Molecular features of early- vs. late-onset gastric cancer: a systematic review and meta-analysis

Molecular features of early- vs. late-onset gastric cancer: a systematic review and meta-analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Systematic Review Molecular features of early- vs. late-onset gastric cancer: a systematic review and meta-analysis Xinmei Lin, Wenhan Shi, Zitong Zhou, Degang Li, Qiuyuan Yao, Sun Yu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6435874/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 14 Jan, 2026 Read the published version in BMC Cancer → Version 1 posted 13 You are reading this latest preprint version Abstract Background: Early-onset gastric cancer (EOGC), diagnosed before the age of 50, exhibits distinct clinicopathological and molecular characteristics compared to late-onset gastric cancer (LOGC). However, the molecular landscape of EOGC remains incompletely defined. This study systematically reviews and meta-analyzes the molecular differences between EOGC and LOGC. Methods: A comprehensive literature search was conducted in PubMed, Embase, and Web of Science to identify studies comparing the molecular features between EOGC and LOGC. Meta-analyses were performed to assess differences in The Cancer Genome Atlas (TCGA) molecular subtypes, frequently mutated genes, key therapeutic biomarkers, and serum tumor markers. Odds ratios (ORs) with 95% confidence intervals (CIs) were calculated, and heterogeneity was evaluated via the I² statistic. Results: EOGC was associated with a significantly greater prevalence of the genomically stable (GS) subtype (OR = 1.71, 95% CI: 1.37–2.12) and a lower prevalence of the chromosomal instability (CIN) subtype (OR = 0.62, 95% CI: 0.50–0.77). CDH1 mutations were more common in EOGC (OR = 3.56, 95% CI: 2.94–4.31), whereas HER2 expression was significantly lower (OR = 0.55, 95% CI: 0.44–0.68). EOGC patients had a lower prevalence of deficient mismatch repair (dMMR)/microsatellite instability-high (MSI-H) (OR = 0.25, 95% CI: 0.12–0.53) but higher PD-L1 expression (OR = 2.11, 95% CI: 1.26–3.54). Serum markers such as CEA (OR = 0.43, 95% CI: 0.36–0.51) and CA19-9 (OR = 0.70, 95% CI: 0.58–0.85) were also less frequently elevated in EOGC. Conclusion: EOGC constitutes a biologically distinct subset of gastric cancer, defined by unique genomic, immune, and serological profiles. These differences highlight the need for age-specific diagnostic and therapeutic strategies and emphasize the value of multiomics approaches to further elucidate the molecular basis of early-onset disease. Early-onset gastric cancer TCGA classification gene mutations immune biomarkers molecular features meta-analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction Gastric cancer (GC) remains a major global health burden, with approximately 968,000 new cases and over 660,000 deaths reported worldwide in 2022 alone 1 . Although advances in early detection, Helicobacter pylori eradication, and public health initiatives have contributed to a general decline in GC incidence and mortality, this downward trend has not been observed uniformly across all age groups 2 . Notably, recent epidemiological data suggest a rising incidence of GC among younger individuals, particularly those under the age of 50 3 . This emerging subgroup—commonly referred to as early-onset gastric cancer (EOGC)—is increasingly recognized as a distinct clinical and molecular entity compared with late-onset gastric cancer (LOGC) 4 . The definition of EOGC varies across studies, with age cutoffs of 40, 45, or 50 years being most commonly used 5 – 8 . While GC in younger individuals often raises suspicion of hereditary cancer syndromes, only approximately 10% of EOGC cases can be attributed to germline mutations or familial predisposition, with the majority occurring sporadically 9 . Clinically, EOGC is more common in females and frequently presents aggressive pathological features, such as poor differentiation, extensive nodal involvement, predominant diffuse-type histology (particularly signet-ring cell carcinoma), and a more advanced TNM stage at diagnosis 10 , 11 . Collectively, these features contribute to a more aggressive disease course and poorer prognosis in younger patients. In addition to clinicopathological characteristics, EOGC and LOGC also present distinct molecular profiles 12 . Recent studies suggest that EOGC is more frequently associated with CDH1 mutations and lower HER2 expression 8 , 13 ; however, these findings remain inconsistent, with several studies reporting contradictory results 14 , 15 . With respect to the Cancer Genome Atlas (TCGA) classification, some authors have reported a higher prevalence of EBV-positive and genomically stable (GS) subtypes in EOGC 13 , 16 , whereas a study by Zhou et al. revealed no significant difference in the distribution of the EBV subtypes 17 . Similarly, discrepancies have been reported in the mutation status of ARID1A 8 , 17 , microsatellite instability (MSI) 10 , 18 , and serum tumor markers such as CA19-9 19,20 . Given the pivotal role of molecular biomarkers in guiding personalized treatment strategies, a comprehensive synthesis of current evidence is urgently needed to clarify these molecular distinctions and inform future research. This study aims to conduct a systematic review and meta-analysis to elucidate the molecular differences between EOGC and LOGC. By comprehensively analyzing TCGA molecular subtypes, frequently mutated genes, targeted and immune-related biomarkers, and serum tumor markers, we sought to define and quantify the molecular characteristics that distinguish these two subtypes of gastric cancer. These findings are expected to provide a theoretical framework for the development of age-specific diagnostic tools, prognostic models, and precision therapeutic strategies tailored to the unique biology of EOGC. 2. Materials and methods This systematic review and meta-analysis was registered with PROSPERO (CRD420251011066) and conducted in accordance with the PRISMA guidelines 21 . 2.1 Literature search A comprehensive and systematic literature search was conducted in PubMed, Embase, and Web of Science from their inception through February 24, 2025. The search strategy incorporated both Medical Subject Headings (MeSH terms) and relevant entry terms, including “Stomach Neoplasms,” “young onset,” and “early-onset gastric cancer.” The full search strategies for each database are detailed in Supplementary material 1 . In addition, the reference lists of all included articles and relevant review papers were manually screened to identify additional eligible studies. 2.2 Inclusion and exclusion criteria Inclusion criteria were as follows: (1) original studies that directly compared molecular characteristics between EOGC (age < 50 years) and LOGC; (2) studies that reported data on at least one of the following: TCGA molecular subtypes; gene mutation frequencies (e.g., CDH1 , TP53 ); targeted or immune-related biomarkers (e.g., HER2, dMMR/MSI-H, PD-L1); or serum tumor markers (e.g., CEA, CA19-9); and (3) studies involving pathologically confirmed samples. Exclusion criteria were as follows: (1) case reports, letters, news articles, and reviews; (2) nonhuman studies or studies lacking definitive EOGC diagnosis; (3) studies with unclear definitions of EOGC; (4) studies without a clearly defined control group; (5) non-English publications; (6) studies with insufficient or invalid data for analysis; and (7) studies with duplicated or overlapping data. 2.3 Data extraction and quality assessment Data extraction was independently performed by two authors (X.L. and W.S.). The extracted information included (1) the number of patients with early-onset and late-onset gastric cancer; (2) the distribution of TCGA molecular subtypes (EBV, MSI, CIN, and GS); (3) the frequencies of commonly mutated genes (e.g., CDH1 , TP53 , ARID1A , PIK3CA , and KRAS ); (4) the expression levels of clinically relevant biomarkers (e.g., HER2, dMMR/MSI-H, PD-L1, COX-2, E-cadherin, and p53); and (5) serum tumor markers (CEA and CA19-9). For studies in which data were presented graphically without corresponding numerical values, values were extracted using GetData Graph Digitizer (version 2.24). Additional study-level characteristics, including first author, publication year, study location, study period, sample size, and molecular detection methods, were also collected. Study quality was assessed using the Newcastle–Ottawa Scale (NOS), which evaluates methodological rigor across three domains: selection (0–4 points), comparability (0–2 points), and outcome assessment (0–3 points). Only studies with an NOS score ≥ 6 were deemed eligible for inclusion. Quality assessments were conducted independently by two authors (Y.S. and X.L.), with discrepancies resolved through discussion. 2.4 Statistical analysis Statistical analyses were conducted to compare the molecular features between EOGC and LOGC. For categorical variables, odds ratios (ORs) and 95% confidence intervals (CIs) were calculated. Heterogeneity across studies was assessed via Cochran’s Q test and the I² statistic. A random-effects model was applied when heterogeneity was significant ( P 50%); otherwise, a fixed-effects model was used. To evaluate the robustness of the pooled estimates, a leave-one-out sensitivity analysis was performed. Publication bias was assessed via funnel plots and Egger’s test. A two-tailed P value < 0.05 was considered statistically significant. All the statistical analyses were performed via Stata (version 12.0) and R (version 4.4.2). 3. Results 3.1 Study selection and characteristics A total of 22,091 records were identified through systematic searches of PubMed, Embase, and Web of Science, from which 4,424 duplicates were removed. After screening titles and abstracts on the basis of predefined eligibility criteria, 17,557 articles were excluded. An additional 88 full-text articles were excluded after detailed assessment. Ultimately, 22 studies met the inclusion criteria and were included in the final analysis 8,10,12–20,22–32 . A PRISMA flow diagram illustrating the study selection process is presented in Fig. 1 . These studies were published between 2000 and 2024 across 10 countries and collectively included data from 5,644 patients diagnosed with EOGC. The key characteristics of the included studies are summarized in Table 1 . Study quality, assessed using the Newcastle–Ottawa Scale (NOS), ranged from 6--9, with a mean score of 7.64, indicating generally high methodological quality. The detailed results of the quality assessment are provided in Supplementary material 2 . 3.2 Distribution of TCGA molecular subtypes Data extracted from studies comparing TCGA molecular subtypes revealed significant differences between early-onset and late-onset gastric cancer. As no significant heterogeneity was observed for the CIN subtype ( P = 0.4274, I² = 0%) or the GS subtype ( P = 0.4186, I² = 0%), a fixed-effects model was applied. The GS subtype was significantly more prevalent in EOGC than in LOGC ( Fig. 2 ; OR = 1.71, 95% CI: 1.37–2.12; P < 0.0001), whereas the CIN subtype was less common in the EOGC group( Fig. 2 ; OR = 0.62, 95% CI: 0.50–0.77; P < 0.0001). However, there were no statistically significant differences in the prevalence of the MSI and EBV subtypes between the two groups ( Fig. 2 ). 3. 3 Mutation profiles of frequently altered genes Analysis of key gene mutations revealed notable differences between EOGC and LOGC. Owing to substantial heterogeneity in the data for CDH1 mutations ( P = 0.0001, I² = 76%), a random effects model was applied. Pooled analysis revealed that CDH1 mutations were significantly more common in EOGC ( Fig. 3 ; OR = 3.56, 95% CI: 2.94–4.31; P < 0.0001). In contrast, the mutation frequencies of other commonly altered genes, including TP53 , ARID1A , PIK3CA , and KRAS , did not differ significantly between the two groups ( Fig. 3 ). 3.4 Expression of therapeutic and immune biomarkers Twelve studies reported data on the expression of molecular biomarkers. Owing to significant heterogeneity in the analysis of PD-L1 expression ( P = 0.0584, I² = 72%), a random effects model was applied. In contrast, for dMMR/MSI-H ( P = 0.3211, I² = 12%) and HER2 ( P = 0.3422, I² = 11%) tumors, heterogeneity was low, and a fixed-effects model was used. Pooled analysis revealed that HER2 expression was significantly lower in EOGC than in LOGC ( Fig. 4 ; OR = 0.55, 95% CI: 0.44–0.68; P < 0.0001). Similarly, the proportion of tumors with dMMR/MSI-H status was significantly lower in EOGC ( Fig. 5 ; OR = 0.25, 95% CI: 0.12–0.53; P = 0.0003). In contrast, PD-L1 expression was significantly higher in EOGC than in LOGC ( Fig. 5 ; OR = 2.11, 95% CI: 1.26–3.54; P = 0.0046). Additional biomarkers, including COX-2, E-cadherin, and p53, were analyzed in studies with substantial heterogeneity; thus, a random effects model was applied. No significant differences in the expression of these markers were observed between EOGC and LOGC ( Fig. 6 ). 3.5 Serum tumor marker levels Four studies reported comparisons of serum tumor markers between EOGC and LOGC patients. The analysis revealed that CEA levels were significantly lower in EOGC than in LOGC ( Fig. 7 ; OR = 0.43, 95% CI: 0.36–0.51; P < 0.0001), with no significant heterogeneity observed ( P = 0.3925, I² = 0%). A fixed-effects model was applied for this analysis. In contrast, for CA19-9, which exhibited significant heterogeneity ( P = 0.0058, I² = 81%), a random effects model was used. This analysis revealed that the incidence of elevated CA19-9 was also significantly lower in EOGC patients than in LOGC patients ( Fig. 7 ; OR = 0.70, 95% CI: 0.58–0.85; P = 0.0003). 3.6 Sensitivity analysis and publication bias A leave-one-out sensitivity analysis was conducted to evaluate the robustness of the pooled estimates. All the results remained stable, except for CA19-9 ( Supplementary material 3 ). The results from the funnel plot and Egger's test collectively indicated no significant publication bias ( Supplementary material 4 ). 4. Discussion EOGC has gained increasing research attention in recent years, driven by its increasing incidence and distinct clinical and pathological features 4 , 5 , 33 . However, its molecular mechanisms remain incompletely understood. Existing studies yield conflicting results regarding the molecular characteristics of EOGC due to factors such as small sample sizes, regional variations, and methodological heterogeneity. This inconsistency has hindered the development of standardized molecular classification systems and age-specific treatment strategies. To address these gaps, we performed the first comprehensive systematic review and meta-analysis comparing EOGC and LOGC across several molecular dimensions, including TCGA subtype distribution, frequently mutated genes, therapeutic biomarkers (e.g., HER2), and serum and immune-related markers. Our study synthesizes and quantitatively analyzes the existing evidence, clarifies key molecular features of EOGC and highlights both therapeutic opportunities and challenges, particularly in the context of targeted and immune-based therapies. These findings provide a theoretical foundation for precision molecular subtyping and individualized treatment strategies tailored to this unique subtype of gastric cancer. Regarding TCGA molecular subtypes, we found that the proportion of the GS subtype was significantly higher in EOGC, whereas the CIN subtype was relatively less common. This distribution aligns closely with the high prevalence of diffuse-type or signet-ring cell carcinomas in EOGC, which are frequently associated with CDH1 mutations 12 , demonstrating consistency between molecular classification and histopathological features. In contrast, the CIN subtype is predominantly observed in intestinal-type tumors, which are more characteristic of LOGC. No significant difference was observed in the prevalence of the EBV subtype between the two groups, which may be attributed to differences in ethnic composition across studies. Given the varying sensitivities of different subtypes to targeted therapies, identifying the predominant molecular subtypes in EOGC is crucial for guiding precision treatment strategies. We further conducted an analysis of high-frequency mutated genes in EOGC. The results showed that CDH1 mutations were significantly more prevalent in EOGC than in LOGC. In contrast, no significant differences in the mutation frequencies of TP53 , ARID1A , KRAS , or PIK3CA were detected between the two groups . CDH1 encodes E-cadherin, a key protein responsible for maintaining epithelial structural integrity 34 . Loss of E-cadherin function is strongly associated with the invasive nature of diffuse-type gastric cancer, which is common in EOGC 35 . Existing literature reported that the CDH1 mutation rate in EOGC ranges from 22.2–42.2%, with a higher prevalence among female patients and a correlation with poorer clinical outcomes 8 , 12 . The NCCN guidelines recommend prophylactic total gastrectomy for individuals with pathogenic CDH1 mutations, highlighting its potential role in screening and managing EOGC. With respect to ARID1A mutations, although some studies have indicated a higher mutation frequency in LOGC 8 , the meta-analysis was significantly influenced by the large sample size of the study by Zhou et al. 17 , leading to no significant difference between the groups. This finding warrants cautious interpretation and further validation in larger, independent cohorts. In terms of treatment-related biomarkers, our analysis revealed distinct features in EOGC compared with LOGC. Specifically, HER2 expression and the prevalence of dMMR/MSI-H status were significantly lower in EOGC, potentially limiting the clinical utility of HER2-targeted therapies and immune checkpoint inhibitors in this younger patient population. In contrast, PD-L1 expression was significantly greater in EOGC, suggesting that a subset of PD-L1–positive, microsatellite-stable (MSS) tumors may still be responsive to immunotherapy. This finding challenges the traditional reliance on MSI-H status as the primary predictor of immunotherapeutic efficacy and highlights the potential role of alternative biomarkers such as PD-L1 in EOGC. A particularly noteworthy observation in our study was the discrepancy between the TCGA-defined MSI subtype and the clinically assessed MSI-H status. Although no significant difference was observed in the proportion of the MSI subtype between EOGC and LOGC, a significant difference in dMMR/MSI-H status was evident. This inconsistency likely reflects fundamental differences in classification criteria and detection techniques. The MSI subtype in the TCGA framework is primarily identified through next-generation sequencing (NGS)-based approaches, which evaluate mutation burden and microsatellite instability across the genome 36 . However, it does not necessarily correspond to the MSI-H status determined through routine clinical methods such as polymerase chain reaction (PCR)-based assays or immunohistochemistry (IHC) for mismatch repair (MMR) proteins 37 . These clinically validated pathology-based techniques provide a more direct and reliable assessment of functional MMR deficiency 38 . Consequently, exclusive reliance on TCGA subtyping may underestimate the true prevalence of MSI-H tumors in EOGC and potentially lead to underrecognition of patients who could benefit from immunotherapy. Additionally, we reevaluated COX-2 expression, which has previously been reported to be downregulated in EOGC 22 , 39 . After adjusting for potential cohort overlap and incorporating data from other international studies, our analysis revealed no significant difference in COX-2 expression between EOGC and LOGC patients. Among the commonly used serum tumor markers in gastric cancer, CEA and CA19-9 are frequently used in clinical practice. In our analysis, both markers were significantly associated with lower positivity rates in EOGC, which may be attributed to the predominance of poorly differentiated and diffuse-type histology in this subgroup—tumor types generally associated with lower marker expression. Sensitivity analysis revealed some variability in the CA19-9 results, primarily due to differences in sample sizes across individual studies. However, the overall trend remained consistent. A large-scale retrospective study by Liu et al. reported a lower CA19-9 positivity rate in EOGC, which further supports our findings 10 . It is important to acknowledge several limitations of this meta-analysis. The definition of EOGC varied across studies, with different age cutoffs adopted, which may have introduced heterogeneity into the pooled estimates. The predominance of retrospective study designs, together with inconsistencies in molecular testing methods and reporting standards, may have introduced selection bias and limited the generalizability of the findings. To address these issues, future studies should aim to standardize EOGC definitions and harmonize molecular testing protocols to reduce interstudy heterogeneity and enhance comparability. Moreover, current evidence regarding the emerging therapeutic target Claudin 18.2 remains inconclusive due to conflicting results across studies 10 , 40 , underscoring the need for further validation to clarify its role in EOGC. Overall, this study systematically delineates the molecular distinctions between EOGC and LOGC across multiple dimensions, reinforcing the concept that EOGC represents a distinct molecular subtype of gastric cancer. These findings lay the foundation for refining molecular classification systems and opening new avenues for improving the clinical management of younger patients. Predictive models based on these insights could facilitate earlier diagnosis and more informed therapeutic decision-making. Furthermore, large-scale prospective cohort studies integrating multiomics approaches will be crucial for identifying novel driver genes and actionable targets, thereby advancing precision immunotherapy and targeted treatment strategies tailored to younger individuals with gastric cancer. 5. Conclusion This meta-analysis demonstrated that EOGC is a biologically distinct entity with molecular features that differ significantly from those of LOGC. Compared with LOGC, EOGC is characterized by a greater prevalence of the GS subtype, a lower prevalence of the CIN subtype, an increased frequency of CDH1 mutations, reduced HER2 expression, lower rates of dMMR/MSI-H, and decreased positivity for serum markers such as CEA and CA19-9. These molecular distinctions underscore the limitations of a uniform treatment approach across age groups and highlight the necessity of developing age-specific diagnostic and therapeutic strategies. Large-scale, multiomics studies are essential to deepen our understanding of the biological underpinnings of EOGC and to accelerate the development of personalized, age-stratified therapeutic strategies. Declarations Author contributions X.L. and W.S. contributed equally to this work. X.L. performed the conceptualization, methodology design, formal analysis, data curation, and original draft writing and contributed to manuscript review and editing. W.S. contributed to data curation, formal analysis, and data visualization and participated in original draft writing and manuscript editing. Z.Z. contributed to data curation. D.L. was responsible for study design. Q.Y. contributed to manuscript revision. Y.S. contributed to study design, provided research supervision, led the main manuscript writing, and participated in editing and revision. Funding This work was supported by the Doctoral Scientific Research Start-up Fund Project (Natural Science Category), Guangxi University of Traditional Chinese Medicine (2021BS027, Q.Y.), the Self-funded Research Project (Youth Fund Category), Administration of Traditional Chinese Medicine of Guangxi Zhuang Autonomous Region, and the First Affiliated Hospital of Guangxi University of Traditional Chinese Medicine (GXZYA20220069, Q.Y.). Data availability All the data included in this systematic review were derived from previously published studies and are publicly available. Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. Contributor information Degang Li, Email: [email protected] . Qiuyuan Yao, Email: [email protected] . Yu Sun, Email: [email protected] . References Bray, F. et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA. Cancer J. Clin. 74, 229–263 (2024). Smyth, E. C., Nilsson, M., Grabsch, H. I., van Grieken, N. C. & Lordick, F. Gastric cancer. Lancet 396, 635–648 (2020). Arnold, M. et al. Is gastric cancer becoming a rare disease? A global assessment of predicted incidence trends to 2035. Gut 69, 823–829 (2020). Mazurek, M., Szewc, M., Sitarz, M. Z., Dudzińska, E. & Sitarz, R. Gastric Cancer: An Up-to-Date Review with New Insights into Early-Onset Gastric Cancer. Cancers (Basel) 16, 3163 (2024). Ugai, T. et al. 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Supplementary Files Table1.docx Supplementarymaterial1.docx Supplementarymaterial2.docx Supplementarymaterial3.docx Supplementarymaterial4.docx Cite Share Download PDF Status: Published Journal Publication published 14 Jan, 2026 Read the published version in BMC Cancer → Version 1 posted Editorial decision: Revision requested 07 Jul, 2025 Reviews received at journal 05 Jul, 2025 Reviewers agreed at journal 24 Jun, 2025 Reviews received at journal 13 Jun, 2025 Reviewers agreed at journal 01 Jun, 2025 Reviews received at journal 17 May, 2025 Reviewers agreed at journal 14 May, 2025 Reviewers agreed at journal 13 May, 2025 Reviewers invited by journal 12 May, 2025 Editor invited by journal 30 Apr, 2025 Editor assigned by journal 13 Apr, 2025 Submission checks completed at journal 13 Apr, 2025 First submitted to journal 12 Apr, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6435874","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Systematic Review","associatedPublications":[],"authors":[{"id":456729597,"identity":"32d2903e-2001-413f-b953-1fc7393d8193","order_by":0,"name":"Xinmei Lin","email":"","orcid":"","institution":"Sun Yat-sen University","correspondingAuthor":false,"prefix":"","firstName":"Xinmei","middleName":"","lastName":"Lin","suffix":""},{"id":456729598,"identity":"db9db257-2f24-42ad-97b1-cb4c68010883","order_by":1,"name":"Wenhan Shi","email":"","orcid":"","institution":"Sun Yat-sen University","correspondingAuthor":false,"prefix":"","firstName":"Wenhan","middleName":"","lastName":"Shi","suffix":""},{"id":456729599,"identity":"e915c15d-f7f6-446f-b5d6-917c288e77c4","order_by":2,"name":"Zitong Zhou","email":"","orcid":"","institution":"Sun Yat-sen University","correspondingAuthor":false,"prefix":"","firstName":"Zitong","middleName":"","lastName":"Zhou","suffix":""},{"id":456729600,"identity":"3890d2b2-46a3-4739-8875-a41709653298","order_by":3,"name":"Degang Li","email":"","orcid":"","institution":"Guangxi University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Degang","middleName":"","lastName":"Li","suffix":""},{"id":456729601,"identity":"a601557e-a2ad-4d2c-9156-871ffeeedb83","order_by":4,"name":"Qiuyuan Yao","email":"","orcid":"","institution":"Guangxi University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Qiuyuan","middleName":"","lastName":"Yao","suffix":""},{"id":456729602,"identity":"83d7f099-a921-46d2-a00c-f74755c2a4c5","order_by":5,"name":"Sun Yu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCElEQVRIiWNgGAWjYJACxg8VNnJAqvEATESCkBZmiTNpxkAtDcRrYeBtOZzYAKSJ0yI/I/eYhGTD4fS17YeBtvw5bG9wgPngbR4GuzxcWgxu5KVJFO5Iz912JrHhAGPb4cQNB9iSrXkYkotxapHIMZOQPGOdu+0ASEvD4QSDAzxm0jwMB8BOxe4woBbeNuZ0s/MPYQ7j/4ZXC8MNsBbnBLMbQFsY2A4zbjjAw4ZXi8GZN8bWwEA23HYDaEtiW3rizMNsxpZzDJJxO6w9x/AmMCrlzc6nP3zw4Y+1Pd/x5oc33lTY4XYYAwMLIhYSGJqBMQu2Hbd6IGD+gMSpw6t0FIyCUTAKRiYAALaWXs6Ak7kSAAAAAElFTkSuQmCC","orcid":"","institution":"Sun Yat-sen University","correspondingAuthor":true,"prefix":"","firstName":"Sun","middleName":"","lastName":"Yu","suffix":""}],"badges":[],"createdAt":"2025-04-12 17:23:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6435874/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6435874/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12885-026-15567-5","type":"published","date":"2026-01-14T16:31:08+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":82886685,"identity":"833b7b64-1eb1-4077-b8cd-f786efd2b748","added_by":"auto","created_at":"2025-05-16 11:54:10","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":32457,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePRISMA flow diagram of study selection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFlowchart depicting the study selection process for the systematic review and meta-analysis.\u003c/p\u003e","description":"","filename":"Binder61.png","url":"https://assets-eu.researchsquare.com/files/rs-6435874/v1/72a870a1d606b6b820028c30.png"},{"id":82886687,"identity":"3518f6e3-268a-4838-9310-91308ef4ce5c","added_by":"auto","created_at":"2025-05-16 11:54:10","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":39687,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDistribution of TCGA molecular subtypes in EOGC vs LOGC\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEOGC was associated with a significantly greater prevalence of the GS subtype and a lower prevalence of the CIN subtype. No significant differences were found in the EBV-positive or MSI subtypes.\u003c/p\u003e","description":"","filename":"Binder62.png","url":"https://assets-eu.researchsquare.com/files/rs-6435874/v1/5ffe154d07f183000bd071c7.png"},{"id":82888414,"identity":"4df93dd2-19de-41a8-a893-ad692bad15b6","added_by":"auto","created_at":"2025-05-16 12:02:10","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":59821,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHigh-frequency gene mutations in EOGC vs LOGC\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOnly \u003cem\u003eCDH1\u003c/em\u003e mutations were significantly more common in EOGC; no significant differences were observed for \u003cem\u003eTP53\u003c/em\u003e, \u003cem\u003eARID1A\u003c/em\u003e, \u003cem\u003ePIK3CA\u003c/em\u003e, or \u003cem\u003eKRAS\u003c/em\u003e.\u003c/p\u003e","description":"","filename":"Binder63.png","url":"https://assets-eu.researchsquare.com/files/rs-6435874/v1/49451c17f4ffb38054a91777.png"},{"id":82886695,"identity":"15d7bebe-8324-4938-aaf5-3906c2dae346","added_by":"auto","created_at":"2025-05-16 11:54:10","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":17909,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHER2 expression in EOGC vs LOGC\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHER2 expression was significantly lower in EOGC than in LOGC.\u003c/p\u003e","description":"","filename":"Binder64.png","url":"https://assets-eu.researchsquare.com/files/rs-6435874/v1/7f9ca51d2554999c28d91f6e.png"},{"id":82886688,"identity":"4789690c-ab2b-4097-bbaf-c735ea826c40","added_by":"auto","created_at":"2025-05-16 11:54:10","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":20047,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExpression of immune biomarkersin EOGC vs LOGC\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCompared with LOGC, EOGC had lower dMMR/MSI-H and higher PD-L1 expression.\u003c/p\u003e","description":"","filename":"Binder65.png","url":"https://assets-eu.researchsquare.com/files/rs-6435874/v1/c973bd7f5a4b170d32d5a2b7.png"},{"id":82886693,"identity":"94cdb5ed-1f55-400e-af42-ace67a7495eb","added_by":"auto","created_at":"2025-05-16 11:54:10","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":32383,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExpression of additional molecular markers in EOGC vs LOGC\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo significant differences in COX-2, E-cadherin, or p53 expression were detected between the groups.\u003c/p\u003e","description":"","filename":"Binder66.png","url":"https://assets-eu.researchsquare.com/files/rs-6435874/v1/bbf4a3f7628db2bb1415b2e4.png"},{"id":82888418,"identity":"e7520e78-af2c-4229-ab75-665f4b2dcb4c","added_by":"auto","created_at":"2025-05-16 12:02:10","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":22577,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSerum tumor marker levels in EOGC vs LOGC\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCEA and CA19-9 were less frequently elevated in EOGCpatients.\u003c/p\u003e","description":"","filename":"Binder67.png","url":"https://assets-eu.researchsquare.com/files/rs-6435874/v1/bba4db0e1776eadfae37bddb.png"},{"id":100616452,"identity":"934be073-9d8d-4cb1-b86b-788e0e716cf2","added_by":"auto","created_at":"2026-01-19 17:43:01","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1069587,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6435874/v1/152cfafa-c44e-4909-bc45-01c681356f2b.pdf"},{"id":82888416,"identity":"cfab1186-3c00-42ea-81f5-c482b4bf410f","added_by":"auto","created_at":"2025-05-16 12:02:10","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":19384,"visible":true,"origin":"","legend":"","description":"","filename":"Table1.docx","url":"https://assets-eu.researchsquare.com/files/rs-6435874/v1/528885c4f246b44f464e205a.docx"},{"id":82888417,"identity":"161f2a84-bc10-41e5-a19f-480d004a2b19","added_by":"auto","created_at":"2025-05-16 12:02:10","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":14363,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial1.docx","url":"https://assets-eu.researchsquare.com/files/rs-6435874/v1/c9ad3c94c985e555ed3a71d1.docx"},{"id":82888415,"identity":"0c646e7d-3cbc-4c5e-8418-5a2c321d0b24","added_by":"auto","created_at":"2025-05-16 12:02:10","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":21166,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial2.docx","url":"https://assets-eu.researchsquare.com/files/rs-6435874/v1/1e5b22bca8a4e0091822acf3.docx"},{"id":82886700,"identity":"a36718e5-7326-40f5-ac65-563b65fdceb6","added_by":"auto","created_at":"2025-05-16 11:54:10","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":1963673,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial3.docx","url":"https://assets-eu.researchsquare.com/files/rs-6435874/v1/61dbfac8b6648a19b65faf2d.docx"},{"id":82886699,"identity":"8350382e-cd5f-402a-a930-f21d5969fc13","added_by":"auto","created_at":"2025-05-16 11:54:10","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":1552293,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial4.docx","url":"https://assets-eu.researchsquare.com/files/rs-6435874/v1/34bc0ee6d1d45b826b37576b.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eMolecular features of early- vs. late-onset gastric cancer: a systematic review and meta-analysis\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eGastric cancer (GC) remains a major global health burden, with approximately 968,000 new cases and over 660,000 deaths reported worldwide in 2022 alone\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Although advances in early detection, \u003cem\u003eHelicobacter pylori\u003c/em\u003e eradication, and public health initiatives have contributed to a general decline in GC incidence and mortality, this downward trend has not been observed uniformly across all age groups\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Notably, recent epidemiological data suggest a rising incidence of GC among younger individuals, particularly those under the age of 50\u003csup\u003e3\u003c/sup\u003e. This emerging subgroup\u0026mdash;commonly referred to as early-onset gastric cancer (EOGC)\u0026mdash;is increasingly recognized as a distinct clinical and molecular entity compared with late-onset gastric cancer (LOGC)\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe definition of EOGC varies across studies, with age cutoffs of 40, 45, or 50 years being most commonly used\u003csup\u003e\u003cspan additionalcitationids=\"CR6 CR7\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. While GC in younger individuals often raises suspicion of hereditary cancer syndromes, only approximately 10% of EOGC cases can be attributed to germline mutations or familial predisposition, with the majority occurring sporadically\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Clinically, EOGC is more common in females and frequently presents aggressive pathological features, such as poor differentiation, extensive nodal involvement, predominant diffuse-type histology (particularly signet-ring cell carcinoma), and a more advanced TNM stage at diagnosis\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Collectively, these features contribute to a more aggressive disease course and poorer prognosis in younger patients.\u003c/p\u003e \u003cp\u003eIn addition to clinicopathological characteristics, EOGC and LOGC also present distinct molecular profiles\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Recent studies suggest that EOGC is more frequently associated with \u003cem\u003eCDH1\u003c/em\u003e mutations and lower HER2 expression\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e; however, these findings remain inconsistent, with several studies reporting contradictory results\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. With respect to the Cancer Genome Atlas (TCGA) classification, some authors have reported a higher prevalence of EBV-positive and genomically stable (GS) subtypes in EOGC\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e, whereas a study by Zhou et al. revealed no significant difference in the distribution of the EBV subtypes\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Similarly, discrepancies have been reported in the mutation status of \u003cem\u003eARID1A\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e, microsatellite instability (MSI)\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e, and serum tumor markers such as CA19-9\u003csup\u003e19,20\u003c/sup\u003e. Given the pivotal role of molecular biomarkers in guiding personalized treatment strategies, a comprehensive synthesis of current evidence is urgently needed to clarify these molecular distinctions and inform future research.\u003c/p\u003e \u003cp\u003eThis study aims to conduct a systematic review and meta-analysis to elucidate the molecular differences between EOGC and LOGC. By comprehensively analyzing TCGA molecular subtypes, frequently mutated genes, targeted and immune-related biomarkers, and serum tumor markers, we sought to define and quantify the molecular characteristics that distinguish these two subtypes of gastric cancer. These findings are expected to provide a theoretical framework for the development of age-specific diagnostic tools, prognostic models, and precision therapeutic strategies tailored to the unique biology of EOGC.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cp\u003eThis systematic review and meta-analysis was registered with PROSPERO (CRD420251011066) and conducted in accordance with the PRISMA guidelines\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Literature search\u003c/h2\u003e \u003cp\u003eA comprehensive and systematic literature search was conducted in PubMed, Embase, and Web of Science from their inception through February 24, 2025. The search strategy incorporated both Medical Subject Headings (MeSH terms) and relevant entry terms, including \u0026ldquo;Stomach Neoplasms,\u0026rdquo; \u0026ldquo;young onset,\u0026rdquo; and \u0026ldquo;early-onset gastric cancer.\u0026rdquo; The full search strategies for each database are detailed in \u003cb\u003eSupplementary material 1\u003c/b\u003e. In addition, the reference lists of all included articles and relevant review papers were manually screened to identify additional eligible studies.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Inclusion and exclusion criteria\u003c/h2\u003e \u003cp\u003eInclusion criteria were as follows: (1) original studies that directly compared molecular characteristics between EOGC (age\u0026thinsp;\u0026lt;\u0026thinsp;50 years) and LOGC; (2) studies that reported data on at least one of the following: TCGA molecular subtypes; gene mutation frequencies (e.g., \u003cem\u003eCDH1\u003c/em\u003e, \u003cem\u003eTP53\u003c/em\u003e); targeted or immune-related biomarkers (e.g., HER2, dMMR/MSI-H, PD-L1); or serum tumor markers (e.g., CEA, CA19-9); and (3) studies involving pathologically confirmed samples.\u003c/p\u003e \u003cp\u003eExclusion criteria were as follows: (1) case reports, letters, news articles, and reviews; (2) nonhuman studies or studies lacking definitive EOGC diagnosis; (3) studies with unclear definitions of EOGC; (4) studies without a clearly defined control group; (5) non-English publications; (6) studies with insufficient or invalid data for analysis; and (7) studies with duplicated or overlapping data.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Data extraction and quality assessment\u003c/h2\u003e \u003cp\u003eData extraction was independently performed by two authors (X.L. and W.S.). The extracted information included (1) the number of patients with early-onset and late-onset gastric cancer; (2) the distribution of TCGA molecular subtypes (EBV, MSI, CIN, and GS); (3) the frequencies of commonly mutated genes (e.g., \u003cem\u003eCDH1\u003c/em\u003e, \u003cem\u003eTP53\u003c/em\u003e, \u003cem\u003eARID1A\u003c/em\u003e, \u003cem\u003ePIK3CA\u003c/em\u003e, and \u003cem\u003eKRAS\u003c/em\u003e); (4) the expression levels of clinically relevant biomarkers (e.g., HER2, dMMR/MSI-H, PD-L1, COX-2, E-cadherin, and p53); and (5) serum tumor markers (CEA and CA19-9). For studies in which data were presented graphically without corresponding numerical values, values were extracted using GetData Graph Digitizer (version 2.24). Additional study-level characteristics, including first author, publication year, study location, study period, sample size, and molecular detection methods, were also collected.\u003c/p\u003e \u003cp\u003eStudy quality was assessed using the Newcastle\u0026ndash;Ottawa Scale (NOS), which evaluates methodological rigor across three domains: selection (0\u0026ndash;4 points), comparability (0\u0026ndash;2 points), and outcome assessment (0\u0026ndash;3 points). Only studies with an NOS score\u0026thinsp;\u0026ge;\u0026thinsp;6 were deemed eligible for inclusion. Quality assessments were conducted independently by two authors (Y.S. and X.L.), with discrepancies resolved through discussion.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Statistical analysis\u003c/h2\u003e \u003cp\u003eStatistical analyses were conducted to compare the molecular features between EOGC and LOGC. For categorical variables, odds ratios (ORs) and 95% confidence intervals (CIs) were calculated. Heterogeneity across studies was assessed via Cochran\u0026rsquo;s Q test and the I\u0026sup2; statistic. A random-effects model was applied when heterogeneity was significant (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.10 or I\u0026sup2; \u0026gt; 50%); otherwise, a fixed-effects model was used. To evaluate the robustness of the pooled estimates, a leave-one-out sensitivity analysis was performed. Publication bias was assessed via funnel plots and Egger\u0026rsquo;s test. A two-tailed P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant. All the statistical analyses were performed via Stata (version 12.0) and R (version 4.4.2).\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003e\u003cstrong\u003e3.1 Study selection and characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 22,091 records were identified through systematic searches of PubMed, Embase, and Web of Science, from which 4,424 duplicates were removed. After screening titles and abstracts on the basis of predefined eligibility criteria, 17,557 articles were excluded. An additional 88 full-text articles were excluded after detailed assessment. Ultimately, 22 studies met the inclusion criteria and were included in the final analysis\u003csup\u003e8,10,12–20,22–32\u003c/sup\u003e. A PRISMA flow diagram illustrating the study selection process is presented in \u003cstrong\u003eFig. 1\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eThese studies were published between 2000 and 2024 across 10 countries and collectively included data from 5,644 patients diagnosed with EOGC. The key characteristics of the included studies are summarized in \u003cstrong\u003eTable 1\u003c/strong\u003e. Study quality, assessed using the Newcastle–Ottawa Scale (NOS), ranged from 6--9, with a mean score of 7.64, indicating generally high methodological quality. The detailed results of the quality assessment are provided in \u003cstrong\u003eSupplementary material 2\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Distribution of TCGA molecular subtypes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData extracted from studies comparing TCGA molecular subtypes revealed significant differences between early-onset and late-onset gastric cancer. As no significant heterogeneity was observed for the CIN subtype (\u003cem\u003eP\u0026nbsp;\u003c/em\u003e= 0.4274, I² = 0%) or the GS subtype (\u003cem\u003eP\u0026nbsp;\u003c/em\u003e= 0.4186, I² = 0%), a fixed-effects model was applied. The GS subtype was significantly more prevalent in EOGC than in LOGC (\u003cstrong\u003eFig. 2\u003c/strong\u003e; OR = 1.71, 95% CI: 1.37–2.12; \u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt; 0.0001), whereas the CIN subtype was less common in the EOGC group(\u003cstrong\u003eFig. 2\u003c/strong\u003e; OR = 0.62, 95% CI: 0.50–0.77; \u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt; 0.0001). However, there were no statistically significant differences in the prevalence of the MSI and EBV subtypes between the two groups (\u003cstrong\u003eFig. 2\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.\u003c/strong\u003e\u003cstrong\u003e3\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eMutation profiles of frequently altered genes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAnalysis of key gene mutations revealed notable differences between EOGC and LOGC. Owing to substantial heterogeneity in the data for \u003cem\u003eCDH1\u003c/em\u003e mutations (\u003cem\u003eP\u0026nbsp;\u003c/em\u003e= 0.0001, I² = 76%), a random effects model was applied. Pooled analysis revealed that \u003cem\u003eCDH1\u003c/em\u003e mutations were significantly more common in EOGC (\u003cstrong\u003eFig. 3\u003c/strong\u003e; OR = 3.56, 95% CI: 2.94–4.31; \u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt; 0.0001). In contrast, the mutation frequencies of other commonly altered genes, including \u003cem\u003eTP53\u003c/em\u003e, \u003cem\u003eARID1A\u003c/em\u003e, \u003cem\u003ePIK3CA\u003c/em\u003e, and \u003cem\u003eKRAS\u003c/em\u003e, did not differ significantly between the two groups (\u003cstrong\u003eFig. 3\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4 Expression of therapeutic and immune biomarkers\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTwelve studies reported data on the expression of molecular biomarkers. Owing to significant heterogeneity in the analysis of PD-L1 expression (\u003cem\u003eP\u0026nbsp;\u003c/em\u003e= 0.0584, I² = 72%), a random effects model was applied. In contrast, for dMMR/MSI-H (\u003cem\u003eP\u0026nbsp;\u003c/em\u003e= 0.3211, I² = 12%) and HER2 (\u003cem\u003eP\u0026nbsp;\u003c/em\u003e= 0.3422, I² = 11%) tumors, heterogeneity was low, and a fixed-effects model was used. Pooled analysis revealed that HER2 expression was significantly lower in EOGC than in LOGC (\u003cstrong\u003eFig. 4\u003c/strong\u003e; OR = 0.55, 95% CI: 0.44–0.68; \u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt; 0.0001). Similarly, the proportion of tumors with dMMR/MSI-H status was significantly lower in EOGC (\u003cstrong\u003eFig. 5\u003c/strong\u003e; OR = 0.25, 95% CI: 0.12–0.53; \u003cem\u003eP\u0026nbsp;\u003c/em\u003e= 0.0003). In contrast, PD-L1 expression was significantly higher in EOGC than in LOGC (\u003cstrong\u003eFig. 5\u003c/strong\u003e; OR = 2.11, 95% CI: 1.26–3.54; \u003cem\u003eP\u0026nbsp;\u003c/em\u003e= 0.0046).\u003c/p\u003e\n\u003cp\u003eAdditional biomarkers, including COX-2, E-cadherin, and p53, were analyzed in studies with substantial heterogeneity; thus, a random effects model was applied. No significant differences in the expression of these markers were observed between EOGC and LOGC (\u003cstrong\u003eFig. 6\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.5 Serum tumor marker levels\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFour studies reported comparisons of serum tumor markers between EOGC and LOGC patients. The analysis revealed that CEA levels were significantly lower in EOGC than in LOGC (\u003cstrong\u003eFig. 7\u003c/strong\u003e; OR = 0.43, 95% CI: 0.36–0.51; \u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt; 0.0001), with no significant heterogeneity observed (\u003cem\u003eP\u0026nbsp;\u003c/em\u003e= 0.3925, I² = 0%). A fixed-effects model was applied for this analysis. In contrast, for CA19-9, which exhibited significant heterogeneity (\u003cem\u003eP\u0026nbsp;\u003c/em\u003e= 0.0058, I² = 81%), a random effects model was used. This analysis revealed that the incidence of elevated CA19-9 was also significantly lower in EOGC patients than in LOGC patients (\u003cstrong\u003eFig. 7\u003c/strong\u003e; OR = 0.70, 95% CI: 0.58–0.85; \u003cem\u003eP\u0026nbsp;\u003c/em\u003e= 0.0003).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.6 Sensitivity analysis and\u0026nbsp;publication bias\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA leave-one-out sensitivity analysis was conducted to evaluate the robustness of the pooled estimates. All the results remained stable, except for CA19-9 (\u003cstrong\u003eSupplementary material 3\u003c/strong\u003e). The results from the funnel plot and Egger's test collectively indicated no significant publication bias (\u003cstrong\u003eSupplementary material 4\u003c/strong\u003e).\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eEOGC has gained increasing research attention in recent years, driven by its increasing incidence and distinct clinical and pathological features\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. However, its molecular mechanisms remain incompletely understood. Existing studies yield conflicting results regarding the molecular characteristics of EOGC due to factors such as small sample sizes, regional variations, and methodological heterogeneity. This inconsistency has hindered the development of standardized molecular classification systems and age-specific treatment strategies. To address these gaps, we performed the first comprehensive systematic review and meta-analysis comparing EOGC and LOGC across several molecular dimensions, including TCGA subtype distribution, frequently mutated genes, therapeutic biomarkers (e.g., HER2), and serum and immune-related markers. Our study synthesizes and quantitatively analyzes the existing evidence, clarifies key molecular features of EOGC and highlights both therapeutic opportunities and challenges, particularly in the context of targeted and immune-based therapies. These findings provide a theoretical foundation for precision molecular subtyping and individualized treatment strategies tailored to this unique subtype of gastric cancer.\u003c/p\u003e \u003cp\u003eRegarding TCGA molecular subtypes, we found that the proportion of the GS subtype was significantly higher in EOGC, whereas the CIN subtype was relatively less common. This distribution aligns closely with the high prevalence of diffuse-type or signet-ring cell carcinomas in EOGC, which are frequently associated with \u003cem\u003eCDH1\u003c/em\u003e mutations\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e, demonstrating consistency between molecular classification and histopathological features. In contrast, the CIN subtype is predominantly observed in intestinal-type tumors, which are more characteristic of LOGC. No significant difference was observed in the prevalence of the EBV subtype between the two groups, which may be attributed to differences in ethnic composition across studies. Given the varying sensitivities of different subtypes to targeted therapies, identifying the predominant molecular subtypes in EOGC is crucial for guiding precision treatment strategies.\u003c/p\u003e \u003cp\u003eWe further conducted an analysis of high-frequency mutated genes in EOGC. The results showed that \u003cem\u003eCDH1\u003c/em\u003e mutations were significantly more prevalent in EOGC than in LOGC. In contrast, no significant differences in the mutation frequencies of \u003cem\u003eTP53\u003c/em\u003e, \u003cem\u003eARID1A\u003c/em\u003e, \u003cem\u003eKRAS\u003c/em\u003e, or \u003cem\u003ePIK3CA were detected between the two groups\u003c/em\u003e. \u003cem\u003eCDH1\u003c/em\u003e encodes E-cadherin, a key protein responsible for maintaining epithelial structural integrity\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. Loss of E-cadherin function is strongly associated with the invasive nature of diffuse-type gastric cancer, which is common in EOGC\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. Existing literature reported that the \u003cem\u003eCDH1\u003c/em\u003e mutation rate in EOGC ranges from 22.2\u0026ndash;42.2%, with a higher prevalence among female patients and a correlation with poorer clinical outcomes\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. The NCCN guidelines recommend prophylactic total gastrectomy for individuals with pathogenic \u003cem\u003eCDH1\u003c/em\u003e mutations, highlighting its potential role in screening and managing EOGC. With respect to \u003cem\u003eARID1A\u003c/em\u003e mutations, although some studies have indicated a higher mutation frequency in LOGC\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e, the meta-analysis was significantly influenced by the large sample size of the study by Zhou et al.\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e, leading to no significant difference between the groups. This finding warrants cautious interpretation and further validation in larger, independent cohorts.\u003c/p\u003e \u003cp\u003eIn terms of treatment-related biomarkers, our analysis revealed distinct features in EOGC compared with LOGC. Specifically, HER2 expression and the prevalence of dMMR/MSI-H status were significantly lower in EOGC, potentially limiting the clinical utility of HER2-targeted therapies and immune checkpoint inhibitors in this younger patient population. In contrast, PD-L1 expression was significantly greater in EOGC, suggesting that a subset of PD-L1\u0026ndash;positive, microsatellite-stable (MSS) tumors may still be responsive to immunotherapy. This finding challenges the traditional reliance on MSI-H status as the primary predictor of immunotherapeutic efficacy and highlights the potential role of alternative biomarkers such as PD-L1 in EOGC. A particularly noteworthy observation in our study was the discrepancy between the TCGA-defined MSI subtype and the clinically assessed MSI-H status. Although no significant difference was observed in the proportion of the MSI subtype between EOGC and LOGC, a significant difference in dMMR/MSI-H status was evident. This inconsistency likely reflects fundamental differences in classification criteria and detection techniques. The MSI subtype in the TCGA framework is primarily identified through next-generation sequencing (NGS)-based approaches, which evaluate mutation burden and microsatellite instability across the genome\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. However, it does not necessarily correspond to the MSI-H status determined through routine clinical methods such as polymerase chain reaction (PCR)-based assays or immunohistochemistry (IHC) for mismatch repair (MMR) proteins\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. These clinically validated pathology-based techniques provide a more direct and reliable assessment of functional MMR deficiency\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. Consequently, exclusive reliance on TCGA subtyping may underestimate the true prevalence of MSI-H tumors in EOGC and potentially lead to underrecognition of patients who could benefit from immunotherapy. Additionally, we reevaluated COX-2 expression, which has previously been reported to be downregulated in EOGC\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. After adjusting for potential cohort overlap and incorporating data from other international studies, our analysis revealed no significant difference in COX-2 expression between EOGC and LOGC patients.\u003c/p\u003e \u003cp\u003eAmong the commonly used serum tumor markers in gastric cancer, CEA and CA19-9 are frequently used in clinical practice. In our analysis, both markers were significantly associated with lower positivity rates in EOGC, which may be attributed to the predominance of poorly differentiated and diffuse-type histology in this subgroup\u0026mdash;tumor types generally associated with lower marker expression. Sensitivity analysis revealed some variability in the CA19-9 results, primarily due to differences in sample sizes across individual studies. However, the overall trend remained consistent. A large-scale retrospective study by Liu et al. reported a lower CA19-9 positivity rate in EOGC, which further supports our findings\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIt is important to acknowledge several limitations of this meta-analysis. The definition of EOGC varied across studies, with different age cutoffs adopted, which may have introduced heterogeneity into the pooled estimates. The predominance of retrospective study designs, together with inconsistencies in molecular testing methods and reporting standards, may have introduced selection bias and limited the generalizability of the findings. To address these issues, future studies should aim to standardize EOGC definitions and harmonize molecular testing protocols to reduce interstudy heterogeneity and enhance comparability. Moreover, current evidence regarding the emerging therapeutic target Claudin 18.2 remains inconclusive due to conflicting results across studies\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e, underscoring the need for further validation to clarify its role in EOGC.\u003c/p\u003e \u003cp\u003eOverall, this study systematically delineates the molecular distinctions between EOGC and LOGC across multiple dimensions, reinforcing the concept that EOGC represents a distinct molecular subtype of gastric cancer. These findings lay the foundation for refining molecular classification systems and opening new avenues for improving the clinical management of younger patients. Predictive models based on these insights could facilitate earlier diagnosis and more informed therapeutic decision-making. Furthermore, large-scale prospective cohort studies integrating multiomics approaches will be crucial for identifying novel driver genes and actionable targets, thereby advancing precision immunotherapy and targeted treatment strategies tailored to younger individuals with gastric cancer.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis meta-analysis demonstrated that EOGC is a biologically distinct entity with molecular features that differ significantly from those of LOGC. Compared with LOGC, EOGC is characterized by a greater prevalence of the GS subtype, a lower prevalence of the CIN subtype, an increased frequency of \u003cem\u003eCDH1\u003c/em\u003e mutations, reduced HER2 expression, lower rates of dMMR/MSI-H, and decreased positivity for serum markers such as CEA and CA19-9. These molecular distinctions underscore the limitations of a uniform treatment approach across age groups and highlight the necessity of developing age-specific diagnostic and therapeutic strategies. Large-scale, multiomics studies are essential to deepen our understanding of the biological underpinnings of EOGC and to accelerate the development of personalized, age-stratified therapeutic strategies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eX.L. and W.S. contributed equally to this work. X.L. performed the conceptualization, methodology design, formal analysis, data curation, and original draft writing and contributed to manuscript review and editing. W.S. contributed to data curation, formal analysis, and data visualization and participated in original draft writing and manuscript editing. Z.Z. contributed to data curation. D.L. was responsible for study design. Q.Y. contributed to manuscript revision. Y.S. contributed to study design, provided research supervision, led the main manuscript writing, and participated in editing and revision.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Doctoral Scientific Research Start-up Fund Project (Natural Science Category), Guangxi University of Traditional Chinese Medicine (2021BS027, Q.Y.), the Self-funded Research Project (Youth Fund Category), Administration of Traditional Chinese Medicine of Guangxi Zhuang Autonomous Region, and the First Affiliated Hospital of Guangxi University of Traditional Chinese Medicine (GXZYA20220069, Q.Y.).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eavailability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the data included in this systematic review were derived from previously published studies and are publicly available.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContributor information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDegang Li, Email: [email protected].\u003c/p\u003e\n\u003cp\u003eQiuyuan Yao, Email: [email protected].\u003c/p\u003e\n\u003cp\u003eYu Sun, Email: [email protected].\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eBray, F. et al. 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A comparison analysis of the somatic mutations in early-onset gastric cancer and traditional gastric cancer. Clin. Res. Hepatol. Gastroenterol. 48, 102287 (2024).\u003c/li\u003e\n \u003cli\u003eChoi, S. et al. CDH1 mutations in gastric cancers are not associated with family history. Pathol Res Pract 216, 152941 (2020).\u003c/li\u003e\n \u003cli\u003eSchildberg, C. W. et al. Gastric cancer patients less than 50 years of age exhibit significant downregulation of E-cadherin and CDX2 compared to older reference populations. Adv. Med. Sci. 59, 142\u0026ndash;146 (2014).\u003c/li\u003e\n \u003cli\u003eWei, P., Jin, M., Zhou, X., Hu, X. \u0026amp; Wang, Y. Programmed death-1 and PD-1 ligand-1 expression in early onset gastric carcinoma and correlation with clinicopathological characteristics. Int. J. Clin. Exp. Path. 11, 1989\u0026ndash;1998 (2018).\u003c/li\u003e\n \u003cli\u003eTan, N. et al. Global, regional, and national burden of early-onset gastric cancer. Cancer Biol. Med. 21, 667\u0026ndash;678 (2024).\u003c/li\u003e\n \u003cli\u003evan Roy, F. Beyond E-cadherin: roles of other cadherin superfamily members in cancer. Nat Rev Cancer 14, 121\u0026ndash;134 (2014).\u003c/li\u003e\n \u003cli\u003eLim, H. J., Zhuang, L. \u0026amp; Fitzgerald, R. C. Current advances in understanding the molecular profile of hereditary diffuse gastric cancer and its clinical implications. J Exp Clin Cancer Res 42, 57 (2023).\u003c/li\u003e\n \u003cli\u003eWang, Q., Liu, G. \u0026amp; Hu, C. Molecular classification of gastric adenocarcinoma. Gastroenterol. Res. 12, 275\u0026ndash;282 (2019).\u003c/li\u003e\n \u003cli\u003eR\u0026uuml;schoff, J. et al. Testing for deficient mismatch repair and microsatellite instability : a focused update. Pathol. (heidelb. Ger.) 44, 61\u0026ndash;70 (2023).\u003c/li\u003e\n \u003cli\u003eAmemiya, K. et al. Simple IHC reveals complex MMR alternations than PCR assays: validation by LCM and next-generation sequencing. Cancer Med. 11, 4479\u0026ndash;4490 (2022).\u003c/li\u003e\n \u003cli\u003eSitarz, R. et al. The COX-2 promoter polymorphism -765 G\u0026gt;C is associated with early-onset, conventional and stump gastric cancers. Mod Pathol 21, 685\u0026ndash;690 (2008).\u003c/li\u003e\n \u003cli\u003eLiu, J. D. et al. [Clinicopathological and molecular diagnostic features of early-onset gastric cancer: a study based on data from a single-center dedicated gastric cancer database]. Zhonghua Wei Chang Wai Ke Za Zhi 26, 963\u0026ndash;967 (2023).\u003cstrong\u003e\u003cbr\u003e\u003c/strong\u003e\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table","content":"\u003cp\u003eTable 1 is available in the Supplementary Files section\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Early-onset gastric cancer, TCGA classification, gene mutations, immune biomarkers, molecular features, meta-analysis","lastPublishedDoi":"10.21203/rs.3.rs-6435874/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6435874/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eEarly-onset gastric cancer (EOGC), diagnosed before the age of 50, exhibits distinct clinicopathological and molecular characteristics compared to late-onset gastric cancer (LOGC). However, the molecular landscape of EOGC remains incompletely defined. This study systematically reviews and meta-analyzes the molecular differences between EOGC and LOGC.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eA comprehensive literature search was conducted in PubMed, Embase, and Web of Science to identify studies comparing the molecular features between EOGC and LOGC. Meta-analyses were performed to assess differences in The Cancer Genome Atlas (TCGA) molecular subtypes, frequently mutated genes, key therapeutic biomarkers, and serum tumor markers. Odds ratios (ORs) with 95% confidence intervals (CIs) were calculated, and heterogeneity was evaluated via the I² statistic.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eEOGC was associated with a significantly greater prevalence of the genomically stable (GS) subtype (OR = 1.71, 95% CI: 1.37–2.12) and a lower prevalence of the chromosomal instability (CIN) subtype (OR = 0.62, 95% CI: 0.50–0.77). \u003cem\u003eCDH1\u003c/em\u003e mutations were more common in EOGC (OR = 3.56, 95% CI: 2.94–4.31), whereas HER2 expression was significantly lower (OR = 0.55, 95% CI: 0.44–0.68). EOGC patients had a lower prevalence of deficient mismatch repair (dMMR)/microsatellite instability-high (MSI-H) (OR = 0.25, 95% CI: 0.12–0.53) but higher PD-L1 expression (OR = 2.11, 95% CI: 1.26–3.54). Serum markers such as CEA (OR = 0.43, 95% CI: 0.36–0.51) and CA19-9 (OR = 0.70, 95% CI: 0.58–0.85) were also less frequently elevated in EOGC.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e EOGC constitutes a biologically distinct subset of gastric cancer, defined by unique genomic, immune, and serological profiles. These differences highlight the need for age-specific diagnostic and therapeutic strategies and emphasize the value of multiomics approaches to further elucidate the molecular basis of early-onset disease.\u003c/p\u003e","manuscriptTitle":"Molecular features of early- vs. late-onset gastric cancer: a systematic review and meta-analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-16 11:54:05","doi":"10.21203/rs.3.rs-6435874/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-07-07T05:47:25+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-05T04:26:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"12952233972513016095225587793652678629","date":"2025-06-24T05:55:50+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-13T09:14:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"215286660761408222324028180001373091599","date":"2025-06-01T17:39:37+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-17T13:31:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"22745021394959277568164604493711587204","date":"2025-05-15T03:50:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"280126204341719469212597276039873977842","date":"2025-05-14T02:02:37+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-05-13T00:20:41+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-04-30T09:35:29+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-14T03:51:01+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-14T03:50:51+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cancer","date":"2025-04-12T17:18:50+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a28bfd4f-b706-4b02-af94-c9344669ba08","owner":[],"postedDate":"May 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-01-19T17:08:51+00:00","versionOfRecord":{"articleIdentity":"rs-6435874","link":"https://doi.org/10.1186/s12885-026-15567-5","journal":{"identity":"bmc-cancer","isVorOnly":false,"title":"BMC Cancer"},"publishedOn":"2026-01-14 16:31:08","publishedOnDateReadable":"January 14th, 2026"},"versionCreatedAt":"2025-05-16 11:54:05","video":"","vorDoi":"10.1186/s12885-026-15567-5","vorDoiUrl":"https://doi.org/10.1186/s12885-026-15567-5","workflowStages":[]},"version":"v1","identity":"rs-6435874","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6435874","identity":"rs-6435874","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}