ETM Experimental and Therapeutic Medicine 1792-0981 1792-1015 D.A. Spandidos ETM-30-1-12883 10.3892/etm.2025.12883 Articles Label-free quantitative proteomics of gastric high-grade intraepithelial neoplasia LinNan LinLiping HuangXinxiang HuangChaozhong GongJinrong Department of Gastroenterology, The Affiliated Hospital of Putian University, Putian, Fujian 351100, P.R. China Correspondence to: Mr. Nan Lin, Department of Gastroenterology, The Affiliated Hospital of Putian University, 999 Dongzhen East Road, Licheng, Putian, Fujian 351100, P.R. China 642803499@qq.com 072025 13052025 30 1 133 12 09 2023 01 05 2025 Copyright: © 2025 Lin et al. 2025 This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

Early detection and diagnosis are key to improving the survival rate and reducing the fatality rate linked to gastric cancer. The precancerous lesion of gastric cancer is referred to as gastric high-grade intraepithelial neoplasia (HGIN). Both the sensitivity and specificity of current biomarkers that aid in the diagnosis of gastric HGIN are still relatively low. Furthermore, proteomic data on gastric HGIN are still scarce. The present study aimed to explore candidate protein biomarkers for gastric HGIN screening with proteomics and bioinformatics technology. A total of 10 serum samples were collected and categorized into two groups, i.e., the gastric HGIN and the healthy control groups. Label-free quantification in conjunction with liquid chromatography with tandem mass spectrometry was employed to identify the probable biomarkers for gastric HGIN. Furthermore, differentially expressed proteins (DEPs) were quantified by proteomics analysis. In total, 1,192 distinct serum proteins were discovered between the gastric HGIN group and the healthy control group. DEPs were identified in the further analyses, utilizing a threshold of a 1.5-fold difference in expression level (P<0.05) in comparison with the control group. There were 18 upregulated and 12 downregulated proteins in the gastric HGIN group in comparison with the control group. Bioinformatics analyses were performed using Gene Ontology and KEGG pathway enrichment analyses. The GO analysis revealed that the DEPs were enriched in biological processes such as ‘cellular’, ‘biological regulation’, ‘multicellular organismal’, ‘developmental’ and ‘reaction to stimulus processes’, localized to ‘cell’, ‘intracellular’ and ‘protein-containing complex’, and involved in molecular functions such as ‘molecular function modulator’, ‘binding’ and ‘catalytic activity’. The KEGG pathway enrichment analysis manifested that the DEPs were predominantly enriched in ‘antigen processing and presentation’, ‘diabetic cardiomyopathy’, ‘Epstein-Barr virus infection’, ‘herpes simplex virus 1 infection’, ‘human immunodeficiency virus 1 infection’ and ‘human cytomegalovirus infection’. In conclusion, the present data provide more biological information for the formation of gastric HGIN and clues for further research on the pathogenesis of early gastric cancer.

 gastric cancer gastric high-grade intraepithelial neoplasia proteomics label-free Funding: This research was funded by the Science and Technology Project Plan of Putian city (grant no. 2020S3F001). Introduction

Gastric cancer is the predominant gastrointestinal malignancy at present. In 2020, there were >1 million new cases of gastric cancer globally and ~769,000 deaths. As a result, it ranks 5th in incidence and 4th in mortalities related to malignant tumors (1). Due to the invasiveness of endoscopic screening and the lack of ideal biomarkers, the early diagnosis rate of gastric cancer is <10% and the proportion of patients at the intermediate and late stage is higher (2). The 5-year survival rate is ~90% for early gastric cancer, while that of intermediate and advanced gastric cancer is only ~30% and the overall therapeutic effect is not ideal (3). Therefore, early detection and diagnosis are key to improving the survival rate and reducing the fatality rate of gastric cancer. However, early gastric cancer and precancerous lesions are mostly asymptomatic or cause nonspecific symptoms (4,5). Gastric cancer is often mistaken for benign diseases, such as gastritis and dyspepsia, in the early stage of the disease, and it fails to get the attention of patients so they easily miss the best treatment period. Most patients have developed advanced gastric cancer when diagnosed, which requires chemotherapy, radiotherapy, surgery and other treatments, increasing the physical and psychological burden of patients and endangering the safety of patients in serious cases (6,7). Currently, gastric cancer diagnosis primarily relies on gastroscopy and pathological diagnosis (8). However, gastroscopy and pathological biopsy are invasive examinations, which are difficult for patients to accept and have a high cost, and they also have certain requirements regarding the operation ability and experience of doctors, so the opportunity for early diagnosis is usually missed (9).

Gastric high-grade intraepithelial neoplasia (HGIN) is a precancerous lesion. Over 80% of gastric HGIN progresses to adenocarcinoma (10). If the lesions can be detected and treated in time at the gastric HGIN stage, the therapeutic effect on patients is significantly improved. Appropriate biomarkers can improve the rates of early diagnosis of gastric cancer and help achieve its prompt treatment (11). Although studies on biomarkers for gastric cancer have been gradually carried out at home and abroad, only a small number of biomarkers can be used for auxiliary diagnosis of clinical gastric cancer so far (12,13), such as carcinoembryonic antigen, carbohydrate antigen 19-9 (CA19-9), CA724 and α-fetoprotein, but the detection rate and accuracy are low, which are not sufficient to replace conventional pathological diagnosis. The gastric HGIN-related diagnostic sensitivity of of 77.8% and specificity of 81.8%, are still relatively low (14,15).

Blood is an ideal sample for detecting tumor biomarkers because some tumor-related specific proteins are released into the blood, and blood sampling is simple, rapid, and minimally invasive, so samples are relatively easy to obtain. Abnormal protein expression plays a principal function in the occurrence as well as the development of gastric cancer (16,17). Proteomic studies have been reported on gastric cancer, and certain differentially expressed proteins (DEPs), such as fibrinogen α-chain, apolipoprotein A-II, vitronectin and clusterin isoform 1, have been screened (18,19). Another study from Singapore, comparing plasma proteomics of normal individuals and patients with early and advanced gastric cancer found that the level of component 9 (C9) in patients with gastric cancer was significantly higher than that in normal subjects, and the subsequent blind test study on 119 plasma samples showed that the sensitivity of C9 could be as high as 90% at a specificity of 74% (20). However, due to different etiologies and different stages of the disease, the results of various research groups were different (21). In addition, proteomic data on gastric HGIN are still scarce.

Label-free quantification (LFQ) is one of the most popular high-throughput screening techniques in quantitative proteomics. The approach may detect >1,000 proteins in a serum sample with a single needle, after which it employs tandem mass spectrometric analysis to compare the relative protein content in up to 10 distinct samples concurrently (22). In addition, this technology has the advantages of high throughput, high sensitivity, low-abundance protein detection, accurate quantification, good repeatability, reliable identification results and detection of a wide range of proteins, and is widely used in the analysis and exploration of DEPs (23). In the present study, LFQ together with liquid chromatography with tandem mass spectrometry (LC-MS/MS) was employed to compare the peripheral blood's protein expression of patients with gastric HGIN and healthy controls, and the protein expression profile in serum was analyzed biologically, providing a basis for finding biomarkers for gastric HGIN.

 Patients and methods <sec> <title>Patients and plasma samples

The present study was approved by the Ethics Committee of the Affiliated Hospital of Putian University (Putian, China; approval no. 202006) (24). All patients enrolled in this study were from the Affiliated Hospital of Putian University (Putian, China), with the enrollment period from January 2022 to March 2022. Each patient who participated signed a written informed consent form before the study began. All patients were divided into a gastric HGIN group and healthy control group, meeting the following inclusion and exclusion criteria: The gastric HGIN group was pathologically confirmed as HGIN, and endoscopic ultrasonography and computed tomography did not confirm metastasis, and there was no previous history of gastric malignancy. The healthy control group underwent gastroscopy to exclude gastric HGIN. The following conditions were excluded in both groups: Associations with other malignant tumors, serious systemic diseases and serious primary diseases. A total of 10 serum samples were collected and all personnel was from the Affiliated Hospital of Putian University. Male individuals with a higher incidence of gastric cancer were selected as the case group, and age-matched healthy individuals were chosen from the same period as the control group. There were 5 cases in the gastric HGIN group and 5 cases in the healthy control group. Among them, 5 patients in the gastric HGIN group underwent endoscopic submucosal dissection (ESD) at the Affiliated Hospital of Putian University, and were diagnosed with HGIN by postoperative pathology. The protocol was performed according to standard procedures. Centrifugation of blood samples was conducted at 1,300 x g for 10 min at 4˚C and immediately stored at -80˚C.

 Protein extraction and trypsin digestion

The centrifugation process was crucial in the isolation of cellular debris contained in the serum sample (12,000 x g at 4˚C for 10 min). After centrifugation, the supernatant was transferred into another centrifuge tube. The Pierce™ Top 14 Abundant Protein Depletion Spin Columns Kit (Thermo Fisher Scientific, Inc.) was utilized to separate the top 14 high-abundance proteins. Afterward, the concentration of protein was ascertained utilizing a bicinchoninic acid kit (cat. no. P0011; Beyotime Institute of Biotechnology) as per the manufacturer's specifications.

The same amount of each sample protein was taken for enzymatic hydrolysis. An appropriate amount of standard protein (cat. no. 26620; Thermo Fisher Scientific, Inc.) was added and the volume was adjusted to be consistent with the lysate. A total of 5 mM dithiothreitol was utilized for the reduction of the protein solution to facilitate its digestion for 30 min at 56˚C. Subsequently, it underwent alkylation with 11 mM iodoacetamide for 15 min at room temperature, which was performed in the dark. The alkylated samples were transferred to ultrafiltration tubes, centrifuged at 12,000 x g for 20 min at room temperature, replaced with 8 M urea for 3 times and then replaced with displacement buffer for 3 times. Finally, trypsin (Promega Corporation) was introduced at 1:50 trypsin-to-protein mass ratios for digestion overnight at 37˚C. The peptides were subjected to centrifugation at 12,000 x g for 10 min at room temperature and then recovered once with ultrapure water, and the two peptide solutions were combined.

 LC-MS/MS analysis

The tryptic peptides were dissolved in solvent A (0.1% formic acid, 2% acetonitrile/in water) and directly loaded onto a home-made reversed-phase analytical column (25 cm length, 100 µm inner diameter) (cat. no. ZTC18M096; MilliporeSigma). The liquid phase gradient was set as follows: 0-68 min, 4-20% solvent B (0.1% formic acid in 90% acetonitrile); 68-82 min, 20-32% B; 82-86 min, 32-80% B; 86-90 min, 80% B, all at a constant flow rate of 500 nl/min on an EASY-nLC 1200 ultra-performance LC system (Thermo Fisher Scientific, Inc.).

The separated peptides were analyzed in Exploris 480™ (Thermo Fisher Scientific, Inc.) with a nano-electrospray ion source. The electrospray voltage applied was 2.3 kV and the compensation voltages were -45 and -70 V. The full MS scan resolution was set to 60,000 for a scan range of 400-1,200 m/z. Up to 25 of the most abundant precursors were then selected for further MS/MS analyses with 30 sec dynamic exclusion. The higher-energy collisional dissociation fragmentation was performed at a normalized collision energy of 27%. The fragments were detected in the Orbitrap at a resolution of 30,000. The fixed first mass was set at 110 m/z. The automatic gain control target was set at 75%, with an intensity threshold of 1E4 ions/sec and the MS2 maximum injection time was set at 100 msec. The MS proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with database identifier PXD036991 (https://www.ebi.ac.uk/pride/profile/reviewer_pxd036991).

 Database search

The resulting MS/MS data were processed using Proteome Discoverer search engine (v2.4.0.305) (https://www.thermofisher.cn/cn/zh/home/industrial/mass-spectrometry/liquid-chromatography-mass-spectrometry-lc-ms/lc-ms-software/multi-omics-data-analysis/proteome-discoverer-software.html?erpType=Global_E1). Tandem mass spectra were searched against the Homo sapiens database (78,120 entries) concatenated with a reverse decoy database. Trypsin (Full) was specified as the cleavage enzyme, allowing up to 2 missing cleavages. The mass tolerance for precursor ions was set at 10 ppm in the first search and the mass tolerance for fragment ions was set at 0.02 Da. Carbamidomethyl on Cys was specified as a fixed modification, and oxidation (methionine; M), acetylation on protein N-term, Met-loss (M) and Met-loss+acetyl (M) were specified as variable modifications. The quantitative method was set as LFQ. The false discovery rate was adjusted to <1% and the minimum score for modified peptides was set to >40. The minimum peptide length was set at 6. All of the other parameters in Proteome Discoverer were set to default values.

 Bioinformatics analysis

Gene Ontology (GO) annotations were acquired from the eggNOG database (v.5.0) (http://eggnog5.embl.de/#/app/home). The eggNOG-mapper software (v.2.0) (http://eggnog-mapper.embl.de/) analyzed these GO categories: Cellular component, molecular function and biological process. The Kyoto Encyclopedia of Genes and Genomes (KEGG) online service tool KAAS (v.2.1) (https://www.genome.jp/tools/kaas/) annotated the protein descriptions and then mapped using the KEGG mapper (v.5.0) (https://www.kegg.jp/kegg/mapper/) to their respective pathways. WoLFPSort (v.0.2) (https://www.genscript.com/wolf-psort.html) was used to investigate the subcellular localization. P<0.05 was deemed statistically significant for both GO terms and KEGG pathways. Gene count thresholds for the GO terms and KEGG pathways were established at ≥20 and ≥4, respectively.

 Statistical analysis

Clinical parameter data were expressed as the mean ± standard deviation. Continuous variables were compared using Student's t-test and the Mann-Whitney U-test depending on whether the data were normally distributed. The protein data along with the intensity values were log-transformed with base 2 and median normalization was carried out to eliminate random errors and sample bias. DEPs were identified by a two-tailed Student's t-test. The pathway enrichment analysis was performed by a two-tailed Fisher's exact test. P<0.05 (two-tailed) was considered to indicate a statistically significant difference. Data were analyzed and visualized with SPSS 21.0 (IBM, Corp.) or GraphPad Prism 7.0 (GraphPad Software, Inc.).

 Results <sec> <title>Characteristics of patients with gastric HGIN and healthy controls

A total of 10 serum samples were collected in this study. In the gastric HGIN group, 5 cases were male, with an average age of 62.6±3.8 years. All cases were diagnosed with HGIN after ESD. The healthy control group consisted of 5 cases, all male, with an average age of 59.8±0.8 years. There was no rmarked difference in terms of age between the patients with gastric HGIN and the healthy controls (P>0.05, two-tailed Student's t-test) (Table I).

 Identification of DEPs

LFQ in conjunction with LC-MS/MS enabled the discovery of 1,192 serum proteins. Upregulated and downregulated DEPs were defined as proteins with expression ratio thresholds of >1.5-fold increase or <0.67-fold decrease (P<0.05, two-tailed Student's t-test), respectively. In comparison with the control group, 18 proteins were upregulated and 12 proteins were downregulated in the gastric HGIN group (Table II; Fig. 1).

 GO analysis

GO analysis evaluated the biological process, cellular component and molecular function terms enriched by the DEPs. Concerning the biological process analysis, the leading five modulated processes were ‘cellular’, ‘biological regulation’, ‘multicellular organismal’, ‘developmental’ and ‘reaction to stimulus processes’. Regarding the cellular component analysis, the most prevalent proteins were primarily linked to the ‘cell’, ‘intracellular’ and ‘protein-containing complex’. On the other hand, the molecular function analysis indicated that the prevalent molecular functions were ‘molecular function modulator’, ‘binding’ and ‘catalytic activity’ (Fig. 2A).

The major subcategories (>40%) for the gastric HGIN proteins were extracellular (50%), cytoplasm (13.33%), cytoplasm/nucleus (10%) and plasma membrane (10%) (Fig. 2B) as per the subcellular structural localization evaluations.

To determine whether the DEPs were remarkably enriched in certain functional terms, GO enrichment analysis was employed to analyze the DEPs. GO enrichment analysis identifies significant enrichment of DEPs in specific functional terms through statistical methods (P<0.05), whereas GO categorization describes the distribution profile of DEPs across GO hierarchy without emphasizing statistical significance. The core distinction resides in the application of statistical testing to assess functional specificity enrichment. In the category biological process, the most significantly enriched items were ‘mononuclear cell migration’, ‘positive modulation of cell killing’ and ‘hemopoiesis modulation’ (Fig. 3A). In cellular component, the most significantly enriched items were ‘brush border membrane’, ‘recycling endosome membrane’ and ‘microvillus’ (Fig. 3B). In the category molecular function, the most significantly enriched items were ‘transforming growth factor beta receptor binding’ and ‘phosphoric diester hydrolase activity’ (Fig. 3C).

 KEGG pathway enrichment analysis

The proteins were integrated into the KEGG database and Fisher's exact test P-values were important to determine the proteins' enrichment levels in order to identify the pathways influenced by the DEPs. The KEGG pathway enrichment analysis manifested that the DEPs were predominantly enriched in ‘antigen processing and presentation’, ‘diabetic cardiomyopathy’, ‘Epstein-Barr virus infection’, ‘herpes simplex virus 1 infection’, ‘human immunodeficiency virus 1 infection’ and ‘human cytomegalovirus infection’ (Fig. 4).

 Discussion

In the present study, 30 DEPs were identified in the gastric HGIN group in comparison with the healthy control group, including 18 and 12 upregulated and downregulated proteins, correspondingly. Bioinformatics analysis showed that the functions of the DEPs mainly included ‘cellular process’, ‘biological regulation’, ‘multicellular organismal process’, ‘binding, molecular function regulator’ and ‘catalytic activity’. The major signaling pathways included ‘antigen processing and presentation’, ‘diabetic cardiomyopathy’, ‘Epstein-Barr virus infection’, ‘herpes simplex virus 1 infection’, ‘human immunodeficiency virus 1 infection’ and ‘human cytomegalovirus infection’. DEP functions, as well as the signaling pathways engaged, can avail an experimental foundation for determining the mechanism of occurrence and advancement of gastric HGIN. The proteomic characteristics and data of gastric HGIN analyzed in the study were different from those of previous gastric cancer proteomics and were heterogeneous (25,26). The discrepancy may be linked to the different stages of gastric cancer in each study.

Collagen alpha-3 (VI) chain (COL6A3) is among the collagen type VI, which is the predominant structural extracellular matrix protein (27). It is primarily expressed in stromal cancer-associated fibroblasts (CAFs) (28). CAFs are a central determinant in the progression of malignancies, as affirmed by a previous study (29). COL6A3 expression was remarkably varied in the normal mucosa in contrast with cancerous tissues, with the greatest and lowest expression levels in primary colorectal cancer and adenoma, respectively. COL6A3 expression was positively directly proportional to the stage of prostate, breast and colorectal cancer (30). Certain studies have reported that the level of expression of COL6A3 in gastric cancer tissue is higher than in normal tissue (31,32). Therefore, COL6A3 could be used as a tumor diagnostic marker. COL6A3 functioned in tumorigenesis as well as in the progression of cholangiocarcinoma via the E2F1/LMCD1-AS1/miR-345-5p/COL6A3 axis (33). In a study on colon cancer, stromal COL6A3 could enhance tumor growth by regulating Hippo and Wnt signaling (34). COL6A3 knockout lowered cell proliferation and invasion, and enhanced cell apoptosis in cancer cell lines (28), further confirming the relationship between COL6A3 and tumor progression. In this study, COL6A3 expression was upregulated, indicating that it may be engaged in the development of gastric HGIN, but the specific mechanism of action remains to be further studied.

Cadherin-17 (CDH17), also referred to as human peptide transporter-1 or liver-intestine cadherin, encompasses seven homologous repeated domains. As one of the cadherin superfamily members, it functions in intercellular conjunction (35). CDH17 can regulate the Ca2+-dependent homophilic cell-cell adhesion, suggesting that it may be related to the occurrence of tumors (36). In general, CDH17 is expressed not only on the enterocytes’ basolateral surfaces but also on goblet cells in the small and large intestines selectively and is rarely discovered in the stomach or liver of a healthy adult (37,38). Previous documentation affirms that the elevated expression level of CDH17, contrary to the lowered levels of various classical cadherins, is linked to cholangiocarcinoma as well as gastric, liver, colorectal and pancreatic cancers, highlighting that CDH17 may have a central function in tumor progression (39). In many of these malignancies, CDH17 expression is linked to tumor stage or unfavorable survival of patients (40). Therefore, CDH17 can be utilized as a sensitive tumor marker for the identification of metastasis of unclear primary origin as well as their subsequent assignation to the gastrointestinal tract (41). Multiple studies on gastric cancer have affirmed that overexpression of CDH17 was positively linked to the histological stage and tumor invasion of gastric cancer, demonstrating that CDH17 expression can be utilized as a valuable indicator for anticipating the progression of gastric cancer (42). High expression of CDH17 can promote cell adhesion and proliferation and participates in the formation and development of gastric cancer mainly via three signaling pathways in the cells, i.e. the Wnt/β-catenin pathway, the NF-κB signaling pathway and the Ras/Raf/MEK/ERK MAPK signaling pathway (43). In the present study, the expression of CDH17 was upregulated in the gastric HGIN group, suggesting that CDH17 influences the occurrence and development of the disease. However, since we are studying precancerous lesions of gastric cancer, which is at a different disease stage from previous studies, whether it has the same mechanism of action needs to be proved by further studies.

The crucial element of the extracellular matrix (ECM), hyaluronic acid (HA) is essentially linked to a complex transmembrane adhesion glycoprotein known as cluster of differentiation 44 (CD44) (44). Numerous studies have affirmed that CD44 is not only important in the various physiological processes, for instance, diverse immune functions, organ development and hematopoiesis (45,46), but also in pathological processes, particularly tumors (47). Increasing evidence infers that CD44 is remarkably overexpressed in various cancer types such as lymphoma, breast, colon, endometrial, prostate, ovarian, gallbladder and oral squamous cell carcinoma, which is linked to aggressive biological behavior (48,49). CD44 expression was also upregulated in previous studies on gastric precancerous lesions and gastric cancer (50). CD44 interacts with different ECM components, cytokines and proteins that exist in the tumor microenvironment. The interaction between ligands, including osteopontin and HA, in addition to matrix metalloproteinases, with the CD44 receptor, can trigger numerous cellular signaling pathways, thus enhancing tumor advancement and aggressiveness (51). In the present study, CD44 expression was upregulated in the gastric HGIN group. Although the role of CD44 in gastric HGIN remains elusive, it may be involved in the formation of HGIN.

The protein disulfide isomerase A3 (PDIA3) was identified as the most remarkable downregulated protein based on the various ratios in protein expression and the related P-values. PDIA3, also referred to as endoplasmic reticulum resident protein 57, is among the principal members of the PDI gene family. It has been discovered as one of the primary enzymatic chaperones that are essential in the reconstruction of misfolded proteins inside the endoplasmic reticulum (52). It has been ascertained that in some malignancies, the expression of PDIA3 is negatively linked to the degree of differentiation. In uterocervical cancer, PDIA3 expression was downregulated in contrast with normal tissue, as well as cervical intraepithelial neoplasia (53). Another study showed that low expression of PDIA3 was significantly linked to poor cause-specific survival in patients with papillary thyroid carcinoma (54). In cancer immunity, PDIA3 creates a complex with major histocompatibility complexes (MHC) class I to enhance antigen processing, thus triggering an immune response against cancerous cells (55). On the contrary, when MHC class I expression is inadequate, cancerous cells can evade the cytotoxic impacts of immune cells that can be seen in gastric cancer (56). In the present study, downregulation of PDIA3 was detected, which indicated the possibility of inactivation of gastric HGIN as a tumor suppressor during the development of HGIN, but its potential impact requires further study.

The present study has certain limitations. First of all, the sample size included in this study is small and the screened DEPs have not been further verified. In the future, it is necessary to further expand the sample and use enzyme-linked immunosorbent assay and other experimental methods to verify the DEPs. Secondly, due to the small sample size, in order to take into account the reliability of the experimental data, only males with a relatively higher incidence of gastric cancer were selected as experimental subjects in this study (57), and it is required to expand the sample to further include females in the study.

To conclude, utilizing LFQ coupled with LC-MS/MS, it was found that certain proteins, such as COL6A3, CDH17, CD44 and PDIA3, could be candidate protein biomarkers for gastric HGIN. However, additional studies are indispensable to ascertain the possible functions of these candidate proteins in the development of gastric HGIN. The present data provide additional biological information regarding the formation of gastric HGIN and provide clues for further research on the pathogenesis of early gastric cancer.

 Acknowledgements

Not applicable.

 Availability of data and materials

The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with database identifier PXD036991 (https://www.ebi.ac.uk/pride/profile/reviewer_pxd036991).

 Authors' contributions

NL, CH and JG designed the study. LL and XH collected data and patients. NL, LL and XH performed the research. NL and CH analyzed the data. NL, CH and JG wrote the manuscript. All authors contributed to editorial changes to the manuscript. NL and LL confirm the authenticity of the raw data. All authors read and approved the final manuscript.

 Ethics approval and consent to participate

The study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of the Affiliated Hospital of Putian University (approval no. 202006). Informed consent was obtained from all subjects involved in the study. Patients were fully aware of the purpose, methods, potential therapeutic benefits and possible adverse reactions of the study, and willingly agreed to participate and fully cooperate with the doctors.

 Patient consent for publication

Not applicable.

 Competing interests

The authors declare that they have no competing interests.

 References SungHFerlayJSiegelRLLaversanneMSoerjomataramIJemalABrayFGlobal cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countriesCA Cancer J Clin7120924920213353833810.3322/caac.21660 WuDZhangPMaJXuJYangLXuWQueHChenMXuHSerum biomarker panels for the diagnosis of gastric cancerCancer Med81576158320193087376010.1002/cam4.2055 LiuKSongSFuTLiuYZhangHYanMHeZZhangWSuHLiZSpatiotemporal trends in the incidence of gastrointestinal neoplasms in Wuwei City of Northwestern China from 1995 to 2016: A hospital-based retrospective observational studyFront Oncol1171285720213455287110.3389/fonc.2021.712857 ShettyABalarajuGShettySPaiCGDiagnostic utility of alarm features in predicting malignancy in patients with dyspeptic symptomsIndian J Gastroenterol4018318820213383044110.1007/s12664-021-01155-x RepettoOMaieroSMagrisRMioloGCozziMRSteffanACanzonieriVCannizzaroRDe ReVQuantitative proteomic approach targeted to fibrinogen β Chain in tissue gastric carcinomaInt J Mol Sci1975920182951893910.3390/ijms19030759 MachlowskaJBajJSitarzMMaciejewskiRSitarzRGastric cancer: Epidemiology, risk factors, classification, genomic characteristics and treatment strategiesInt J Mol Sci21401220203251269710.3390/ijms21114012 LiuCWangYLiLHeDChiJLiQWuYZhaoYZhangSWangLEngineered extracellular vesicles and their mimetics for cancer immunotherapyJ Control Release34967969820223587872810.1016/j.jconrel.2022.05.062 LiuZSuWAoJWangMJiangQHeJGaoHLeiSNieJYanXInstant diagnosis of gastroscopic biopsy via deep-learned single-shot femtosecond stimulated Raman histologyNat Commun13405020223583129910.1038/s41467-022-31339-8 Delgado-GuillenaPGMorales-AlvaradoVJElosua-GonzálezAMurcia PomaresOPérez-AisaACórdovaHAlcedoJCalvetXFernández-EsparrachGGastroenterologists' attitudes on the detection and management of gastric premalignant conditions: Results of a nationwide survey in SpainEur J Cancer Prev3043143620213336994710.1097/CEJ.0000000000000648 Ray-OfforEObiorahCCHelicobacter pylori and precancerous lesions of the stomach in a Nigerian Metropolis: A cohort studyNiger J Clin Pract2137537920182951998910.4103/njcp.njcp_302_17 JiLLiuZZhouBCaiYAnFWangLLvZXiaMYangJYuanJCommunity-based pilot study of a screening program for gastric cancer in a Chinese populationCancer Prev Res (Phila)13738220203179646710.1158/1940-6207.CAPR-19-0372 FengFTianYXuGLiuZLiuSZhengGGuoMLianXFanDZhangHDiagnostic and prognostic value of CEA, CA19-9, AFP and CA125 for early gastric cancerBMC Cancer1773720172912187210.1186/s12885-017-3738-y TohJHoppeMMThakurTYangHTanKTPangBHoSRoyRHoKYYeohKGProfiling of gastric cancer cell-surface markers to achieve tumour-normal discriminationBMJ Open Gastroenterol7e00045220203281695610.1136/bmjgast-2020-000452 ZhaoJZhouRZhangQShuPLiHWangXShenZLiuFChenWQinJSunYEstablishment of risk evaluation model of peritoneal metastasis in gastric cancer and its predictive valueZhonghua Wei Chang Wai Ke Za Zhi204752201728105619(In Chinese) WangZMoTMTianLChenJQGastrin-17 combined with CEA, CA12-5 and CA19-9 improves the sensitivity for the diagnosis of gastric cancerInt J Gen Med148087809520213479551510.2147/IJGM.S335985 ShiXJWeiYJiBSystems biology of gastric cancer: Perspectives on the omics-based diagnosis and treatmentFront Mol Biosci720320203300562910.3389/fmolb.2020.00203 NiclaussNGütgemannIDohmenJKalffJCLingohrPNovel biomarkers of gastric adenocarcinoma: Current research and future perspectivesCancers (Basel)13566020213483081510.3390/cancers13225660 ShiFWuHQuKSunQLiFShiCLiYXiongXQinQYuTIdentification of serum proteins AHSG, FGA and APOA-I as diagnostic biomarkers for gastric cancerClin Proteomics151820182971949410.1186/s12014-018-9194-0 YooMWParkJHanHSYunYMKangJWChoiDYLeeJWJungJHLeeKYKimKPDiscovery of gastric cancer specific biomarkers by the application of serum proteomicsProteomics17160033220172813390710.1002/pmic.201600332 AhnHSSohnTSKimMJChoBKKimSMKimSTYiECLeeCSEPROGADIC-serum protein-based gastric cancer prediction model for prognosis and selection of proper adjuvant therapySci Rep81689220183044293910.1038/s41598-018-34858-x Rostami-NejadMRezaei-TaviraniMMansouriVAkbariZAbdiSImpact of proteomics investigations on gastric cancer treatment and diagnosisGastroenterol Hepatol Bed Bench12 (Suppl 1)S1S7201932099594 Panner SelvamMKFinelliRAgarwalAHenkelRProteomics and metabolomics-current and future perspectives in clinical andrologyAndrologia53e1371120213259856610.1111/and.13711 LiuDZhouLHuangLZuoZHoVJinLLuYChenXZhaoJQianDMicrofluidic integrated capacitive biosensor for C-reactive protein label-free and real-time detectionAnalyst1465380538820213433825910.1039/d1an00464f WuYSangJZhouJFangYComparative analysis of differences between preoperative endoscopic biopsy and postoperative pathological examination for diagnosis of gastric intraepithelial neoplasiaJ Int Med Res4930006052199492920213373651610.1177/0300060521994929 Fernández-CotoDLGilJHernándezAHerrera-GoepfertRCastro-RomeroIHernández-MárquezEArenas-LinaresASCalderon-SosaVTSanchez-AlemanMÁMendez-TenorioAQuantitative proteomics reveals proteins involved in the progression from non-cancerous lesions to gastric cancerJ Proteomics186152720183004877410.1016/j.jprot.2018.07.013 HuangAZhangMLiTQinXSerum proteomic analysis by tandem mass Tags (TMT) based quantitative proteomics in gastric cancer patientsClin Lab6485586620182973906410.7754/Clin.Lab.2018.171129 SvoronosCTsoulfasGSouvatziMChatzitheoklitosEPrognostic value of COL6A3 in pancreatic adenocarcinomaAnn Hepatobiliary Pancreat Surg24525620203218142910.14701/ahbps.2020.24.1.52 LiuWLiLYeHTaoHHeHRole of COL6A3 in colorectal cancerOncol Rep392527253620182962022410.3892/or.2018.6331 DaiLLiMZhangWLTangYJTangYLLiangXHFibroblasts in cancer dormancy: Foe or friend?Cancer Cell Int2118420213377115610.1186/s12935-021-01883-2 WangJPanWThe biological role of the collagen alpha-3 (VI) Chain and its cleaved C5 domain fragment endotrophin in cancerOnco Targets Ther135779579320203260678910.2147/OTT.S256654 LuYKongXZhongWHuMLiCDiagnostic, therapeutic, and prognostic value of the thrombospondin family in gastric cancerFront Mol Biosci864709520213399690310.3389/fmolb.2021.647095 BessaCMatosPJordanPGonçalvesVAlternative splicing: Expanding the landscape of cancer biomarkers and therapeuticsInt J Mol Sci21903220203326113110.3390/ijms21239032 YuJZhangBZhangHQiYWangYWangWWangYWangYE2F1-induced upregulation of long non-coding RNA LMCD1-AS1 facilitates cholangiocarcinoma cell progression by regulating miR-345-5p/COL6A3 pathwayBiochem Biophys Res Commun51215015520193087669110.1016/j.bbrc.2019.03.054 HuangYLiGWangKMuZXieQQuHLvHHuBCollagen type VI alpha 3 Chain promotes epithelial-mesenchymal transition in bladder cancer cells via transforming growth factor β (TGF-β)/Smad pathwayMed Sci Monit245346535420183006669810.12659/MSM.909811 LeeCWLinSETsaiHISuPJHsiehCHKuoYCSungCMLinCYTsaiCNYuMCCadherin 17 is related to recurrence and poor prognosis of cytokeratin 19-positive hepatocellular carcinomaOncol Lett1555956720182938723410.3892/ol.2017.7320 CasalJIBartoloméRABeyond N-cadherin, relevance of cadherins 5, 6 and 17 in cancer progression and metastasisInt J Mol Sci20337320193132405110.3390/ijms20133373 ValverdeAPovedanoERuiz-Valdepeñas MontielVYáñez-SedeñoPGarranzo-AsensioMRodríguezNDomínguezGBarderasRCampuzanoSPingarrónJMDetermination of cadherin-17 in tumor tissues of different metastatic grade using a single incubation-step amperometric immunosensorAnal Chem90111611116720183013410810.1021/acs.analchem.8b03506 KangMHJeongGSSmootDTAshktorabHHwangCMKimBSKimHSParkYYVerteporfin inhibits gastric cancer cell growth by suppressing adhesion molecule FAT1Oncotarget8988879889720172922873510.18632/oncotarget.21946 LiuXHuangYYuanHQiXManjunathYAvellaDKaifiJTMiaoYLiMJiangKLiGDisruption of oncogenic liver-intestine cadherin (CDH17) drives apoptotic pancreatic cancer deathCancer Lett45420421420193100470110.1016/j.canlet.2019.04.022 KmeidMLukoseGHodgeKChoDKimKALeeHAberrant expression of SATB2, CDX2, CDH17 and CK20 in hepatocellular carcinoma: A pathological, clinical and outcome studyHistopathology7976877820213403662910.1111/his.14420 Altree-TachaDTyrrellJHaasTCDH17 is a more sensitive marker for gastric adenocarcinoma than CK20 and CDX2Arch Pathol Lab Med14114415020172802990710.5858/arpa.2015-0404-OA JiangXJLinJCaiQHZhaoJFZhangHJCDH17 alters MMP-2 expression via canonical NF-κB signalling in human gastric cancerGene6829210020192978307010.1016/j.gene.2018.05.042 LongZWZhouMLFuJWChuXQWangYNAssociation between cadherin-17 expression and pathological characteristics of gastric cancer: A meta-analysisWorld J Gastroenterol213694370520152583433810.3748/wjg.v21.i12.3694 ThorneRFWangYZhangYJingXZhangXDde BockCEOliveiraCSEvaluating nuclear translocation of surface receptors: Recommendations arising from analysis of CD44Histochem Cell Biol153778720203183448510.1007/s00418-019-01835-y ChenCZhaoSKarnadAFreemanJWThe biology and role of CD44 in cancer progression: Therapeutic implicationsJ Hematol Oncol116420182974768210.1186/s13045-018-0605-5 XuHNiuMYuanXWuKLiuACD44 as a tumor biomarker and therapeutic targetExp Hematol Oncol93620203330302910.1186/s40164-020-00192-0 SenbanjoLTChellaiahMACD44: A multifunctional cell surface adhesion receptor is a regulator of progression and metastasis of cancer cellsFront Cell Dev Biol51820172832630610.3389/fcell.2017.00018 NimmakayalaRKBatraSKPonnusamyMPUnraveling the journey of cancer stem cells from origin to metastasisBiochim Biophys Acta Rev Cancer1871506320193041931410.1016/j.bbcan.2018.10.006 HeYXueCYuYChenJChenXRenFRenZCuiGSunRCD44 is overexpressed and correlated with tumor progression in gallbladder cancerCancer Manag Res103857386520183028811710.2147/CMAR.S175681 TongtaweeTWattanawongdonWSimawaranonTKaewpitoonSKaengpenkaeSJintabanditwongNTangjanyathamPRatchapolWKangwantasKDechsukhumCExpression of cancer stem cell marker CD44 and its polymorphisms in patients with chronic gastritis, precancerous gastric lesion, and gastric cancer: A cross-sectional multicenter study in ThailandBiomed Res Int201743848220172944573810.1155/2017/4384823 WangLZuoXXieKWeiDThe role of CD44 and cancer stem cellsMethods Mol Biol1692314220182898688410.1007/978-1-4939-7401-6_3 HettinghouseALiuRLiuCJMultifunctional molecule ERp57: From cancer to neurodegenerative diseasesPharmacol Ther181344820182872341310.1016/j.pharmthera.2017.07.011 WalshRJTanDSPThe role of immunotherapy in the treatment of advanced cervical cancer: Current status and future perspectivesJ Clin Med10452320213464054110.3390/jcm10194523 KureSChibaTEbinaATodaKJikuzonoTMotodaNMitaniHSugitaniITakeuchiKOhashiRCorrelation between low expression of protein disulfide isomerase A3 and lymph node metastasis in papillary thyroid carcinoma and poor prognosis: A clinicopathological study of 1,139 cases with long-term follow-upEndocr J6927328120223473260410.1507/endocrj.EJ21-0394 ShimodaTWadaRKureSIshinoKKudoMOhashiRFujitaIUchidaEYoshidaHNaitoZExpression of protein disulfide isomerase A3 and its clinicopathological association in gastric cancerOncol Rep412265227220193072011710.3892/or.2019.6999 SongDLiuHWuJGaoXHaoJFanDInsights into the role of ERp57 in cancerJ Cancer122456246420213375862210.7150/jca.48707 MorganEArnoldMCamargoMCGiniAKunzmannATMatsudaTMeheusFVerhoevenRHAVignatJLaversanneMThe current and future incidence and mortality of gastric cancer in 185 countries, 2020-40: A population-based modelling studyEClinicalMedicine4710140420223549706410.1016/j.eclinm.2022.101404

Quantification of DEPs. (A) The quantity of DEPs utilizing a threshold of 1.5-fold. (B) A volcanic scatter plot showing a threshold of 1.5. DEP, differentially expressed protein; HIGN, high-grade neoplasia; CON, control.

GO annotation analysis revealing the possible biomarkers principally engaged in the gastric HGIN group. (A) GO terms’ names, namely cellular component, molecular function and biological process in HGIN-linked proteins. (B) Subcellular structural localization evaluation in HGIN-linked proteins. GO, gene ontology; HGIN, high-grade intraepithelial neoplasia.

GO enrichment analyses. (A) GO enrichment analyses for biological process. (B) GO enrichment analyses for cellular component. (C) GO enrichment analyses for molecular function. GO, gene ontology.

Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis comparing differentially expressed proteins.

Demographics and clinicopathological characteristics of patients and healthy controls.

Characteristics Patients Healthy controls P-value
Sex, n (%)     1.000
     Male 5(100) 5(100)  
     Female 0 (0) 0 (0)  
Mean age ± SD, years 62.6±3.8 59.8±0.8 0.168
Pathology, n (%)     0.004
     Gastric HGIN 5(100) 0  
     Normal 0 5(100)  

HGIN, high-grade intraepithelial neoplasia.

Differentially expressed proteins between the gastric HGIN group and the control group.

A, Upregulated proteins
Protein accession no. Protein description Gene name HGIN/CON ratio HGIN/CON P-value
F6SYF8 Dickkopf-related protein 3 DKK3 4.698 0.030
P55259 Pancreatic secretory granulemembrane major glycoprotein GP2 GP2 2.938 0.022
A0A0G2JMX5 Leukocyte immunoglobulin-like receptor subfamily B member 5 LILRB5 2.802 0.005
O76076 CCN family member 5 CCN5 2.667 0.036
A0A0B4J2D9 Immunoglobulin κ variable 1D-13 IGKV1D-13 2.639 0.029
Q9NQ38 Serine protease inhibitor Kazal-type 5 SPINK5 2.585 0.029
E7ENL6 Collagen α-3(VI) chain COL6A3 2.383 0.032
H0YCU9 Transgelin (Fragment) TAGLN 2.381 0.031
Q92484 Acid sphingomyelinase-like phosphodiesterase 3a SMPDL3A 2.009 0.026
Q12864 Cadherin-17 CDH17 1.995 0.050
A0A1W2PRS4 Junctional adhesion molecule-like JAML 1.964 0.023
Q9BRV8 Suppressor of IKBKE 1 SIKE1 1.836 0.035
A0A0A0MSN4 Angiotensin-converting enzyme ACE 1.748 0.031
P17931 Galectin-3 LGALS3 1.738 0.042
Q15828 Cystatin-M CST6 1.611 0.007
P16070 CD44 antigen CD44 1.538 0.035
P61769 β-2-microglobulin B2M 1.53 0.037
Q9NPY3 Complement component C1q receptor CD93 1.516 0.003
B, Downregulated proteins
Protein accession no. Protein description Gene name HGIN/CON ratio HGIN/CON P-value
P30101 Protein disulfide-isomerase A3 PDIA3 0.659 0.034
P28070 Proteasome subunit β type-4 PSMB4 0.618 0.043
P26447 Protein S100-A4 S100A4 0.591 0.018
A0A0J9YVY3 Immunoglobulin heavy variable 7-4-1 IGHV7-4-1 0.551 0.049
Q12882 Dihydropyrimidine dehydrogenase [NADP(+)] DPYD 0.539 0.024
B5MDQ0 DNA excision repair protein ERCC-6-like ERCC6L 0.531 0.044
O95897 Noelin-2 OLFM2 0.484 0.050
O14793 Growth/differentiation factor 8 MSTN 0.456 0.007
P04406 Glyceraldehyde-3-phosphate dehydrogenase GAPDH 0.421 0.033
Q9HBB8 Cadherin-related family member 5 CDHR5 0.415 0.040
Q8N1N4 Keratin, type II cytoskeletal 78 KRT78 0.337 0.038
P01705 Immunoglobulin λ variable 2-23 IGLV2-23 0.279 0.033

HGIN, high-grade neoplasia; CON, control.