Clinicopathological significance of histological diversity in gastric adenocarcinoma with primitive enterocyte phenotype: A methylation-driven aggressive entity

Abstract

BACKGROUND

Gastric adenocarcinoma with primitive phenotypes has recently attracted increasing attention due to its aggressive nature and challenging diagnosis. Gastric adenocarcinoma with enteroblastic differentiation (GAED) and hepatoid adenocarcinoma (HAC) were previously regarded as gastric adenocarcinoma with primitive enterocyte phenotype (GAPEP). GAPEP is known for its poor prognosis, and the accurate diagnosis of GAPEP directly affects therapeutic decision-making. Despite their poor prognosis and morphological heterogeneity, the molecular drivers of GAPEP, particularly methylation-driven mechanisms, remain poorly explored.

AIM

To investigate the clinicopathological and molecular characteristics of GAPEP and establish an integrative diagnostic strategy to guide therapeutic decision-making.

METHODS

Based on the expression profile and morphology, patients were divided into three groups: GAPEP (including GAED and HAC), conventional gastric cancer (CGC), and CGC expressing primitive phenotypic markers. We analyzed clinicopathological features and overall survival. Data from The Cancer Genome Atlas were also analyzed, and functional enrichment analysis was conducted.

RESULTS

GAPEP showed diverse morphology, and immunohistochemical staining alone was not adequate for accurate diagnosis. Histologically, GAPEP was characterized by large, polygonal tumor cells with supranuclear or subnuclear vacuoles, a “piano keyboard-like” appearance, and clear or eosinophilic cytoplasms. Compared to CGC and CGC expressing primitive phenotypic markers, GAPEP displayed more aggressive clinical features. Molecular analysis showed significant differences in molecular subtypes, TP53 mutation, ERBB2 amplification, ARID1A mutation, MSI status, and CpG island methylator phenotype category. Genomic analysis revealed that TP53 mutations, APC mutations, and ERBB2 amplifications were more frequent in GAPEP. Genes involved in methylation processes were highly upregulated in GAPEP. HAC and GAED shared similar clinicopathological and genetic characteristics. Functional enrichment analysis highlighted the critical role of methylation in the development of GAPEP.

CONCLUSION

The diversity and aggressiveness of GAPEP are driven by deregulated methylation, necessitating the integration of morphological and immunohistochemical diagnosis. Targeting methylation can provide new therapeutic opportunities for treating this aggressive cancer.

Key Words: Gastric cancer; Gastric adenocarcinoma with enteroblastic differentiation; Hepatoid adenocarcinoma; Alpha-fetoprotein; Bioinformatics; Methylation

Core Tip: Gastric adenocarcinoma with primitive enterocyte phenotype (GAPEP) exhibits aggressive behavior and diverse morphologies, including tubular-papillary and solid types, which necessitate integrated histopathological, immunohistochemical, and molecular experiments for accurate identification. This study highlighted GAPEP’s methylation-driven pathogenesis, with methylation dysregulation (e.g., DNMT3A/B/L, TET1/3 and EZH2 upregulation). Gastric adenocarcinoma with enteroblastic differentiation and hepatoid adenocarcinoma shared similar clinicopathological and genetic features, suggesting a common origin. Targeting methylation pathways (e.g., DNMT inhibitors) offers potential therapeutic avenues for this lethal cancer, emphasizing the critical role of epigenetic mechanisms in the pathogenesis of GAPEP.

INTRODUCTION

Gastric cancer (GC) is among the most common malignant tumors of the digestive system, ranking third in terms of cancer-related deaths in China[1]. A major contributor to the development of GC is chronic Helicobacter pylori infection, which remodels the environment of the stomach in atrophic gastritis and induces the phenotypic transformation of gastric stem cells[2-4]. Gastric mucosal stem cells differ in origin, proliferation, differentiation, and expression profiles across different anatomical sites, contributing to GC’s heterogeneity[5]. Advances in high-throughput sequencing have shifted the focus of pathology from morphology-based to molecular-based classifications. However, the heterogeneity of GC continues to complicate precise diagnosis and treatment strategies, even with The Cancer Genome Atlas (TCGA) molecular subtypes[6].

GC’s striking histological heterogeneity features have led to the development of various classification systems[7-9]. Alpha-fetoprotein (AFP)-producing GC is an example of GC heterogeneity. Regardless of morphology, cases with positive AFP in immunohistochemistry (IHC) or elevated serum levels of AFP were previously reported and considered to have AFP-producing GC[10-15]. In practice, the diagnosis of AFP-producing GC does not rely on positive IHC staining for AFP or elevated serum levels of AFP. These entities often expressed specific markers, including AFP, glypican-3 (GPC3), and spalt-like transcription factor 4 (SALL4), which reflect embryonic characteristics. This has led researchers to describe them as gastric adenocarcinoma with primitive enterocyte phenotype (GAPEP), which predominantly were intestinal-type cancers[10]. GAPEP includes aggressive subtypes like gastric adenocarcinoma with enteroblastic differentiation (GAED), hepatoid adenocarcinoma (HAC), and yolk sac tumor-like carcinomas, which were classified as unique histological subtypes in the fifth edition of the World Health Organization classification[8]. Previous studies indicated that GAPEP subtypes share similar clinicopathological and molecular features, such as aggressive behavior and high frequency of TP53 mutation[12,15,16].

Although GAPEP subtypes share similar immunophenotypes, their histomorphology may overlap, making diagnosis challenging. Previous studies demonstrated that the coexistence of GAED and HAC can be frequently observed[15]. The relationship between GAED and HAC has been challenging for researchers. Histologically, GAED has a low-grade tubule-papillary architecture and a distinctly clear cytoplasm that resembles the primitive gut. In contrast, HAC consists of large polygonal eosinophilic cells resembling hepatocellular carcinoma. These cells are arranged in various patterns, including pseudo-glandular, cord-like, or solid patterns[15]. IHC for primitive markers, including AFP, GPC3, and SALL4, is crucial to distinguish GAPEP from other types of GC, though these markers can also be seen in more conventional types. This overlap complicates accurate diagnosis and highlights the phenotypic plasticity of GAPEP. GAPEP is known for its poor prognosis, and its morphological heterogeneity makes diagnosis difficult. The accurate diagnosis of GAPEP directly affects therapeutic decision-making.

Currently, there is limited information regarding the morphological and molecular features of GAPEP, underscoring the importance of further research. In this paper, we present a series of typical cases of GAPEP to delineate the morphological and molecular characteristics of this aggressive tumor. Our study systematically identified that the histological heterogeneity of GAPEP is intrinsically linked with methylation aberrations, providing critical molecular evidence for stratified therapeutic strategies. Additionally, we conducted a comparative analysis of clinicopathological and molecular characteristics between GAED and HAC to ascertain their relationship.

MATERIALS AND METHODS

Patients

Inclusion and exclusion criteria: Patients with progressive GC from the Fujian Provincial Hospital (January 2014 to December 2020) who were pathologically confirmed were included in this study. Patients were included in two cohorts. 407 consecutive patients who underwent surgical resection of GC from January 2014 to December 2015 were included in cohort I, and 114 patients with GC expressing at least one of three primitive phenotypic markers (AFP, GPC3, and SALL4) were randomly included in cohort II. The classification criteria of various histological subtypes are defined as follows (Figure 1): (1) GAPEP: GC expressing at least one of the primitive phenotypic markers and histologically exhibiting features of GAED or HAC; (2) Conventional GC (CGC) primitive phenotypic markers (CGC-P): GC lacking the histological features of GAED or HAC, but expressing primitive phenotypic markers; and (3) CGC: GC expressing none of the primitive phenotypic markers. GC expressing none of the primitive phenotypic markers but exhibiting features of GAED or HAC were not included in this study because rare. Among the three categories, AFP (serum or IHC) expression is not necessary. The inclusion criteria were as follows: (1) Pathologically diagnosed with progressive gastric adenocarcinoma; and (2) Undergoing radical gastrectomy. Exclusion criteria were as follows: (1) Recurrent or metastatic GC; (2) Undergoing neoadjuvant therapy; (3) Having a history of germ cell tumors or hepatocellular carcinoma; and (4) Death within one month after surgery.

Figure 1 The classification criteria of various histological gastric cancer subtypes. AFP: Alpha-fetoprotein; SALL4: Spalt-like transcription factor 4; GPC3: Glypican-3.

Clinicopathological characteristics and follow-up: Four pathologists (Li HQ, Zhang X, Lin L and Chen XY) reviewed all original hematoxylin and eosin slides and recorded clinicopathological parameters from the hospital medical records. The following clinical pathological parameters were recorded: Age, sex, tumor location, histologic type, lymphovascular invasion, perineural invasion, tumor budding, tumor size, depth of tumor invasion, and lymph node metastasis. All cases with complete follow-up data and a follow-up period of more than 5 years were included in the survival analysis. The tumor-node-metastasis staging was conducted following the eighth edition of the American Joint Committee on Cancer staging manual[17]. The detailed histological classification was determined according to the criteria of the sixth edition of the Japanese GC Association classification[7].

Immunohistochemical staining: The 4-μm-thick unstained sections of the representative regions of samples were prepared and then stuck to Superfrost Plus glass slides (Matsunami Glass Industry, Japan). Of each tissue, one slide was stained with hematoxylin and eosin to confirm the presence of representative tumors. The slides were stained with the following antibodies (Supplementary Table 1): SALL4, GPC3, AFP, MutL homolog 1, postmeiotic segregation increased 2, Mut S homolog (MSH) 2, and MSH6 on the Lumatas platform (Maixin Biotechnology Co. Ltd, China). HER-2 and p53 staining was performed using a Ventana Benchmark Ultra immunostainer (Ventana Medical Systems, Inc., United States). Phosphate-buffered saline was used as the negative control. The IHC automated staining protocol was performed as described previously[18]. We conducted nuclear staining for SALL4 and cytoplasmic staining for AFP and GPC3, as primitive phenotypic markers. The staining was considered positive in the presence of ≥ 10% cytoplasmic staining for AFP and GPC3 and ≥ 10% nuclear staining for SALL4. HER-2 immunostaining was interpreted according to the American Society of Clinical Oncology guidelines[19]. The presence of p53 expression in ≥ 70% of nuclei or the absence of p53 expression was considered TP53 mutation. The four mismatched repair (MMR) proteins, including MutL homolog 1, postmeiotic segregation increased 2, MSH2, and MSH6, were nuclear stained. Proficient MMR was defined as no loss of the MMR proteins, and defective MMR was defined as loss of one or more MMR proteins[20].

Fluorescence in situ hybridization: The fluorescence in situ hybridization assay was conducted using the HER-2 DNA Probe Kit II (PathVysion Kit II, Abbott, United States) on the paraffin-embedded tissue blocks. The fluorescence in situ hybridization assay was conducted following the instructions, and the results were analyzed using the methods described by the American Society of Clinical Oncology guidelines[19]. The total numbers of HER2 and CEP17 signals were counted in 20 adjacent interphase tumor cell nuclei, using fluorescent microscopes and appropriate filters. The ratios of HER2 signals to CEP17 signals were calculated regardless of IHC status as follows: When the ratio was < 1.8, the gene was considered non-amplified, and when it was > 2.2, the gene was considered to be amplified. If the ratio was within the range of 1.8 to 2.2 at the initial count, an additional 20 tumor cells were counted. If the final ratio for 40 nuclei was 2.0 or higher, the case was deemed to have HER2 amplification.

Data mining from the TCGA databases

Data from the Stomach Adenocarcinoma (TCGA, Nature 2014) dataset were obtained using the public tool: CBioPortal (http://www.cbioportal.org). The clinicopathological information and molecular data, including the gene expression profile and mutated genes, were obtained from the TCGA database. We analyzed the expression of primitive phenotypic genes (SALL4, GPC3, and AFP) at the mRNA level. Clinical parameters, including clinicopathological features and patient prognosis, were extracted. The whole slide images of tumors were reviewed following the Genomic Data Commons portal (https://portal.gdc.cancer.gov/). In cBioPortal, Onco Query Language was used to identify samples with specific expression values. For mRNA (RNA Seq V2 RSEM) expression, z-score thresholds were set to ± 1. We analyzed the correlation between GAPEP, CGC-P, and CGC and clinicopathological features, including patient prognosis and somatic genomic alterations, including somatic mutations, DNA copy-number alterations, mRNA expression, and DNA methylation. Differently expressed genes in the TCGA-Stomach Adenocarcinoma RNA-seq dataset were identified using the cBioPortal. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were conducted using the R package “cluster profiler” (3.14.3) to explore the functional and pathway differences between GAPEP, CGC-P, and CGC. A heatmap was produced using the R package “pheatmap”. For each analysis, the permutation number was set to 1000 times. Enrichment results with P < 0.05 and false discovery rate q-value < 0.05 were considered statistically significant.

Statistical analysis

GraphPad Prism 9.0 (La Jolla, CA, United States) was used to draw graphs and conduct statistical analyses. Descriptive variables are presented as percentages. Continuous data were compared using the Mann-Whitney U test. Categorical variables were compared using the χ² test and Fisher’s exact test. A log-rank test was performed for survival analysis, and R studio was used to generate Kaplan-Meier curves. For molecular genetics data, continuous data were used to analyze copy-number alterations. For all other parameters (mRNA, methylation, and protein expression), normalized continuous values were used for statistical analysis. The Kruskal-Wallis test was used to assess differences in DNA copy-number alterations, mRNA, and methylation between various types of GC. The Mann-Whitney U test was employed to compare individual groups. Log2 ratio test/control thresholds of 0.25 and -0.25 were defined as copy number gains and losses, respectively. Statistical significance was defined as a two-tailed P < 0.05.

RESULTS

General information

The results of single-center dataset analysis: The results of IHC for cohort I showed that 133 of 407 (32.7%) tumors had at least one of the three primitive phenotypes, including SALL4 (63/407, 15.5%), GPC3 (85/407, 20.9%), and AFP (6/407, 1.5%). In the cohort I, 63 cases (15.5%) were GAPEP, 70 cases (17.2%) were CGC-P. Meanwhile, 71 cases of cohort II were GAPEP. In total, 134 cases of GAPEP (including 63 GAED, 49 HAC and 22 cases of a mixture of GAED and HAC) were included in subsequent analyses of clinicopathological data. This cohort of 134 GAPEP cases represents the largest single-center cohort reported to date. Additionally, 92 cases of CGC-P and 274 cases of CGC were included.

Results of TCGA datasets: After excluding cases with no mRNA data for any of the three genes and cases with preoperative neoadjuvant therapy or palliative surgery, 228 cases were included for subsequent analyses. Of these cases, 71 cases (31.14%) had a high mRNA expression of SALL4, GPC3, and AFP. In total, 39 cases (17.1%) had a high expression of SALL4, 46 cases (20.2%) had a high expression of GPC3, and 10 cases (4.4%) had a high expression of AFP. Moreover, 17 cases (7.5%) had a high expression of SALL4 and GPC3, and seven cases (3%) had a high expression of SALL4, GPC3, and AFP. Meanwhile, we reviewed all whole-slide imaging sections for all cases. Only cases exhibiting histologically features of GAED and HAC were included. Finally, 31 cases (13.6%) of GAPEP (including 19 GAED and 12 HAC), 40 cases of CGC-P (17.5%), and 157 cases of CGC were included in subsequent analyses.

GAPEP showed histopathological feature diversity

The morphology of GAPEP was diverse, with classical features visible in all cases. In the local dataset, the morphology of GAPEP was classified into two main types. The “tubular-papillary type” was characterized by prominent tubular and papillary structures with columnar and cuboidal cells containing hyaline cytoplasm similar to the intestinal epithelium of a newly formed fetus (Figure 2A and B). The tubular and papillary structures in GAPEP presented with supranuclear and subnuclear vacuoles, showing a “piano keyboard-like” appearance (Figure 2A). Most glandular lumens contained a large amount of eosinophilic secretion, which may be a feature of this type of GAPEP (Figure 2C). In some cases, the tumor exhibited sieve-like and sac-like structures (Figure 2D), tumor cells had a flat shape, and the tumor had a villi-like structure (Figure 2E). The “solid type” possessed a morphology similar to hepatocellular carcinoma (Figure 2F and G). It had large polygonal cells with eosinophilic or clear cytoplasm and round to oval nuclei in nested mass structure. The “solid type” contained eosinophilic intracellular and extracellular hyaline microspheres. Extracellular and intracellular mucus formation was seen in a subset of tumors (Figure 2H and I). In rare instances, tumor cells did not adhere to each other, similar to the poorly cohesive gastric carcinoma (Figure 2J). A combination of tubule-papillary and solid components was observed in more than 20 cases, and their migratory capacity was identified (Figure 2K-M). Although the “tubule-papillary type” had various amounts of “solid type” components, no “tubule-papillary type” GAPEP was composed only of solid tumors with abundant eosinophilic cytoplasm. Clear cytoplasm was predominant over the eosinophilic cytoplasm in the solid component (Figure 2N). Some cases exhibited significant nuclear atypia (irregular chromatin, prominent nucleoli, and nuclear divisions) and tumor necrosis, indicating high-grade transformation (Figure 2O and P). A mixture of conventional intestinal adenocarcinoma was observed in almost all cases. Tumors with tubulopapillary patterns exhibited a wide range of solid component content, with histological analysis indicating a migratory nature. Figure 2Q shows the transformation of the tumor from “tubular-papillary type” to “solid type”. Samples from the TCGA dataset showed similar histopathological features. The presence of the aforementioned histological features (e.g., glands with supranuclear and subnuclear vacuoles, clear or eosinophilic cytoplasm of tumor cells, and large polygonal cells) is the characteristic feature that can differentiate GAPEP from CGC-P and CGC. These morphological features were not found in CGC-P and CGC. The morphological characteristics of CGC-P and CGC were similar.

Figure 2 Histopathological diversity of gastric adenocarcinoma with primitive enterocyte phenotype. A and B: Tubular-papillary subtype of gastric adenocarcinoma with primitive enterocyte phenotype characterized by glandular structures with supranuclear and subnuclear vacuoles, creating a distinctive “piano keyboard-like” architecture; C: Luminal eosinophilic secretions within glandular spaces (asterisks); D and E: Uncommon architectural patterns, including sieve-like/sac-like formations and villous projections with tumor cells exhibiting flattened morphology; F and G: Solid subtype resembling hepatocellular carcinoma, featuring large polygonal tumor cells with eosinophilic or clear cytoplasm arranged in nested sheets; H and I: Intracellular and extracellular mucin deposition (asterisks) observed in select cases; J: Rare poorly cohesive growth pattern akin to gastric poorly cohesive carcinoma; K and L: High-grade cytological atypia, including nuclear pleomorphism and prominent nucleoli; M-O: Transitional zones demonstrating admixture and migratory progression between hepatoid adenocarcinoma and gastric adenocarcinoma with enteroblastic differentiation components; P: Predominance of clear cytoplasmic features over eosinophilic differentiation in solid areas; Q: Schematic representation of phenotypic evolution from tubular-papillary to solid morphology.

GAPEP was associated with aggressive clinicopathological characteristics

Results of the local dataset: The mean age of patients with GAPEP was 67 years in the local dataset (range: 44-90), with a male predominance (3:1). Most tumors were located in the fundus, body, and cardia (62.22%), with a median tumor size of 5 cm (range: 1.5-15 cm). Differentiated subtypes were the main histological types of GAPEP (73.9%), and most cases showed well-differentiated tumors (69.17%). However, GAPEP showed aggressiveness, and most cases were in stage T4 (73.13%). Compared with CGC, GAPEP was significantly and positively correlated with the age of onset (P < 0.001), tumor size (P < 0.0001), T stage (P < 0.0001), tumor differentiation (P = 0.0001), differentiated subtypes (P = 0.0001), perineural invasion (P < 0.001), lymph node metastasis (P < 0.05), MMR (P < 0.05), p53 expression (P = 0.001), and HER2/ERBB2 amplification (P < 0.001) (Table 1). In contrast, no differences were found in gender, tumor location, lymphovascular embolus, tumor budding, and American Joint Committee on Cancer stage. CGC-P and CGC had similar clinicopathological characteristics (Table 1).

Table 1 The comparison of clinicopathologic features and stratification in gastric adenocarcinoma with primitive enterocyte phenotype.

Parament	GAPEP (n = 134)	CGC-P (n = 92)	CGC (n = 274)	HAC (n = 49)	GAED (n = 63)	P value
						GAPEP vs CGC	GAPEP vs CGC-P	CGC-P vs CGC	HAC vs GAED
Age (years)						< 0.01	< 0.05	0.4103	< 0.05
> 65	74	38	100	21	39
≤ 65	60	54	174	28	24
Gender						0.407	0.4426	0.1206	0.568
Male	102	74	198	35	48
Female	32	18	76	14	15
Location						0.749	0.6455	0.4288	0.4778
Fundus-body and cardia	84	60	166	32	37
Antrum	51	32	108	17	26
Size (cm)						< 0.0001	< 0.0001	0.5841	0.2630
> 5	65	21	219	27	28
≤ 5	69	71	55	22	35
Histological grade						< 0.0001	< 0.01	0.4677	< 0.001
G1-2	92	47	128	25	53
G3	41	45	146	24	10
JGCA type						0.0001	0.0822	0.1309	< 0.001
Differentiated	99	58	148	26	54
Undifferentiated	35	34	126	23	9
Vascular invasion						0.846	0.6119	0.6883	0.1278
Positive	105	70	214	41	45
Negative	28	22	60	8	18
Perineural invasion						< 0.0001	< 0.05	0.1168	0.7850
Positive	98	58	158	37	46
Negative	31	34	136	12	17
T-staging						< 0.001	0.4323	< 0.0001	0.8249
T2-3	9	20	53	11	16
T4	124	72	221	38	47
AJCC-staging						0.6875	0.0967	0.127	0.5434
I-II	42	39	92	14	22
III-IV	91	53	182	35	41
Tumor budding						0.0656	0.6422	0.2247	NA
Bd1	16	16	47	NA	NA
Bd 2-3	43	52	227	NA	NA
MMR
pMMR	86	209	71	30	40	< 0.05	0.9554	0.0571	0.7564
dMMR	13	63	11	6	6
p53						0.001	0.5465	< 0.05	0.7880
Mutant type	81	59	128	28	42
Wild type	21	19	83	6	11
HER2						< 0.01	0.3301	0.1179	0.6284
Amplification	29	16	30	8	14
Not amplification	93	72	228	36	45

GAPEP: Gastric adenocarcinoma with primitive enterocyte phenotype; CGC: Conventional gastric cancer; CGC-P: Conventional gastric cancer expressing primitive phenotypic markers; HAC: Hepatoid adenocarcinoma; GAED: Gastric adenocarcinoma with enteroblastic differentiation; NA: Not available; JGCA: Japanese Gastric Cancer Association; AJCC: American Joint Committee on Cancer; MMR: Mismatched repair; pMMR: Proficient mismatched repair; dMMR: Defective mismatched repair; NA: Not available.

TCGA dataset results: Data mining from TCGA databases demonstrated that the mean age of GAPEP was 71.86 years (range: 51-90), with a male predominance (3:2). Most tumors were located in the fundus, body, and cardia (62.1%). Intestinal subtypes were the main histological types (87.1%), and most cases were in the T3-T4 stage (77.8%). In terms of molecular subtypes defined by the TCGA project, the majority of GAPEP cases in TCGA datasets were chromosome instability (83.9%), and the remaining subtypes, microsatellite stability (MSI), genomic stability and tumors positive for Epstein-Barr virus, constituted a small proportion of GAPEP (16.1%). TP53 mutation was frequently observed in GAPEP (67.7%), and ERBB2 was amplified in seven cases (22.6%). Compared with CGC, GAPEP was significantly and positively correlated with the age of onset (P < 0.05), intestinal type (P < 0.05), World Health Organization classification (P < 0.05), TP53 mutation (P < 0.01), HER2/ERBB2 amplification (P = 0.0183), ARID1A mutation (P < 0.05), MSI status (P < 0.05), CpG island methylator phenotype (CIMP) category (P < 0.05), and molecular subtypes (P < 0.001). Moreover, comparing other molecular genetic characteristics between GAPEP and CGC showed significant differences in the fraction genome alteration, copy number cluster, methylation cluster, gene expression cluster, and hyper-mutation. Meanwhile, there were significant differences between GAPEP and CGC-P in clinicopathological and molecular genetic characteristics. Subgroup analysis revealed that HAC and GAED shared similar clinicopathological and molecular genetic characteristics. Significant differences in gene expression cluster (P < 0.05) and CIMP category (P < 0.05) were shown by comparing HAC and GAED (Table 2).

Table 2 The comparison of clinicopathologic features and stratification in gastric adenocarcinoma with primitive enterocyte phenotype.

Parament	Statisticians	P value
		GAPEP (n = 31) vs CGC (n = 40)	GAPEP (n = 31) vs CGC-P (n = 157)	HAC (n = 12) vs GAED (n = 19)
Sex	χ2 test	0.652	0.893	0.518
Diagnosis age	Kruskal-Wallis test	< 0.05	0.0346	0.525
Lauren class	χ2 test	< 0.05	< 0.01	0.366
WHO class	χ2 test	< 0.05	< 0.05	0.216
TNM stage	χ2 test	0.572	0.129	0.435
AJCC stage	χ2 test	0.319	0.0878	0.778
T stage	χ2 test	0.071	-	-
NDS-AJCC	χ2 test	0.443	0.277	0.299
EBV present	χ2 test	0.48	0.132	0.814
Molecular-subtypes	χ2 test	< 0.0001	< 0.05	0.183
Fraction genome altered	Kruskal-Wallis test	< 0.0001	< 0.01	0.598
Copy number cluster	χ2 test	< 0.0001	< 0.05	0.295
Molecular subtype	χ2 test	< 0.001	< 0.05	0.136
Absolute extract ploidy	Kruskal-Wallis test	< 0.001	0.126	0.653
Methylation cluster	χ2 test	< 0.001	0.0688	0.0625
Gene expression cluster	χ2 test	< 0.05	< 0.05	< 0.05
CIMP category	χ2 test	< 0.05	0.167	< 0.05
Hyper-mutated	χ2 test	< 0.05	0.738	0.244
TP53 mutation	χ2 test	< 0.01	< 0.05	0.869
CDKN2A silencing	χ2 test	0.0622	0.26	0.0894
ARID1A mutation	χ2 test	< 0.05	0.175	0.249
MSI status	χ2 test	< 0.05	0.85	0.145
MLH1 silencing	χ2 test	0.0655	0.648	0.809
ERBB2 amplificated	χ2 test	< 0.05	0.2799	0.363
Percent tumor nuclei	Kruskal-Wallis test	0.285	< 0.05	0.0418
MET skipped exons 18 and 19	χ2 test	0.129	0.682	0.571
Percent lymphocyte infiltration	Kruskal-Wallis test	0.444	0.902	0.0833
ARHGAP26-ARHGAP6-CLDN18 rearrangement	χ2 test	0.86	0.821	0.814
KRAS mutation	χ2 test	0.156	0.123	-
PIK3CA mutation	χ2 test	0.15	0.42	0.613
MicroRNA expression cluster	χ2 test	0.377	0.0688	-
Mutation count	Kruskal-Wallis test	0.208	0.262	0.389
Mutation rate	Kruskal-Wallis test	0.19	0.285	0.378
MET skipped exon 2	χ2 test	0.957	0.105	0.237
TMB	Kruskal-Wallis test	0.18	0.378	0.655
RHOA mutation	χ2 test	0.239	-	-
Percent tumor cells	Kruskal-Wallis test	0.277	< 0.01	0.178
Estimated leukocyte percentage	Kruskal-Wallis test	< 0.05	0.101	0.372
Intestinal type subclass	χ2 test	0.133	0.257	0.527
Anatomic region	χ2 test	0.732	0.72	0.0754
NDS-AJCC	χ2 test	0.443	0.277	0.299
EBV present	χ2 test	0.48	0.132	0.814

GAPEP: Gastric adenocarcinoma with primitive enterocyte phenotype; CGC: Conventional gastric cancer; CGC-P: Conventional gastric cancer expressing primitive phenotypic markers; HAC: Hepatoid adenocarcinoma; GAED: Gastric adenocarcinoma with enteroblastic differentiation; WHO: World Health Organization; TNM: Tumor-node-metastasis staging; TMB: Tumor mutational burden; NDS, Nodal disease staging; AJCC: American Joint Committee on Cancer; EBV: Epstein-Barr virus; CIMP: CpG island methylator phenotype; MSI: Microsatellite stability; MET: Mesenchymal-epithelial transition factor gene.

GAPEP was markedly associated with poor prognosis: Prognosis analysis of our data indicated that compared with CGC-P and CGC, GAPEP was associated with poorer overall survival (P < 0.01 and P < 0.0001, respectively) (Figure 3A). Stratified analysis revealed no differences in prognosis between GAED and HAC (P = 0.0810) (Figure 3B). Moreover, the TCGA dataset was analyzed to better clarify the differences in prognosis between GAPEP, CGC-P, and CGC. The results showed that GAPEP had worse overall survival compared to CGC-P (P < 0.05) and CGC (P < 0.05). There was no difference in prognosis between CGC-P and CGC (P = 0.9960) (Figure 3C). The results of stratified analysis of data from the TCGA dataset also exhibited no differences in prognosis between GAED and HAC (P = 0.0810) (Figure 3D).

Figure 3 Prognostic significance of gastric adenocarcinoma with primitive enterocyte phenotype compared to other gastric cancer subtypes. A: Kaplan-Meier survival curves using our institutional cohort demonstrate significantly worse overall survival (OS) in gastric adenocarcinoma with primitive enterocyte phenotype patients vs conventional gastric cancer and conventional gastric cancer expressing primitive phenotypic markers; B: Stratified analysis across cohorts (in-house); C: Kaplan-Meier survival curves using The Cancer Genome Atlas data demonstrate significantly worse OS in gastric adenocarcinoma with primitive enterocyte phenotype patients vs conventional gastric cancer and conventional gastric cancer expressing primitive phenotypic markers; D: Stratified analysis across cohorts (The Cancer Genome Atlas showed no OS difference between hepatoid adenocarcinoma and gastric adenocarcinoma with enteroblastic differentiation (P = 0.081 and P = 0.318, respectively), supporting their shared clinicopathological spectrum. GAPEP: Gastric adenocarcinoma with primitive enterocyte phenotype; CGC: Conventional gastric cancer; CGC-P: CGC: Conventional gastric cancer expressing primitive phenotypic markers; HAC: Hepatoid adenocarcinoma; GAED: Gastric adenocarcinoma with enteroblastic differentiation.

Genetic alteration analysis

Mutation analysis: After applying our filtering strategy, 361, 525, and 984 known cancer-associated genes were detected among the mutated genes in GAPEP, CGC-P, and CGC, respectively. The TP53 was the most frequently mutated gene in GAPEP with a mutation frequency of 67.7%, followed by LRP1B (25.8%), APC (22.6%), PREX2 (22.6%), PCLO (19.4%), AFF3 (19.4%), SPEN (12.9%), CAMTA1 (12.9%), PTPRD (12.9%), and ARID1A (12.9%). In CGC-P, TP53 exhibited the highest mutation frequency (42.5%), followed by ARID1A (30%), LRP1B (22.5%), RNF43 (20.5%), PIK3CA (20%), PCLO (17.5%), TRRAP (16.8%), FAT4 (15%), PTPRT (15%), KMT2C (12.5%), RELN (12.5%), and KMT2D (12.5%). Moreover, CGC harbored the highest number of mutated cancer genes, including TP53 (40.5%), ARID1A (36.2%), FAT4 (30.3%), LRP1B (28.1%), KMT2D (27%), PCLO (25.4%), PIK3CA (23.2%), RNF43 (20.5%), KMT2C (19.5%), TRRAP (16.8%), ZFHX3 (16.8%), and RELN (16.8%) (Figure 4A). TP53 mutations were frequently observed in GAPEP, CGC-P, and CGC. TP53, APC, and AFF3 were more frequently mutated in GAPEP than in CGC-P and CGC. However, CGC-P and CGC had a higher frequency of ARID1A, PIK3CA, KMT2C, and FAT4 mutations. No significant differences were found in gene mutation between GAPEP, CGC-P, and CGC. CGC and CGC-P exhibited similar profiles of gene mutation.

Figure 4 Distinct genomic landscapes across gastric cancer subtypes. A: Somatic mutation analysis identifying the top 10 most frequently mutated genes in gastric adenocarcinoma with primitive enterocyte phenotype (blue), conventional gastric cancer (CGC) (orange) cohorts and CGC expressing primitive phenotypic markers (purple), with mutation frequencies expressed as percentages; B: Somatic mutation analysis revealing the top 10 most frequently mutated genes in hepatoid adenocarcinoma (brown) and gastric adenocarcinoma with enteroblastic differentiation (black) cohorts, with both groups exhibiting notably high TP53 mutation frequencies; C: Copy number alteration analysis highlighting the top 10 genes with the highest alteration frequencies in gastric adenocarcinoma with primitive enterocyte phenotype (blue), CGC expressing primitive phenotypic markers (purple), and CGC (orange) cohorts; D: Copy number alteration analysis showcasing the top 10 genes with the highest alteration frequencies in hepatoid adenocarcinoma (brown) and gastric adenocarcinoma with enteroblastic differentiation (black) cohorts. GAPEP: Gastric adenocarcinoma with primitive enterocyte phenotype; CGC: Conventional gastric cancer; CGC-P: Conventional gastric cancer expressing primitive phenotypic markers; HAC: Hepatoid adenocarcinoma; GAED: Gastric adenocarcinoma with enteroblastic differentiation.

In GAPEP, the HAC group harbored the highest number of mutated genes, including TP53 (66.7%), LRP1B (41.7%), PREX2 (41.7%), PCLO (33.3%), APC (25%), AFF3 (25%), KMT2D (25%), BRCA2 (25%), ARID1A (25%), and PDE4DIP (25%). On the other hand, the GAED group had the highest number of mutated genes, including TP53 (68.4%), APC (21.1%), PTPRD (21.1%), LRP1B (15.8%), AFF3 (15.8%), CDKN2A (10.5%), NIN (10.5%), SPEN (10.5%), PCLO (10.5%), and ARID4B (10.5%) (Figure 4B). Both HAC and GAED showed extremely high frequencies of TP53 mutation. There was no significant genetic difference between HAC and GAED.

Copy number alteration analysis: After applying the filtering strategy, we detected 672, 371, and 1295 known cancer-associated genes with DNA copy number alteration in GAPEP, CGC-P, and CGC, respectively. CCNE1 most frequently showed copy number alteration in GAPEP with an alteration frequency of 25.8%, followed by IKZF3 (22.60%), ZNF217 (22.60%), PTPN1 (22.60%), ERBB2 (22.6%), RICTOR (19.40%), TERT (19.40%), GATA6 (19.40%), TRIP13 (19.40%), and LIFR (19.40%). The most frequently altered gene in CGC-P was VEGFA (17.9%), followed by ERBB2 (15.4%), RARA (12.8%), MYC (12.8%), CCND3 (12.8%), NFKBIE (12.8%), HSP90AB1 (12.8%), PTK7 (12.8%), CDK12 (12.8%), and TFEB (12.8%). Moreover, MYC (10.30%), CDKN2A (9.80%), CDK6 (9.2%), ERBB2 (9.2%), CDKN2B (9.2%), MTAP (9.2%), GATA4 (8.7%), IKZF3 (8.2%), TRRAP (7.6%), and ZNF217 (7.6%) exhibited the highest number of DNA copy-number alterations in CGC. The amplified genes, such as CCNE1, IKZF3, ZNF217, PTPN1, ERBB2, RICTOR, TERT, GATA6, TRIP13, and LIFR, all had higher frequencies in GAPEP than in CGC and CGC-P; however, no significant differences were found (Figure 4C). Further analysis showed that DNA copy number alteration in CIC (P < 0.0001), DUSP22 (P < 0.0001), IRF4 (P < 0.0001), BRCA1 (P < 0.001), and ETV4 (P < 0.001) was significantly more prevalent in GAPEP compared to CGC-P and CGC.

Moreover, stratified analyses showed that in GAPEP, the HAC group harbored more genes with copy number alterations, such as TRIP13 (33.3%), LIFR (33.3%), DROSHA (33.3%), RICTOR (33.3%), IL7R (33.3%), FGF10 (33.3%), TERT (33.3%), SDHA (33.3%), MYC (25%), and CCNE1 (25%). On the other hand, the GAED group had the highest copy number alterations in ERBB2 (31.6%), GATA6 (31.6%), IKZF3 (31.6%), CCNE1 (26.3%), ZNF217 (26.3%), PTPN1 (26.3%), RARA (21.1%), NFATC2 (21.1%), SALL4 (21.1%), and SMARCE1 (21.1%) (Figure 4D); however, no significant differences were found between HAC and GAED.

mRNA expression level and key signaling pathways: According to the standard deviation of log2 expression, the following genes showed the highest levels of mRNA expression in GAPEP: GAPDH, HSP90AB1, CALR, P4HB, HSP90B1, ATP5F1B, TUBB, MDH2, HSPA5, and NCL. In CGC-P, the following genes showed the highest levels of mRNA expression: TMSB4X, LYZ, TXNIP, JUN, CEBPB, TGM2, ZNF703, BTG2, STAT6, and CD44. In CGC, the following genes showed the highest levels of mRNA expression: STN1, MRPL54, CCNDBP1, POLR3GL, GHITM, NDUFS4, ABTB1, FAS, BRK1, and RPL36AL. The lowest level of mRNA expression in GAPEP was observed for the OR5M8 gene, while in CGC-P and CGC, the lowest levels of mRNA expression were observed for AGAP9 and ANXA1 genes, respectively.

In total, 3541 differentially expressed genes (DEGs) were identified between GAPEP, CGC-P, and CGC. The heatmap showed the top 50 DEGs of GAPEP, CGC-P, and CGC (Figure 5A). Further analysis revealed that the DNMT, TET, TDG, IDH, and EZH2 genes, which are involved in methylation, were upregulated in GAPEP. Specifically, TET1 (P < 0.0001), TET3 (P < 0.0001), DNMT3A (P < 0.0001), DNMT3B (P < 0.0001), DNMT3 L (P < 0.0001), EZH2 (P < 0.01), IDH1 (P < 0.01), IDH3B (P < 0.01), and TDG (P < 0.0001) were upregulated in GAPEP compared with CGC-P and CGC (Figure 5B). These findings indicated that methylation-related genes play a crucial role in the development of GAPEP.

Figure 5 Multi-omics profiling reveals methylation-driven molecular features of gastric adenocarcinoma with primitive enterocyte phenotype. A: Heatmap illustrating the top 50 differentially expressed genes across gastric adenocarcinoma with primitive enterocyte phenotype (GAPEP) (blue), conventional gastric cancer (CGC) (orange), and CGC expressing primitive phenotypic markers (purple) highlights distinct gene expression patterns, with GAPEP forming a separate cluster; B: Box plots confirm significant upregulation of methylation regulators (TET, DNMT3, and EZH2) in GAPEP compared to CGC expressing primitive phenotypic markers and CGC; C: Gene Ontology analysis detailing the biological processes, cellular components, and molecular functions, with results underscoring the critical role of “methyl-CpG binding” in GAPEP development (left). Additionally, Kyoto Encyclopedia of Genes and Genomes pathway analysis identified key signaling pathways associated with GAPEP (right); D: Comparative analysis showing LGLS2 and SFTA2 as the only two genes with statistically significant differential expression between hepatoid adenocarcinoma and gastric adenocarcinoma with enteroblastic differentiation. GAPEP: Gastric adenocarcinoma with primitive enterocyte phenotype; CGC: Conventional gastric cancer; CGC-P: CGC expressing primitive phenotypic markers; HAC: Hepatoid adenocarcinoma; GAED: Gastric adenocarcinoma with enteroblastic differentiation.

GO and KEGG pathway analyses were performed to unravel the biological function and molecular mechanism of the DEGs in GAPEP, CGC-P, and CGC. The top 1000 positively and negatively correlated DEGs were selected for analysis. The GO analysis revealed that biological process terms were implicated in nuclear division and chromosome segregation. The cellular component terms were related to chromosomal regions, condensed chromosomes, and centromeric regions. The molecular function terms were associated with ATP hydrolysis, methyl-CpG binding, and helicase activity (Figure 5C). The KEGG pathway analysis indicated that the GAPEP-related signaling pathways were enriched in cell cycle signaling, cholesterol metabolism, Parkinson’s disease, and motor proteins (Figure 5C).

Stratified analyses showed that the GAED group harbored the highest expression of mRNA in the following genes: ACTB, GAPDH, FTL, MT-ND5, ACTG1, TMSB10, RPLP1, TPT1, RPS11, and RPL27. The HAC group harbored the highest mRNA expression of the following genes: GAPDH, ACTB, ACTG1, RPS11, RPS6, RPLP1, FTL, TPT1, TMSB10, and B2M. Both the GAED and HAC groups had extremely high expression of GAPDH and ACTB. SFTA2 and LGALS2 were the only genes that showed significantly different expression between HAC and GAED (P < 0.001 and P < 0.001, respectively) (Figure 5D).

DISCUSSION

According to the research results of Yamazawa et al[10], GAPEP is a subtype of GC that expresses at least one primitive phenotypic marker. However, this study has certain limitations, as it primarily focuses on the value of immunophenotype in diagnosing GAPEP and does not delve into the morphological significance. Our study indicated that some cases of conventional tubular or papillary adenocarcinomas, mucinous adenocarcinomas, and indolent cell carcinomas can also express SALL4, GPC3, and AFP. These types of adenocarcinomas were classified as CGC-P in this article, as they do not have the morphological characteristics of GAPEP (including HAC or GAED). Further analyses also showed that CGC-P did not exhibit aggressive clinicopathological characteristics and unfavorable prognosis compared to GAPEP, and it was comparable to CGC. Additionally, molecular studies demonstrated that GAPEP and CGC-P exhibited significantly different profiles of gene mutation. It is evident that the differential diagnosis of GAPEP, CGC-P, and CGC is of significant importance. Immunohistochemical staining for SALL4, GPC3, and AFP can determine the presence of primitive phenotypes. However, the diagnosis of GAPEP more often relies on morphological features. In this article, we demonstrated the importance of histopathological assessment in diagnosing GAPEP. Pathologically, GAPEP was characterized by multilineage differentiation and diverse morphology. We suggested a comprehensive definition of GAPEP. It was characterized by the expression of primitive phenotypic markers, including AFP, SALL4, and GPC3, unique histological features, pressure on glands with supranuclear and subnuclear vacuoles or “piano keyboard-like” appearance, clear or eosinophilic cytoplasm, and nested mass structures with large polygonal cells.

Compared to CGC-P and CGC, GAPEP was associated with aggressive clinicopathological features, in terms of tumor size, tumor differentiation, differentiated subtypes, perineural invasion, lymph node metastasis, and p53 overexpression. Bioinformatic analysis revealed that GAPEP, CGC-P, and CGC were significantly different in terms of molecular subtype, TP53 mutation, ERBB2 amplification, ARID1A mutation, MSI status, fraction genome altered, copy number cluster, CIMP category, and hyper-mutated. Subgroup analysis unveiled that HAC and GAED shared similar clinicopathological and molecular genetic characteristics. There were no significant differences in prognosis between GAED and HAC. Genetically, there was no significant difference between GAED and HAC, suggesting that they originated from the same clone. Moreover, histological analysis indicated that migration occurs between tubule-papillary and solid components, supporting the transdifferentiation hypothesis from GAED to HAC. This feature has been previously reported in other studies[15,21-24]. Kishimoto et al[21] described the possibility of HAC transdifferentiation, whereby adenocarcinomas may show hepatic differentiation during tumor progression. Akiyama et al[22] reported that adenocarcinomatous and hepatoid components possess monoclonal origin, suggesting that adenocarcinomas may exhibit hepatic differentiation during tumor progression. Kumashiro et al[23] found that some HAC lesions contain tubular or papillary adenocarcinoma components with “hepatic-like” features. Moreover, He et al[24] demonstrated that the coexistence of GAED and HAC can be frequently observed, with typical transitions between histologic subtypes. Therefore, we recommend subcategorizing HAC as a solid-type GAED.

We also analyzed data from the TCGA to clarify the molecular aspects of GAPEP and found that methylation modifiers, including DNMT, TET, TDG, IDH, and EZH2, were upregulated in GAPEP. Methylation is a crucial epigenetic modification that regulates gene expression and plays a critical role in various biological processes. Methylation of CpG islands by DNMT enzymes leads to gene silencing. DNMT3A and DNMT3B are the most important DNMT enzymes responsible for de novo methylation. They are highly expressed in early embryonic development and are necessary for normal development and growth. Mutations in these enzymes can lead to various disorders, including immunodeficiency, neurological disorders, and developmental defects[25]. Members of the TET family of enzymes are the main drivers of DNA demethylation. At the molecular level, TET-mediated oxidation counteracts DNMT-mediated hypermethylation of the genome[26]. However, the study of methylation patterns in mice without DNMT3A and TET2 suggests a model of cooperative inhibition by epigenetic modifiers[27]. Another upregulated methylation modifier is EZH2, which has been linked to the development, progression, invasion, metastasis, and poor prognosis of GC[27-29]. Signaling pathway analysis showed that the DEGs of GAPEP play a significant role in methyl-CpG binding. In the present study, we discovered methylation modifiers that are frequently methylated in GAPEP. Epigenetic modulators, such as DNA methyltransferase inhibitors, have shown promise in reactivating silenced genes and restoring normal gene expression patterns in cancer cells. Studying the methylation dynamics can provide new insights into the histological heterogeneity of GAPEP and potential therapeutic strategies for GAPEP.

The pathogenesis of GAPEP remains to be found in future studies. Between embryonic days 17-23 of human gestation, the caudal and terminal ventral walls of the foregut develop into the stomach and liver, respectively[30]. Therefore, the histological features and immunophenotypes of GAPEP are similar to those of the early fetal gut and embryonic liver, both of which express SALL4, GPC3, and AFP. The development of GAPEP may be a reenactment of foregut differentiation during embryonic development[10]. SALL4 is a critical marker of stem cells and plays a key role in the self-renewal of embryonic stem cells[31]. Overexpression of SALL4 contributes to the proliferation, development, invasion, and migration of cancer cells through various signaling pathways, such as the Wnt/β-catenin[32,33], phosphatidylinositol 3-kinase/protein kinase B[34], and transforming growth factor-β/small mothers against decapentaplegic pathways[35]. SALL4 also plays a key role in tumor progression by modulating the immune microenvironment and DNA methylation[36-39]. Recent studies have shown that SALL4 plays a crucial role in maintaining the stem-like phenotype through various epigenetic factors[40,41]. For example, DNA methylation can affect the transcriptional activity of SALL4[37]. SALL4 inhibits transcription by recruiting DNMTs, including DNMT1, DNMT3A, and DNMT3B[38]. We hypothesized that GAPEP may develop through primitive phenotypic transformation, which is likely triggered by an epigenetic mechanism. Further studies are needed to unravel the complex mechanisms underlying the development of GAPEP and determine the precise role of epigenetic modifications in this process.

Our study has several limitations. First, although our single-center cohort represents the largest GAPEP cohort, the sample size remains modest due to the low incidence of this subtype, potentially limiting the generalizability of our conclusions. To enhance the robustness of our findings, we validated key methylation differences between GAPEP and CGC/CGC-P using the TCGA-Stomach Adenocarcinoma cohort and confirmed their consistency. Future multi-center collaborations should expand the cohort size and assess the stability of molecular features across diverse populations. Second, although the TCGA database provides an authoritative resource, we could not access all whole-slide imaging sections for all cases. Therefore, we could not comprehensively assess the histologic pattern of the disease. The retrospective design of this study and reliance on the TCGA database may introduce selection bias. Prospective validation of clinical relevance is needed. Third, although our study highlighted the role of methylation dysregulation as a key feature, we did not explore the methylation mechanism in depth. Specifically, the roles of specific methylation-related pathways or driver genes were not validated. Further studies should leverage organoid models or mouse xenograft experiments to elucidate these mechanisms and confirm their functional significance.

CONCLUSION

The aggressiveness of GAPEP is closely associated with methylation dysregulation, and achieving a precise diagnosis necessitates a combined approach integrating morphological analysis with molecular biomarkers. Accurate diagnosis of GAPEP is of great importance. The morphology of GAPEP was diverse, suggesting a multilineage differentiation. We also emphasized that HAC and GAED have a monoclonal origin. Targeting methylation could provide new therapeutic opportunities for treating this aggressive cancer.

ACKNOWLEDGEMENTS

We would like to sincerely thank Professor Giulia De Falco from the School of Biological and Chemical Sciences, Queen Mary University of London, and Professor Jiang Huang from Guangzhou Huayin Healthcare Group.