HLA-G High-expressor 3’UTR Markers Are Linked to Gastric Cancer Development and Survival

HLA-G High-expressor 3’UTR Markers Are Linked to Gastric Cancer Development and Survival | 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 Short Report HLA-G High-expressor 3’UTR Markers Are Linked to Gastric Cancer Development and Survival Christian Vaquero-Yuste, Ignacio Juarez, Marta Molina-Alejandre, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4437900/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 16 Nov, 2024 Read the published version in Cancer Immunology, Immunotherapy → Version 1 posted 7 You are reading this latest preprint version Abstract Gastric cancer ranks fifth in both world prevalence and lethality, with a 5-year survival of less than 30%. HLA-G, a non-classical class I HLA gene, has emerged as a potential marker for cancer susceptibility and prognosis due to its immunomodulatory properties. Its level of expression is regulated by polymorphisms in the 3’ untranslated region (3’UTR) polymorphisms, which form various combined haplotypes (UTR-1 to -9). In this study, we examined HLA-G 3’UTR polymorphisms in paired tissue samples from 111 patients with gastric adenocarcinoma and 119 healthy controls. Polymorphism analysis was performed using PCR and Sanger sequencing, followed by statistical analysis using SNPStats software. Survival analysis was conducted using Kaplan-Meier curves and multivariate Cox regression models. High-expressor HLA-G 3’UTR haplotypes (UTR-1 and UTR-6) were significantly associated with gastric cancer susceptibility, indicating a potential role in tumor immune evasion. Additionally, the 14 base pair insertion/deletion polymorphism (14bp I/D) emerged as a prognostic marker, with D/D genotype carriers showing lower survival rates compared to I/D and I/I genotype carriers. Our study highlights the clinical relevance of HLA-G polymorphisms in gastric cancer, suggesting their potential as prognostic markers and therapeutic targets. Further elucidation of HLA-G-related pathways could lead to personalized treatment strategies and improved patient outcomes in gastric cancer. Figures Figure 1 Figure 2 INTRODUCTION Gastric cancer ranks as the fifth most prevalent cancer worldwide, exhibiting an annual incidence of 11.1 new cases per 100,000 individuals. Furthermore, it stands as the 5th most lethal cancer, with 7.7 deaths per 100,000 individuals annually, underscoring its aggressive nature. The 5-year survival rate for this cancer subtype remains at 29.5%, persisting over the past three decades ( 1 , 2 ). HLA-G, a non-classical class I HLA gene, encodes molecules with tolerogenic properties. The HLA-G gene comprises 7 introns and 8 exons, and exon 8 constitutes an untranslated region (3'UTR) with regulatory properties ( 3 ). Specific polymorphic sites within the 3'UTR region, such as the 14 base pair (14bp) INS/DEL (I/D), + 3142C/G, and + 3187A/G polymorphisms, influence mRNA stability and HLA-G expression levels. These polymorphisms can give rise to extended haplotypes (denoted as UTR-1 to UTR-7), which either upregulate (UTR-1 or UTR-6) or downregulate (UTR-2 or UTR-5) HLA-G expression ( 4 ). Previous research has documented HLA-G expression in gastric tumors ( 5 ). Thus, investigating polymorphisms associated with expression levels, such as those within the 3’UTR of this molecule, could help identify new genetic markers implicated in the risk, severity, or prognosis of this pathology. In this work, we focused on studying the influence of the aforementioned HLA-G UTR polymorphisms in paired tissue samples (tumoral and non-tumoral) obtained from a cohort of patients with gastric adenocarcinoma. Such an approach will allow us to assess the suitability of HLA-G variants as potential risk markers for gastric adenocarcinoma and elucidate their significance in the prognosis of this type of cancer. MATERIALS AND METHODS The study was approved by the local ethics committee and carried out in compliance with the ethical guidelines outlined in the Declarations of Helsinki. Prior to the collection of blood samples, all patients provided written informed consent. A total of 111 patients with gastric cancer were included (see Table 1 ), alongside a control group of 119 healthy Spanish individuals. Genomic DNA was extracted from paired (tumor and distal) tissues as well as blood samples obtained from patients with gastric cancer using the Nucleon BACC kit (GE Healthcare). DNA extraction from saliva samples collected from controls was performed using the Oragene DNA 500 kit (DNA Genotek) and purified with PrepIT-L2P (DNA Genotek). Quantity and purity of DNA was assessed spectrophotometrically in a Nanodrop-One (Thermo-Scientific). Table 1 Demographic and clinical characteristics of patients. Patient characteristics Total N. (%) Patients N Range, % Age (y) Median (range) 70 (33–89) Sex, N. (%) Male 66 (60.6%) Female 43 (55.4%) NA 1 Stage, N. (%) I 24 (22.0%) II 39 (36.0%) III 23 (21.0%) IV 23 (21.0%) NA 2 Treatment a, N. (%) Surgery 111 (100%) Chemotherapy 111 (100%) Localization, N. (%) Fundus 14 (13.6%) Antrum 37 (35.9%) Body 34 (33.0%) Difuse 6 (5.8%) Cardia 4 (3.9%) Gastric Bypass 7 (6.8%) NA 8 Type, N. (%) Intestinal 51 (52.7%) Non-intestinal 52 (47.3%) NA 8 Overall survival, N. (%) 5 y 43 (41.7%) NA 8 HLA-G polymorphism analysis was performed using PCR (see Table 2 ). The 14bp polymorphism was analyzed by band size discrimination ( 6 ) and the remaining UTR SNPs were analyzed by Sanger sequencing. Primers and PCR conditions are detailed in Table 2 . Table 2 Primers and PCR programs employed in this study. Polymorphism Primers PCR conditions Forward Reverse Denaturalization Annealing Elongation 14bp INS/DEL (30 cycles) 5’-GTGATGGGCTGTTTAAAGTGTCACC − 3’ 5’-GGAAGGA ATGCAGTTC AGCATGA − 3’ 94°C 64°C 72°C 2 min 30 sec 60 sec 60 sec 10 min UTR SNPs (32 cycles) 5’-CATGCTG AACTGCAT TCCTTCC − 3’ 5’-CTGGTGG GACAAGGT TCTACTG − 3’ 94°C 65.5°C 72°C 5 min 30 sec 30 sec 60 sec 5 min The PCR or PCR-RFLP data of the studied polymorphisms were analyzed using the software SNPStats. This software allows the evaluation of Hardy-Weinberg Equilibrium (exact test), chi-square test, odds ratio (OR) estimation for the association between polymorphisms, the analysis of linkage disequilibrium using the D statistic and correlation coefficient, and haplotype analysis employing the EM algorithm ( 7 , 8 ). Kaplan-Meier method was used to estimate the 5-year survival function of patients with gastric cancer considering various genetic factors (GraphPad Prism 8.0 software). Multivariate Cox regression models were used to simultaneously assess the effect of genetic factors along with other variables such as comorbidities, clinical features and demographic characteristics on 5-year survival of patients (software R). In all Cox regression fits, both individual and global Schoenfeld test indicated that no covariate in the model nor the model as a whole violate the Proportional Hazard assumption, meaning that the hazard ratio stays constant over time ( 9 ). P-values below 0.05 were considered statistically significant. When required, the Holm-Bonferroni (HB) sequential correction method for multiple testing was applied to the statistical analyses. This method compares the k-ranked p-value to the nominal significance level (0.05) divided by (n-k + 1), where in this case n = 2 (the number of polymorphisms) and k = 1 and 2. RESULTS 1. Hardy-Weinberg equilibrium and linkage disequilibrium The analysis showed that UTR polymorphisms were in Hardy-Weinberg equilibrium (not shown). Additionally, genetic distance analysis demonstrated that the polymorphisms were in linkage disequilibrium (Fig. 1), suggesting that these variants form combined haplotypes, as described in previous works. We then proceeded to analyze the distribution of these haplotypes in our study population. 2. UTR-1 and 6 haplotypes are increased in patients with gastric cancer The analysis of the 9 polymorphisms forming the HLA-G 3’UTR haplotypes showed an elevated frequency of UTR-1 and UTR-6, which are associated with increased HLA-G expression. Specifically, UTR-1 showed an odds ratio (OR) of 2.42 (1.12–5.21 95% CI, p-value = 0.025) and UTR-6 an OR of 3.02 (1.03–8.90 95% CI, p-value = 0.046), with frequencies of 32.1% and 6.9%, respectively, compared to the control group (25.5% and 4.6%, respectively) (Table 3 ). Table 3 HLA-G 3’-UTR haplotype frequencies estimation and distribution. UTR 1 and 6 are overrepresented in patients with gastric cáncer. 3'UTR Haplotypes analysis UTR 14bp + 3092 G > T + 3107 C > G + 3111 G > A + 3121 T > C + 3142 C > G + 3187 A > G + 3196 C > G + 3227 G > A Control Gastric Cancer Mean Freq. OR (95% CI) P-value 5 I G C G T G A C G 0.116 0.069 0.094 1.00 --- 1 D G C G T C G C G 0.254 0.321 0.284 2.42 (1.12–5.21) 0.025 2 I G C G T G A G G 0.254 0.235 0.245 1.82 (0.84–3.93) 0.131 3 D G C G T G A C G 0.149 0.135 0.142 1.75 (0.75–4.09) 0.195 4 D G C G T C A C G 0.126 0.131 0.128 2.11 (0.85–5.24) 0.107 6 D G C G T C A C A 0.046 0.069 0.060 3.02 (1.03–8.90) 0.046 rare * * * * * * * * * 0.020 0 0.047 1.62 (0.51–5.12) 0.409 This data provides evidence of the role of the 3’UTR region polymorphisms of the HLA-G gene in gastric cancer susceptibility. 3. Patients with the 14bp D/D polymorphism show lower survival rate Survival curve analysis showed that the 14bp polymorphism was the only one implicated in the survival of patients with gastric cancer. Specifically, patients with the 14bp D/D genotype exhibited a lower survival rate (p = 0.023, survival below 25%), compared to 14bp I/D and 14bp I/I patients combined (survival above 50%) (Fig. 2A). Moreover, the corresponding Cox regression model along with TNM staging, showed that the hazard ratio estimate of D/D vs. I/D + I/I is 2.7 (95% CI 1.17–6.3, p-value = 0.021) (Fig. 2B) DISCUSSION In recent years, significant interest has been directed towards the interplay between HLA-G, immunoediting, and cancer ( 10 , 11 , 12 ). Indeed, the HLA-G-mediated signaling pathway is currently acknowledged as a novel therapeutic immune checkpoint, alongside other firmly established checkpoints ( 13 ). This study shows that individuals harboring genetic markers in the 3’-UTR region associated with elevated HLA-G expression are more susceptible to developing gastric cancer. In our cohort, we observed a heightened frequency of 3’-UTRs inducing high HLA-G high expression (namely UTR-1 and UTR-6,) among patients with gastric cancer, The UTR-1 and 6 haplotypes displayed an increased frequency in patients with gastric cancer. These haplotypes encompass variants known to increase HLA-G levels ( 4 ), thereby fostering an HLA-G mediated immunosuppressive microenvironment, as proposed in our initial hypothesis. Several authors have identified mechanisms that regulate HLA-G expression probably related to the 3’ UTR variants. For instance, the 14bp I/D is associated with mRNA stability ( 14 ), while + 3142 G/C and + 3187 G/A (located in an AU-rich element, ARE) may participate in miRNA-mediated post-transcriptional regulation ( 15 ) or in mRNA degradation ( 16 ), respectively. Additionally, other SNPs (namely + 3001 C/T, + 3003 T/C, + 3010 G/C, + 3027 C/A, + 3035 C/T and + 3196 C/G), have been proposed as miRNA targets, suggesting a potential role in post-transcriptional regulation of HLA-G ( 17 ). Elevated HLA-G levels have been linked to numerous physiological ( 18 ) and pathological conditions, such as cancer, while lower levels are associated with autoimmune and inflammatory diseases ( 19 ), in line with the results described herein. These findings underscore HLA-G as a promising target for potential therapeutic interventions. Strategies such as miRNA-mediated downregulation of HLA-G expression or inhibition of its interaction with its cognate receptors, akin to current PD-1/PD-L1 immunotherapies, hold promise for stimulating immune responses against the tumor. Furthermore, exploring the functional implications of these HLA-G polymorphisms and their relationship with disease susceptibility and progression could shed light on the association between HLA-G markers and pathological conditions. Other works and meta-analysis described an association of HLA-G 3’UTR haplotypes to the development and severity of different malignancies, such as breast and colorectal cancer, were 14bp Del/Del and + 3142 C/C, both forming UTR-1 and UTR6 haplotypes, showed association to risk of developing both cancers ( 20 , 21 ). Moreover, other authors showed an association of HLA-G levels of expression and the 14bp polymorphism in gynecologic cancers ( 22 ), and, in the same vein of thinking, loss of miR-152 increases HLA-G expression in vitro ( 23 ), mimicking the effect of + 3142 polymorphisms, as miR-152 is not able to bind + 3142 C/C. All these findings not only support the results of our work, but also provide a functional explanation for them, as it seems clear that there is a link between variants in the 3'UTR of HLA-G and the expression levels of this protein and, consequently, with the risk of developing tumors of epithelial origin. This work has some limitations. First, it is focused on genetic variants with a known effect on HLA-G expression. However, further studies in a large cohort of patients may enable to link this polymorphisms and UTR haplotypes with data of protein in sera and tissue of patients, thus comparing the actual effect of these variants in the level of expression of HLA-G. Although it is an interesting point, several factors can modify HLA-G levels of expression that diverge from the regulation of classic class I HLA molecules ( 24 ), such as inflammation and cellular stress ( 23 , 25 ), making it difficult to link the effect of genetic variants with HLA-G expression in the context of an evolving pathology like cancer. Moreover, HLA-G gene is in a high linkage disequilibrium with other class I genes, as all of them are located in the same chromosome. For instance, the HLA-A locus is surrounded by HLA class Ib genes: HLA-E, HLA-H, HLA-G and HLA-F ( 26 ), meaning that the association of genetic variants to a certain phenotype may be mediated by other genes in that haplotype. Finally, although several authors described the effect of HLA-G in the immune system ( 27 , 28 ), functional analysis may enlight the actual immunomodulatory properties of this molecule in the context of gastric cancer. In our group, we already described the expression of HLA-G in gastric tumors (6, Supplementary Material), and a closer look to the in vivo effect of the immunomodulatory properties of HLA-G in the tumor microenvironment should address the importance of this molecule in the evolution and possible therapies of this pathology. Declarations ETHICS STATEMENT The studies involving human participants were reviewed and approved by Comité ético de investigación clínica, Hospital Clínico San Carlos, Madrid, Spain. The patients/participants provided their written informed consent to participate in this study. The study was performed in accordance with the ethical standards as laid down in the Declarations of Helsinki and approved by the local ethics committee. Written informed consent was obtained from all subjects. ACKNOWLEDGEMENTS We thank Prof. E. D. Carosella (Saint Louis Hospital, Paris, France) for scientific support. CONFLICT OF INTEREST STATEMENT The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. FUNDING This work was supported by grants from Instituto de Salud Carlos III (PI18/00626 and PI18/00721), with funds from the European Union (Fondo Europeo de Desarrollo Regional FEDER) and PR3/23 30834 AUTHORSHIP CV-Y and IJ: manuscript writing, investigation, and analysis. IJ: design, analysis, and supervision. MM-A: investigation support and validation. EM-L: analysis. AG-C, AL-G, IL, and RG: patient follow-up, sample and data collection. AA-V: critical review, project administration, and funding acquisition. JM-V: manuscript writing and revision, supervision, project administration, and funding acquisition. All authors contributed to the article and approved the submitted version. References WHO Cancer Today: Data Visualization Tools for Exploring the Global Cancer Burden in 2020. https://gco.iarc.fr/today/home Sung H, Ferlay J, Siegel RL et al (2021) Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin 71(3):209–249. https://doi.org/10.3322/caac.21660 Arnaiz-Villena A, Juarez I, Suarez-Trujillo F et al (2020) HLA-G: Function, Polymorphisms and Pathology. 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Cite Share Download PDF Status: Published Journal Publication published 16 Nov, 2024 Read the published version in Cancer Immunology, Immunotherapy → Version 1 posted Editorial decision: Revision requested 30 May, 2024 Reviews received at journal 28 May, 2024 Reviewers agreed at journal 20 May, 2024 Reviewers invited by journal 20 May, 2024 Submission checks completed at journal 19 May, 2024 Editor assigned by journal 19 May, 2024 First submitted to journal 17 May, 2024 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-4437900","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Short Report","associatedPublications":[],"authors":[{"id":307621684,"identity":"e6253b74-5521-4b39-8439-bb3ac771460a","order_by":0,"name":"Christian Vaquero-Yuste","email":"","orcid":"","institution":"Universidad Complutense de Madrid","correspondingAuthor":false,"prefix":"","firstName":"Christian","middleName":"","lastName":"Vaquero-Yuste","suffix":""},{"id":307621685,"identity":"ed9c8e87-adce-4d60-8e64-aaed8368382a","order_by":1,"name":"Ignacio 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de Asturias","correspondingAuthor":false,"prefix":"","firstName":"Inmaculada","middleName":"","lastName":"Lasa","suffix":""},{"id":307621691,"identity":"1442d8a0-8890-4e75-a484-961bff1f80a3","order_by":7,"name":"Remedios Gómez","email":"","orcid":"","institution":"Hospital Universitario Príncipe de Asturias","correspondingAuthor":false,"prefix":"","firstName":"Remedios","middleName":"","lastName":"Gómez","suffix":""},{"id":307621692,"identity":"38d82295-8ba2-48a0-a90f-d17bbbda2489","order_by":8,"name":"Antonio Arnaiz-Villena","email":"","orcid":"","institution":"Universidad Complutense de Madrid","correspondingAuthor":false,"prefix":"","firstName":"Antonio","middleName":"","lastName":"Arnaiz-Villena","suffix":""},{"id":307621693,"identity":"c2daeb17-0df1-40ac-890c-bc14ab3f7c32","order_by":9,"name":"Jose Manuel Martin-Villa","email":"","orcid":"","institution":"Universidad Complutense de Madrid","correspondingAuthor":false,"prefix":"","firstName":"Jose","middleName":"Manuel","lastName":"Martin-Villa","suffix":""}],"badges":[],"createdAt":"2024-05-17 16:38:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4437900/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4437900/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00262-024-03771-w","type":"published","date":"2024-11-16T15:57:21+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":57721485,"identity":"d6212492-ffd3-4df7-b922-048d7249f88a","added_by":"auto","created_at":"2024-06-04 18:58:10","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":89651,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e3’UTR SNPs are in linkage disequilibrium, forming a combined haplotype. \u003c/strong\u003eAccording to the values obtained with the SNPStats software (D’ and r), these polymorphisms lie in close vicinity and are in linkage disequilibrium, allowing them to form a combined haplotype in our patients, as has been already described previously.\u003c/p\u003e","description":"","filename":"OnlineFIGURE1.png","url":"https://assets-eu.researchsquare.com/files/rs-4437900/v1/e5d837c234abce28670ebf55.png"},{"id":57721486,"identity":"7d7ee577-0212-4ab9-a910-c742a5f5b2f2","added_by":"auto","created_at":"2024-06-04 18:58:10","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":73580,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e14bp D/D genotype predicts a lower survival rate in patients with gastric cancer.\u003c/strong\u003e A) Kaplan-Meier curve depicting survival rates for 14bp D/D and 14bp I/D+I/I combined genotypes, showing a lower survival rate in patients with D homozygosity. B) Cox-regression analysis with 14bp and TNM staging show an increased hazard ratio in patients with the 14bp genotype compared to patients with the I/D or I/I genotype, with TNM as a co-factor implicated in survival.\u003c/p\u003e","description":"","filename":"OnlineFIGURE2.png","url":"https://assets-eu.researchsquare.com/files/rs-4437900/v1/81abe6b25f363cb3d94f30d9.png"},{"id":69274959,"identity":"4e6dd17e-d8b2-434e-bfae-8f30251d3735","added_by":"auto","created_at":"2024-11-18 16:41:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":783534,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4437900/v1/35b6b757-9e63-409d-9235-0c81d029a580.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eHLA-G High-expressor 3’UTR Markers Are Linked to Gastric Cancer Development and Survival\u003c/p\u003e","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eGastric cancer ranks as the fifth most prevalent cancer worldwide, exhibiting an annual incidence of 11.1 new cases per 100,000 individuals. Furthermore, it stands as the 5th most lethal cancer, with 7.7 deaths per 100,000 individuals annually, underscoring its aggressive nature. The 5-year survival rate for this cancer subtype remains at 29.5%, persisting over the past three decades (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHLA-G, a non-classical class I HLA gene, encodes molecules with tolerogenic properties. The HLA-G gene comprises 7 introns and 8 exons, and exon 8 constitutes an untranslated region (3'UTR) with regulatory properties (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Specific polymorphic sites within the 3'UTR region, such as the 14 base pair (14bp) INS/DEL (I/D), +\u0026thinsp;3142C/G, and +\u0026thinsp;3187A/G polymorphisms, influence mRNA stability and HLA-G expression levels. These polymorphisms can give rise to extended haplotypes (denoted as UTR-1 to UTR-7), which either upregulate (UTR-1 or UTR-6) or downregulate (UTR-2 or UTR-5) HLA-G expression (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePrevious research has documented HLA-G expression in gastric tumors (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Thus, investigating polymorphisms associated with expression levels, such as those within the 3\u0026rsquo;UTR of this molecule, could help identify new genetic markers implicated in the risk, severity, or prognosis of this pathology.\u003c/p\u003e \u003cp\u003eIn this work, we focused on studying the influence of the aforementioned HLA-G UTR polymorphisms in paired tissue samples (tumoral and non-tumoral) obtained from a cohort of patients with gastric adenocarcinoma. Such an approach will allow us to assess the suitability of HLA-G variants as potential risk markers for gastric adenocarcinoma and elucidate their significance in the prognosis of this type of cancer.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cp\u003eThe study was approved by the local ethics committee and carried out in compliance with the ethical guidelines outlined in the Declarations of Helsinki. Prior to the collection of blood samples, all patients provided written informed consent.\u003c/p\u003e\n\u003cp\u003eA total of 111 patients with gastric cancer were included (see Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e), alongside a control group of 119 healthy Spanish individuals. Genomic DNA was extracted from paired (tumor and distal) tissues as well as blood samples obtained from patients with gastric cancer using the Nucleon BACC kit (GE Healthcare). DNA extraction from saliva samples collected from controls was performed using the Oragene DNA 500 kit (DNA Genotek) and purified with PrepIT-L2P (DNA Genotek). Quantity and purity of DNA was assessed spectrophotometrically in a Nanodrop-One (Thermo-Scientific).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eDemographic and clinical characteristics of patients.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003cth rowspan=\"3\" align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003ePatient characteristics\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eTotal N. (%)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003ePatients\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eN\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRange, %\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eAge (y)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMedian (range)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e70\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(33\u0026ndash;89)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eSex, N. (%)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e66\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(60.6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFemale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e43\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(55.4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"5\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eStage, N. (%)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(22.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eII\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e39\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(36.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIII\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e23\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(21.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIV\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e23\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(21.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eTreatment a, N. (%)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSurgery\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e111\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(100%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eChemotherapy\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e111\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(100%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"7\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eLocalization, N. (%)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFundus\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(13.6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAntrum\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e37\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(35.9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBody\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e34\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(33.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDifuse\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(5.8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCardia\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(3.9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGastric Bypass\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(6.8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eType, N. (%)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIntestinal\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e51\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(52.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNon-intestinal\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e52\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(47.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eOverall survival, N. (%)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;5 y\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e60\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(58.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026gt;\u0026thinsp;5 y\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e43\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(41.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eHLA-G polymorphism analysis was performed using PCR (see Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). The 14bp polymorphism was analyzed by band size discrimination (\u003cspan class=\"CitationRef\"\u003e6\u003c/span\u003e) and the remaining UTR SNPs were analyzed by Sanger sequencing. Primers and PCR conditions are detailed in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003ePrimers and PCR programs employed in this study.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003ePolymorphism\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003ePrimers\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"5\" align=\"left\"\u003e\n\u003cp\u003ePCR conditions\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eForward\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eReverse\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eDenaturalization\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAnnealing\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eElongation\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e14bp INS/DEL (30 cycles)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e5\u0026rsquo;-GTGATGGGCTGTTTAAAGTGTCACC \u0026minus;\u0026thinsp;3\u0026rsquo;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e5\u0026rsquo;-GGAAGGA ATGCAGTTC AGCATGA \u0026minus;\u0026thinsp;3\u0026rsquo;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e94\u0026deg;C\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e64\u0026deg;C\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e72\u0026deg;C\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2 min\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e30 sec\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e60 sec\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e60 sec\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10 min\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eUTR SNPs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(32 cycles)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e5\u0026rsquo;-CATGCTG AACTGCAT TCCTTCC \u0026minus;\u0026thinsp;3\u0026rsquo;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e5\u0026rsquo;-CTGGTGG GACAAGGT TCTACTG \u0026minus;\u0026thinsp;3\u0026rsquo;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e94\u0026deg;C\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e65.5\u0026deg;C\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e72\u0026deg;C\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5 min\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e30 sec\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e30 sec\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e60 sec\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5 min\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThe PCR or PCR-RFLP data of the studied polymorphisms were analyzed using the software SNPStats. This software allows the evaluation of Hardy-Weinberg Equilibrium (exact test), chi-square test, odds ratio (OR) estimation for the association between polymorphisms, the analysis of linkage disequilibrium using the D statistic and correlation coefficient, and haplotype analysis employing the EM algorithm (\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eKaplan-Meier method was used to estimate the 5-year survival function of patients with gastric cancer considering various genetic factors (GraphPad Prism 8.0 software). Multivariate Cox regression models were used to simultaneously assess the effect of genetic factors along with other variables such as comorbidities, clinical features and demographic characteristics on 5-year survival of patients (software R). In all Cox regression fits, both individual and global Schoenfeld test indicated that no covariate in the model nor the model as a whole violate the Proportional Hazard assumption, meaning that the hazard ratio stays constant over time (\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eP-values below 0.05 were considered statistically significant. When required, the Holm-Bonferroni (HB) sequential correction method for multiple testing was applied to the statistical analyses. This method compares the k-ranked p-value to the nominal significance level (0.05) divided by (n-k\u0026thinsp;+\u0026thinsp;1), where in this case n\u0026thinsp;=\u0026thinsp;2 (the number of polymorphisms) and k\u0026thinsp;=\u0026thinsp;1 and 2.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003e1. Hardy-Weinberg equilibrium and linkage disequilibrium\u003c/p\u003e\n\u003cp\u003eThe analysis showed that UTR polymorphisms were in Hardy-Weinberg equilibrium (not shown). Additionally, genetic distance analysis demonstrated that the polymorphisms were in linkage disequilibrium (Fig.\u0026nbsp;1), suggesting that these variants form combined haplotypes, as described in previous works. We then proceeded to analyze the distribution of these haplotypes in our study population.\u003c/p\u003e\n\u003cp\u003e2. UTR-1 and 6 haplotypes are increased in patients with gastric cancer\u003c/p\u003e\n\u003cp\u003eThe analysis of the 9 polymorphisms forming the HLA-G 3\u0026rsquo;UTR haplotypes showed an elevated frequency of UTR-1 and UTR-6, which are associated with increased HLA-G expression. Specifically, UTR-1 showed an odds ratio (OR) of 2.42 (1.12\u0026ndash;5.21 95% CI, p-value\u0026thinsp;=\u0026thinsp;0.025) and UTR-6 an OR of 3.02 (1.03\u0026ndash;8.90 95% CI, p-value\u0026thinsp;=\u0026thinsp;0.046), with frequencies of 32.1% and 6.9%, respectively, compared to the control group (25.5% and 4.6%, respectively) (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab3\" style=\"width: 1003px;\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eHLA-G 3\u0026rsquo;-UTR haplotype frequencies estimation and distribution. UTR 1 and 6 are overrepresented in patients with gastric c\u0026aacute;ncer.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth style=\"width: 919px;\" colspan=\"15\" align=\"left\"\u003e\n\u003cp\u003e3'UTR Haplotypes analysis\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth style=\"width: 28px;\" align=\"left\"\u003e\n\u003cp\u003eUTR\u003c/p\u003e\n\u003c/th\u003e\n\u003cth style=\"width: 35px;\" align=\"left\"\u003e\n\u003cp\u003e14bp\u003c/p\u003e\n\u003c/th\u003e\n\u003cth style=\"width: 66px;\" align=\"left\"\u003e\n\u003cp\u003e+\u0026thinsp;3092 G\u0026thinsp;\u0026gt;\u0026thinsp;T\u003c/p\u003e\n\u003c/th\u003e\n\u003cth style=\"width: 66.8958px;\" align=\"left\"\u003e\n\u003cp\u003e+\u0026thinsp;3107 C\u0026thinsp;\u0026gt;\u0026thinsp;G\u003c/p\u003e\n\u003c/th\u003e\n\u003cth style=\"width: 65.1042px;\" align=\"left\"\u003e\n\u003cp\u003e+\u0026thinsp;3111 G\u0026thinsp;\u0026gt;\u0026thinsp;A\u003c/p\u003e\n\u003c/th\u003e\n\u003cth style=\"width: 65px;\" align=\"left\"\u003e\n\u003cp\u003e+\u0026thinsp;3121 T\u0026thinsp;\u0026gt;\u0026thinsp;C\u003c/p\u003e\n\u003c/th\u003e\n\u003cth style=\"width: 66px;\" align=\"left\"\u003e\n\u003cp\u003e+\u0026thinsp;3142 C\u0026thinsp;\u0026gt;\u0026thinsp;G\u003c/p\u003e\n\u003c/th\u003e\n\u003cth style=\"width: 66px;\" align=\"left\"\u003e\n\u003cp\u003e+\u0026thinsp;3187 A\u0026thinsp;\u0026gt;\u0026thinsp;G\u003c/p\u003e\n\u003c/th\u003e\n\u003cth style=\"width: 66px;\" align=\"left\"\u003e\n\u003cp\u003e+\u0026thinsp;3196 C\u0026thinsp;\u0026gt;\u0026thinsp;G\u003c/p\u003e\n\u003c/th\u003e\n\u003cth style=\"width: 66px;\" align=\"left\"\u003e\n\u003cp\u003e+\u0026thinsp;3227 G\u0026thinsp;\u0026gt;\u0026thinsp;A\u003c/p\u003e\n\u003c/th\u003e\n\u003cth style=\"width: 51px;\" align=\"left\"\u003e\n\u003cp\u003eControl\u003c/p\u003e\n\u003c/th\u003e\n\u003cth style=\"width: 80px;\" align=\"left\"\u003e\n\u003cp\u003eGastric Cancer\u003c/p\u003e\n\u003c/th\u003e\n\u003cth style=\"width: 61px;\" align=\"left\"\u003e\n\u003cp\u003eMean Freq.\u003c/p\u003e\n\u003c/th\u003e\n\u003cth style=\"width: 89px;\" align=\"left\"\u003e\n\u003cp\u003eOR (95% CI)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth style=\"width: 48px;\" align=\"left\"\u003e\n\u003cp\u003eP-value\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 28px;\" align=\"left\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 35px;\" align=\"left\"\u003e\n\u003cp\u003eI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 66px;\" align=\"left\"\u003e\n\u003cp\u003eG\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 66.8958px;\" align=\"left\"\u003e\n\u003cp\u003eC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 65.1042px;\" align=\"left\"\u003e\n\u003cp\u003eG\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 65px;\" align=\"left\"\u003e\n\u003cp\u003eT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 66px;\" align=\"left\"\u003e\n\u003cp\u003eG\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 66px;\" align=\"left\"\u003e\n\u003cp\u003eA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 66px;\" align=\"left\"\u003e\n\u003cp\u003eC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 66px;\" align=\"left\"\u003e\n\u003cp\u003eG\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 51px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.116\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 80px;\" align=\"left\"\u003e\n\u003cp\u003e0.069\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 61px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.094\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 89px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 48px;\" align=\"left\"\u003e\n\u003cp\u003e---\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 28px;\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 35px;\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eD\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 66px;\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eG\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 66.8958px;\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eC\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 65.1042px;\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eG\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 65px;\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eT\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 66px;\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eC\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 66px;\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eG\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 66px;\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eC\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 66px;\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eG\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 51px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.254\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 80px;\" align=\"left\"\u003e\n\u003cp\u003e0.321\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 61px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.284\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 89px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e2.42 (1.12\u0026ndash;5.21)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 48px;\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.025\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 28px;\" align=\"left\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 35px;\" align=\"left\"\u003e\n\u003cp\u003eI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 66px;\" align=\"left\"\u003e\n\u003cp\u003eG\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 66.8958px;\" align=\"left\"\u003e\n\u003cp\u003eC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 65.1042px;\" align=\"left\"\u003e\n\u003cp\u003eG\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 65px;\" align=\"left\"\u003e\n\u003cp\u003eT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 66px;\" align=\"left\"\u003e\n\u003cp\u003eG\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 66px;\" align=\"left\"\u003e\n\u003cp\u003eA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 66px;\" align=\"left\"\u003e\n\u003cp\u003eG\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 66px;\" align=\"left\"\u003e\n\u003cp\u003eG\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 51px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.254\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 80px;\" align=\"left\"\u003e\n\u003cp\u003e0.235\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 61px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.245\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 89px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.82 (0.84\u0026ndash;3.93)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 48px;\" align=\"left\"\u003e\n\u003cp\u003e0.131\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 28px;\" align=\"left\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 35px;\" align=\"left\"\u003e\n\u003cp\u003eD\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 66px;\" align=\"left\"\u003e\n\u003cp\u003eG\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 66.8958px;\" align=\"left\"\u003e\n\u003cp\u003eC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 65.1042px;\" align=\"left\"\u003e\n\u003cp\u003eG\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 65px;\" align=\"left\"\u003e\n\u003cp\u003eT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 66px;\" align=\"left\"\u003e\n\u003cp\u003eG\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 66px;\" align=\"left\"\u003e\n\u003cp\u003eA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 66px;\" align=\"left\"\u003e\n\u003cp\u003eC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 66px;\" align=\"left\"\u003e\n\u003cp\u003eG\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 51px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.149\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 80px;\" align=\"left\"\u003e\n\u003cp\u003e0.135\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 61px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.142\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 89px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.75 (0.75\u0026ndash;4.09)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 48px;\" align=\"left\"\u003e\n\u003cp\u003e0.195\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 28px;\" align=\"left\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 35px;\" align=\"left\"\u003e\n\u003cp\u003eD\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 66px;\" align=\"left\"\u003e\n\u003cp\u003eG\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 66.8958px;\" align=\"left\"\u003e\n\u003cp\u003eC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 65.1042px;\" align=\"left\"\u003e\n\u003cp\u003eG\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 65px;\" align=\"left\"\u003e\n\u003cp\u003eT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 66px;\" align=\"left\"\u003e\n\u003cp\u003eC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 66px;\" align=\"left\"\u003e\n\u003cp\u003eA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 66px;\" align=\"left\"\u003e\n\u003cp\u003eC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 66px;\" align=\"left\"\u003e\n\u003cp\u003eG\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 51px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.126\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 80px;\" align=\"left\"\u003e\n\u003cp\u003e0.131\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 61px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.128\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 89px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.11 (0.85\u0026ndash;5.24)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 48px;\" align=\"left\"\u003e\n\u003cp\u003e0.107\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 28px;\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e6\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 35px;\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eD\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 66px;\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eG\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 66.8958px;\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eC\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 65.1042px;\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eG\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 65px;\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eT\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 66px;\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eC\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 66px;\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 66px;\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eC\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 66px;\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 51px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.046\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 80px;\" align=\"left\"\u003e\n\u003cp\u003e0.069\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 61px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.060\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 89px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e3.02 (1.03\u0026ndash;8.90)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 48px;\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.046\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 28px;\" align=\"left\"\u003e\n\u003cp\u003erare\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 35px;\" align=\"left\"\u003e\n\u003cp\u003e*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 66px;\" align=\"left\"\u003e\n\u003cp\u003e*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 66.8958px;\" align=\"left\"\u003e\n\u003cp\u003e*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 65.1042px;\" align=\"left\"\u003e\n\u003cp\u003e*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 65px;\" align=\"left\"\u003e\n\u003cp\u003e*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 66px;\" align=\"left\"\u003e\n\u003cp\u003e*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 66px;\" align=\"left\"\u003e\n\u003cp\u003e*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 66px;\" align=\"left\"\u003e\n\u003cp\u003e*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 66px;\" align=\"left\"\u003e\n\u003cp\u003e*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 51px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.020\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 80px;\" align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 61px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.047\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 89px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.62 (0.51\u0026ndash;5.12)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 48px;\" align=\"left\"\u003e\n\u003cp\u003e0.409\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThis data provides evidence of the role of the 3\u0026rsquo;UTR region polymorphisms of the HLA-G gene in gastric cancer susceptibility.\u003c/p\u003e\n\u003cp\u003e3. Patients with the 14bp D/D polymorphism show lower survival rate\u003c/p\u003e\n\u003cp\u003eSurvival curve analysis showed that the 14bp polymorphism was the only one implicated in the survival of patients with gastric cancer.\u003c/p\u003e\n\u003cp\u003eSpecifically, patients with the 14bp D/D genotype exhibited a lower survival rate (p\u0026thinsp;=\u0026thinsp;0.023, survival below 25%), compared to 14bp I/D and 14bp I/I patients combined (survival above 50%) (Fig.\u0026nbsp;2A).\u003c/p\u003e\n\u003cp\u003eMoreover, the corresponding Cox regression model along with TNM staging, showed that the hazard ratio estimate of D/D vs. I/D\u0026thinsp;+\u0026thinsp;I/I is 2.7 (95% CI 1.17\u0026ndash;6.3, p-value\u0026thinsp;=\u0026thinsp;0.021) (Fig.\u0026nbsp;2B)\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eIn recent years, significant interest has been directed towards the interplay between HLA-G, immunoediting, and cancer (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Indeed, the HLA-G-mediated signaling pathway is currently acknowledged as a novel therapeutic immune checkpoint, alongside other firmly established checkpoints (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis study shows that individuals harboring genetic markers in the 3\u0026rsquo;-UTR region associated with elevated HLA-G expression are more susceptible to developing gastric cancer. In our cohort, we observed a heightened frequency of 3\u0026rsquo;-UTRs inducing high HLA-G high expression (namely UTR-1 and UTR-6,) among patients with gastric cancer,\u003c/p\u003e \u003cp\u003eThe UTR-1 and 6 haplotypes displayed an increased frequency in patients with gastric cancer. These haplotypes encompass variants known to increase HLA-G levels (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e), thereby fostering an HLA-G mediated immunosuppressive microenvironment, as proposed in our initial hypothesis.\u003c/p\u003e \u003cp\u003eSeveral authors have identified mechanisms that regulate HLA-G expression probably related to the 3\u0026rsquo; UTR variants. For instance, the 14bp I/D is associated with mRNA stability (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e), while\u0026thinsp;+\u0026thinsp;3142 G/C and +\u0026thinsp;3187 G/A (located in an AU-rich element, ARE) may participate in miRNA-mediated post-transcriptional regulation (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e) or in mRNA degradation (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e), respectively. Additionally, other SNPs (namely\u0026thinsp;+\u0026thinsp;3001 C/T, +\u0026thinsp;3003 T/C, +\u0026thinsp;3010 G/C, +\u0026thinsp;3027 C/A, +\u0026thinsp;3035 C/T and +\u0026thinsp;3196 C/G), have been proposed as miRNA targets, suggesting a potential role in post-transcriptional regulation of HLA-G (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eElevated HLA-G levels have been linked to numerous physiological (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e) and pathological conditions, such as cancer, while lower levels are associated with autoimmune and inflammatory diseases (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e), in line with the results described herein.\u003c/p\u003e \u003cp\u003eThese findings underscore HLA-G as a promising target for potential therapeutic interventions. Strategies such as miRNA-mediated downregulation of HLA-G expression or inhibition of its interaction with its cognate receptors, akin to current PD-1/PD-L1 immunotherapies, hold promise for stimulating immune responses against the tumor.\u003c/p\u003e \u003cp\u003eFurthermore, exploring the functional implications of these HLA-G polymorphisms and their relationship with disease susceptibility and progression could shed light on the association between HLA-G markers and pathological conditions.\u003c/p\u003e \u003cp\u003eOther works and meta-analysis described an association of HLA-G 3\u0026rsquo;UTR haplotypes to the development and severity of different malignancies, such as breast and colorectal cancer, were 14bp Del/Del and +\u0026thinsp;3142 C/C, both forming UTR-1 and UTR6 haplotypes, showed association to risk of developing both cancers (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Moreover, other authors showed an association of HLA-G levels of expression and the 14bp polymorphism in gynecologic cancers (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e), and, in the same vein of thinking, loss of miR-152 increases HLA-G expression \u003cem\u003ein vitro\u003c/em\u003e (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e), mimicking the effect of +\u0026thinsp;3142 polymorphisms, as miR-152 is not able to bind\u0026thinsp;+\u0026thinsp;3142 C/C.\u003c/p\u003e \u003cp\u003eAll these findings not only support the results of our work, but also provide a functional explanation for them, as it seems clear that there is a link between variants in the 3'UTR of HLA-G and the expression levels of this protein and, consequently, with the risk of developing tumors of epithelial origin.\u003c/p\u003e \u003cp\u003eThis work has some limitations. First, it is focused on genetic variants with a known effect on HLA-G expression. However, further studies in a large cohort of patients may enable to link this polymorphisms and UTR haplotypes with data of protein in sera and tissue of patients, thus comparing the actual effect of these variants in the level of expression of HLA-G. Although it is an interesting point, several factors can modify HLA-G levels of expression that diverge from the regulation of classic class I HLA molecules (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e), such as inflammation and cellular stress (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e), making it difficult to link the effect of genetic variants with HLA-G expression in the context of an evolving pathology like cancer.\u003c/p\u003e \u003cp\u003eMoreover, HLA-G gene is in a high linkage disequilibrium with other class I genes, as all of them are located in the same chromosome. For instance, the HLA-A locus is surrounded by HLA class Ib genes: HLA-E, HLA-H, HLA-G and HLA-F (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e), meaning that the association of genetic variants to a certain phenotype may be mediated by other genes in that haplotype.\u003c/p\u003e \u003cp\u003eFinally, although several authors described the effect of HLA-G in the immune system (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e), functional analysis may enlight the actual immunomodulatory properties of this molecule in the context of gastric cancer. In our group, we already described the expression of HLA-G in gastric tumors (6, Supplementary Material), and a closer look to the \u003cem\u003ein vivo\u003c/em\u003e effect of the immunomodulatory properties of HLA-G in the tumor microenvironment should address the importance of this molecule in the evolution and possible therapies of this pathology.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003eETHICS STATEMENT\u003c/p\u003e\n\u003cp\u003eThe studies involving human participants were reviewed and approved by Comit\u0026eacute; \u0026eacute;tico de investigaci\u0026oacute;n cl\u0026iacute;nica, Hospital Cl\u0026iacute;nico San Carlos, Madrid, Spain. The patients/participants provided their written informed consent to participate in this study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe study was performed in accordance with the ethical standards as laid down in the Declarations of Helsinki and approved by the local ethics committee. Written informed consent was obtained from all subjects.\u003c/p\u003e\n\u003cp\u003eACKNOWLEDGEMENTS\u003c/p\u003e\n\u003cp\u003eWe thank Prof. E. D. Carosella (Saint Louis Hospital, Paris, France) for scientific support.\u003c/p\u003e\n\u003cp\u003eCONFLICT OF INTEREST STATEMENT\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e\n\u003cp\u003eFUNDING\u003c/p\u003e\n\u003cp\u003eThis work was supported by grants from Instituto de Salud Carlos III (PI18/00626 and PI18/00721), with funds from the European Union (Fondo Europeo de Desarrollo Regional FEDER) and PR3/23 30834\u003c/p\u003e\n\u003cp\u003eAUTHORSHIP\u003c/p\u003e\n\u003cp\u003eCV-Y and IJ: manuscript writing, investigation, and analysis. IJ: design, analysis, and supervision. MM-A: investigation support and validation. EM-L: analysis. AG-C, AL-G, IL, and RG: patient follow-up, sample and data collection. AA-V: critical review, project administration, and funding acquisition. JM-V: manuscript writing and revision, supervision, project administration, and funding acquisition. All authors contributed to the article and approved the submitted version.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWHO Cancer Today: Data Visualization Tools for Exploring the Global Cancer Burden in 2020. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://gco.iarc.fr/today/home\u003c/span\u003e\u003cspan address=\"https://gco.iarc.fr/today/home\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSung H, Ferlay J, Siegel RL et al (2021) Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. 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Proc Natl Acad Sci U S A 101(18):7064\u0026ndash;7069. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1073/pnas.0401922101\u003c/span\u003e\u003cspan address=\"10.1073/pnas.0401922101\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\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":"cancer-immunology-immunotherapy","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ciim","sideBox":"Learn more about [Cancer Immunology, Immunotherapy](http://link.springer.com/journal/262)","snPcode":"262","submissionUrl":"https://submission.nature.com/new-submission/262/3","title":"Cancer Immunology, Immunotherapy","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-4437900/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4437900/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eGastric cancer ranks fifth in both world prevalence and lethality, with a 5-year survival of less than 30%. HLA-G, a non-classical class I HLA gene, has emerged as a potential marker for cancer susceptibility and prognosis due to its immunomodulatory properties. Its level of expression is regulated by polymorphisms in the 3\u0026rsquo; untranslated region (3\u0026rsquo;UTR) polymorphisms, which form various combined haplotypes (UTR-1 to -9).\u003c/p\u003e \u003cp\u003eIn this study, we examined HLA-G 3\u0026rsquo;UTR polymorphisms in paired tissue samples from 111 patients with gastric adenocarcinoma and 119 healthy controls. Polymorphism analysis was performed using PCR and Sanger sequencing, followed by statistical analysis using SNPStats software. Survival analysis was conducted using Kaplan-Meier curves and multivariate Cox regression models.\u003c/p\u003e \u003cp\u003eHigh-expressor HLA-G 3\u0026rsquo;UTR haplotypes (UTR-1 and UTR-6) were significantly associated with gastric cancer susceptibility, indicating a potential role in tumor immune evasion. Additionally, the 14 base pair insertion/deletion polymorphism (14bp I/D) emerged as a prognostic marker, with D/D genotype carriers showing lower survival rates compared to I/D and I/I genotype carriers.\u003c/p\u003e \u003cp\u003eOur study highlights the clinical relevance of HLA-G polymorphisms in gastric cancer, suggesting their potential as prognostic markers and therapeutic targets. Further elucidation of HLA-G-related pathways could lead to personalized treatment strategies and improved patient outcomes in gastric cancer.\u003c/p\u003e","manuscriptTitle":"HLA-G High-expressor 3’UTR Markers Are Linked to Gastric Cancer Development and Survival","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-04 18:58:05","doi":"10.21203/rs.3.rs-4437900/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-05-31T03:36:46+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-05-28T09:08:10+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"177222349530247421035772752399287623320","date":"2024-05-21T01:21:33+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-05-20T20:15:00+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-05-19T10:09:47+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-05-19T10:09:47+00:00","index":"","fulltext":""},{"type":"submitted","content":"Cancer Immunology, Immunotherapy","date":"2024-05-17T16:26:27+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"cancer-immunology-immunotherapy","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ciim","sideBox":"Learn more about [Cancer Immunology, Immunotherapy](http://link.springer.com/journal/262)","snPcode":"262","submissionUrl":"https://submission.nature.com/new-submission/262/3","title":"Cancer Immunology, Immunotherapy","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"c11f1ef5-7d99-40d4-b96c-97ac51d3b00f","owner":[],"postedDate":"June 4th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-11-18T16:01:15+00:00","versionOfRecord":{"articleIdentity":"rs-4437900","link":"https://doi.org/10.1007/s00262-024-03771-w","journal":{"identity":"cancer-immunology-immunotherapy","isVorOnly":false,"title":"Cancer Immunology, Immunotherapy"},"publishedOn":"2024-11-16 15:57:21","publishedOnDateReadable":"November 16th, 2024"},"versionCreatedAt":"2024-06-04 18:58:05","video":"","vorDoi":"10.1007/s00262-024-03771-w","vorDoiUrl":"https://doi.org/10.1007/s00262-024-03771-w","workflowStages":[]},"version":"v1","identity":"rs-4437900","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4437900","identity":"rs-4437900","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}