OLR1 is closely related to poor prognosis and immune cell infiltration in gastric cancer

OLR1 is closely related to poor prognosis and immune cell infiltration in gastric cancer | 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 Article OLR1 is closely related to poor prognosis and immune cell infiltration in gastric cancer Meixia Zhang, Qingzi Fu, Bo Jiang, Zhenzhen Yang, Qiaofeng Chen, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6509290/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 24 Nov, 2025 Read the published version in Scientific Reports → Version 1 posted 14 You are reading this latest preprint version Abstract Background : Oxidized low-density lipoprotein receptor 1 (OLR1) in gastric cancer (GC) progression and immune cell infiltration remains unclear. This study aimed to investigate the impact of OLR1 on GC progression and to elucidate its association with immune cell infiltration in GC. Materials and Methods : OLR1 expression profiles in GC and paired normal tissues were analyzed using datasets from the TCGA, GTEx, and GEO databases, with validation conducted through quantitative real-time PCR (qPCR) and immunohistochemistry (IHC). Differentially expressed genes (DEGs) associated with OLR1 were identified through gene set enrichment analysis (GSEA) and functional enrichment analysis. We evaluated the role of OLR1 in GC immune cell infiltration. In addition, Cox regression and Kaplan-Meier analyses were conducted on OLR1 for its prognostic significance and correlation with clinical variables. Finally, survival probabilities in GC patients were predicted by nomogram construction. Results : OLR1 expression in GC tissues significantly increased relative to that in matched normal samples (P < 0.001), as confirmed by qPCR and IHC (both P < 0.05). There were 268 DEGs associated with Staphylococcus aureus infection, protein digestion and absorption signaling, and the estrogen signaling pathway. OLR1 expression was positively related to macrophage infiltration (r = 0.742, P < 0.001, as validated by IHC). Additionally, OLR1 expression was strongly related to histological grade (P < 0.001), histological type (P < 0.05), T stage (P < 0.01), pathological stage (P < 0.05), and unfavorable overall survival (OS, P < 0.05). Our established nomogram effectively predicted the 1-, 3-, and 5-year OS probabilities in patients with GC (C-index [95% CI] = 0.627 [0.601–0.652]). Conclusion : OLR1 is strongly associated with a dismal prognostic outcome and immune cell infiltration in patients with GC. Biological sciences/Cancer/Cancer genetics Biological sciences/Cancer/Cancer microenvironment Biological sciences/Cancer/Gastrointestinal cancer Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Gastric cancer (GC), a highly aggressive malignancy of the gastrointestinal tract, ranks fifth in cancer-related mortality and is the fourth leading cause of cancer-associated deaths globally ( 1 ). Although the early diagnosis rate of GC exceeds 50% in certain countries, such as South Korea and Japan ( 2 ), it remains markedly low in most parts of the world, contributing to poor prognostic outcomes ( 3 ). Therefore, the identification of biomarkers closely associated with early GC detection and prognosis is critically needed. As the receptor for oxidized low-density lipoprotein (ox-LDL), oxidized low-density lipoprotein receptor 1 (OLR1) has important effects on various inflammatory diseases. In cardiovascular and metabolic diseases, the upregulation of ORL1 increases DNA damage resulting from intracellular reactive oxygen species, promoting hypoxia and angiogenesis( 4 ). According to numerous studies in recent years, OLR1 is tightly linked to cancer genesis and progression. OLR1 may increase HMGA2 transcription and c-Myc expression to promote the metastasis of pancreatic cancer( 5 ). Increased OLR1 expression has been observed in breast cancer tissues, which is related to unfavorable clinical outcomes and advanced clinicopathological factors( 6 ). In vitro experimental results have demonstrated that OLR1 overexpression promotes lung cancer cell proliferation, migration, and immune evasion ( 7 ), while OLR1 silencing significantly inhibits colonic cancer cell growth and reduces chemotherapy ( 8 )resistance ( 7 ). However, the role of OLR1 in GC has not yet been elucidated. The emergence of immunotherapy has markedly improved clinical outcomes for many patients with advanced stage malignancies( 9 ). Compared with conventional chemotherapy, anti-her2 therapy and anti-programmed cell death 1 (PD-1) antibodies have shown remarkable therapeutic effects in untreated GC patients ( 10 ). New anti-HER2 therapeutics, such as disitamab vedotin (RC48) and T-DXd, have made great achievements in the treatment of GC( 8 ). Antitumor immunotherapy efficacy should be predicated by taking effector cell infiltration in the tumor microenvironment (TME) as a prerequisite ( 11 ). Anti-ctla-4 (T lymphocyte-associated antigen 4) monoclonal antibodies exert their effects on tumors through restoring T-cell growth and extending T-cell stimulation( 12 ). T cells are among the tumor-infiltrating lymphocytes (TILs), while additional prevalent TILs include NK cells and macrophages. Consequently, comprehending both the TME and TILs in GC might establish a basis for enhancing the anti-immunotherapy efficacy for GC. Nevertheless, it remains unreported whether OLR1 can impact TIL levels in GC. The present work examined the effect of OLR1 on GC via the Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO) and Genotype-Tissue Expression (GTEx) databases through bioinformatics analyses, which cover differential gene (DEG) identification, functional enrichment, immune cell infiltration, clinical correlation, survival analyses and gene set enrichment analysis (GSEA). Additionally, overall survival (OS) in patients with GC was predicted through the construction of a nomogram. Finally, the aforementioned bioinformatics analyses were validated using clinical data and tissue samples from patients with histologically confirmed GC. Materials and Methods 2.1 Data Acquisition and Processing RNA-seq data were collected from 414 TCGA-derived GC and 36 adjacent non-carcinoma tissues (https://portal.gdc.cancer.gov/, November 20, 2024). Additionally, RNA-seq data from 174 non-carcinoma gastric tissues were acquired from the GTEx project through the UCSC Xena platform (http://xena.ucsc.edu/, November 20, 2024). Duplicate samples or those with insufficient information were excluded from subsequent analyses. R software (version 4.2.1) was used to perform log2-transformation of the RNA-seq data. Table 1 presents the clinical features of the GC patients. 2.2 OLR1 Differential Expression in Pan-cancer and GC Tissues Statistical analyses were conducted using appropriate methods tailored to the distributional characteristics of the data, employing the stats and car packages in R. Data visualization was performed using the ggplot2 package. ROC curve analyses were carried out with the pROC package, and the resulting outputs were visualized using ggplot2.To further validate the differential expression of OLR1 in GC compared with adjacent non-carcinoma tissues, two gene expression profile datasets from the GEO—GSE13911 and GSE54129—were analyzed using the GEO2R online tool (https://www.ncbi.nlm.nih.gov/geo/, accessed November 23, 2024). Subsequently, ROC curve analyses were performed to evaluate the prognostic significance of OLR1 in GC. Quantitative real-time PCR (q-PCR) of OLR1 expression in GC and non-carcinoma samples The Ethics Committee of the First Affiliated Hospital of Nanchang University (CDYFY-IACUC-202312QR022) approved our study. All human participants provided informed consent, and the study conformed to the Declaration of Helsinki. Every participant provided informed consent. Between March and June 2024, GC and non-carcinoma biopsy samples were continuously harvested from 20 GC patients at the Endoscopy Center of the First Affiliated Hospital of Nanchang University (Nanchang, Jiangxi). These obtained tissues were soaked in RNA protective solution before immediate storage in a –80 °C freezer. No patients received previous antitumor treatments, such as chemotherapy or radiotherapy. Moreover, patients who developed concomitant cancers were excluded from the analysis. 2.3 RNA Extraction and q-PCR First, TRIzol reagent (R0016, Beyotime) was used to extract total tissue RNA, which was subsequently used to prepare first-strand cDNA using the PrimeScript™ RT Reagent Kit with gDNA Eraser (RR047A, Takara). Thereafter, we adopted the 2 –∆∆Ct method to compute OLR1 gene expression, with GAPDH being used as an endogenous reference. Primer sequences: OLR1-F: 5’- TCCTGTCCCTAACCAAGCCT-3’ OLR1-R: 5’- AGGCAAAGGACCCCTAGAGT-3’ GAPDH-F: 5’-GTCAGCCGCATCTTCTTT-3’ GAPDH-R: 5’-CGCCCAATACGACCAAAT-3’ 2.4 IHC This study retrospectively enrolled 80 patients who underwent surgery for GC at the First Affiliated Hospital of Nanchang University between June 2022 and December 2022. All cases were pathologically confirmed as primary gastric adenocarcinoma, with no prior history of other malignancies or previous anticancer treatments. For the IHC analysis, paired GC and matched non-carcinoma samples (≥5 cm away from the tumor edge) were processed. Formalin-fixed, paraffin-embedded blocks were prepared to construct tissue microarrays. Sections (4 μm) were subjected to xylene deparaffinization and gradient alcohol rehydration. Following antigen retrieval and blocking, a primary antibody against OLR1 (57946; Cell Signaling Technology; 1:500) was added and incubated with the slides at 4 °C for 12 h. Subsequently, an HRP-conjugated secondary polymer (KIT-5009, MXB Biotechnologies, 1:10000) was applied and incubated for 40 min at 26 °C. Chromogenic detection was performed with diaminobenzidine (DAB) substrate (P0202, Beyotime). Three randomly selected fields per sample were imaged using a light microscope (Nikon Eclipse Ci-L). Staining intensity was quantitatively analyzed with ImageJ software (version 1.53t). Graphical presentation and statistical analyses were conducted using GraphPad Prism version 8.0. 2.5 Identification of DEGs in the high- and low-OLR1 expression groups In line with OLR1 expression, the top 50% of samples were assigned to the high-expression group, whereas the remaining samples were assigned to the low-expression group. The DESeq2 package in R was used to identify DEGs for which P.adj < 0.05 and the logFC absolute value was greater than 1.5. The results of the identified differential genes are presented in volcano maps. 2.6 Functional enrichment of DEGs When the IDs of the DEGs were converted via the “Hs.eg.db” package of R software, the clusterProfiler package was utilized for subsequent functional enrichment with thresholds of a q value<0.2 and p.adj <0.05. GSEA visualization and statistical analysis were completed with the clusterProfiler package, utilizing the C2 collection in MSigDB. Gene set permutations were conducted 10,000 times, with the significance thresholds adjusted to P < 0.05 and a false discovery rate (FDR) < 0.25. 2.7 Immune cell infiltration Immune cell infiltration within tumor tissues was assessed through single-sample GSEA (ssGSEA) with the GSVA package(13), which utilizes published gene signatures for quantification(14). Statistical analysis was carried out to investigate the relationship between OLR1 expression and immune infiltration levels via Spearman's correlation, and the Wilcoxon signed-rank test was used to analyze differential immune infiltration patterns in the OLR1 expression groups. For immunohistochemical evaluation, OLR1 expression levels were quantified by calculating the average optical density (AOD) from tissue samples of 80 GC patients using ImageJ software. Patients were stratified into high and low OLR1 expression cohorts based on the median AOD value. From each cohort, 10 cases were randomly selected for additional immunohistochemical staining to assess macrophage populations, following the protocol outlined in Section 2.5. We selected CD68 (MAB-0041, MXB biotechnologies, 1:500) as the molecular marker for identifying macrophages. Quantitative analysis of marker-positive areas was conducted via ImageJ, and GraphPad Prism 8.0 was used to visualize and analyze the data. 2.8 Clinical correlation analysis of OLR1 in GC The relationships between OLR1 expression levels and clinicopathological features were assessed using logistic regression analysis and Wilcoxon signed-rank tests. To assess how the OLR1 level and additional clinicopathological features affect survival outcomes in GC patients, univariate and multivariate Cox regression analyses were conducted. Multivariate Cox regression was further utilized to identify factors independently predicting GC prognosis. 2.9 Nomogram establishment and validation We developed a nomogram integrating all independent prognostic factors to estimate 1-, 3-, and 5-year survival probabilities for GC patients. The prognostic variables included were based on previously published studies(15). Nomogram construction was performed using R software, specifically employing the rms and survival packages. Model parameters were determined through bootstrap resampling, with 200 iterations conducted for each sample group to enhance stability. The predictive performance of the nomogram was assessed using the concordance index (C-index), with values approaching 1 indicating higher predictive accuracy. Results 2.10 OLR1 expression significantly increases inside pan-cancer and GC OLR1 is significantly upregulated in multiple malignancies, such as GC (Fig. 1A, P < 0.001). TCGA- and GTEx-derived data, comprising 414 GC tissues and 210 normal or adjacent non-tumor tissues, revealed that OLR1 expression markedly increased in GC samples compared with that in their non-carcinoma counterparts (Fig. 1B, P < 0.001). Furthermore, in 27 paired GC and adjacent tissue samples from the TCGA, OLR1 expression apparently increased inside tumors compared with that in their matched non-tumor counterparts (Fig. 1C, P < 0.001). In addition, ROC curve analysis (Fig. 1D) demonstrated that OLR1 expression has a high predictive value for GC diagnosis (AUC = 0.904, 95% CI: 0.881–0.927). IHC analysis of 80 GC patients confirmed that OLR1 expression inside tumor samples apparently increased compared with that in non-carcinoma samples (Fig. 2A-E, P<0.001). Similarly, according to the real-time PCR results, the OLR1 transcript levels in the GC samples apparently increased relative to those in the non-carcinoma samples (Fig. 2F, P<0.0001). To analyze differential OLR1 expression and its diagnostic predictive value, we analyzed RNA-seq data from the GSE13911 and GSE54129 datasets obtained from GEO. The results revealed that OLR1 expression in GC samples was markedly greater than that in adjacent samples in both datasets (Fig. 3A-B, P<0.001). Moreover, OLR1 expression exhibited strong predictive power for GC diagnosis (Fig. 3C, GSE13911: AUC = 0.823, 95% CI: 0.722–0.925; Fig. 3D, GSE54129: AUC = 0.842, 95% CI: 0.774–0.910). 2.11 DEG detection and functional enrichment A total of 199 downregulated DEGs and 69 upregulated DEGs related to OLR1 expression were identified (Fig. 4A). To further analyze DEG functions, enrichment analyses involving biological process (BP), cellular component (CC), and molecular function (MF) categories and KEGG pathways were conducted. As shown in Fig. 4B, the DEGs were predominantly associated with the following terms: BP (keratinization, keratinocyte differentiation, and epidermis development), CC (keratin filament, intermediate filament, and tertiary granule), MF (signaling receptor activator activity, structural constituent of skin epidermis, and receptor ligand activity), and KEGG (staphylococcus aureus infection, protein digestion and absorption, and the estrogen pathway). Only the top three enriched terms are displayed here; detailed enrichment results are provided in Table 2. GSEA revealed that OLR1-associated DEGs were most significantly enriched in six pathways, ranked in descending order by NES: Reactome immunoregulatory interactions between a lymphoid and a non-lymphoid cell, Reactome FCGR activation, Reactome FCGR3A-mediated IL-10 synthesis, Reactome extracellular matrix organization, Reactome FCERI-mediated Ca 2+ mobilization, and Reactome role of LAT2/NTAL/LAB in calcium mobilization (Fig. 4C-H). 2.12 OLR1 expression is tightly linked to immune cell infiltration OLR1 expression was positively correlated with macrophage and Th1 cell infiltration (Fig. 5A-C, macrophages, R = 0.742, P<0.001; Th1, R = 0.535, P<0.001). Compared with the low-OLR1 samples, the high-OLR1 samples presented significantly higher enrichment scores for both macrophages and Th1 cells (Fig. 5D-E, P<0.001). Detailed correlation analyses of OLR1 expression with immune cell infiltration are presented in Table 3. To further investigate these associations, we performed IHC staining of macrophages, which demonstrated the strongest correlation with OLR1 expression. Notably, compared with non-carcinoma samples, GC samples presented a significantly greater proportion of CD68-positive areas (a specific molecular marker of macrophages) (Fig. 5F-H, P<0.0001). 2.13 OLR1 expression is strongly associated with GC clinicopathological variables We obtained and analyzed RNAseq and clinical data from 375 STAD cases derived from TCGA. Statistical analysis revealed that OLR1 expression was significantly associated with histological type (Fig. 6A: Diffuse type vs. Tubular type, P < 0.01; Mucinous type vs. Tubular type, P < 0.05; Not otherwise specified vs. Tubular type, P < 0.001), histological grade (Fig. 6B: G1&G2 vs. G3, P < 0.001), pathological stage (Fig. 6C: Stage I vs. Stages II, III, and IV, P < 0.05), and pathological T stage (Fig. 6D: T1 vs. T2, P < 0.01; T1 vs. T3, P < 0.01; T1 vs. T4, P < 0.001). However, no significant associations were observed between OLR1 expression and pathological M stage, N stage, Helicobacter pylori infection status, primary therapy outcome, sex, race, or age (Fig. 6E-L). We further expanded our investigation by collecting clinicopathological variables from an additional cohort of 80 GC patients and analyzing their relationship with OLR1 expression levels. The analysis revealed statistically significant correlations between OLR1 expression and pathological stage (Table 4, pathological stage I vs. II & III & IV, P = 0.027), T stage (T1 vs. T2 & T3 & T4, P = 0.032), histological grade (G1 & G2 vs. G3, P = 0.015), and histological type (tubular type vs. non-tubular type, P = 0.001). These clinical findings provide substantial validation and reinforce the accuracy of our initial data mining analysis. 2.14 High OLR1 expression is related to unfavorable GC prognostic outcomes We further performed survival analysis using clinical data from patients with GC obtained from the TCGA database. OLR1 expression was strongly associated with OS, with the high-OLR1 expression group showing poorer OS (Fig. 7A, P = 0.024). However, no significant correlation between OLR1 expression and disease-specific survival (DSS) or the progression-free interval (PFI) was detected (Fig. 7B and 7C, P > 0.05). According to multivariate Cox regression, the OLR1 expression level, pathological M stage, and age were found to independently predict OS (Table 5). In contrast, OLR1 expression was not significantly related to either DSS or PFI (Tables 6 and 7). 2.15 Nomogram establishment and validation According to multivariate Cox regression, risk factors independently affecting GC OS (OLR1 expression level, pathological M stage, and age) were incorporated to construct a nomogram (Figure 7D) for predicting 1-, 3-, and 5-year survival rates in patients with GC. Calibration plots (Figure 7E) demonstrated that our nomogram performed satisfactorily in predicting the 1-, 3-, and 5-year survival probabilities (C-index [95% CI] = 0.627 [0.601–0.652]). Discussion According to bioinformatics analysis, OLR1 expression markedly increased in GC samples. These findings were further validated by qPCR and IHC analyses. Moreover, as revealed by the analysis of ROC curves, OLR1 is a potential diagnostic biomarker related to GC. A total of 199 downregulated and 69 upregulated DEGs associated with OLR1 were identified. GO annotation revealed that these DEGs were primarily enriched in terms related to keratinization, keratin filament, and the structural constituent of the skin epidermis. Emerging evidence indicates that aberrant expression of keratin 7 (KRT7), a key member of the cytokeratin family, may contribute to the progression of various malignancies( 16 ). In GC, KRT7 expression is markedly upregulated, and its knockdown has been shown to significantly inhibit tumor cell proliferation and migration( 17 ). In vitro studies have demonstrated that KRT80 interference inhibits viability, proliferation, and migration while promoting apoptosis in GC cells( 17 ). Keratin 18 (KRT18) has been shown to induce GC growth invasion and migration by activating the MAPK pathway( 18 ). KEGG analysis revealed that the Staphylococcus aureus infection pathway was significantly enriched. The high abundance of bacteria such as Staphylococcus in gastric juice leads to the production of N-nitroso compounds, which may promote gastric carcinogenesis by inducing the overexpression of proto-oncogenes and angiogenesis and the inhibition of apoptosis( 19 ). Non- H. pylori urease-positive bacteria, including Staphylococcus , are crucial for H. pylori -related GC severity ( 20 ). The GSEA results revealed the following: immunoregulatory interactions between a lymphoid and a non-lymphoid cell and a Reactome extracellular matrix organization. The heterogeneous TME fosters immune escape and contributes to resistance against both conventional therapies and immunotherapies( 21 ). The TME comprises various components, including stromal cells and immune cells, which interact dynamically to influence cancer progression and metastasis( 22 , 23 ). The complex crosstalk between stromal and immune elements plays a pivotal role in shaping the TME and driving the development of GC( 23 ). Moreover, interactions between the extracellular matrix and its receptors significantly impact the structure and function of the TME( 24 ), influencing carcinogenesis( 25 ), and tumor development( 25 ). Our analytical results indicate that OLR1-related DEGs are closely associated with two reactome pathways: immunoregulatory interactions between lymphoid and non-lymphoid cells and extracellular matrix organization. Furthermore, the immune infiltration results revealed that OLR1 expression was strongly correlated with immune cell infiltration, especially that of macrophages. M2-like macrophages promote GC metastasis by secreting multiple potent proangiogenic cytokines( 26 ). Consequently, OLR1 may have a significant effect on modulating the TME and extracellular matrix in GC, warranting further investigation. OLR1 not only has an important effect on metabolic diseases( 27 ) but is also closely related to different tumors. In head and neck squamous cell carcinoma (HNSCC), OLR1 expression is correlated with unfavorable prognostic outcomes and may influence epithelial-mesenchymal transition (EMT), stemness, and resistance outcomes( 28 ). OLR1 silencing may downregulate c-MYC, leading to the suppression of SULT2B1, thereby inhibiting glycolytic metabolism and reducing the growth and chemoresistance of colonic cancer cells( 29 ). Additionally, OLR1 may function as an oncogene by activating NF-κB target genes implicated in cellular proliferation, migration, and the inhibition of apoptosis, as well as genes associated with de novo lipogenesis( 30 ). The study revealed significant associations between OLR1 expression and GC features such as histological type (tubular type vs. non-tubular type), histological grade (G1 & G2 vs. G3), pathological stage (stage I vs. stages II & III & IV), and pathological T stage (T1 vs. T2/T3/T4). High OLR1 expression was strongly correlated with poor OS. Multivariate Cox regression analysis revealed that age, pathological M stage, and OLR1 expression were prognostic factors that independently predict the OS of GC patients. From these factors, a prognostic nomogram was established to predict survival probability in patients with GC. The calibration curves demonstrated good predictive accuracy of the established nomogram, suggesting its potential clinical utility for guiding individualized treatment strategies in GC management. Conclusions The main findings of this study can be summarized as follows: First, OLR1 expression is significantly elevated in GC tissues compared to non-carcinoma samples, suggesting its potential as a diagnostic biomarker for GC. Second, OLR1 may contribute to tumor progression and prognosis by remodeling the TME, highlighting its promise as a therapeutic target. Third, age, pathological M stage, and OLR1 expression level appear to function as independent risk factors influencing overall survival in GC patients. Finally, the nomogram developed in this study demonstrated predictive accuracy for estimating patient survival probability. Nonetheless, despite the integration of data from multiple online databases and clinical specimens, the findings require further validation in larger, independent cohorts to confirm their reliability. Declarations Declaration of interests 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. Author Contributions Shiyu Zhang: Conceptualization, Methodology, Software. Meixia Zhang.: Data curation, Writing- Original draft preparation. Qingzi Fu: Data curation, Writing- Original draft preparation. Bo Jiang: Visualization. Zhenzhen Yang: Supervision. Validation. Qiaofeng Chen, Data curation. Peng Chen: Writing- Reviewing and Editing. Funding This work was supported by the National Natural Science Foundation of China [Grant No.82460134]; Natural Science Foundation of Jiangxi Province [Grant No.20232BAB206021]; National Natural Science Foundation of China (Grant No.82003121)；Natural Science Foundation of Jiangxi Province (Grant No. 20242BAB20409). Acknowledgements This work was supported by the Key Laboratory Project of Digestive Diseases in Jiangxi Province (2024SSY06101), and Jiangxi Clinical Research Center for Gastroenterology (20223BCG74011). Data availability statement Data will be made available on request. References Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global Cancer Statistics 2020: Globocan Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA: a cancer journal for clinicians (2021) 71(3):209-49. Epub 2021/02/05. doi: 10.3322/caac.21660. So JBY, Kapoor R, Zhu F, Koh C, Zhou L, Zou R, et al. 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Epub 2021/12/19. doi: 10.1038/s41419-021-04174-w. Khaidakov M, Mitra S, Kang BY, Wang X, Kadlubar S, Novelli G, et al. Oxidized Ldl Receptor 1 (Olr1) as a Possible Link between Obesity, Dyslipidemia and Cancer. PLoS One (2011) 6(5):e20277. Epub 2011/06/04. doi: 10.1371/journal.pone.0020277. Tables Table 1 The clinical characteristic of Stomach Adenocarcinoma. Characteristic levels Overall n 375 Gender, n (%) Female 134 (35.7%) Male 241 (64.3%) Age, n (%) 65 207 (55.8%) T stage, n (%) T1 19 (5.2%) T2 80 (21.8%) T3 168 (45.8%) T4 100 (27.2%) N stage, n (%) N0 111 (31.1%) N1 97 (27.2%) N2 75 (21%) N3 74 (20.7%) M stage, n (%) M0 330 (93%) M1 25 (7%) Histological type, n (%) Diffuse Type 63 (16.8%) Mucinous Type 19 (5.1%) Not Otherwise Specified 207 (55.3%) Papillary Type 5 (1.3%) Signet Ring Type 11 (2.9%) Tubular Type 69 (18.4%) Pathologic stage, n (%) Stage I 53 (15.1%) Stage II 111 (31.5%) Stage III 150 (42.6%) Stage IV 38 (10.8%) Histologic grade, n (%) G1 10 (2.7%) G2 137 (37.4%) G3 219 (59.8%) Residual tumor, n (%) R0 298 (90.6%) R1 15 (4.6%) R2 16 (4.9%) Primary therapy outcome, n (%) PD 65 (20.5%) SD 17 (5.4%) PR 4 (1.3%) CR 231 (72.9%) H pylori infection, n (%) No 145 (89%) Yes 18 (11%) Barretts esophagus, n (%) No 193 (92.8%) Yes 15 (7.2%) Anatomic neoplasm subdivision, n (%) Antrum/Distal 138 (38.2%) Cardia/Proximal 48 (13.3%) Fundus/Body 130 (36%) Gastroesophageal Junction 41 (11.4%) Other 4 (1.1%) Age, median (IQR) 67 (58, 73) Table 2 Details of functional enrichment analysis results of DEGs. Ontology ID Description Gene Ratio Bg Ratio P value p.adjust BP GO:0031424 keratinization 16/161 85/18800 1.85e-17 3.5e-14 BP GO:0030216 keratinocyte differentiation 19/161 167/18800 2.91e-16 2.75e-13 BP GO:0008544 epidermis development 25/161 355/18800 4.48e-16 2.82e-13 BP GO:0009913 epidermal cell differentiation 19/161 230/18800 1.09e-13 5.15e-11 BP GO:0043588 skin development 19/161 296/18800 9.7e-12 3.66e-09 CC GO:0045095 keratin filament 11/173 102/19594 1.64e-09 2.61e-07 CC GO:0005882 intermediate filament 14/173 216/19594 7.89e-09 6.27e-07 CC GO:0070820 tertiary granule 12/173 164/19594 2.51e-08 1.33e-06 CC GO:0045111 intermediate filament cytoskeleton 14/173 257/19594 7.05e-08 2.8e-06 CC GO:0001533 cornified envelope 7/173 45/19594 1.27e-07 4.03e-06 MF GO:0030280 structural constituent of skin epidermis 7/162 37/18410 2.96e-08 8.22e-06 MF GO:0048018 receptor ligand activity 17/162 489/18410 1.53e-06 0.0002 MF GO:0030546 signaling receptor activator activity 17/162 496/18410 1.86e-06 0.0002 MF GO:0004252 serine-type endopeptidase activity 10/162 174/18410 3.29e-06 0.0002 MF GO:0004175 endopeptidase activity 15/162 432/18410 6.63e-06 0.0003 KEGG hsa05150 Staphylococcus aureus infection 10/71 96/8164 8.02e-09 1.13e-06 KEGG hsa04915 Estrogen signaling pathway 7/71 138/8164 0.0002 0.0129 KEGG hsa04974 Protein digestion and absorption 5/71 103/8164 0.0020 0.0703 KEGG hsa04145 Phagosome 6/71 152/8164 0.0020 0.0703 DEGs: Differential Expression Genes; BP: Biological Process; MF: Molecular Function; CC: Cellular Component; KEGG: Kyoto Encyclopedia of Genes and Genomes. Table 3 Correlation between OLR1 expression and immune cell infiltration var1 var2 df statistic_spearman cor_spearman pvalue_spearman OLR1 Macrophages 373 2270320.629 0.741686127 1.12478E-66 OLR1 Th1 cells 373 4090677.733 0.534568468 4.23145E-29 OLR1 iDC 373 4494495.756 0.488622624 6.72707E-24 OLR1 DC 373 4768941.771 0.457396544 8.69907E-21 OLR1 Neutrophils 373 5286105.801 0.398554352 9.97013E-16 OLR1 Cytotoxic cells 373 5354327.805 0.390792149 3.93382E-15 OLR1 Eosinophils 373 5369216.805 0.389098099 5.28249E-15 OLR1 TReg 373 5470588.979 0.377564117 3.75922E-14 OLR1 aDC 373 5478625.812 0.376649697 4.37759E-14 OLR1 T cells 373 5600877.819 0.362740037 4.18739E-13 OLR1 NK CD56dim cells 373 5928372.837 0.325478116 1.0574E-10 OLR1 Tem 373 6051237.844 0.311498709 7.00005E-10 OLR1 NK cells 373 6370984.862 0.275118345 6.13918E-08 OLR1 CD8 T cells 373 6433830.866 0.267967816 1.37534E-07 OLR1 TFH 373 6873542.891 0.217938003 2.06682E-05 OLR1 pDC 373 7010034.292 0.202408204 7.89825E-05 OLR1 Mast cells 373 7101655.904 0.191983627 0.000183885 OLR1 T helper cells 373 7419125.922 0.155862337 0.002472368 OLR1 B cells 373 7512561.927 0.14523132 0.004833008 OLR1 Tcm 373 7568665.931 0.138847886 0.00708427 OLR1 Th2 cells 373 7877074.948 0.103757544 0.044646382 OLR1 Tgd 373 8217730.968 0.064998183 0.209184629 OLR1 NK CD56bright cells 373 9529880.042 -0.084296284 0.103134706 OLR1 Th17 cells 373 9564862.044 -0.088276487 0.0878046 Table 4 The correlation between OLR1 expression and clinical variables in 80 gastric cancer patients Characteristics High Low P value n 42 38 Gender (M/F), n (%) 0.438 F 12 (15%) 8 (10%) M 30 (37.5%) 30 (37.5%) Age (years), median (IQR) 66 (59, 70.75) 63 (57.25, 71.5) 0.606 Pathologic.stage, n (%) 0.027 I 10 (12.5%) 18 (22.5%) II & III & IV 32 (40%) 20 (25%) T.stage, n (%) 0.032 T1 5 (6.2%) 12 (15%) T2 & T3 & T4 37 (46.2%) 26 (32.5%) N.stage, n (%) 0.554 N1 7 (8.8%) 10 (12.5%) N0 10 (12.5%) 11 (13.8%) N2 12 (15%) 7 (8.8%) N3 13 (16.2%) 10 (12.5%) Histological type, n (%) 0.001 Tubular Type 5 (6.2%) 17 (21.2%) Non-tubular type 37 (46.2%) 21 (26.2%) Histological grade, n (%) 0.015 G1 & G2 13 (16.2%) 22 (27.5%) G3 29 (36.2%) 16 (20%) Residual tumor, n (%) 0.952 R0 29 (36.2%) 26 (32.5%) R1 & R2 13 (16.2%) 12 (15%) Primary therapy outcome, n (%) 0.463 CR 32 (40%) 24 (30%) SD 4 (5%) 3 (3.8%) PD 5 (6.2%) 9 (11.2%) PR 1 (1.2%) 2 (2.5%) Anatomic neoplasm subdivision, n (%) 0.195 other 3 (3.8%) 0 (0%) Cardia 14 (17.5%) 16 (20%) Fundus/Body 20 (25%) 14 (17.5%) Antrum 5 (6.2%) 8 (10%) Table 5 Univariate and multivariate regression analysis of Overall Survival (OS) related factors in patients with gastric cancer. Characteristics Total(N) Univariate analysis Multivariate analysis Hazard ratio (95% CI) P value Hazard ratio (95% CI) P value Pathologic T stage 362 T1 18 Reference Reference T4&T3&T2 344 8.829 (1.234 - 63.151) 0.030 4.832 (0.616 - 37.905) 0.134 Pathologic N stage 352 N0 107 Reference Reference N1&N2&N3 245 1.925 (1.264 - 2.931) 0.002 1.589 (0.941 - 2.685) 0.083 OLR1 370 Low 184 Reference Reference High 186 1.462 (1.051 - 2.034) 0.024 1.552 (1.080 - 2.229) 0.017 Pathologic M stage 352 M0 327 Reference Reference M1 25 2.254 (1.295 - 3.924) 0.004 2.579 (1.398 - 4.757) 0.002 Age 367 65 204 1.620 (1.154 - 2.276) 0.005 1.782 (1.233 - 2.578) 0.002 Race 320 White 236 Reference Asian&Black or African American 84 0.801 (0.515 - 1.247) 0.326 Gender 370 Female 133 Reference Male 237 1.267 (0.891 - 1.804) 0.188 Pathologic stage 347 Stage I 50 Reference Reference Stage III&Stage IV&Stage II 297 2.247 (1.210 - 4.175) 0.010 1.087 (0.466 - 2.538) 0.846 Histologic grade 361 G1 10 Reference G3&G2 351 1.957 (0.484 - 7.910) 0.346 Table 6 Univariate and multivariate regression analysis of Disease Specific Survival (DSS) related factors in patients with gastric cancer. Characteristics Total(N) Univariate analysis Multivariate analysis Hazard ratio (95% CI) P value Hazard ratio (95% CI) P value Pathologic stage 331 Stage I&Stage II 154 Reference Stage III&Stage IV 177 2.146 (1.352-3.404) 0.001 1.550 (0.773-3.107) 0.217 N stage 334 N0 104 Reference N1&N2&N3 230 1.807 (1.075-3.036) 0.025 1.101 (0.532-2.279) 0.796 M stage 333 M0 311 Reference M1 22 2.438 (1.221-4.870) 0.012 1.625 (0.768-3.442) 0.204 OLR1 349 Low 178 Reference High 171 1.361 (0.897-2.066) 0.147 T stage 345 T1&T2 90 Reference T3&T4 255 2.089 (1.192-3.660) 0.010 1.529 (0.780-2.997) 0.217 Table 7 Univariate and multivariate regression analysis of Progress Free Interval (PFI) related factors in patients with gastric cancer. Characteristics Total(N) Univariate analysis Multivariate analysis Hazard ratio (95% CI) P value Hazard ratio (95% CI) P value Pathologic stage 349 Stage I&Stage II 161 Reference Stage III&Stage IV 188 1.676 (1.154-2.435) 0.007 1.061 (0.613-1.837) 0.831 N stage 354 N0 108 Reference N1&N2&N3 246 1.640 (1.075-2.501) 0.022 1.302 (0.740-2.291) 0.359 M stage 353 M0 328 Reference M1 25 2.224 (1.194-4.144) 0.012 1.667 (0.851-3.264) 0.136 OLR1 372 Low 186 Reference High 186 1.144 (0.804-1.627) 0.456 T stage 364 T1&T2 97 Reference T3&T4 267 1.705 (1.095-2.654) 0.018 1.560 (0.907-2.683) 0.108 Additional Declarations No competing interests reported. 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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-6509290","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":464531222,"identity":"6955bbc1-f851-43d5-ba1e-676f45a20b00","order_by":0,"name":"Meixia Zhang","email":"","orcid":"","institution":"The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang 330006, Jiangxi, China","correspondingAuthor":false,"prefix":"","firstName":"Meixia","middleName":"","lastName":"Zhang","suffix":""},{"id":464531223,"identity":"abcbbbd5-4903-4fe5-b161-d00ac96e78e0","order_by":1,"name":"Qingzi Fu","email":"","orcid":"","institution":"Jiangxi Maternal and Child Health Hospital","correspondingAuthor":false,"prefix":"","firstName":"Qingzi","middleName":"","lastName":"Fu","suffix":""},{"id":464531224,"identity":"6c78474b-789d-44d8-91e4-170bd5346f91","order_by":2,"name":"Bo Jiang","email":"","orcid":"","institution":"The First People's Hospital of Suqian","correspondingAuthor":false,"prefix":"","firstName":"Bo","middleName":"","lastName":"Jiang","suffix":""},{"id":464531225,"identity":"28f48836-a467-4b58-9e25-b7a2812a1adc","order_by":3,"name":"Zhenzhen Yang","email":"","orcid":"","institution":"The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang 330006, Jiangxi, China","correspondingAuthor":false,"prefix":"","firstName":"Zhenzhen","middleName":"","lastName":"Yang","suffix":""},{"id":464531226,"identity":"4913fa9e-1fab-4c03-bab6-b1578190e741","order_by":4,"name":"Qiaofeng Chen","email":"","orcid":"","institution":"The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang 330006, Jiangxi, China","correspondingAuthor":false,"prefix":"","firstName":"Qiaofeng","middleName":"","lastName":"Chen","suffix":""},{"id":464531227,"identity":"81428e38-8b7d-4798-841e-15637f9e3245","order_by":5,"name":"Peng Chen","email":"","orcid":"","institution":"The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang 330006, Jiangxi, China","correspondingAuthor":false,"prefix":"","firstName":"Peng","middleName":"","lastName":"Chen","suffix":""},{"id":464531228,"identity":"16b4b105-ae3b-4acf-8326-e67b7608a052","order_by":6,"name":"Shiyu Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8UlEQVRIiWNgGAWjYFCCBIYPDAY2cvbHmw9ABA4Q1sI4g6EizZjhzLEEUrScOZzIcMPHgDgt5uw5hs28bYeBGnm+PfjZxiDHdyOB8XMBHi2WPW9AWtLzmKV7txv2tjEYS95IYJaegUeLwY0c88e8bdbFbDJnt0kztjEkbriRwMbMg18LyBbmxB6JnGcgLfXEaeE545w4QyKHDaQlwYCgljPPChvnAAPZgOeYmWTPOQnDmWceNkvj1XI8eWPDG2BUGrA3P5P4UWYjz3c8+eBnfFoYGDgMmJAUSAAxYwNeDQwM7A8YfxBQMgpGwSgYBSMcAAB4+lCqBzWZxwAAAABJRU5ErkJggg==","orcid":"","institution":"The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang 330006, Jiangxi, China","correspondingAuthor":true,"prefix":"","firstName":"Shiyu","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2025-04-23 06:08:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6509290/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6509290/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-25552-w","type":"published","date":"2025-11-24T15:58:39+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":83814828,"identity":"2ce01b02-6331-4b1e-8c5f-2c3c9aca74a3","added_by":"auto","created_at":"2025-06-03 07:31:55","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":367074,"visible":true,"origin":"","legend":"\u003cp\u003eOLR1 expression patterns across cancers and associated DEGs. (A) Comparison of OLR1 expression between cancer and non-carcinoma samples in the TCGA dataset. (B, C) Differential OLR1 expression in gastric cancer (GC). (D) Diagnostic performance of OLR1 in distinguishing GC from non-tumor tissues, as evaluated by ROC curve analysis. NS, \u003cem\u003eP\u003c/em\u003e≥0.05; *, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.05; **, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.01; ***,\u003cem\u003e P\u003c/em\u003e\u0026lt;0.001.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-6509290/v1/0d97b0ff50f4a424c8d7e0c9.png"},{"id":83814834,"identity":"9e5dfa63-6231-470b-a4a1-f29eee9ade2c","added_by":"auto","created_at":"2025-06-03 07:31:55","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":5945568,"visible":true,"origin":"","legend":"\u003cp\u003eIHC and qRT-PCR analysis of OLR1 expression. (A-D) Representative IHC staining for OLR1 ingastric cancer (GC) and non-carcinoma samples at 200× and 400× magnification. (E) Statistical analysis of OLR1 protein expression in 80 clinically confirmed GC cases. (F) Comparative qRT-PCR assay for OLR1 mRNA levels inside GC and matchednormal samples from 20 patients. *, \u003cem\u003eP\u003c/em\u003e\u0026lt; 0.05.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-6509290/v1/4ad0953d5c7a0e6982bf55dd.png"},{"id":83816188,"identity":"ddd9435e-d73c-4fb1-949a-929a3db5657d","added_by":"auto","created_at":"2025-06-03 07:47:55","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":213901,"visible":true,"origin":"","legend":"\u003cp\u003eDifferential OLR1 expression in gastric cancer and its diagnostic value in GEO datasets. (A-B) Differential OLR1 expression in GC and matched non-tumor samples in two independent GEO datasets (GSE13911 and GSE54129). (C-D) ROC curves evaluating the ability of OLR1 to discriminate GC samples from non-carcinoma samples in the respective datasets. ***, \u003cem\u003eP\u003c/em\u003e\u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-6509290/v1/e73ed8a60c94af8f75bd5a32.png"},{"id":83815808,"identity":"1bbde713-7793-4874-9263-bc4e8e5de063","added_by":"auto","created_at":"2025-06-03 07:39:55","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":815715,"visible":true,"origin":"","legend":"\u003cp\u003eOLR1-associated DEGs and functional enrichment. (A) Volcano plot showing OLR1-related DEGs. The red dots represent genes whose expression was significantly upregulated, whereas the blue dots indicate those whose expression was downregulated (|log2FC| \u0026gt; 1.5, adjusted P \u0026lt; 0.05). (B) Top three significantly enriched functional terms in the GO annotation, namely, BP, MF and CC, as well as KEGG. (C-H) The top six associated pathways obtained from GSEA. NES, normalized enrichment score; p.adj, adjusted p-value; FDR, false discovery rate.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-6509290/v1/948f43886dbd8ddc3c1f3f8d.png"},{"id":83815810,"identity":"8d618aaf-2435-4e49-ba89-420d5692e91c","added_by":"auto","created_at":"2025-06-03 07:39:55","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":2169142,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of OLR1-associated immune cell infiltration within GCs. (A) Lollipop plot illustrating the association of OLR1 with immune cell infiltration levels inside GCs. The circle size represents the strength of the correlation (larger size = stronger correlation), whereas the color intensity indicates statistical significance (blue gradient: darker = lower P value). (B-C) Scatter plots demonstrating the relationships of OLR1 with (B) macrophage and (C) Th1 cell infiltration levels. (D-E) Box plots comparing the enrichment scores of (D) macrophages and (E) Th1 cells in the OLR1-high versus OLR1-low-expression groups. (F-G) Representative immunohistochemical (IHC) staining of CD68 (a macrophage-specific marker) in (F) adjacent non-tumor tissues and (G) gastric cancer tissues at 200× magnification. Brown regions indicate positive staining. (H) Quantitative comparison of CD68-positive areas between gastric cancer and matched adjacent tissues. ***, \u003cem\u003eP\u003c/em\u003e\u0026lt; 0.001; ****, \u003cem\u003eP\u003c/em\u003e\u0026lt; 0.0001.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-6509290/v1/33a415cd2824e2c9521f1e2d.png"},{"id":83815811,"identity":"07c8b1fc-5286-404f-ad6e-9abf354d1f3c","added_by":"auto","created_at":"2025-06-03 07:39:55","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":545069,"visible":true,"origin":"","legend":"\u003cp\u003eRelationship of OLR1 expression with GC clinicopathological features from the TCGA database. (A-L) Correlations between OLR1 expression levels and (A) histological type, (B) Histological grade, (C) Pathological stage, (D) Pathological T stage, (E) Pathological N stage, (F) Pathological M stage, (G) \u003cem\u003eHelicobacter pylori\u003c/em\u003einfection status, (H) Primary therapy outcome, (I) Sex, (J) Race, (K) Age, (L) Barrett's esophagus. *, \u003cem\u003eP\u003c/em\u003e\u0026lt; 0.05; **, \u003cem\u003eP\u003c/em\u003e\u0026lt; 0.01; ***, \u003cem\u003eP\u003c/em\u003e\u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-6509290/v1/66ea6f4f8501e281f6de4701.png"},{"id":83814831,"identity":"be6add42-20e8-4f6d-acd6-e14de304dfe7","added_by":"auto","created_at":"2025-06-03 07:31:55","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":479639,"visible":true,"origin":"","legend":"\u003cp\u003e(A-C) Kaplan-Meier survival curves comparing OS, DSS, and PFI between the OLR1-high and OLR1-low-expression groups.\u003cstrong\u003e \u003c/strong\u003e(D) The nomogram constructed to predict 1-, 3-, and 5-year OS in patients with GC. (E) Calibration plots for our constructed nomogram.\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-6509290/v1/fd23b07d260d2944a302549c.png"},{"id":97178641,"identity":"28e3ebca-bef2-4b82-b38f-90c2ff6dc0a6","added_by":"auto","created_at":"2025-12-01 16:12:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":11557315,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6509290/v1/4ae10f23-b036-4cf7-820f-2fea4a98c275.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"OLR1 is closely related to poor prognosis and immune cell infiltration in gastric cancer","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGastric cancer (GC), a highly aggressive malignancy of the gastrointestinal tract, ranks fifth in cancer-related mortality and is the fourth leading cause of cancer-associated deaths globally (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Although the early diagnosis rate of GC exceeds 50% in certain countries, such as South Korea and Japan (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e), it remains markedly low in most parts of the world, contributing to poor prognostic outcomes (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Therefore, the identification of biomarkers closely associated with early GC detection and prognosis is critically needed.\u003c/p\u003e \u003cp\u003eAs the receptor for oxidized low-density lipoprotein (ox-LDL), oxidized low-density lipoprotein receptor 1 (OLR1) has important effects on various inflammatory diseases. In cardiovascular and metabolic diseases, the upregulation of ORL1 increases DNA damage resulting from intracellular reactive oxygen species, promoting hypoxia and angiogenesis(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). According to numerous studies in recent years, OLR1 is tightly linked to cancer genesis and progression. OLR1 may increase HMGA2 transcription and c-Myc expression to promote the metastasis of pancreatic cancer(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Increased OLR1 expression has been observed in breast cancer tissues, which is related to unfavorable clinical outcomes and advanced clinicopathological factors(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). In vitro experimental results have demonstrated that OLR1 overexpression promotes lung cancer cell proliferation, migration, and immune evasion (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e), while OLR1 silencing significantly inhibits colonic cancer cell growth and reduces chemotherapy (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e)resistance (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). However, the role of OLR1 in GC has not yet been elucidated.\u003c/p\u003e \u003cp\u003eThe emergence of immunotherapy has markedly improved clinical outcomes for many patients with advanced stage malignancies(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Compared with conventional chemotherapy, anti-her2 therapy and anti-programmed cell death 1 (PD-1) antibodies have shown remarkable therapeutic effects in untreated GC patients (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). New anti-HER2 therapeutics, such as disitamab vedotin (RC48) and T-DXd, have made great achievements in the treatment of GC(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Antitumor immunotherapy efficacy should be predicated by taking effector cell infiltration in the tumor microenvironment (TME) as a prerequisite (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Anti-ctla-4 (T lymphocyte-associated antigen 4) monoclonal antibodies exert their effects on tumors through restoring T-cell growth and extending T-cell stimulation(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). T cells are among the tumor-infiltrating lymphocytes (TILs), while additional prevalent TILs include NK cells and macrophages. Consequently, comprehending both the TME and TILs in GC might establish a basis for enhancing the anti-immunotherapy efficacy for GC. Nevertheless, it remains unreported whether OLR1 can impact TIL levels in GC.\u003c/p\u003e \u003cp\u003eThe present work examined the effect of OLR1 on GC via the Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO) and Genotype-Tissue Expression (GTEx) databases through bioinformatics analyses, which cover differential gene (DEG) identification, functional enrichment, immune cell infiltration, clinical correlation, survival analyses and gene set enrichment analysis (GSEA). Additionally, overall survival (OS) in patients with GC was predicted through the construction of a nomogram. Finally, the aforementioned bioinformatics analyses were validated using clinical data and tissue samples from patients with histologically confirmed GC.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003ch2\u003e2.1 Data Acquisition and Processing\u003c/h2\u003e\n\u003cp\u003eRNA-seq data were collected from 414 TCGA-derived GC and 36 adjacent non-carcinoma tissues (https://portal.gdc.cancer.gov/, November 20, 2024). Additionally, RNA-seq data from 174 non-carcinoma gastric tissues were acquired from the GTEx project through the UCSC Xena platform (http://xena.ucsc.edu/, November 20, 2024). Duplicate samples or those with insufficient information were excluded from subsequent analyses. R software (version 4.2.1) was used to perform log2-transformation of the RNA-seq data. Table 1 presents the clinical features of the GC patients.\u003c/p\u003e\n\u003ch2\u003e2.2 OLR1 Differential Expression in Pan-cancer and GC Tissues\u003c/h2\u003e\n\u003cp\u003eStatistical analyses were conducted using appropriate methods tailored to the distributional characteristics of the data, employing the stats and car packages in R. Data visualization was performed using the ggplot2 package. ROC curve analyses were carried out with the pROC package, and the resulting outputs were visualized using ggplot2.To further validate the differential expression of OLR1 in GC compared with adjacent non-carcinoma tissues, two gene expression profile datasets from the GEO\u0026mdash;GSE13911 and GSE54129\u0026mdash;were analyzed using the GEO2R online tool (https://www.ncbi.nlm.nih.gov/geo/, accessed November 23, 2024). Subsequently, ROC curve analyses were performed to evaluate the prognostic significance of OLR1 in GC.\u003c/p\u003e\n\u003ch2\u003eQuantitative real-time PCR (q-PCR) of OLR1 expression in GC and non-carcinoma samples\u003c/h2\u003e\n\u003cp\u003eThe Ethics Committee of the First Affiliated Hospital of Nanchang University (CDYFY-IACUC-202312QR022) approved our study. All human participants provided informed consent, and the study conformed to the Declaration of Helsinki. Every participant provided informed consent. Between March and June 2024, GC and non-carcinoma biopsy samples were continuously harvested from 20 GC patients at the Endoscopy Center of the First Affiliated Hospital of Nanchang University (Nanchang, Jiangxi). These obtained tissues were soaked in RNA protective solution before immediate storage in a \u0026ndash;80 \u0026deg;C freezer. No patients received previous antitumor treatments, such as chemotherapy or radiotherapy. Moreover, patients who developed concomitant cancers were excluded from the analysis.\u003c/p\u003e\n\u003ch2\u003e2.3 RNA Extraction and q-PCR\u003c/h2\u003e\n\u003cp\u003eFirst, TRIzol reagent (R0016, Beyotime) was used to extract total tissue RNA, which was subsequently used to prepare first-strand cDNA using the PrimeScript\u0026trade; RT Reagent Kit with gDNA Eraser (RR047A, Takara). Thereafter, we adopted the 2\u003csup\u003e\u0026ndash;∆∆Ct\u003c/sup\u003e method to compute OLR1 gene expression, with GAPDH being used as an endogenous reference.\u003c/p\u003e\n\u003cp\u003ePrimer sequences:\u003c/p\u003e\n\u003cp\u003eOLR1-F: 5\u0026rsquo;- TCCTGTCCCTAACCAAGCCT-3\u0026rsquo;\u003c/p\u003e\n\u003cp\u003eOLR1-R: 5\u0026rsquo;- AGGCAAAGGACCCCTAGAGT-3\u0026rsquo;\u003c/p\u003e\n\u003cp\u003eGAPDH-F: 5\u0026rsquo;-GTCAGCCGCATCTTCTTT-3\u0026rsquo;\u003c/p\u003e\n\u003cp\u003eGAPDH-R: 5\u0026rsquo;-CGCCCAATACGACCAAAT-3\u0026rsquo;\u003c/p\u003e\n\u003ch2\u003e2.4 IHC\u003c/h2\u003e\n\u003cp\u003eThis study retrospectively enrolled 80 patients who underwent surgery for GC at the First Affiliated Hospital of Nanchang University between June 2022 and December 2022. All cases were pathologically confirmed as primary gastric adenocarcinoma, with no prior history of other malignancies or previous anticancer treatments. For the IHC analysis, paired GC and matched non-carcinoma samples (\u0026ge;5 cm away from the tumor edge) were processed. Formalin-fixed, paraffin-embedded blocks were prepared to construct tissue microarrays. Sections (4 \u0026mu;m) were subjected to xylene deparaffinization and gradient alcohol rehydration. Following antigen retrieval and blocking, a primary antibody against OLR1 (57946; Cell Signaling Technology; 1:500) was added and incubated with the slides at 4 \u0026deg;C for 12 h. Subsequently, an HRP-conjugated secondary polymer (KIT-5009, MXB Biotechnologies, 1:10000) was applied and incubated for 40 min at 26 \u0026deg;C. Chromogenic detection was performed with\u0026nbsp;diaminobenzidine (DAB) substrate (P0202, Beyotime).\u003c/p\u003e\n\u003cp\u003eThree randomly selected fields per sample were imaged using a light microscope (Nikon Eclipse Ci-L). Staining intensity was quantitatively analyzed with ImageJ software (version 1.53t). Graphical presentation and statistical analyses were conducted using GraphPad Prism version 8.0.\u003c/p\u003e\n\u003ch2\u003e2.5 Identification of DEGs in the high- and low-OLR1 expression groups\u003c/h2\u003e\n\u003cp\u003eIn line with OLR1 expression, the top 50% of samples were assigned to the high-expression group, whereas the remaining samples were assigned to the low-expression group. The DESeq2 package in R was used to identify DEGs for which P.adj \u0026lt; 0.05 and the logFC absolute value was greater than 1.5. The results of the identified differential genes are presented in volcano maps.\u003c/p\u003e\n\u003ch2\u003e2.6 Functional enrichment of DEGs\u003c/h2\u003e\n\u003cp\u003eWhen the IDs of the DEGs were converted via the \u0026ldquo;Hs.eg.db\u0026rdquo; package of R software, the clusterProfiler package was utilized for subsequent functional enrichment with thresholds of a q value\u0026lt;0.2 and p.adj \u0026lt;0.05. GSEA visualization and statistical analysis were completed with the clusterProfiler package, utilizing the C2 collection in MSigDB. Gene set permutations were conducted 10,000 times, with the significance thresholds adjusted to P \u0026lt; 0.05 and a false discovery rate (FDR) \u0026lt; 0.25.\u003c/p\u003e\n\u003ch2\u003e2.7 Immune cell infiltration\u003c/h2\u003e\n\u003cp\u003eImmune cell infiltration within tumor tissues was assessed through single-sample GSEA (ssGSEA) with the GSVA package(13), which utilizes published gene signatures for quantification(14). Statistical analysis was carried out to investigate the relationship between OLR1 expression and immune infiltration levels via Spearman\u0026apos;s correlation, and the Wilcoxon signed-rank test was used to analyze differential immune infiltration patterns in the OLR1 expression groups.\u003c/p\u003e\n\u003cp\u003eFor immunohistochemical evaluation, OLR1 expression levels were quantified by calculating the average optical density (AOD) from tissue samples of 80 GC patients using ImageJ software. Patients were stratified into high and low OLR1 expression cohorts based on the median AOD value. From each cohort, 10 cases were randomly selected for additional immunohistochemical staining to assess macrophage populations, following the protocol outlined in Section 2.5. We selected CD68 (MAB-0041, MXB biotechnologies, 1:500) as the molecular marker for identifying macrophages. Quantitative analysis of marker-positive areas was conducted via ImageJ, and GraphPad Prism 8.0 was used to visualize and analyze the data.\u003c/p\u003e\n\u003ch2\u003e2.8 Clinical correlation analysis of OLR1 in GC\u003c/h2\u003e\n\u003cp\u003eThe relationships between OLR1 expression levels and clinicopathological features were assessed using logistic regression analysis and Wilcoxon signed-rank tests. To assess how the OLR1 level and additional clinicopathological features affect survival outcomes in GC patients, univariate and multivariate Cox regression analyses were conducted. Multivariate Cox regression was further utilized to identify factors independently predicting GC prognosis.\u003c/p\u003e\n\u003ch2\u003e2.9 Nomogram establishment and validation\u003c/h2\u003e\n\u003cp\u003eWe developed a nomogram integrating all independent prognostic factors to estimate 1-, 3-, and 5-year survival probabilities for GC patients. The prognostic variables included were based on previously published studies(15). \u0026nbsp;Nomogram construction was performed using R software, specifically employing the rms and survival packages. Model parameters were determined through bootstrap resampling, with 200 iterations conducted for each sample group to enhance stability. The predictive performance of the nomogram was assessed using the concordance index (C-index), with values approaching 1 indicating higher predictive accuracy.\u003c/p\u003e"},{"header":"Results","content":"\u003ch2\u003e2.10 OLR1 expression significantly increases inside pan-cancer and GC\u003c/h2\u003e\n\u003cp\u003eOLR1 is significantly upregulated in multiple malignancies, such as GC (Fig. 1A, P \u0026lt; 0.001). TCGA- and GTEx-derived data, comprising 414 GC tissues and 210 normal or adjacent non-tumor tissues, revealed that OLR1 expression markedly increased in GC samples compared with that in their non-carcinoma counterparts (Fig. 1B, P \u0026lt; 0.001). Furthermore, in 27 paired GC and adjacent tissue samples from the TCGA, OLR1 expression apparently increased inside tumors compared with that in their matched non-tumor counterparts (Fig. 1C, P \u0026lt; 0.001). In addition, ROC curve analysis (Fig. 1D) demonstrated that OLR1 expression has a high predictive value for GC diagnosis (AUC = 0.904, 95% CI: 0.881\u0026ndash;0.927).\u003c/p\u003e\n\u003cp\u003eIHC analysis of 80 GC patients confirmed that OLR1 expression inside tumor samples apparently increased compared with that in non-carcinoma samples (Fig. 2A-E, P\u0026lt;0.001). Similarly, according to the real-time PCR results, the OLR1 transcript levels in the GC samples apparently increased relative to those in the non-carcinoma samples (Fig. 2F, P\u0026lt;0.0001).\u003c/p\u003e\n\u003cp\u003eTo analyze differential OLR1 expression and its diagnostic predictive value, we analyzed RNA-seq data from the GSE13911 and GSE54129 datasets obtained from GEO. The results revealed that OLR1 expression in GC samples was markedly greater than that in adjacent samples in both datasets (Fig. 3A-B, P\u0026lt;0.001). Moreover, OLR1 expression exhibited strong predictive power for GC diagnosis (Fig. 3C, GSE13911: AUC = 0.823, 95% CI: 0.722\u0026ndash;0.925; Fig. 3D, GSE54129: AUC = 0.842, 95% CI: 0.774\u0026ndash;0.910).\u003c/p\u003e\n\u003ch2\u003e2.11 DEG\u0026nbsp;detection and functional enrichment\u003c/h2\u003e\n\u003cp\u003eA total of 199 downregulated DEGs and 69 upregulated DEGs related to OLR1 expression were identified (Fig. 4A).\u003c/p\u003e\n\u003cp\u003eTo further analyze DEG functions, enrichment analyses involving biological\u0026nbsp;process (BP), cellular component (CC), and molecular function (MF) categories and KEGG pathways were conducted. As shown in Fig. 4B, the DEGs were predominantly associated with the following terms: BP (keratinization, keratinocyte differentiation, and epidermis development), CC (keratin filament, intermediate filament, and tertiary granule), MF (signaling receptor activator activity, structural constituent of skin epidermis, and receptor ligand activity), and KEGG (staphylococcus aureus infection, protein digestion and absorption, and the estrogen pathway). Only the top three enriched terms are displayed here; detailed enrichment results are provided in Table 2.\u003c/p\u003e\n\u003cp\u003eGSEA revealed that OLR1-associated DEGs were most significantly enriched in six pathways, ranked in descending order by NES: Reactome immunoregulatory interactions between a lymphoid and a non-lymphoid cell, Reactome FCGR activation, Reactome FCGR3A-mediated IL-10 synthesis, Reactome extracellular matrix organization, Reactome FCERI-mediated Ca\u003csup\u003e2+\u003c/sup\u003e mobilization, and Reactome role of LAT2/NTAL/LAB in calcium mobilization (Fig. 4C-H).\u003c/p\u003e\n\u003ch2\u003e2.12 OLR1 expression is\u0026nbsp;tightly linked to\u0026nbsp;immune cell infiltration\u003c/h2\u003e\n\u003cp\u003eOLR1 expression was positively correlated with macrophage and Th1 cell infiltration (Fig. 5A-C, macrophages, R\u0026nbsp;=\u0026nbsp;0.742, P\u0026lt;0.001; Th1, R\u0026nbsp;=\u0026nbsp;0.535, P\u0026lt;0.001). Compared with the low-OLR1 samples, the high-OLR1 samples presented significantly higher enrichment scores for both macrophages and Th1 cells (Fig. 5D-E, P\u0026lt;0.001). Detailed correlation analyses of OLR1 expression with immune cell infiltration are presented in Table 3. To further investigate these associations, we performed IHC staining of macrophages, which demonstrated the strongest correlation with OLR1 expression. Notably, compared with non-carcinoma samples, GC samples presented a significantly greater proportion of CD68-positive areas (a specific molecular marker of macrophages) (Fig. 5F-H, P\u0026lt;0.0001).\u003c/p\u003e\n\u003ch2\u003e2.13 OLR1 expression is\u0026nbsp;strongly associated with GC\u0026nbsp;clinicopathological variables\u003c/h2\u003e\n\u003cp\u003eWe obtained and analyzed RNAseq and clinical data from 375 STAD cases derived from TCGA. Statistical analysis revealed that OLR1 expression was significantly associated with histological type (Fig. 6A: Diffuse type vs. Tubular type, P \u0026lt; 0.01; Mucinous type vs. Tubular type, P \u0026lt; 0.05; Not otherwise specified vs. Tubular type, P \u0026lt; 0.001), histological grade (Fig. 6B: G1\u0026amp;G2 vs. G3, P \u0026lt; 0.001), pathological stage (Fig. 6C: Stage I vs. Stages II, III, and IV, P \u0026lt; 0.05), and pathological T stage (Fig. 6D: T1 vs. T2, P \u0026lt; 0.01; T1 vs. T3, P \u0026lt; 0.01; T1 vs. T4, P \u0026lt; 0.001). However, no significant associations were observed between OLR1 expression and pathological M stage, N stage,\u0026nbsp;\u003cem\u003eHelicobacter pylori\u003c/em\u003e infection status, primary therapy outcome, sex, race, or age (Fig. 6E-L).\u003c/p\u003e\n\u003cp\u003eWe further expanded our investigation by collecting clinicopathological variables from an additional cohort of 80 GC patients and analyzing their relationship with OLR1 expression levels. The analysis revealed statistically significant correlations between OLR1 expression and pathological stage (Table 4, pathological stage I vs. II \u0026amp; III \u0026amp; IV, P = 0.027), T stage (T1 vs. T2 \u0026amp; T3 \u0026amp; T4, P = 0.032), histological grade (G1 \u0026amp; G2 vs. G3, P = 0.015), and histological type (tubular type vs. non-tubular type, P = 0.001). These clinical findings provide substantial validation and reinforce the accuracy of our initial data mining analysis.\u003c/p\u003e\n\u003ch2\u003e2.14 High OLR1\u0026nbsp;expression\u0026nbsp;is related to unfavorable GC prognostic outcomes\u003c/h2\u003e\n\u003cp\u003eWe further performed survival analysis using clinical data from patients with GC obtained from the TCGA database. OLR1 expression was strongly associated with OS, with the high-OLR1 expression group showing poorer OS (Fig. 7A, P = 0.024). However, no significant correlation between OLR1 expression and disease-specific survival (DSS) or the progression-free interval (PFI) was detected (Fig. 7B and 7C, P \u0026gt; 0.05).\u003c/p\u003e\n\u003cp\u003eAccording to multivariate Cox regression, the OLR1 expression level, pathological M stage, and age were found to independently predict OS (Table 5). In contrast, OLR1 expression was not significantly related to either DSS or PFI (Tables 6 and 7).\u003c/p\u003e\n\u003ch2\u003e2.15 Nomogram\u0026nbsp;establishment and validation\u003c/h2\u003e\n\u003cp\u003eAccording to multivariate Cox regression, risk factors independently affecting GC OS (OLR1 expression level, pathological M stage, and age) were incorporated to construct a nomogram (Figure 7D) for predicting 1-, 3-, and 5-year survival rates in patients with GC. Calibration plots (Figure 7E) demonstrated that our nomogram performed satisfactorily in predicting the 1-, 3-, and 5-year survival probabilities (C-index [95% CI] = 0.627 [0.601\u0026ndash;0.652]).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eAccording to bioinformatics analysis, OLR1 expression markedly increased in GC samples. These findings were further validated by qPCR and IHC analyses. Moreover, as revealed by the analysis of ROC curves, OLR1 is a potential diagnostic biomarker related to GC.\u003c/p\u003e \u003cp\u003eA total of 199 downregulated and 69 upregulated DEGs associated with OLR1 were identified. GO annotation revealed that these DEGs were primarily enriched in terms related to keratinization, keratin filament, and the structural constituent of the skin epidermis. Emerging evidence indicates that aberrant expression of keratin 7 (KRT7), a key member of the cytokeratin family, may contribute to the progression of various malignancies(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). In GC, KRT7 expression is markedly upregulated, and its knockdown has been shown to significantly inhibit tumor cell proliferation and migration(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). In vitro studies have demonstrated that KRT80 interference inhibits viability, proliferation, and migration while promoting apoptosis in GC cells(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Keratin 18 (KRT18) has been shown to induce GC growth invasion and migration by activating the MAPK pathway(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). KEGG analysis revealed that the Staphylococcus aureus infection pathway was significantly enriched. The high abundance of bacteria such as Staphylococcus in gastric juice leads to the production of N-nitroso compounds, which may promote gastric carcinogenesis by inducing the overexpression of proto-oncogenes and angiogenesis and the inhibition of apoptosis(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Non-\u003cem\u003eH. pylori\u003c/em\u003e urease-positive bacteria, including \u003cem\u003eStaphylococcus\u003c/em\u003e, are crucial for \u003cem\u003eH. pylori\u003c/em\u003e-related GC severity (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). The GSEA results revealed the following: immunoregulatory interactions between a lymphoid and a non-lymphoid cell and a Reactome extracellular matrix organization. The heterogeneous TME fosters immune escape and contributes to resistance against both conventional therapies and immunotherapies(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). The TME comprises various components, including stromal cells and immune cells, which interact dynamically to influence cancer progression and metastasis(\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). The complex crosstalk between stromal and immune elements plays a pivotal role in shaping the TME and driving the development of GC(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Moreover, interactions between the extracellular matrix and its receptors significantly impact the structure and function of the TME(\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e), influencing carcinogenesis(\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e), and tumor development(\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). Our analytical results indicate that OLR1-related DEGs are closely associated with two reactome pathways: immunoregulatory interactions between lymphoid and non-lymphoid cells and extracellular matrix organization. Furthermore, the immune infiltration results revealed that OLR1 expression was strongly correlated with immune cell infiltration, especially that of macrophages. M2-like macrophages promote GC metastasis by secreting multiple potent proangiogenic cytokines(\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). Consequently, OLR1 may have a significant effect on modulating the TME and extracellular matrix in GC, warranting further investigation.\u003c/p\u003e \u003cp\u003eOLR1 not only has an important effect on metabolic diseases(\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e) but is also closely related to different tumors. In head and neck squamous cell carcinoma (HNSCC), OLR1 expression is correlated with unfavorable prognostic outcomes and may influence epithelial-mesenchymal transition (EMT), stemness, and resistance outcomes(\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). OLR1 silencing may downregulate c-MYC, leading to the suppression of SULT2B1, thereby inhibiting glycolytic metabolism and reducing the growth and chemoresistance of colonic cancer cells(\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Additionally, OLR1 may function as an oncogene by activating NF-κB target genes implicated in cellular proliferation, migration, and the inhibition of apoptosis, as well as genes associated with de novo lipogenesis(\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). The study revealed significant associations between OLR1 expression and GC features such as histological type (tubular type vs. non-tubular type), histological grade (G1 \u0026amp; G2 vs. G3), pathological stage (stage I vs. stages II \u0026amp; III \u0026amp; IV), and pathological T stage (T1 vs. T2/T3/T4). High OLR1 expression was strongly correlated with poor OS.\u003c/p\u003e \u003cp\u003eMultivariate Cox regression analysis revealed that age, pathological M stage, and OLR1 expression were prognostic factors that independently predict the OS of GC patients. From these factors, a prognostic nomogram was established to predict survival probability in patients with GC. The calibration curves demonstrated good predictive accuracy of the established nomogram, suggesting its potential clinical utility for guiding individualized treatment strategies in GC management.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThe main findings of this study can be summarized as follows:\u003c/p\u003e \u003cp\u003eFirst, OLR1 expression is significantly elevated in GC tissues compared to non-carcinoma samples, suggesting its potential as a diagnostic biomarker for GC. Second, OLR1 may contribute to tumor progression and prognosis by remodeling the TME, highlighting its promise as a therapeutic target. Third, age, pathological M stage, and OLR1 expression level appear to function as independent risk factors influencing overall survival in GC patients. Finally, the nomogram developed in this study demonstrated predictive accuracy for estimating patient survival probability. Nonetheless, despite the integration of data from multiple online databases and clinical specimens, the findings require further validation in larger, independent cohorts to confirm their reliability.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eDeclaration of interests\u003c/strong\u003e\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\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eShiyu Zhang:\u0026nbsp;Conceptualization, Methodology, Software. Meixia Zhang.: Data curation, Writing- Original draft preparation.\u0026nbsp;Qingzi Fu: Data curation, Writing- Original draft preparation. Bo Jiang: Visualization. Zhenzhen Yang:\u0026nbsp;Supervision. Validation. Qiaofeng Chen, Data curation. Peng Chen:\u0026nbsp;Writing- Reviewing and Editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Natural Science Foundation of China [Grant No.82460134]; Natural Science Foundation of Jiangxi Province [Grant No.20232BAB206021]; National Natural Science Foundation of China (Grant No.82003121)；Natural Science Foundation of Jiangxi Province (Grant No. 20242BAB20409).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Key Laboratory Project of Digestive Diseases in Jiangxi Province (2024SSY06101), and Jiangxi Clinical Research Center for Gastroenterology (20223BCG74011).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData will be made available on request.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. 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Epub 2011/06/04. doi: 10.1371/journal.pone.0020277.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1 The clinical characteristic of Stomach Adenocarcinoma.\u0026nbsp;\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003e\u003cstrong\u003elevels\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOverall\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003en\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e375\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003eGender, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e134 (35.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e241 (64.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003eAge, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003e\u0026lt;=65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e164 (44.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003e\u0026gt;65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e207 (55.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003eT stage, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003eT1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e19 (5.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003eT2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e80 (21.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003eT3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e168 (45.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003eT4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e100 (27.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003eN stage, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003eN0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e111 (31.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003eN1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e97 (27.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003eN2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e75 (21%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003eN3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e74 (20.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003eM stage, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003eM0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e330 (93%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003eM1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e25 (7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003eHistological type, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003eDiffuse Type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e63 (16.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003eMucinous Type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e19 (5.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003eNot Otherwise Specified\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e207 (55.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003ePapillary Type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e5 (1.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003eSignet Ring Type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e11 (2.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003eTubular Type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e69 (18.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003ePathologic stage, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003eStage I\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e53 (15.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003eStage II\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e111 (31.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003eStage III\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e150 (42.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003eStage IV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e38 (10.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003eHistologic grade, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003eG1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e10 (2.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003eG2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e137 (37.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003eG3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e219 (59.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003eResidual tumor, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003eR0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e298 (90.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003eR1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e15 (4.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003eR2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e16 (4.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003ePrimary therapy outcome, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003ePD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e65 (20.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e17 (5.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003ePR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e4 (1.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003eCR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e231 (72.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003eH pylori infection, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e145 (89%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e18 (11%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003eBarretts esophagus, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e193 (92.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e15 (7.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003eAnatomic neoplasm subdivision, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003eAntrum/Distal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e138 (38.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003eCardia/Proximal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e48 (13.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003eFundus/Body\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e130 (36%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003eGastroesophageal Junction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e41 (11.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e4 (1.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003eAge, median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e67 (58, 73)\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\u003e\u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 2 Details of functional enrichment analysis results of DEGs.\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" align=\"\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOntology\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eID\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDescription\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGene Ratio\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBg Ratio\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep.adjust\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003eGO:0031424\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003ekeratinization\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e16/161\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e85/18800\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e1.85e-17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e3.5e-14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003eGO:0030216\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003ekeratinocyte differentiation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e19/161\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e167/18800\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e2.91e-16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e2.75e-13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003eGO:0008544\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003eepidermis development\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e25/161\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e355/18800\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e4.48e-16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e2.82e-13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003eGO:0009913\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003eepidermal cell differentiation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e19/161\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e230/18800\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e1.09e-13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e5.15e-11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003eGO:0043588\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003eskin development\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e19/161\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e296/18800\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e9.7e-12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e3.66e-09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003eGO:0045095\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003ekeratin filament\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e11/173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e102/19594\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e1.64e-09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e2.61e-07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003eGO:0005882\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003eintermediate filament\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e14/173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e216/19594\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e7.89e-09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e6.27e-07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003eGO:0070820\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003etertiary granule\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e12/173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e164/19594\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e2.51e-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e1.33e-06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003eGO:0045111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003eintermediate filament cytoskeleton\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e14/173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e257/19594\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e7.05e-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e2.8e-06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003eGO:0001533\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003ecornified envelope\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e7/173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e45/19594\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e1.27e-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e4.03e-06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eMF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003eGO:0030280\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003estructural constituent of skin epidermis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e7/162\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e37/18410\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e2.96e-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e8.22e-06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eMF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003eGO:0048018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003ereceptor ligand activity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e17/162\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e489/18410\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e1.53e-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e0.0002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eMF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003eGO:0030546\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003esignaling receptor activator activity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e17/162\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e496/18410\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e1.86e-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e0.0002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eMF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003eGO:0004252\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003eserine-type endopeptidase activity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e10/162\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e174/18410\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e3.29e-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e0.0002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eMF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003eGO:0004175\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003eendopeptidase activity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e15/162\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e432/18410\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e6.63e-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e0.0003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eKEGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003ehsa05150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003eStaphylococcus aureus infection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e10/71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e96/8164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e8.02e-09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e1.13e-06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eKEGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003ehsa04915\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003eEstrogen signaling pathway\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e7/71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e138/8164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e0.0002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e0.0129\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eKEGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003ehsa04974\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003eProtein digestion and absorption\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e5/71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e103/8164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e0.0020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e0.0703\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eKEGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003ehsa04145\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003ePhagosome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e6/71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e152/8164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e0.0020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e0.0703\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eDEGs: Differential Expression Genes; BP: Biological Process; MF: Molecular Function; CC: Cellular Component; KEGG: Kyoto Encyclopedia of Genes and Genomes.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 3 Correlation between OLR1 expression and immune cell infiltration\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"613\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cstrong\u003evar1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 115px;\"\u003e\n \u003cp\u003e\u003cstrong\u003evar2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cstrong\u003edf\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 127px;\"\u003e\n \u003cp\u003e\u003cstrong\u003estatistic_spearman\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ecor_spearman\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003epvalue_spearman\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003eOLR1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 115px;\"\u003e\n \u003cp\u003eMacrophages\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e373\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 127px;\"\u003e\n \u003cp\u003e2270320.629\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0.741686127\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e1.12478E-66\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003eOLR1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 115px;\"\u003e\n \u003cp\u003eTh1 cells\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e373\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 127px;\"\u003e\n \u003cp\u003e4090677.733\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0.534568468\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e4.23145E-29\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003eOLR1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 115px;\"\u003e\n \u003cp\u003eiDC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e373\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 127px;\"\u003e\n \u003cp\u003e4494495.756\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0.488622624\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e6.72707E-24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003eOLR1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 115px;\"\u003e\n \u003cp\u003eDC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e373\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 127px;\"\u003e\n \u003cp\u003e4768941.771\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0.457396544\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e8.69907E-21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003eOLR1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 115px;\"\u003e\n \u003cp\u003eNeutrophils\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e373\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 127px;\"\u003e\n \u003cp\u003e5286105.801\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0.398554352\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e9.97013E-16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003eOLR1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 115px;\"\u003e\n \u003cp\u003eCytotoxic cells\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e373\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 127px;\"\u003e\n \u003cp\u003e5354327.805\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0.390792149\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e3.93382E-15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003eOLR1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 115px;\"\u003e\n \u003cp\u003eEosinophils\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e373\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 127px;\"\u003e\n \u003cp\u003e5369216.805\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0.389098099\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e5.28249E-15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003eOLR1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 115px;\"\u003e\n \u003cp\u003eTReg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e373\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 127px;\"\u003e\n \u003cp\u003e5470588.979\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0.377564117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e3.75922E-14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003eOLR1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 115px;\"\u003e\n \u003cp\u003eaDC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e373\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 127px;\"\u003e\n \u003cp\u003e5478625.812\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0.376649697\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e4.37759E-14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003eOLR1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 115px;\"\u003e\n \u003cp\u003eT cells\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e373\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 127px;\"\u003e\n \u003cp\u003e5600877.819\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0.362740037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e4.18739E-13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003eOLR1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 115px;\"\u003e\n \u003cp\u003eNK CD56dim cells\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e373\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 127px;\"\u003e\n \u003cp\u003e5928372.837\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0.325478116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e1.0574E-10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003eOLR1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 115px;\"\u003e\n \u003cp\u003eTem\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e373\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 127px;\"\u003e\n \u003cp\u003e6051237.844\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0.311498709\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e7.00005E-10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003eOLR1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 115px;\"\u003e\n \u003cp\u003eNK cells\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e373\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 127px;\"\u003e\n \u003cp\u003e6370984.862\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0.275118345\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e6.13918E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003eOLR1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 115px;\"\u003e\n \u003cp\u003eCD8 T cells\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e373\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 127px;\"\u003e\n \u003cp\u003e6433830.866\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0.267967816\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e1.37534E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003eOLR1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 115px;\"\u003e\n \u003cp\u003eTFH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e373\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 127px;\"\u003e\n \u003cp\u003e6873542.891\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0.217938003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e2.06682E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003eOLR1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 115px;\"\u003e\n \u003cp\u003epDC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e373\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 127px;\"\u003e\n \u003cp\u003e7010034.292\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0.202408204\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e7.89825E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003eOLR1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 115px;\"\u003e\n \u003cp\u003eMast cells\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e373\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 127px;\"\u003e\n \u003cp\u003e7101655.904\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0.191983627\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e0.000183885\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003eOLR1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 115px;\"\u003e\n \u003cp\u003eT helper cells\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e373\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 127px;\"\u003e\n \u003cp\u003e7419125.922\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0.155862337\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e0.002472368\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003eOLR1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 115px;\"\u003e\n \u003cp\u003eB cells\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e373\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 127px;\"\u003e\n \u003cp\u003e7512561.927\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0.14523132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e0.004833008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003eOLR1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 115px;\"\u003e\n \u003cp\u003eTcm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e373\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 127px;\"\u003e\n \u003cp\u003e7568665.931\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0.138847886\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e0.00708427\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003eOLR1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 115px;\"\u003e\n \u003cp\u003eTh2 cells\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e373\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 127px;\"\u003e\n \u003cp\u003e7877074.948\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0.103757544\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e0.044646382\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003eOLR1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 115px;\"\u003e\n \u003cp\u003eTgd\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e373\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 127px;\"\u003e\n \u003cp\u003e8217730.968\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0.064998183\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e0.209184629\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003eOLR1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 115px;\"\u003e\n \u003cp\u003eNK CD56bright cells\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e373\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 127px;\"\u003e\n \u003cp\u003e9529880.042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 100px;\"\u003e\n \u003cp\u003e-0.084296284\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e0.103134706\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003eOLR1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 115px;\"\u003e\n \u003cp\u003eTh17 cells\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e373\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 127px;\"\u003e\n \u003cp\u003e9564862.044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 100px;\"\u003e\n \u003cp\u003e-0.088276487\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e0.0878046\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 4 The correlation between OLR1 expression and clinical variables in 80 gastric cancer patients\u0026nbsp;\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 213px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHigh\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLow\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 213px;\"\u003e\n \u003cp\u003en\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 213px;\"\u003e\n \u003cp\u003eGender (M/F), n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003e0.438\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 213px;\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e12 (15%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e8 (10%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 213px;\"\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e30 (37.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e30 (37.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 213px;\"\u003e\n \u003cp\u003eAge (years), median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e66 (59, 70.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e63 (57.25, 71.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003e0.606\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 213px;\"\u003e\n \u003cp\u003ePathologic.stage, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.027\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 213px;\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e10 (12.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e18 (22.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 213px;\"\u003e\n \u003cp\u003eII \u0026amp; III \u0026amp; IV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e32 (40%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e20 (25%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 213px;\"\u003e\n \u003cp\u003eT.stage, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.032\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 213px;\"\u003e\n \u003cp\u003eT1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e5 (6.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e12 (15%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 213px;\"\u003e\n \u003cp\u003eT2 \u0026amp; T3 \u0026amp; T4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e37 (46.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e26 (32.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 213px;\"\u003e\n \u003cp\u003eN.stage, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003e0.554\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 213px;\"\u003e\n \u003cp\u003eN1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e7 (8.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e10 (12.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 213px;\"\u003e\n \u003cp\u003eN0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e10 (12.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e11 (13.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 213px;\"\u003e\n \u003cp\u003eN2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e12 (15%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e7 (8.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 213px;\"\u003e\n \u003cp\u003eN3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e13 (16.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e10 (12.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 213px;\"\u003e\n \u003cp\u003eHistological type, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 213px;\"\u003e\n \u003cp\u003eTubular Type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e5 (6.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e17 (21.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 213px;\"\u003e\n \u003cp\u003eNon-tubular type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e37 (46.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e21 (26.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 213px;\"\u003e\n \u003cp\u003eHistological grade, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.015\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 213px;\"\u003e\n \u003cp\u003eG1 \u0026amp; G2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e13 (16.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e22 (27.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 213px;\"\u003e\n \u003cp\u003eG3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e29 (36.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e16 (20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 213px;\"\u003e\n \u003cp\u003eResidual tumor, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003e0.952\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 213px;\"\u003e\n \u003cp\u003eR0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e29 (36.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e26 (32.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 213px;\"\u003e\n \u003cp\u003eR1 \u0026amp; R2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e13 (16.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e12 (15%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 213px;\"\u003e\n \u003cp\u003ePrimary therapy outcome, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003e0.463\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 213px;\"\u003e\n \u003cp\u003eCR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e32 (40%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e24 (30%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 213px;\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e4 (5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e3 (3.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 213px;\"\u003e\n \u003cp\u003ePD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e5 (6.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e9 (11.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 213px;\"\u003e\n \u003cp\u003ePR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e1 (1.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e2 (2.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 213px;\"\u003e\n \u003cp\u003eAnatomic neoplasm subdivision, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003e0.195\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 213px;\"\u003e\n \u003cp\u003eother\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e3 (3.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 213px;\"\u003e\n \u003cp\u003eCardia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e14 (17.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e16 (20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 213px;\"\u003e\n \u003cp\u003eFundus/Body\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e20 (25%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e14 (17.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 213px;\"\u003e\n \u003cp\u003eAntrum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e5 (6.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e8 (10%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003e\u0026nbsp;\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\u003e\u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 5 Univariate and multivariate regression analysis of Overall Survival (OS) related factors in patients with gastric cancer.\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"625\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal(N)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 214px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnivariate analysis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 31px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 214px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMultivariate analysis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHazard ratio (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHazard ratio (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003ePathologic T stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e362\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003eT1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003eReference\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003eReference\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003eT4\u0026amp;T3\u0026amp;T2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e344\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e8.829 (1.234 - 63.151)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.030\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e4.832 (0.616 - 37.905)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e0.134\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003ePathologic N stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e352\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003eN0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003eReference\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003eReference\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003eN1\u0026amp;N2\u0026amp;N3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e245\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e1.925 (1.264 - 2.931)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e1.589 (0.941 - 2.685)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e0.083\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003eOLR1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e370\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e184\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003eReference\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003eReference\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e186\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e1.462 (1.051 - 2.034)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.024\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e1.552 (1.080 - 2.229)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.017\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003ePathologic M stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e352\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003eM0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e327\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003eReference\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003eReference\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003eM1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e2.254 (1.295 - 3.924)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.004\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e2.579 (1.398 - 4.757)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e367\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026lt;= 65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003eReference\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003eReference\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026gt; 65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e204\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e1.620 (1.154 - 2.276)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.005\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e1.782 (1.233 - 2.578)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003eRace\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e320\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003eWhite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e236\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003eReference\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003eAsian\u0026amp;Black or African American\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e0.801 (0.515 - 1.247)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e0.326\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e370\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003eReference\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e237\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e1.267 (0.891 - 1.804)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e0.188\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003ePathologic stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e347\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003eStage I\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003eReference\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003eReference\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003eStage III\u0026amp;Stage IV\u0026amp;Stage II\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e297\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e2.247 (1.210 - 4.175)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.010\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e1.087 (0.466 - 2.538)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e0.846\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003eHistologic grade\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e361\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003eG1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003eReference\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003eG3\u0026amp;G2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e351\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e1.957 (0.484 - 7.910)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e0.346\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\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\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 6 Univariate and multivariate regression analysis of Disease Specific Survival (DSS) related factors in patients with gastric cancer.\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"619\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal(N)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 211px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnivariate analysis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 31px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 211px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMultivariate analysis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHazard ratio (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHazard ratio (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003ePathologic stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e331\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003eStage I\u0026amp;Stage II\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003eStage III\u0026amp;Stage IV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e177\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e2.146 (1.352-3.404)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e1.550 (0.773-3.107)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e0.217\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003eN stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e334\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003eN0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003eN1\u0026amp;N2\u0026amp;N3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e1.807 (1.075-3.036)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.025\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e1.101 (0.532-2.279)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e0.796\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003eM stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e333\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003eM0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e311\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003eM1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e2.438 (1.221-4.870)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.012\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e1.625 (0.768-3.442)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e0.204\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003eOLR1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e349\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e171\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e1.361 (0.897-2.066)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e0.147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003eT stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e345\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003eT1\u0026amp;T2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003eT3\u0026amp;T4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e255\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e2.089 (1.192-3.660)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.010\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e1.529 (0.780-2.997)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e0.217\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\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 7 Univariate and multivariate regression analysis of Progress Free Interval (PFI) related factors in patients with gastric cancer.\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"619\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal(N)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 211px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnivariate analysis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 31px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 211px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMultivariate analysis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHazard ratio (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHazard ratio (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003ePathologic stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e349\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003eStage I\u0026amp;Stage II\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e161\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003eStage III\u0026amp;Stage IV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e188\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e1.676 (1.154-2.435)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.007\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e1.061 (0.613-1.837)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e0.831\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003eN stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e354\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003eN0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n 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Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-6509290/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6509290/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e: Oxidized low-density lipoprotein receptor 1 (OLR1) in gastric cancer (GC) progression and immune cell infiltration remains unclear. This study aimed to investigate the impact of OLR1 on GC progression and to elucidate its association with immune cell infiltration in GC.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMaterials and Methods\u003c/strong\u003e: OLR1 expression profiles in GC and paired normal tissues were analyzed using datasets from the TCGA, GTEx, and GEO databases, with validation conducted through quantitative real-time PCR (qPCR) and immunohistochemistry (IHC). Differentially expressed genes (DEGs) associated with OLR1 were identified through gene set enrichment analysis (GSEA) and functional enrichment analysis. We evaluated the role of OLR1 in GC immune cell infiltration. In addition, Cox regression and Kaplan-Meier analyses were conducted on OLR1 for its prognostic significance and correlation with clinical variables. Finally, survival probabilities in GC patients were predicted by nomogram construction.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: OLR1 expression in GC tissues significantly increased relative to that in matched normal samples (P \u0026lt; 0.001), as confirmed by qPCR and IHC (both P \u0026lt; 0.05). There were 268 DEGs associated with \u003cem\u003eStaphylococcus\u003c/em\u003e \u003cem\u003eaureus\u003c/em\u003e infection, protein digestion and absorption signaling, and the estrogen signaling pathway. OLR1 expression was positively related to macrophage infiltration (r = 0.742, P \u0026lt; 0.001, as validated by IHC). Additionally, OLR1 expression was strongly related to histological grade (P \u0026lt; 0.001), histological type (P \u0026lt; 0.05), T stage (P \u0026lt; 0.01), pathological stage (P \u0026lt; 0.05), and unfavorable overall survival (OS, P \u0026lt; 0.05). Our established nomogram effectively predicted the 1-, 3-, and 5-year OS probabilities in patients with GC (C-index [95% CI] = 0.627 [0.601–0.652]).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e: OLR1 is strongly associated with a dismal prognostic outcome and immune cell infiltration in patients with GC.\u003c/p\u003e","manuscriptTitle":"OLR1 is closely related to poor prognosis and immune cell infiltration in gastric cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-03 07:31:50","doi":"10.21203/rs.3.rs-6509290/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-06-24T06:10:13+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-23T06:02:59+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-20T09:51:23+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-11T13:13:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"298059089403418713900569257187285622884","date":"2025-06-11T12:46:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"265673608945739696101317861521437470840","date":"2025-06-11T09:04:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"166082973843522182246066144682802330217","date":"2025-06-02T14:53:43+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-31T10:58:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"235599832342279741921173558765484592669","date":"2025-05-29T16:44:13+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-05-29T15:25:19+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-29T14:36:13+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-05-07T06:18:43+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-05T09:10:11+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-04-23T05:53:18+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"55158d8a-0505-49c9-be67-121b2ee2bb00","owner":[],"postedDate":"June 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":49325512,"name":"Biological sciences/Cancer/Cancer genetics"},{"id":49325513,"name":"Biological sciences/Cancer/Cancer microenvironment"},{"id":49325514,"name":"Biological sciences/Cancer/Gastrointestinal cancer"}],"tags":[],"updatedAt":"2025-12-01T16:04:57+00:00","versionOfRecord":{"articleIdentity":"rs-6509290","link":"https://doi.org/10.1038/s41598-025-25552-w","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-11-24 15:58:39","publishedOnDateReadable":"November 24th, 2025"},"versionCreatedAt":"2025-06-03 07:31:50","video":"","vorDoi":"10.1038/s41598-025-25552-w","vorDoiUrl":"https://doi.org/10.1038/s41598-025-25552-w","workflowStages":[]},"version":"v1","identity":"rs-6509290","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6509290","identity":"rs-6509290","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}