Cuproplasia-associated LOXL2 cooperates with Helicobacter pylori infection to promote gastric cancer progression via the FAK/Src signaling axis

Introduction

Gastric cancer (GC) is one of the most prevalent malignancies of the digestive system globally, with a high incidence rate and mortality rate, posing a serious threat to human health (1). The occurrence and development of GC are a complex process involving multiple factors and stages (2). There are numerous etiological factors, among which Helicobacter pylori (H. pylori) infection, which is closely related, is recognized by the World Health Organization (WHO) as a class 1 carcinogen in the development of GC (3). Numerous epidemiological and clinical studies have confirmed that H. pylori infection can cause chronic gastric mucosal inflammation, gastric mucosal atrophy, intestinal metaplasia, and other lesions, ultimately leading to the development of GC (4,5). The mechanism of H. pylori infection is relatively complex, mainly involving inflammatory response, abnormal cell proliferation, apoptosis inhibition, DNA damage, and epigenetic modification (6,7). However, it is worth noting that not all individuals infected with H. pylori will progress to GC; only 3% of H. pylori-infected patients ultimately develop GC (8,9).

Cuproplasia is a newly proposed concept in recent years, referring to the phenomenon of cell growth, proliferation, and phenotypic transformation that relies critically on copper ions. It includes hyperplasia (an increase in cell number), metaplasia (a change in cell type), and neoplasia (malignant transformation of cells) (10). Research evidence indicates that disturbances in intracellular copper homeostasis directly promote the occurrence and progression of tumors such as breast cancer, lung cancer, colorectal cancer, and GC (11-14). Tumor cells require higher levels of copper than normal cells to support their energy needs. Since copper is a co-factor for MT-CO1 and MT-CO2, this metal is crucial for mitochondrial respiration and adenosine triphosphate (ATP) synthesis, especially during rapid division of cancer cells. Copper can replace zinc in p53, leading to misfolding of the protein and loss of its tumor suppressor activity, and may also increase the probability of mutations in TP53 (the gene encoding p53), resulting in abnormalities in tumor suppressor proteins (15). As an emerging concept recently proposed to describe copper-dependent tumor growth and progression, cuproplasia provides a complementary perspective to cuproptosis for investigating the role of copper metabolism in cancer biology. Therefore, the cuproplasia framework was adopted in the present study to explore the potential contribution of copper-associated genes to H. pylori-related gastric carcinogenesis.

LOX and LOXL are both copper enzymes associated with the invasion and migration of various types of tumors. LOX and LOXL can oxidize lysine residues and generate aldehyde derivatives, which in turn trigger the activation of PTK2 signaling, leading to the activation of transcription factors and promoting tumor cell proliferation, migration, invasion, and distant metastasis (16). The persistent colonization of H. pylori in gastric mucosa may induce chronic inflammation and immune imbalance, accompanied by the release of a large amount of reactive oxygen species (ROS) and inflammatory cytokines, leading to epithelial DNA damage and abnormal proliferation (17,18). Simultaneously, cuproplasia disrupts copper homeostasis, further exacerbating oxidative stress and promoting the continuous activation of multiple cancer-promoting signaling pathways, interfering with the function of tumor suppressor proteins. The combined effect of these factors not only accelerates the accumulation of gene mutations but also enhances immune escape by upregulating immune checkpoint molecules (PD-L1) or affecting immune cell phenotypes, making the gastric mucosa more prone to malignant transformation on the basis of inflammatory damage and abnormal proliferation (19,20). Consequently, the lesions caused by H. pylori infection, coupled with the energy and signal support provided by cuproplasia, jointly lead to the development of GC.

In this study, we retrieved GC data from the public database and successfully constructed a risk nomogram model for H. pylori-positive GC based on five cuproplasia-related genes. The clinical predictive efficacy of this model was validated through various methods. Additionally, we identified a significant biological marker, LOXL2. Then, immunohistochemistry (IHC) and Western blotting demonstrated a gradual increase in LOXL2 expression during the progression of H. pylori-infected GC. Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) suggested that the messenger RNA (mRNA) expression level of LOXL2 in GC tissues is higher than that in normal adjacent tissues. Positive expression of LOXL2 in GC tissues is related to tumor size, tumor-node-metastasis (TNM) staging, differentiation degree, lymph node involvement, distant metastasis, and H. pylori positivity in GC patients. Furthermore, through Western blotting, we identified the high-expression strain HGC27 and the low-expression strain SNU-1, and successfully constructed overexpression and knockdown models. Through cell cloning experiments, scratch experiments, and Transwell experiments, we found that overexpression of LOXL2 significantly enhances the proliferation, migration, and invasion abilities of GC cells, while knockdown of LOXL2 expression has an inhibitory effect. This phenomenon is further pronounced after H. pylori infection. Finally, we found that the mechanism by which LOXL2 promotes the progression of GC may be through inducing FAK/Src phosphorylation, inhibiting E-cadherin, and promoting Snail, thereby facilitating the occurrence of epithelial-mesenchymal transition (EMT), indicating the important role of LOXL2 in H. pylori-infected GC and its potential as a new therapeutic target. Although the prognostic significance of LOXL2 in GC has been previously reported, its role in H. pylori-associated GC progression and the underlying molecular mechanisms remain incompletely understood. Therefore, the present study focused on investigating the biological role of LOXL2 and its interaction with H. pylori infection in GC. We present this article in accordance with the TRIPOD and MDAR reporting checklists (available at https://jgo.amegroups.com/article/view/10.21037/jgo-2026-0380/rc).

Methods

Gene acquisition and screening

RNA sequencing (RNA-seq) data of patients with stomach adenocarcinoma (STAD) were obtained from The Cancer Genome Atlas (TCGA) database. Cuproplasia-associated genes (CAGs) were collated and screened from literature databases such as PubMed and Web of Science. After clarifying the sample grouping and copper-related gene list, we utilized the commonly used differential expression analysis packages in R language, namely limma or DESeq2, to perform pairwise comparisons among the three groups of samples, with special attention paid to the gene expression differences between the H. pylori-positive tumor group [H. pylori-positive tumor tissue (Hp⁺ T)] and the H. pylori-positive normal group [H. pylori-positive normal tissue (Hp⁺ N)].

Construction and validation of risk prediction model

Based on the expression data from the H. pylori-positive GC tissue group (Hp⁺ T; n=163) and the normal tissue group (Hp⁺ N; n=29), a model was constructed. The samples were randomly divided into a training set (n=115) and a validation set (n=77) in a 3:2 ratio for further analysis. In the training set, the least absolute shrinkage and selection operator (LASSO) algorithm was performed to obtain the corresponding regression coefficients (β). The risk score for each patient was calculated using the following formula: risk score = exp(β₁X₁ + β₂X₂ + … + β_nX_n). Here, β represents the coefficient obtained from the LASSO regression analysis, and X represents the expression level of the corresponding gene. Subsequently, using the risk score, gender, and age as predictive variables, a logistic regression model was constructed to predict the probability of GC occurrence in H. pylori-positive patients. A forest plot was generated to show the contribution of each variable to the risk, and a nomogram was ultimately constructed. To evaluate the predictive ability of the model, we assessed its practicality and generalizability from the perspective of clinical benefit through receiver operating characteristic (ROC) curves, area under the curve (AUC), and decision curve analysis (DCA). Finally, based on the median risk score, patients were divided into high- and low-risk groups. Kaplan-Meier (KM) survival analysis was performed to compare the overall survival (OS) of the two groups, exploring whether the risk score had prognostic value.

Functional enrichment analysis of differential genes

The selected differentially expressed genes (DEGs) were subjected to Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. The analysis results were sorted according to P values, with the screening criterion set at P<0.05. Additionally, the Metascape platform (https://metascape.org) was utilized for further functional enrichment analysis and visual representation of the aforementioned genes.

Clinical tissue specimens

Gastric mucosal tissue specimens at different stages were obtained from patients who underwent gastroscopy and pathological biopsy at The First Affiliated Hospital of Soochow University. These specimens were divided into two groups based on whether there was a history of H. pylori infection, with each group containing information from three stages: gastritis, intestinal metaplasia, and tumor. Gastroscopy reports were obtained from the Department of Gastroenterology at The First Affiliated Hospital of Soochow University, and corresponding pathological reports and specimens were collected from the Department of Pathology at The First Affiliated Hospital of Soochow University. Inclusion criteria: (I) the patient’s clinical and pathological information is sufficient for this experiment; (II) the patient had not received any treatment for H. pylori before the examination; (III) the patient had a corresponding pathological report and meets the group screening criteria, without any comorbidities or malignancies; and (IV) prior to inclusion, informed consent was obtained from the patient and their family members. A total of 180 samples were included.

Tissue specimens from GC patients were obtained from those who underwent inpatient surgery at The First Affiliated Hospital of Soochow University between September 2021 and February 2024. Tissues located more than 5 cm away from the edge of the cancerous tissue during surgery were considered adjacent normal tissues and were confirmed by a pathologist’s report to be free of cancer cell infiltration or invasion. All collected tissue specimens were collected under sterile conditions within 30 minutes after tumor removal and stored in a −80 ℃ liquid nitrogen tank. Inclusion criteria: (I) the patient’s clinical pathological information is sufficient for this experiment; (II) the patient did not undergo radiotherapy, neoadjuvant therapy, or immunotherapy before surgery; (III) postoperative pathology confirmed GC without any comorbidities such as other malignancies or systemic major diseases; and (IV) prior to inclusion, informed consent was obtained from the patient and their family members. A total of 100 cases were included in the data.

IHC

Fix the gastric tissue with 10% formalin solution, perform routine dehydration and paraffin embedding. Prepare 5 µm thick tissue sections. Dewax with xylene, dehydrate with anhydrous ethanol and gradient alcohol. Perform high-temperature antigen retrieval with antigen retrieval solution for 20 minutes, wash with phosphate-buffered saline (PBS) for 5 minutes × 3 times. Incubate with 3% H₂O₂ at room temperature in the dark for 15 minutes, wash with PBS for 5 minutes × 3 times. Incubate with 3% bovine serum albumin (BSA) solution at 37 ℃ in the dark for 45 minutes. Add LOXL2 primary antibody (1:800; Abcam, Waltham, MA, USA) and incubate in the dark at 4 ℃ overnight. Add secondary antibody (1:500; Abcam) and incubate at 37 ℃ for 1 hour. Stain with 3,3’-diaminobenzidine (DAB) solution, rinse with PBS, and then counterstain with hematoxylin for 1–2 minutes. Dehydrate with anhydrous ethanol, dry, and then add neutral gum for mounting. Take photos under a microscope.

Result judgment criteria: LOXL2 is primarily localized in the nuclei and extracellular matrix of gastric mucosal tissues. Positive staining is characterized by brown or brownish-yellow staining in the nuclei and extracellular regions. Three high-power fields (20×) are randomly selected from each slide for evaluation. A semi-quantitative scoring system is used based on staining intensity and the proportion of positive cells: staining intensity scoring: strong positive (3 points), moderate staining (2 points), weak positive (1 point), and no staining (0 points); proportion of positive cells scoring: >75% positive cells (4 points), 51% to 75% positive cells (3 points), 25% to 50% positive cells (2 points), and <25% positive cells (1 point). The final score is the product of the two scores mentioned above. The interpretation of the results is as follows: a comprehensive score of ≥3 points is considered positive expression (+), and <3 points is considered negative expression (−).

Quantitative real-time polymerase chain reaction (PCR)

Tumor cells and their corresponding normal adjacent tissues were stored at -80°C via liquid nitrogen flash freezing. Prior to the experiment, the specimens were retrieved and promptly ground on ice, or directly cultured cells in logarithmic growth phase were used. After lysis with buffer RL, samples were prepared for extraction. RNA was extracted following the instructions provided in the reagent kit (Novavax Biotech Co., Ltd., Nanjing, China), and the concentration and purity were confirmed to be satisfactory using a spectrophotometer. Reverse transcription was performed using a reaction system consisting of 4 µL of 5× reverse transcription mix, 1 µL of 20× genomic DNA (gDNA) remover, an appropriate amount of RNA template, and RNase-free water to make up the total reaction volume to 20 µL. The reaction program was set as follows: 37 ℃ for 2 minutes to remove residual gDNA, 55 ℃ for 15 minutes for reverse transcription, 85 ℃ for 5 minutes to terminate the reaction, and finally maintained at 4 ℃. The quantitative PCR (qPCR) reaction system consisted of a 10-fold dilution of complementary DNA (cDNA) template, 2.5 µL of cDNA, 2.5 µL of primer, and 5 µL of SYBR. Glyceraldehyde 3-phosphate dehydrogenase (GAPDH; Rui’an Biotechnology Co., Ltd., Suzhou, China): forward “GAGTCAACGGATTTGGTCGT”; reverse: “GACAAGCTTCCCGTTCTCAG”. LOXL2 (Rui’an Biotechnology Co., Ltd.): forward “TGTACCGCCATGACATCGAC”; reverse: “TAGCGGCTCCTGCATTTCAT”. The relative expression level of mRNA was calculated using the 2^−ΔΔCt method.

Western blotting

Tissue was lysed using radioimmunoprecipitation assay (RIPA) lysis buffer, and phenylmethylsulfonyl fluoride (PMSF) and protease/phosphatase inhibitors were added. The lysate was incubated on ice for 30 minutes. After centrifugation at 12,000 rpm for 15 minutes, the supernatant was collected and the protein concentration was measured. Loading buffer was added, and the mixture was heated in a metal bath at 95 ℃ for 5 minutes to fully denature the proteins. A 10% sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) was used for electrophoresis, and a 10× Tris-glycine-SDS electrophoresis buffer was prepared. Using a wet transfer method, the membrane was transferred to a polyvinylidene difluoride (PVDF) membrane using 10× Tris-glycine transfer buffer at 300 mA for 90 minutes. The membrane was blocked with 5% skim milk powder or BSA solution at room temperature for 1 hour. A dilution of LOXL2 antibody (1:1,000; Abcam) was prepared, and the primary antibody was added to the PVDF membrane and incubated overnight at 4 ℃. Horseradish peroxidase (HRP)-labeled secondary antibody (1:10,000; Abcam) was added and incubated at 37 ℃ for 1 hour. The membrane was washed five times with PBS for 5 minutes each, and the water was absorbed before adding enhanced chemiluminescence (ECL) reagent. Detection was performed using a ChemiDoc imaging system. ImageJ software was used for grayscale analysis to calculate the relative expression level of LOXL2 protein. The experiment was repeated three times.

Cell culture

Two human gastric-derived cell lines were screened out through the online website (proteinatlas.org), including HGC27 and SNU-1, purchased from Wuhan Procell Life Science & Technology Co., Ltd. (Wuhan, China). Culture system: add 50 mL fetal bovine serum (FBS) and 5 mL penicillin-streptomycin mixture to 450 mL RPMI-1640 basic medium, mix well, filter through a 0.22 µm sterile filter, and store at 4 ℃ for future use. Culture conditions: cells were cultured in a cell incubator at 37 ℃, 5% CO₂, and 95% relative humidity.

Cell transfection

24 hours before the experiment, SNU-1 and HGC27 cells were seeded into 24-well plates, ensuring that the cell density was controlled at around 70% during transfection. Each well was filled with 0.45 mL of antibiotic-free RPMI-1640 complete medium. SNU-1 cells were performed to construct an LOXL2 overexpression model by transfecting the pcDNA3.1-LOXL2 plasmid (Genemac Biotechnology Co., Ltd., Suzhou, China). HGC27 cells were performed to construct an LOXL2 knockdown model by transfecting the sh-LOXL2 interference vector (Genemac Biotechnology Co., Ltd.).

Each well uses 0.67 µg of plasmid or short hairpin RNA (shRNA) fragment, diluted with 25 µL of serum-free RPMI-1640 medium, and mixed evenly for use. Another 5 µL of Lipofectamine™ 2000 transfection reagent is taken, and the plasmid dilution solution and transfection reagent mixture are combined. After gently pipetting and mixing, they are incubated at room temperature for 20 minutes. The prepared transfection complex (50 µL) is added to the cell suspension in each well, and the cells are then placed in the incubator for further incubation. After transfection, the cells are cultured for 24 to 72 hours for sample collection and functional testing. The successfully transfected cells are used for subsequent colony formation.

Treatment with H. pylori supernatant

The standard strain of H. pylori ATCC 43504 was inoculated into Brucella liquid medium and cultured under microaerobic conditions (5% O₂, 10% CO₂, 85% N₂) at 37 ℃ for 72 hours. After the incubation, the bacterial culture medium was transferred to sterile centrifuge tubes and centrifuged at 4,000 rpm for 10 minutes to collect the supernatant. The collected supernatant was sterilized and filtered using a 0.22 µm sterile filter membrane to obtain H. pylori supernatant (Hp supernatant). After 24 hours of cell transfection, the adherent HGC27 and SNU-1 cells were respectively added to the H. pylori supernatant for stimulation treatment. The control group was treated with an equal volume of sterile Brucella medium. The cells were then incubated in the incubator for 24 to 48 hours. During this period, if the cell status remained stable and there was no significant toxic damage, the cells could be used for subsequent colony formation experiments.

Colony formation assay

Select the cells HGC27 and SNU-1 that have undergone transfection treatment, and divide them into two groups based on whether they receive stimulation from H. pylori supernatant: the control group and the experimental group. After preparing the cells into a single-cell suspension, adjust the cell concentration to 500 cells/mL. Add 1 mL of cell suspension to each well of a six-well plate, ensuring that 200 cells are seeded per well, with three parallel replicates for each group. Place the cell culture plate in an incubator and let it incubate for 10 to 14 days, during which fresh medium can be added appropriately based on the cell status, but the original medium should not be replaced. When cell colonies are visible to the naked eye (individual cell colonies >50 cells), discard the medium and gently wash twice with PBS buffer to remove floating cells. Add 1 mL of methanol to each well for fixation for 15 minutes, discard the fixative, and allow natural air drying. Add 0.1% crystal violet staining solution to each well at 1 mL, stain at room temperature in the dark for 20 minutes, and then thoroughly wash with PBS until the background is clean. Observe the staining results using an inverted microscope and take photos for recording. Count the number of colonies in each well.

Wound healing assay

Four groups of cells were incubated in six-well plates at 37 ℃ for 24 hours with 1×10⁶ cells per well in the presence of 5% CO₂. Subsequently, two parallel straight lines were created in the uniform monolayer after two washes with PBS using the tip of a 200 µL pipette. RPMI-1640 medium without FBS was added for further incubation. Inverted microscopes were performed to capture wound healing images at 0, 6, 12, and 18 hours for measuring wound healing width. Each measurement was conducted in triplicate.

Transwell migration assay

Take out the cultured cells and resuspend them to a cell suspension of 20×10⁴/mL. After air-drying the Transwell chamber (Abcam), place it in a 24-well plate. Add 100 µL of cell suspension to the upper chamber and 300 uL of 1640 medium to the lower chamber, and incubate for 24 hours. After incubation, remove the chamber and discard the cell supernatant and RPMI-1640 medium from both the upper and lower chambers. Wash three times with PBS. Add 4% paraformaldehyde to immerse the bottom of the chamber, stain at room temperature for 30 minutes, and then stain with 0.1% crystal violet for 20 minutes. After washing with PBS and air-drying, randomly select five fields under the microscope for cell counting and statistics.

Statistical analysis

Statistical analyses were performed using GraphPad Prism 9.01 and SPSS 26.0 software. Quantitative data are presented as mean ± standard deviation (SD). The normality of continuous variables and homogeneity of variances were assessed before performing parametric analyses. Inter-group comparisons of quantitative data were analyzed using t-tests. Inter-group comparisons of qualitative data were analyzed using Chi-squared tests. Because the analyses performed in the present study were primarily based on predefined pairwise comparisons rather than exploratory multiple testing, multiple comparison correction was not routinely applied. A P value <0.05 suggested statistical significance.

Results

Screening of DEGs associated with cuproplasia

By analyzing four sets of data: GC tissues with positive H. pylori infection (Hp⁺ T; n=163), adjacent non-cancerous tissues with positive H. pylori infection (Hp⁺ N; n=29), GC tissues with negative H. pylori infection [H. pylori-negative tumor tissue (Hp⁻ T), n=249], and adjacent non-cancerous tissues with negative H. pylori infection [H. pylori-negative normal tissue (Hp⁻ N), n=7], in which the H. pylori-negative cohorts were incorporated as internal negative controls, differential expression analysis was conducted for three pairs of samples: Hp⁺ T vs. Hp⁺ N, Hp⁺ T vs. Hp⁻ T, and Hp⁺ N vs. Hp⁻ N. The screening criteria were |log fold change (logFC)| ≥0.5 and P value <0.05. As a result, 496 DEGs were identified that were commonly expressed across all stages of H. pylori infection (Figure 1A). By intersecting these DEGs with 141 CAGs collated from the literature, nine CAGs associated with H. pylori infection were identified (Figure 1B).

Figure 1 Flowchart for screening DEGs related to cuproplasia. (A) Venn diagram of the results of differential expression analysis among three groups. (B) Nine DEGs related to copper proliferation and H. pylori infection were obtained by intersecting the two sets of data. CAG, cuproplasia-associated gene; DE-CAG, differentially expressed cuproplasia-associated gene; DEG, differentially expressed gene; H. pylori, Helicobacter pylori; Hp⁺ N, H. pylori-positive normal tissue; Hp⁺ T, H. pylori-positive tumor tissue; Hp⁻ N, H. pylori-negative normal tissue; Hp⁻ T, H. pylori-negative tumor tissue.

Screening of target gene and construction of a risk prediction model

In the training set, five CAGs significantly associated with the occurrence of GC caused by H. pylori infection and their risk coefficient graphs were obtained through LASSO regression analysis and cross-validation (Figure 2A,2B). The five CAGs ultimately identified were: AOC3, LOXL2, PDGFRB, SOD3, and SPARC. The risk score was calculated using the coefficients obtained from the LASSO regression model, with the formula as follows: risk score = −0.084712 × AOC3 expression value + 0.290105 × LOXL2 expression value + 0.039206 × PDGFRB expression value − 0.057207 × SOD3 expression value + 0.178017 × SPARC expression value.

Figure 2 Construction of hub genes related to the occurrence of GC with H. pylori infection and risk prediction model. (A) LASSO regression analysis. (B) Cross-validation. The differently colored curves represent the coefficient trajectories of the candidate cuproplasia-associated genes across the LASSO regularization path (logλ); each colored curve corresponds to one gene. (C) Volcano plot of differential genes. (D) Heatmap of five copper proliferation-related genes. (E) Univariate logistic regression forest plot. CI, confidence interval; GC, gastric cancer; H. pylori, Helicobacter pylori; LASSO, least absolute shrinkage and selection operator; OR, odds ratio.

The differential expression of the aforementioned genes was then visualized through a volcano plot (Figure 2C). The results suggested that LOXL2, SPARC, and PDGFRB were upregulated, while AOC3 and SOD3 were downregulated. Notably, LOXL2 stood out in terms of both expression level and statistical significance. Furthermore, a heatmap of DEGs was constructed, illustrating the differential expression of these five key genes in H. pylori-infected GC tissues and adjacent normal tissues (Figure 2D). Compared to normal tissues, LOXL2, PDGFRB, and SPARC genes were significantly upregulated in tumor tissues, whereas AOC3 and SOD3 were more highly expressed in normal tissues, showing a downward trend. Logistic regression analysis was conducted to evaluate the predictive effect of risk score, gender, and age on the occurrence of H. pylori-infected GC. The results revealed that risk score was the only statistically significant independent predictor [odds ratio (OR) =4.18; 95% confidence interval (CI): 2.76–6.33; P<0.001], while the P values for gender and age were 0.128 and 0.213, respectively, indicating no statistical significance (Figure 2E).

Validation of risk prediction model

ROC analysis was performed to evaluate the discriminatory ability of the prediction model. ROC curves were plotted for both the training and validation sets. The AUC values were 0.977 in the training set and 0.952 in the validation set, indicating that the model had excellent ability to distinguish patients with H. pylori-associated GC from controls (Figure 3A,3B). To facilitate individualized risk assessment, a nomogram was constructed to provide a visual and clinically applicable tool for estimating the probability of GC occurrence in patients with H. pylori infection (Figure 3C). This enhanced the practical operability of the model in clinical settings. DCA curves were plotted for both the training set and the validation set (Figure 3D,3E). DCA was performed to evaluate the potential clinical utility of the prediction model. The results demonstrated that, across most threshold probability ranges, the prediction model provided greater net clinical benefit than either treating all patients or treating no patients.

Figure 3 Validation of the risk prediction model. (A,B) ROC curves and AUC values for the training and validation sets. (C) Nomogram for predicting the risk of GC development in H. pylori infection based on gene risk score. (D,E) DCA curves for the training and validation sets. AUC, area under the curve; DCA, decision curve analysis; GC, gastric cancer; H. pylori, Helicobacter pylori; ROC, receiver operating characteristic.

The relationship between risk prediction models and prognosis

To investigate whether this risk score has an impact on prognosis, the median risk score was performed to distinguish between high- and low-risk groups. The KM curve demonstrated that in both the training and validation sets, the OS of the high-risk group was significantly shorter than that of the low-risk group, and the difference was statistically significant (P<0.05) (Figure 4A,4B).

Figure 4 Relationship between risk prediction model and prognosis. (A) KM curve in training set. (B) KM curve in validation set. KM, Kaplan-Meier.

Enrichment analysis

The GO enrichment analysis revealed that these genes were significantly enriched in multiple GO terms closely related to tumorigenesis, oxidative stress, and metal ion metabolism. In terms of biological processes (BPs), the DEGs were significantly enriched in pathways such as “copper ion response”, “epithelial cell proliferation”, “reactive oxygen species metabolic process”, and “oxidative stress response”, suggesting that these genes may participate in the growth and proliferation of tumor cells by regulating copper metabolism and cellular redox balance. In terms of cellular components (CCs), the enrichment analysis results suggested that these genes were mainly localized in subcellular structural regions such as “extracellular matrix”, “secretory granule lumen”, and “Golgi complex lumen”, indicating their potential key role in regulating tumor microenvironment construction, cellular secretion function, and protein processing and modification. In terms of molecular functions (MFs), the related genes were significantly enriched in “copper ion binding”, “antioxidant activity”, and “growth factor binding”, reflecting their biological roles in maintaining metal ion homeostasis, eliminating oxidative stress products, and mediating extracellular signal transduction (Figure 5A). The KEGG analysis results demonstrated that these genes were significantly enriched in multiple classic tumor-related signaling pathways such as the PI3K-Akt signaling pathway, MAPK signaling pathway, Ras signaling pathway, JAK-STAT signaling pathway, and cancer-related microRNAs, suggesting that cuproplasia may further promote the malignant progression of tumor cells by affecting their metabolism (Figure 5B). A more in-depth functional network analysis and visualization were conducted through the Metascape platform. These genes were significantly enriched in BPs and pathways closely related to tumorigenesis and progression, such as copper ion response, oxidative stress response, cell migration regulation, and reactive oxygen species metabolic process regulation. These results are consistent with the aforementioned GO and KEGG enrichment analysis results, further supporting the hypothesis that the screened genes play an important role in the development of H. pylori-associated GC (Figure 5C,5D).

Figure 5 Enrichment analysis further revealed the role of pathways such as copper metabolism in the development of H. pylori-associated GC. (A) GO enrichment analysis. (B) KEGG enrichment analysis. (C) Metascape analysis diagram. (D) Metascape analysis diagram. BP, biological process; CC, cellular component; GC, gastric cancer; GO, Gene Ontology; H. pylori, Helicobacter pylori; KEGG, Kyoto Encyclopedia of Genes and Genomes; MF, molecular function.

Expression status of LOXL2 in public databases and experimental verification

Using gene expression data from the TCGA and GTEx databases on the Gene Expression Profiling Interactive Analysis 2 (GEPIA2) website, we analyzed the differential expression of LOXL2 across different tumors. It was observed that LOXL2 has higher expression levels in tissues such as GC (STAD), pancreatic cancer [pancreatic adenocarcinoma (PAAD)], renal clear cell carcinoma [kidney renal clear cell carcinoma (KIRC)], and hepatocellular carcinoma [liver hepatocellular carcinoma (LIHC)] compared to normal tissues. The expression difference of LOXL2 in STAD was statistically significant (P<0.05) (Figure 6A). The results of RT-qPCR demonstrated that the relative mRNA expression level of LOXL2 in GC tissues was significantly higher than that in adjacent normal tissues, and the difference was statistically significant (P<0.001) (Figure 6B). Online KM plotter analysis revealed that patients with high LOXL2 expression had significantly lower OS and progression-free survival (PFS) compared to those with low expression, and the differences were statistically significant (P<0.001) (Figure 6C).

Figure 6 Analysis of LOXL2 expression level in GC and its correlation with patient prognosis. (A) GEPIA2 online analysis reveals differential expression of LOXL2 between different tumors and normal tissues, with red indicating significantly upregulated expression. LOXL2 is significantly upregulated in GC tissues. (B) Verification in tissue specimens shows that LOXL2 mRNA is significantly overexpressed in GC tissues. (C) Online KM plotter analysis revealed that patients with high LOXL2 expression exhibited significantly shorter OS and PFS than those with low LOXL2 expression (P<0.001), suggesting a potential association between elevated LOXL2 expression and unfavorable prognosis in GC. *, P<0.05; ***, P<0.001. CI, confidence interval; GC, gastric cancer; GEPIA2, Gene Expression Profiling Interactive Analysis 2; HR, hazard ratio; KM, Kaplan-Meier; mRNA, messenger RNA; N, normal; OS, overall survival; PFS, progression-free survival; T, tumor; TPM, transcripts per million.

The expression levels of LOXL2 protein in GC tissues

Immunohistochemical detection was performed to assess the expression of LOXL2 in various gastric mucosal tissue specimens. Microscopic observation revealed that positive staining was primarily located in the extracellular matrix and nuclei. Further examination suggested that LOXL2 expression was higher in GC tissues compared to intestinal metaplasia tissues, which in turn had higher expression than gastritis tissues. This phenomenon was more pronounced in the context of H. pylori infection and was statistically significant (P<0.05) (Figure 7A,7B, Table 1). Western blotting results suggested that the protein content of LOXL2 gradually increased during the progression of H. pylori-infected GC (Figure 7C).

Figure 7 Expression differences of LOXL2 in different gastric mucosal tissues. (A,B) Immunohistochemical detection of LOXL2 expression levels in different gastric mucosal tissues. (C) Western blotting detection of LOXL2 expression levels in different gastric mucosal tissues. ****, P<0.0001. CG, chronic gastritis; H&E, hematoxylin and eosin; H. pylori, Helicobacter pylori; Hp, Helicobacter pylori; Hp⁺ CG, H. pylori-positive chronic gastritis tissue; Hp⁺ IM, H. pylori-positive intestinal metaplasia tissue; Hp⁺ T, H. pylori-positive tumor tissue; Hp⁻ CG, H. pylori-negative chronic gastritis tissue; Hp⁻ IM, H. pylori-negative intestinal metaplasia tissue; Hp⁻ T, H. pylori-negative tumor tissue; IHC, immunohistochemistry; IM, intestinal metaplasia; T, tumor.

The relationship between the expression of LOXL2 in GC tissues and the clinicopathological characteristics of patients

The expression of LOXL2 was significantly elevated in GC tissues, and its positive expression was closely correlated with multiple clinical pathological characteristics. In terms of tumor size, the positive expression rate of LOXL2 in the group with tumor diameter ≤5 cm was 43.5%, while it significantly increased to 64.8% in the group with tumor diameter >5 cm. In terms of histological differentiation, the positive rate of LOXL2 in undifferentiated or poorly differentiated tissues was 68.3%, which was significantly higher than that in moderately and well-differentiated tissues (35.0%) (P<0.05). As the TNM staging of the tumor progresses, the positive expression of LOXL2 gradually increased: the positive expression rate in patients with stages I–II was 33.3%, while it reached 67.2% in patients with stages III–IV. In terms of lymph node metastasis status, among patients with lymph node metastasis, 76.2% (45 cases) were LOXL2 positive, while in patients without lymph node metastasis, the proportion was 29.4% (10 cases); the difference is statistically significant (P<0.05). In terms of distant metastasis, the expression of LOXL2 also demonstrated a similar trend. Among patients with distant metastasis, 72.7% (24 cases) were LOXL2 positive, while in patients without distant metastasis, the proportion was 46.3% (P<0.05). Furthermore, in terms of H. pylori infection status, the expression level of LOXL2 in H. pylori-positive patients was also significantly higher than that in H. pylori-negative patients, suggesting its potential involvement in the development and progression of H. pylori-associated GC (Table 2).

Construction and validation of LOXL2 gene overexpression and knockdown

The pcDNA3.1-LOXL2 overexpression plasmid was transfected into SNU-1 cells, and the shRNA interference fragment (sh-LOXL2) was transfected into HGC27 cells, with empty vector or negative control serving as the control group, to construct a functional regulation model of the LOXL2 gene. Western blot results suggested that LOXL2 protein expression was significantly upregulated in SNU-1 cells after transfection, while LOXL2 expression was effectively inhibited in HGC27 cells, both achieving statistically significant differences (P<0.01). This demonstrated that both overexpression and knockdown of the LOXL2 gene were successful (Figure 8A). Cells were stimulated with the supernatant of the ATCC 43504 strain culture. The SNU-1 oe-LOXL2 group and HGC27 sh-LOXL2 group were treated with H. pylori supernatant and compared with the untreated control group. Western blot results demonstrated that after H. pylori stimulation, LOXL2 protein expression was upregulated in both cell lines, suggesting that H. pylori may be involved in regulating LOXL2 expression (Figure 8B).

Figure 8 Construction and transfection efficiency verification of LOXL2 overexpression and knockdown models. (A) Protein expression level of LOXL2 after transfection. (B) Expression level of LOXL2 after treatment with H. pylori supernatant. H. pylori, Helicobacter pylori; Hp⁺, H. pylori-positive; NC, negative control; oe, overexpression; sh, short hairpin RNA.

LOXL2 and H. pylori coordinate to promote the proliferation, migration, and invasion of GC cells

To further verify the effects of LOXL2 and H. pylori stimulation on the proliferation, migration, and invasion abilities of GC cells, and to explore the synergistic effect of H. pylori stimulation in this process, a colony formation assay, scratch assay, and Transwell assay were conducted in the SNU-1 oe-LOXL2 group and HGC27 sh-LOXL2 group, respectively. The colony formation assay results demonstrated that LOXL2 overexpression significantly enhanced the colony-forming ability (P<0.0001), and Hp stimulation further promoted the number of colonies (P<0.001); LOXL2 knockdown significantly inhibited colony formation (P<0.0001), and although H. pylori stimulation slightly improved it, the difference was not statistically significant (Figure 9A). The scratch assay found that LOXL2 knockdown significantly inhibited the migration ability of SNU-1 cells (P<0.01), while H. pylori stimulation enhanced the migration ability of SNU-1 cells (P<0.01) (Figure 9B). The Transwell assay revealed that LOXL2 overexpression promoted the invasion of SNU-1 cells, and H. pylori stimulation further enhanced their invasive ability (P<0.001); LOXL2 knockdown inhibited the invasion of HGC27 cells, and H. pylori stimulation enhanced the invasive ability of SNU-1 cells (P<0.001) (Figure 9C). The above results suggested that LOXL2 plays a promoting role in the proliferation, migration, and invasion of GC cells, and H. pylori stimulation can enhance these effects associated with LOXL2, suggesting a possible synergistic promotion mechanism between the two.

Figure 9 LOXL2 promotes the proliferation, invasion, and migration of GC cells. (A) Cell colony formation assay was used to evaluate the proliferation ability of SNU-1 and HGC27 cells under both with and without H. pylori stimulation. (B) Scratch assay was used to evaluate the migration ability of HGC27 cells under both with and without H. pylori stimulation. (C) Transwell assay was used to evaluate the invasion ability of SNU-1 and HGC27 cells under both with and without H. pylori stimulation. Staining: (A) 0.1% crystal violet after methanol fixation (colony formation assay); (C) 0.1% crystal violet after 4% paraformaldehyde fixation (Transwell assay). Magnification: (B) 100×; (C) 200×. *, P<0.05; **, P<0.01; ***, P<0.001; ****, P<0.0001. GC, gastric cancer; H. pylori, Helicobacter pylori; Hp⁺, H. pylori-positive; NC, negative control; oe, overexpression; sh, short hairpin RNA.

Possible mechanism of coordinated promotion of GC progression by LOXL2 and H. pylori

To further explore the possible mechanism by which LOXL2 promotes the progression of GC and whether H. pylori has a synergistic effect, we investigated the effects of overexpressing and knocking down LOXL2, as well as H. pylori stimulation, on SNU-1 and HGC27 cells. The results demonstrated that overexpressing LOXL2 promoted the expression and phosphorylation of FAK/Src, and H. pylori stimulation further enhanced this effect. Activation of FAK/Src is known to promote EMT (21). Further research revealed that overexpressing LOXL2 promotes the expression of snail, one of the main transcription factors of EMT, and inhibits the expression of E-cadherin, the epithelial phenotype of EMT (22). H. pylori stimulation can also further enhance this effect. Knocking down LOXL2 significantly inhibits the expression and phosphorylation of FAK/Src, suppresses the expression of snail, and promotes the expression of E-cadherin. However, H. pylori stimulation can partially reverse these effects (Figure 10). These results suggested that LOXL2 and H. pylori can synergize to promote EMT by inducing the activation of FAK/Src, ultimately promoting the malignant progression of GC.

Figure 10 LOXL2 promotes EMT by activating the FAK/Src pathway, which promotes the expression of Snail and inhibits the expression of E-cadherin. EMT, epithelial-mesechymal transition; Hp, Helicobacter pylori; NC, negative control; oe, overexpression; sh, short hairpin RNA.

Discussion

The occurrence and development of GC is a complex process driven by environmental factors, pathogen infection, and host molecular abnormalities (23). Although H. pylori infection is common in the general population, only a small percentage of people eventually progress to GC, indicating that H. pylori is not the only determinant of GC occurrence (24). Host genetic background, molecular abnormalities, and changes in tumor microenvironment also play important roles in disease progression (25). The current clinical screening for GC mainly relies on gastroscopy, tissue biopsy, and routine tumor markers. However, in H. pylori-positive populations, these methods still have insufficient sensitivity and specificity for early identification of high-risk individuals (26). H. pylori-positive GC is often diagnosed only after clinical features are obvious, leading to many patients losing the opportunity for cure when discovered. Despite the continuous development of imaging, molecular diagnosis, and other technologies in recent years, and the development of various prediction models in existing research, there is still a lack of a convenient, effective, and scalable prediction method for the risk of H. pylori-positive GC in clinical practice (27). Therefore, the search for novel molecular markers that can reflect the risk and malignant progression of H. pylori-related GC is of great significance for improving early screening efficiency and achieving precise stratified management.

Copper, as an important trace element involved in cellular metabolism and redox reactions, has been found in recent years that its steady-state imbalance may promote tumor development through various mechanisms such as oxidative stress, cell migration, and inflammatory response (28,29). This study focuses on cuproplasia-related genes and further focuses on LOXL2, attempting to explain the development and progression of H. pylori-related GC from the perspective of the “infection metabolism tumor” interaction. In this study, a H. pylori-positive GC risk prediction model was constructed based on five cuproplasia-related genes and demonstrated high discriminatory efficacy. The AUC of the training set and validation set reached 0.977 and 0.952, respectively. The column chart and DCA further suggest that the model has good clinical application potential, while KM survival analysis shows that the OS of high-risk scoring patients is significantly worse, indicating that the model not only has diagnostic and predictive significance, but also has certain prognostic evaluation value. GO and KEGG enrichment analysis further demonstrated that the model involved genes mainly enriched in classic tumor-related pathways such as PI3K-Akt, MAPK, JAK-STAT, as well as BPs such as copper ion response, oxidative stress, and cell migration regulation. This suggests that under the background of H. pylori infection, copper homeostasis disorder may jointly promote the development and progression of GC by activating multiple oncogenic signaling axes.

Among the key genes that make up the model, LOXL2 exhibits particularly outstanding research value. As a copper-dependent lysyl oxidase, LOXL2 not only participates in collagen and elastin cross-linking and regulates extracellular matrix hardness, but also participates in tumor malignant progression by promoting cell migration, invasion, and EMT (30). At the same time, genes such as PDGFRB, SPARC, SOD3, and AOC3 are involved in angiogenesis, matrix remodeling, redox homeostasis, and inflammatory microenvironment regulation, respectively, suggesting that copper metabolism imbalance may form a synergistic network with H. pylori infection (31-34). Among them, LOXL2 is not only a factor with significant influence weight in the model, but also has copper dependence and EMT-promoting effects. Therefore, it is likely to be an important molecular hub connecting copper proliferation, chronic inflammation, and GC progression.

At the histological and molecular expression levels, this study consistently confirmed through public databases, IHC, Western blot, and RT-qPCR that LOXL2 is significantly overexpressed in GC tissues, and its expression gradually increases as gastric mucosal lesions progress from gastritis and intestinal metaplasia to GC, especially in the context of H. pylori infection. It has been reported that LOXL2 is highly expressed in a variety of solid tumors (including breast cancer, pancreatic cancer, lung cancer, etc.), and plays a key role in tumor genesis and development by promoting EMT, regulating extracellular matrix remodeling, enhancing cell migration and invasion, and other mechanisms (11,13,14,35). The above research results are consistent with the conclusions of this study. Therefore, we speculate that H. pylori infection induces progressive overexpression of LOXL2 by activating chronic inflammatory responses and related signaling axes, promoting gastric epithelial cell transformation and tumor progression. The concentration of copper ions affects the activity of LOXL2, and the high expression of LOXL2 may play a role in the copper homeostasis changes in the tumor microenvironment. This suggests that LOXL2 not only participates in the formation of GC, but may also be involved in the continuous process of H. pylori-related gastric mucosal malignancy. Further analysis showed that high LOXL2 expression was associated with tumor enlargement, poor differentiation, advanced TNM stage, lymph node metastasis, distant metastasis, and H. pylori positivity, whereas no significant association was observed with age, sex, or tumor location. These findings suggest that elevated LOXL2 expression is associated with aggressive clinicopathological features and unfavorable clinical outcomes in GC. However, because major clinicopathological and molecular confounding factors were not adjusted for in the present study, the prognostic significance of LOXL2 should be interpreted as associative rather than indicative of independent prognostic value.

It is worth noting that cell experiments further support the functional role of LOXL2 in H. pylori-associated GC. After constructing the LOXL2 overexpression and knockdown models, it was found that H. pylori stimulation can further induce an increase in LOXL2 expression. Functional experiments have shown that overexpression of LOXL2 can significantly enhance the colony-forming ability and invasion and migration level of GC cells, while H. pylori stimulation can further amplify this pro-proliferative effect; on the contrary, knocking down LOXL2 significantly inhibits cell proliferation, migration, and invasion, and can partially recover under H. pylori stimulation. This indicates that LOXL2 plays a crucial role in regulating the proliferation of GC cells, and its function is significantly enhanced by H. pylori infection, further supporting the possibility of a synergistic mechanism between the two in the development and progression of GC. In addition to GC, LOXL2 has also been proven to have important pathogenic significance in various malignant tumors and benign lesions. LOXL2 is highly expressed in liver cancer, breast cancer, lung cancer, pancreatic cancer, and other tumors, and is closely related to cell migration, matrix remodeling, EMT transformation, and angiogenesis (36). In non-neoplastic diseases, LOXL2 is widely involved in the fibrosis process of pulmonary fibrosis, hepatic fibrosis, diabetes nephropathy, and systemic sclerosis, leading to organ structure and function abnormalities mainly by promoting collagen cross-linking, ECM accumulation, and tissue stiffness and other mechanisms (37-39). For example, in idiopathic pulmonary fibrosis (IPF), LOXL2 expression is significantly upregulated and is considered a potential target for the development of anti-fibrotic drugs (40,41).

Mechanistically, existing results suggest that H. pylori may induce upregulation of LOXL2 expression through multiple inflammation- and stress-related pathways, including NF-κB, STAT3, HIF-1α, and ROS-related signaling (42,43). The H. pylori virulence factor CagA can activate the SRC/ERK pathway and enhance inflammatory transcriptional activity, while VacA may induce ROS accumulation and stress response through mitochondrial damage, thereby jointly promoting the enhancement of LOXL2 transcription and expression (44,45). EMT is the gradual transformation and transformation of epithelial cells into mesenchymal phenotype, which is extremely important in the invasion and metastasis of GC, thus enabling the transformed cells to have the ability to invade, migrate, metastasize, and resist apoptosis. Snail, as a transcription factor, can directly bind to the E-cadherin promoter, which is an epithelial cell phenotype (46,47). The results of this study demonstrated that overexpression of LOXL2 may promote the expression and phosphorylation of FAK/Src, upregulate the key EMT transcription factor snail, and downregulate the epithelial marker E-cadherin, while H. pylori stimulation can further enhance this effect; on the contrary, knocking down LOXL2 inhibits FAK/Src activation, reduces snail, and restores E-cadherin expression, while H. pylori can only partially reverse this inhibitory effect. Previous studies have confirmed that LOXL2 may promote the occurrence of EMT in breast cancer and pancreatic cancer cells by activating the FAK/Src pathway (48,49), which is consistent with the conclusion of this study. The above indicates that LOXL2 and H. pylori may synergistically activate the FAK/Src signaling axis, thereby inducing EMT-related molecular reprogramming and ultimately promoting the invasion, metastasis, and malignant progression of GC cells.

Conclusions

In summary, this study suggests that LOXL2 plays an important promoting role in the development and progression of H. pylori-related GC from multiple perspectives, including risk prediction, tissue expression, clinical relevance, cellular function, and molecular mechanisms. LOXL2 is not only expected to serve as a potential biomarker for high-risk screening and prognostic evaluation of H. pylori-positive GC, but also as a therapeutic target for connecting cuproplasia, inflammatory stimulation, and EMT activation. Of course, this study still has certain limitations, such as the risk model being mainly constructed based on the TCGA database and lacking external large-scale independent queue validation; the clinical sample size is relatively limited and it is a single-center retrospective study; the cell experiment uses H. pylori strain supernatant instead of live bacteria intervention, which cannot fully simulate the real infection state. This experimental strategy mainly reflects the biological effects mediated by H. pylori-derived secretory factors and cannot fully reproduce the complex interactions between live H. pylori and gastric epithelial cells, particularly those involving bacterial adhesion and type IV secretion system-dependent signaling. Therefore, future studies using live H. pylori infection models are warranted to further validate the molecular mechanisms identified in the present study. In addition, the research on the downstream mechanism of LOXL2 is still not deep enough, lacking further validation through animal models and more clinical samples. In the future, it is still necessary to combine prospective clinical studies, tissue chips, and in vivo experiments to further improve the mechanism chain of “H. pylori infection copper metabolism imbalance LOXL2 activation gastric cancer progression”.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the TRIPOD and MDAR reporting checklists. Available at https://jgo.amegroups.com/article/view/10.21037/jgo-2026-0380/rc

Data Sharing Statement: Available at https://jgo.amegroups.com/article/view/10.21037/jgo-2026-0380/dss

Peer Review File: Available at https://jgo.amegroups.com/article/view/10.21037/jgo-2026-0380/prf

Funding: This study was supported by grants from the Key Project of Science and Education Promoting Health of Suzhou City (No. ZDXM2024003), the National Science Foundation of China (NSFC; No. 82073180), the Scientific Research Project of Suzhou Industrial Park Healthcare Talent Support Initiative (No. YQWS202510), the Suzhou Industrial Park Healthcare Talent Support Initiative [No. (2024) 66], and the Research of Medical Innovation and Application, Science and Technology Development Project of Suzhou City (No. SKY2023109).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://jgo.amegroups.com/article/view/10.21037/jgo-2026-0380/coif). The 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. All patients were informed about the study and provided written informed consent and their family members. The present study was approved by the Ethics Committee of The First Affiliated Hospital of Soochow University (Suzhou, China). The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.

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/.

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