Genetic architecture of gastric adenocarcinoma in West Asia

IF 6.8 1区 医学 Q1 MEDICINE, RESEARCH & EXPERIMENTAL
Saeid Latifi-Navid, Esmat Abdi, Tianpei Wang, Farhad Pourfarzi, Abbas Yazdanbod, Seyed Alireza Salami, Reza Safaralizadeh, Omolbanin Amjadi, Hamid Latifi-Navid, Bahareh Safarnejad, Mahmoud Shokrabadi, Iradj Maleki, Vahid Hosseini, Seyed Mohammad Valizadeh, Mehdi Pourghasemian, Negin Abediasl, Arash Kazemi, Mohammad Eslami Jouybari, Zohreh Bari, Tarang Taghvaei, Caiwang Yan, Amir Taher Eftekhar Sadat, Seyed Yaghoub Moaddab, Ghasem Janbabaei, Mohammad Hossein Somi, Alireza Sadjadi, Ramin Shakeri, Farideh Siavoshi, Hafez Fakheri, Hossein Poustchi, Reza Malekzadeh, Guangfu Jin
{"title":"Genetic architecture of gastric adenocarcinoma in West Asia","authors":"Saeid Latifi-Navid,&nbsp;Esmat Abdi,&nbsp;Tianpei Wang,&nbsp;Farhad Pourfarzi,&nbsp;Abbas Yazdanbod,&nbsp;Seyed Alireza Salami,&nbsp;Reza Safaralizadeh,&nbsp;Omolbanin Amjadi,&nbsp;Hamid Latifi-Navid,&nbsp;Bahareh Safarnejad,&nbsp;Mahmoud Shokrabadi,&nbsp;Iradj Maleki,&nbsp;Vahid Hosseini,&nbsp;Seyed Mohammad Valizadeh,&nbsp;Mehdi Pourghasemian,&nbsp;Negin Abediasl,&nbsp;Arash Kazemi,&nbsp;Mohammad Eslami Jouybari,&nbsp;Zohreh Bari,&nbsp;Tarang Taghvaei,&nbsp;Caiwang Yan,&nbsp;Amir Taher Eftekhar Sadat,&nbsp;Seyed Yaghoub Moaddab,&nbsp;Ghasem Janbabaei,&nbsp;Mohammad Hossein Somi,&nbsp;Alireza Sadjadi,&nbsp;Ramin Shakeri,&nbsp;Farideh Siavoshi,&nbsp;Hafez Fakheri,&nbsp;Hossein Poustchi,&nbsp;Reza Malekzadeh,&nbsp;Guangfu Jin","doi":"10.1002/ctm2.70489","DOIUrl":null,"url":null,"abstract":"<p>Dear Editor,</p><p>Gastric cancer (GC) is a significant global health concern, with 968 000 new cases and 660 000 deaths in 2022, with male predominance.<span><sup>1</sup></span> Despite a declining trend, the absolute number of GC cases is anticipated to rise, particularly in East and West Asia.<span><sup>2</sup></span> Most genome-wide association studies (GWASs) have focused on East Asian populations,<span><sup>3, 4</sup></span> leaving a gap in understanding the genetic contributions to GC risk in West Asia, particularly in countries like Iran, where GC incidence is notably high. Most of Iran's northern and northwestern regions are located in the GC belt of West Asia. <i>Helicobacter pylori</i> infection, high salt intake, and smoking are major risk factors, along with gastroesophageal reflux disease, which contributes to higher cardia GC rates.<span><sup>5</sup></span> This study presents a GWAS analysis of 4095 Iranian samples from high-risk areas, with subsequent replication in a large Chinese dataset of 21 168 samples, aiming to delineate susceptibility loci associated with GC.</p><p>The analysis strategy is thoroughly outlined in Figure 1A (Supporting Information Methods). Of 2061 patients, 1531 (74.3%) were male; among 2034 controls, 1503 (73.9%) were male. The average age (mean ± SD) was 65.8 ± 11.0 and 67.8 ± 10.9 years for the patients and controls, respectively (Table S1). After filtering and quality control, 3686 subjects (1880 cases and 1806 controls) with 9 159 468 genetic variants were retained in the GWAS dataset (Figure 1A). A quantile‒quantile plot did not show substantial evidence of an inflation rate, with <i>λ</i> = 1.07 (Figure S1). Ethnicity and population structure were determined by the top two principal components for each study (Figure S2). Manhattan plots from the GWAS and multimarker analysis of the GenoMic Annotation (MAGMA) gene-based analyses<span><sup>6</sup></span> are shown in Figure 1B. Previous GWASs have identified a number of susceptibility loci (or common variants), including 1q22, 1p35.2, 2p11.2, 3q13.31, 5q14.3, 5p13.1, 6p22.1, 6p21.1, 8q24.3, 9q34.2, 10q23.33, 12q24.11–12, and 20q11.21.<span><sup>3, 4</sup></span> In the present study, three loci reached genome-wide significance (reported: 1q22 and 8q24.3; novel: 1p33; <i>p </i>&lt; 5 × 10<sup>−8</sup>). Compared with previous results, consistent associations were observed for single nucleotide polymorphisms (SNPs) in <i>MUC1</i> at 1q22 (lead SNP: rs760077, OR = 1.39, 95% CI = 1.27–1.53; functional SNP: rs4072037, OR = 1.34, 95% CI = 1.22–1.47) and <i>PSCA</i> at 8q24.3 (lead SNP: rs2717562, OR = 1.39, 95% CI = 1.27–1.53). At 1p33, the lead (intergenic) SNP rs498352 near <i>FOXD2</i> was first found to be associated with GC risk (OR = 1.73, 95% CI = 1.43–2.10, <i>p </i>= 2.26 × 10<sup>−8</sup>; Table 1A; Figure 2A). FOXD2 binding reconfigures chromatin structure to suppress colorectal cancer by reprogramming enhancer interactions.<span><sup>7</sup></span> In addition, four previously reported loci, 4q28.1 (<i>ANKRD50</i>), 5p13.1 (<i>PRKAA1</i>), 10q23.33 (<i>PLCE1, NOC3L</i>), and 12q24.11-12 (<i>CUX2</i>), were also replicated in the present GWAS of West Asians (<i>p </i>&lt; .05; Table S2).</p><p>The present study (discovery study) identified 1108 SNPs associated with GC risk with <i>p </i>&lt; 5×10<sup>−5</sup>. We validated these SNPs in the largest Chinese GC GWAS dataset (replication study; 10 254 GC patients and 10 914 controls).<span><sup>8</sup></span> Four novel loci were identified at 3p11.1, 3p22.1, 10q25.2, and 17q21.31 (Table 1A). There were consistent associations for SNPs rs4859012 near <i>ABCF2P1</i> at 3p11.1 (discovery: OR = 1.25, 95% CI = 1.14–1.38; replication: OR = 1.05, 95% CI = 1.00–1.10), rs11720364 near <i>POMGNT2</i> at 3p22.1 (discovery: OR = 1.60, 95% CI = 1.30–1.97; replication: OR = 1.57, 95% CI = 1.08–2.29), rs7899485 in <i>RBM20</i> at 10q25.2 (discovery: OR = 1.24, 95% CI = 1.12–1.37; replication: OR = 1.05, 95% CI = 1.01–1.10), and rs114469358 in <i>MAPT-AS1</i> at 17q21.31 (discovery: OR = 1.41, 95% CI = 1.20–1.65; replication: OR = 1.18, 95% CI = 1.01–1.37; Figure 2B–E). We also developed a polygenic risk score (PRS) based on 93 potential novel SNPs and six replicated loci. There was a significant difference between the case and control distributions (Tables S3 and S4; Figure S3).</p><p>Among the 1880 GC patients (vs. 1806 controls), 833, 906, and 141 individuals were diagnosed with cardia, noncardia, and not otherwise specified (NOS) GC, respectively. We identified three novel loci that showed different effects between cardia and noncardia GCs (Table 1B). The intronic SNPs rs17009774 in <i>RP11-287D1.3:SLC4A5</i> at 2p13.1 and rs1230471 in <i>LCP1</i> at 13q14.13 were associated only with cardia GCs; the ORs (95% CIs) were.76 (.59–.97) and 1.16 (1.01–1.34), respectively (Figure S4A,C). In contrast, the intronic SNP rs10217067 in <i>CSMD1</i> at 8p23.2 was associated with only noncardia GCs, with an OR (95% CI) of 1.18 (1.02–1.36) (Figure S4B).</p><p>SLC4A5 is biologically plausible as a cancer-relevant transporter involved in pH regulation and trafficking; however, specific functional data in GC are limited or lacking. Although LCP1 may play a complex role in GC, gaps remain in understanding its differential expression and functional impact based on tumour location—specifically when comparing cardia and noncardia gastric cancers, which differ in aetiology, histology, and molecular characteristics. Mechanistically, infection with <i>H. pylori</i> induces GC cells to express LCP1 via the CagA-activated ERK signalling pathway, which mediates the binding of SP1 to the LCP1 promoter. Furthermore, increased LCP1 expression facilitates the growth and metastasis of GC in vivo.<span><sup>9</sup></span> Another protein, CSMD1, acts as a tumour suppressor gene, while microRNA-10b drives GC cell invasion and metastasis by inhibiting CSMD1, activating the NF-κB pathway, and upregulating c-Myc, cyclin D1, and epithelial–mesenchymal transition (EMT) markers. Nevertheless, the expression levels and functional implications of CSMD1 concerning the tumour's location remain poorly understood.<span><sup>10</sup></span></p><p>Among the 1880 GC patients, 1091, 483, and 306 individuals were diagnosed with intestinal, diffuse, and mixed-type GC, respectively. Among previously reported loci, 1q22 and 5p13.1 were identified to play different roles in intestinal-type and diffuse-type GC (DGC; Table 1B). At 1q22, the exonic SNP rs4072037 in the <i>MUC1</i> gene showed a significant association with only DGC, with an OR (95% CI) of 1.19 (1.00–1.40). At this locus, the <i>p</i>-values for the exonic SNPs rs760077 and rs2990223 were &gt;.05. However, the effect of these SNPs on intestinal- and diffuse-type GC differed (<i>p</i> for heterogeneity &lt;.05). At 5p13.1, the intronic SNP rs10036575 in <i>PTGER4</i> (EP-4) was associated with only DGC, with an OR (95% CI) of.80 (.66–.97; Figure S4D–E<b>)</b>.</p><p>MUC1-C (the C-terminal subunit) plays a crucial role in the loss of cell polarity, the induction of EMT, the activation of stem cell characteristics, and epigenetic reprogramming. It promotes cell growth by activating multiple signalling pathways, including RTKs, PI3K/AKT, and WNT/β-catenin. Additionally, MUC1-C regulates inflammation, protects against cell death, and facilitates immune evasion. Patients with positive MUC1 expression exhibit higher rates of aggressive pathological features, such as DGC, lymph node metastasis, and distant metastasis.<span><sup>11, 12</sup></span> DGC is characterised by a scattered and infiltrative growth pattern, often associated with defects in CDH1/E-cadherin. The EP4 signalling pathway promotes cellular migration and increases the population of stem-like CD133⁺/CD44⁺ cells, which are associated with EMT and peritoneal dissemination—both key characteristics of DGC. EP4 activity also supports inflammatory and fibrotic cascades, including NLRP3 and phosphorylated p65 (p-p65), which can prime the peritoneal environment. Notably, EP4 blockade reduces peritoneal fibrosis in preclinical models, a finding that is particularly relevant given the frequent intraperitoneal spread of DGC.<span><sup>13, 14</sup></span></p><p>In the present GWAS, we identified 93 potential novel GC risk loci (<i>p </i>&lt; 5 × 10<sup>−5</sup>). However, only four of these loci were replicated in the Chinese dataset (<i>p </i>&lt; .05). This could be explained by the genetic diversity between West and East Asian populations. It was found that Iranians had a population structure between Europeans and East Asians, but were more closely related to Europeans. Even among the replicated ones, the risk alleles, allele frequencies, and effect sizes differed between populations. These findings underscore the importance of conducting GWASs in diverse populations to identify missed genetic variants that contribute to GC risk across different ancestry groups.</p><p>Functional analysis identified 89 genomic risk loci. Most of the 2732 annotated SNPs (93.1%) were in the intergenic, intronic, and ncRNA-intronic regions. Forty-seven genes were implicated by at least two mapping strategies and 14 by all three strategies; 17 had pLI scores of ≥.9, indicating that they were highly sensitive to mutations that lead to truncated or nonfunctional proteins. In the MAGMA gene analysis, 13 genes (all mapped by FUMA<span><sup>15</sup></span>) had aggregate associations based on all SNPs in a gene on chromosomes 1 and 8, with a stringent <i>p</i>-value threshold of less than 2.58 × 10<sup>−6</sup> (Figure 1B; Tables S5–S7; Figure S5A–M). Notably, <i>THBS3</i> and <i>TRIM46</i> exhibited high sensitivity to loss-of-function mutations, indicated by their pLI scores of 1.2 and.99, respectively. MAGMA gene set analysis identified the top 10 gene sets (<i>p </i>≤ .0007), but the associations did not reach significance after Bonferroni correction (<i>p</i><sub>bon</sub> &lt; .05). GSEA identified 627 GO biological processes and 76 pathways from Biocarta, KEGG, and Reactome (FDR ≤ .05). However, 14 hallmark gene sets summarised well-defined biological states or processes coherently (Tables S8–S11). Furthermore, the 1q22, 4q28.1, and 8q24.3 risk loci showed evidence of colocalization<span><sup>16</sup></span> in stomach tissue (GTEx v8; PP4 = .84,.96, and.85, respectively), reflecting the fact that these loci contain shared GC causal variants in West Asia. Although novel loci, especially 11q14.1 and 13q14.11, did show some potential colocalization signals, PP4 did not reach the significance threshold (&lt;.7; Table S12; Figure 3A–E). The study's strengths include a large sample size and subgroup analyses, although it acknowledged limitations such as the exclusion of lifestyle, dietary habits, and <i>H. pylori</i> infection. However, the latter may not be relevant because the prevalence of <i>H. pylori</i> infection is significantly high in northern/northwestern Iran, especially in Ardabil (89%), where more than 90% of adults aged 40 or older have <i>H. pylori</i>-related chronic gastritis and GC is the most common malignancy (31%), with an ASR of 49.1/10<sup>5</sup> for males and 25.4/10<sup>5</sup> for females.<span><sup>5</sup></span></p><p>In the present study, the developed PRS showed a significant difference between case and control distributions, which marks a pivotal advancement in personalised medicine. It quantifies an individual's genetic predisposition to diseases, which can enable risk stratification and potentially inform preventive care and treatment decisions. The implementation of these scores can help identify high-risk individuals, allowing for tailored interventions that may improve overall patient outcomes. However, applying this PRS to large-scale, long-term Persian cohorts—especially those from high-risk areas—will be essential to validate its predictive power and facilitate its eventual integration into clinical workflows.</p><p>In conclusion, we identified the GC risk-related loci in West Asia, including those related to tumour site and pathology, which may contribute to future clinical risk assessments and genetic screening in West Asia.</p><p>Saeid Latifi-Navid, Abbas Yazdanbod, Farhad Pourfarzi, Guangfu Jin, and Reza Malekzadeh conceptualised the study. Saeid Latifi-Navid, Tianpei Wang, and Caiwang Yan performed the statistical analyses. Abbas Yazdanbod, Farhad Pourfarzi, Esmat Abdi, Reza Safaralizadeh, Omolbanin Amjadi, Hamid Latifi-Navid, Bahareh Safarnejad, Mahmoud Shokrabadi, Iradj Maleki, Vahid Hosseini, Seyed Mohammad Valizadeh, Mehdi Pourghasemian, Negin Abediasl, Arash Kazemi, Mohammad Eslami Jouybari, Zohreh Bari, Tarang Taghvaei, Amir Taher Eftekhar Sadat, Seyed Yaghoub Moaddab, Ghasem Janbabaei, Mohammad Hossein Somi, Alireza Sadjadi, Ramin Shakeri, Farideh Siavoshi, Hafez Fakheri, Hossein Poustchi, and Reza Malekzadeh contributed to evaluate and diagnose patients, sample and data collection or data interpretation. Saeid Latifi-Navid, Esmat Abdi, and Seyed Alireza Salami conducted experiments. Saeid Latifi-Navid, Tianpei Wang, and Guangfu Jin reviewed data and provided critical comments or suggestions. Saeid Latifi-Navid wrote the manuscript. Guangfu Jin and Tianpei Wang revised the manuscript. Saeid Latifi-Navid and Guangfu Jin supervised the study. All authors reviewed or revised the manuscript and approved the final draft for submission.</p><p>The authors declare no conflict of interest.</p><p>This study was funded by the National Institute for Medical Research Development (NIMAD) (grants 958117 and 962249), Tehran, Iran, and the National Natural Science Foundation of China (82125033 and 82230110).</p><p>The research was performed on the basis of ethical principles of human research declared by the 1975 Declaration of Helsinki. All patients and/or their legal guardians signed written informed consent. The research was approved by the Ethics Committee of the National Institute for Medical Research Development (NIMAD)/IR.NIMAD.REC.1396.097.</p><p>Not applicable</p>","PeriodicalId":10189,"journal":{"name":"Clinical and Translational Medicine","volume":"15 10","pages":""},"PeriodicalIF":6.8000,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ctm2.70489","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Clinical and Translational Medicine","FirstCategoryId":"3","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/ctm2.70489","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MEDICINE, RESEARCH & EXPERIMENTAL","Score":null,"Total":0}
引用次数: 0

Abstract

Dear Editor,

Gastric cancer (GC) is a significant global health concern, with 968 000 new cases and 660 000 deaths in 2022, with male predominance.1 Despite a declining trend, the absolute number of GC cases is anticipated to rise, particularly in East and West Asia.2 Most genome-wide association studies (GWASs) have focused on East Asian populations,3, 4 leaving a gap in understanding the genetic contributions to GC risk in West Asia, particularly in countries like Iran, where GC incidence is notably high. Most of Iran's northern and northwestern regions are located in the GC belt of West Asia. Helicobacter pylori infection, high salt intake, and smoking are major risk factors, along with gastroesophageal reflux disease, which contributes to higher cardia GC rates.5 This study presents a GWAS analysis of 4095 Iranian samples from high-risk areas, with subsequent replication in a large Chinese dataset of 21 168 samples, aiming to delineate susceptibility loci associated with GC.

The analysis strategy is thoroughly outlined in Figure 1A (Supporting Information Methods). Of 2061 patients, 1531 (74.3%) were male; among 2034 controls, 1503 (73.9%) were male. The average age (mean ± SD) was 65.8 ± 11.0 and 67.8 ± 10.9 years for the patients and controls, respectively (Table S1). After filtering and quality control, 3686 subjects (1880 cases and 1806 controls) with 9 159 468 genetic variants were retained in the GWAS dataset (Figure 1A). A quantile‒quantile plot did not show substantial evidence of an inflation rate, with λ = 1.07 (Figure S1). Ethnicity and population structure were determined by the top two principal components for each study (Figure S2). Manhattan plots from the GWAS and multimarker analysis of the GenoMic Annotation (MAGMA) gene-based analyses6 are shown in Figure 1B. Previous GWASs have identified a number of susceptibility loci (or common variants), including 1q22, 1p35.2, 2p11.2, 3q13.31, 5q14.3, 5p13.1, 6p22.1, 6p21.1, 8q24.3, 9q34.2, 10q23.33, 12q24.11–12, and 20q11.21.3, 4 In the present study, three loci reached genome-wide significance (reported: 1q22 and 8q24.3; novel: 1p33; p < 5 × 10−8). Compared with previous results, consistent associations were observed for single nucleotide polymorphisms (SNPs) in MUC1 at 1q22 (lead SNP: rs760077, OR = 1.39, 95% CI = 1.27–1.53; functional SNP: rs4072037, OR = 1.34, 95% CI = 1.22–1.47) and PSCA at 8q24.3 (lead SNP: rs2717562, OR = 1.39, 95% CI = 1.27–1.53). At 1p33, the lead (intergenic) SNP rs498352 near FOXD2 was first found to be associated with GC risk (OR = 1.73, 95% CI = 1.43–2.10, p = 2.26 × 10−8; Table 1A; Figure 2A). FOXD2 binding reconfigures chromatin structure to suppress colorectal cancer by reprogramming enhancer interactions.7 In addition, four previously reported loci, 4q28.1 (ANKRD50), 5p13.1 (PRKAA1), 10q23.33 (PLCE1, NOC3L), and 12q24.11-12 (CUX2), were also replicated in the present GWAS of West Asians (p < .05; Table S2).

The present study (discovery study) identified 1108 SNPs associated with GC risk with p < 5×10−5. We validated these SNPs in the largest Chinese GC GWAS dataset (replication study; 10 254 GC patients and 10 914 controls).8 Four novel loci were identified at 3p11.1, 3p22.1, 10q25.2, and 17q21.31 (Table 1A). There were consistent associations for SNPs rs4859012 near ABCF2P1 at 3p11.1 (discovery: OR = 1.25, 95% CI = 1.14–1.38; replication: OR = 1.05, 95% CI = 1.00–1.10), rs11720364 near POMGNT2 at 3p22.1 (discovery: OR = 1.60, 95% CI = 1.30–1.97; replication: OR = 1.57, 95% CI = 1.08–2.29), rs7899485 in RBM20 at 10q25.2 (discovery: OR = 1.24, 95% CI = 1.12–1.37; replication: OR = 1.05, 95% CI = 1.01–1.10), and rs114469358 in MAPT-AS1 at 17q21.31 (discovery: OR = 1.41, 95% CI = 1.20–1.65; replication: OR = 1.18, 95% CI = 1.01–1.37; Figure 2B–E). We also developed a polygenic risk score (PRS) based on 93 potential novel SNPs and six replicated loci. There was a significant difference between the case and control distributions (Tables S3 and S4; Figure S3).

Among the 1880 GC patients (vs. 1806 controls), 833, 906, and 141 individuals were diagnosed with cardia, noncardia, and not otherwise specified (NOS) GC, respectively. We identified three novel loci that showed different effects between cardia and noncardia GCs (Table 1B). The intronic SNPs rs17009774 in RP11-287D1.3:SLC4A5 at 2p13.1 and rs1230471 in LCP1 at 13q14.13 were associated only with cardia GCs; the ORs (95% CIs) were.76 (.59–.97) and 1.16 (1.01–1.34), respectively (Figure S4A,C). In contrast, the intronic SNP rs10217067 in CSMD1 at 8p23.2 was associated with only noncardia GCs, with an OR (95% CI) of 1.18 (1.02–1.36) (Figure S4B).

SLC4A5 is biologically plausible as a cancer-relevant transporter involved in pH regulation and trafficking; however, specific functional data in GC are limited or lacking. Although LCP1 may play a complex role in GC, gaps remain in understanding its differential expression and functional impact based on tumour location—specifically when comparing cardia and noncardia gastric cancers, which differ in aetiology, histology, and molecular characteristics. Mechanistically, infection with H. pylori induces GC cells to express LCP1 via the CagA-activated ERK signalling pathway, which mediates the binding of SP1 to the LCP1 promoter. Furthermore, increased LCP1 expression facilitates the growth and metastasis of GC in vivo.9 Another protein, CSMD1, acts as a tumour suppressor gene, while microRNA-10b drives GC cell invasion and metastasis by inhibiting CSMD1, activating the NF-κB pathway, and upregulating c-Myc, cyclin D1, and epithelial–mesenchymal transition (EMT) markers. Nevertheless, the expression levels and functional implications of CSMD1 concerning the tumour's location remain poorly understood.10

Among the 1880 GC patients, 1091, 483, and 306 individuals were diagnosed with intestinal, diffuse, and mixed-type GC, respectively. Among previously reported loci, 1q22 and 5p13.1 were identified to play different roles in intestinal-type and diffuse-type GC (DGC; Table 1B). At 1q22, the exonic SNP rs4072037 in the MUC1 gene showed a significant association with only DGC, with an OR (95% CI) of 1.19 (1.00–1.40). At this locus, the p-values for the exonic SNPs rs760077 and rs2990223 were >.05. However, the effect of these SNPs on intestinal- and diffuse-type GC differed (p for heterogeneity <.05). At 5p13.1, the intronic SNP rs10036575 in PTGER4 (EP-4) was associated with only DGC, with an OR (95% CI) of.80 (.66–.97; Figure S4D–E).

MUC1-C (the C-terminal subunit) plays a crucial role in the loss of cell polarity, the induction of EMT, the activation of stem cell characteristics, and epigenetic reprogramming. It promotes cell growth by activating multiple signalling pathways, including RTKs, PI3K/AKT, and WNT/β-catenin. Additionally, MUC1-C regulates inflammation, protects against cell death, and facilitates immune evasion. Patients with positive MUC1 expression exhibit higher rates of aggressive pathological features, such as DGC, lymph node metastasis, and distant metastasis.11, 12 DGC is characterised by a scattered and infiltrative growth pattern, often associated with defects in CDH1/E-cadherin. The EP4 signalling pathway promotes cellular migration and increases the population of stem-like CD133⁺/CD44⁺ cells, which are associated with EMT and peritoneal dissemination—both key characteristics of DGC. EP4 activity also supports inflammatory and fibrotic cascades, including NLRP3 and phosphorylated p65 (p-p65), which can prime the peritoneal environment. Notably, EP4 blockade reduces peritoneal fibrosis in preclinical models, a finding that is particularly relevant given the frequent intraperitoneal spread of DGC.13, 14

In the present GWAS, we identified 93 potential novel GC risk loci (p < 5 × 10−5). However, only four of these loci were replicated in the Chinese dataset (p < .05). This could be explained by the genetic diversity between West and East Asian populations. It was found that Iranians had a population structure between Europeans and East Asians, but were more closely related to Europeans. Even among the replicated ones, the risk alleles, allele frequencies, and effect sizes differed between populations. These findings underscore the importance of conducting GWASs in diverse populations to identify missed genetic variants that contribute to GC risk across different ancestry groups.

Functional analysis identified 89 genomic risk loci. Most of the 2732 annotated SNPs (93.1%) were in the intergenic, intronic, and ncRNA-intronic regions. Forty-seven genes were implicated by at least two mapping strategies and 14 by all three strategies; 17 had pLI scores of ≥.9, indicating that they were highly sensitive to mutations that lead to truncated or nonfunctional proteins. In the MAGMA gene analysis, 13 genes (all mapped by FUMA15) had aggregate associations based on all SNPs in a gene on chromosomes 1 and 8, with a stringent p-value threshold of less than 2.58 × 10−6 (Figure 1B; Tables S5–S7; Figure S5A–M). Notably, THBS3 and TRIM46 exhibited high sensitivity to loss-of-function mutations, indicated by their pLI scores of 1.2 and.99, respectively. MAGMA gene set analysis identified the top 10 gene sets (p ≤ .0007), but the associations did not reach significance after Bonferroni correction (pbon < .05). GSEA identified 627 GO biological processes and 76 pathways from Biocarta, KEGG, and Reactome (FDR ≤ .05). However, 14 hallmark gene sets summarised well-defined biological states or processes coherently (Tables S8–S11). Furthermore, the 1q22, 4q28.1, and 8q24.3 risk loci showed evidence of colocalization16 in stomach tissue (GTEx v8; PP4 = .84,.96, and.85, respectively), reflecting the fact that these loci contain shared GC causal variants in West Asia. Although novel loci, especially 11q14.1 and 13q14.11, did show some potential colocalization signals, PP4 did not reach the significance threshold (<.7; Table S12; Figure 3A–E). The study's strengths include a large sample size and subgroup analyses, although it acknowledged limitations such as the exclusion of lifestyle, dietary habits, and H. pylori infection. However, the latter may not be relevant because the prevalence of H. pylori infection is significantly high in northern/northwestern Iran, especially in Ardabil (89%), where more than 90% of adults aged 40 or older have H. pylori-related chronic gastritis and GC is the most common malignancy (31%), with an ASR of 49.1/105 for males and 25.4/105 for females.5

In the present study, the developed PRS showed a significant difference between case and control distributions, which marks a pivotal advancement in personalised medicine. It quantifies an individual's genetic predisposition to diseases, which can enable risk stratification and potentially inform preventive care and treatment decisions. The implementation of these scores can help identify high-risk individuals, allowing for tailored interventions that may improve overall patient outcomes. However, applying this PRS to large-scale, long-term Persian cohorts—especially those from high-risk areas—will be essential to validate its predictive power and facilitate its eventual integration into clinical workflows.

In conclusion, we identified the GC risk-related loci in West Asia, including those related to tumour site and pathology, which may contribute to future clinical risk assessments and genetic screening in West Asia.

Saeid Latifi-Navid, Abbas Yazdanbod, Farhad Pourfarzi, Guangfu Jin, and Reza Malekzadeh conceptualised the study. Saeid Latifi-Navid, Tianpei Wang, and Caiwang Yan performed the statistical analyses. Abbas Yazdanbod, Farhad Pourfarzi, Esmat Abdi, Reza Safaralizadeh, Omolbanin Amjadi, Hamid Latifi-Navid, Bahareh Safarnejad, Mahmoud Shokrabadi, Iradj Maleki, Vahid Hosseini, Seyed Mohammad Valizadeh, Mehdi Pourghasemian, Negin Abediasl, Arash Kazemi, Mohammad Eslami Jouybari, Zohreh Bari, Tarang Taghvaei, Amir Taher Eftekhar Sadat, Seyed Yaghoub Moaddab, Ghasem Janbabaei, Mohammad Hossein Somi, Alireza Sadjadi, Ramin Shakeri, Farideh Siavoshi, Hafez Fakheri, Hossein Poustchi, and Reza Malekzadeh contributed to evaluate and diagnose patients, sample and data collection or data interpretation. Saeid Latifi-Navid, Esmat Abdi, and Seyed Alireza Salami conducted experiments. Saeid Latifi-Navid, Tianpei Wang, and Guangfu Jin reviewed data and provided critical comments or suggestions. Saeid Latifi-Navid wrote the manuscript. Guangfu Jin and Tianpei Wang revised the manuscript. Saeid Latifi-Navid and Guangfu Jin supervised the study. All authors reviewed or revised the manuscript and approved the final draft for submission.

The authors declare no conflict of interest.

This study was funded by the National Institute for Medical Research Development (NIMAD) (grants 958117 and 962249), Tehran, Iran, and the National Natural Science Foundation of China (82125033 and 82230110).

The research was performed on the basis of ethical principles of human research declared by the 1975 Declaration of Helsinki. All patients and/or their legal guardians signed written informed consent. The research was approved by the Ethics Committee of the National Institute for Medical Research Development (NIMAD)/IR.NIMAD.REC.1396.097.

Not applicable

Abstract Image

西亚地区胃腺癌的遗传结构
胃癌(GC)是一个重大的全球健康问题,2022年有96.8万例新病例和66万例死亡,其中男性占主导地位尽管有下降的趋势,但预计胃癌病例的绝对数量将上升,特别是在东亚和西亚。2大多数全基因组关联研究(GWASs)都集中在东亚人群,3,4在了解遗传因素对西亚胃癌风险的影响方面存在空白,特别是在胃癌发病率特别高的伊朗等国家。伊朗北部和西北部大部分地区位于西亚GC带。幽门螺杆菌感染、高盐摄入和吸烟是主要的危险因素,以及胃食管反流病,这些疾病导致了较高的贲门GC率本研究对来自高风险地区的4095份伊朗样本进行了GWAS分析,并随后在中国的大型数据集中复制了21 168份样本,旨在描绘与GC相关的易感性位点。分析策略在图1A(支持信息方法)中进行了全面概述。2061例患者中,男性1531例(74.3%);在2034例对照中,男性1503例(73.9%)。患者和对照组的平均年龄(mean±SD)分别为65.8±11.0岁和67.8±10.9岁(表S1)。经过筛选和质量控制,GWAS数据集中保留了3686名受试者(1880例和1806例对照),共有9 159 468个遗传变异(图1A)。分位数-分位数图没有显示通货膨胀率的实质性证据,λ = 1.07(图S1)。种族和人口结构由每个研究的前两个主成分决定(图S2)。来自GWAS的曼哈顿图和基因组注释(MAGMA)基因分析的多标记分析如图1B所示。先前的GWASs已经鉴定出了许多易感位点(或常见变异),包括1q22、1p35.2、2p11.2、3q13.31、5q14.3、5p13.1、6p22.1、6p21.1、8q24.3、9q34.2、10q23.33、12q24.11-12和20q11.21.3, 4在本研究中,三个位点达到了全基因组意义(报道:1q22和8q24.3;新发现:1p33; p &lt; 5 × 10−8)。与先前的结果相比,MUC1在1q22位点的单核苷酸多态性(lead SNP: rs760077, OR = 1.39, 95% CI = 1.27-1.53;功能性SNP: rs4072037, OR = 1.34, 95% CI = 1.22-1.47)和PSCA在8q24位点(lead SNP: rs2717562, OR = 1.39, 95% CI = 1.27-1.53)之间存在一致的关联。在1p33处,首次发现FOXD2附近的铅(基因间)SNP rs498352与GC风险相关(OR = 1.73, 95% CI = 1.43-2.10, p = 2.26 × 10−8;表1A;图2A)。FOXD2结合重新配置染色质结构,通过重编程增强子相互作用来抑制结直肠癌此外,四个先前报道的位点,4q28.1 (ANKRD50), 5p13.1 (PRKAA1), 10q23.33 (PLCE1, NOC3L)和12q24.11-12 (CUX2),也在西亚人的GWAS中被复制(p &lt; 0.05;表S2)。本研究(发现研究)确定了1108个与GC风险相关的snp, p &lt; 5×10−5。我们在最大的中国GC - GWAS数据集(复制研究,10254例GC患者和10914例对照)中验证了这些snp在3p11.1、3p22.1、10q25.2和17q21.31位点鉴定出4个新的位点(表1A)。附近有一致的关联snp rs4859012 ABCF2P1 p11.1三点(发现:或= 1.25,95% CI -1.38 = 1.14;复制:= 1.05,95% CI = 1.00 - -1.10), rs11720364 POMGNT2附近3 p22 . 1(发现:或= 1.60,95% CI -1.97 = 1.30;复制:= 1.57,95% CI = 1.08 - -2.29),在RBM20 rs7899485 10 q25.2(发现:或= 1.24,95% CI -1.37 = 1.12;复制:= 1.05,95% CI = 1.01 - -1.10), 17岁和rs114469358 MAPT-AS1 q21.31(发现:或= 1.41,95% CI = 1.20 - -1.65;复制:OR = 1.18, 95% CI = 1.01-1.37;图2中)。我们还基于93个潜在的新snp和6个复制位点开发了多基因风险评分(PRS)。病例和对照分布之间存在显著差异(表S3和S4;图S3)。在1880例胃癌患者(对照1806例)中,分别有833例、906例和141例被诊断为贲门、非贲门和非其他特异性(NOS)胃癌。我们确定了三个新的基因座,它们在贲门和非贲门GCs中表现出不同的作用(表1B)。RP11-287D1.3中的内含子snp rs17009774、2p13.1中的SLC4A5和13q14.13中的rs1230471仅与心脏gc相关;or (95% ci)为0.76(0.59 - 0.97)和1.16(1.01-1.34)(图S4A,C)。相比之下,CSMD1中8p23.2位点的内含子SNP rs10217067仅与非贲门癌相关,OR (95% CI)为1.18(1.02-1.36)(图S4B)。SLC4A5在生物学上可能是一种参与pH调节和运输的癌症相关转运体;然而,GC中特定的功能数据是有限的或缺乏的。 尽管LCP1可能在胃癌中发挥复杂的作用,但在比较病因、组织学和分子特征不同的贲门癌和非贲门癌时,在了解其基于肿瘤位置的差异表达和功能影响方面仍然存在差距。从机制上讲,幽门螺杆菌感染诱导GC细胞通过caga激活的ERK信号通路表达LCP1,介导SP1与LCP1启动子的结合。此外,LCP1表达的增加促进了GC在体内的生长和转移另一种蛋白CSMD1作为肿瘤抑制基因,而microRNA-10b通过抑制CSMD1、激活NF-κB通路、上调c-Myc、cyclin D1和上皮-间质转化(epithelial-mesenchymal transition, EMT)标志物来驱动GC细胞的侵袭和转移。然而,CSMD1的表达水平和与肿瘤位置有关的功能意义仍然知之甚少。在1880例胃癌患者中,诊断为肠型胃癌1091例,弥漫性胃癌483例,混合型胃癌306例。在先前报道的基因座中,鉴定出1q22和5p13.1在肠型和弥漫性GC中发挥不同的作用(DGC;表1B)。在1q22处,MUC1基因的外显子SNP rs4072037仅与DGC显著相关,OR (95% CI)为1.19(1.00-1.40)。该位点外显子snp rs760077和rs2990223的p值为&gt; 0.05。然而,这些snp对肠型和弥漫性GC的影响不同(p表示异质性&lt; 0.05)。在5p13.1处,PTGER4 (EP-4)中的内含子SNP rs10036575仅与DGC相关,OR (95% CI)为0.80(0.66 - 0.97;图S4D-E)。MUC1-C (c端亚基)在细胞极性丧失、EMT诱导、干细胞特性激活和表观遗传重编程中起着至关重要的作用。它通过激活多种信号通路促进细胞生长,包括RTKs、PI3K/AKT和WNT/β-catenin。此外,MUC1-C调节炎症,防止细胞死亡,并促进免疫逃避。MUC1阳性表达的患者表现出更高的侵袭性病理特征,如DGC、淋巴结转移和远处转移。11,12 DGC的特征是分散和浸润性生长模式,通常与CDH1/ e -钙粘蛋白缺陷有关。EP4信号通路促进细胞迁移,增加了干细胞样CD133 + /CD44 +细胞的数量,这两种细胞与EMT和腹膜传播有关,这两种细胞都是DGC的关键特征。EP4活性也支持炎症和纤维化级联反应,包括NLRP3和磷酸化p65 (p-p65),它们可以激活腹膜环境。值得注意的是,在临床前模型中,EP4阻断可减少腹膜纤维化,这一发现与dgc在腹腔内的频繁扩散特别相关。13,14在目前的GWAS中,我们确定了93个潜在的新的GC风险位点(p &lt; 5 × 10−5)。然而,这些基因座中只有4个在中国数据集中被复制(p &lt; 0.05)。这可以用东亚和东亚人群的遗传多样性来解释。结果发现,伊朗人的人口结构介于欧洲人和东亚人之间,但与欧洲人的关系更密切。即使在被复制的基因中,风险等位基因、等位基因频率和效应大小在人群之间也存在差异。这些发现强调了在不同人群中进行GWASs的重要性,以确定在不同祖先群体中导致GC风险的缺失遗传变异。功能分析鉴定出89个基因组风险位点。在2732个被注释的snp中,大多数(93.1%)位于基因间区、内含子区和ncrna -内含子区。47个基因被至少两种定位策略所牵连,14个基因被所有三种策略所牵连;17例pLI评分≥0.9,表明它们对导致蛋白截断或无功能的突变高度敏感。在MAGMA基因分析中,13个基因(全部由FUMA15定位)基于1号和8号染色体上一个基因的所有snp具有聚合关联,严格的p值阈值小于2.58 × 10−6(图1B;表S5-S7;图S5A-M)。值得注意的是,THBS3和TRIM46对功能丧失突变表现出高度敏感性,这表明它们的pLI评分为1.2和。99年,分别。MAGMA基因集分析鉴定出前10个基因集(p≤0.0007),但经Bonferroni校正后相关性不显著(pbon &lt; 0.05)。GSEA鉴定了来自Biocarta、KEGG和Reactome的627种氧化石墨烯生物过程和76种途径(FDR≤0.05)。然而,14个标志基因集一致地总结了定义明确的生物状态或过程(表S8-S11)。此外,1q22、4q28.1和8q24.3风险位点在胃组织中显示了共定位的证据(GTEx v8; PP4 = .84)。96年,。85),反映了这些基因座包含西亚共有的GC因果变异的事实。 虽然新基因座,尤其是11q14.1和13q14.11确实显示了一些潜在的共定位信号,但PP4没有达到显著性阈值(&lt;.7;表S12;图3A-E)。该研究的优势在于样本量大和亚组分析,尽管它承认排除生活方式、饮食习惯和幽门螺杆菌感染等局限性。然而,后者可能不相关,因为幽门螺杆菌感染的患病率在伊朗北部/西北部非常高,特别是在阿达比尔(89%),那里超过90%的40岁或以上的成年人患有幽门螺杆菌相关的慢性胃炎,GC是最常见的恶性肿瘤(31%),男性的ASR为49.1/105,女性为25.4/105。在本研究中,开发的PRS在病例和对照分布之间显示出显著差异,这标志着个性化医疗的关键进步。它量化了个人对疾病的遗传易感性,可以实现风险分层,并可能为预防保健和治疗决策提供信息。这些评分的实施可以帮助识别高风险个体,允许量身定制的干预措施,可能会改善患者的整体结果。然而,将这种PRS应用于大规模、长期的波斯队列——特别是那些来自高风险地区的队列——对于验证其预测能力并促进其最终整合到临床工作流程至关重要。总之,我们确定了西亚地区胃癌风险相关基因座,包括与肿瘤部位和病理相关的基因座,这可能有助于西亚地区未来的临床风险评估和遗传筛查。Saeid Latifi-Navid, Abbas Yazdanbod, Farhad Pourfarzi, Guangfu Jin和Reza Malekzadeh概念化了这项研究。Saeid Latifi-Navid, Tianpei Wang, Caiwang Yan进行了统计分析。Abbas Yazdanbod、Farhad Pourfarzi、Esmat Abdi、Reza Safaralizadeh、Omolbanin Amjadi、Hamid Latifi-Navid、Bahareh Safarnejad、Mahmoud Shokrabadi、Iradj Maleki、Vahid Hosseini、Seyed Mohammad Valizadeh、Mehdi Pourghasemian、Negin Abediasl、Arash Kazemi、Mohammad Eslami Jouybari、Zohreh Bari、Tarang Taghvaei、Amir Taher Eftekhar Sadat、Seyed Yaghoub Moaddab、Ghasem Janbabaei、Mohammad Hossein Somi、Alireza Sadjadi、Ramin Shakeri、Farideh Siavoshi、Hafez Fakheri、Hossein Poustchi、Reza Malekzadeh为评估和诊断患者、样本和数据收集或数据解释做出了贡献。Saeid Latifi-Navid, Esmat Abdi和Seyed Alireza Salami进行了实验。Saeid Latifi-Navid、Tianpei Wang和Guangfu Jin审阅了数据并提出了重要的意见或建议。Saeid Latifi-Navid写了手稿。金光甫、王天培对原稿进行了修改。Saeid Latifi-Navid和Guangfu Jin监督了这项研究。所有作者审阅或修改稿件,并批准最终稿提交。作者声明无利益冲突。本研究由伊朗德黑兰国家医学研究发展研究所(NIMAD)(拨款958117和962249)和中国国家自然科学基金(82125033和82230110)资助。这项研究是根据1975年赫尔辛基宣言宣布的人类研究伦理原则进行的。所有患者和/或其法定监护人签署书面知情同意书。该研究已获得美国国家医学研究发展研究所(NIMAD)伦理委员会/IR.NIMAD.REC.1396.097的批准。不适用
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来源期刊
CiteScore
15.90
自引率
1.90%
发文量
450
审稿时长
4 weeks
期刊介绍: Clinical and Translational Medicine (CTM) is an international, peer-reviewed, open-access journal dedicated to accelerating the translation of preclinical research into clinical applications and fostering communication between basic and clinical scientists. It highlights the clinical potential and application of various fields including biotechnologies, biomaterials, bioengineering, biomarkers, molecular medicine, omics science, bioinformatics, immunology, molecular imaging, drug discovery, regulation, and health policy. With a focus on the bench-to-bedside approach, CTM prioritizes studies and clinical observations that generate hypotheses relevant to patients and diseases, guiding investigations in cellular and molecular medicine. The journal encourages submissions from clinicians, researchers, policymakers, and industry professionals.
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