to incorporate functional gene associations. GeneMANIA's predictive algorithms helped to identify additional interactions and potential functional modules within the network. The differential genes associated with PAPPA, as identified in the TCGA-STAD dataset, were submitted to the analysis section of the database, specifically within the 'Multiple proteins' module. In order to evaluate the degree of immune infiltration present in the tumors under study, the GSVA package (version 1.34.0) was employed for this purpose. Statistical analysis methods, including the Wilcoxon rank sum test and Spearman correlation test, were employed to explore the relationship between PAPPA expression and immune cell infiltration levels. Prior to applying the Spearman correlation analysis, we assessed the normality of the data using the Kolmogorov–Smirnov test or the Shapiro–Wilk test. The results indicated that the data did not conform to a normal distribution (P < 0.05), which substantiated our decision to select the Spearman correlation test for further analysis.

KEGG, GO and GSEA

Utilizing Gene Ontology (GO) [20] analysis provides a concise summary of the disparate differential genes, offering a more comprehensive understanding of the underlying biological response. Meanwhile, KEGG analysis allows for the examination of signaling pathways linked to these differential genes.

These analyses were conducted using the clusterProfiler package (version 3.14.3) in conjunction with the org.HS.eg.db package [20] (version 3.10.0). Gene set enrichment analysis (GSEA) [21] was also performed utilizing the clusterProfiler package (version 3.14.3). The statistical significance of pathways was assessed using a traditional P-value threshold of less than 0.05, in combination with a false discovery rate (FDR) q-value cutoff of less than 0.25.

Cell culture and transfection

The MKN28, MKN45, HGC27 GC cell lines were obtained from Xiamen Yimo Biotech Co., Ltd. The AGS, BGC823, MGC803 GC cell lines were obtained from Anhui Baioujing Medical Technology Co., Ltd. The cell cultures were maintained in RPMI-1640 medium supplemented with 10% fetal bovine serum (FBS) and incubated at 37 °C in a humidified atmosphere with 5% CO2.

Cellular transfection

The transfection of cells in 24-well plates with Lipofectamine 2000 (Invitrogen, USA) was carried out using a specific siRNA targeting PAPPA. Cells were then used for subsequent assays 48 h after transfection. The experimental procedures were repeated three times in order to confirm the precision and reliability of the results. The plasmid for overexpressing PAPPA was obtained from Anhui Baioujing Medical Technology Co., Ltd. The sequences of the oligonucleotides used in the experiment were as follows:

#Si-PAPPA-1: 5'- GAAGUGCAAAGUGCUCAUGUUTT-3';

#Si-PAPPA-2: 5'- CAGAGCCUACUUGGAUGUUAATT −3';

#Si-PAPPA-3: 5'- CGGUACGUGUGAGCUUCAGUUTT −3'.

QRT-PCR

TRIzol reagent (Invitrogen, USA) was employed for the extraction of cellular RNA, followed by qRT-PCR analysis according to established protocols [22]. The specific primers utilized are detailed below: PAPPA: Forward—ACA AAG ACC CAC GCT ACTT TT; Reverse—CAT GAA CTG CCC ATC ATAG GTG; GAPDH: Forward—AGA AGG CTG GGG CTC ATTT G; Reverse—AGG GGC CAT CCA CAG TCT TC. The experimental procedures were repeated three times in order to confirm the precision and reliability of the results.

Western blotting assay

Western blot analysis was carried out in triplicate following established protocols [23]. To determine the protein concentration, the BCA assay was employed. Following this, identical quantities of protein were resolved using SDS-PAGE and then moved onto PVDF membranes. Subsequently, the membrane was blocked with 5% non-fat milk at room temperature for a duration of 1 h. Following the blocking, the membrane was subjected to overnight incubation at 4 °C with primary antibodies: anti-PAPPA (Abcam, ab174314, dilution 1:1000) and anti-α-tubulin (Proteintech, 11224-1-AP, dilution 1:10,000). Following three washes of 10 min each, the membranes were exposed to either Goat anti-rabbit IgG(H + L) secondary antibody (Thermo Pierce, 31210, dilution 1:5000) or HRP conjugated Rabbit Anti-Goat secondary antibody (Pierce, 31402, dilution 1:5000) at room temperature for a duration of 1 h. After another three washes with TBST, the protein bands were visualized using the Super Signal West Dura western blot substrate (Thermo Fisher, 34057) and a Gel Photography System (Shanghai, China). The protein expression levels in each sample were normalized to α-tubulin for accurate comparison.

CCK-8 assay

A volume of 10 µl CCK-8 solution obtained from Biomiky in Shanghai, China, was carefully dispensed into 96-well plates according to the manufacturer's recommended protocol. The plates were then inoculated with 2.5 × 104 cells and placed in an incubator at 37 °C for a period of 2 h. Following incubation, the optical density at a wavelength of 450 nm was determined utilizing a spectrophotometer. This experimental procedure was repeated thrice for accuracy.

Wound healing and transwell assay

In the wound healing experiments, cells (1.5 × 105 per well) were plated onto a plate and a scratch was made using a sterile needle. The wound was imaged at 0,24-, and 48-h using fluorescence microscopy (Olympus, China). In invasion assays, cells were seeded onto wells of a rig and subsequently introduced into a chamber at a concentration of 1.5 × 105 in 100 µL of medium in the upper chamber, while 600 µL of medium with 5% bovine serum albumin was placed in the lower chamber. After incubating for 24 h, the cells were fixed using a 4% paraformaldehyde solution and then stained with 1% crystal. For each transwell chamber, the field of view was randomly selected and 3 100X photographs were taken photographs, counted, and data analyzed.

Cell cycle and cell apoptosis assay

The cells were collected and labeled using the PI Cell Cycle Kit and Annexin V FITC/PI Cell Apoptosis Detection Kit (Biyuntian, Shanghai, China) as per the manufacturer's guidelines. The acquisition and analysis of data were carried out using a BD Biosciences Flow Cytometer equipped with WinMDI software.

Statistical analysis

The results were presented as average values accompanied by their respective standard deviations (SD), with each trial being conducted thrice to ensure precision. Statistical analysis was conducted using Student’s t-test, with the exception of cases where the χ2 test was more appropriate. Survival rates were compared using KM survival curves and the Log-rank test.

Statistical analysis for cell experiments was conducted utilizing SPSS 22.0 and R (version 3.6.3). The quantitative data was represented by mean values accompanied by the standard deviation. Analysis of QRT-PCR and Western blot results involved using one-way ANOVA and t-test to compare means between different groups. A significance threshold of P < 0.05 was set for all analyses, with results reported as follows: ns (not significant), *P < 0.05, **P < 0.01, ***P < 0.001 to indicate statistical significance.

Results

Expression and prognosis analysis of PAPPA in the database

Clinical baseline information of GC patients was downloaded from the TCGA database, encompassing details such as gender, age, ethnicity, TNM stage, pathological stage, histological type, histological grade, anatomical site of the tumor, efficacy analysis, postoperative residual tumor status, H. pylori infection status, history of reflux and receipt of anti-reflux treatment, and presence of Barrett's esophagus status (Table 1). PAPPA gene expression in STAD and normal tissues from the TCGA database revealed a notable upregulation of PAPPA in tumors compared to normal tissues (Fig. 1A). It was observed that PAPPA expression levels correlated positively with the stage and grade of the tumor, peaking at stage 4 and grade 3 (Fig. 1B–D). Comparing GC tissues to normal tissues, PAPPA expression was elevated in with or without lymph node metastasis (Fig. 1E).

Table 1 Clinical baseline information of GC patients
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Fig. 1
figure 1

Expression and prognosis analysis of PAPPA in the database. A PAPPA gene expression in STAD and normal tissues from the TCGA database revealed a notable upregulation of PAPPA in tumors compared to normal tissues. B Relative expression levels of PAPPA in 415 GC tissues and 34 normal tissues from TCGA database. C The expression levels of PAPPA vary across different stages of GC tissues, with a positive correlation observed between PAPPA expression and tumor stage progression. D Expression levels of the PAPPA gene across various grades of GC tissues demonstrate a positive correlation with tumor grading, with the highest expression observed at grade 3. E Comparing GC tissues to normal tissues, PAPPA expression was elevated in with or without lymph node metastasis *P < 0.05, **P < 0.01, ***P < 0.001

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PAPPA expression correlates with patient clinicopathologic parameters

The KM curves revealed that patients who expressed elevated levels of PAPPA had a decreased overall survival compared to patients with lower levels of PAPPA (Fig. 2A). Similarly, Disease Specific Survival (DSS) (Fig. 2B) and Progression Free Interval (PFI) (Fig. 2C) were notably elevated in patients with low expression of PAPPA compared to those with high expression of PAPPA. Survival analysis was conducted for OS, DSS, and PFI among different subgroups. The findings revealed that individuals with elevated PAPPA levels exhibited significantly worse prognoses within the T3&T4, N0&N1, and M0 subgroups of OS, DSS, and PFI (Fig. 2D–F). Additionally, we have observed a trend towards a lower PFI in patients with higher levels of PAPPA expression. We also performed a survival analysis of PAPPA expression and prognosis of GC patients based on the KM database and obtained similar results (Fig. 2G, H).

Fig. 2
figure 2

Kaplan–Meier survival curves comparing the high and low expression of PAPPA in different types of cancer. A The KM curves revealed that patients who expressed elevated levels of PAPPA had a decreased OS compared to patients with lower levels of PAPPA. B, C DSS and PFI were notably elevated in patients with low expression of PAPPA compared to those with high expression of PAPPA. D–F In the T3&T4, N0&N1, and M0 subgroups, individuals with higher levels of PAPPA expression showed significantly worse outcomes in OS, DSS, and PFI. G–I Kaplan–Meier survival curve analysis of PAPPA in GC from all datasets, GSE62254, GSE511005 datasets

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PAPPA serves as a prognostic risk for outcomes in GC

The predictive significance of PAPPA expression, along with factors such as pathological stage, T stage, N stage, primary therapy outcome, and residual tumor, was evaluated in GC patients. Results from Cox regression analysis revealed that PAPPA emerged as a significant prognostic indicator for OS in GC (Figs. 3, 4A). Furthermore, through the development of a nomogram model, it was established that PAPPA could effectively forecast patient prognosis at 1 and 3 years (Fig. 4B). This was further corroborated by the calibration curve (Fig. 4C).

Fig. 3
figure 3

Forest plot of univariate Cox survival regression analysis in patients with GC. The predictive significance of PAPPA expression, along with factors such as pathological stage, T stage, N stage, primary therapy outcome, and residual tumor, was evaluated in GC patients. Results from Cox regression analysis revealed that PAPPA emerged as a significant prognostic indicator for OS in GC

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Fig. 4
figure 4

Prognostic model for patients with GC. Through the development of a nomogram model, it was established that PAPPA could effectively forecast patient prognosis at 1 and 3 years. A Nomogram for predicting the probability of GC patients at 1 and 3 years. B Calibration curves for the nomogram model for 1 and 3 years. C Prognostic risk factor map for GC patients

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Investigating the relationship between PAPPA expression and immune cell infiltration

Spearman correlation analysis was utilized to investigate the relationship between the expression level of PAPPA and the extent of immune cell infiltration quantified by ssGSEA. The findings revealed that the scores of Tem and macrophages were notably higher in the high PAPPA expression group compared to the low PAPPA expression group (Fig. 5A, B). KM survival curve analysis demonstrated that patients with STAD who had low PAPPA expression and low macrophage infiltration exhibited a higher overall survival rate than those with high expression levels of both PAPPA and macrophages (Fig. 5C). Furthermore, the results of tumor immune cell infiltration analysis indicated a positive correlation between PAPPA expression and the presence of B cells, follicular helper CD4+ T cells (TFH), CD8+ T cells, Mast cells, and Neutrophils (Fig. 5D).

Fig. 5
figure 5

Correlation of PAPPA expression with immune infiltration level in GC. A Correlation between the abundances of immune cells and PAPPA expression level. B Enrichment scores of macrophages and Tem in PAPPA-high group is higher than PAPPA-low group (p < 0.001). C KM survival curve analysis demonstrated that patients with STAD who had low PAPPA expression and low macrophage infiltration exhibited a higher overall survival rate than those with high expression levels of both PAPPA and macrophages. D Correlation between the relative abundances of B cells, follicular helper CD4 T cells (TFH), CD8 T cells, Mast cell, Neutrophils and PAPPA expression level. The results of tumor immune cell infiltration analysis indicated a positive correlation between PAPPA expression and the presence of B cells, follicular helper CD4 + T cells (TFH), CD8 + T cells, Mast cells, and Neutrophils

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Identification of PAPPA-related DEGs in GC

The analysis was conducted separately for high and low PAPPA expression in GC, identifying a total of 291 differentially expressed genes (DEGs). In this comprehensive investigation, a total of 291 genes exhibiting differential expression (DEGs) were identified. Among these, 221 genes displayed upregulation, while 70 genes exhibited downregulation. These findings exhibited statistical significance between the two cohorts, with an adjusted p-value of less than 0.05 and an absolute log2-fold change greater than 1.5 (Fig. 6A). A heatmap was generated to visually represent the relative expression levels of the top 21 DEGs identified in this study (Fig. 6B).

Fig. 6
figure 6

The possible biological pathways of PAPPA. A Volcano map demonstrates the distribution of differential genes associated with PAPPA in GC. B Heat map shows the top 20 differential genes. C PAPPA gene GO, KEGG analysis bubble diagram. D GSEA enrichment analysis of PAPPA in GC. The pathways that are enriched in extracellular matrix structures includes nuclear matrix pathway, extracellular matrix glycoprotein pathway, secretory factors and other pathways. E, F PAPPA protein interaction network diagram. Analysis of the network indicated that genes closely associated with PAPPA include IGF1, IGF2, IGFBP4, IGFBP5, IGFALS, and PRG2

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Functional enrichment and analyses of PAPPA-related DEGs in GC

We conducted a GO and KEGG enrichment analysis in order to anticipate the functional enrichment details of the genes interacting with PAPPA. The outcomes of the GO enrichment analysis indicated that the genes associated with PAPPA play a role in cellular components (CCs), such as intermediate filaments, intermediate filament cytoskeleton, plasma membrane rafts, keratinization envelope, and keratin filaments.

Biological processes (BPs) were primarily enriched in cornification, keratinization, differentiation of keratinocytes, and development of the epidermis. Molecular functions (MFs) are mainly related to receptor-ligand, hormones, neuropeptide hormone activity, etc. We conducted an evaluation of the associations between PAPPA expression and signaling pathways through the utilization of KEGG enrichment analysis.

The examined dataset included evaluations of neuroactive ligand-receptor interactions, the IL-17 signaling pathway, and insulin secretion (Fig. 6C). Additionally, the Gene Set Enrichment Analysis (GSEA) uncovered a strong correlation between PAPPA expression and components of the Extracellular Matrix (ECM), as well as pathways related to adherent plaques. The pathways that are enriched in extracellular matrix structures includes nuclear matrix pathway, extracellular matrix glycoprotein pathway, secretory factors and other pathways (Fig. 6D).

PPI network analysis

The PPI network linked to PAPPA was constructed using 11 nodes and 26 edges, with an average node degree of 4.73. The enrichment evaluation yielded a significant p-value of 4.27e-05. Through the application of the K-means algorithm, the network was partitioned into 5 distinct clusters. Analysis of the network indicated that genes closely associated with PAPPA include IGF1, IGF2, IGFBP4, IGFBP5, IGFALS, and PRG2.

The construction of the PPI network associated with PAPPA was anchored in a dataset that encompasses 11 distinct protein nodes and 26 interaction edges. This network architecture is characterized by an average node degree of 4.73, which quantifies the median connectivity of the proteins involved, hinting at a balanced network of interactions. Statistical validation of the network's interactions was achieved through enrichment analysis, yielding a p-value of 4.27e-05. This significant result affirms the non-random assembly of the network and its biological pertinence, particularly within the realm of PAPPA-associated processes. Our in-depth analysis has pinpointed several genes with robust associations to PAPPA. These include Insulin-Like Growth Factor 1 (IGF1), Insulin-Like Growth Factor 2 (IGF2), Insulin-Like Growth Factor Binding Protein 4 (IGFBP4), Insulin-Like Growth Factor Binding Protein 5 (IGFBP5), Insulin-Like Growth Factor Binding Protein, Apolipoprotein E-Binding Protein (IGFALS), and Pregnancy Zone Protein 2 (PRG2). The proximity of these genes to PAPPA within the network (Fig. 6E F).

Down‐regulation of PAPPA expression inhibits the proliferation of GC cells

In order to understand how PAPPA contributes to the advancement of GC, we conducted assessments of PAPPA levels in GC cells through qRT‐PCR and WB analyses. The levels of PAPPA mRNA and protein were found to be notably elevated in the MGC803 and BGC823 GC cell lines in comparison to the GES‐1 cell line (Fig. 7A, B, S1). To produce cell lines with sustained low levels of PAPPA expression, we utilized PAPPA-lentivirus expression vectors. The effectiveness of transfection was evaluated through qRT-PCR and WB analysis to ensure optimal transfection efficiency. (Fig. 7C, D, S2-3). Among the three different siPAPPAs tested, we selected siPAPPA#3 for the subsequent experiments based on the initial assessment. We consistently used siPAPPA#3 throughout the remaining experiments to maintain experimental continuity and control for variability. Suppression of PAPPA expression with siPAPPA#3 led to a marked decrease in the proliferation of gastric cancer cells. (Fig. 7E, F).

Fig. 7
figure 7

Expression of PAPPA in GC cell lines. A, B The levels of PAPPA mRNA and protein were found to be notably elevated in the MGC803 and BGC823 GC cell lines in comparison to the GES-1 cell line. C knockdown efficiency of PAPPA in BGC823 and MGC803 cells by qRT‐PCR. D knockdown efficiency of PAPPA in BGC823 and MGC803 cells by Western blot. E The proliferation of BGC823 cells with PAPPA down‐regulation was detected by CCK8 assay. F The proliferation of MGC803 cells with PAPPA down‐regulation was detected by CCK8 assay. *P < 0.05, **P < 0.01, ***P < 0.001

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Down‐regulation of PAPPA promotes apoptosis

Flow cytometry was employed to analyze apoptosis in GC cells. The levels of apoptosis observed in PAPPA‐knockdown BGC823 and MGC803 cells were significantly elevated in comparison to the control groups (Fig. 8A, B).

Fig. 8
figure 8

Effect of knockdown of PAPPA on apoptosis in GC cells. A, B Flow cytometry detection of apoptosis. The levels of apoptosis observed in PAPPA-knockdown BGC823 and MGC803 cells were significantly elevated in comparison to the control groups. C, D Flow Cytometer detected the cell cycle and the proportion of each cell cycle. We have noticed a rise in the quantity of cells present in the G1 phase within the PAPPA-knockdown cells as opposed to the control group. Conversely, was decrease in the number of cells in the S stage among the PAPPA-knockdown cells. *P < 0.05, **P < 0.01, ***P < 0.001

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Down‐regulation of PAPPA promotes G1-S arrest

To examine the effects of PAPPA on the growth of GC cells, we performed a cell cycle analysis utilizing flow cytometry. We have noticed a rise in the quantity of cells present in the G1 phase within the PAPPA-knockdown cells as opposed to the control group. Conversely, was decrease in the number of cells in the S stage among the PAPPA-knockdown cells (Fig. 8C, D). It can be seen that after knockdown of PAPPA gene, GC cells were blocked in G1-S, and the proliferation rate was slowed down. However, whether apoptosis is induced after blockade or just G1 prolongation needs to be investigated in further experiments.

The suppression of PAPPA expression effectively hinders the migration and invasiveness of gastric cancer cells

To elucidate the role of PAPPA in the metastatic and relapse processes of GC, we conducted a series of experiments to assess the impact of PAPPA levels on the migratory and invasive capabilities of GC cells. Our analysis included wound healing assays and invasion assays, the results of which are presented in Fig. 9. In the wound healing assays (Fig. 9A–D), we observed that PAPPA-knockdown cells displayed a significant reduction in their migratory capacity compared to the control cells. Specifically, for the BGC823 cell line, the siPAPPA group exhibited an average migration rate of 0.39 at 24 h and 0.67 at 48 h, which was notably lower than the siCtrl group's rates of 0.56 at 24 h and 0.84 at 48 h. Similarly, the MGC803 cell line showed a siPAPPA group average migration rate of 0.19 at 24 h and 0.25 at 48 h, compared to the siCtrl group's 0.30 at 24 h and 0.46 at 48 h. Paired T-tests revealed that these differences were statistically significant (p < 0.01) for both cell lines at the respective time points. The invasion assays (Fig. 9E, F) further confirmed the inhibitory effect of PAPPA knockdown on GC cell invasion. In the BGC823 cell line, the siCtrl group had an average of 174 migrated cells per field of view, with a standard deviation of 4.83, while the siPAPPA group showed a significantly reduced average of 60 cells per field of view, with a standard deviation of 2.17. The average migration fold for the siCtrl group was 1, with a standard deviation of 0.03, whereas the siPAPPA group had an average fold of 0.34, with a standard deviation of 0.01. For the MGC803 cell line, the siCtrl group had 375 cells per field of view, with a standard deviation of 22.99, and the siPAPPA group had 171 cells per field of view, with a standard deviation of 10.27. The average migration fold for the siCtrl group was 1, with a standard deviation of 0.06, and for the siPAPPA group, it was 0.46, with a standard deviation of 0.03.These findings collectively demonstrate that PAPPA plays a critical role in the migration and invasion of GC cells, suggesting its potential as a therapeutic target for the treatment of gastric cancer metastasis and recurrence.

Fig. 9
figure 9

PAPPA regulates GC cell proliferation, migration and invasion. A–D Wound healing assay was used for the BGC823 and MGC803 cells with PAPPA down‐regulation. we observed that PAPPA-knockdown cells displayed a significant reduction in their migratory capacity compared to the control cells. Specifically, for the BGC823 cell line, the siPAPPA group exhibited an average migration rate of 0.39 at 24 h and 0.67 at 48 h, which was notably lower than the siCtrl group's rates of 0.56 at 24 h and 0.84 at 48 h. Similarly, the MGC803 cell line showed a siPAPPA group average migration rate of 0.19 at 24 h and 0.25 at 48 h, compared to the siCtrl group's 0.30 at 24 h and 0.46 at 48 h. E, F Migration and invasion assay were used for BGC823 and MGC803 cells with PAPPA down‐regulation. The invasion assays (E, F) further confirmed the inhibitory effect of PAPPA knockdown on GC cell invasion. In the BGC823 cell line, the siCtrl group had an average of 174 migrated cells per field of view, with a standard deviation of 4.83, while the siPAPPA group showed a significantly reduced average of 60 cells per field of view, with a standard deviation of 2.17. The average migration fold for the siCtrl group was 1, with a standard deviation of 0.03, whereas the siPAPPA group had an average fold of 0.34, with a standard deviation of 0.01. For the MGC803 cell line, the siCtrl group had 375 cells per field of view, with a standard deviation of 22.99, and the siPAPPA group had 171 cells per field of view, with a standard deviation of 10.27. The average migration fold for the siCtrl group was 1, with a standard deviation of 0.06, and for the siPAPPA group, it was 0.46, with a standard deviation of 0.03. *P < 0.05, **P < 0.01, ***P < 0.001

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Discussion

GC ranks as the quintessential malignancy globally, occupying the fourth position in the mortality hierarchy of major neoplasms [24]. Despite significant advancements in diagnostic and therapeutic strategies over the preceding decades, the prognosis for GC afflicted patients remains bleak, with a persistently low five-year survival rate [25]. A significant volume of academic research on molecularly targeted treatments and molecular pathways for GC has provided valuable insights into the disease's development and has played a crucial role in improving the outlook for patients with GC [26, 27]. For instance, estradiol possessed the capacity to target pivotal proteins implicated in cancer progression, thereby impeding the proliferation of gastric neoplastic cells [28]. The histone deacetylase inhibitor known as trichostatin-A (TSA) has demonstrated the ability to effectively trigger cell cycle arrest and apoptosis in various cancer cell lines, including solid tumors like GC [29]. However, the full clinical utilization of these biomarkers remains unrealized, underscoring the imperative need for the identification of efficacious biomarkers and therapeutic targets.

The gene encoding the PAPPA is located on the long arm of chromosome 9, specifically in region 3, band 3 [30]. This has been acknowledged for its crucial contribution to the process of pregnancy [31]. This protein functioned as an oncogene, fostering tumor cell proliferation, invasion, and metastasis [32]. Research has indicated an association between PAPPA and hepatocellular carcinoma [33], breast cancer [9], and ovarian cancer [34]. Yet, its correlation with GC remains unreported. A comprehensive investigation into the biological behavior of PAPPA could potentially unveil the mechanisms underlying tumor proliferation and invasion.

Within the confines of this investigation, we procured patient data and RNA high-throughput sequencing pertaining to GC from the TCGA database. We scrutinized the expression magnitude of PAPPA across various cancers and its consequential effect on GC prognosis. Our findings revealed a statistically significant differential expression of PAPPA between tumorous and normal tissue across a multitude of cancer types, including CHOL, DLBC, ESCA, GBM, HNSC, LGG, PAAD, STAD, and THYM. To delve deeper into the role and implications of PAPPA expression, we proceeded to examine its correlation with clinical and pathological characteristics, as well as prognosis. The ROC curve delineated the diagnostic potential of PAPPA expression levels for GC. Survival analysis outcomes suggested that elevated PAPPA expression portends a less favorable prognosis. Logistic regression analysis unveiled a strong association between PAPPA expression levels and pathological stage type, histological grade, and postoperative gastroesophageal reflux symptoms. Univariate Cox regression analysis posited that N stage, age, efficacy assessment, postoperative tumor residual status, and PAPPA expression constituted risk factors influencing GC patient prognosis. The risk factor scores and Nomogram model, predicated on PAPPA expression levels, were authenticated through Calibration analysis, yielding satisfactory fitting results, thereby affirming the reliability of the model constructed within this study. This insinuates that PAPPA levels could potentially serve as a novel indicator of poor prognosis in GC.

The Pearson and Spearman correlation analyses revealed a positive association between PAPPA expression and B cells, follicular helper CD4+ T cells (TFH), CD8+ T cells, Mast cells, and Neutrophils. Additionally, these cell populations were found to be positively associated with the infiltration depth of effector memory T cells (Tem), mast cells, plasmacytoid dendritic cells (pDC), and neutrophils. Conversely, there was a negative correlation with the abundance of helper T cell 2 (Th2) and NK CD56 bright cell subsets. This association between PAPPA expression and immune cells suggested a regulatory role in tumor immunity during the progression of GC.

Furthermore, we identified genes with differential PAPPA expression, among which the upregulated genes encompassed IGF1 and IGFBP4 genes. Previously, we constructed a PPI network and screened for immune-related hub genes including IGF1, IGF2, IGFBP4, IGFBP5. Therefore, we analyzed the correlation between PAPPA and the above genes via Spearman analysis. (Fig.S4). IGF-1 is widely recognized for its ability to promote cell proliferation, enhance cell survival, increase cell motility, stimulate anabolic processes, and trigger growth in various tissues [35]. IGF1R serves as the central signaling molecule responsible for a wide array of biological effects triggered by IGFs, which are further regulated by IGFBPs [36]. IGFBPs play crucial roles within the pericellular environment by controlling intracellular signal transduction via IGF1R. The primary function of canonical IGFBPs at the cell surface is the binding of IGF-1 with high affinity, effectively blocking access to the receptor [37]. A common method used to decrease the strength of the IGF-IGFBP interaction in order to facilitate the release of IGF and the activation of IGF1R signaling is through controlled proteolysis [38]. Various categories of proteolytic enzymes have been associated with the degradation of IGFBPs, such as serine proteases, metalloproteinases, and cathepsins [39], in recent years, there has been a notable focus on pappalysins, also referred to as PAPPA, which are zinc-binding endopeptidases. These enzymes have garnered particular attention for their unique characteristics and functions [40].

PAPPA showed a preference for cleaving IGFBP-4 when IGF-1 or IGF-2 were present, also exhibiting some effectiveness against IGFBP-2 and IGFBP-5 [41]. On the other hand, PAPP-A2's activity was not dependent on IGF and specifically focused on targeting IGFBP-5, with a lesser impact on IGFBP-3 [40]. Nevertheless, there are other validated mechanisms through which IGFBPs may potentially regulate IGF1R-dependent pathways. The evidence supporting regulatory systems implicated in various disease conditions related to pappalysin cleavage of IGFBPs was compelling, reinforced by research involving protease-resistant IGFBP-4 and comprehensive cellular and in vivo experiments [42, 43]. In summary, these studies highlight the importance of IGFBP proteolysis, particularly IGFBP-4, in increasing IGF bioavailability. This process is further regulated by PAPPA inhibitors, namely stanniocalcin-1and −2. For example, the overexpression of stanniocalcin-2 in mice resulted in the formation of an inhibitory complex with PAPPA, leading to a decrease in IGFBP proteolytic activity. This ultimately reduced IGF bioavailability and resulted in significant growth inhibition. It is noteworthy that this regulation of IGF is believed to occur post-translationally at the tissue level, without impacting IGF gene expression or circulating levels [44]. Consequently, we postulated that PAPPA fostered the development of GC in intimate association with IGFBP4. To probe the biological functions of PAPPA in GC, we conducted enrichment analysis of differential genes. The outcomes of GO enrichment analysis revealed that PAPPA-related genes were implicated in cellular keratinization and associated with receptor-ligand, hormone, and neuropeptide hormone activities. GSEA findings illuminated that the elevated expression of PAPPA was predominantly concentrated in the ensuing categories of pathways. Primarily, they were abundant in Extracellular Matrix (ECM) structures and adherent plaque-associated pathways. The pathways that were abundant in extracellular matrix structures encompassed nuclear matrix pathway, extracellular matrix glycoprotein pathway, secretory factors, and other pathways. The ECM was a sophisticated complex constituted of fibronectin, glycoprotein, and other secretory factors [45]. Modifications in the structure and composition of the ECM exerted a profound influence on the functional behavior of cells and were linked to a multitude of pathological states [46]. Antecedent research has underscored those modifications in ECM-related genes profoundly impacted the histological type (intestinal, diffuse), depth of invasion, lymph node metastasis, and survival of gastric adenocarcinoma [47].

In biological processes, the adhesion of cells to the matrix was pivotal, and the creation of specialized structures at the point of contact between the cells and the extracellular matrix was denominated adhesion spots [48]. It instigated downstream signaling through the integrin-mediated FAK pathway, which impacts the reorganization of the cytoskeleton and hence tumor progression. It has been demonstrated that significant integrin-mediated FAK phosphorylation was observed in GC patients [49]. Nonetheless, therapies targeting the ECM receptor were profoundly dependent on the tumor type and the disease stage. The subsequent step was to mediate the formation of a keratinized cell envelope. In normal epidermal tissues, keratinocytes formed and shed at comparable rates. If the keratinization process was mediated upregulation, hyper keratinization was formed, which readily induced malignant tumor formation, commonly in squamous carcinomas. Ultimately, it was a process implicated in the diapedesis of the myometrium.

In this investigation, we substantiated that the manifestation degree of PAPPA could serve as a dependable prognostic determinant for GC patients, utilizing biomimetic analysis. We posited that PAPPA might function as an oncogene, contributing significantly to the evolution and advancement of GC. Concurrently, the facilitation of GC by PAPPA could potentially be correlated with ECM. Another conceivable pathway we hypothesized involves PAPPA activating IGF1R by accelerating the hydrolysis of the IGFBP4 protein, thereby instigating the development of GC. Regrettably, we did not conduct pertinent experimental analyses in this context. We delineated the potential mechanism pattern by which PAPPA facilitates GC (Fig. 10). The findings indicated that the silencing of PAPPA could stimulate apoptosis, obstruct GC cells in G1-S, and decelerate proliferation. Transwell assay and wound healing revealed that the suppression of PAPPA could impede the migration and invasion capability of GC cells.

Fig. 10
figure 10

Conjectural model diagram of the mechanism by which PAPPA promotes GC. The dotted lines represent the pathways through which PAPPA may act to promote GC. Figure created via Biorender

Full size image

Nevertheless, this study was not without its limitations. Initially, the clinical samples of GC patients from the department of general surgery in the 900th Hospital of Joint Logistics Support Force were not incorporated in this study, and the expression of PAPPA in the clinical samples from our center necessitated subsequent observation via immunohistochemical assays. Secondly, despite the observation of PAPPA promoting GC progression in this study, the specific biological mechanism remains unelucidated. To scrutinize the downstream targets of PAPPA, further research is necessitated to ascertain whether the heightened expression of PAPPA facilitates the progression of GC by modifying the tumor microenvironment through adherent plaques and ECM, or whether the effect is mediated by the IGF signaling pathway.

Conclusion

In summary, PAPPA expression was significantly upregulated in GC tissues. It regulated immune infiltrating cells and proteins of various pathways during GC, while being associated with malignant progression in GC patients. By gaining insight into the function, the high expression of PAPPA can be used as a prognostic observer of GC. It is expected to be an effective tool for GC diagnosis and treatment, as well as a new molecular target for therapy.

Availability of data and materials

No datasets were generated or analysed during the current study.

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Acknowledgements

The authors thank the staffs from the General Surgery Department at the 900th Hospital of Joint Logistics Support Force for their help.

Funding

This study was supported by the Provincial General Program [Grant Number 2024J011155] awarded to H.Z, the Guided Project of Fujian Provincial Natural Science Foundation [Grant Number 2024Y0050] and the Key Discipline Support Project for In-house Research [Grant Number 2023XKPW0], both awarded to Y.W, as well as the Qi-hang Foundation of Fujian Medical University [Grant Number 2021QH1327] owned by J.W.C.

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Author notes

  1. Shiya Yu, Shiyao Zheng, and Jinwei Qiu have contributed equally to this work.

Authors and Affiliations

  1. Department of General Surgery, The 900th Hospital of Joint Logistics Support Force, 156 Xierhuan Northern Road, Fuzhou, 350025, Fujian, People’s Republic of China

    Shiya Yu, Hongming Lin, Huiyan Chen, Junhong Wu, Weihang Wu, Jiong Chen, Jianwei Chen, Hu Zhao & Yu Wang

  2. Fuzong Clinical Medical College, Fujian Medical University, Fuzhou, 350025, Fujian, People’s Republic of China

    Shiya Yu, Hongming Lin, Xiaojuan Yu, Huiyan Chen, Junhong Wu, Weihang Wu, Jiong Chen, Jianwei Chen, Hu Zhao & Yu Wang

  3. College of Clinical Medicine for Oncology, Fujian Medical University, Fuzhou, 350004, Fujian, People’s Republic of China

    Shiyao Zheng

  4. Department of General Surgery, The 900th Hospital of Joint Logistics Support Force, Xiamen University, Fuzhou, 350025, Fujian, People’s Republic of China

    Xiaojuan Yu

  5. Department of General Surgery, The 73rd Army Group Hospital, Xiamen, 361003, Fujian, People’s Republic of China

    Jinwei Qiu

  6. Department of Gastrointestinal Surgery, Xiamen Hospital Affiliated with Fudan University Cancer Hospital, Xiamen, 361026, Fujian, People’s Republic of China

    Yongyuan Chen

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Contributions

S.Y. and H.Z. were responsible for study design and writing. Y.W. and S.Z. was involved in the study design and were responsible for scientific revision. S.Y., S.Z and J.Q. contributed the same to this paper as the co-first author. H.C., J.W., H.L. and W.W were responsible for data collection and analysis. S.Y, Y.C, J.C contributed to the image painting. All authors read and approved the final manuscript.

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Correspondence to Jianwei Chen, Hu Zhao or Yu Wang.

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Supplementary Information

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Supplementary material 1: Figure1 Relative Protein Expression of PAPPA in Gastric Cancer Cell Lines. The bar graph represents the mean relative protein expression levels of PAPPA in different gastric cancer cell lines compared to the GES-1 cell line, which is set as the reference. Error bars indicate standard deviation. Statistical significance is denoted by asterisks: **p < 0.01, ***p < 0.001. The data suggest a varied expression pattern of PAPPA among the tested cell lines, with significant upregulation in BGC823 and downregulation in MNK45 and MGC803

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Supplementary material 2: Figure2 Relative Protein Expression of PAPPA in BGC823 Cell Line Post SiRNA Treatment. The bar graph shows the mean relative protein expression levels of PAPPA in BGC823 cells treated with siNC-1and three siPAPPA constructs. The expression level in the siNC-1 group is normalized to a baseline value of 1.0. Error bars indicate the standard deviation. Statistical significance is denoted by asterisks: ***p < 0.001, indicating a highly significant decrease in PAPPA expression with siPAPPA treatment compared to the non-targeting control

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Supplementary material 3: Figure3 Relative Protein Expression of PAPPA in MGC803 Cell Line Following SiRNA Treatment. The bar chart displays the mean relative protein expression levels of PAPPA in MGC803 cells transfected with siNC-1and three siPAPPA constructs. The expression level in the siNC-1 group is set as the reference. Error bars indicate standard deviation. Statistical significance is marked with asterisks: ***p < 0.001, indicating a highly significant reduction in PAPPA expression upon treatment with siPAPPA constructs as compared to the non-targeting control

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Supplementary material 4: Figure4 PAPPA correlates with IGF and its binding proteins.PAPPA correlates with IGF1.PAPPA correlates with IGF2.PAPPA correlates with IGFBP4.PAPPA correlates with IGFBP5

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Yu, S., Zheng, S., Qiu, J. et al. Overexpression of metalloproteinase PAPPA accelerates cancer progression and correlates with immune cell infiltration in gastric cancer: insights from bioinformatics and in vitro investigations. Cancer Cell Int 25, 38 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12935-025-03650-z

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  • DOI: https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12935-025-03650-z

Keywords

Cancer Cell International

ISSN: 1475-2867

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