Poorer prognosis of early gastric cardia cancer compared to early gastric non-cardia cancer: evidence from SEER database analysis

Introduction

Gastric cancer remains the fifth most common malignancy, with over one million new cases and 769,000 deaths in 2021 worldwide (1). Despite advances in medical knowledge and therapeutic interventions, the prognosis of patients with gastric cancer remains unfavorable, particularly in advanced stages (2). In recent years, the diagnosis of early gastric cancer (EGC) has seen a sharp rise driven by advancements in universal screening methods and refinements in endoscopic techniques. EGC is defined as a tumor confined to the mucosa and submucosa regardless of lymph node metastasis (LNM) (3). The primary approach for EGC involves endoscopic resection, which offers a favorable prognosis and effective treatment outcomes (4).

Gastric cancer is generally classified into two categories: gastric cardia cancer (GCC) and gastric non-cardia cancer (GNCC) (5). GCC, located at the gastroesophageal junction (GEJ), represents a unique subset within the spectrum of gastric malignancies (6). The GEJ has a unique anatomy and is located at the junction of the squamous and columnar epithelium. To date, there has been no uniform classification standard for GCC. The complicated histology and biological characteristics have resulted in many controversies regarding the classification. In 1998, Siewert et al. proposed a classification in which tumors were located within 5 cm above and below the GEJ, based on the morphological and anatomical location of the tumor center (7). Three subtypes were further described: (I) type I: 1–5 cm above the GEJ; (II) type II: 1–2 cm below the GEJ (true carcinoma of the cardia); and (III) type III: 2–5 cm below the GEJ. However, this classification schema is not universally accepted. Nevertheless, there have been different trends in the incidence of GCC and GNCC over the past two decades. The incidence of GCC has significantly increased in Western countries and Asia, while the incidence rate of GNCC has decreased proportionately (8-10).

The etiology and pathogenesis of GCC remain unclear. Helicobacter pylori (HP) infection plays a crucial role in the development of gastric cancer via chronic inflammation, atrophy, intestinal metaplasia (IM), and dysplasia (11). Some studies have found that HP prevalence rates are decreasing, especially in developed countries, but the level of infection remains higher in underdeveloped regions (1,12,13). A previous study reported an inverse association between HP infection and the risk of GCC (14). Kamangar et al. also considered HP to be a strong risk factor for GNCC, but inversely associated with the risk of GCC (15). Therefore, in previous decades, the decrease in HP infection may have been an important factor in the rising incidence of GCC in Western populations (16). However, reverse research has indicated a positive association between HP infection and GCC based on the population of Asian regions (17,18). Except for HP infection, other risk factors, such as gastroesophageal reflux, tobacco smoking, and obesity, are associated with the incidence of GCC (19-21).

Previous researches have illustrated different clinicopathological features and prognosis between GCC and GNCC, with GCC showing a worse prognosis than GNCC (22,23). However, these studies analyzed patients with advanced gastric cancer. Currently, no related research has investigated the differences between the clinicopathological characteristics and prognosis of early GCC (EGCC) and early GNCC (EGNCC). In this context, the present study drew upon the Surveillance, Epidemiology, and End Results (SEER) Program, a comprehensive cancer registry covering diverse demographics and regions across the United States. Leveraging this extensive dataset, we embarked on a thorough comparative analysis of EGCC and EGNCC, encompassing a spectrum of clinical, pathological, and survival variables. Our study aimed to unravel the distinct epidemiological profiles, disease characteristics, and survival patterns of EGCC and EGNCC, ultimately contributing to an enhanced understanding of the disease landscape and guiding tailored clinical approaches. We present this article in accordance with the TRIPOD reporting checklist (available at https://jgo.amegroups.com/article/view/10.21037/jgo-2025-107/rc).

Methods

The data for EGC were obtained from the SEER database, which covers approximately 28% of the cases in the United States (24). The variables in each case included clinical characteristics, tumor characteristics, treatment specifics, and survival information. To explain the different classification schema, we regarded the “cardia” as synonymous with the “EGJ” according to the International Classification of Diseases for Oncology (ICD-O), version 3. GCC was defined as tumors with an epicenter located in the cardia (C16.0), whereas GNCC included tumors located in the fundus (C16.1), body (C16.2), antrum (C16.3), pylorus (C16.4), lesser curve (C16.5), and greater curve (C16.6). Patients with EGC who met the following criteria were enrolled in our study: (I) infiltration depth was limited to T1a and T1b; (II) patients were at least 18 years of age between 2004 and 2015; (III) EGC was confirmed via biopsy; and (IV) patients had complete survival data and active follow-up. The following were excluded: (I) patients who received radiotherapy and chemotherapy; (II) multiple primary tumors; (III) retrieved lymph nodes; (IV) unknown primary sites; and (V) distant metastasis.

Other variables included race (White, Black, other), sex (male, female), age (18–60, >60 years), year of diagnosis (2004–2009, 2010–2015), cell type (intestinal type, diffuse type, other types), grade (well/moderately differentiated, poorly differentiated/undifferentiated, unknown), invasion depth (T1a, T1b), LNM (N0, N1, N2, N3), tumor size (≤2, >2 and <5, ≥5 cm, unknown), examined lymph nodes (<15 and ≥15), and lymph node dissection (negative, positive). The survival time was calculated in months from the date of diagnosis to the date of death. Disease-specific survival (DSS) was the primary survival outcome in this study. Overall survival (OS) was defined as the secondary endpoint. DSS was defined as the time from cancer diagnosis to death from gastric cancer. OS was defined as the period from the date of diagnosis to the last follow-up or death from any cause. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.

Statistical analyses

In the descriptive analysis, continuous variables were compared using Student’s t-test, whereas categorical variables were analyzed using the χ² test or Fisher’s exact test. The Kaplan-Meier method was used to analyze survival rates, and differences between groups in survival curves were evaluated using log-rank tests. To investigate potential risk factors for LNM, we performed multivariate logistic regression to identify the risk factors for LNM, with results presented as odds ratio (OR) with 95% confidence interval (CI). Univariate and multivariate Cox regression models were used to identify independent risk factors associated with DSS and to calculate hazard ratio (HR) and 95% CI. We used the cox.zph function in the R Survival package to verify the assumption of proportional hazards (PH) in the Cox analysis. When the PH assumption in Cox regression does not hold, it can be addressed using the following methods: stratified Cox model, time-varying coefficient Cox model, delayed-entry Cox model, or generalized Cox model. A nomogram was constructed to estimate the likelihood of 1-, 3-, and 5-year DSS based on the Cox model findings. Concordance index (C-index), receiver operating characteristic (ROC) curve, area under the curve (AUC), and calibration plots were used to evaluate model calibration and discrimination. The calibration plot used a 1,000 bootstrap validation approach to verify the performance of the nomogram. We also adopted propensity score matching (PSM) to balance baseline characteristics between the two groups. A 1:1 ratio PSM was conducted using a 0.05 calliper between EGCC and EGNCC. SPSS software (version 27.0; IBM Corporation, Armonk, NY, USA) and R statistical software (version 4.2.3, StataCorp LLC, College Station, TX, USA) were used for all the statistical analyses. A two-sided P value less than 0.05 (two-sided) was considered statistically significant.

Results

The epidemiological trends of GCC and GNCC

Between 2004 and 2015, 78,258 patients were diagnosed with gastric cancer using the SEER database. The trends of diagnosed cases of GCC and GNCC over time are illustrated in Figure 1. In general, the number of confirmed cases of GNCC has slightly increased in the past decade, whereas GCC has markedly increased, especially after 2010. The shifting epidemiological patterns of gastric cancer, especially the rise in GCC among individuals with gastric cancer, demand attention and swift action.

Figure 1 The epidemiological trends of GCC and GNCC patients. GCC, gastric cardia cancer; GNCC, gastric non-cardia cancer.

Comparison of clinicopathologic features between EGCC and EGNCC

According to our inclusion and exclusion criteria, the final cohort consisted of 2,591 patients with surgically resected EGC, including 656 EGCC and 1,935 EGNCC (Figure S1). The clinicopathological characteristics of the patients with EGCC and EGNCC are summarized in Table 1. There were significant differences in sex, race, age, cell type, grade, and tumor size between the two groups (P<0.05). Patients with EGCC were more common in the white race than those with EGNCC (88.6% vs. 49.4%). Regarding sex, more male patients were diagnosed with EGCC than with EGNCC (78.8% vs. 53.7%). In this cohort, more patients with EGCC were less than 60 years old (35.2% vs. 25.1%), which indicated that patients diagnosed with EGCC were younger than those diagnosed with EGNCC. Additionally, compared with EGNCC, patients with EGCC tended to have a well-differentiated type (60.5% vs. 46.2%), intestinal type (86.7% vs. 63.4%), and smaller tumor size (59.3% vs. 51.9%).

Risk of LNM among EGCC and EGNCC

A total of 218 EGC (8.4%) had LNM, with LNM rates of 8.4% and 8.4% in EGCC and EGNCC, respectively. In the multivariate logistic model, the results showed no association was observed between the tumor location of EGC and LNM (OR: 0.87; 95% CI: 0.60–1.25; P=0.44). Other variables associated with a higher risk of LNM included older age (OR: 1.51; 95% CI: 1.04–2.19; P=0.03), poorly differentiated or undifferentiated histology (OR: 1.59; 95% CI: 1.15–2.21; P=0.006), deeper invasion depth (OR: 2.95; 95% CI: 2.05–4.24; P<0.001), tumor size >2 and <5 cm (OR: 2.51; 95% CI: 1.80–3.50; P<0.001), tumor size ≥5 cm (OR: 4.44; 95% CI: 2.67–7.40; P<0.001), and a higher number of examined lymph nodes (OR: 1.36; 95% CI: 1.01–1.82; P=0.04). Other factors such as race, sex, and cell type did not increase the risk of LNM (P>0.05). The results are shown in the form of a forest plot in Figure 2.

Figure 2 Risk of LNM in EGC patients. CI, confidence interval; EGC, early gastric cancer; N, number; LNM, lymph node metastasis; OR, odds ratio.

Comparison of survival between EGCC and EGNCC

Our results revealed that patients with EGNCC had a better DSS than those with EGCC (DSS: P<0.001, log-rank test; Figure 3A). Total EGC had a 5-year DSS rate of 85.5%, with a 5-year DSS rate of 87.3% for EGNCC and 80.3% for EGCC. However, compared to EGNCC patients, EGCC patients had comparable OS (OS: P=0.22, log-rank test; Figure 3B). We then stratified the EGC patients according to the invasion depth. When EGC was limited to the mucosa, the results showed that patients with EGCC and EGNCC had the same 5-year OS (P=0.26; Figure 3C). As the depth of infiltration reached the submucosa, EGCC patients had a significantly worse DSS compared to EGNCC patients (5-year DSS: 73.9% vs. 85.7%, P<0.001; Figure 3D).

Figure 3 Comparison of survival between EGCC and EGNCC patients. (A) DSS for patients with EGCC and EGNCC. (B) OS for patients with EGCC and EGNCC. (C) OS for patients with EGCC and EGNCC when the tumor was limited to the mucosa. (D) DSS for patients with EGCC and EGNCC when the tumor was limited to the submucosa. DSS, disease-specific survival; EGCC, early gastric cardia cancer; EGNCC, early gastric non-cardia cancer; OS, overall survival.

Prognostic factors of patients with EGC

As indicated in Table 2, we included 11 demographic and clinical characteristics in the univariate Cox regression analysis. The results showed that all covariates had P values greater than 0.05, and the overall model test had a P value of 0.65, indicating that the model as a whole satisfied the PH assumption. Univariate Cox analysis revealed that sex, age, tumor location, depth, LNM, examined lymph nodes, lymph node dissection, and tumor size were risk factors for DSS (P<0.05). In the multivariate Cox regression model, older age (HR: 2.58; 95% CI: 1.90–3.52; P<0.001), poorly differentiated or undifferentiated tumor grade (HR: 1.35; 95% CI: 1.01–1.68; P=0.006), T1b (HR: 1.28; 95% CI: 1.01–1.61, P=0.04), advanced LNM stage (HR: 7.35; 95% CI: 2.99–18.08; P<0.001), and large tumor size (HR: 1.89; 95% CI: 1.24–2.89; P=0.003) were significant predictors of poor survival in EGC patients. Of note, after adjustment for the multivariate analysis, the HR for location was 1.94 (95% CI: 1.54–2.43; P<0.001), indicating that cardia invasion is an independent risk factor for EGC prognosis. In addition, more than 15 retrieved lymph nodes (HR: 0.59; 95% CI: 0.47–0.75; P<0.001) was an independent protective factor for improving DSS.

Prognostic nomogram for DSS

The establishment of the nomogram, which included factors based on the multivariate Cox regression analysis, is shown in Figure 4. Each factor was assigned a corresponding score, and aggregating these scores might predict the 1-, 3-, and 5-year DSS of patients with EGC. LNM staging emerged as the most significant prognostic factor, followed by patient age and tumor location, with invasion depth having the least impact. We then evaluated the performance of the nomogram using C-index, AUC, calibration curve, and DCA. The C-index of the nomogram for DSS prediction was 0.701. The ROC curve was used to evaluate the discriminatory performance of the nomogram. The AUC values at 0.700, 0.721, and 0.710 for 1-, 3-, and 5-year, respectively (Figure 5). The calibration curves for 1, 3, and 5-year were close to 45°, suggesting that the projected outcomes agreed well with actual measurements (Figure S2).

Figure 4 Nomograms for predicting 1-, 3-, and 5-year DSS in EGC patients. DSS, disease-specific survival; EGC, early gastric cancer; EGCC, early gastric cardia cancer; EGNCC, early gastric non-cardia cancer; LNM, lymph node metastasis.

Figure 5 ROC curves for 1-, 3-, and 5-year DSS in EGC patients. AUC, area under the curve; DSS, disease-specific survival; EGC, early gastric cancer; ROC, receiver operating characteristic.

PSM and survival analysis in matched groups

We used the PSM method to perform a 1:1 matched cohort analysis to eliminate the effect of confounding factors on baseline characteristics and ensure that our observations were reliable and stable. After matching, 528 patients were subsequently generated in the EGCC and EGNCC groups (Figure S3). Similar basic clinical characteristics were observed after matching the EGCC and EGNCC groups. We also investigated the prognosis of the patients with EGCC and EGNCC. There was still a considerable difference in the 5-year DSS between EGCC and EGNCC, but they had a similar prognosis in terms of OS (Figure S4). These results proved that our analysis was credible and reliable and that EGCC had a worse prognosis than EGNCC.

Discussion

To the best of our knowledge, the present study is the first to investigate the differences in LNM and prognosis between EGCC and EGNCC, and to identify the main clinical risk factors for LNM in EGC patients. We demonstrated that EGCC had a similar risk of LNM and a worse prognosis compared to EGNCC. Meanwhile, we stratified patients with EGC according to invasion depth and found that EGCC patients had a similar prognosis to EGNCC patients in the mucosa but significantly worse survival in the submucosa. Endoscopic therapy might only be appropriate for invasion depth limited to the mucosal layer in patients with EGCC. The worse prognosis of submucosal lesions in EGCC patients requires conventional gastrectomy with lymph node dissection.

In the past two decades, we have noticed significant changes in epidemiological characteristics and operative methods for EGC. Many population-based studies have shown that the incidence rates of GCC have increased in Western countries and Asian countries, such as Japan and Korea (8,25,26). Similarly, our research demonstrated a constant upward trend in the occurrence of GCC over time, juxtaposed with a decline in GNCC occurrence. This trend may be attributed to various factors such as changes in dietary habits, obesity, gastroesophageal reflux disease, HP infection, environmental factors, and genetic and molecular factors (27-30). Histopathological examination is crucial for elucidating the underlying tissue characteristics and differentiation patterns of GCC and GNCC. Our findings align with previous research demonstrating notable histological differences between the two groups, in which patients with GCC had more intestinal-type tumors than those with GNCC (31). In this study, we investigated the histological differences between EGCC and EGNCC to shed light on the distinct characteristics of these two subtypes.

In this study, we investigated the prognostic differences between EGCC and EGNCC. The EGCC group exhibited a notably lower median survival time than the EGNCC group, consistent with the results of a large retrospective study (10). Our findings have put forth a new perspective, which suggests that the survival outcome of EGCC is dependent on the depth of tumor infiltration. When the tumor was confined to the mucosal layer, we found no significant survival difference between EGCC and EGNCC patients. We believe that superficially invasive tumors are less likely to disseminate and have comparable survival rates. However, the landscape shifts when tumors penetrate the submucosal layer. The results of our study revealed a significant disparity in survival rates between patients with EGCC and EGNCC when the tumor was limited to the submucosal layer. This finding aligns with the studies by Saragoni et al. and Dao et al., which emphasized the importance of submucosal invasion as a critical factor influencing prognosis (32,33).

Several prior studies have developed nomogram models using SEER data to predict outcomes in EGC. For example, Zhang et al. constructed a nomogram to stratify postoperative EGC patients and guide decisions on adjuvant chemotherapy, identifying risk groups that benefit from adjuvant chemotherapy (34). Zhang et al. developed and externally validated a nomogram for predicting cancer-specific survival in middle-aged EGC patients (35). While both models demonstrated strong predictive performance, they focused on treatment decision-making or specific demographic subsets. However, our study provides a unique perspective by stratifying EGC patients based on tumor location (EGCC vs. EGNCC). Based on the univariate and multivariate analysis results, we constructed a nomogram model for DSS in EGC patients. The development of accurate prognostic tools is essential to inform clinical decision making and provide patients with personalized treatment strategies. Our nomogram integrated various clinicopathological variables that have been shown to influence survival outcomes in patients with gastric cancer. The incorporation of multiple factors into a nomogram allows for more comprehensive and individualized prediction of survival probabilities. Our model includes well-established prognostic factors, such as age, tumor site, grade, depth, and LNM. Previous studies have consistently linked these factors to the prognosis of patients (36,37). By combining these factors, our nomogram provides a holistic approach for estimating survival rates considering the complex interplay of different variables. The nomogram showed excellent and promising predictive performance, as validated by C-index, calibration, and ROC curves.

Several mechanisms may underlie the observed disparity in survival. The more aggressive behavior of GCC may be attributed to the unique anatomical and biological characteristics of the GEJ, including its distinct microenvironment and complex lymphatic drainage patterns (38). Genome-wide association studies have identified susceptibility loci such as PRKAA1 and MUC1 that are significantly associated with the risk of GCC (39-41). In comparison to GNCC, the expression of oncogenes such as PAK1 and KRAS is markedly upregulated in GCC, contributing to its unfavorable prognosis (42). Epigenetic changes, particularly DNA methylation, also play a critical role in carcinogenesis (43). ADHFE1 promoter methylation has been reported to increase the risk of GCC (44). However, most of these studies focus on advanced-stage GCC, and limited data are available regarding the early molecular events in EGCC. It is well recognized that IM and intraepithelial neoplasia (IEN) are precursor lesions of GCC (45). Recent findings suggest that OLFM4 expression, which is absent in normal cardia mucosa, emerges in IM and increases significantly in IEN. This pattern of expression may help identify individuals with cardia IM who are at higher malignant potential, thereby addressing the diagnostic challenge posed by the heterogeneity of IM (46). Moreover, EPHA2 mutations have been found to be more prevalent in precancerous lesions and early gastric cardia adenocarcinoma (GCA), especially in high-grade IEN and early-stage GCA (47). This suggests that EPHA2 may serve as a potential biomarker for the early detection and diagnosis of precancerous and early malignant lesions in the cardia region. In future clinical studies, we aim to further elucidate the molecular pathogenesis of EGCC by expanding the patient cohort, conducting comprehensive molecular profiling, and integrating genomic, histopathological, and clinical data. These efforts may enhance our understanding of EGCC biology and facilitate the development of more precise surveillance protocols and individualized treatment strategies.

In this study, we aimed to analyze the clinical characteristics and prognostic disparities between EGCC and EGNCC using SEER data. Although the database offers extensive patient information, there are several limitations that must be acknowledged in our analysis. First, the accuracy and completeness of the SEER database depend heavily on the quality of the data entry and reporting. Furthermore, the SEER database predominantly captures information on cancer-related variables and lacks detailed clinical data, treatment modalities, and patient comorbidities. This limits our ability to comprehensively assess the impact of these factors on observed clinical characteristics and prognostic differences. Second, EGC classification of EGC might vary across institutions, leading to potential misclassification bias. The Siewert classification has been widely accepted as a crucial prognostic tool for EGJ tumors management (48). However, our study could not obtain Siewert classification information for EGJ cancer cases. Establishing standardized reporting protocols for EGJ cancer cases will facilitate accurate data collection and enhance the reliability of the SEER database for future research. Finally, in terms of external validity, the SEER database primarily represents the United States population, potentially limiting the generalizability of our findings to other regions or populations with differing demographics, healthcare systems, and environmental factors. Extrapolating our results to a global context requires caution considering the potential variations in cancer incidence, risk factors, and treatment approaches. Despite these limitations, the SEER database remains a valuable resource for generating hypotheses, informing research directions, and identifying trends in cancer outcome. To mitigate these limitations, future studies should complement SEER data with more comprehensive clinical data, incorporate advanced statistical methods to address biases, and explore collaborations among multiple cancer databases to enhance the robustness and generalizability of the findings.

Conclusions

Our analysis of the clinical characteristics and prognostic differences between EGCC and EGNCC using the SEER database provides valuable insights into cancer epidemiology. Future studies could incorporate prospective designs and molecular profiling to further unravel the intricacies of EGCC.

Acknowledgments

We would like to thank the SEER database for providing the data for our study.

Footnote

Reporting Checklist: The authors have completed the TRIPOD reporting checklist. Available at https://jgo.amegroups.com/article/view/10.21037/jgo-2025-107/rc

Peer Review File: Available at https://jgo.amegroups.com/article/view/10.21037/jgo-2025-107/prf

Funding: This study was funded by the Tianjin Health Science and Technology Research Project (No. TJWJ2021MS005) and the Science and Technology Program of Tianjin (No. 21JCQNJC00990).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://jgo.amegroups.com/article/view/10.21037/jgo-2025-107/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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