OLFM4 promotes the progression of intestinal metaplasia through activation of the MYH9/GSK3β/β-catenin pathway

OLFM4 promotes the progression of intestinal metaplasia through activation of the MYH9/GSK3β/β-catenin pathway | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article OLFM4 promotes the progression of intestinal metaplasia through activation of the MYH9/GSK3β/β-catenin pathway wei hongfa, Wenchao Li, Leli Zeng, Ni Ding, Kuan Li, Hong Yu, and 11 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4014155/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Background Intestinal metaplasia (IM) is classified into complete intestinal metaplasia (CIM) and incomplete intestinal metaplasia (IIM). Patients diagnosed with IIM face an elevated susceptibility to the development of gastric cancer, underscoring the critical need for early screening measures. In addition to the complexities associated with diagnosis, the exact mechanisms driving the progression of gastric cancer in IIM patients remain poorly understood. OLFM4 is overexpressed in several types of tumors, including colorectal, gastric, pancreatic, and ovarian cancers, and its expression has been associated with tumor progression. Methods In this study, we used pathological sections from two clinical centers, biopsies of IM tissues, precancerous lesions of gastric cancer (PLGC) cell models, animal models, and organoids to explore the role of OLFM4 in IIM. Results Our results show that OLFM4 expression is highly increased in IIM, with superior diagnostic accuracy of IIM when compared to CDX2 and MUC2. OLFM4, along with MYH9, was overexpressed in IM organoids and PLGC animal models. Furthermore, OLFM4, in combination with Myosin heavy chain 9 (MYH9), accelerated the ubiquitination of GSK3β and resulted in increased β-catenin levels through the Wnt signaling pathway, promoting the proliferation and invasion abilities of PLGC cells. Conclusions OLFM4 represents a novel biomarker for IIM and could be utilized as an important auxiliary means to delimit the key population for early gastric cancer screening. Finally, our study identifies cell signaling pathways involved in the progression of IM. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Gastric cancer (GC) is a significant health concern, ranking fifth and fourth in terms of morbidity and mortality 1 . Early diagnosis rates for gastric cancer worldwide are currently below 20%, considerably lower than the 50%-60% rates seen in Japan and South Korea. This is primarily due to the hidden onset of gastric cancer, and the substantial cost of intensive early screening, which is not currently feasible on a widespread scale 2-6 . Targeted surveillance of precancerous lesions, including them in the high-risk group for critical monitoring, can improve the efficiency and financial benefits of screening for early gastric cancer (EGC). Chronic stimulation of normal gastric mucosa by factors like Helicobacter pylori (HP), carcinogens, high salt, bile acids, tobacco, or alcohol can lead to pathological progression from chronic atrophic gastritis (CAG), intestinal metaplasia (IM), gastric dysplasia, and ultimately adenocarcinoma, a process known as the "Correa cascade" 7,8 . Intestinal metaplasia represents a significant portion of precancerous lesions associated with gastric cancer. Based on Lauren's classification, approximately 80% of gastric cancer cases are categorized as the intestinal type, which originates from the mucosa undergoing intestinal metaplasia 9-11 . Intestinal metaplasia results from chronic inflammatory stimulation of the stomach's normal mucosal epithelium, leading to atrophy of parietal cells and the formation of goblet cells and enterocytes. While enteroid epithelium replaces the lost gastric glands, this process is marked by impaired differentiation and atypical regeneration, increasing the risk of cancer 8,12-14 . Consequently, patients with intestinal metaplasia have a significantly higher risk of developing cancer compared to healthy individuals 13,15 . IIM is associated with a 4- to 11-fold higher risk of developing gastric adenocarcinoma compared to CIM 16-18 . Since various subtypes of intestinal metaplasia exhibit different levels of cancer risk, recognizing IIM is essential. Currently, HE staining and AB-PAS staining are the primary methods used for assessing IIM. However, staining interpretation can be challenging for non-pathologists as well as non-gastrointestinal pathological experts 13 . Therefore, developing appropriate markers for IIM diagnosis, identifying "high-risk" IIM groups, and defining key screening targets can enhance endoscopy efficiency, increase EGC diagnosis rates, and significantly reduce associated healthcare costs. OLFM4, also known as Olfactomedin-4, GW112, or hGC-1, is a glycoprotein belonging to the olfactory regulatory protein family 19 . OLFM4 expression is absent in normal gastric mucosa but is present in the small intestine and colon 20,21 . Research has highlighted OLFM4's crucial role in regulating intestinal stem cells 22,23 . In fact, one of OLFM4's key biological functions is to regulate cell adhesion and cell migration by interacting with adhesion molecules, the cytoskeleton, and the extracellular matrix 24 . Studies have shown elevated OLFM4 expression in gastric cancer and colorectal cancer, particularly in the early stages of tumor formation 25 . However, previous studies have not explored the differential expression of OLFM4 in different severity of intestinal metaplasia and the mechanism by which OLFM4 promotes the progression of intestinal metaplasia. The activation of the Wnt/β-catenin signaling pathway is crucial for tumor invasion and metastasis 26-28 . Consequently, Wnt/β-catenin expression is positive in 86% of intestinal metaplasia and 95% of gastric cancer cases 29 . Expanding upon the aforementioned literature review, our research study centered on evaluating OLFM4 expression in IIM. We aimed to construct a predictive model utilizing OLFM4 as a classifier for distinguishing different types of intestinal metaplasia. Additionally, we investigated potential mechanisms and activated signaling pathways by which OLFM4 contributes to the progression of gastric mucosal intestinal metaplasia. Materials and Methods Patient samples and cell lines This study received approval from the Medical Ethical Committee of the Seventh Affiliated Hospital of Sun Yat-sen University and The People's Hospital of Fengqing in Yunnan Province (KY-2021-105-01, 2021/12/02). Written informed consent, following the Declaration of Helsinki guidelines, was obtained from every patient. Paraffin-embedded archived samples were collected between 2018 and 2022 from two distinct clinical centers. The samples of the first clinical center included 78 newly diagnosed intestinal metaplasia patients and 40 healthy individuals from the Seventh Affiliated Hospital (Training set). The samples of the second clinical center consisted of 63 newly diagnosed intestinal metaplasia patients and 40 healthy individuals from The People's Hospital of Fengqing (Validation set). Histological diagnoses were based on the Third edition of Pathology 30 , and the classification of intestinal metaplasia was determined according to Pathology and classical literature 30-33 . GES-1 cells were purchased from ATCC and underwent short tandem repeat (STR) analysis. Patient-derived organoids (PDO) were cultured in the laboratory of the Seventh Affiliated Hospital. Biopsy tissues were obtained from patients with intestinal metaplasia or healthy individuals, and these tissues were processed into PDO and cultured in an organoid medium. DEGs in intestinal metaplasia tissues We selected the GSE78523 dataset from the Gene Expression Omnibus database (GEO), which comprises samples from both normal gastric mucosa and intestinal metaplasia tissues. Differential Expression Genes (DEGs) were analyzed using the "DESeq2" package in R. The volcano plots and heatmaps were generated using the "ggplot2" package or GraphPad Prism 8.0.2 based on the results of the DEGs analysis. Identification of a new PLGC subgroup in intestinal metaplasia tissues We selected and analyzed the GSE134520 dataset, a single-cell RNA sequencing (scRNA-seq) dataset that includes non-atrophic gastritis (NAG), intestinal metaplasia (IM), and early gastric cancer (EGC), using the "Seurat" R package. Data normalization was performed using the "NormalizeData" function, and inconsequential sources of variation were removed with the "ScaleData" function. The "FindVariableFeatures" function was used to identify highly variable genes (HVGs), and the "RunPCA" function identified 50 significant principal component analyses (PCA). We embedded cells into the graph structure of PCAs using the "FindNeighbors" and "FindClusters" functions. The spatial correlation of expression data was presented through Uniform Manifold Approximation and Projection (UMAP) plots based on RunUMAP and Dimplot. We selected all epithelial cells using classical epithelial markers "EPCAM" and "KRT19". A total of 11 clusters were identified based on HVGs. To assess the malignancy of glandular cells, the "inferCNV" package was used to determine cellular heterogeneity by identifying chromosome copy number variation (CNV) in scRNA-seq. Gastric cancer cells served as a positive control for CNV, and precancerous lesions of gastric carcinoma (PLGC) cells were identified by "inferCNV" in comparison. The "Monocle" function was employed to display the evolution of gastric mucosa during EGC development by performing pseudotime analysis, projecting high-dimensional data into one dimension. The "Cytotrace" function was utilized to create a critical RNA-based feature for developmental potential and to establish a platform for delineating cellular hierarchies, attempting to predict differentiation states from single-cell RNA sequencing (scRNA-seq). Reagents N-Methyl-N”-nitro-N-nitrosoguanidine (MNNG) was obtained from Meilunbio (MB0455-2, China), and Blebbistatin (the inhibitor of MYH9) was bought from GlpBio (GC12341, USA). Cell transfection Lentivirus vectors encoding shOLFM4, oeOLFM4, shMYH9, oeMYH9, control, or HA-ubiquitin plasmids were from GeneCopoeia (Guangzhou, China). HA-ubiquitin plasmids were transfected into cells for 48 hours, followed by lysis for immunoblotting with anti-HA antibodies. Immunohistochemistry (IHC), Immunofluorescence (IF) staining, Alcian blue-periodic acid-Schiff (AB-PAS) straining, and hematoxylin-eosin (HE) staining We conducted IHC, IF, AB-PAS, and HE staining using standard protocols. IF staining was observed either through fluorescence microscopy (Leica, DM6B) or confocal microscopy (ZEISS, LSM-880). Two independent observers, unaware of the patient's clinical information, evaluated the staining results at separate intervals. The IHC score was determined using Image J to calculate the proportion of stained areas. Primary antibodies used included OLFM4 (14369S, CST), CDX2 (A19030, Abclonal), MUC2 (sc-515032, Santa Cruz Biotechnology), MYH9 (11128-1-AP, Proteintech), E-cadherin (60335-1-Ig, Proteintech), Vimentin (60330-1-Ig, Proteintech), and Ki67 (ab16667, Abcam). Western blotting Total protein extraction and Western Blotting were performed following standard methods. The primary antibodies included OLFM4 (14369S, CST), MYH9 (11128-1-AP, Proteintech), GSK3-β (22104-1-AP, Proteintech), β-catenin (51067-2-AP, Proteintech), β-actin (66009-1-Ig, Proteintech), STAT3 (10253-2-AP, Proteintech), c-Myc (10828-1-AP, Proteintech), N-cadherin (22018-1-AP, Proteintech), E-cadherin (60335-1-Ig, Proteintech), Vimentin (60330-1-Ig, Proteintech), Snai1 (13099-1-AP, Proteintech) Ubiquitin (10201-2-AP, Proteintech), and GAPDH (60004-1-Ig, Proteintech). Quantitative Reverse Transcription-PCR (qRT-PCR) Total RNA was harvested, and cDNA was generated by a reverse transcription reagent kit (AG11706, Accurate Biology, China). Then, the cDNA template was used for amplification with specific primers. qRT-PCR was conducted using SYBR-green PCR Master Mix and 45 cycles of 95℃ for 10s, 60℃ for 20s, and 72℃ for 20s. These sequences of primers are defined as follows: Forward Reverse CDX2 TTCACTACAGTCGCTACATCACCA CTGCGGTTCTGAAACCAGATT MUC1 TTCACCACCACCATGACACC GGGGCTGTGGTAGCTGTAAG MUC2 GGGGAGTGCTGTAAGAAGTGTGA GTTGGAGACGGACGAGATGAG OLFM4 GAGAAATCGTGGCTCTGAAGAC CAGACGGTTTGCTGATGTTC GSK3β CATCCTTGGACTAAGGTCTTCCG CATTTGTGGGGGTTGAAGCAG β-actin TCAAGATCATTGCTCCTCCTGAG ACATCTGCTGGAAGGTGGACA Cell proliferation assay, colony-formation assay, EdU assay, Wound healing assay, and Transwell assay Cell Proliferation: We assessed cell proliferation using the Cell Counting Kit-8 (CCK-8) assay kit (Biosharp, China) and the Microplate Reader (BioTeK, USA). We seeded 2,000 cells into 96-well plates and cultured them for 1-5 days. Each day, we mixed the CCK-8 reagent with the cell culture medium at a 1:9 ratio and incubated the cells for 90 minutes. We measured absorbance at 450 nanometers using a spectrophotometer in each culture dish. Cell Colony Formation: For colony formation, we inoculated 800 cells in six-well plates and cultured them for 14 days. The number of cell colonies was determined by microscopy after staining with crystal violet dye. Cell Viability (EdU Assay): We measured cell viability using the EdU assay. We plated 6,000 cells into 96-well plates, treated them with EdU reagent (10 μM, Beyotime, China), and observed them with fluorescence microscopy (Leica, DMI8). Wound Healing Assay: Cells were plated and grown to confluence in six-well plates. We created scratches with a pipette tip and examined the cell migration process under a microscope at 0 and 24 hours. Cell Migration and Invasion: We evaluated cell migration and invasion using 24-well transwells (8.0 μm, Corning, USA), precoated with Matrigel in invasion assay but without Matrigel in migration assay. In the lower chamber, we added 500 μL RMPI-1640 with 10% FBS. We seeded 5 × 10 4 treated cells suspended in 500 μL RMPI-1640 without FBS in the upper chamber and cultured them at 37°C for 36 hours. We counted the number of GES-1 cells in the lower chamber using a cell counting plate. Co-immunoprecipitation (IP) and mass spectrometry Protein extraction and purification were performed using primary antibodies for IP and Protein A/G Magnetic Beads (B23202, Selleck). Mass spectrometry was performed by Baiqu Tech. co. LTD (Hangzhou, China) and results were provided in Table 5. The primary antibodies included OLFM4 (14369S, CST), MYH9 (60233-1-Ig, Proteintech), and GSK3-β (22104-1-AP, Proteintech). Cycloheximide (CHX) chase assay GES-1 cells were incubated with 2 μM MG132 (HY-13259, MCE, USA) or left untreated. After the treatment of 20 μg/mL CHX (C7698, Sigma-Aldrich) for different times, cells were harvested and prepared for Western blotting. PLGC animal model The Institutional Animal Care and Use Committee (IACUC) (TopBiotech Co., LTD., Shenzhen) approved the experimental methods and animal use and care protocols. We obtained twenty male Sprague Dawley (SD) rats five-week-old for each group from Gempharmatech company (Jiangsu, China). We prepared an MNNG solution with a concentration of 170 µg/ml by dissolving MNNG in drinking water containing 5% alcohol. The rats received the MNNG solution by gavage every two days, with a regimen of one day of a normal diet and one day of fasting. This procedure continued for 24 weeks. Statistical analysis Statistical analyses were performed by using SPSS 22.0 (SPSS Inc., Chicago). Histogram Graphing was performed with GraphPad Prism 8.0.2 (GraphPad Software). Each in vitro experiment was repeated three times or more and experimental data were depicted as mean ± standard deviation (SD). Quantitative variables were analyzed using a Student t-test for Gaussian distribution and non-parametric tests for non-Gaussian distribution. Differences were considered statistically significant at p < 0.05 (*p < 0.05, **p < 0.01, ***p < 0.001). Results 1. OLFM4 is remarkably differentially expressed in intestinal metaplasia tissue Intestinal metaplasia tissues are closely associated with gastric cancer and are considered the origin of intestinal-type gastric cancer. In HE staining of pathological sections, the mucosal layer or submucosal layer of gastric cancer tissues, and para-cancerous tissues were observed to be accompanied by intestinal metaplasia lesions (Fig. 1a). To identify significant Differentially Expressed Genes (DEGs) in intestinal metaplasia tissue, we analyzed the RNA-seq dataset GSE78523, revealing high expression of OLFM4 and MUC2 in intestinal metaplasia tissue (Fig. 1b, Fig. S1a). As MUC2 is established as a conventional biomarker for intestinal metaplasia, our research endeavors have been directed towards exploring the role of OLFM4. Although OLFM4 has been reported among the highly expressed genes in intestinal metaplasia tissues by transcriptome sequencing, its expression profile and the mechanism by which it promotes progression in incomplete intestinal metaplasia remain unexplored 34,35 . To further investigate OLFM4 in intestinal metaplasia, we analyzed the scRNA-seq dataset GSE134520. Traditional biomarkers, "EPCAM" and "KRT19", were used to distinguish epithelial cells and stromal cells, with epithelial cells constituting 88.4% of all cells (Fig. S1b-d). Epithelial cells were categorized into 11 clusters, yielding 7 cell subgroups based on their cell markers (Fig. 1c, Fig. S1e-f). To assess malignancy of epithelial cells, copy number variations (CNV) were analyzed in the 11 clusters using inferCNV (Fig. 1d). Clusters 2, 3, and 7 were identified as gastric cancer cells by their cancerous origin and frequent CNV (Fig. 1d, Fig. S1e). Cluster 8 was classified as PLGC cells based on their comparable frequency of copy number variations (CNV) to gastric cancer cells and their partial cellular origin from intestinal metaplasia tissue (Fig. 1d, Fig. S1e). PLGC cells exhibited less differentiation across all subgroups and expressed high levels of OLFM4 (Fig. 1e-g) which was confirmed as a cell marker in PLGC cells (Fig. 1h-i, Fig. S1f-g). Pseudotime analyses indicated overlapping differentiation trajectories between PLGC cells and gastric cancer cells, suggesting a tendency toward malignancy in PLGC cells (Fig. 1g). Consequently, we speculated that OLFM4 might be one of the biomarkers in PLGC cells. 2. OLFM4 is a novel biomarker of incomplete intestinal metaplasia We selected IM biopsies and normal mucosa samples for qPCR and Western Blot analyses. Traditional biomarkers for IM, CDX2, and MUC2, exhibited significant increases in expression [A1] . Our target molecule, OLFM4, showed elevated expression in IM biopsies (Fig. 2a-b). To diagnose IM, we utilized HE staining and AB-PAS staining to assess the presence of Goblet cell in pathological sections of gastric lesions. Goblet cells are bright in HE staining, but dark blue in the AB-PAS staining (Fig. 2c, Fig. S2a). Statistical analysis revealed increased expression of CDX2, MUC2, and OLFM4 in both the training set and validation set. OLFM4 demonstrated an AUC value of 0.825 in the training set and 0.915 in the validation set (Fig. 2d, Table 1, Table 2). Certainly, the diagnostic effectiveness of OLFM4 in identifying IIM was comparable to that of CDX2 and MUC2. (Fig. 2d). To distinguish between complete intestinal metaplasia (CIM) and incomplete intestinal metaplasia (IIM), we conducted HE and AB-PAS immunohistochemical experiments (Fig. 2e). HE staining revealed eosinophilic secretory granules in Paneth cells and intact brush borders in CIM tissues (Fig. 2f). In comparison to CIM, OLFM4 showed higher expression in IIM, while CDX2 and MUC2 displayed no significant differences (Fig. 2g, Fig. S2b). OLFM4 exhibited an AUC value of 0.729 in the Training set and 0.786 in the Validation set, indicating relatively better diagnostic performance compared to the other two biomarkers in IIM (Fig. 2g, Table 3, Table 4). Consequently, OLFM4 might serve as a superior biomarker for distinguishing IIM tissues in immunohistochemistry. Table 1 The ROC curve of IM in the Training set Genes Area ±SE P value 95% CI Youden index Up Down Sensitivity Specificity CDX2 0.846 ±0.045 <0.0001 0.759 0.934 88.5% 77.5% MUC2 0.874 ±0.032 <0.0001 0.812 0.936 70.5% 92.5% OLFM4 0.829 ±0.038 <0.0001 0.753 0.904 82.1% 70.0% Table 2 The ROC curve of IM in the Validation set Genes Area ±SE P value 95% CI Youden index Up Down Sensitivity Specificity CDX2 0.915 ±0.027 <0.0001 0.862 0.968 77.8% 92.5% MUC2 0.918 ±0.016 <0.0001 0.877 0.938 76.2% 99.5% OLFM4 0.868 ±0.034 <0.0001 0.802 0.935 85.7% 72.5% Table 3 The ROC curve of IIM in the Training set Genes Area ±SE P value 95% CI Youden index Up Down Sensitivity Specificity CDX2 0.501 ±0.073 0.992 0.357 0.642 81.3% 19.2% MUC2 0.510 ±0.068 0.886 0.376 0.644 87.5% 23.3% OLFM4 0.729 ±0.056 0.001 0.620 0.838 52.0% 93.3% Table 4 The ROC curve of IIM in the Validation set Genes Area ±SE P value 95% CI Youden index Up Down Sensitivity Specificity CDX2 0.551 ±0.070 0.494 0.407 0.694 92.9% 28.6% MUC2 0.522 ±0.074 0.485 0.407 0.696 82.1% 34.3% OLFM4 0.786 ±0.069 <0.0001 0.651 0.922 71.4% 94.3% 3. Increased expression of OLFM4 in PLGC cells promotes cellular proliferation and invasion To investigate the relationship between OLFM4 and intestinal metaplasia progression, we used a PLGC cell model by inducing GES-1 cells with MNNG exposure. The control group received PBS and the experimental group received MNNG dissolved in 0.3% DMSO to avoid cellular toxicity. As expected MNNG induced a significant increase in classical intestinal metaplasia biomarkers in PLGC cells, especially at a concentration of 200μmol/L for 48 hours (Fig. 3a-d). Subsequent transcriptome sequencing of PLGC cells confirmed markedly high expressions of CDX2 and OLFM4, validating the PLGC cells model (Fig. 3e-f). Consistent with a role for OLFM4 in cancer cell proliferation, our experiments showed increased proliferation and enhanced invasion capabilities of both PLGC cells and OLFM4-overexpressing GES-1 cells (oeOLFM4) (Fig. 3g-j, Fig. S3a-f). Conversely, the knockdown of OLFM4 (shOLFM4) in PLGC cells led to decreased proliferation and invasion capabilities (Fig. 3k-n, Fig. S3g-l). These data confirm the role of OLFM4 in PLGC progression. 4. OLFM4 cooperates with MYH9 to activate the Wnt signaling pathway and enhance the progression of intestinal metaplasia We employed bioinformatics methods, specifically Gene Set Enrichment Analysis (GSEA) in Hallmark and KEGG, to analyze the RNA-seq dataset GSE78523 for mechanistic insights. High OLFM4 expression in intestinal metaplasia tissues was associated with enrichment in the Myc, EMT, and Wnt signaling pathways (Fig. 4a). Additionally, utilizing the AddModuleScore analysis on the scRNA-seq dataset GSE134520, we observed that PLGC cells with elevated OLFM4 expression exhibited the highest scores in Cancer, EMT, and Wnt/β-catenin signaling pathways (Fig. 4b). Western Blot assays confirmed a positive correlation between OLFM4, Wnt pathway markers, EMT transition markers, and c-Myc (Fig. 4c-d). Immunofluorescent staining confirmed an overlap expression of OLFM4, Ki67 and Vimentin in intestinal metaplasia tissues suggesting that OFLM4 stimulates tumor cell proliferation (Fig. 4e-f). To identify OLFM4-interacting proteins and associated signaling pathways involved in intestinal metaplasia, we conducted Co-immunoprecipitation (CoIP) assays and mass spectrometry-based quantitative proteomics. Table 5 displays the top five proteins and their interaction scores. Interestingly, MYH9 a gene closely related to the progression and poor prognosis of gastric cancer and esophageal cancer ranked first. Confocal immunofluorescence and CoIP assays confirmed endogenous MYH9 as a novel OLFM4-associated protein (Fig. 5a-c). In addition, MYH9 expression increased with OLFM4 overexpression in GES-1 cells (Fig. 5d). Western Blot assays suggest that the interaction between OLFM4 and MYH9 activate the Wnt signaling pathway (Fig. 5e-f). Furthermore, the knockdown of MYH9 in PLGC cells or its suppression with the inhibitor Blebbistatin in oeOLFM4 cells significantly weakened cellular proliferation and invasion abilities (Fig. 5g-n). Table 5 The first 5 proteins that bind to OLFM4 protein in mass spectrometry ID Gene MW [kDa] Score Sequest HT Abundance Protein Unique Peptides Unique Peptides Peptides (by Search Engine) Wnt/β-catenin pathway P35579 MYH9 226.4 250.94 1306113914 55 60 70 [ 36,37 ] P35580 MYH10 229.0 183.77 228611562 39 43 53 [ 38,39 ] P60709 ACTB 41.7 98.44 2971294207 0 3 14 [ 40 ] P63267 ACTG2 41.9 79.64 4936448 0 3 27 [ 41 ] P21333 FLNA 280.7 75.08 31837103 27 27 18 [ 42,43 ] 5. OLFM4 regulates Wnt signaling pathway by influencing the ubiquitination of GSK3β in intestinal metaplasia Previous studies have shown that GSK3β interact with MYH9 to regulate the Wnt pathway 37 . Our study revealed that an increase in OLFM4 protein expression is associated with a decrease in GSK3β protein expression. Since, GSK3β mRNA levels remained unchanged (Fig. 6a), we analyzed the effects of OLFM4 on GSK3β protein turnover. Indeed, CHX assays demonstrated that knocking down of either OLFM4 or MYH9 extended the half-life of GSK3β in PLGC cells (Fig. 6b). CoIP assays were performed to elucidate the role of OLFM4 and MYH9 in the promotion between ubiquitin and GSK3β in PLGC cells (Fig. 6c). Eleven candidate structure-damaged variants of the OLFM4 protein with highest Polyphen prediction score were predicted in Missense3D database (Supplemental table 1). The Pymol software was used to analyze the protein-protein docking between OLFM4 and MYH9. Two localizations (D388 and G438) of OLFM4, which had polar contacts with MYH9, were screened out for further mutation experiments (Fig. 6d). The 3D structural changes of missense mutations were constructed (Fig. 6e). Our results showed that the D388 mutation but not the G438 mutation of OLFM4 upregulated the β-catenin and activated the Wnt signal pathway (Fig. 6f-g). The G438 mutation downregulated the ubiquitination level of GSK3β in oeOLFM4 cells (Fig. 6h). Further CoIP assay showed that the G438 mutation of OLFM4 protein could not have interaction with MYH9 (Fig. 6i). Our studies suggest that OLFM4 can interact with MYH9 to facilitate the ubiquitination of GSK3β, consequently activating the Wnt signaling pathway. 6. OLFM4 and MYH9 increased in intestinal metaplasia organoids and PLGC animal models PDO experiments using both intestinal metaplasia and normal gastric mucosa organoids revealed significantly elevated expressions of CDX2 and MUC2 in the intestinal metaplasia organoids. The immunofluorescence and immunohistochemical assays demonstrated robust expression of OLFM4 and MYH9 in the intestinal metaplasia organoids (Fig. 6j-k, Fig. S5a). In the PLGC animal model induced by MNNG, the gastric mucosa exhibited deep, extensive ulcers surrounded by red and swollen tissue (Fig. 6l). Under light microscopy, the gastric mucosal glands of PLGC models in HE staining exhibited swelling and disorganization, with a significant increase in heterogeneous cells that were notably aggravated (Fig. S5b). Furthermore, the conventional biomarkers CDX2 and MUC2, alongside OLFM4 and MYH9, displayed heightened expression levels in the PLGC models (Fig. 6m, Fig. S5b). Overall, OLFM4 was the biomarker of IIM tissue and might promote the progression of intestinal metaplasia through the MYH9/GSK3β/β-catenin pathway (Fig. 7). Discussion In this study, we identified OLFM4 as a novel biomarker of incomplete intestinal metaplasia (IIM) and discovered that OLFM4 promotes the progression of IIM through the MYH9/GSK3β/β-catenin pathway. These findings have significant implications for the development of novel biomarkers for IIM and novel therapeutic strategies for preventing the progression of intestinal metaplasia. The annual incidence of gastric cancer in patients with intestinal metaplasia was 12.4/10000 (95%CI: 10.7–14.3), which was significantly higher than that in healthy people (2/100000-5/100000) 44 . A study from Sweden showed that 1 in 39 patients with intestinal metaplasia will develop gastric cancer within 20 years, a much higher incidence than in healthy people 45 . However, the prevalence of intestinal-type gastric cancer was greater in IIM, which had a 4- to 11-fold higher risk of suffering gastric adenocarcinoma than CIM, and the risk rose with the severity of IIM 16–18 . In agreement with previous studies, we found that intestinal-type gastric cancers were frequently accompanied by incomplete intestinal metaplasia tissues suggesting that IIM is an important precancerous lesion in gastric cancer development. Unfortunately, while being good indicators for intestinal metaplasia, CDX2, and MUC2 cannot be employed as biomarkers for IIM 46,47 . This result was also validated by the pathology data from multi-center studies used in our investigation. Therefore, the identification of incomplete intestinal metaplasia (IIM) lesions is of utmost importance in determining the high-priority population for early screening of gastric cancer. Nevertheless, the diagnosis of incomplete intestinal metaplasia (IIM) poses significant challenges, and there is a no good biomarkers specific to incomplete intestinal metaplasia tissues. Furthermore, few investigations focused on the progression of intestinal metaplasia. Studies have reported that the subtype of IM can be determined by CD10 staining of the brush surface of IM cells in the gastric mucosa, with a sensitivity of 87.5% and a specificity of 96.7% 48 . However, accurately identifying incomplete intestinal metaplasia (IIM) based solely on sporadic incomplete brush border features was not feasible 33 . In HID-AB staining, the mucin profile in intestinal metaplasia cells can be used as a criterion when identifying the classification of intestinal metaplasia: mucin sulfate or salivary mucin in IIM cells can be stained brownish black by HID solution, while proteoglycans and hyaluronic acid in CIM cells are stained blue by Alcian solution. However, both HID-AB staining and AB-PAS staining methods were unable to provide clear insights into the stemness characteristics and proliferation of incomplete intestinal metaplasia (IIM). Additionally, the HID-AB staining method had toxicity concerns 49 . Therefore, the HID-AB staining has limited application in clinical practice. In the same study, it was suggested that Das1 staining could detect goblet cells in incomplete intestinal metaplasia (IIM) tissues. However, goblet cells were not found to be a distinguishing feature of IIM, and the Das1 staining method exhibited limited specificity and sensitivity. Consequently, it could not categorize intestinal metaplasia 48 . OLFM4 expression is closely related to cell stemness, reflecting the strong proliferative ability of cells with high OLFM4 expression 22,50 . OLFM4 had been identified from a gene signature of Lgr5 + stem cells as a strong marker for murine small intestine stem cells 22 . The precancerous lesions in the stomach and esophagus exhibited significant levels of OLFM4 expression, which was positively correlated with the severity of diseases 25 . Here, we demonstrated that OLFM4 is a more effective biomarker for identifying IIM because it is expressed in intestinal metaplasia, its detection was highly specific in IIM, and OFLM4 had higher diagnostic performance. Identifying and diagnosing incomplete intestinal metaplasia (IIM) can play a crucial role in identifying individuals who should be prioritized for early screening of gastric cancer. This can significantly enhance the effectiveness of endoscopic screening and serve as a valuable auxiliary tool for early detection in screening programs. The role of OLFM4, a gene associated with stemness properties, has received limited attention in the context of intestinal metaplasia. The amount of OLFM4 + cells is positively associated with the number of intestinal stem cells and the expression of OLFM4 was highly confined to the Lgr5 + stem cell area 22,25,50 . Some studies reported that OLFM4 can promote gastric cancer progression by promoting cell proliferation and invasion 51,52 . In support of this, knockdown of OLFM4 expression greatly decreased cell growth and promoted apoptosis of gastric cancer cells 52–54 . Nonetheless, there is limited literature that specifically highlights the elevated expression of OLFM4 in intestinal metaplasia tissue 25,34 . OLFM4 expression is elevated in gastric and colorectal cancer, particularly in the early stages of tumor formation 25 . Other investigations have found that OLFM4 expression is higher in early-stage, moderately differentiated, and well-differentiated cancers, while it was considerably lower in late-stage, poorly differentiated, and undifferentiated tumors 55,56 . OLFM4 expression was also associated with activation of the Wnt/β-catenin signaling pathway, which played a key role in regulating cell growth and differentiation 26–28,57 . However, no literature explored the clinical application value, the biological function, and the mechanism of OLFM4 in intestinal metaplasia, especially in IIM. OLFM4 is significantly increased in IIM, possibly because OLFM4 matched the specific stemness characteristic of IIM 58 . Nevertheless, due to the absence of an extended follow-up period, it remains unclear whether IIM patients exhibiting high OLFM4 expression eventually develop gastric cancer. Collectively, our findings indicate that OLFM4 expression could serve as a pathologic predictor in IIM. MNNG, a chemical carcinogen, has been used to induce precancerous lesions of gastric cancer (PLGC) cells models and mimic PLGC in animal models 59–62 . The PLGC cells, induced by MNNG, is a widely recognized cell model of intestinal metaplasia 63–65 . We performed transcriptome sequencing (RNA-seq) on PLGC cells, and discovered that they strongly expressed CDX2, making them an ideal cell model for researching intestinal metaplasia. Concurrently, our study confirmed the significant upregulation of OLFM4 in RNA-seq analysis of PLGC cells. Previous research has indicated that PLGC cells exhibit enhanced proliferative and invasive characteristics, consistent with our own observations from functional experiments conducted on PLGC cells 63–65 . Previous studies have shown that c-Ras, c-met, and ErbB2 mutations in PLGC cells caused GES-1 carcinogenesis in MNNG induced PLGC animal models 66,67 . Oral administration of MNNG in Sprague Dawley (SD) rats is associated with the development of PLGC 68,69 . In this study, we cultured intestinal metaplasia organoids which simulates and retains the information and functionality of intestinal metaplasia tissues 70 . The OLFM4 and MYH9 expression were both shown to be higher in intestinal metaplasia organoids than normal gastric mucosa, which is compatible with the PLGC cells model. In this study, we also demonstrated that OLFM4, combined with MYH9, was responsible for intestinal metaplasia progression. The over-expression of OLFM4 and the ability of malignant biological behavior including proliferation, migration and invasion were synchronously enhanced in PLGC cells. Mechanistic investigations revealed that OLFM4 plays a role in activating the downstream signaling pathways of Wnt/β-catenin during the formation of intestinal metaplasia. This activation is involved in mediating tumor growth and the process of epithelial-mesenchymal transition (EMT). The Wnt pathway has been reported to be expressed in 86% of intestinal metaplasia, suggesting that Wnt plays an important role in intestinal metaplasia 29 . Intriguingly, we demonstrated that MYH9 and OLFM4 cooperate to regulate the ubiquitination and degradation of GSK3β and to establish a positive regulatory loop MYH9/GSK3β/β-catenin involved in the progression of intestinal metaplasia. We discovered that GSK3β is ubiquitinated in addition to being phosphorylated, especially in intestinal metaplasia 71 . Myosin heavy chain 9 (MYH9) encoded non-muscle myosin II (NMM-II) plays a crucial role in cell adhesion, migration, proliferation, and differentiation. Studies have found that MYH9 can promote the progression of liver cancer and lymphoma through the Wnt pathway 36,37 . Considering that MYH9 overexpression has been associated with gastric cancer (GC) progression and unfavorable prognosis, it is noteworthy that MYH9 exhibits significant positive correlations with parameters such as depth of invasion (T stage), lymph node metastasis (N stage), distant metastasis (M stage), and the overall pTNM stage of gastric cancer 72 . MYH9 overexpression in gastric cancer cells has been shown to enhance their capacity for invasion and metastasis 73 . Blebbistatin is a MYH9 inhibitor, as a tumor suppressor in several malignancies 74 . In our study, we observed that Blebbistatin exhibited significant inhibition of the progression of incomplete intestinal metaplasia (IM) in vitro. In summary, our results established, for the first time, the high expression and oncogenic significance of OLFM4 in intestinal metaplasia, as well as the therapeutic efficacy of Blebbistatin in the treatment of intestinal metaplasia. Overall, our results show that OLFM4 is a novel target for intestinal metaplasia treatment and that blebbistatin represents a potent and effective treatment option for intestinal metaplasia. Abbreviations IM: intestinal metaplasia CIM: complete intestinal metaplasia IIM: incomplete intestinal metaplasia PLGC: precancerous lesions of gastric carcinoma GC: gastric cancer EGC: early gastric cancer HP: Helicobacter pylori scRNA-seq: single-cell RNA sequencing CAG: chronic atrophic gastritis PDO: Patient-derived organoids GEO: Gene Expression Omnibus database CNV: chromosome copy number variation IHC: immunohistochemistry HE: hematoxylin-eosin AB-PAS: Alcian blue-periodic acid-Schiff AUC: area under the curve DEGs: differentially expressed genes Declarations Acknowledgments We thank the Seventh Affiliated Hospital of Sun Yat-sen University assisted in this study. Funding This work was supported by the He Yulong Expert Workstation of Yunnan Province (202104AC100001-B03), Science and Technology Special Project of the National Sustainable Development Agenda Innovation Demonstration Zone of Yunnan Province (202104 AC100001-A10), the National Natural Science Foundation of China (22307151), Guangdong Provincial Key Laboratory of Digestive Cancer Research (No. 2021B1212040006), Guangdong Basic and Applied Basic Research Foundation (2023A1515010156), the Science and Technology Planning Project of Shenzhen Municipality (No. JCYJ20220530144815035, JCYJ20220818102011022, GJHZ20220913142400001) Author information Hongfa Wei and Wenchao Li contributed equally. Authors and Affiliations Digestive Diseases Center, The Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen, China Hongfa Wei, Wenchao Li, Kuan Li1, Hong Yu, Haofan Yin, Zhijian Huang, Songyao Chen, Shangjiu Yang, Cuncan Deng, Nan Cai, Xiancong Chen, Hui Zhou, Guofei Deng, Yulong He, Changhua Zhang Scientific Research Center, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, China, Ni Ding, Fei Jiang, Mo Yang, Xiao-Yong Zhan, Yu Xia, Wei Chen, Zhangsen Huang, Liang Li, Hui Chen, Fuhui Wang, Liang Gu, Leli Zeng Department of General Surgery, The First Affiliated Hospital of Shantou University Medical College, Jinping, Shantou, Guangdong 515041, P.R. China. Hongfa Wei, Feiran Zhang Contributions C.H.Z., J.L., and Y.L.H. designed the study, K.L., N.D., H.Y., F.J., and C.W. diagnosed patients, evaluated patients, and provided samples; H.F.Y., Z.J.H., M.Y., X.Y.Z., C.W., and X.Y. were responsible for providing technological guidance; H.F.W., W.C.L., C.C.D., D.G.F., and S.J.Y. performed and analyzed bioinformatics experiments; H.F.W., W.C.L., S.Y.C., and L.L. contributed to gastric tissue analysis and genomic studies; Z.S.H., H.C., X.C.C. performed and contributed to the single cell analysis; G.F.D., L.G., and F.H.W. performed statistical analyses; H.F.W., W.C.L., C.C.D., and D.N. interpreted data and contributed to figures and tables; F.R.Z., L.L.Z., and C.H.Z. supervised the study and wrote the manuscript with input from all authors. All authors reviewed and approved the final manuscript. Corresponding authors Correspondence to Changhua Zhang, Leli Zeng, Yulong He, or Feiran Zhang. Ethics declarations Ethics approval and consent to participate This study was performed according to the ethical standards of the Declaration of Helsinki and was approved by the ethics committee of the Seventh Affiliated Hospital of Sun Yat-sen University and The People's Hospital of Fengqing in Yunnan Province. The animal experiments were approved by the Institutional Animal Care and Use Committee (IACUC) (TopBiotech Co., LTD., Shenzhen). Consent for publication We have obtained consent to publish this paper from all the participants of this study. Conflict of interest The authors declare no potential conflicts of interest. Additional information Publisher’s Note Springer Nature remains neutral about jurisdictional claims in published maps and institutional affiliations. References Sung H, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. Cancer J Clin. 2021;71:209–49. 10.3322/caac.21660 . Leung WK, et al. 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University","correspondingAuthor":false,"prefix":"","firstName":"Nan","middleName":"","lastName":"Cai","suffix":""},{"id":277076754,"identity":"d03db91f-ead5-455e-92dd-80ac132ac3f2","order_by":11,"name":"Xiancong Chen","email":"","orcid":"","institution":"The Seventh Affiliated Hospital of Sun Yat-sen University","correspondingAuthor":false,"prefix":"","firstName":"Xiancong","middleName":"","lastName":"Chen","suffix":""},{"id":277076755,"identity":"6a727b90-bfee-4947-8f63-a942acf918f8","order_by":12,"name":"Liang Gu","email":"","orcid":"","institution":"The Seventh Affiliated Hospital of Sun Yat-sen University","correspondingAuthor":false,"prefix":"","firstName":"Liang","middleName":"","lastName":"Gu","suffix":""},{"id":277076756,"identity":"cf3fa799-e7fb-413d-a4ad-bc93e5d9f288","order_by":13,"name":"Feiran Zhang","email":"","orcid":"","institution":"The First Affiliated Hospital of Shantou University Medical College","correspondingAuthor":false,"prefix":"","firstName":"Feiran","middleName":"","lastName":"Zhang","suffix":""},{"id":277076757,"identity":"667ce42c-9336-42e7-9ce1-411e5f80d151","order_by":14,"name":"Yulong He","email":"","orcid":"","institution":"The Seventh Affiliated Hospital of Sun Yat-sen University","correspondingAuthor":false,"prefix":"","firstName":"Yulong","middleName":"","lastName":"He","suffix":""},{"id":277076758,"identity":"4eef9856-abf1-48ff-b907-f9a1cba178e4","order_by":15,"name":"Jia Li","email":"","orcid":"","institution":"The Seventh Affiliated Hospital of Sun Yat-sen University","correspondingAuthor":false,"prefix":"","firstName":"Jia","middleName":"","lastName":"Li","suffix":""},{"id":277076759,"identity":"4db01c88-bdf3-4edc-b817-e25f21a15240","order_by":16,"name":"Changhua Zhang","email":"","orcid":"","institution":"The Seventh Affiliated Hospital of Sun Yat-sen University","correspondingAuthor":false,"prefix":"","firstName":"Changhua","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2024-03-04 19:15:42","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4014155/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4014155/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":52303083,"identity":"810a6325-6d56-44ed-a2d8-6ebcd9d16323","added_by":"auto","created_at":"2024-03-08 18:54:28","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2932338,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOLFM4 was the remarkable DEG in PLGC tissue: \u003c/strong\u003e(a) HE staining of pathological sections in T1N0M0 stage gastric cancer: Intestinal metaplasia lesions co-occurring with gastric cancer infiltration throughout the mucosal layer (left), infiltration that breaks through the basement membrane and invades the submucosa (middle), and normal adjacent glandular epithelium with accompanying intestinal metaplastic lesions (right). \u003cstrong\u003eKey indicators:\u003c/strong\u003e \u003cstrong\u003eThe Black dashed line\u003c/strong\u003e represents the basement membrane, and \u003cstrong\u003ethe yellow arrow\u003c/strong\u003e highlights gastric cancer invading the basement membrane with intestinal metaplasia. (b) Volcano plot from GSE78523 highlighting the top two DEGs, OLFM4 and MUC2, in intestinal metaplasia tissues. (c) Cluster analysis of epithelial cells in GSE134520. (d) Analysis of CNV in epithelial cell subgroups using inferCNV. (e-g) Assessment of the differentiation of epithelial cell subgroups with Cytotrace. (h-i) Evaluation of OLFM4 expression in tissues and cell subgroups. \u003cstrong\u003eIM: Intestinal metaplasia\u003c/strong\u003e.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4014155/v1/cabc1bd7dd98dd8b214406f0.png"},{"id":52301297,"identity":"a36120ef-0a51-488a-b8b4-e81bc08e77b8","added_by":"auto","created_at":"2024-03-08 18:38:28","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":3866946,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOLFM4 was the biomarker of IIM tissue:\u003c/strong\u003e (a-b) qPCR and Western Blot were used to assess CDX2, MUC2, and OLFM4 expression in normal gastric mucosa and intestinal metaplasia tissues. (c) HE staining and AB-PAS staining confirmed intestinal metaplasia diagnosis and the immunohistochemical status of CDX2, MUC2, and OLFM4 was assessed in the Training set. (d) Statistical analysis of CDX2, MUC2, and OLFM4 immunohistochemical scores and diagnostic efficacy for intestinal metaplasia in both the Training sets and Validation sets. (e) HE staining and AB-PAS staining confirmed intestinal metaplasia subtypes, while OLFM4 immunohistochemistry was used in the Training set. (f) HE staining of intestinal metaplasia demonstrated red-stained Paneth cells (\u003cstrong\u003eblack arrow\u003c/strong\u003e) and an intact brush border (\u003cstrong\u003ered dashed line\u003c/strong\u003e), confirming CIM. (g) Statistical analysis of CDX2, MUC2, and OLFM4 immunohistochemical scores and diagnostic efficacy for IIM in both the Training set and Validation set. Statistics are expressed as mean ± SD. *p\u0026lt;0.05，**p\u0026lt;0.01， ***p\u0026lt;0.001， ****p\u0026lt;0.0001. \u003cstrong\u003eN：Normal gastric mucosa；IM：Intestinal metaplasia.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4014155/v1/832f0b4f19a8d6d07c27e24b.png"},{"id":52301293,"identity":"551d0e9e-e8cc-4695-9f0b-f6aca8929f27","added_by":"auto","created_at":"2024-03-08 18:38:28","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":489989,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOLFM4 is a biomarker of PLGC cells:\u003c/strong\u003e(a-b) The most significant changes in CDX2 expression in PLGC cells were observed at an MNNG incubation concentration of 200μmol/L, (c-d) after 48-hour incubation with MNNG. (e-f) Transcriptome sequencing of GES-1 cells induced with 200μmol/L MNNG for 48 hours showed significant overexpression of the classical intestinal metaplasia biomarker CDX2, confirming the successful construction of MNNG-induced PLGC cells. (g-j) Statistical analysis of repeated experimental data for cell cloning, EdU, wound healing, and transwell assays in PLGC cells and GES-1 cells overexpressing OLFM4 (oeOLFM4). (k-n) Statistical analysis of repeated experimental data for cell cloning, EdU, wound healing, and transwell assays after OLFM4 knockdown in PLGC cells (shOLFM4). Statistics are expressed as mean ± SD. *p\u0026lt;0.05，**p\u0026lt;0.01， ***p\u0026lt;0.001， ****p\u0026lt;0.0001.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4014155/v1/2539098e36cdb3ee2bec7797.png"},{"id":52301299,"identity":"38b5d3e9-12da-4e14-8447-9fa9137542bb","added_by":"auto","created_at":"2024-03-08 18:38:28","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":3575037,"visible":true,"origin":"","legend":"\u003cp\u003ePLGC cells with high OLFM4 expression display activation of the Wnt signaling pathway\u003cstrong\u003e:\u003c/strong\u003e(a) Functional enrichment analysis of the GSE78523 RNA-seq dataset in Hallmark genes and KEGG analysis revealed enrichment in the Myc, EMT, and Wnt pathways. (b) Addmodulescore [A1] analysis of the scRNA-seq dataset GSE134520 demonstrated that PLGC cells with high OLFM4 expression were functionally enriched in EMT, Wnt, and Cancer pathways. (c-d) Western Blotting confirmed that OLFM4 positively regulated the expression of molecules related to Wnt/β-catenin signaling pathway, EMT transition, and c-Myc. (e-f) In intestinal metaplasia, confocal immunofluorescence revealed overexpression of OLFM4, Ki67 and Vimentin, while E-cadherin expression was low.\u003c/p\u003e\n\u003cp\u003e[A1]?\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-4014155/v1/75243ce3c344f16a61a9ceae.png"},{"id":52303082,"identity":"49f89d21-c9c7-4bc9-afbf-e2a2b11c0192","added_by":"auto","created_at":"2024-03-08 18:54:28","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":929864,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOLFM4 interaction with MYH9 stimulates activation of the Wnt pathway: \u003c/strong\u003e(a) Confocal immunofluorescence demonstrated co-localization of OLFM4 and MYH9 proteins in PLGC cells, with overlapping fluorescence signal peaks. (b-c) CoIP and Western Blotting experiments confirmed mutual binding between OLFM4 and MYH9 proteins in PLGC cells. (d) Confocal immunofluorescence revealed overexpression of MYH9 in GES-1 cells with OLFM4 overexpression. (e-f) Western Blotting indicated that OLFM4 interacts with MYH9 to regulate the Wnt signaling pathway. (g-n) Statistical analysis of repeated experimental data for cell cloning, EdU, wound healing, and transwell assays after MYH9 knockdown in PLGC cells (shMYH9). Statistics are expressed as mean ± SD. *p\u0026lt;0.05，**p\u0026lt;0.01， ***p\u0026lt;0.001， ****p\u0026lt;0.0001.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-4014155/v1/9dc4e392c8339e28d6062778.png"},{"id":52302450,"identity":"7745257f-0ea9-4990-9fd4-5c3fc3b0557c","added_by":"auto","created_at":"2024-03-08 18:46:28","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1752211,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOLFM4 interacts with MYH9 to facilitate the ubiquitination of GSK3β:\u003c/strong\u003e (a) qPCR assays demonstrated that altering OLFM4 expression had no significant impact on GSK3β mRNA levels. (b) CHX assay indicated that OLFM4 or MYH9 knockdown reduced the degradation rate of GSK3β. (c) Knockdown of OLFM4 or MYH9 suppressed GSK3β ubiquitination and increased GSK3β protein levels. (d) The Pymol software constructed two crucial protein-protein dockings between OLFM4 and MYH9. (e) The Missense3D database predicted and constructed two mutations of OLFM4 protein. (f-g) GES-1 cells were transfected with OLFM4 mutation plasmids, and then subjected to qPCR and Western Blotting for analysis of Wnt signaling pathway. (h) GES-1 cells were transfected with OLFM4 mutation plasmids and conducted with CoIP assay for the ubiquitination level of GSK3β. (i) GES-1 cells were transfected with G438 mutation plasmids, and the CoIP assay was executed to the interaction between OLFM4 and MYH9. (j) Immunofluorescence assays highlighted the elevated expression of OLFM4 and MYH9 in intestinal metaplasia organoids compared to normal gastric mucosa. (k) Statistical analysis of immunohistochemical scores for CDX2, MUC2, OLFM4, and MYH9 in intestinal metaplasia organoids. (l) Illustration of the PLGC animal model construction process and the presentation of rat gastric mucosa. (n) Statistical analysis of immunohistochemical scores of CDX2, MUC2, OLFM4, and MYH9 in the gastric mucosa of PLGC rats. Statistics are expressed as mean ± SD. *p\u0026lt;0.05， **p\u0026lt;0.01， ***p\u0026lt;0.001， ****p\u0026lt;0.0001.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-4014155/v1/1450a24703c19122fb263659.png"},{"id":52302447,"identity":"452cbfff-de84-4c20-adcd-5c8350e7199f","added_by":"auto","created_at":"2024-03-08 18:46:28","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":2033949,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe study overview and the mechanism of OLFM4 promoting intestinal metaplasia: \u003c/strong\u003eOLFM4 is highly expressed in incomplete intestinal metaplasia, and interacts with MYH9 to promote the progression of intestinal metaplasia via GSK3β/β-catenin signaling pathway.\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-4014155/v1/072d11a0e5c4e7ee405259f8.png"},{"id":52303894,"identity":"f7be35d6-34e2-4d64-b03d-99099319ba16","added_by":"auto","created_at":"2024-03-08 19:02:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6333815,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4014155/v1/56c18e0a-c4f7-44d0-8f81-e188f5f71d94.pdf"},{"id":52301301,"identity":"9d407eb5-decf-423a-ad96-384e4dc549e3","added_by":"auto","created_at":"2024-03-08 18:38:30","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":17453068,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementalmaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-4014155/v1/433923a2b764ad2373154733.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"OLFM4 promotes the progression of intestinal metaplasia through activation of the MYH9/GSK3β/β-catenin pathway","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGastric cancer (GC) is a significant health concern, ranking fifth and fourth in terms of morbidity and mortality\u0026nbsp;\u003csup\u003e1\u003c/sup\u003e. Early diagnosis rates for gastric cancer worldwide are currently below 20%, considerably lower than the 50%-60% rates seen in Japan and South Korea. This is primarily due to the hidden onset of gastric cancer, and the substantial cost of intensive early screening, which is not currently feasible on a widespread scale\u0026nbsp;\u003csup\u003e2-6\u003c/sup\u003e. Targeted surveillance of precancerous lesions, including them in the high-risk group for critical monitoring, can improve the efficiency and financial benefits of screening for early gastric cancer (EGC). Chronic stimulation of normal gastric mucosa by factors like Helicobacter pylori (HP), carcinogens, high salt, bile acids, tobacco, or alcohol can lead to pathological progression from chronic atrophic gastritis (CAG), intestinal metaplasia (IM), gastric dysplasia, and ultimately adenocarcinoma, a process known as the \u0026quot;Correa cascade\u0026quot;\u0026nbsp;\u003csup\u003e7,8\u003c/sup\u003e. Intestinal metaplasia represents a significant portion of precancerous lesions associated with gastric cancer. Based on Lauren\u0026apos;s classification, approximately 80% of gastric cancer cases are categorized as the intestinal type, which originates from the mucosa undergoing intestinal metaplasia\u0026nbsp;\u003csup\u003e9-11\u003c/sup\u003e. Intestinal metaplasia results from chronic inflammatory stimulation of the stomach\u0026apos;s normal mucosal epithelium, leading to atrophy of parietal cells and the formation of goblet cells and enterocytes. While enteroid epithelium replaces the lost gastric glands, this process is marked by impaired differentiation and atypical regeneration, increasing the risk of cancer\u0026nbsp;\u003csup\u003e8,12-14\u003c/sup\u003e. Consequently, patients with intestinal metaplasia have a significantly higher risk of developing cancer compared to healthy individuals\u0026nbsp;\u003csup\u003e13,15\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIIM is associated with a 4- to 11-fold higher risk of developing gastric adenocarcinoma compared to CIM\u0026nbsp;\u003csup\u003e16-18\u003c/sup\u003e. Since various subtypes of intestinal metaplasia exhibit different levels of cancer risk, recognizing IIM is essential. Currently, HE staining and AB-PAS staining are the primary methods used for assessing IIM. However, staining interpretation can be challenging for non-pathologists as well as non-gastrointestinal pathological experts\u0026nbsp;\u003csup\u003e13\u003c/sup\u003e. Therefore, developing appropriate markers for IIM diagnosis, identifying \u0026quot;high-risk\u0026quot; IIM groups, and defining key screening targets can enhance endoscopy efficiency, increase EGC diagnosis rates, and significantly reduce associated healthcare costs.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOLFM4, also known as Olfactomedin-4, GW112, or hGC-1, is a glycoprotein belonging to the olfactory regulatory protein family\u0026nbsp;\u003csup\u003e19\u003c/sup\u003e. OLFM4 expression is absent in normal gastric mucosa but is present in the small intestine and colon\u0026nbsp;\u003csup\u003e20,21\u003c/sup\u003e. Research has highlighted OLFM4\u0026apos;s crucial role in regulating intestinal stem cells\u0026nbsp;\u003csup\u003e22,23\u003c/sup\u003e. In fact, one of OLFM4\u0026apos;s key biological functions is to regulate cell adhesion and cell migration by interacting with adhesion molecules, the cytoskeleton, and the extracellular matrix\u0026nbsp;\u003csup\u003e24\u003c/sup\u003e.\u0026nbsp;Studies have shown elevated OLFM4 expression in gastric cancer and colorectal cancer, particularly in the early stages of tumor formation\u0026nbsp;\u003csup\u003e25\u003c/sup\u003e. However, previous studies have not explored the differential expression of OLFM4 in different severity of intestinal metaplasia and the mechanism by which OLFM4 promotes the progression of intestinal metaplasia. The activation of the Wnt/\u0026beta;-catenin signaling pathway is crucial for tumor invasion and metastasis\u0026nbsp;\u003csup\u003e26-28\u003c/sup\u003e. Consequently, Wnt/\u0026beta;-catenin expression is positive in 86% of intestinal metaplasia and 95% of gastric cancer cases\u0026nbsp;\u003csup\u003e29\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eExpanding upon the aforementioned literature review, our research study centered on evaluating OLFM4 expression in IIM. We aimed to construct a predictive model utilizing OLFM4 as a classifier for distinguishing different types of intestinal metaplasia. Additionally, we investigated potential mechanisms and activated signaling pathways by which OLFM4 contributes to the progression of gastric mucosal intestinal metaplasia.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cstrong\u003ePatient samples and cell lines\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study received approval from the Medical Ethical Committee of the Seventh Affiliated Hospital of Sun Yat-sen University and The People\u0026apos;s Hospital of Fengqing in Yunnan Province (KY-2021-105-01, 2021/12/02). Written informed consent, following the Declaration of Helsinki guidelines, was obtained from every patient. Paraffin-embedded archived samples were collected between 2018 and 2022 from two distinct clinical centers. The samples of the first clinical center included 78 newly diagnosed intestinal metaplasia patients and 40 healthy individuals from the Seventh Affiliated Hospital (Training set). The samples of the second clinical center consisted of 63 newly diagnosed intestinal metaplasia patients and 40 healthy individuals from The People\u0026apos;s Hospital of Fengqing (Validation set). Histological diagnoses were based on the Third edition of Pathology \u003csup\u003e30\u003c/sup\u003e, and the classification of intestinal metaplasia was determined according to Pathology and classical literature \u003csup\u003e30-33\u003c/sup\u003e. GES-1 cells were purchased from ATCC and underwent short tandem repeat (STR) analysis.\u003c/p\u003e\n\u003cp\u003ePatient-derived organoids (PDO) were cultured in the laboratory of the Seventh Affiliated Hospital. Biopsy tissues were obtained from patients with intestinal metaplasia or healthy individuals, and these tissues were processed into PDO and cultured in an organoid medium.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDEGs in intestinal metaplasia tissues\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe selected the GSE78523 dataset from the Gene Expression Omnibus database (GEO), which comprises samples from both normal gastric mucosa and intestinal metaplasia tissues. Differential Expression Genes (DEGs) were analyzed using the \u0026quot;DESeq2\u0026quot; package in R. The volcano plots and heatmaps were generated using the \u0026quot;ggplot2\u0026quot; package or GraphPad Prism 8.0.2 based on the results of the DEGs analysis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIdentification of a new PLGC subgroup in intestinal metaplasia tissues\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe selected and analyzed the GSE134520 dataset, a single-cell RNA sequencing (scRNA-seq) dataset that includes non-atrophic gastritis (NAG), intestinal metaplasia (IM), and early gastric cancer (EGC), using the \u0026quot;Seurat\u0026quot; R package. Data normalization was performed using the \u0026quot;NormalizeData\u0026quot; function, and inconsequential sources of variation were removed with the \u0026quot;ScaleData\u0026quot; function. The \u0026quot;FindVariableFeatures\u0026quot; function was used to identify highly variable genes (HVGs), and the \u0026quot;RunPCA\u0026quot; function identified 50 significant principal component analyses (PCA). We embedded cells into the graph structure of PCAs using the \u0026quot;FindNeighbors\u0026quot; and \u0026quot;FindClusters\u0026quot; functions. The spatial correlation of expression data was presented through Uniform Manifold Approximation and Projection (UMAP) plots based on RunUMAP and Dimplot. We selected all epithelial cells using classical epithelial markers \u0026quot;EPCAM\u0026quot; and \u0026quot;KRT19\u0026quot;. A total of 11 clusters were identified based on HVGs. To assess the malignancy of glandular cells, the \u0026quot;inferCNV\u0026quot; package was used to determine cellular heterogeneity by identifying chromosome copy number variation (CNV) in scRNA-seq. Gastric cancer cells served as a positive control for CNV, and precancerous lesions of gastric carcinoma (PLGC) cells were identified by \u0026quot;inferCNV\u0026quot; in comparison. The \u0026quot;Monocle\u0026quot; function was employed to display the evolution of gastric mucosa during EGC development by performing pseudotime analysis, projecting high-dimensional data into one dimension. The \u0026quot;Cytotrace\u0026quot; function was utilized to create a critical RNA-based feature for developmental potential and to establish a platform for delineating cellular hierarchies, attempting to predict differentiation states from single-cell RNA sequencing (scRNA-seq).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eReagents\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eN-Methyl-N\u0026rdquo;-nitro-N-nitrosoguanidine (MNNG) was obtained from Meilunbio (MB0455-2, China), and Blebbistatin (the inhibitor of MYH9) was bought from GlpBio (GC12341, USA). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCell transfection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLentivirus vectors encoding shOLFM4, oeOLFM4, shMYH9, oeMYH9, control, or HA-ubiquitin plasmids were from GeneCopoeia (Guangzhou, China). HA-ubiquitin plasmids were transfected into cells for 48 hours, followed by lysis for immunoblotting with anti-HA antibodies.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImmunohistochemistry (IHC),\u003c/strong\u003e \u003cstrong\u003eImmunofluorescence (IF) staining, Alcian blue-periodic acid-Schiff (AB-PAS) straining, and hematoxylin-eosin (HE) staining\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe conducted IHC, IF, AB-PAS, and HE staining using standard protocols. IF staining was observed either through fluorescence microscopy (Leica, DM6B) or confocal microscopy (ZEISS, LSM-880). Two independent observers, unaware of the patient\u0026apos;s clinical information, evaluated the staining results at separate intervals. The IHC score was determined using Image J to calculate the proportion of stained areas. Primary antibodies used included OLFM4 (14369S, CST), CDX2 (A19030, Abclonal), MUC2 (sc-515032, Santa Cruz Biotechnology), MYH9 (11128-1-AP, Proteintech), E-cadherin (60335-1-Ig, Proteintech), Vimentin (60330-1-Ig, Proteintech), and Ki67 (ab16667, Abcam).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWestern blotting\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTotal protein extraction and Western Blotting were performed following standard methods. The primary antibodies included OLFM4 (14369S, CST), MYH9 (11128-1-AP, Proteintech), GSK3-\u0026beta; (22104-1-AP, Proteintech), \u0026beta;-catenin (51067-2-AP, Proteintech), \u0026beta;-actin (66009-1-Ig, Proteintech), STAT3 (10253-2-AP, Proteintech), c-Myc (10828-1-AP, Proteintech), N-cadherin (22018-1-AP, Proteintech), E-cadherin (60335-1-Ig, Proteintech), Vimentin (60330-1-Ig, Proteintech), Snai1 (13099-1-AP, Proteintech) Ubiquitin (10201-2-AP, Proteintech), and GAPDH (60004-1-Ig, Proteintech).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eQuantitative Reverse Transcription-PCR (qRT-PCR)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTotal RNA was harvested, and cDNA was generated by a reverse transcription reagent kit (AG11706, Accurate Biology, China). Then, the cDNA template was used for amplification with specific primers. qRT-PCR was conducted using SYBR-green PCR Master Mix and 45 cycles of 95℃ for 10s, 60℃ for 20s, and 72℃ for 20s. These sequences of primers are defined as follows:\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.24780316344464%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"45.518453427065026%\" valign=\"top\"\u003e\n \u003cp\u003eForward\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"43.23374340949034%\" valign=\"top\"\u003e\n \u003cp\u003eReverse\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.24780316344464%\" valign=\"top\"\u003e\n \u003cp\u003eCDX2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"45.518453427065026%\" valign=\"top\"\u003e\n \u003cp\u003eTTCACTACAGTCGCTACATCACCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"43.23374340949034%\" valign=\"top\"\u003e\n \u003cp\u003eCTGCGGTTCTGAAACCAGATT\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.24780316344464%\" valign=\"top\"\u003e\n \u003cp\u003eMUC1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"45.518453427065026%\" valign=\"top\"\u003e\n \u003cp\u003eTTCACCACCACCATGACACC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"43.23374340949034%\" valign=\"top\"\u003e\n \u003cp\u003eGGGGCTGTGGTAGCTGTAAG\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.24780316344464%\" valign=\"top\"\u003e\n \u003cp\u003eMUC2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"45.518453427065026%\" valign=\"top\"\u003e\n \u003cp\u003eGGGGAGTGCTGTAAGAAGTGTGA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"43.23374340949034%\" valign=\"top\"\u003e\n \u003cp\u003eGTTGGAGACGGACGAGATGAG\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.24780316344464%\" valign=\"top\"\u003e\n \u003cp\u003eOLFM4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"45.518453427065026%\" valign=\"top\"\u003e\n \u003cp\u003eGAGAAATCGTGGCTCTGAAGAC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"43.23374340949034%\" valign=\"top\"\u003e\n \u003cp\u003eCAGACGGTTTGCTGATGTTC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.24780316344464%\" valign=\"top\"\u003e\n \u003cp\u003eGSK3\u0026beta;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"45.518453427065026%\" valign=\"top\"\u003e\n \u003cp\u003eCATCCTTGGACTAAGGTCTTCCG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"43.23374340949034%\" valign=\"top\"\u003e\n \u003cp\u003eCATTTGTGGGGGTTGAAGCAG\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.24780316344464%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026beta;-actin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"45.518453427065026%\" valign=\"top\"\u003e\n \u003cp\u003eTCAAGATCATTGCTCCTCCTGAG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"43.23374340949034%\" valign=\"top\"\u003e\n \u003cp\u003eACATCTGCTGGAAGGTGGACA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCell proliferation assay, colony-formation assay, EdU assay, Wound healing assay, and Transwell assay\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCell Proliferation:\u003c/strong\u003e We assessed cell proliferation using the Cell Counting Kit-8 (CCK-8) assay kit (Biosharp, China) and the Microplate Reader (BioTeK, USA). We seeded 2,000 cells into 96-well plates and cultured them for 1-5 days. Each day, we mixed the CCK-8 reagent with the cell culture medium at a 1:9 ratio and incubated the cells for 90 minutes. We measured absorbance at 450 nanometers using a spectrophotometer in each culture dish.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCell Colony Formation:\u003c/strong\u003e For colony formation, we inoculated 800 cells in six-well plates and cultured them for 14 days. The number of cell colonies was determined by microscopy after staining with crystal violet dye.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCell Viability (EdU Assay):\u0026nbsp;\u003c/strong\u003eWe measured cell viability using the EdU assay. We plated 6,000 cells into 96-well plates, treated them with EdU reagent (10 \u0026mu;M, Beyotime, China), and observed them with fluorescence microscopy (Leica, DMI8).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWound Healing Assay:\u003c/strong\u003e Cells were plated and grown to confluence in six-well plates. We created scratches with a pipette tip and examined the cell migration process under a microscope at 0 and 24 hours.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCell Migration and Invasion:\u003c/strong\u003e We evaluated cell migration and invasion using 24-well transwells (8.0 \u0026mu;m, Corning, USA), precoated with Matrigel in invasion assay but without Matrigel in migration assay. In the lower chamber, we added 500 \u0026mu;L RMPI-1640 with 10% FBS. We seeded 5 \u0026times; 10\u003csup\u003e4\u003c/sup\u003e treated cells suspended in 500 \u0026mu;L RMPI-1640 without FBS in the upper chamber and cultured them at 37\u0026deg;C for 36 hours. We counted the number of GES-1 cells in the lower chamber using a cell counting plate.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCo-immunoprecipitation (IP) and mass spectrometry\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eProtein extraction and purification were performed using primary antibodies for IP and Protein A/G Magnetic Beads (B23202, Selleck). Mass spectrometry was performed by Baiqu Tech. co. LTD (Hangzhou, China) and results were provided in Table 5. The primary antibodies included OLFM4 (14369S, CST), MYH9 (60233-1-Ig, Proteintech), and GSK3-\u0026beta; (22104-1-AP, Proteintech).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCycloheximide (CHX) chase assay\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGES-1 cells were incubated with 2 \u0026mu;M MG132 (HY-13259, MCE, USA) or left untreated. After the treatment of 20 \u0026mu;g/mL CHX (C7698, Sigma-Aldrich) for different times, cells were harvested and prepared for Western blotting.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePLGC animal model\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Institutional Animal Care and Use Committee (IACUC) (TopBiotech Co., LTD., Shenzhen) approved the experimental methods and animal use and care protocols. We obtained twenty male Sprague Dawley (SD) rats five-week-old for each group from Gempharmatech company (Jiangsu, China).\u003c/p\u003e\n\u003cp\u003eWe prepared an MNNG solution with a concentration of 170 \u0026micro;g/ml by dissolving MNNG in drinking water containing 5% alcohol. The rats received the MNNG solution by gavage every two days, with a regimen of one day of a normal diet and one day of fasting. This procedure continued for 24 weeks.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStatistical analyses were performed by using SPSS 22.0 (SPSS Inc., Chicago). Histogram Graphing was performed with GraphPad Prism 8.0.2 (GraphPad Software). Each in vitro experiment was repeated three times or more and experimental data were depicted as mean \u0026plusmn; standard deviation (SD). Quantitative variables were analyzed using a Student t-test for Gaussian distribution and non-parametric tests for non-Gaussian distribution. Differences were considered statistically significant at p \u0026lt; 0.05 (*p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001).\u003c/p\u003e"},{"header":"Results","content":"\u003ch2\u003e1. OLFM4 is remarkably differentially expressed in intestinal metaplasia tissue\u003c/h2\u003e\n\u003cp\u003eIntestinal metaplasia tissues are closely associated with gastric cancer and are considered the origin of intestinal-type gastric cancer. In HE staining of pathological sections, the mucosal layer or submucosal layer of gastric cancer tissues, and para-cancerous tissues were observed to be accompanied by intestinal metaplasia lesions (Fig. 1a). To identify significant Differentially Expressed Genes (DEGs) in intestinal metaplasia tissue, we analyzed the RNA-seq dataset GSE78523, revealing high expression of OLFM4 and MUC2 in intestinal metaplasia tissue (Fig. 1b, Fig. S1a). As MUC2 is established as a conventional biomarker for intestinal metaplasia, our research endeavors have been directed towards exploring the role of OLFM4. Although OLFM4 has been reported among the highly expressed genes in intestinal metaplasia tissues by transcriptome sequencing, its expression profile and the mechanism by which it promotes progression in incomplete intestinal metaplasia remain unexplored \u003csup\u003e34,35\u003c/sup\u003e. To further investigate OLFM4 in intestinal metaplasia, we analyzed the scRNA-seq dataset GSE134520. Traditional biomarkers, \u0026quot;EPCAM\u0026quot; and \u0026quot;KRT19\u0026quot;, were used to distinguish epithelial cells and stromal cells, with epithelial cells constituting 88.4% of all cells (Fig. S1b-d). Epithelial cells were categorized into 11 clusters, yielding 7 cell subgroups based on their cell markers (Fig. 1c, Fig. S1e-f). To assess malignancy of epithelial cells, copy number variations (CNV) were analyzed in the 11 clusters using inferCNV (Fig. 1d). Clusters 2, 3, and 7 were identified as gastric cancer cells by their cancerous origin and frequent CNV (Fig. 1d, Fig. S1e). Cluster 8 was classified as PLGC cells based on their comparable frequency of copy number variations (CNV) to gastric cancer cells and their partial cellular origin from intestinal metaplasia tissue (Fig. 1d, Fig. S1e). PLGC cells exhibited less differentiation across all subgroups and expressed high levels of OLFM4 (Fig. 1e-g) which was confirmed as a cell marker in PLGC cells (Fig. 1h-i, Fig. S1f-g). Pseudotime analyses indicated overlapping differentiation trajectories between PLGC cells and gastric cancer cells, suggesting a tendency toward malignancy in PLGC cells (Fig. 1g). Consequently, we speculated that OLFM4 might be one of the biomarkers in PLGC cells.\u003c/p\u003e\n\u003ch2\u003e2. OLFM4 is a novel biomarker of incomplete intestinal metaplasia\u003c/h2\u003e\n\u003cp\u003eWe selected IM biopsies and normal mucosa samples for qPCR and Western Blot analyses. Traditional biomarkers for IM, CDX2, and MUC2, exhibited significant increases in expression\u003ca id=\"_anchor_1\" href=\"#_msocom_1\" language=\"JavaScript\" name=\"_msoanchor_1\"\u003e[A1]\u003c/a\u003e . Our target molecule, OLFM4, showed elevated expression in IM biopsies (Fig. 2a-b). To diagnose IM, we utilized HE staining and AB-PAS staining to assess the presence of Goblet cell in pathological sections of gastric lesions. Goblet cells are bright in HE staining, but dark blue in the AB-PAS staining (Fig. 2c, Fig. S2a). Statistical analysis revealed increased expression of CDX2, MUC2, and OLFM4 in both the training set and validation set. OLFM4 demonstrated an AUC value of 0.825 in the training set and 0.915 in the validation set (Fig. 2d, Table 1, Table 2). Certainly, the diagnostic effectiveness of OLFM4 in identifying IIM was comparable to that of CDX2 and MUC2. (Fig. 2d).\u003c/p\u003e\n\u003cp\u003eTo distinguish between complete intestinal metaplasia (CIM) and incomplete intestinal metaplasia (IIM), we conducted HE and AB-PAS immunohistochemical experiments (Fig. 2e). HE staining revealed eosinophilic secretory granules in Paneth cells and intact brush borders in CIM tissues (Fig. 2f). In comparison to CIM, OLFM4 showed higher expression in IIM, while CDX2 and MUC2 displayed no significant differences (Fig. 2g, Fig. S2b). OLFM4 exhibited an AUC value of 0.729 in the Training set and 0.786 in the Validation set, indicating relatively better diagnostic performance compared to the other two biomarkers in IIM (Fig. 2g, Table 3, Table 4). Consequently, OLFM4 might serve as a superior biomarker for distinguishing IIM tissues in immunohistochemistry.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1 The ROC curve of IM in the Training set\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.010830324909747%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eGenes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.303249097472925%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eArea\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.371841155234657%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026plusmn;SE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.71841155234657%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eP value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.20216606498195%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.2490974729241877%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.144404332129962%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eYouden index\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.805194805194805%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eUp\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.12987012987013%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDown\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.8441558441558445%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.675324675324674%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSensitivity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.545454545454547%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSpecificity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.010830324909747%\"\u003e\n \u003cp\u003eCDX2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.303249097472925%\"\u003e\n \u003cp\u003e0.846\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.371841155234657%\"\u003e\n \u003cp\u003e\u0026plusmn;0.045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.71841155234657%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.010830324909747%\"\u003e\n \u003cp\u003e0.759\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.191335740072201%\"\u003e\n \u003cp\u003e0.934\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.2490974729241877%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.71841155234657%\" valign=\"top\"\u003e\n \u003cp\u003e88.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.425992779783392%\" valign=\"top\"\u003e\n \u003cp\u003e77.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.010830324909747%\"\u003e\n \u003cp\u003eMUC2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.303249097472925%\"\u003e\n \u003cp\u003e0.874\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.371841155234657%\"\u003e\n \u003cp\u003e\u0026plusmn;0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.71841155234657%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.010830324909747%\"\u003e\n \u003cp\u003e0.812\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.191335740072201%\"\u003e\n \u003cp\u003e0.936\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.2490974729241877%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.71841155234657%\" valign=\"top\"\u003e\n \u003cp\u003e70.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.425992779783392%\" valign=\"top\"\u003e\n \u003cp\u003e92.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.010830324909747%\"\u003e\n \u003cp\u003eOLFM4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.303249097472925%\"\u003e\n \u003cp\u003e0.829\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.371841155234657%\"\u003e\n \u003cp\u003e\u0026plusmn;0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.71841155234657%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.010830324909747%\"\u003e\n \u003cp\u003e0.753\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.191335740072201%\"\u003e\n \u003cp\u003e0.904\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.2490974729241877%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.71841155234657%\" valign=\"top\"\u003e\n \u003cp\u003e82.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.425992779783392%\" valign=\"top\"\u003e\n \u003cp\u003e70.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2 The ROC curve of IM in the Validation set\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.010830324909747%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eGenes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.303249097472925%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eArea\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.371841155234657%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026plusmn;SE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.71841155234657%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eP value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.20216606498195%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.2490974729241877%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.144404332129962%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eYouden index\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.805194805194805%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eUp\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.12987012987013%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDown\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.8441558441558445%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.675324675324674%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSensitivity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.545454545454547%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSpecificity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.010830324909747%\" valign=\"top\"\u003e\n \u003cp\u003eCDX2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.303249097472925%\" valign=\"top\"\u003e\n \u003cp\u003e0.915\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.371841155234657%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026plusmn;0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.71841155234657%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.010830324909747%\" valign=\"top\"\u003e\n \u003cp\u003e0.862\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.191335740072201%\" valign=\"top\"\u003e\n \u003cp\u003e0.968\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.2490974729241877%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.71841155234657%\" valign=\"top\"\u003e\n \u003cp\u003e77.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.425992779783392%\" valign=\"top\"\u003e\n \u003cp\u003e92.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.010830324909747%\" valign=\"top\"\u003e\n \u003cp\u003eMUC2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.303249097472925%\" valign=\"top\"\u003e\n \u003cp\u003e0.918\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.371841155234657%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026plusmn;0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.71841155234657%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.010830324909747%\" valign=\"top\"\u003e\n \u003cp\u003e0.877\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.191335740072201%\" valign=\"top\"\u003e\n \u003cp\u003e0.938\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.2490974729241877%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.71841155234657%\" valign=\"top\"\u003e\n \u003cp\u003e76.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.425992779783392%\" valign=\"top\"\u003e\n \u003cp\u003e99.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.010830324909747%\" valign=\"top\"\u003e\n \u003cp\u003eOLFM4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.303249097472925%\" valign=\"top\"\u003e\n \u003cp\u003e0.868\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.371841155234657%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026plusmn;0.034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.71841155234657%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.010830324909747%\" valign=\"top\"\u003e\n \u003cp\u003e0.802\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.191335740072201%\" valign=\"top\"\u003e\n \u003cp\u003e0.935\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.2490974729241877%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.71841155234657%\" valign=\"top\"\u003e\n \u003cp\u003e85.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.425992779783392%\" valign=\"top\"\u003e\n \u003cp\u003e72.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3 The ROC curve of IIM in the Training set\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.010830324909747%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eGenes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.303249097472925%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eArea\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.371841155234657%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026plusmn;SE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.71841155234657%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eP value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.20216606498195%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.2490974729241877%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.144404332129962%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eYouden index\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.805194805194805%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eUp\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.12987012987013%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDown\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.8441558441558445%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.675324675324674%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSensitivity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.545454545454547%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSpecificity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.010830324909747%\" valign=\"top\"\u003e\n \u003cp\u003eCDX2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.303249097472925%\" valign=\"top\"\u003e\n \u003cp\u003e0.501\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.371841155234657%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026plusmn;0.073\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.71841155234657%\" valign=\"top\"\u003e\n \u003cp\u003e0.992\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.010830324909747%\" valign=\"top\"\u003e\n \u003cp\u003e0.357\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.191335740072201%\" valign=\"top\"\u003e\n \u003cp\u003e0.642\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.2490974729241877%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.71841155234657%\" valign=\"top\"\u003e\n \u003cp\u003e81.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.425992779783392%\" valign=\"top\"\u003e\n \u003cp\u003e19.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.010830324909747%\" valign=\"top\"\u003e\n \u003cp\u003eMUC2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.303249097472925%\" valign=\"top\"\u003e\n \u003cp\u003e0.510\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.371841155234657%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026plusmn;0.068\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.71841155234657%\" valign=\"top\"\u003e\n \u003cp\u003e0.886\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.010830324909747%\" valign=\"top\"\u003e\n \u003cp\u003e0.376\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.191335740072201%\" valign=\"top\"\u003e\n \u003cp\u003e0.644\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.2490974729241877%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.71841155234657%\" valign=\"top\"\u003e\n \u003cp\u003e87.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.425992779783392%\" valign=\"top\"\u003e\n \u003cp\u003e23.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.010830324909747%\" valign=\"top\"\u003e\n \u003cp\u003eOLFM4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.303249097472925%\" valign=\"top\"\u003e\n \u003cp\u003e0.729\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.371841155234657%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026plusmn;0.056\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.71841155234657%\" valign=\"top\"\u003e\n \u003cp\u003e0.001\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.010830324909747%\" valign=\"top\"\u003e\n \u003cp\u003e0.620\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.191335740072201%\" valign=\"top\"\u003e\n \u003cp\u003e0.838\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.2490974729241877%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.71841155234657%\" valign=\"top\"\u003e\n \u003cp\u003e52.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.425992779783392%\" valign=\"top\"\u003e\n \u003cp\u003e93.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4 The ROC curve of IIM in the Validation set\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.010830324909747%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eGenes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.303249097472925%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eArea\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.371841155234657%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026plusmn;SE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.71841155234657%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eP value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.20216606498195%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.2490974729241877%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.144404332129962%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eYouden index\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.805194805194805%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eUp\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.12987012987013%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDown\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.8441558441558445%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.675324675324674%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSensitivity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.545454545454547%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSpecificity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.010830324909747%\" valign=\"top\"\u003e\n \u003cp\u003eCDX2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.303249097472925%\" valign=\"top\"\u003e\n \u003cp\u003e0.551\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.371841155234657%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026plusmn;0.070\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.71841155234657%\" valign=\"top\"\u003e\n \u003cp\u003e0.494\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.010830324909747%\" valign=\"top\"\u003e\n \u003cp\u003e0.407\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.191335740072201%\" valign=\"top\"\u003e\n \u003cp\u003e0.694\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.2490974729241877%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.71841155234657%\" valign=\"top\"\u003e\n \u003cp\u003e92.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.425992779783392%\" valign=\"top\"\u003e\n \u003cp\u003e28.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.010830324909747%\" valign=\"top\"\u003e\n \u003cp\u003eMUC2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.303249097472925%\" valign=\"top\"\u003e\n \u003cp\u003e0.522\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.371841155234657%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026plusmn;0.074\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.71841155234657%\" valign=\"top\"\u003e\n \u003cp\u003e0.485\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.010830324909747%\" valign=\"top\"\u003e\n \u003cp\u003e0.407\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.191335740072201%\" valign=\"top\"\u003e\n \u003cp\u003e0.696\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.2490974729241877%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.71841155234657%\" valign=\"top\"\u003e\n \u003cp\u003e82.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.425992779783392%\" valign=\"top\"\u003e\n \u003cp\u003e34.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.010830324909747%\" valign=\"top\"\u003e\n \u003cp\u003eOLFM4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.303249097472925%\" valign=\"top\"\u003e\n \u003cp\u003e0.786\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.371841155234657%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026plusmn;0.069\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.71841155234657%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.010830324909747%\" valign=\"top\"\u003e\n \u003cp\u003e0.651\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.191335740072201%\" valign=\"top\"\u003e\n \u003cp\u003e0.922\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.2490974729241877%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.71841155234657%\" valign=\"top\"\u003e\n \u003cp\u003e71.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.425992779783392%\" valign=\"top\"\u003e\n \u003cp\u003e94.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003e3. Increased expression of OLFM4 in PLGC cells promotes cellular proliferation and invasion\u003c/h2\u003e\n\u003cp\u003eTo investigate the relationship between OLFM4 and intestinal metaplasia progression, we used a PLGC cell model by inducing GES-1 cells with MNNG exposure. The control group received PBS and the experimental group received MNNG dissolved in 0.3% DMSO to avoid cellular toxicity. As expected MNNG induced a significant increase in classical intestinal metaplasia biomarkers in PLGC cells, especially at a concentration of 200\u0026mu;mol/L for 48 hours (Fig. 3a-d). Subsequent transcriptome sequencing of PLGC cells confirmed markedly high expressions of CDX2 and OLFM4, validating the PLGC cells model (Fig. 3e-f). Consistent with a role for OLFM4 in cancer cell proliferation, our experiments showed increased proliferation and enhanced invasion capabilities of both PLGC cells and OLFM4-overexpressing GES-1 cells (oeOLFM4) (Fig. 3g-j, Fig. S3a-f). Conversely, the knockdown of OLFM4 (shOLFM4) in PLGC cells led to decreased proliferation and invasion capabilities (Fig. 3k-n, Fig. S3g-l). These data confirm the role of OLFM4 in PLGC progression.\u003c/p\u003e\n\u003ch2\u003e4. OLFM4 cooperates with MYH9 to activate the Wnt signaling pathway and enhance the progression of intestinal metaplasia\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eWe employed bioinformatics methods, specifically Gene Set Enrichment Analysis (GSEA) in Hallmark and KEGG, to analyze the RNA-seq dataset GSE78523 for mechanistic insights. High OLFM4 expression in intestinal metaplasia tissues was associated with enrichment in the Myc, EMT, and Wnt signaling pathways (Fig. 4a). Additionally, utilizing the AddModuleScore analysis on the scRNA-seq dataset GSE134520, we observed that PLGC cells with elevated OLFM4 expression exhibited the highest scores in Cancer, EMT, and Wnt/\u0026beta;-catenin signaling pathways (Fig. 4b). Western Blot assays confirmed a positive correlation between OLFM4, Wnt pathway markers, EMT transition markers, and c-Myc (Fig. 4c-d). Immunofluorescent staining confirmed an overlap expression of OLFM4, Ki67 and Vimentin in intestinal metaplasia tissues suggesting that OFLM4 stimulates tumor cell proliferation (Fig. 4e-f).\u003c/p\u003e\n\u003cp\u003eTo identify OLFM4-interacting proteins and associated signaling pathways involved in intestinal metaplasia, we conducted Co-immunoprecipitation (CoIP) assays and mass spectrometry-based quantitative proteomics. Table 5 displays the top five proteins and their interaction scores. Interestingly, MYH9 a gene closely related to the progression and poor prognosis of gastric cancer and esophageal cancer ranked first. Confocal immunofluorescence and CoIP assays confirmed endogenous MYH9 as a novel OLFM4-associated protein (Fig. 5a-c). In addition, MYH9 expression increased with OLFM4 overexpression in GES-1 cells (Fig. 5d). Western Blot assays suggest that the interaction between OLFM4 and MYH9 activate the Wnt signaling pathway (Fig. 5e-f). Furthermore, the knockdown of MYH9 in PLGC cells or its suppression with the inhibitor Blebbistatin in oeOLFM4 cells significantly weakened cellular proliferation and invasion abilities (Fig. 5g-n).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5 The first 5 proteins that bind to OLFM4 protein in mass spectrometry\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"539\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.594795539033457%\" valign=\"top\"\u003e\n \u003cp\u003eID\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.408921933085502%\" valign=\"top\"\u003e\n \u003cp\u003eGene\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.736059479553903%\" valign=\"top\"\u003e\n \u003cp\u003eMW [kDa]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.851301115241636%\" valign=\"top\"\u003e\n \u003cp\u003eScore Sequest HT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.613382899628252%\" valign=\"top\"\u003e\n \u003cp\u003eAbundance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.66542750929368%\" valign=\"top\"\u003e\n \u003cp\u003eProtein Unique Peptides\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.594795539033457%\" valign=\"top\"\u003e\n \u003cp\u003eUnique Peptides\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.736059479553903%\" valign=\"top\"\u003e\n \u003cp\u003ePeptides (by Search Engine)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.799256505576208%\" valign=\"top\"\u003e\n \u003cp\u003eWnt/\u0026beta;-catenin\u0026nbsp;pathway\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.594795539033457%\" valign=\"top\"\u003e\n \u003cp\u003eP35579\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.408921933085502%\" valign=\"top\"\u003e\n \u003cp\u003eMYH9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.736059479553903%\" valign=\"top\"\u003e\n \u003cp\u003e226.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.851301115241636%\" valign=\"top\"\u003e\n \u003cp\u003e250.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.613382899628252%\" valign=\"top\"\u003e\n \u003cp\u003e1306113914\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.66542750929368%\" valign=\"top\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.594795539033457%\" valign=\"top\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.736059479553903%\" valign=\"top\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.799256505576208%\" valign=\"top\"\u003e\n \u003cp\u003e[\u003csup\u003e36,37\u003c/sup\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.594795539033457%\" valign=\"top\"\u003e\n \u003cp\u003eP35580\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.408921933085502%\" valign=\"top\"\u003e\n \u003cp\u003eMYH10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.736059479553903%\" valign=\"top\"\u003e\n \u003cp\u003e229.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.851301115241636%\" valign=\"top\"\u003e\n \u003cp\u003e183.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.613382899628252%\" valign=\"top\"\u003e\n \u003cp\u003e228611562\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.66542750929368%\" valign=\"top\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.594795539033457%\" valign=\"top\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.736059479553903%\" valign=\"top\"\u003e\n \u003cp\u003e53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.799256505576208%\" valign=\"top\"\u003e\n \u003cp\u003e[\u003csup\u003e38,39\u003c/sup\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.594795539033457%\" valign=\"top\"\u003e\n \u003cp\u003eP60709\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.408921933085502%\" valign=\"top\"\u003e\n \u003cp\u003eACTB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.736059479553903%\" valign=\"top\"\u003e\n \u003cp\u003e41.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.851301115241636%\" valign=\"top\"\u003e\n \u003cp\u003e98.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.613382899628252%\" valign=\"top\"\u003e\n \u003cp\u003e2971294207\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.66542750929368%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.594795539033457%\" valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.736059479553903%\" valign=\"top\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.799256505576208%\" valign=\"top\"\u003e\n \u003cp\u003e[\u003csup\u003e40\u003c/sup\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.594795539033457%\" valign=\"top\"\u003e\n \u003cp\u003eP63267\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.408921933085502%\" valign=\"top\"\u003e\n \u003cp\u003eACTG2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.736059479553903%\" valign=\"top\"\u003e\n \u003cp\u003e41.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.851301115241636%\" valign=\"top\"\u003e\n \u003cp\u003e79.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.613382899628252%\" valign=\"top\"\u003e\n \u003cp\u003e4936448\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.66542750929368%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.594795539033457%\" valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.736059479553903%\" valign=\"top\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.799256505576208%\" valign=\"top\"\u003e\n \u003cp\u003e[\u003csup\u003e41\u003c/sup\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.594795539033457%\" valign=\"top\"\u003e\n \u003cp\u003eP21333\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.408921933085502%\" valign=\"top\"\u003e\n \u003cp\u003eFLNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.736059479553903%\" valign=\"top\"\u003e\n \u003cp\u003e280.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.851301115241636%\" valign=\"top\"\u003e\n \u003cp\u003e75.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.613382899628252%\" valign=\"top\"\u003e\n \u003cp\u003e31837103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.66542750929368%\" valign=\"top\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.594795539033457%\" valign=\"top\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.736059479553903%\" valign=\"top\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.799256505576208%\" valign=\"top\"\u003e\n \u003cp\u003e[\u003csup\u003e42,43\u003c/sup\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003e5. OLFM4 regulates Wnt signaling pathway by influencing the ubiquitination of GSK3\u0026beta; in intestinal metaplasia\u003c/h2\u003e\n\u003cp\u003ePrevious studies have shown that GSK3\u0026beta; interact with MYH9 to regulate the Wnt pathway \u003csup\u003e37\u003c/sup\u003e. Our study revealed that an increase in OLFM4 protein expression is associated with a decrease in GSK3\u0026beta; protein expression. Since, GSK3\u0026beta; mRNA levels remained unchanged (Fig. 6a), we analyzed the effects of OLFM4 on GSK3\u0026beta; protein turnover. Indeed, CHX assays demonstrated that knocking down of either OLFM4 or MYH9 extended the half-life of GSK3\u0026beta; in PLGC cells (Fig. 6b). CoIP assays were performed to elucidate the role of OLFM4 and MYH9 in the promotion between ubiquitin and GSK3\u0026beta; in PLGC cells (Fig. 6c). Eleven candidate structure-damaged variants of the OLFM4 protein with highest Polyphen prediction score were predicted in Missense3D database (Supplemental table 1). The Pymol software was used to analyze the protein-protein docking between OLFM4 and MYH9. Two localizations (D388 and G438) of OLFM4, which had polar contacts with MYH9, were screened out for further mutation experiments (Fig. 6d). The 3D structural changes of missense mutations were constructed (Fig. 6e). Our results showed that the D388 mutation but not the G438 mutation of OLFM4 upregulated the \u0026beta;-catenin and activated the Wnt signal pathway (Fig. 6f-g). The G438 mutation downregulated the ubiquitination level of GSK3\u0026beta; in oeOLFM4 cells (Fig. 6h). Further CoIP assay showed that the G438 mutation of OLFM4 protein could not have interaction with MYH9 (Fig. 6i). Our studies suggest that OLFM4 can interact with MYH9 to facilitate the ubiquitination of GSK3\u0026beta;, consequently activating the Wnt signaling pathway.\u003c/p\u003e\n\u003ch2\u003e6. OLFM4 and MYH9 increased in intestinal metaplasia organoids and PLGC animal models\u003c/h2\u003e\n\u003cp\u003ePDO experiments using both intestinal metaplasia and normal gastric mucosa organoids revealed significantly elevated expressions of CDX2 and MUC2 in the intestinal metaplasia organoids. The immunofluorescence and immunohistochemical assays demonstrated robust expression of OLFM4 and MYH9 in the intestinal metaplasia organoids (Fig. 6j-k, Fig. S5a).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn the PLGC animal model induced by MNNG, the gastric mucosa exhibited deep, extensive ulcers surrounded by red and swollen tissue (Fig. 6l). Under light microscopy, the gastric mucosal glands of PLGC models in HE staining exhibited swelling and disorganization, with a significant increase in heterogeneous cells that were notably aggravated (Fig. S5b). Furthermore, the conventional biomarkers CDX2 and MUC2, alongside OLFM4 and MYH9, displayed heightened expression levels in the PLGC models (Fig. 6m, Fig. S5b).\u003c/p\u003e\n\u003cp\u003eOverall, OLFM4 was the biomarker of IIM tissue and might promote the progression of intestinal metaplasia through the MYH9/GSK3\u0026beta;/\u0026beta;-catenin pathway (Fig. 7).\u003c/p\u003e\n\u003cdiv id=\"_com_2\" language=\"JavaScript\"\u003e\u003cbr\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we identified OLFM4 as a novel biomarker of incomplete intestinal metaplasia (IIM) and discovered that OLFM4 promotes the progression of IIM through the MYH9/GSK3β/β-catenin pathway. These findings have significant implications for the development of novel biomarkers for IIM and novel therapeutic strategies for preventing the progression of intestinal metaplasia.\u003c/p\u003e \u003cp\u003eThe annual incidence of gastric cancer in patients with intestinal metaplasia was 12.4/10000 (95%CI: 10.7\u0026ndash;14.3), which was significantly higher than that in healthy people (2/100000-5/100000) \u003csup\u003e44\u003c/sup\u003e. A study from Sweden showed that 1 in 39 patients with intestinal metaplasia will develop gastric cancer within 20 years, a much higher incidence than in healthy people \u003csup\u003e45\u003c/sup\u003e. However, the prevalence of intestinal-type gastric cancer was greater in IIM, which had a 4- to 11-fold higher risk of suffering gastric adenocarcinoma than CIM, and the risk rose with the severity of IIM \u003csup\u003e16\u0026ndash;18\u003c/sup\u003e. In agreement with previous studies, we found that intestinal-type gastric cancers were frequently accompanied by incomplete intestinal metaplasia tissues suggesting that IIM is an important precancerous lesion in gastric cancer development. Unfortunately, while being good indicators for intestinal metaplasia, CDX2, and MUC2 cannot be employed as biomarkers for IIM \u003csup\u003e46,47\u003c/sup\u003e. This result was also validated by the pathology data from multi-center studies used in our investigation. Therefore, the identification of incomplete intestinal metaplasia (IIM) lesions is of utmost importance in determining the high-priority population for early screening of gastric cancer. Nevertheless, the diagnosis of incomplete intestinal metaplasia (IIM) poses significant challenges, and there is a no good biomarkers specific to incomplete intestinal metaplasia tissues. Furthermore, few investigations focused on the progression of intestinal metaplasia.\u003c/p\u003e \u003cp\u003eStudies have reported that the subtype of IM can be determined by CD10 staining of the brush surface of IM cells in the gastric mucosa, with a sensitivity of 87.5% and a specificity of 96.7% \u003csup\u003e48\u003c/sup\u003e. However, accurately identifying incomplete intestinal metaplasia (IIM) based solely on sporadic incomplete brush border features was not feasible \u003csup\u003e33\u003c/sup\u003e. In HID-AB staining, the mucin profile in intestinal metaplasia cells can be used as a criterion when identifying the classification of intestinal metaplasia: mucin sulfate or salivary mucin in IIM cells can be stained brownish black by HID solution, while proteoglycans and hyaluronic acid in CIM cells are stained blue by Alcian solution. However, both HID-AB staining and AB-PAS staining methods were unable to provide clear insights into the stemness characteristics and proliferation of incomplete intestinal metaplasia (IIM). Additionally, the HID-AB staining method had toxicity concerns \u003csup\u003e49\u003c/sup\u003e. Therefore, the HID-AB staining has limited application in clinical practice. In the same study, it was suggested that Das1 staining could detect goblet cells in incomplete intestinal metaplasia (IIM) tissues. However, goblet cells were not found to be a distinguishing feature of IIM, and the Das1 staining method exhibited limited specificity and sensitivity. Consequently, it could not categorize intestinal metaplasia \u003csup\u003e48\u003c/sup\u003e. OLFM4 expression is closely related to cell stemness, reflecting the strong proliferative ability of cells with high OLFM4 expression \u003csup\u003e22,50\u003c/sup\u003e. OLFM4 had been identified from a gene signature of Lgr5\u003csup\u003e+\u003c/sup\u003e stem cells as a strong marker for murine small intestine stem cells \u003csup\u003e22\u003c/sup\u003e. The precancerous lesions in the stomach and esophagus exhibited significant levels of OLFM4 expression, which was positively correlated with the severity of diseases \u003csup\u003e25\u003c/sup\u003e. Here, we demonstrated that OLFM4 is a more effective biomarker for identifying IIM because it is expressed in intestinal metaplasia, its detection was highly specific in IIM, and OFLM4 had higher diagnostic performance. Identifying and diagnosing incomplete intestinal metaplasia (IIM) can play a crucial role in identifying individuals who should be prioritized for early screening of gastric cancer. This can significantly enhance the effectiveness of endoscopic screening and serve as a valuable auxiliary tool for early detection in screening programs.\u003c/p\u003e \u003cp\u003eThe role of OLFM4, a gene associated with stemness properties, has received limited attention in the context of intestinal metaplasia. The amount of OLFM4\u003csup\u003e+\u003c/sup\u003e cells is positively associated with the number of intestinal stem cells and the expression of OLFM4 was highly confined to the Lgr5\u003csup\u003e+\u003c/sup\u003e stem cell area \u003csup\u003e22,25,50\u003c/sup\u003e. Some studies reported that OLFM4 can promote gastric cancer progression by promoting cell proliferation and invasion \u003csup\u003e51,52\u003c/sup\u003e. In support of this, knockdown of OLFM4 expression greatly decreased cell growth and promoted apoptosis of gastric cancer cells \u003csup\u003e52\u0026ndash;54\u003c/sup\u003e. Nonetheless, there is limited literature that specifically highlights the elevated expression of OLFM4 in intestinal metaplasia tissue \u003csup\u003e25,34\u003c/sup\u003e. OLFM4 expression is elevated in gastric and colorectal cancer, particularly in the early stages of tumor formation \u003csup\u003e25\u003c/sup\u003e. Other investigations have found that OLFM4 expression is higher in early-stage, moderately differentiated, and well-differentiated cancers, while it was considerably lower in late-stage, poorly differentiated, and undifferentiated tumors \u003csup\u003e55,56\u003c/sup\u003e. OLFM4 expression was also associated with activation of the Wnt/β-catenin signaling pathway, which played a key role in regulating cell growth and differentiation \u003csup\u003e26\u0026ndash;28,57\u003c/sup\u003e. However, no literature explored the clinical application value, the biological function, and the mechanism of OLFM4 in intestinal metaplasia, especially in IIM. OLFM4 is significantly increased in IIM, possibly because OLFM4 matched the specific stemness characteristic of IIM \u003csup\u003e58\u003c/sup\u003e. Nevertheless, due to the absence of an extended follow-up period, it remains unclear whether IIM patients exhibiting high OLFM4 expression eventually develop gastric cancer. Collectively, our findings indicate that OLFM4 expression could serve as a pathologic predictor in IIM. MNNG, a chemical carcinogen, has been used to induce precancerous lesions of gastric cancer (PLGC) cells models and mimic PLGC in animal models \u003csup\u003e59\u0026ndash;62\u003c/sup\u003e. The PLGC cells, induced by MNNG, is a widely recognized cell model of intestinal metaplasia \u003csup\u003e63\u0026ndash;65\u003c/sup\u003e. We performed transcriptome sequencing (RNA-seq) on PLGC cells, and discovered that they strongly expressed CDX2, making them an ideal cell model for researching intestinal metaplasia. Concurrently, our study confirmed the significant upregulation of OLFM4 in RNA-seq analysis of PLGC cells. Previous research has indicated that PLGC cells exhibit enhanced proliferative and invasive characteristics, consistent with our own observations from functional experiments conducted on PLGC cells \u003csup\u003e63\u0026ndash;65\u003c/sup\u003e. Previous studies have shown that c-Ras, c-met, and ErbB2 mutations in PLGC cells caused GES-1 carcinogenesis in MNNG induced PLGC animal models \u003csup\u003e66,67\u003c/sup\u003e. Oral administration of MNNG in Sprague Dawley (SD) rats is associated with the development of PLGC \u003csup\u003e68,69\u003c/sup\u003e. In this study, we cultured intestinal metaplasia organoids which simulates and retains the information and functionality of intestinal metaplasia tissues \u003csup\u003e70\u003c/sup\u003e. The OLFM4 and MYH9 expression were both shown to be higher in intestinal metaplasia organoids than normal gastric mucosa, which is compatible with the PLGC cells model.\u003c/p\u003e \u003cp\u003eIn this study, we also demonstrated that OLFM4, combined with MYH9, was responsible for intestinal metaplasia progression. The over-expression of OLFM4 and the ability of malignant biological behavior including proliferation, migration and invasion were synchronously enhanced in PLGC cells. Mechanistic investigations revealed that OLFM4 plays a role in activating the downstream signaling pathways of Wnt/β-catenin during the formation of intestinal metaplasia. This activation is involved in mediating tumor growth and the process of epithelial-mesenchymal transition (EMT). The Wnt pathway has been reported to be expressed in 86% of intestinal metaplasia, suggesting that Wnt plays an important role in intestinal metaplasia \u003csup\u003e29\u003c/sup\u003e. Intriguingly, we demonstrated that MYH9 and OLFM4 cooperate to regulate the ubiquitination and degradation of GSK3β and to establish a positive regulatory loop MYH9/GSK3β/β-catenin involved in the progression of intestinal metaplasia. We discovered that GSK3β is ubiquitinated in addition to being phosphorylated, especially in intestinal metaplasia\u003csup\u003e71\u003c/sup\u003e. Myosin heavy chain 9 (MYH9) encoded non-muscle myosin II (NMM-II) plays a crucial role in cell adhesion, migration, proliferation, and differentiation. Studies have found that MYH9 can promote the progression of liver cancer and lymphoma through the Wnt pathway \u003csup\u003e36,37\u003c/sup\u003e. Considering that MYH9 overexpression has been associated with gastric cancer (GC) progression and unfavorable prognosis, it is noteworthy that MYH9 exhibits significant positive correlations with parameters such as depth of invasion (T stage), lymph node metastasis (N stage), distant metastasis (M stage), and the overall pTNM stage of gastric cancer \u003csup\u003e72\u003c/sup\u003e. MYH9 overexpression in gastric cancer cells has been shown to enhance their capacity for invasion and metastasis \u003csup\u003e73\u003c/sup\u003e. Blebbistatin is a MYH9 inhibitor, as a tumor suppressor in several malignancies\u003csup\u003e74\u003c/sup\u003e. In our study, we observed that Blebbistatin exhibited significant inhibition of the progression of incomplete intestinal metaplasia (IM) in vitro.\u003c/p\u003e \u003cp\u003eIn summary, our results established, for the first time, the high expression and oncogenic significance of OLFM4 in intestinal metaplasia, as well as the therapeutic efficacy of Blebbistatin in the treatment of intestinal metaplasia. Overall, our results show that OLFM4 is a novel target for intestinal metaplasia treatment and that blebbistatin represents a potent and effective treatment option for intestinal metaplasia.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eIM: intestinal metaplasia\u003c/p\u003e\n\u003cp\u003eCIM: complete intestinal metaplasia\u003c/p\u003e\n\u003cp\u003eIIM: incomplete intestinal metaplasia\u003c/p\u003e\n\u003cp\u003ePLGC: precancerous lesions of gastric carcinoma\u003c/p\u003e\n\u003cp\u003eGC: gastric cancer\u003c/p\u003e\n\u003cp\u003eEGC: early gastric cancer\u003c/p\u003e\n\u003cp\u003eHP: Helicobacter pylori\u003c/p\u003e\n\u003cp\u003escRNA-seq: single-cell RNA sequencing\u003c/p\u003e\n\u003cp\u003eCAG: chronic atrophic gastritis\u003c/p\u003e\n\u003cp\u003ePDO: Patient-derived organoids\u003c/p\u003e\n\u003cp\u003eGEO: Gene Expression Omnibus database\u003c/p\u003e\n\u003cp\u003eCNV: chromosome copy number variation\u003c/p\u003e\n\u003cp\u003eIHC: immunohistochemistry\u003c/p\u003e\n\u003cp\u003eHE: hematoxylin-eosin\u003c/p\u003e\n\u003cp\u003eAB-PAS: Alcian blue-periodic acid-Schiff\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAUC: area under the curve\u003c/p\u003e\n\u003cp\u003eDEGs: differentially expressed genes\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eAcknowledgments \u003c/p\u003e\n\u003cp\u003eWe thank the Seventh Affiliated Hospital of Sun Yat-sen University assisted in this study.\u003c/p\u003e\n\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis work was supported by the He Yulong Expert Workstation of Yunnan Province (202104AC100001-B03), Science and Technology Special Project of the National Sustainable Development Agenda Innovation Demonstration Zone of Yunnan Province (202104 AC100001-A10), the National Natural Science Foundation of China (22307151), Guangdong Provincial Key Laboratory of Digestive Cancer Research (No. 2021B1212040006), Guangdong Basic and Applied Basic Research Foundation (2023A1515010156), the Science and Technology Planning Project of Shenzhen Municipality (No. JCYJ20220530144815035, JCYJ20220818102011022, GJHZ20220913142400001)\u003c/p\u003e\n\n\u003cp\u003eAuthor information\u003c/p\u003e\n\u003cp\u003eHongfa Wei and Wenchao Li contributed equally.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eAuthors and Affiliations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDigestive Diseases Center, The Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen, China\u003c/p\u003e\n\u003cp\u003eHongfa Wei, Wenchao Li, Kuan Li1, Hong Yu, Haofan Yin, Zhijian Huang, Songyao Chen, Shangjiu Yang, Cuncan Deng, Nan Cai, Xiancong Chen, Hui Zhou, Guofei Deng, Yulong He, Changhua Zhang\u003c/p\u003e\n\n\u003cp\u003eScientific Research Center, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, China,\u003c/p\u003e\n\u003cp\u003eNi Ding, Fei Jiang, Mo Yang, Xiao-Yong Zhan, Yu Xia, Wei Chen, Zhangsen Huang, Liang Li, Hui Chen, Fuhui Wang, Liang Gu, Leli Zeng\u003c/p\u003e\n\n\u003cp\u003eDepartment of General Surgery, The First Affiliated Hospital of Shantou University Medical College, Jinping, Shantou, Guangdong 515041, P.R. China.\u003c/p\u003e\n\u003cp\u003eHongfa Wei, Feiran Zhang\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eContributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eC.H.Z., J.L., and Y.L.H. designed the study, K.L., N.D., H.Y., F.J., and C.W. diagnosed patients, evaluated patients, and provided samples; H.F.Y., Z.J.H., M.Y., X.Y.Z., C.W., and X.Y. were responsible for providing technological guidance; H.F.W., W.C.L., C.C.D., D.G.F., and S.J.Y. performed and analyzed bioinformatics experiments; H.F.W., W.C.L., S.Y.C., and L.L. contributed to gastric tissue analysis and genomic studies; Z.S.H., H.C., X.C.C. performed and contributed to the single cell analysis; G.F.D., L.G., and F.H.W. performed statistical analyses; H.F.W., W.C.L., C.C.D., and D.N. interpreted data and contributed to figures and tables; F.R.Z., L.L.Z., and C.H.Z. supervised the study and wrote the manuscript with input from all authors. All authors reviewed and approved the final manuscript.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eCorresponding authors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCorrespondence to Changhua Zhang, Leli Zeng, Yulong He, or Feiran Zhang.\u003c/p\u003e\n\n\u003cp\u003eEthics declarations\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was performed according to the ethical standards of the Declaration of Helsinki and was approved by the ethics committee of the Seventh Affiliated Hospital of Sun Yat-sen University and The People\u0026apos;s Hospital of Fengqing in Yunnan Province. The animal experiments were approved by the Institutional Animal Care and Use Committee (IACUC) (TopBiotech Co., LTD., Shenzhen).\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe have obtained consent to publish this paper from all the participants of this study.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no potential conflicts of interest.\u003c/p\u003e\n\n\u003cp\u003eAdditional information\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePublisher\u0026rsquo;s Note\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSpringer Nature remains neutral about jurisdictional claims in published maps and institutional affiliations. \u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSung H, et al. 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Trends Biochem Sci. 2018;43:700\u0026ndash;13. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.tibs.2018.06.006\u003c/span\u003e\u003cspan address=\"10.1016/j.tibs.2018.06.006\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"molecular-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"molc","sideBox":"Learn more about [Molecular Cancer](http://gsejournal.biomedcentral.com/)","snPcode":"12943","submissionUrl":"https://submission.nature.com/new-submission/12943/3","title":"Molecular Cancer","twitterHandle":"@SN_Oncology","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-4014155/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4014155/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eIntestinal metaplasia (IM) is classified into complete intestinal metaplasia (CIM) and incomplete intestinal metaplasia (IIM). Patients diagnosed with IIM face an elevated susceptibility to the development of gastric cancer, underscoring the critical need for early screening measures. In addition to the complexities associated with diagnosis, the exact mechanisms driving the progression of gastric cancer in IIM patients remain poorly understood. OLFM4 is overexpressed in several types of tumors, including colorectal, gastric, pancreatic, and ovarian cancers, and its expression has been associated with tumor progression.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eIn this study, we used pathological sections from two clinical centers, biopsies of IM tissues, precancerous lesions of gastric cancer (PLGC) cell models, animal models, and organoids to explore the role of OLFM4 in IIM.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eOur results show that OLFM4 expression is highly increased in IIM, with superior diagnostic accuracy of IIM when compared to CDX2 and MUC2. OLFM4, along with MYH9, was overexpressed in IM organoids and PLGC animal models. Furthermore, OLFM4, in combination with Myosin heavy chain 9 (MYH9), accelerated the ubiquitination of GSK3β and resulted in increased β-catenin levels through the Wnt signaling pathway, promoting the proliferation and invasion abilities of PLGC cells.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eOLFM4 represents a novel biomarker for IIM and could be utilized as an important auxiliary means to delimit the key population for early gastric cancer screening. Finally, our study identifies cell signaling pathways involved in the progression of IM.\u003c/p\u003e","manuscriptTitle":"OLFM4 promotes the progression of intestinal metaplasia through activation of the MYH9/GSK3β/β-catenin pathway","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-08 18:38:23","doi":"10.21203/rs.3.rs-4014155/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-03-26T14:08:01+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-03-18T16:37:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"b61352bb-5b34-45e8-92d4-82e4a07b6e33","date":"2024-03-08T10:49:01+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-03-06T14:08:23+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-03-06T09:09:41+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-03-05T22:02:24+00:00","index":"","fulltext":""},{"type":"submitted","content":"Molecular Cancer","date":"2024-03-04T18:13:29+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"molecular-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"molc","sideBox":"Learn more about [Molecular Cancer](http://gsejournal.biomedcentral.com/)","snPcode":"12943","submissionUrl":"https://submission.nature.com/new-submission/12943/3","title":"Molecular Cancer","twitterHandle":"@SN_Oncology","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e584f18d-5a94-4755-a05e-d4f3df4c81ca","owner":[],"postedDate":"March 8th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-05-04T00:42:40+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-08 18:38:23","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4014155","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4014155","identity":"rs-4014155","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}