Published online Jul 7, 2026. doi: 10.3748/wjg.118842
Revised: February 24, 2026
Accepted: March 25, 2026
Published online: July 7, 2026
Processing time: 169 Days and 6.2 Hours
Gastric intestinal metaplasia (GIM) is a precancerous condition associated with gastric cancer (GC). However, biomarkers that predict progression from GIM to GC remain unclear.
To investigate the potential of FOLH1 as a novel tissue-based biomarker for identifying patients with GIM at high risk of progression to GC.
This single-center retrospective cohort study was conducted at Wangjing Hospital and included 68 patients diagnosed with GIM. GIM samples were obtained sequentially from the antrum using endoscopic biopsies. Patients were assigned to the progressive group (P) if they developed high-grade intraepithelial neoplasia, intramucosal carcinoma, or adenocarcinoma during the 5-year follow-up; patients whose GIM remained stable were assigned to the non-progression group (N). A subset of 8 patients (4 from group P and 4 from group N) was used to identify potential biomarkers using differentially expressed genes and weighted gene correlation network analysis based on digital spatial profiling. The remaining 60 patients were used to validate candidate biomarkers using histologic evaluation, immunohistochemical staining, and immunofluorescent staining.
In the 8-patient discovery cohort, FOLH1 expression was significantly decreased in the epithelium of patients in group P compared with those in group N. In the 60-patient validation cohort, FOLH1 was a reliable biomarker for predicting malignant transformation from GIM to GC, with a sensitivity of 0.967 and a specificity of 1.000. Using Youden’s index, the optimal diagnostic cutoff value for FOLH1 was 0.302, corresponding to an integral optical density of 31.46.
FOLH1 may serve as a tissue-based biomarker for predicting progression from GIM to GC.
Core Tip: In this study, digital spatial profiling was used to identify differentially expressed genes between progressive and nonprogressive gastric intestinal metaplasia (GIM). Among 14 candidate genes, FOLH1 showed the most significant difference. Validation in 60 patients using immunohistochemical and immunofluorescent staining confirmed that low FOLH1 expression strongly predicted progression, with excellent 4 diagnostic accuracy, establishing FOLH1 as a novel biomarker for risk stratification in GIM.
- Citation: He S, Guo XF, Wang Y, Bai JQ, Wang JP, Shi JH, Shi YY, Yuan J, Wei W, Yang Y, Shi XH, Feng Y, Ding X, Gao W, Lu D, Zhou WX, Zhang P. FOLH1 as a novel biomarker for risk stratification in gastric intestinal metaplasia. World J Gastroenterol 2026; 32(25): 118842
- URL: https://www.wjgnet.com/1007-9327/full/v32/i25/118842.htm
- DOI: https://dx.doi.org/10.3748/wjg.118842
Gastric cancer (GC) is the fifth most common cancer and the third leading cause of cancer-related mortality worldwide[1]. Because early-stage disease often lacks specific clinical manifestations, diagnosis is frequently delayed, and most cases are identified at advanced stages, which substantially worsens survival outcomes[2,3]. According to the well-established Correa cascade, GC typically progresses from atrophic gastritis to gastric intestinal metaplasia (GIM), then to dysplasia and, ultimately, to invasive carcinoma[4]. Compared with the transition from dysplasia to GC, progression from intestinal metaplasia (IM) to malignancy generally takes longer, providing a broader window for clinical intervention[5]. Timely intervention at the GIM stage may prevent or delay progression to dysplasia and GC[6].
GIM is recognized as a precancerous condition because of its potential to progress to GC[5-7]. A cohort study involving 26000 patients with non-dysplastic GIM reported an annual GC incidence of 12.4 per 10000 person-years[8,9]. GIM is classified as complete (type I) and incomplete (types II and III) based on its histologic resemblance to small intestinal or colonic epithelium[10]. Endoscopic examination is considered the gold standard for diagnosing GC; however, its high cost and invasiveness limit widespread use[11,12]. Histochemical stains, such as Alcian blue (AB) potential of hydrogen (pH) = 2.5/periodic acid-Schiff (PAS) and high iron diamine, are used to classify GIM subtypes[10,13], but these stains often fail to accurately reflect the overall pathology of gastric mucosal lesions. Accumulating evidence indicates that serum biomarkers including pepsinogen I (PGI), pepsinogen II (PGII), the pepsinogen I/II ratio (PGR), Helicobacter pylori
We enrolled 68 patients with GIM at Wangjing Hospital, China Academy of Chinese Medical Sciences. A subset of 8 patients [4 from progressive group (P) and 4 from non-progression group (N)] was used to identify potential biomarkers, and the remaining 60 patients constituted the validation cohort. Inclusion criteria were: (1) Age 18-80 years; and (2) A pathologic diagnosis of antral GIM. Exclusion criteria were: (1) A history of gastric surgery; (2) The presence of regene
GIM was diagnosed based on the presence of goblet cells, Paneth cells, and absorptive enterocytes in the gastric mucosa on hematoxylin and eosin staining. Subtyping as complete (type I) or incomplete (types II/III) GIM was performed using AB-PAS staining, which visualizes acidic (blue) and neutral (magenta) mucins, according to the updated Sydney system biopsy protocol[18]. Clinical and pathologic variables, including sex, age, H. pylori infection, and GIM subtype, were recorded.
Patients were categorized into discovery and validation cohorts. The 8-patient discovery cohort (4 per group) was used to identify candidate biomarkers based on progression to GC during the 5-year follow-up; digital spatial analysis was used to screen for biomarkers predictive of disease progression (Table 1). Biomarker performance was subsequently evaluated in the 60-patient validation cohort, which included 30 patients in the P group and 30 in the N group. Diagnostic performance was assessed using receiver operating characteristic (ROC) curve analysis, with discrimination quantified by the area under the curve. The optimal cutoff value was determined using Youden’s index (sensitivity and specificity).
| Characteristics | N group | P group |
| Male | 2 | 3 |
| Female | 2 | 1 |
| Age (year) | 51.5 ± 9.52 | 67.25 ± 4.03 |
| GIM | ||
| Moderate | 2 | 2 |
| Severe | 2 | 2 |
| OLGIM | ||
| Type I | 4 | 4 |
| Helicobacter pylori | 2 | 2 |
Approvals were obtained in accordance with the Declaration of Helsinki, and the study protocol was approved by the Ethics Committee of Wangjing Hospital. This retrospective clinical study was registered in the Chinese Clinical Trial Registry.
Tissue specimens were trimmed immediately after surgery and divided into several fragments measuring 20 mm × 3 mm. Each fragment was embedded, snap-frozen on dry ice, and stored at -80 °C. Two experienced pathologists independently reviewed each slide and selected antral fragments containing normal tissue (CTR), IM, and cancerous lesions (CA) for digital spatial profiling (DSP).
For NanoString GeoMx DSP RNA assays, slides were prepared according to the RNA Slide Preparation Protocol described in the GeoMx DSP Slide Preparation User Manual (NanoString; MAN-10115-05; software v2.3). SYTO13, pan-cytokeratin (PanCK), and cluster of differentiation (CD) 45 were used as fluorescent markers for tissue imaging and cell-type identification (NanoString). For spatial sampling, slides were placed in the slide holder of the GeoMx DSP instrument and secured by closing the slide tray clamp. Each slide was covered with 2 mL of buffer S, and the underside of the slide was cleaned with 70% ethanol before the slide tray was loaded into the instrument. Scanning parameters were configured to restrict scanning to tissue areas, with tissue boundaries defined by adjusting the X-axis and Y-axis scanning limits.
A total of 116 rectangular regions of interest (ROIs) were selected by two experienced pathologists. Channel thresholds for segmentation were manually adjusted to maximize inclusion of ROI signal areas while minimizing nonspecific background. The following general settings were applied: Erode 1-2 μm, N-dilate 2 μm, hole size 160 μm2, and particle size 50. After approval of the 116 ROIs, the GeoMx DSP system photocleaved the ultraviolet-cleavable barcoded linkers from the bound RNA probes, enabling the collection of each segmented area into separate wells of the DSP collection plate.
Differentially expressed genes (DEGs) were identified using DESeq2. A gene was considered significantly changed if |log2 fold change (FC)| > 0.6, and the adjusted P value was < 0.05. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of the DEGs were conducted using the enrichplot and clusterProfiler R packages, and the Benjamini-Hochberg procedure was applied for multiple-testing correction.
Weighted correlation network analysis (version 1.72.1) was used to construct a network comprising 16449 genes from PanCK-expressing and CD45-expressing regions. Pearson correlation coefficients were calculated for all module eigengenes (MEs). A topological overlap matrix was generated for hierarchical clustering, and modules were automatically identified and merged using the dynamic tree cut function. Subsequently, the average distance between the MEs of all modules was defined as 1 Pearson’s correlation coefficient.
Formalin-fixed tissues were processed, embedded in paraffin, and sectioned at a thickness of 5 μm. Hematoxylin and eosin staining was performed using a commercial kit (AB2458801KIT, Abcam) according to standard clinical laboratory protocols. Immunohistochemical (IHC) staining was performed using primary antibodies against CES2 (bs-20183R, 1:700, Bioss), CRIP1 (bs-14056R, 1:200, Bioss), DHRS11 (bs-18921R, 1:300, Bioss), EPCAM (bs-1513R, 1:500, Bioss), FOLH1 (BM4078, 1:30, Boster Biological Technology), NPC1 L1 (bs-8849R, 1:500, Bioss), S100A10 (bs-8503R, 1:10000, Bioss), and TMEM176B (PHC0758S, 1:300, Abmart). Tissue sections were deparaffinized in xylene and rehydrated through a graded ethanol series. Antigen retrieval was performed by heating slides in boiling sodium citrate buffer for 20 minutes. After blocking with 3% hydrogen peroxide and bovine serum albumin, tissue sections were incubated with primary antibodies at 4 °C overnight. After washing, the corresponding immunochromogenic reagent (IHC-BF10-03, LBP Medicine Science and Technology) was applied. Color development was performed using a diaminobenzidine substrate (Sigma-Aldrich, D-7304), and tissue sections were counterstained with hematoxylin.
For immunofluorescence (IF) staining, an anti-FOLH1 primary antibody (BM4078, 1:30; Boster Biological Technology) was used. Corresponding secondary antibodies (PS300, Abcarta) were used for fluorescent detection, and 4’,6-diamidino-2-phenylindole was used to counterstain nuclei. Images were acquired using a Nikon DS-Ri2 microscope. The integrated optical density (IOD) of the positive signal was measured using Image-Pro Plus 6.0.
Data were analyzed using GraphPad Prism 9.0. Differences between groups were assessed using Student’s t-test or one-way analysis of variance. Diagnostic efficacy was evaluated using ROC curve analysis and the area under the curve. The cutoff value was determined by maximizing the Youden index. Kaplan-Meier survival analysis with the log-rank test was used to compare progression-free probability between patients stratified by FOLH1 expression levels. Univariable and multivariable Cox proportional hazards regression models were constructed to evaluate the independent prognostic value of FOLH1, with results reported as hazard ratios (HRs) and 95% confidence intervals (CIs). FOLH1 was analyzed both as a binary variable (using the optimal cutoff value) and as a continuous variable. All tests were two-sided, and P < 0.05 was considered statistically significant.
A total of 68 patients from Wangjing Hospital, China Academy of Chinese Medical Sciences were consecutively enrolled in this study. Patients were assigned to the P group if endoscopic biopsies revealed high-grade intraepithelial neoplasia, intramucosal carcinoma, or adenocarcinoma within 5 years, whereas patients whose GIM remained unchanged during the 5-year follow-up were assigned to the N group. The start of observation was defined as the time of the first gastroscopic biopsy (N1 and P1). For P2, the endpoint was defined as the time of endoscopic examination at which trans
| Characteristics | N1IM | P1IM |
| Male | 17 | 22 |
| Female | 13 | 8 |
| Age (year) | 52.4 ± 12.98 | 63.97 ± 8.65 |
| GIM | ||
| Mild | 23 | 10 |
| Moderate | 5 | 15 |
| Severe | 2 | 5 |
| OLGIM | ||
| Type I | 26 | 12 |
| Type II | 3 | 3 |
| Type III | 1 | 15 |
| Helicobacter pylori | 16 | 16 |
As illustrated in Figure 1, 116 ROIs, including CTR, IM, and CA, were selected. Representative hematoxylin-eosin and IF staining of ROIs from each group delineated the CTR, IM, and CA areas (Figure 2A). To specifically examine transcriptional changes in epithelial and stromal regions, we evaluated the PanCK-positive (PanCK+) and immune-positive (CD45+) fractions within each ROI. Principal component analysis (PCA) revealed clear segregation of ROIs in the PanCK+ region (Figure 2B). The PCA results reflected histologic variation, with clustering of expression signatures from the N1-IM, P1-IM, P2-IM, and P2-CA groups.
To investigate gene expression differences between the N1-IM and P1-IM groups, DESeq2 was used to identify DEGs. A total of 2385 genes were upregulated, whereas 667 genes were downregulated (P1-IM vs N1-IM; |log2FC| > 0.6, P < 0.05) (Figure 3A). The top 300 upregulated and downregulated genes were selected for subsequent KEGG pathway and GO analyses.
KEGG analysis indicated that the transformation of GIM to GC was predominantly associated with metabolic pathways, shigellosis, regulation of the actin cytoskeleton, neurodegenerative disease pathways, and cytoskeletal pathways in muscle cells, among others (Figure 3B). GO analysis showed the following: (1) Biological processes were mainly enriched in retinal rod cell development, cell differentiation, cilium movement, positive regulation of DNA-binding transcription factor activity, central nervous system development, cell migration, and cell adhesion, among others; (2) The main cellular components included the axoneme, extracellular region, motile cilium, perinuclear region of the cytoplasm, external side of the plasma membrane, Golgi membrane, mitochondrion, and mitochondrial inner membrane, among others; and (3) The main molecular functions included lipoprotein lipase activity, sequence-specific double-stranded DNA binding, triacylglycerol lipase activity, transmembrane signaling receptor activity, and oxidoreductase activity, among others (Figure 3C).
A total of 14 genes were identified as enriched in the PanCK region (Figure 4) based on the overlap of significant DEGs (log2FC > 0.6, P < 0.05) across the comparisons of N1-IM vs N1-CTR, N1-IM vs N2-IM, N1-IM vs P1-IM, and N1-IM vs P2-IM. Compared with the P1-IM group, the N1-IM group exhibited significantly higher expression levels of ALDH1A1, CD68, CES2, CFL1, CRIP1, DHRS11, EPCAM, EBP, FOLH1, MGST3, NPC1 L1, NDUFA4, S100A10, and TMEM176B (P < 0.05). IHC staining revealed that FOLH1 was exclusively expressed in N1-IM, with levels > 200-fold higher than those in P1-IM, enabling a clear distinction between the two subtypes. Therefore, FOLH1 was selected as a potential biomarker in this study (Figure 5).
Sequential samples from 60 patients (30 in the N group and 30 in the P group) were used to validate the diagnostic efficacy of FOLH1 in distinguishing GIM that progressed to GC from nonprogressive GIM (Table 2 and Supplementary Figures 1 and 2). IHC analysis indicated that FOLH1 expression was significantly higher in the epithelial cells of N1-IM than in those of P1-IM (Figure 6A and B). In addition, FOLH1 demonstrated strong predictive performance, with a sensitivity of 0.967 and a specificity of 1.00. Using the Youden index, the optimal diagnostic cutoff value for FOLH1 was determined to be 0.302. Moreover, the IOD of FOLH1 was 31.46, indicating that an IOD > 31.46 may suggest nonprogressive GIM, whereas an IOD < 31.46 may indicate progressive GIM (Figure 6C). IF results also demonstrated significantly higher FOLH1 expression in N1-IM than in P1-IM (Figure 7).
To further evaluate the clinical utility of FOLH1 as a biomarker for risk stratification, a longitudinal follow-up study was conducted in the validation cohort of 60 patients with GIM over a 5-year period. Kaplan-Meier analysis was used to assess the association between FOLH1 expression levels and progression-free probability in patients with GIM (Figure 8A). Patients were stratified using the optimal diagnostic cutoff of 31.46 for FOLH1 IOD. The high-FOLH1 group (IOD ≥ 31.46) remained stable, with negligible malignant transformation throughout the follow-up period. Conversely, the low-FOLH1 group (IOD < 31.46) showed a significant and rapid decline in progression-free probability over time. The log-rank test revealed a highly significant difference between the two groups (P < 0.0001), indicating that low FOLH1 expression is a robust predictor of progression from GIM to GC.
To adjust for potential confounding clinical variables, including age, sex, and baseline histopathologic grade, multiple Cox proportional hazards regression models were constructed (Figure 8B). When FOLH1 was modeled as a binary variable, low FOLH1 expression was identified as a strong independent risk factor for disease progression, with a HR of 65.5 (95%CI: 8.40-510.3, P < 0.001). In this model, conventional clinical indicators such as age (P = 0.205) and sex did not show independent statistical significance. When FOLH1 expression was modeled as a continuous variable, it showed a significant protective effect (HR = 0.92, 95%CI: 0.87-0.97, P = 0.002). This suggests that for each unit increase in baseline FOLH1 expression, the risk of malignant transformation decreases by approximately 8%. In the comprehensive predictive model. “Severe GIM” remained a significant risk indicator (HR = 4.07, P = 0.044), whereas operative link on GIM (OLGIM) staging (type II and type III) did not reach independent statistical significance (P > 0.05) after adjustment for FOLH1 levels.
The diagnostic robustness of FOLH1 was further confirmed by ROC analysis (Figure 8C). In the validation cohort (n = 60), FOLH1 effectively discriminated between progressors and non-progressors, with an area under the curve of 0.9978, a sensitivity of 0.967, and a specificity of 1.00. These results indicate that FOLH1 significantly outperforms current standard serologic markers and clinical staging systems in predicting GIM progression.
Although GIM is a recognized precursor to GC, the OLGIM staging system has limited sensitivity for predicting progression. A previous study demonstrated that although incomplete IM (OLGIM II/III) confers a higher risk than complete metaplasia (OLGIM I), the subtype does not predict outcome.
To date, no clinically applicable molecular markers are available to differentiate GIM subtypes with respect to risk of transformation to GC[19,20]. Abnormal levels of several serological markers, including PGI, PGII, PGR, H. pylori, and G17, may indicate the possibility of gastric malignancy; however, their utility for predicting the transformation of GIM to GC[15-17] is limited. Therefore, a novel tissue-based biomarker is needed to distinguish GIM subtypes that are likely to remain stable from those at high risk of transformation to GC[19-22]. In this study, we integrated multiregional transcriptomic profiling by DSP with high-dimensional characterization to identify DEGs predictive of GIM-to-GC transformation. Based on comparative analyses of nonprogressive and progressive GIM, FOLH1 was selected as a potential biomarker, with expression in nonprogressive GIM more than 200-fold higher than that observed in progressive GIM.
The FOLH1 gene encodes folate hydrolase 1, a protein expressed on the brush border membranes of the small intestine and duodenum that facilitates the digestion and absorption of dietary folate[23,24]. Notably, FOLH1 expression is significantly increased in intestinal biopsies from patients with inflammatory bowel disease[25]. An analysis of FOLH1 immunoreactivity in 71 benign and malignant gastric tumors showed that only 14.5% (60 negative, 5 weakly positive, 2 moderately positive, and 2 strongly positive) expressed FOLH1[26]. Previous studies have established associations between folate metabolism and cancer progression[27,28]. Folate deficiency has been linked to increased reactive oxygen species production and enhanced tumorigenesis in experimental models[29]. In addition, many gastrointestinal mucosal lesions are associated with folate deficiency[30,31], and GC patients with low plasma folate levels have been reported to have an increased risk of developing preneoplastic metaplasia[32].
In this study, we observed that FOLH1 was expressed at significantly higher levels in nonprogressive GIM than in progressive GIM (96.7% sensitivity, 100% specificity), which is consistent with previously reported findings. Although we established a statistical correlation between FOLH1 expression and progression risk, the causal relationship between FOLH1 expression and folate metabolism remains to be experimentally validated.
FOLH1 IHC staining could be readily integrated into routine pathological assessment of GIM biopsies because it is compatible with standard tissue processing. Patients with low FOLH1 expression (IOD < 31.46) may warrant more frequent surveillance, whereas those with high expression could follow standard protocols. The marked difference in expression between progressive and nonprogressive GIM positions FOLH1 as a potential companion diagnostic for enriching high-risk populations in clinical trials. Furthermore, the link between FOLH1 and folate metabolism raises the possibility of targeted prevention strategies, such as folate supplementation, which warrants further investigation. Future directions include multicenter validation, integration with emerging biomarkers, and the development of automated quantification algorithms to enhance reproducibility and clinical adoption.
Several limitations should be acknowledged. The retrospective design may introduce selection bias; therefore, prospective cohort studies are needed to confirm the predictive value of FOLH1. The sample size was relatively small, which constrained the identification of candidate genes, and the single-center design limits generalizability to populations with diverse genetic and environmental backgrounds. Multicenter validation in diverse cohorts is required before clinical implementation. Established GC markers, including CDX2, MUC2, and p53, were not identified in the DSP discovery screen. This may reflect that these markers, while valuable for diagnosing IM or dysplasia, are less effective at distinguishing progression risk within GIM than FOLH1. However, this interpretation is indirect and requires further validation.
In addition, our analysis was confined to antral GIM; whether FOLH1 has predictive value in other gastric regions remains unknown and should be evaluated with multiregional sampling. Finally, the biological mechanisms linking FOLH1 to GIM progression remain hypothetical. Although we discuss plausible connections to folate metabolism, oxidative stress, and immune modulation, functional studies are needed to establish causality and clarify the underlying molecular pathways.
By integrating cutting-edge spatial transcriptomics with traditional histopathological validation in well-characterized patient cohorts, this study provides evidence that FOLH1 is a novel, promising tissue-based biomarker for risk stratification in GIM. Future studies should address the limitations described above through prospective designs, geographic diversification, standardized protocols, and mechanistic investigations.
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