Pan-Cancer Analysis Reveals Prognostic Potential of ANGPTL2 and Its Implications in Tumor Microenvironment

Pan-Cancer Analysis Reveals Prognostic Potential of ANGPTL2 and Its Implications in Tumor Microenvironment | 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 Research Article Pan-Cancer Analysis Reveals Prognostic Potential of ANGPTL2 and Its Implications in Tumor Microenvironment Junyu Ke, Zhikun He, Yilin Duan, Yaqing Zhu, Yingjian Xu, Hengli Zhou, and 9 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4552153/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Angiopoietin-like protein 2(ANGPTL2)stimulates inflammatory and angiogenic pathways,promoting tumor growth and metastasis.However,research on the prognostic significance,immune infiltration,expression patterns,and underlying mechanisms of ANGPTL2 in various malignancies is sparse. Methods We used different online platforms and datasets to conduct a comprehensive investigation of ANGPTL2 in various human malignancies,including mutation status,methylation levels,and expression profiles.Our study looked at the impact of ANGPTL2 on survival prognosis in various tumour types,its correlation with immune checkpoint genes,immune and stromal scores in tumours,its functional relevance in different cancer types,associated signalling pathways and biological functions,validation of its expression in gastric cancer,and its effects on cell proliferation,migration,and invasion using cell models. Results ANGPTL2 mutations were predominantly missense and truncation.In 31 tumour types,ANGPTL2 expression differed significantly from normal tissue( P < 0.05).Survival analysis revealed that the highest ANGPTL2 expression had worst results.Notably,patients with reduced ANGPTL2 expression showed increased overall survival(OS)in gastric adenocarcinoma,lung cancer and bladder cancer( P < 0.05).Immune infiltration analysis showed positive correlations between ANGPTL2 expression and immune infiltration in 36 tumour types( P < 0.05).Furthermore,ANGPTL2 was found to be positively associated with immune checkpoint genes in most cancers( P < 0.05).In uveal melanoma and retinoblastoma,ANGPTL2 expression was positively correlated with angiogenesis,inflammation,stemness,but negatively correlated with DNA damage,DNA repair,and cell cycle.In the AngPTL2-overexpressed cell model,the proliferation,migration and invasion of GES-1 cells were significantly enhanced. Conclusions Increased ANGPTL2 expression positively correlates with immune cell infiltration,immune checkpoint genes and immune scores in most tumours.In addition,ANGPTL2 has been linked to significant migration and invasion capabilities in clinical samples and in vitro experiments. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Cancer remains a major cause of mortality worldwide,with high incidence and death rates[ 1 – 6 ].Pan-cancer analysis,which involves utilizing cancer gene sequences from multiple genomic databases,along with next-generation sequencing(NGS)[ 7 ],pan-cancer model systems and pan-cancer project[ 8 ]plays a crucial role in analyzing differences and similarities among various cancer types.This research is essential for understanding the fundamental dynamics of tumor development,as well as for diagnosis,prognosis,and treatment[ 9 ]. ANGPTL2,an important member of the angiopoietin-like protein family[ 10 ],is closely associated with angiogenesis,inflammatory regulation,tumor growth,and metabolic control.Its functions include promoting migration and proliferation of vascular endothelial cells,regulating inflammatory responses,and influencing the tumor microenvironment[ 11 – 14 ].Studies have revealed that ANGPTL2 is upregulated in various human tumor tissues,and its expression levels correlate positively with tumor size and malignancy,including lung cancer,liver cancer,osteosarcoma,and breast cancer[ 15 – 16 ].Additionally,serum levels of ANGPTL2 in cancer patients are inversely related to overall survival in breast cancer,lung cancer,colon cancer,and gastric cancer patients.Overexpression of ANGPTL2 in gastric cancer tissue is associated with tumor progression,early recurrence,and poor prognosis.Interestingly,while tumor cell-expressed ANGPTL2 promotes tumor cell proliferation and invasion through multiple signaling pathways,ANGPTL2 derived from tumor stromal fibroblasts enhances anti-tumor immune responses and suppresses tumor development.These findings highlight the need for comprehensive pan-cancer research and exploration of the specific mechanisms underlying ANGPTL2’s role in cancer. In this study,we conducted an comprehensive pan-cancer analysis of ANGPTL2,including its mutational status across various cancers. We summarized ANGPTL2 expression and its prognostic significance in pan-cancer. Furthermore,we explored the correlation between ANGPTL2 expression and tumor immune checkpoints and immune infiltration using single cell sequencing data.Experimental validation was performed using clinical specimens from gastric cancer patients and human gastric mucosal epithelial cells(GES-1).Our evaluation aimed to assess the potential value of ANGPTL2 in cancer detection,tumor prognosis,and immunotherapy. Methods 2.1 Mutation Analysis of ANGPTL2 Single cell sequencing The cBioPortal online database( http://cbioportal.org),i s specifically designed to address the unique data integration challenges posed by large-scale cancer genomics projects.It gives the entire cancer research community easier and more direct access to the data generated by the large-scale cancer Genome Project [ 17 ].The platform currently contains 5 published datasets and 15 interim TCGA datasets.It also currently provides data access to more than 5,000 tumor samples from 20 cancer studies [ 18 ] .We used cBioPortal online database to study the mutation of ANGPTL2 gene.By entering “ANGPTL2” in the search box and navigating to the “cancer types summary” tab,we obtained a visual grapof ANGPTL2 gene mutations in pan-cancer. 2.2 Pan-Cancer Expression Levels of ANGPTL2 Sangerbox,a bioinformatics analysis tool,which can perform a variety of bioinformatics analyses and visual mapping[ 19 ].Utilizing Sangerbox,we studied ANGPTL2 expression across different tumors in the TCGA cohort,which includes RNA sequencing,somatic mutations,phenotype data,and relevant clinical data for 34 human cancers.in the search box of Sangerbox interface,we entered “ANGPTL2,” selected the data source as “TCGA + GTEx,” chose all samples as the sample source,and applied a log2 transformation(x + 0.001).This allowed us to visualize the differential expression of ANGPTL2 in pan-cancer.Additionally,we used the Gene Expression Profiling Interactive Analysis(GEPIA)database,[ 20 ] which integrates RNA sequencing expression data from TCGA,TARGET,and GTEx.In GEPIA,we selected the “box plot” module under the “Expression” option,input “ANGPTL2,” set the data conversion mode as |Log2FC| Cutoff:1,and set p-value cutoff of 0.01.We then added tumor types showing differential ANGPTL2 expression in pan-cancer and exported the results from the “TCGA + GTEx” dataset.To explore ANGPTL2 expression differences across cancer stages,we used the “stage plot” module on the same website to analyze the association between ANGPTL2 and tumor staging. 2.3 survival analysis Based on Multiple Databases The GEPIA contains expression data from 9,736 tumors and 8,587 normal samples obtained from the TCGA and GTEx.In the study,we defined high and low expression groups for ANGPTL2 using a threshold of 50%.We performed survival analysis on specific survival endpoints with log-rank p-values using the GEPIA2 “survival analysis” module.Furthermore,we examined the correlation between ANGPTL2 expression and overall survival(OS)and disease-free survival(DFS)data for various tumour types using the SangerBox portal website.For the purpose of differential gene expression analysis,one-way ANOVA was employed,with a threshold of ≥ 1.5-fold expression change between cancer and normal tissues( P < 0.05).Finally,we employed the Kaplan-Meier Plotter( www.kmplot.com) ,a tool that integrates gene expression data and survival information from GEO,EGA,and TCGA microarray datasets,to assess the impact of ANGPTL2 on survival across 21 cancer types.We selected the“start KM plotter pan-cancer” option on the homepage,entered “ANGPTL2,” and chose overall survival(OS)as the primary endpoint for this study,resulting in relevant outcome plots. 2.4 Immune Score and Immune Checkpoint Analysis for ANGPTL2 We downloaded a standardized pan-cancer dataset(TCGA,TARGET,GTEx)from UCSC( https://xenabrowser.net/).Th e ENSG00000136859(ANGPTL2)gene expression data from a variety of samples were subjected to a log2 transformation(X + 0.001),after which the transformed expression profiles were mapped to GeneSymbol.Further,we used the R package ESTIMATE(version 1.0.13)which can calculate the immune score,stromal score,and ESTIMATE score for each tumor sample in the TCGA database to calculate ESTIMATE scores for each patient in each tumor based on gene expression.[ 22 ] Additionally,We downloaded a standardized pan-cancer dataset(TCGA,TARGET,GTEx)from UCSC( https://xenabrowser.net/).Further,w e extracted ENSG00000136859(ANGPTL2)gene and 60 genes of Immune checkpoint pathway(Inhibitory(24)and Stimulatory(36),derived from The Immune Landscape of Cancer.DOI: 10.1016/j.i mmuni.2018.03.023)of marker genes expressed in each sample data,we screened the sample source for further: Primary Solid Tumor,Primary Tumor,Primary Blood Derived Cancer-Bone Marrow,Primary Blood Derived Cancer-Peripheral For Blood samples,we also filtered all normal samples,and further performed log2(x + 0.001)transformations for each expression value.Next,we calculated the pearson correlation between ENSG00000136859(ANGPTL2)and the marker genes of five immune pathways. 2.5 Single-Cell Sequencing Analysis Related to ANGPTL2 CancerSEA( http://biocc.hrbmu.edu.cn/CancerSEA/home.jsp)i s the first dedicated database to analyse the different functional states of different cancer cells at the single-cell level(23).It encompasses 14 functional states of 41,900 cancer single cells from 25 different cancer types .We used the "correlation plot" module of the CancerSEA single-cell database to analyse the correlation between ANGPTL2 expression and functional status in different tumours at the single-cell level,and combined it with the "Functional relevance" module.correlation" module,we further analysed the correlation data between ANGPTL2 expression and the functional status of different tumours. meanwhile,by combining the “Functional relevance” module,we further investigated the relationshipbetween ANGPTL2 expression and tumor-specific functions.The correlation threshold for ANGPTL2 and cancer functional states was set at a correlation strength of 0.3 and a P -value less than 0.05. 2.6 Protein-Protein Interaction and Functional Enrichment Analysis BioGRID is a selected biological database of protein-protein interactions,genetic interactions,chemical interactions,and post-translational modifications,include1,728,498 proteins and genetic interactions.In our study,we imported a co-expressed gene list for ANGPTL2 and constructed and visualized protein-protein interaction(PPI)networks using Cytoscape 3.0.1 software.We set the filtering criteria to focus on Homo sapiens species and searched by gene,aiming to determine the intrinsic associations or significance of cancer-related genes within the system network.Additionally,we performed functional enrichment analysis of ANGPTL2 using the GO and KEGG databases.Genes were considered significantly enriched when the P -value was less than 0.05 and the false discovery rate(FDR)was less than 0.25. 2.7 Hematoxylin-Eosin(HE)Staining and Immunohistochemistry of ANGPTL2 in Gastric Cancer Tissues To enhance the persuasiveness of our findings,we included 60 clinical gastric cancer samples for HE staining and immunohistochemistry.These tumor specimens were obtained from the Department of Gastroenterology at Gaozhou Traditional Chinese Medicine Hospital.Ethical approval(NO: yxllyjky20220410)was obtained from the hospital,and the study adhered to the principles of the Helsinki Declaration.Prior to immunohistochemistry,we embedded tumor tissues in paraffin and performed HE staining.Following tissue sectioning and rehydration with xylene and alcohol,we stained the nuclei and cytoplasm with hematoxylin and eosin(H&E)to observe the pathological features of tumor and adjacent tissues under a microscope(Lonardi et al.,2021).For immunohistochemistry,we used primary antibody ANGPTL2 antibodies (Proteintech,Cat No: 12316-1-AP).Each slide was captured at ×200 magnification,and subsequent statistical analysis was performed using ImageJ software to quantify the average optical density(AOD),which represents integrated optical density and area.The average AOD from three fields of view in each slide was used as the density value for that sample. 2.8 GES-1 Cell Culture and Plasmid Transfection GES-1 cells were purchased from Wuhan Xeville Biotechnology Co.LTD.(STR identification correct,item No.STCC10401P-1).The cells were cultured in DMEM complete medium(10%FBS + 1%PS)and placed in a cell incubator at 37℃ and 5% CO2. During transfection of plasmid,GES-1 cells were first routinely digested and then inoculated into six-well plates with about 200,000 cells per well for culture,so that the cell density could reach about 80% the next day.After the cell density reached about 80%,each well was replaced with 2ml fresh DMEM medium(containing serum,without double antibody).For the cells in each well of the six-well plate to be transfected,two clean aseptic centrifuge tubes were taken and 125µl DMEM medium without Penicillin-Streptomycin Solution and serum was added,respectively.2.5µg overexpressed plasmid DNA was added to one tube and 5µl Lipo6000 transfection reagent was added to the other tube.After standing at room temperature for 5 minutes,the medium containing plasmids was gently added into the medium containing Lipo6000" transfection reagent with a pipette,and the centrifuge tube was gently reversed and left for 5 minutes at room temperature.Then add 250µl Lipo6000 transfection reagent/plasmid mixture to each well and gently mix.The cells were cultured for 4–6 hours after transfection and then replaced with fresh complete culture medium.After continued culture for about 48 hours,1.5 ml fresh DMEM medium(containing serum,without double antibody)was replaced per well,and 1.5µl puromycin was added to each well,and stable cell lines were screened. 2.9 Cell invasion assay of AngPTL2-overexpressed cells In order to measure the migration ability of cells,we judged the growth and migration ability of cells based on the ability of cells at the edge of scratches to gradually enter the blank area to heal the "scratch".We used a sterile 200mL pipette tipto draw a straight line across the central area of cell growth.Subsequently,the medium was replaced with a DMEM medium of 2%FBS and the shed cells were removed.After 24 hours,the cells were observed under a light microscope(magnification,100×).Cell invasion assay rate is calculated using the following formula: cell invasion assay rate(0h scratch width − 24h scratch width)/0h scratch width. To determine cell invasion ability,GRS-1 cells were re-suspended in serum-free medium and inoculated into the Transwell upper chamber,which was pre-coated with Matrigel at room temperature for 25min as the extracellular matrix for cell invasion analysis.At the same time,complete culture medium of 20%FBS was added to the lower chamber.It was then transferred to a 5%CO2 incubator at 37°C for 24h.After 24h,the chamber was fixed in anhydrous ethanol at room temperature for 30min,and then stained with 0.1% crystal violet solution at room temperature for 30min.The invading cells were observed and counted from at least five fields under an optical microscope(magnification,100×),and finally counted using the ImageJ software. 2.10 Statistical Analysis of Clinical Information and Prognosis In this study,we used the median expression level of ANGPTL2 as the basis for grouping and comparing subjects to assess the relationshipbetween ANGPTL2 expression and various clinical features.Categorical variables were analyzed using Fisher’s exact test,while continuous variables were analyzed using the Wilcoxon rank-sum test.survival analysis involved constructing survival curves using the Kaplan-Meier method,with groupdifferences assessed using the log-rank test.Additionally,we performed univariate and multivariate Cox regression analyses to identify independent prognostic factors.All statistical analyses were conducted using SPSS 27.0 software,and two-tailed P -values ≤ 0.05 were considered statistically significant. Results 3.1 Mutation Levels of ANGPTL2 in Different Cancers DNA modifications are classified into two types: mutations(truncating mutation and missense)and copy number alterations(amplification and deepdeletion).As depicted in Figure 2A,among the topfive cancers in pan-cancer study,endometrial carcinoma and melanoma have mutation rates exceeding 3%,while colorectal cancer mainly exhibits high mutation rates(>2%).Prostate cancer primarily exhibits amplification as the major type of mutation,with a rate exceeding 1.5%.Esophageal and gastric cancers predominantly exhibit high mutation rates(>1%),accompanied by alterations such as amplification and deepdeletion.As shown in Figure 2B,we find that the main mutation mode of ANGPTL2 is missense,followed by truncating mutation.Figure 2C displays the A198V/T alteration in the three-dimensional structure of the ANGPTL2 protein. 3.2 Expression of ANGPTL2 in Pan-Cancer Tissues We investigated the expression of ANGPTL2 in various tumors from the TCGA cohort using the Sangerbox database.As shown in Figure 2D,ANGPTL2 shows varying expression levels among 31 distinct tumor types.Notably,ANGPTL2 expression is significantly lower than that in adjacent normal tissues in 16 types of cancer,including endometrial carcinoma(UCEC),breast invasive carcinoma(BRCA),cervical squamous cell carcinoma(CESC),lung adenocarcinoma(LUAD),esophageal carcinoma(ESCA),kidney renal papillary cell carcinoma(KIRP),colon adenocarcinoma(COAD),colon adenocarcinoma/rectum adenocarcinoma(COADREAD),prostate adenocarcinoma(PRAD),lung squamous cell carcinoma(LUSC),skin cutaneous melanoma(SKCM),bladder urothelial carcinoma(BLCA),thyroid carcinoma(THCA),ovarian serous cystadenocarcinoma(OV),uterine carcinosarcoma(UCS)and pheochromocytoma and paragangliomas(KICH).On the contrary,ANGPTL2 has been found in GBM(Glioblastoma multiforme),GBMLGG(Glioma),LGG(Brain Lower Grade Glioma),KIPAN(Pan-kidney cohort)(KICH+KIRC+KIRP),STAD(Stomach adenocarcinoma),HNSC(Head and Neck squamous cell carcinoma),KIRC(Kidney)renal clear cell carcinoma,WT,PAAD(Pancreatic adenocarcinoma pancreatic cancer),TGCT(Testicular Germ Cell)Testicular germ cell Tumors),ALL(Acute lymphoblastic leukemia),LAML(Acute Myeloid Leukemia),PCPG(Pheochromocytoma and The expression of Paraganglioma was considerably reduced in 15 types of tumors,including Paraganglioma and paraganglioma,Adrenocortical carcinoma(ACC)and Cholangio carcinoma(CHOL).Given that certain data in normal tissues were unavailable,we obtained RNA sequencing expression data from TCGA,TARGET,and GTEx databases and analyzed ANGPTL2 expression in the 31 tumors mentioned above.As shown in Figure 2E,ANGPTL2 expression differed between 16 tumors.ANGPTL2 expression was considerably higher in eight types of cancers compared to surrounding normal tissues.Including STAD,Thymoma(Thymoma),KIRC,GBM,DLBC(Lymphoid Neoplasm Diffuse Large B-cell Lymphoma),PAAD,LGG,ACC expression up-regulated.However,we also observed downregulation of ANGPTL2 expression in UCS,UCEC,READ,OV,KICH,CESC,BRCA,BLCA and other tumors. In addition,to assess CNN1 expression in various tumor stages,we also used GEPIA2.0 to explore the relationshipbetween ANGPTL2 expression level and tumor pathological stage.As shown in Figure 2F,ANGPTL2 expression was different in different tumor stages of 5 types of tumors,including STAD,KIRP,TGCT,BRCA,and BLCA. 3.3 Correlation between ANGPTL2 expression and survival prognosis of tumor patients In the pan-cancer survival analysis of ANGPTL2,we first investigated the correlation between ANGPTL2 expression and overall survival(OS)across multiple cancers using the GEPIA database.As shown in Figure 3A,the expression of ANGPTL2 is correlated with the overall survival of five tumors,namely COAD,LGG,LIHC,LUSC,and PAAD,among which the high expression of ANGPTL2 in COAD,LIHC,LUSC,and PAAD is often significantly correlated with poor prognosis( P <0.05).As shown in Figure 3B,ANGPTL2 expression was correlated with disease-free survival of five tumors,namely COAD,DLBC,KIRC,LGG,and READ,among which high ANGPTL2 expression in COAD,KIRC,and READ was often significantly correlated with poor prognosis( P <0.05). Next,we analyzed the expression of ANGPTL2 in relation to overall survival in ten different cancers using the Kaplan-Meier plotter online platform.As shown in Figure 3C,the expression of ANGPTL2 correlated with prognosis in UCEC,STAD,PAAD,OV,LUSC,LIHC,KIRP,HNSC,CESC and BLCA.Specifically,high ANGPTL2 expression was significantly associated with poor prognosis in STAD( P <0.001),UCEC( P <0.05),PAAD( P <0.05),OV( P <0.01),LUSC( P <0.05),LIHC( P <0.05),KIRP( P <0.01),CESC( P <0.05)and BLCA( P <0.05).Moving on to the correlation between ANGPTL2 protein expression levels and immune checkpoint genes,as well as immune cell infiltration,we extracted ANGPTL2 gene expression data from TCGA,TARGET,and GTEx databases.Using the SangerBox online platform,we evaluated the relationshipbetween ANGPTL2 expression and 60 genes in the immune checkpoint pathway.Remarkably,ANGPTL2 expression showed strong correlations with immune checkpoint genes in CHOL,PRAD,KICH,OV,PAAD,KIPAB,BLCA,READ,COAD,COADREAD,LGG,GBM and TGCT(Figure 4A).Specifically,ANGPTL2 expression positively correlated with immune checkpoint genes in CHOL，PRAD，KICH，OV，PAAD，KIPA，BLCA，READ，COAD and COADREAD.Pronounced effects were observed in OV、PAAD、KIPA、READ、COAD and COAREADE(Figure 4A).Conversely,the expression of ANGPTL2 is negatively correlated with immune checkpoint genes in LGG,GBMLGG,and TGCT(Figure 4A).These findings suggest that high ANGPTL2 expression is generally associated with a better prognosis.However,in the context of immunotherapy for LGG,GBMLGG and TGCT,ANGPTL2 inhibitors may lead to improved outcomes. 3.4 Correlation of ANGPTL2 protein expression level with immune checkpoint genes and immune infiltration The results of the estimated immune score in Figure 4B showed that ANGPTL2 expression was positively correlated with the immune score of 23 cancers.Such as STAD (p=1.8e-10),BRCA ( P =1.3e-10),LUAD ( P =2.3e-8),STES ( P =2.8e-17),ESCA ( P =1.7e-5),SARC ( P =3.0e-6),KIPR ( P =9.2e-7),KIPA ( P =6).0e-59),COAD ( P =2.2e-29),COADREAD ( P =7.9e-35),PRAD ( P =1.8e-14),HNSC ( P =3.6e-5),LIHC ( P =2.6e-6),OV ( P =2.5e-4),UVW ( P =8.7e-4),LUSC ( P =3.8e-1),BLCA ( P =1.5e-18),READ ( P =3.1e-7),PAAD ( P =1.7e-6),PCPG ( P =1.0e-5),SKCM ( P =8.9e-3),THCA ( P =3.0e-3),KICH ( P =2.5e3),etc.,However,there were four significant negative correlations,such as GBMLGG ( P =2.8e-26),LGG ( P =1.9e-14),TGCT ( P =6.1e-6) and ALL ( P =1.6e-4). 3.5 Single-Cell Expression Patterns of ANGPTL2 in Various Cancers To explore the impact of ANGPTL2 in different cancers,we analysed its correlation with 14 different functional categories in 10 cancer types by using the CancerSEA single-cell database.In retinoblastoma(RB),ANGPTL2 expression was negatively correlated with cell cycle,DNA damage and DNA repair.Conversely,ANGPTL2 expression was positively correlated with angiogenesis,differentiation,inflammation,tumour metastasis,quiescence and stemness.In uveal melanoma(UM),ANGPTL2 expression was negatively correlated with tumour biological behaviour such as cell apoptosis,DNA damage and invasion,while positively correlated with cell cycle,hypoxia and stemness,albeit weakly.Notably,ANGPTL2 showed a stronger association with high-grade glioma(HGG).Specifically,ANGPTL2 was positively correlated with cell cycle,DNA damage,DNA repair and stemness( P <0.05),while negatively correlated with angiogenesis,cell apoptosis,epithelial-mesenchymal transition,hypoxia,inflammation,tumour metastasis and cell proliferation( P <0.01)(Figure 5A,B).Interestingly,ANGPTL2 showed a positive correlation with stemness in most cancers(Figure 5A),but a negative correlation with DNA repair(Figure 5A). 3.6 Functional Enrichment Analysis of ANGPTL2 Next,we performed functional enrichment analysis to evaluate the potential molecular mechanisms of ANGPTL2 in tumour initiation and progression.As shown in Figure 5C,we identified 10 molecules that interact with ANGPTL2 from the BioGRID network tool.Subsequently,we conducted a KEGG-GO enrichment analysis(FIG.5D&E),which showed that ANGPTL2 co-expressed genes played a regulatory role in tyrosine phosphorylation,angiogenesis,vasculature development,and actin cytoskeleton regulation.In addition,ANGPTL2 is closely associated with 12 signalling pathways,including the PI3K-Akt,Jak-STAT and Rap1 pathways. 3.7 HE Staining and Immunohistochemistry of ANGPTL2 in Gastric Cancer Tissues HE staining results showed that early stage gastric cancer cells had loosely arranged cell layers with minimal inflammatory cells.In contrast,advanced gastric cancer cells showed significant changes,with deepand dense nuclear concentration,serious disordered nuclear arrangement,and obvious heterogeneity .In the advanced stage IV group,the number of stained cells in the center of the tumor tissue was reduced,and the periphery of the tumor tissue was significantly atrophied(Figure 6A).Immunohistochemistry revealed that ANGPTL2 expression was significantly lower in early stage gastric cancer tissues compared to late stage cases(Figure 6B).These findings suggest that ANGPTL2 may serve as a sensitive marker in the tumour microenvironment of gastric cancer patients and warrant further investigation. 3.8 Impact of overexpression of ANGPTL2 on GES-1 cell invasion assay To further investigate the effects of ANGPTL2 on malignant biological behaviours such as cell proliferation and migration,we performed ANGPTL2 overexpression plasmid transfection in GES-1 gastric mucosal epithelial cells.According to Wound healing experiments and Transwell test,compared with scratch area 0h after the initial test and 48h after the initial test,the cell invasion assay of ANGPTL2 overexpression groupwas significantly higher than that of NC group( P <0.01).In the Transwell experiment,we further confirmed the enhanced migration ability of cells in the ANGPTL2 overexpression group(Figure 6C,D). The results showed that the number of cells attached to the lower surface of the chamber in the NC groupwas significantly lower than that in the ANGPTL2 overexpression group,indicating that the number of cells migrated in the ANGPTL2 overexpression groupwas significantly higher than that in the NC group( P <0.01),which also proved that the increase of ANGPTL2 expreThe results showed that the number of cells attached to the lower surface of the chamber in the NC groupwas significantly lower than that in the ANGPTL2 overexpression group,indicating that the number of cells migrated in the ANGPTL2 overexpression groupwas significantly higher than that in the NC group( P <0.01),which also proved that the increase of ANGPTL2 expression level could enhance the migration and invasion ability of cells to a certain extent. 3.9 Correlation between ANGPTL2 expression and clinical prognosis This is a retrospective study in which all patients received continuous follow-up.Table 1 shows the baseline clinicopathological features of patients with gastric cancer.We performed immunohistochemistry on paraffin sections of 60 patients with gastric cancer to assess the expression of ANGPTL2 in cancer tissues.The positive expression sites were quantitatively analyzed using Image J software,and the expression of ANGPTL2 in GC patient tissues was further divided into low and high levels according to the median expression.Then,the relationshipbetween ANGPTL2 expression level and clinicopathological features was analyzed based on the clinical data of patients.In univariate analysis,there were statistical differences in TNM staging between the two groups( P = 0.035,Table 2,Figure 6C),and no significant differences in other clinical features.In order to further assess whether ANGPTL2 levels in patients with gastric cancer can be used as a prognostic predictors of patients and to explore whether there is a correlation between its expression and survival outcomes,we used Cox regression model for clinical pathological factors affecting the prognosis of the single factor analysis,the results showed that ANGPTL2 expression levels( P <0.001,HR=6.118,95%CI:[2.176-17.205]); TNM staging( P <0.001,HR=0.113,95%CI[0.035 -- 0370]); The degree of differentiation( P =0.018,HR=3.780,95%CI :[1.251-11.428])and age( P = 0.002,HR=2.198,95%CI [0.056-0.509])were associated with poorer prognosis(Table 3). TABLE 1 Clinicopathological features of patients with gastric cancer. Patient characteristics Number of pataients (N=60) Percentage(%) Gender Male 42 70 Female 18 30 Age(year) ＜60 43 71.67 ≥60 17 28.33 Histological type Adenocarcinoma 47 78.33 Non-adenocarcinoma 13 21.67 Differentiation high differentiation 30 50 moderately differentiated 25 41.67 poorly differentiated 5 8.33 TNM staging Ⅰ 10 16.67 Ⅱ 27 45 Ⅲ 18 30 Ⅳ 5 8.33 Depth of intestinal wall infiltration mucous membrane layer 3 5 muscle layer 11 18.33 serous layer 43 71.67 outer layer 3 5 Tumour size ＜5 cm 29 48.33 ≥5 cm 31 51.67 Lymph node metastasis Without 39 65 With 21 35 CEA ＜5 43 71.67 ≥5 17 28.33 CA199 ＜37 48 80 ≥37 12 20 Survival state death 25 41.67 survival 35 58.33 CD11 expression low expression 30 50 high expression 30 50 TABLE 2 Single-factor Cox regression analysis. Clinicopathologic Hazard 95.0% CI for Exp(B) P value characteristics ratio Lower Upper Gender Male 1 0.592 4.663 0.334 Female 1.662 Age(year) ＜60 1 0.056 0.509 0.002 ** ≥60 0.169 Histological type Adenocarcinoma 1 0.813 7.765 0.11 mucoid adenocarcinoma 2.512 Depth of tumor invasion T1&T2 1 0.429 7.792 0.414 T3&T4 1.829 degree of differentiation high and moderately differentiation 1 1.251 11.428 0.018 * poorly differentiated 3.78 Tumour size ＜5 cm 1 0.393 2.762 0.934 ≥5 cm 1.042 TNM staging Ⅰ&Ⅱ stage 1 0.035 0.37 ＜0.001 *** Ⅲ&Ⅳ stage 0.113 Lymph node metastasis Without 1 0.27 2.178 0.619 With 0.768 CEA ＜5 ng/mL 1 0.679 4.037 0.267 ≥5 ng/mL 1.656 CA199 ＜37 U/mL 1 0.48 0.178 1.625 ≥37 U/mL 0.538 ANGPTL2 Low expression 1 2.176 17.205 ＜0.001 *** High expression 6.118 TABLE 3 Correlation between ANGPTL2 and clinicopathologic characteristics. Clinicopathological paramete N ANGPTL2 Expression P 值 Low expression High expression Gender Male 42 21 21 1 Female 18 9 9 Age(year) ＜60 43 19 24 0.152 ≥60 17 11 6 Histological type Adenocarcinoma 47 23 24 0.754 mucoid adenocarcinoma 13 7 6 Depth of tumor invasion T1&T2 6 4 2 0.389 T3&T4 54 26 28 degree of differentiation high and moderately differentiation 30 16 14 0.606 poorly differentiated 30 14 16 Tumour size ＜5 cm 29 14 15 0.796 ≥5 cm 31 16 15 TNM staging Ⅰ&Ⅱ stage 24 16 8 0.035 * Ⅲ&Ⅳ stage 36 14 22 Lymph node metastasis Without 39 19 20 0.787 With 21 11 10 CEA ＜5 ng/mL 43 21 22 0.774 ≥5 ng/mL 17 9 8 CA199 ＜37 U/mL 48 25 23 0.519 ≥37 U/mL 12 5 7 Discussion ANGPTL2,a crucial member of the angiopoietin-like protein family [ 25 ],it can not only shares functional similarities with angiopoietins in promoting angiogenesis but also regulates endothelial cell function through autocrine or paracrine mechanisms,inducing vascular formation and maintaining tissue homeostasis[ 26 ].Numerous studies have confirmed elevated ANGPTL2 expression in various malignant tumor cells [ 27 ],affecting tumor cell proliferation,invasion,and migration.Charan Manish et al.found significantly higher serum levels of ANGPTL2 in osteosarcoma patients compared to healthy controls.Their study revealed that tumor cell-secreted ANGPTL2 induces neutrophil recruitment in the lungs,creating a favorable pre-metastatic tumor microenvironment for tumor dissemination [ 28 ].Yuji Toiyama et al.demonstrated that in vitro knockdown of ANGPTL2 significantly inhibited cell proliferation,migration,and invasion.Furthermore,clinical samples from colorectal cancer patients indicated a close association between high ANGPTL2 expression and lymph node metastasis,liver metastasis,and disease prognosis [ 29 ].Endo et al.found that the expression of ANGPTL2 in human primary lung cancer tissues was much higher than that in normal lung tissues,and its high expression was related to patients' DFS,and ANGPTL2 derived from tumor cells could induce angiogenesis by activating the α5β1-Rac signaling pathway,further promoting the remote metastasis of tumor cells [ 30 ]. In our study,we first explored ANGPTL2 expression across different tumors in the TCGA dataset using the Sangerbox and GEPIA online platforms.Based on Sangerbox results,ANGPTL2 exhibited differential expression in 31 tumor types.Notably,ANGPTL2 expression was significantly higher in 15 tumor types,including gastric cancer,compared to adjacent normal tissues.Conversely,ANGPTL2 expression was lower in 16 tumor types,such as endometrial cancer,relative to adjacent normal tissues.These findings were consistent with GEPIA results,where ANGPTL2 expression was significantly elevated in 8 tumor types(including gastric cancer)and decreased in 8 tumor types(including endometrial cancer)compared to adjacent normal tissues.The source of ANGPTL2 secretion may contribute to these expression patterns.Immunohistochemistry analysis of gastric cancer clinical tissues confirmed significantly higher ANGPTL2 expression in late-stage gastric cancer tissues compared to early-stage gastric cancer tissues.Haruki Horiguchi et al.reported that ANGPTL2 knockout in a renal cell carcinoma mouse model slowed disease progression and metastasis.However,ANGPTL2 in host tumor microenvironment cells also exhibited tumor-suppressive effects,including promoting CD8 + T cell priming,enhancing anti-tumor immune responses,and activating dendritic cells to improve tumor vaccine efficacy.Although the expression patterns of ANGPTL2 in tumors and their microenvironments require further exploration,it is evident that ANGPTL2 expression correlates with malignant progression in most cancers [ 31 ],covering various stages of tumor development in gastric cancer and other tumor types.With the meaningful role of ANGPTL2 expression in tumor development established,we further assessed its potential for predicting tumor survival outcomes.Our results,integrating GEPIA data on overall survival(OS)and disease-free survival(DFS),revealed significant correlations between ANGPTL2 expression and survival prognosis in COAD,LGG,LIHC,LUSC,PAAD,DLBC,KIRC,and READ,with high ANGPTL2 expression often associated with poor prognosis.Kaplan-Meier plotter analysis also demonstrated ANGPTL2’s relevance to survival outcomes in 10 tumor types,with high ANGPTL2 expression linked to adverse prognosis in 9 of these types,including STAD.These findings suggest that elevated ANGPTL2 expression is associated with poor prognosis in most tumors.Shozo Ide et al.similarly reported that esophageal cancer patients with high ANGPTL2 expression had worse overall survival and disease-free survival compared to low-expression counterparts [ 32 ].To further validate ANGPTL2’s impact on cancer survival prognosis,we conducted survival analysis on clinical information from gastric cancer patients,confirming that high ANGPTL2 expression in gastric cancer correlates with poorer survival outcomes. Increasing evidence suggests that immunotherapy alone or in combination with chemotherapy,radiation therapy,and targeted treatments significantly improves cancer management [ 33 ].Immune checkpoint blockade,as a promising therapeutic strategy,is being evaluated in clinical applications [ 34 ].ESTIMATE immune scores reveal a significant correlation between ANGPTL2 expression and immune infiltration across 27 tumor types,particularly a positive association with 23 tumor types,including STAD.In studies investigating the relationshipbetween ANGPTL2 and immune checkpoints,as shown in Fig. 4 A,ANGPTL2 exhibits a significant positive correlation with immune checkpoints in 37 tumor types,including STAD.These findings collectively suggest that ANGPTL2 is a potential and promising immunotherapeutic target. Regarding the molecular mechanisms of ANGPTL2,single cell sequencing data from the CancerSEA platform indicate that ANGPTL2 is associated with various functional states in tumors such as BRCA,AST,GBM,Glioma,HGG,ODG,LUAD,MEL,RB,and UM.These functional states encompass processes related to angiogenesis,inflammatory responses,epithelial-mesenchymal transition(EMT),DNA damage,DNA repair,cell cycle,cell apoptosis,hypoxia,metastasis,invasion,differentiation,and stemness.Functional enrichment analysis further reveals that ANGPTL2 co-expressed genes play regulatory roles in tyrosine phosphorylation,angiogenesis,vascular system development,and actin cytoskeleton.Key signaling pathways include the PI3K-Akt,Jak-STAT,and Rap1 pathways.Dysregulation of tyrosine phosphorylation levels can lead to various diseases,particularly tumors and metabolic disorders.Oike et al.demonstrated that transgenic mice overexpressing ANGPTL2 exhibited significantly increased blood vessels in the dermis.ANGPTL2 upregulated membrane-type matrix metalloproteinase 1 expression,further promoting endothelial cell functions such as migration,proliferation,and lumen formation [ 35 , 36 ].These findings collectively suggest that ANGPTL2 may promote tumor cell proliferation,invasion,and other malignant behaviors through multiple signaling pathways. Based on the bioinformatics research conclusions and ANGPTL2’s significant expression in gastric cancer,we conducted further in vitro experiments to explore ANGPTL2.Scratch assays and Transwell experiments revealed that ANGPTL2 overexpression significantly enhanced migration and invasion abilities in GES-1 cells.We hypothesize that this effect is related to ANGPTL2’s angiogenic function.ANGPTL2 can regulate endothelial cell function through autocrine or paracrine mechanisms,inducing vascular formation and maintaining tissue homeostasis.Neovascularization provides nutrients to tumor cells and plays a crucial role in tumor proliferation,invasion,and metastasis [ 37 ]. In summary,ANGPTL2 exhibits upregulated expression in various tumor types,impacting different stages of tumor development.High ANGPTL2 expression often correlates with poor prognosis.Furthermore,ANGPTL2 shows significant associations with immune cell infiltration in most tumors.Elevated ANGPTL2 expression further enhances tumor migration and invasion.By modulating the tumor immune microenvironment and promoting cancer angiogenesis,ANGPTL2 plays a potential role in pan-cancer immunotherapy. Declarations Institution and Ethics approval and informed consent: These tumor specimens were obtained from the Department of Gastroenterology at Gaozhou Traditional Chinese Medicine Hospital.Ethical approval(NO: yxllyjky20220410)was obtained from the hospital,and the study adhered to the principles of the Helsinki Declaration.Written informed consent was obtained from all participants. Disclosure (Authors): The authors declare no conflicts of interest. Disclaimer: None Ethics approval and consent to participate: These tumor specimens were obtained from the Department of Gastroenterology at Gaozhou Traditional Chinese Medicine Hospital.Ethical approval(NO: yxllyjky20220410)was obtained from the hospital,and the study adhered to the principles of the Helsinki Declaration.Written informed consent was obtained from all participants. Funding: Grant sponsor: Guangdong Provincial Bureau of Traditional Chinese Medicine research project ; Grant number: 20231396 . Author Contribution Authors must provide a statement. Junyu Ke participated in the conception or design of the work; Zhikun He and Yilin Duan participated in the acquisition, analysis, or interpretation of data for the work;Yaqing Zhu, Yingjian Xu,Heng-li Zhou,Jie Lei,Haiyan Wang,Zejun Shan,Yingying Zhang,Yating Wei,Yuyin Zeng,Jiali Zhang and Yao Lu drafted the work or revised it critically for important intellectual content;Junyu Ke give final approval of the version to be published; Junyu Ke agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. Acknowledgement We thank Director YW and his colleagues at the Departmentof Surgery Il(Gaozhou Hospital of Traditional ChineseMedicine)for the processing and staining of clinical tissuesamples. Data Availability Data is provided within the manuscript or supplementary information files. References Sobolewski C, Sanduja S, Blanco FF, Hu L, Dixon DA. 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Medicine","correspondingAuthor":false,"prefix":"","firstName":"Yao","middleName":"","lastName":"Lu","suffix":""},{"id":326749893,"identity":"2450f3bb-f25a-40fb-8300-34f391d7ff30","order_by":14,"name":"Yongqiang Wu","email":"","orcid":"","institution":"Gaozhou Hospital of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Yongqiang","middleName":"","lastName":"Wu","suffix":""}],"badges":[],"createdAt":"2024-06-09 01:53:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4552153/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4552153/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":60618514,"identity":"bfeb7cd6-26d4-487e-b7f0-d711c6c28f1b","added_by":"auto","created_at":"2024-07-18 20:37:07","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":983555,"visible":true,"origin":"","legend":"\u003cp\u003eA flowchart showing the steps of this study including mutation analysis of the ANGPTL2 gene,pan-cancer analysis,survival analysis,immune infiltration analysis,single cell sequencing,KEGG-GO enrichment analysis,and validation in cell models and gastric cancer tissues\u003c/p\u003e","description":"","filename":"OnlineFIGURE1.png","url":"https://assets-eu.researchsquare.com/files/rs-4552153/v1/925f9a388c1d1b4acf9ef13a.png"},{"id":60618519,"identity":"b8d4ea7b-2486-43a6-8b47-c474267baefa","added_by":"auto","created_at":"2024-07-18 20:37:08","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":434252,"visible":true,"origin":"","legend":"\u003cp\u003eMutations of the ANGPTL2 gene in various cancers and its expression in pan-cance\u003c/p\u003e\n\u003cp\u003e(A,B)cBioPortal showed the change frequency of ANGPTL2 in different mutation types(A)and mutation sites(B)in pan-cancer.\u003c/p\u003e\n\u003cp\u003e(C)The three-dimensional structure ANGPTL2 protein showed the corresponding mutation site of A198V/T.\u003c/p\u003e\n\u003cp\u003e(D)Expression of ANGPTL2 in different tumors in Sangerbox.*\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\n\u003cp\u003e(E)Expression differences of ANGPTL2 in UCS,UCEC,READ,OV,KICH,CESC,BRCA,BLCA,STAD,THYM,KIRC,GBM,DLBC,PAAD,LGG and ACC in GTEx,TARGET and TCGA.***\u003cem\u003eP\u003c/em\u003e\u0026lt;0.001\u003c/p\u003e\n\u003cp\u003e(F)The expression level of ANGPTL2 in GEPIA was correlated with the tumor pathological stage of 5 cancers\u003c/p\u003e","description":"","filename":"OnlineFIGURE2.png","url":"https://assets-eu.researchsquare.com/files/rs-4552153/v1/25f8a0c876836069210960bd.png"},{"id":60619432,"identity":"61a76661-8b2b-4efb-8fc0-4593fe8e9c49","added_by":"auto","created_at":"2024-07-18 20:45:07","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":297084,"visible":true,"origin":"","legend":"\u003cp\u003ePrognostic value of ANGPTL2 expression in pan-cancer.\u003c/p\u003e\n\u003cp\u003e(A,B) The influence of ANGPTL2 gene expression on the prognosis of pan-cancer patients,including OS(A) and DFS(B),was analyzed based on GEPIA2.0.\u003c/p\u003e\n\u003cp\u003e(C) ANGPTL2 pan-cancer prognosis analysis based on Kaplan-Meier database.*\u003cem\u003eP\u003c/em\u003e\u0026lt;0.01; **\u003cem\u003eP\u003c/em\u003e\u0026lt;0.001; ***\u003cem\u003eP\u003c/em\u003e\u0026lt;0.0001\u003c/p\u003e","description":"","filename":"OnlineFIGURE3.png","url":"https://assets-eu.researchsquare.com/files/rs-4552153/v1/16b17136f910e372bab4a03b.png"},{"id":60619435,"identity":"96419eaf-2f5a-45ce-afe7-3a0acc96be11","added_by":"auto","created_at":"2024-07-18 20:45:08","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":724911,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation between ANGPTL2 expression and immune checkpoint genes and immune scores.\u003c/p\u003e\n\u003cp\u003e(A)The relationshipbetween ANGPTL2 expression and immune checkpoint genes.The lower triangle of each tile represents the coefficient calculated by Pearson correlation test,and the upper triangle represents the \u003cem\u003eP\u003c/em\u003e-value after log10 transformation.*\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\n\u003cp\u003e(B)Correlation of ANGPTL2 expression with immune score,stromal score,and ESTIMATE score in multiple cancers\u003c/p\u003e","description":"","filename":"OnlineFIGURE4.png","url":"https://assets-eu.researchsquare.com/files/rs-4552153/v1/7696b12b483e56d45c9230f4.png"},{"id":60618515,"identity":"1615e784-dbfd-4d29-8c6d-2e0148ffb6e4","added_by":"auto","created_at":"2024-07-18 20:37:07","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":311911,"visible":true,"origin":"","legend":"\u003cp\u003eExpression level of ANGPTL2 in Single-cell RNA-sequence and gene \u003cem\u003efunctional\u003c/em\u003e \u003cem\u003eenrichment\u003c/em\u003e \u003cem\u003eanalysis\u003c/em\u003e of related genes.\u003c/p\u003e\n\u003cp\u003e(A,B)CancerSEA tool investigated the relationshipbetween ANGPTL2 expression and different functional states in tumors.*\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\n\u003cp\u003e(C)ANGPTL2-related genes were obtained from the BioGRID network tool and 10 proteins were displayed.\u003c/p\u003e\n\u003cp\u003e(D)KEGG pathway enrichment chord diagram of ANGPTL2.\u003c/p\u003e\n\u003cp\u003e(E)GO enrichment analysis of ANGPTL2-related genes\u003c/p\u003e","description":"","filename":"OnlineFIGURE5.png","url":"https://assets-eu.researchsquare.com/files/rs-4552153/v1/75ff10b2b45ae07e8c4ee0d4.png"},{"id":60618517,"identity":"4ce42f1e-7d83-4d4f-bd5a-7eb9747c0b15","added_by":"auto","created_at":"2024-07-18 20:37:08","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1360329,"visible":true,"origin":"","legend":"\u003cp\u003eThe expression of ANGPTL2 in stage I-II and III-IV of gastric cancer was determined by HE and immunohistochemistry.Kaplan-meier survival analysis of gastric cancer samples and Cell invasion assay and Wound healing experiments of GES-1 cell models.\u003c/p\u003e\n\u003cp\u003e(A)Pathological changes of gastric cancer tumors and adjacent tissues as observed by HE staining(magnification:200×).\u003c/p\u003e\n\u003cp\u003e(B)The expression level of ANGPTL2 was evaluated by IHC(magnification :200×and 400×; *\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\n\u003cp\u003e(C)Correlation between ANGPTL2 expression and clinical prognosis.\u003c/p\u003e\n\u003cp\u003e(D)Wound healing experiments was used to measure the migration and invasion ability of cells.****\u003cem\u003eP\u003c/em\u003e\u0026lt;0.0001\u003c/p\u003e\n\u003cp\u003e(E)Cell invasion assay was used to evaluate the migration capacity of GES-1 cells.The magnifications in the figure are 20× and 40×.****\u003cem\u003eP\u003c/em\u003e\u0026lt;0.0001\u003c/p\u003e","description":"","filename":"OnlineFIGURE6.png","url":"https://assets-eu.researchsquare.com/files/rs-4552153/v1/aba407b0c7b979ff5d976923.png"},{"id":63449791,"identity":"56eb26e3-4079-40e4-bd26-609d3987cf50","added_by":"auto","created_at":"2024-08-28 09:07:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6610665,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4552153/v1/0778a6a5-9341-41ba-b6eb-1e1d6b3bdb87.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Pan-Cancer Analysis Reveals Prognostic Potential of ANGPTL2 and Its Implications in Tumor Microenvironment","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCancer remains a major cause of mortality worldwide,with high incidence and death rates[\u003cspan additionalcitationids=\"CR2 CR3 CR4 CR5\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].Pan-cancer analysis,which involves utilizing cancer gene sequences from multiple genomic databases,along with next-generation sequencing(NGS)[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e],pan-cancer model systems and pan-cancer project[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]plays a crucial role in analyzing differences and similarities among various cancer types.This research is essential for understanding the fundamental dynamics of tumor development,as well as for diagnosis,prognosis,and treatment[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eANGPTL2,an important member of the angiopoietin-like protein family[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e],is closely associated with angiogenesis,inflammatory regulation,tumor growth,and metabolic control.Its functions include promoting migration and proliferation of vascular endothelial cells,regulating inflammatory responses,and influencing the tumor microenvironment[\u003cspan additionalcitationids=\"CR12 CR13\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].Studies have revealed that ANGPTL2 is upregulated in various human tumor tissues,and its expression levels correlate positively with tumor size and malignancy,including lung cancer,liver cancer,osteosarcoma,and breast cancer[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].Additionally,serum levels of ANGPTL2 in cancer patients are inversely related to overall survival in breast cancer,lung cancer,colon cancer,and gastric cancer patients.Overexpression of ANGPTL2 in gastric cancer tissue is associated with tumor progression,early recurrence,and poor prognosis.Interestingly,while tumor cell-expressed ANGPTL2 promotes tumor cell proliferation and invasion through multiple signaling pathways,ANGPTL2 derived from tumor stromal fibroblasts enhances anti-tumor immune responses and suppresses tumor development.These findings highlight the need for comprehensive pan-cancer research and exploration of the specific mechanisms underlying ANGPTL2\u0026rsquo;s role in cancer.\u003c/p\u003e \u003cp\u003eIn this study,we conducted an comprehensive pan-cancer analysis of ANGPTL2,including its mutational status across various cancers. We summarized ANGPTL2 expression and its prognostic significance in pan-cancer. Furthermore,we explored the correlation between ANGPTL2 expression and tumor immune checkpoints and immune infiltration using single cell sequencing data.Experimental validation was performed using clinical specimens from gastric cancer patients and human gastric mucosal epithelial cells(GES-1).Our evaluation aimed to assess the potential value of ANGPTL2 in cancer detection,tumor prognosis,and immunotherapy.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Mutation Analysis of ANGPTL2 Single cell sequencing\u003c/h2\u003e \u003cp\u003eThe cBioPortal online database(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://cbioportal.org),i\u003c/span\u003e\u003cspan address=\"http://cbioportal.org),i\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003es specifically designed to address the unique data integration challenges posed by large-scale cancer genomics projects.It gives the entire cancer research community easier and more direct access to the data generated by the large-scale cancer Genome Project [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].The platform currently contains 5 published datasets and 15 interim TCGA datasets.It also currently provides data access to more than 5,000 tumor samples from 20 cancer studies [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] .We used cBioPortal online database to study the mutation of ANGPTL2 gene.By entering \u0026ldquo;ANGPTL2\u0026rdquo; in the search box and navigating to the \u0026ldquo;cancer types summary\u0026rdquo; tab,we obtained a visual grapof ANGPTL2 gene mutations in pan-cancer.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Pan-Cancer Expression Levels of ANGPTL2\u003c/h2\u003e \u003cp\u003eSangerbox,a bioinformatics analysis tool,which can perform a variety of bioinformatics analyses and visual mapping[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].Utilizing Sangerbox,we studied ANGPTL2 expression across different tumors in the TCGA cohort,which includes RNA sequencing,somatic mutations,phenotype data,and relevant clinical data for 34 human cancers.in the search box of Sangerbox interface,we entered \u0026ldquo;ANGPTL2,\u0026rdquo; selected the data source as \u0026ldquo;TCGA\u0026thinsp;+\u0026thinsp;GTEx,\u0026rdquo; chose all samples as the sample source,and applied a log2 transformation(x\u0026thinsp;+\u0026thinsp;0.001).This allowed us to visualize the differential expression of ANGPTL2 in pan-cancer.Additionally,we used the Gene Expression Profiling Interactive Analysis(GEPIA)database,[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] which integrates RNA sequencing expression data from TCGA,TARGET,and GTEx.In GEPIA,we selected the \u0026ldquo;box plot\u0026rdquo; module under the \u0026ldquo;Expression\u0026rdquo; option,input \u0026ldquo;ANGPTL2,\u0026rdquo; set the data conversion mode as |Log2FC| Cutoff:1,and set p-value cutoff of 0.01.We then added tumor types showing differential ANGPTL2 expression in pan-cancer and exported the results from the \u0026ldquo;TCGA\u0026thinsp;+\u0026thinsp;GTEx\u0026rdquo; dataset.To explore ANGPTL2 expression differences across cancer stages,we used the \u0026ldquo;stage plot\u0026rdquo; module on the same website to analyze the association between ANGPTL2 and tumor staging.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 survival analysis Based on Multiple Databases\u003c/h2\u003e \u003cp\u003eThe GEPIA contains expression data from 9,736 tumors and 8,587 normal samples obtained from the TCGA and GTEx.In the study,we defined high and low expression groups for ANGPTL2 using a threshold of 50%.We performed survival analysis on specific survival endpoints with log-rank p-values using the GEPIA2 \u0026ldquo;survival analysis\u0026rdquo; module.Furthermore,we examined the correlation between ANGPTL2 expression and overall survival(OS)and disease-free survival(DFS)data for various tumour types using the SangerBox portal website.For the purpose of differential gene expression analysis,one-way ANOVA was employed,with a threshold of \u0026ge;\u0026thinsp;1.5-fold expression change between cancer and normal tissues(\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).Finally,we employed the Kaplan-Meier Plotter(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.kmplot.com)\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.kmplot.com)\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e,a tool that integrates gene expression data and survival information from GEO,EGA,and TCGA microarray datasets,to assess the impact of ANGPTL2 on survival across 21 cancer types.We selected the\u0026ldquo;start KM plotter pan-cancer\u0026rdquo; option on the homepage,entered \u0026ldquo;ANGPTL2,\u0026rdquo; and chose overall survival(OS)as the primary endpoint for this study,resulting in relevant outcome plots.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Immune Score and Immune Checkpoint Analysis for ANGPTL2\u003c/h2\u003e \u003cp\u003eWe downloaded a standardized pan-cancer dataset(TCGA,TARGET,GTEx)from UCSC(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://xenabrowser.net/).Th\u003c/span\u003e\u003cspan address=\"https://xenabrowser.net/).Th\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003ee ENSG00000136859(ANGPTL2)gene expression data from a variety of samples were subjected to a log2 transformation(X\u0026thinsp;+\u0026thinsp;0.001),after which the transformed expression profiles were mapped to GeneSymbol.Further,we used the R package ESTIMATE(version 1.0.13)which can calculate the immune score,stromal score,and ESTIMATE score for each tumor sample in the TCGA database to calculate ESTIMATE scores for each patient in each tumor based on gene expression.[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eAdditionally,We downloaded a standardized pan-cancer dataset(TCGA,TARGET,GTEx)from UCSC(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://xenabrowser.net/).Further,w\u003c/span\u003e\u003cspan address=\"https://xenabrowser.net/).Further,w\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003ee extracted ENSG00000136859(ANGPTL2)gene and 60 genes of Immune checkpoint pathway(Inhibitory(24)and Stimulatory(36),derived from The Immune Landscape of Cancer.DOI: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.i\u003c/span\u003e\u003cspan address=\"10.1016/j.i\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e mmuni.2018.03.023)of marker genes expressed in each sample data,we screened the sample source for further: Primary Solid Tumor,Primary Tumor,Primary Blood Derived Cancer-Bone Marrow,Primary Blood Derived Cancer-Peripheral For Blood samples,we also filtered all normal samples,and further performed log2(x\u0026thinsp;+\u0026thinsp;0.001)transformations for each expression value.Next,we calculated the pearson correlation between ENSG00000136859(ANGPTL2)and the marker genes of five immune pathways.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Single-Cell Sequencing Analysis Related to ANGPTL2\u003c/h2\u003e \u003cp\u003eCancerSEA(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://biocc.hrbmu.edu.cn/CancerSEA/home.jsp)i\u003c/span\u003e\u003cspan address=\"http://biocc.hrbmu.edu.cn/CancerSEA/home.jsp)i\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003es the first dedicated database to analyse the different functional states of different cancer cells at the single-cell level(23).It encompasses 14 functional states of 41,900 cancer single cells from 25 different cancer types .We used the \"correlation plot\" module of the CancerSEA single-cell database to analyse the correlation between ANGPTL2 expression and functional status in different tumours at the single-cell level,and combined it with the \"Functional relevance\" module.correlation\" module,we further analysed the correlation data between ANGPTL2 expression and the functional status of different tumours.\u003c/p\u003e \u003cp\u003emeanwhile,by combining the \u0026ldquo;Functional relevance\u0026rdquo; module,we further investigated the relationshipbetween ANGPTL2 expression and tumor-specific functions.The correlation threshold for ANGPTL2 and cancer functional states was set at a correlation strength of 0.3 and a \u003cem\u003eP\u003c/em\u003e-value less than 0.05.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Protein-Protein Interaction and Functional Enrichment Analysis\u003c/h2\u003e \u003cp\u003eBioGRID is a selected biological database of protein-protein interactions,genetic interactions,chemical interactions,and post-translational modifications,include1,728,498 proteins and genetic interactions.In our study,we imported a co-expressed gene list for ANGPTL2 and constructed and visualized protein-protein interaction(PPI)networks using Cytoscape 3.0.1 software.We set the filtering criteria to focus on Homo sapiens species and searched by gene,aiming to determine the intrinsic associations or significance of cancer-related genes within the system network.Additionally,we performed functional enrichment analysis of ANGPTL2 using the GO and KEGG databases.Genes were considered significantly enriched when the \u003cem\u003eP\u003c/em\u003e-value was less than 0.05 and the false discovery rate(FDR)was less than 0.25.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e2.7 Hematoxylin-Eosin(HE)Staining and Immunohistochemistry of ANGPTL2 in Gastric Cancer Tissues\u003c/h3\u003e\n\u003cp\u003eTo enhance the persuasiveness of our findings,we included 60 clinical gastric cancer samples for HE staining and immunohistochemistry.These tumor specimens were obtained from the Department of Gastroenterology at Gaozhou Traditional Chinese Medicine Hospital.Ethical approval(NO: yxllyjky20220410)was obtained from the hospital,and the study adhered to the principles of the Helsinki Declaration.Prior to immunohistochemistry,we embedded tumor tissues in paraffin and performed HE staining.Following tissue sectioning and rehydration with xylene and alcohol,we stained the nuclei and cytoplasm with hematoxylin and eosin(H\u0026amp;E)to observe the pathological features of tumor and adjacent tissues under a microscope(Lonardi et al.,2021).For immunohistochemistry,we used primary antibody ANGPTL2 antibodies (Proteintech,Cat No: 12316-1-AP).Each slide was captured at \u0026times;200 magnification,and subsequent statistical analysis was performed using ImageJ software to quantify the average optical density(AOD),which represents integrated optical density and area.The average AOD from three fields of view in each slide was used as the density value for that sample.\u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8 GES-1 Cell Culture and Plasmid Transfection\u003c/h2\u003e \u003cp\u003eGES-1 cells were purchased from Wuhan Xeville Biotechnology Co.LTD.(STR identification correct,item No.STCC10401P-1).The cells were cultured in DMEM complete medium(10%FBS\u0026thinsp;+\u0026thinsp;1%PS)and placed in a cell incubator at 37℃ and 5% CO2.\u003c/p\u003e \u003cp\u003eDuring transfection of plasmid,GES-1 cells were first routinely digested and then inoculated into six-well plates with about 200,000 cells per well for culture,so that the cell density could reach about 80% the next day.After the cell density reached about 80%,each well was replaced with 2ml fresh DMEM medium(containing serum,without double antibody).For the cells in each well of the six-well plate to be transfected,two clean aseptic centrifuge tubes were taken and 125\u0026micro;l DMEM medium without Penicillin-Streptomycin Solution and serum was added,respectively.2.5\u0026micro;g overexpressed plasmid DNA was added to one tube and 5\u0026micro;l Lipo6000 transfection reagent was added to the other tube.After standing at room temperature for 5 minutes,the medium containing plasmids was gently added into the medium containing Lipo6000\" transfection reagent with a pipette,and the centrifuge tube was gently reversed and left for 5 minutes at room temperature.Then add 250\u0026micro;l Lipo6000 transfection reagent/plasmid mixture to each well and gently mix.The cells were cultured for 4\u0026ndash;6 hours after transfection and then replaced with fresh complete culture medium.After continued culture for about 48 hours,1.5 ml fresh DMEM medium(containing serum,without double antibody)was replaced per well,and 1.5\u0026micro;l puromycin was added to each well,and stable cell lines were screened.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.9 Cell invasion assay of AngPTL2-overexpressed cells\u003c/h2\u003e \u003cp\u003eIn order to measure the migration ability of cells,we judged the growth and migration ability of cells based on the ability of cells at the edge of scratches to gradually enter the blank area to heal the \"scratch\".We used a sterile 200mL pipette tipto draw a straight line across the central area of cell growth.Subsequently,the medium was replaced with a DMEM medium of 2%FBS and the shed cells were removed.After 24 hours,the cells were observed under a light microscope(magnification,100\u0026times;).Cell invasion assay rate is calculated using the following formula: cell invasion assay rate(0h scratch width \u0026minus;\u0026thinsp;24h scratch width)/0h scratch width.\u003c/p\u003e \u003cp\u003eTo determine cell invasion ability,GRS-1 cells were re-suspended in serum-free medium and inoculated into the Transwell upper chamber,which was pre-coated with Matrigel at room temperature for 25min as the extracellular matrix for cell invasion analysis.At the same time,complete culture medium of 20%FBS was added to the lower chamber.It was then transferred to a 5%CO2 incubator at 37\u0026deg;C for 24h.After 24h,the chamber was fixed in anhydrous ethanol at room temperature for 30min,and then stained with 0.1% crystal violet solution at room temperature for 30min.The invading cells were observed and counted from at least five fields under an optical microscope(magnification,100\u0026times;),and finally counted using the ImageJ software.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.10 Statistical Analysis of Clinical Information and Prognosis\u003c/h2\u003e \u003cp\u003eIn this study,we used the median expression level of ANGPTL2 as the basis for grouping and comparing subjects to assess the relationshipbetween ANGPTL2 expression and various clinical features.Categorical variables were analyzed using Fisher\u0026rsquo;s exact test,while continuous variables were analyzed using the Wilcoxon rank-sum test.survival analysis involved constructing survival curves using the Kaplan-Meier method,with groupdifferences assessed using the log-rank test.Additionally,we performed univariate and multivariate Cox regression analyses to identify independent prognostic factors.All statistical analyses were conducted using SPSS 27.0 software,and two-tailed \u003cem\u003eP\u003c/em\u003e-values\u0026thinsp;\u0026le;\u0026thinsp;0.05 were considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003ch3\u003e\u003cstrong\u003e3.1 Mutation Levels of ANGPTL2 in Different Cancers\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eDNA modifications are classified into two types: mutations(truncating mutation and missense)and copy number alterations(amplification and deepdeletion).As depicted in Figure 2A,among the topfive cancers in pan-cancer study,endometrial carcinoma and melanoma have mutation rates exceeding 3%,while colorectal cancer mainly exhibits high mutation rates(\u0026gt;2%).Prostate cancer primarily exhibits amplification as the major type of mutation,with a rate exceeding 1.5%.Esophageal and gastric cancers predominantly exhibit high mutation rates(\u0026gt;1%),accompanied by alterations such as amplification and deepdeletion.As shown in Figure 2B,we find that the main mutation mode of ANGPTL2 is missense,followed by truncating mutation.Figure 2C displays the A198V/T alteration in the three-dimensional structure of the ANGPTL2 protein.\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003e3.2 Expression of ANGPTL2 in Pan-Cancer Tissues\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eWe investigated the expression of ANGPTL2 in various tumors from the TCGA cohort using the Sangerbox database.As shown in Figure 2D,ANGPTL2 shows varying expression levels among 31 distinct tumor types.Notably,ANGPTL2 expression is significantly lower than that in adjacent normal tissues in 16 types of cancer,including endometrial carcinoma(UCEC),breast invasive carcinoma(BRCA),cervical squamous cell carcinoma(CESC),lung adenocarcinoma(LUAD),esophageal carcinoma(ESCA),kidney renal papillary cell carcinoma(KIRP),colon adenocarcinoma(COAD),colon adenocarcinoma/rectum adenocarcinoma(COADREAD),prostate adenocarcinoma(PRAD),lung squamous cell carcinoma(LUSC),skin cutaneous melanoma(SKCM),bladder urothelial carcinoma(BLCA),thyroid carcinoma(THCA),ovarian serous cystadenocarcinoma(OV),uterine carcinosarcoma(UCS)and pheochromocytoma and paragangliomas(KICH).On the contrary,ANGPTL2 has been found in GBM(Glioblastoma multiforme),GBMLGG(Glioma),LGG(Brain Lower Grade Glioma),KIPAN(Pan-kidney cohort)(KICH+KIRC+KIRP),STAD(Stomach adenocarcinoma),HNSC(Head and Neck squamous cell carcinoma),KIRC(Kidney)renal clear cell carcinoma,WT,PAAD(Pancreatic adenocarcinoma pancreatic cancer),TGCT(Testicular Germ Cell)Testicular germ cell Tumors),ALL(Acute lymphoblastic leukemia),LAML(Acute Myeloid Leukemia),PCPG(Pheochromocytoma and The expression of Paraganglioma was considerably reduced in 15 types of tumors,including Paraganglioma and paraganglioma,Adrenocortical carcinoma(ACC)and Cholangio carcinoma(CHOL).Given\u0026nbsp;that\u0026nbsp;certain\u0026nbsp;data in normal tissues were unavailable,we obtained RNA sequencing expression data from TCGA,TARGET,and GTEx databases and analyzed ANGPTL2 expression in the 31 tumors mentioned above.As shown in Figure 2E,ANGPTL2 expression\u0026nbsp;differed between\u0026nbsp;16 tumors.ANGPTL2 expression was considerably higher in eight types of cancers compared to surrounding normal tissues.Including STAD,Thymoma(Thymoma),KIRC,GBM,DLBC(Lymphoid Neoplasm Diffuse Large B-cell Lymphoma),PAAD,LGG,ACC expression up-regulated.However,we also observed downregulation of ANGPTL2 expression in UCS,UCEC,READ,OV,KICH,CESC,BRCA,BLCA and other tumors.\u003c/p\u003e\n\u003cp\u003eIn addition,to assess CNN1 expression in various tumor stages,we also used GEPIA2.0 to explore the relationshipbetween ANGPTL2 expression level and tumor pathological stage.As shown in Figure 2F,ANGPTL2 expression was different in different tumor stages of 5 types of tumors,including STAD,KIRP,TGCT,BRCA,and BLCA.\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003e3.3 Correlation between ANGPTL2 expression and survival prognosis of tumor patients\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eIn the pan-cancer survival analysis of ANGPTL2,we first investigated the correlation between ANGPTL2 expression and overall survival(OS)across multiple cancers using the GEPIA database.As shown in Figure 3A,the expression of ANGPTL2 is correlated with the overall survival of five tumors,namely COAD,LGG,LIHC,LUSC,and PAAD,among which the high expression of ANGPTL2 in COAD,LIHC,LUSC,and PAAD is often significantly correlated with poor prognosis(\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05).As shown in Figure 3B,ANGPTL2 expression was correlated with disease-free survival of five tumors,namely COAD,DLBC,KIRC,LGG,and READ,among which high ANGPTL2 expression in COAD,KIRC,and READ was often significantly correlated with poor prognosis(\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05).\u003c/p\u003e\n\u003cp\u003eNext,we analyzed the expression of ANGPTL2 in relation to overall survival in ten different cancers using the Kaplan-Meier plotter online platform.As shown in Figure 3C,the expression of ANGPTL2 correlated with prognosis in UCEC,STAD,PAAD,OV,LUSC,LIHC,KIRP,HNSC,CESC and BLCA.Specifically,high ANGPTL2 expression was significantly associated with poor prognosis in STAD(\u003cem\u003eP\u003c/em\u003e\u0026lt;0.001),UCEC(\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05),PAAD(\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05),OV(\u003cem\u003eP\u003c/em\u003e\u0026lt;0.01),LUSC(\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05),LIHC(\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05),KIRP(\u003cem\u003eP\u003c/em\u003e\u0026lt;0.01),CESC(\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05)and BLCA(\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05).Moving on to the correlation between ANGPTL2 protein expression levels and immune checkpoint genes,as well as immune cell infiltration,we extracted ANGPTL2 gene expression data from TCGA,TARGET,and GTEx databases.Using the SangerBox online platform,we evaluated the relationshipbetween ANGPTL2 expression and 60 genes in the immune checkpoint pathway.Remarkably,ANGPTL2 expression showed strong correlations with immune checkpoint genes in CHOL,PRAD,KICH,OV,PAAD,KIPAB,BLCA,READ,COAD,COADREAD,LGG,GBM and TGCT(Figure 4A).Specifically,ANGPTL2 expression positively correlated with immune checkpoint genes in CHOL，PRAD，KICH，OV，PAAD，KIPA，BLCA，READ，COAD and COADREAD.Pronounced effects were observed in OV、PAAD、KIPA、READ、COAD and COAREADE(Figure 4A).Conversely,the expression of ANGPTL2 is negatively correlated with immune checkpoint genes in LGG,GBMLGG,and TGCT(Figure 4A).These findings suggest that high ANGPTL2 expression is generally associated with a better prognosis.However,in the context of immunotherapy for LGG,GBMLGG and TGCT,ANGPTL2 inhibitors may lead to improved outcomes.\u003c/p\u003e\n\u003ch3\u003e3.4 Correlation of ANGPTL2 protein expression level with immune checkpoint genes and immune infiltration\u003c/h3\u003e\n\u003cp\u003eThe results of the estimated immune score in Figure 4B showed that ANGPTL2 expression was positively correlated with the immune score of 23 cancers.Such as STAD (p=1.8e-10),BRCA (\u003cem\u003eP\u003c/em\u003e=1.3e-10),LUAD (\u003cem\u003eP\u003c/em\u003e=2.3e-8),STES (\u003cem\u003eP\u003c/em\u003e=2.8e-17),ESCA (\u003cem\u003eP\u003c/em\u003e=1.7e-5),SARC (\u003cem\u003eP\u003c/em\u003e=3.0e-6),KIPR (\u003cem\u003eP\u003c/em\u003e=9.2e-7),KIPA (\u003cem\u003eP\u003c/em\u003e=6).0e-59),COAD (\u003cem\u003eP\u003c/em\u003e=2.2e-29),COADREAD (\u003cem\u003eP\u003c/em\u003e=7.9e-35),PRAD (\u003cem\u003eP\u003c/em\u003e=1.8e-14),HNSC (\u003cem\u003eP\u003c/em\u003e=3.6e-5),LIHC (\u003cem\u003eP\u003c/em\u003e=2.6e-6),OV (\u003cem\u003eP\u003c/em\u003e=2.5e-4),UVW (\u003cem\u003eP\u003c/em\u003e=8.7e-4),LUSC (\u003cem\u003eP\u003c/em\u003e=3.8e-1),BLCA (\u003cem\u003eP\u003c/em\u003e=1.5e-18),READ (\u003cem\u003eP\u003c/em\u003e=3.1e-7),PAAD (\u003cem\u003eP\u003c/em\u003e=1.7e-6),PCPG (\u003cem\u003eP\u003c/em\u003e=1.0e-5),SKCM (\u003cem\u003eP\u003c/em\u003e=8.9e-3),THCA (\u003cem\u003eP\u003c/em\u003e=3.0e-3),KICH (\u003cem\u003eP\u003c/em\u003e=2.5e3),etc.,However,there were four significant negative correlations,such as GBMLGG (\u003cem\u003eP\u003c/em\u003e=2.8e-26),LGG (\u003cem\u003eP\u003c/em\u003e=1.9e-14),TGCT (\u003cem\u003eP\u003c/em\u003e=6.1e-6) and ALL (\u003cem\u003eP\u003c/em\u003e=1.6e-4).\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003e3.5 Single-Cell Expression Patterns of ANGPTL2 in Various Cancers\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eTo explore the impact of ANGPTL2 in different cancers,we analysed its correlation with 14 different functional categories in 10 cancer types by using the CancerSEA single-cell database.In retinoblastoma(RB),ANGPTL2 expression was negatively correlated with cell cycle,DNA damage and DNA repair.Conversely,ANGPTL2 expression was positively correlated with angiogenesis,differentiation,inflammation,tumour metastasis,quiescence and stemness.In uveal melanoma(UM),ANGPTL2 expression was negatively correlated with tumour biological behaviour such as cell apoptosis,DNA damage and invasion,while positively correlated with cell cycle,hypoxia and stemness,albeit weakly.Notably,ANGPTL2 showed a stronger association with high-grade glioma(HGG).Specifically,ANGPTL2 was positively correlated with cell cycle,DNA damage,DNA repair and stemness(\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05),while negatively correlated with angiogenesis,cell apoptosis,epithelial-mesenchymal transition,hypoxia,inflammation,tumour metastasis and cell proliferation(\u003cem\u003eP\u003c/em\u003e\u0026lt;0.01)(Figure 5A,B).Interestingly,ANGPTL2 showed a positive correlation with stemness in most cancers(Figure 5A),but a negative correlation with DNA repair(Figure 5A).\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003e3.6 Functional Enrichment Analysis of ANGPTL2\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eNext,we performed functional enrichment analysis to evaluate the potential molecular mechanisms of ANGPTL2 in tumour initiation and progression.As shown in Figure 5C,we identified 10 molecules that interact with ANGPTL2 from the BioGRID network tool.Subsequently,we conducted a KEGG-GO enrichment analysis(FIG.5D\u0026amp;E),which showed that ANGPTL2 co-expressed genes played a regulatory role in tyrosine phosphorylation,angiogenesis,vasculature development,and actin cytoskeleton regulation.In addition,ANGPTL2 is closely associated with 12 signalling pathways,including the PI3K-Akt,Jak-STAT and Rap1 pathways.\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003e3.7 HE Staining and Immunohistochemistry of ANGPTL2 in Gastric Cancer Tissues\u003c/strong\u003e\u0026nbsp;\u003c/h3\u003e\n\u003cp\u003eHE staining results showed that early stage gastric cancer cells had loosely arranged cell layers with minimal inflammatory cells.In contrast,advanced gastric cancer cells showed significant changes,with deepand dense nuclear concentration,serious disordered nuclear arrangement,and obvious heterogeneity .In the advanced stage IV group,the number of stained cells in the center of the tumor tissue was reduced,and the periphery of the tumor tissue was significantly atrophied(Figure 6A).Immunohistochemistry revealed that ANGPTL2 expression was significantly lower in early stage gastric cancer tissues compared to late stage cases(Figure 6B).These findings suggest that ANGPTL2 may serve as a sensitive marker in the tumour microenvironment of gastric cancer patients and warrant further investigation.\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003e3.8 Impact of overexpression of ANGPTL2 on GES-1 cell\u0026nbsp;invasion\u0026nbsp;assay\u003c/strong\u003e\u0026nbsp;\u003c/h3\u003e\n\u003cp\u003eTo further investigate the effects of ANGPTL2 on malignant biological behaviours such as cell proliferation and migration,we performed ANGPTL2 overexpression plasmid transfection in GES-1 gastric mucosal epithelial cells.According to Wound\u0026nbsp;healing\u0026nbsp;experiments and Transwell test,compared with scratch area 0h after the initial test and 48h after the initial test,the cell\u0026nbsp;invasion\u0026nbsp;assay of ANGPTL2 overexpression groupwas significantly higher than that of NC group(\u003cem\u003eP\u003c/em\u003e\u0026lt;0.01).In the Transwell experiment,we further confirmed the enhanced migration ability of cells in the ANGPTL2 overexpression group(Figure 6C,D).\u003c/p\u003e\n\u003cp\u003eThe results showed that the number of cells attached to the lower surface of the chamber in the NC groupwas significantly lower than that in the ANGPTL2 overexpression group,indicating that the number of cells migrated in the ANGPTL2 overexpression groupwas significantly higher than that in the NC group(\u003cem\u003eP\u003c/em\u003e\u0026lt;0.01),which also proved that the increase of ANGPTL2 expreThe results showed that the number of cells attached to the lower surface of the chamber in the NC groupwas significantly lower than that in the ANGPTL2 overexpression group,indicating that the number of cells migrated in the ANGPTL2 overexpression groupwas significantly higher than that in the NC group(\u003cem\u003eP\u003c/em\u003e\u0026lt;0.01),which also proved that the increase of ANGPTL2 expression level could enhance the migration and invasion ability of cells to a certain extent.\u003c/p\u003e\n\u003ch3\u003e3.9 Correlation between ANGPTL2 expression and clinical prognosis\u003c/h3\u003e\n\u003cp\u003eThis is a retrospective study in which all patients received continuous follow-up.Table 1 shows the baseline clinicopathological features of patients with gastric cancer.We performed immunohistochemistry on paraffin sections of 60 patients with gastric cancer to assess the expression of ANGPTL2 in cancer tissues.The positive expression sites were quantitatively analyzed using Image J software,and the expression of ANGPTL2 in GC patient tissues was further divided into low and high levels according to the median expression.Then,the relationshipbetween ANGPTL2 expression level and clinicopathological features was analyzed based on the clinical data of patients.In univariate analysis,there were statistical differences in TNM staging between the two groups(\u003cem\u003eP\u003c/em\u003e= 0.035,Table 2,Figure 6C),and no significant differences in other clinical features.In order to further assess whether ANGPTL2 levels in patients with gastric cancer can be used as a prognostic predictors of patients and to explore whether there is a correlation between its expression and survival outcomes,we used Cox regression model for clinical pathological factors affecting the prognosis of the single factor analysis,the results showed that ANGPTL2 expression levels(\u003cem\u003eP\u003c/em\u003e\u0026lt;0.001,HR=6.118,95%CI:[2.176-17.205]); TNM staging(\u003cem\u003eP\u003c/em\u003e\u0026lt;0.001,HR=0.113,95%CI[0.035 -- 0370]); The degree of differentiation(\u003cem\u003eP\u003c/em\u003e=0.018,HR=3.780,95%CI :[1.251-11.428])and age(\u003cem\u003eP\u003c/em\u003e= 0.002,HR=2.198,95%CI [0.056-0.509])were associated with poorer prognosis(Table 3).\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"535\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"83.17757009345794%\" colspan=\"9\"\u003e\n \u003cp\u003e\u003cstrong\u003eTABLE 1 Clinicopathological features of patients with gastric cancer.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.822429906542055%\" colspan=\"4\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"49.250936329588015%\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003ePatient characteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.670411985018726%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of pataients (N=60)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.224719101123597%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003ePercentage(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.853932584269664%\" colspan=\"4\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"49.250936329588015%\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.670411985018726%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.224719101123597%\" colspan=\"3\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.853932584269664%\" colspan=\"4\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"49.250936329588015%\" colspan=\"4\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.670411985018726%\" colspan=\"2\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.224719101123597%\" colspan=\"3\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.853932584269664%\" colspan=\"4\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"49.250936329588015%\" colspan=\"4\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.670411985018726%\" colspan=\"2\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.224719101123597%\" colspan=\"3\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.853932584269664%\" colspan=\"4\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"49.250936329588015%\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge(year)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.670411985018726%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.224719101123597%\" colspan=\"3\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.853932584269664%\" colspan=\"4\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"49.250936329588015%\" colspan=\"4\"\u003e\n \u003cp\u003e＜60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.670411985018726%\" colspan=\"2\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.224719101123597%\" colspan=\"3\"\u003e\n \u003cp\u003e71.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.853932584269664%\" colspan=\"4\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"49.250936329588015%\" colspan=\"4\"\u003e\n \u003cp\u003e\u0026ge;60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.670411985018726%\" colspan=\"2\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.224719101123597%\" colspan=\"3\"\u003e\n \u003cp\u003e28.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.853932584269664%\" colspan=\"4\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"49.250936329588015%\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eHistological type\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.670411985018726%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.224719101123597%\" colspan=\"3\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.853932584269664%\" colspan=\"4\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"49.250936329588015%\" colspan=\"4\"\u003e\n \u003cp\u003eAdenocarcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.670411985018726%\" colspan=\"2\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.224719101123597%\" colspan=\"3\"\u003e\n \u003cp\u003e78.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.853932584269664%\" colspan=\"4\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"49.250936329588015%\" colspan=\"4\"\u003e\n \u003cp\u003eNon-adenocarcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.670411985018726%\" colspan=\"2\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.224719101123597%\" colspan=\"3\"\u003e\n \u003cp\u003e21.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.853932584269664%\" colspan=\"4\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"49.250936329588015%\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eDifferentiation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.670411985018726%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.224719101123597%\" colspan=\"3\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.853932584269664%\" colspan=\"4\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"49.250936329588015%\" colspan=\"4\"\u003e\n \u003cp\u003ehigh differentiation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.670411985018726%\" colspan=\"2\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.224719101123597%\" colspan=\"3\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.853932584269664%\" colspan=\"4\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"49.250936329588015%\" colspan=\"4\"\u003e\n \u003cp\u003emoderately differentiated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.670411985018726%\" colspan=\"2\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.224719101123597%\" colspan=\"3\"\u003e\n \u003cp\u003e41.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.853932584269664%\" colspan=\"4\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"49.250936329588015%\" colspan=\"4\"\u003e\n \u003cp\u003epoorly differentiated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.670411985018726%\" colspan=\"2\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.224719101123597%\" colspan=\"3\"\u003e\n \u003cp\u003e8.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.853932584269664%\" colspan=\"4\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"49.250936329588015%\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eTNM staging\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.670411985018726%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.224719101123597%\" colspan=\"3\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.853932584269664%\" colspan=\"4\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"49.250936329588015%\" colspan=\"4\"\u003e\n \u003cp\u003eⅠ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.670411985018726%\" colspan=\"2\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.224719101123597%\" colspan=\"3\"\u003e\n \u003cp\u003e16.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.853932584269664%\" colspan=\"4\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"49.250936329588015%\" colspan=\"4\"\u003e\n \u003cp\u003eⅡ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.670411985018726%\" colspan=\"2\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.224719101123597%\" colspan=\"3\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.853932584269664%\" colspan=\"4\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"49.250936329588015%\" colspan=\"4\"\u003e\n \u003cp\u003eⅢ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.670411985018726%\" colspan=\"2\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.224719101123597%\" colspan=\"3\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.853932584269664%\" colspan=\"4\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"49.250936329588015%\" colspan=\"4\"\u003e\n \u003cp\u003eⅣ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.670411985018726%\" colspan=\"2\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.224719101123597%\" colspan=\"3\"\u003e\n \u003cp\u003e8.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.853932584269664%\" colspan=\"4\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"49.250936329588015%\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eDepth of intestinal wall infiltration\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.670411985018726%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.224719101123597%\" colspan=\"3\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.853932584269664%\" colspan=\"4\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"49.250936329588015%\" colspan=\"4\"\u003e\n \u003cp\u003emucous membrane layer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.670411985018726%\" colspan=\"2\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.224719101123597%\" colspan=\"3\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.853932584269664%\" colspan=\"4\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"49.250936329588015%\" colspan=\"4\"\u003e\n \u003cp\u003emuscle layer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.670411985018726%\" colspan=\"2\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.224719101123597%\" colspan=\"3\"\u003e\n \u003cp\u003e18.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.853932584269664%\" colspan=\"4\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"49.250936329588015%\" colspan=\"4\"\u003e\n \u003cp\u003eserous layer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.670411985018726%\" colspan=\"2\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.224719101123597%\" colspan=\"3\"\u003e\n \u003cp\u003e71.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.853932584269664%\" colspan=\"4\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"49.250936329588015%\" colspan=\"4\"\u003e\n \u003cp\u003eouter layer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.670411985018726%\" colspan=\"2\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.224719101123597%\" colspan=\"3\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.853932584269664%\" colspan=\"4\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"49.250936329588015%\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eTumour size\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.670411985018726%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.224719101123597%\" colspan=\"3\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.853932584269664%\" colspan=\"4\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"49.250936329588015%\" colspan=\"4\"\u003e\n \u003cp\u003e＜5 cm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.670411985018726%\" colspan=\"2\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.224719101123597%\" colspan=\"3\"\u003e\n \u003cp\u003e48.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.853932584269664%\" colspan=\"4\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"49.250936329588015%\" colspan=\"4\"\u003e\n \u003cp\u003e\u0026ge;5 cm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.670411985018726%\" colspan=\"2\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.224719101123597%\" colspan=\"3\"\u003e\n \u003cp\u003e51.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.853932584269664%\" colspan=\"4\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"49.250936329588015%\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eLymph node metastasis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.670411985018726%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.224719101123597%\" colspan=\"3\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.853932584269664%\" colspan=\"4\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"49.250936329588015%\" colspan=\"4\"\u003e\n \u003cp\u003eWithout\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.670411985018726%\" colspan=\"2\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.224719101123597%\" colspan=\"3\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.853932584269664%\" colspan=\"4\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"49.250936329588015%\" colspan=\"4\"\u003e\n \u003cp\u003eWith\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.670411985018726%\" colspan=\"2\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.224719101123597%\" colspan=\"3\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.853932584269664%\" colspan=\"4\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"49.250936329588015%\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eCEA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.670411985018726%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.224719101123597%\" colspan=\"3\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.853932584269664%\" colspan=\"4\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"49.250936329588015%\" colspan=\"4\"\u003e\n \u003cp\u003e＜5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.670411985018726%\" colspan=\"2\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.224719101123597%\" colspan=\"3\"\u003e\n \u003cp\u003e71.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.853932584269664%\" colspan=\"4\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"49.250936329588015%\" colspan=\"4\"\u003e\n \u003cp\u003e\u0026ge;5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.670411985018726%\" colspan=\"2\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.224719101123597%\" colspan=\"3\"\u003e\n \u003cp\u003e28.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.853932584269664%\" colspan=\"4\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"49.250936329588015%\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eCA199\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.670411985018726%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.224719101123597%\" colspan=\"3\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.853932584269664%\" colspan=\"4\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"49.250936329588015%\" colspan=\"4\"\u003e\n \u003cp\u003e＜37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.670411985018726%\" colspan=\"2\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.224719101123597%\" colspan=\"3\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.853932584269664%\" colspan=\"4\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"49.250936329588015%\" colspan=\"4\"\u003e\n \u003cp\u003e\u0026ge;37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.670411985018726%\" colspan=\"2\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.224719101123597%\" colspan=\"3\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.853932584269664%\" colspan=\"4\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"49.250936329588015%\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eSurvival state\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.670411985018726%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.224719101123597%\" colspan=\"3\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.853932584269664%\" colspan=\"4\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"49.250936329588015%\" colspan=\"4\"\u003e\n \u003cp\u003edeath\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.670411985018726%\" colspan=\"2\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.224719101123597%\" colspan=\"3\"\u003e\n \u003cp\u003e41.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.853932584269664%\" colspan=\"4\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"49.250936329588015%\" colspan=\"4\"\u003e\n \u003cp\u003esurvival\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.670411985018726%\" colspan=\"2\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.224719101123597%\" colspan=\"3\"\u003e\n \u003cp\u003e58.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.853932584269664%\" colspan=\"4\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"49.250936329588015%\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eCD11 expression\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.670411985018726%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.224719101123597%\" colspan=\"3\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.853932584269664%\" colspan=\"4\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"49.250936329588015%\" colspan=\"4\"\u003e\n \u003cp\u003elow expression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.670411985018726%\" colspan=\"2\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.224719101123597%\" colspan=\"3\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.853932584269664%\" colspan=\"4\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"49.250936329588015%\" colspan=\"4\"\u003e\n \u003cp\u003ehigh expression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.670411985018726%\" colspan=\"2\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.224719101123597%\" colspan=\"3\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.853932584269664%\" colspan=\"4\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e \n \u003c/tbody\u003e\n\u003c/table\u003e\u003cbr\u003e\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"535\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"13\"\u003e\n \u003cp\u003e\u003cstrong\u003eTABLE 2 Single-factor Cox regression analysis.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.35514018691589%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eClinicopathologic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.336448598130842%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eHazard\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.102803738317757%\" colspan=\"6\"\u003e\n \u003cp\u003e\u003cstrong\u003e95.0% CI for Exp(B)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.205607476635514%\" colspan=\"2\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP value\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"54.030501089324616%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003echaracteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.379084967320262%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eratio\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.904139433551197%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eLower\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.686274509803921%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eUpper\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.35514018691589%\" colspan=\"3\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.336448598130842%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.644859813084112%\" colspan=\"3\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.457943925233645%\" colspan=\"3\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.205607476635514%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.35514018691589%\" colspan=\"3\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.336448598130842%\" colspan=\"2\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.644859813084112%\" colspan=\"3\" rowspan=\"2\"\u003e\n \u003cp\u003e0.592\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.457943925233645%\" colspan=\"3\" rowspan=\"2\"\u003e\n \u003cp\u003e4.663\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.205607476635514%\" colspan=\"2\" rowspan=\"2\"\u003e\n \u003cp\u003e0.334\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"78.98089171974522%\" colspan=\"3\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.019108280254777%\" colspan=\"2\"\u003e\n \u003cp\u003e1.662\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.35514018691589%\" colspan=\"3\"\u003e\n \u003cp\u003eAge(year)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.336448598130842%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.644859813084112%\" colspan=\"3\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.457943925233645%\" colspan=\"3\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.205607476635514%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.35514018691589%\" colspan=\"3\"\u003e\n \u003cp\u003e＜60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.336448598130842%\" colspan=\"2\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.644859813084112%\" colspan=\"3\" rowspan=\"2\"\u003e\n \u003cp\u003e0.056\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.457943925233645%\" colspan=\"3\" rowspan=\"2\"\u003e\n \u003cp\u003e0.509\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.205607476635514%\" colspan=\"2\" rowspan=\"2\"\u003e\n \u003cp\u003e0.002\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"78.98089171974522%\" colspan=\"3\"\u003e\n \u003cp\u003e\u0026ge;60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.019108280254777%\" colspan=\"2\"\u003e\n \u003cp\u003e0.169\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.35514018691589%\" colspan=\"3\"\u003e\n \u003cp\u003eHistological type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.336448598130842%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.644859813084112%\" colspan=\"3\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.457943925233645%\" colspan=\"3\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.205607476635514%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.35514018691589%\" colspan=\"3\"\u003e\n \u003cp\u003eAdenocarcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.336448598130842%\" colspan=\"2\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.644859813084112%\" colspan=\"3\" rowspan=\"2\"\u003e\n \u003cp\u003e0.813\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.457943925233645%\" colspan=\"3\" rowspan=\"2\"\u003e\n \u003cp\u003e7.765\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.205607476635514%\" colspan=\"2\" rowspan=\"2\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"78.98089171974522%\" colspan=\"3\"\u003e\n \u003cp\u003emucoid adenocarcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.019108280254777%\" colspan=\"2\"\u003e\n \u003cp\u003e2.512\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.35514018691589%\" colspan=\"3\"\u003e\n \u003cp\u003eDepth of tumor invasion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.336448598130842%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.644859813084112%\" colspan=\"3\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.457943925233645%\" colspan=\"3\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.205607476635514%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.35514018691589%\" colspan=\"3\"\u003e\n \u003cp\u003eT1\u0026amp;T2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.336448598130842%\" colspan=\"2\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.644859813084112%\" colspan=\"3\" rowspan=\"3\"\u003e\n \u003cp\u003e0.429\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.457943925233645%\" colspan=\"3\" rowspan=\"3\"\u003e\n \u003cp\u003e7.792\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.205607476635514%\" colspan=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003e0.414\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"78.98089171974522%\" colspan=\"3\"\u003e\n \u003cp\u003eT3\u0026amp;T4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.019108280254777%\" colspan=\"2\"\u003e\n \u003cp\u003e1.829\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"78.98089171974522%\" colspan=\"3\"\u003e\n \u003cp\u003edegree of differentiation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.019108280254777%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.35514018691589%\" colspan=\"3\"\u003e\n \u003cp\u003ehigh and moderately differentiation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.336448598130842%\" colspan=\"2\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.644859813084112%\" colspan=\"3\" rowspan=\"2\"\u003e\n \u003cp\u003e1.251\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.457943925233645%\" colspan=\"3\" rowspan=\"2\"\u003e\n \u003cp\u003e11.428\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.205607476635514%\" colspan=\"2\" rowspan=\"2\"\u003e\n \u003cp\u003e0.018\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"78.98089171974522%\" colspan=\"3\"\u003e\n \u003cp\u003epoorly differentiated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.019108280254777%\" colspan=\"2\"\u003e\n \u003cp\u003e3.78\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.35514018691589%\" colspan=\"3\"\u003e\n \u003cp\u003eTumour size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.336448598130842%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.644859813084112%\" colspan=\"3\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.457943925233645%\" colspan=\"3\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.205607476635514%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.35514018691589%\" colspan=\"3\"\u003e\n \u003cp\u003e＜5 cm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.336448598130842%\" colspan=\"2\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.644859813084112%\" colspan=\"3\" rowspan=\"2\"\u003e\n \u003cp\u003e0.393\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.457943925233645%\" colspan=\"3\" rowspan=\"2\"\u003e\n \u003cp\u003e2.762\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.205607476635514%\" colspan=\"2\" rowspan=\"2\"\u003e\n \u003cp\u003e0.934\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"78.98089171974522%\" colspan=\"3\"\u003e\n \u003cp\u003e\u0026ge;5 cm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.019108280254777%\" colspan=\"2\"\u003e\n \u003cp\u003e1.042\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.35514018691589%\" colspan=\"3\"\u003e\n \u003cp\u003eTNM staging\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.336448598130842%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.644859813084112%\" colspan=\"3\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.457943925233645%\" colspan=\"3\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.205607476635514%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.35514018691589%\" colspan=\"3\"\u003e\n \u003cp\u003eⅠ\u0026amp;Ⅱ stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.336448598130842%\" colspan=\"2\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.644859813084112%\" colspan=\"3\" rowspan=\"2\"\u003e\n \u003cp\u003e0.035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.457943925233645%\" colspan=\"3\" rowspan=\"2\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.205607476635514%\" colspan=\"2\" rowspan=\"2\"\u003e\n \u003cp\u003e＜0.001\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"78.98089171974522%\" colspan=\"3\"\u003e\n \u003cp\u003eⅢ\u0026amp;Ⅳ stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.019108280254777%\" colspan=\"2\"\u003e\n \u003cp\u003e0.113\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.35514018691589%\" colspan=\"3\"\u003e\n \u003cp\u003eLymph node metastasis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.336448598130842%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.644859813084112%\" colspan=\"3\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.457943925233645%\" colspan=\"3\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.205607476635514%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.35514018691589%\" colspan=\"3\"\u003e\n \u003cp\u003eWithout\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.336448598130842%\" colspan=\"2\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.644859813084112%\" colspan=\"3\" rowspan=\"2\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.457943925233645%\" colspan=\"3\" rowspan=\"2\"\u003e\n \u003cp\u003e2.178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.205607476635514%\" colspan=\"2\" rowspan=\"2\"\u003e\n \u003cp\u003e0.619\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"78.98089171974522%\" colspan=\"3\"\u003e\n \u003cp\u003eWith\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.019108280254777%\" colspan=\"2\"\u003e\n \u003cp\u003e0.768\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.35514018691589%\" colspan=\"3\"\u003e\n \u003cp\u003eCEA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.336448598130842%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.644859813084112%\" colspan=\"3\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.457943925233645%\" colspan=\"3\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.205607476635514%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.35514018691589%\" colspan=\"3\"\u003e\n \u003cp\u003e＜5 ng/mL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.336448598130842%\" colspan=\"2\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.644859813084112%\" colspan=\"3\" rowspan=\"2\"\u003e\n \u003cp\u003e0.679\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.457943925233645%\" colspan=\"3\" rowspan=\"2\"\u003e\n \u003cp\u003e4.037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.205607476635514%\" colspan=\"2\" rowspan=\"2\"\u003e\n \u003cp\u003e0.267\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"78.98089171974522%\" colspan=\"3\"\u003e\n \u003cp\u003e\u0026ge;5 ng/mL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.019108280254777%\" colspan=\"2\"\u003e\n \u003cp\u003e1.656\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.35514018691589%\" colspan=\"3\"\u003e\n \u003cp\u003eCA199\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.336448598130842%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.644859813084112%\" colspan=\"3\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.457943925233645%\" colspan=\"3\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.205607476635514%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.35514018691589%\" colspan=\"3\"\u003e\n \u003cp\u003e＜37 U/mL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.336448598130842%\" colspan=\"2\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.644859813084112%\" colspan=\"3\" rowspan=\"2\"\u003e\n \u003cp\u003e0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.457943925233645%\" colspan=\"3\" rowspan=\"2\"\u003e\n \u003cp\u003e0.178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.205607476635514%\" colspan=\"2\" rowspan=\"2\"\u003e\n \u003cp\u003e1.625\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"78.98089171974522%\" colspan=\"3\"\u003e\n \u003cp\u003e\u0026ge;37 U/mL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.019108280254777%\" colspan=\"2\"\u003e\n \u003cp\u003e0.538\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.35514018691589%\" colspan=\"3\"\u003e\n \u003cp\u003eANGPTL2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.336448598130842%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.644859813084112%\" colspan=\"3\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.457943925233645%\" colspan=\"3\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.205607476635514%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.35514018691589%\" colspan=\"3\"\u003e\n \u003cp\u003eLow expression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.336448598130842%\" colspan=\"2\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.644859813084112%\" colspan=\"3\" rowspan=\"2\"\u003e\n \u003cp\u003e2.176\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.457943925233645%\" colspan=\"3\" rowspan=\"2\"\u003e\n \u003cp\u003e17.205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.205607476635514%\" colspan=\"2\" rowspan=\"2\"\u003e\n \u003cp\u003e＜0.001\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"78.98089171974522%\" colspan=\"3\"\u003e\n \u003cp\u003eHigh expression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.019108280254777%\" colspan=\"2\"\u003e\n \u003cp\u003e6.118\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e \n \u003c/tbody\u003e\n\u003c/table\u003e\u003cbr\u003e\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"535\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"92.5233644859813%\" colspan=\"12\"\u003e\n \u003cp\u003e\u003cstrong\u003eTABLE 3 Correlation between ANGPTL2 and clinicopathologic characteristics.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.4766355140186915%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.6822429906542%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eClinicopathological paramete\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.177570093457944%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40%\" colspan=\"8\"\u003e\n \u003cp\u003e\u003cstrong\u003eANGPTL2 Expression\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.663551401869159%\" colspan=\"2\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e值\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.4766355140186915%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.338582677165356%\" colspan=\"5\"\u003e\n \u003cp\u003e\u003cstrong\u003eLow expression\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.91338582677165%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eHigh expression\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.748031496062993%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.6822429906542%\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.177570093457944%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.626168224299064%\" colspan=\"5\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.373831775700936%\" colspan=\"3\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.663551401869159%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.4766355140186915%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.6822429906542%\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.177570093457944%\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.626168224299064%\" colspan=\"5\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.373831775700936%\" colspan=\"3\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.663551401869159%\" colspan=\"2\" rowspan=\"2\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.4766355140186915%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.1417004048583%\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.4412955465587043%\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.25506072874494%\" colspan=\"5\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.06477732793522%\" colspan=\"3\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.097165991902834%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.6822429906542%\"\u003e\n \u003cp\u003eAge(year)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.177570093457944%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.626168224299064%\" colspan=\"5\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.373831775700936%\" colspan=\"3\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.663551401869159%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.4766355140186915%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.6822429906542%\"\u003e\n \u003cp\u003e＜60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.177570093457944%\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.626168224299064%\" colspan=\"5\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.373831775700936%\" colspan=\"3\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.663551401869159%\" colspan=\"2\" rowspan=\"2\"\u003e\n \u003cp\u003e0.152\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.4766355140186915%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.1417004048583%\"\u003e\n \u003cp\u003e\u0026ge;60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.4412955465587043%\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.25506072874494%\" colspan=\"5\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.06477732793522%\" colspan=\"3\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.097165991902834%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.6822429906542%\"\u003e\n \u003cp\u003eHistological type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.177570093457944%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.626168224299064%\" colspan=\"5\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.373831775700936%\" colspan=\"3\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.663551401869159%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.4766355140186915%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.6822429906542%\"\u003e\n \u003cp\u003eAdenocarcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.177570093457944%\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.626168224299064%\" colspan=\"5\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.373831775700936%\" colspan=\"3\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.663551401869159%\" colspan=\"2\" rowspan=\"2\"\u003e\n \u003cp\u003e0.754\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.4766355140186915%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.1417004048583%\"\u003e\n \u003cp\u003emucoid adenocarcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.4412955465587043%\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.25506072874494%\" colspan=\"5\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.06477732793522%\" colspan=\"3\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.097165991902834%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.6822429906542%\"\u003e\n \u003cp\u003eDepth of tumor invasion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.177570093457944%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.626168224299064%\" colspan=\"5\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.373831775700936%\" colspan=\"3\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.663551401869159%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.4766355140186915%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.6822429906542%\"\u003e\n \u003cp\u003eT1\u0026amp;T2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.177570093457944%\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.626168224299064%\" colspan=\"5\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.373831775700936%\" colspan=\"3\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.663551401869159%\" colspan=\"2\" rowspan=\"2\"\u003e\n \u003cp\u003e0.389\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.4766355140186915%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.1417004048583%\"\u003e\n \u003cp\u003eT3\u0026amp;T4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.4412955465587043%\"\u003e\n \u003cp\u003e54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.25506072874494%\" colspan=\"5\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.06477732793522%\" colspan=\"3\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.097165991902834%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.6822429906542%\"\u003e\n \u003cp\u003edegree of differentiation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.177570093457944%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.626168224299064%\" colspan=\"5\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.373831775700936%\" colspan=\"3\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.663551401869159%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.4766355140186915%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.6822429906542%\"\u003e\n \u003cp\u003ehigh and moderately differentiation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.177570093457944%\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.626168224299064%\" colspan=\"5\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.373831775700936%\" colspan=\"3\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.663551401869159%\" colspan=\"2\" rowspan=\"2\"\u003e\n \u003cp\u003e0.606\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.4766355140186915%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.1417004048583%\"\u003e\n \u003cp\u003epoorly differentiated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.4412955465587043%\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.25506072874494%\" colspan=\"5\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.06477732793522%\" colspan=\"3\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.097165991902834%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.6822429906542%\"\u003e\n \u003cp\u003eTumour size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.177570093457944%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.626168224299064%\" colspan=\"5\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.373831775700936%\" colspan=\"3\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.663551401869159%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.4766355140186915%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.6822429906542%\"\u003e\n \u003cp\u003e＜5 cm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.177570093457944%\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.626168224299064%\" colspan=\"5\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.373831775700936%\" colspan=\"3\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.663551401869159%\" colspan=\"2\" rowspan=\"2\"\u003e\n \u003cp\u003e0.796\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.4766355140186915%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.1417004048583%\"\u003e\n \u003cp\u003e\u0026ge;5 cm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.4412955465587043%\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.25506072874494%\" colspan=\"5\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.06477732793522%\" colspan=\"3\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.097165991902834%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.6822429906542%\"\u003e\n \u003cp\u003eTNM staging\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.177570093457944%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.626168224299064%\" colspan=\"5\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.373831775700936%\" colspan=\"3\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.663551401869159%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.4766355140186915%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.6822429906542%\"\u003e\n \u003cp\u003eⅠ\u0026amp;Ⅱ stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.177570093457944%\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.626168224299064%\" colspan=\"5\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.373831775700936%\" colspan=\"3\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.663551401869159%\" colspan=\"2\" rowspan=\"2\"\u003e\n \u003cp\u003e0.035\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.4766355140186915%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.1417004048583%\"\u003e\n \u003cp\u003eⅢ\u0026amp;Ⅳ stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.4412955465587043%\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.25506072874494%\" colspan=\"5\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.06477732793522%\" colspan=\"3\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.097165991902834%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.6822429906542%\"\u003e\n \u003cp\u003eLymph node metastasis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.177570093457944%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.626168224299064%\" colspan=\"5\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.373831775700936%\" colspan=\"3\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.663551401869159%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.4766355140186915%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.6822429906542%\"\u003e\n \u003cp\u003eWithout\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.177570093457944%\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.626168224299064%\" colspan=\"5\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.373831775700936%\" colspan=\"3\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.663551401869159%\" colspan=\"2\" rowspan=\"2\"\u003e\n \u003cp\u003e0.787\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.4766355140186915%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.1417004048583%\"\u003e\n \u003cp\u003eWith\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.4412955465587043%\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.25506072874494%\" colspan=\"5\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.06477732793522%\" colspan=\"3\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.097165991902834%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.6822429906542%\"\u003e\n \u003cp\u003eCEA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.177570093457944%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.626168224299064%\" colspan=\"5\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.373831775700936%\" colspan=\"3\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.663551401869159%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.4766355140186915%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.6822429906542%\"\u003e\n \u003cp\u003e＜5 ng/mL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.177570093457944%\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.626168224299064%\" colspan=\"5\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.373831775700936%\" colspan=\"3\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.663551401869159%\" colspan=\"2\" rowspan=\"2\"\u003e\n \u003cp\u003e0.774\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.4766355140186915%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.1417004048583%\"\u003e\n \u003cp\u003e\u0026ge;5 ng/mL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.4412955465587043%\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.25506072874494%\" colspan=\"5\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.06477732793522%\" colspan=\"3\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.097165991902834%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.6822429906542%\"\u003e\n \u003cp\u003eCA199\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.177570093457944%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.626168224299064%\" colspan=\"5\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.373831775700936%\" colspan=\"3\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.663551401869159%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.4766355140186915%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.6822429906542%\"\u003e\n \u003cp\u003e＜37 U/mL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.177570093457944%\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.626168224299064%\" colspan=\"5\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.373831775700936%\" colspan=\"3\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.663551401869159%\" colspan=\"2\" rowspan=\"2\"\u003e\n \u003cp\u003e0.519\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.4766355140186915%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.1417004048583%\"\u003e\n \u003cp\u003e\u0026ge;37 U/mL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.4412955465587043%\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.25506072874494%\" colspan=\"5\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.06477732793522%\" colspan=\"3\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.097165991902834%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Discussion","content":"\u003cp\u003eANGPTL2,a crucial member of the angiopoietin-like protein family [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e],it can not only shares functional similarities with angiopoietins in promoting angiogenesis but also regulates endothelial cell function through autocrine or paracrine mechanisms,inducing vascular formation and maintaining tissue homeostasis[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].Numerous studies have confirmed elevated ANGPTL2 expression in various malignant tumor cells [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e],affecting tumor cell proliferation,invasion,and migration.Charan Manish et al.found significantly higher serum levels of ANGPTL2 in osteosarcoma patients compared to healthy controls.Their study revealed that tumor cell-secreted ANGPTL2 induces neutrophil recruitment in the lungs,creating a favorable pre-metastatic tumor microenvironment for tumor dissemination [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].Yuji Toiyama et al.demonstrated that in vitro knockdown of ANGPTL2 significantly inhibited cell proliferation,migration,and invasion.Furthermore,clinical samples from colorectal cancer patients indicated a close association between high ANGPTL2 expression and lymph node metastasis,liver metastasis,and disease prognosis [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].Endo et al.found that the expression of ANGPTL2 in human primary lung cancer tissues was much higher than that in normal lung tissues,and its high expression was related to patients' DFS,and ANGPTL2 derived from tumor cells could induce angiogenesis by activating the α5β1-Rac signaling pathway,further promoting the remote metastasis of tumor cells [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn our study,we first explored ANGPTL2 expression across different tumors in the TCGA dataset using the Sangerbox and GEPIA online platforms.Based on Sangerbox results,ANGPTL2 exhibited differential expression in 31 tumor types.Notably,ANGPTL2 expression was significantly higher in 15 tumor types,including gastric cancer,compared to adjacent normal tissues.Conversely,ANGPTL2 expression was lower in 16 tumor types,such as endometrial cancer,relative to adjacent normal tissues.These findings were consistent with GEPIA results,where ANGPTL2 expression was significantly elevated in 8 tumor types(including gastric cancer)and decreased in 8 tumor types(including endometrial cancer)compared to adjacent normal tissues.The source of ANGPTL2 secretion may contribute to these expression patterns.Immunohistochemistry analysis of gastric cancer clinical tissues confirmed significantly higher ANGPTL2 expression in late-stage gastric cancer tissues compared to early-stage gastric cancer tissues.Haruki Horiguchi et al.reported that ANGPTL2 knockout in a renal cell carcinoma mouse model slowed disease progression and metastasis.However,ANGPTL2 in host tumor microenvironment cells also exhibited tumor-suppressive effects,including promoting CD8\u0026thinsp;+\u0026thinsp;T cell priming,enhancing anti-tumor immune responses,and activating dendritic cells to improve tumor vaccine efficacy.Although the expression patterns of ANGPTL2 in tumors and their microenvironments require further exploration,it is evident that ANGPTL2 expression correlates with malignant progression in most cancers [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e],covering various stages of tumor development in gastric cancer and other tumor types.With the meaningful role of ANGPTL2 expression in tumor development established,we further assessed its potential for predicting tumor survival outcomes.Our results,integrating GEPIA data on overall survival(OS)and disease-free survival(DFS),revealed significant correlations between ANGPTL2 expression and survival prognosis in COAD,LGG,LIHC,LUSC,PAAD,DLBC,KIRC,and READ,with high ANGPTL2 expression often associated with poor prognosis.Kaplan-Meier plotter analysis also demonstrated ANGPTL2\u0026rsquo;s relevance to survival outcomes in 10 tumor types,with high ANGPTL2 expression linked to adverse prognosis in 9 of these types,including STAD.These findings suggest that elevated ANGPTL2 expression is associated with poor prognosis in most tumors.Shozo Ide et al.similarly reported that esophageal cancer patients with high ANGPTL2 expression had worse overall survival and disease-free survival compared to low-expression counterparts [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].To further validate ANGPTL2\u0026rsquo;s impact on cancer survival prognosis,we conducted survival analysis on clinical information from gastric cancer patients,confirming that high ANGPTL2 expression in gastric cancer correlates with poorer survival outcomes.\u003c/p\u003e \u003cp\u003eIncreasing evidence suggests that immunotherapy alone or in combination with chemotherapy,radiation therapy,and targeted treatments significantly improves cancer management [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].Immune checkpoint blockade,as a promising therapeutic strategy,is being evaluated in clinical applications [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].ESTIMATE immune scores reveal a significant correlation between ANGPTL2 expression and immune infiltration across 27 tumor types,particularly a positive association with 23 tumor types,including STAD.In studies investigating the relationshipbetween ANGPTL2 and immune checkpoints,as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA,ANGPTL2 exhibits a significant positive correlation with immune checkpoints in 37 tumor types,including STAD.These findings collectively suggest that ANGPTL2 is a potential and promising immunotherapeutic target.\u003c/p\u003e \u003cp\u003eRegarding the molecular mechanisms of ANGPTL2,single cell sequencing data from the CancerSEA platform indicate that ANGPTL2 is associated with various functional states in tumors such as BRCA,AST,GBM,Glioma,HGG,ODG,LUAD,MEL,RB,and UM.These functional states encompass processes related to angiogenesis,inflammatory responses,epithelial-mesenchymal transition(EMT),DNA damage,DNA repair,cell cycle,cell apoptosis,hypoxia,metastasis,invasion,differentiation,and stemness.Functional enrichment analysis further reveals that ANGPTL2 co-expressed genes play regulatory roles in tyrosine phosphorylation,angiogenesis,vascular system development,and actin cytoskeleton.Key signaling pathways include the PI3K-Akt,Jak-STAT,and Rap1 pathways.Dysregulation of tyrosine phosphorylation levels can lead to various diseases,particularly tumors and metabolic disorders.Oike et al.demonstrated that transgenic mice overexpressing ANGPTL2 exhibited significantly increased blood vessels in the dermis.ANGPTL2 upregulated membrane-type matrix metalloproteinase 1 expression,further promoting endothelial cell functions such as migration,proliferation,and lumen formation [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e].These findings collectively suggest that ANGPTL2 may promote tumor cell proliferation,invasion,and other malignant behaviors through multiple signaling pathways.\u003c/p\u003e \u003cp\u003eBased on the bioinformatics research conclusions and ANGPTL2\u0026rsquo;s significant expression in gastric cancer,we conducted further in vitro experiments to explore ANGPTL2.Scratch assays and Transwell experiments revealed that ANGPTL2 overexpression significantly enhanced migration and invasion abilities in GES-1 cells.We hypothesize that this effect is related to ANGPTL2\u0026rsquo;s angiogenic function.ANGPTL2 can regulate endothelial cell function through autocrine or paracrine mechanisms,inducing vascular formation and maintaining tissue homeostasis.Neovascularization provides nutrients to tumor cells and plays a crucial role in tumor proliferation,invasion,and metastasis [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn summary,ANGPTL2 exhibits upregulated expression in various tumor types,impacting different stages of tumor development.High ANGPTL2 expression often correlates with poor prognosis.Furthermore,ANGPTL2 shows significant associations with immune cell infiltration in most tumors.Elevated ANGPTL2 expression further enhances tumor migration and invasion.By modulating the tumor immune microenvironment and promoting cancer angiogenesis,ANGPTL2 plays a potential role in pan-cancer immunotherapy.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eInstitution and\u003c/strong\u003e \u003cstrong\u003eEthics approval and informed consent:\u0026nbsp;\u003c/strong\u003e\u003cbr\u003eThese tumor specimens were obtained from the Department of Gastroenterology at Gaozhou Traditional Chinese Medicine Hospital.Ethical approval(NO: yxllyjky20220410)was obtained from the hospital,and the study adhered to the principles of the Helsinki Declaration.Written informed consent was obtained from all participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisclosure (Authors):\u003c/strong\u003e \u003cem\u003eThe authors declare no conflicts of interest.\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisclaimer:\u003c/strong\u003e\u0026nbsp; \u003cem\u003eNone\u003c/em\u003e\u003c/p\u003e\u003cp\u003e \u003ch2\u003eEthics approval and consent to participate:\u003c/h2\u003e \u003cp\u003eThese tumor specimens were obtained from the Department of Gastroenterology at Gaozhou Traditional Chinese Medicine Hospital.Ethical approval(NO: yxllyjky20220410)was obtained from the hospital,and the study adhered to the principles of the Helsinki Declaration.Written informed consent was obtained from all participants.\u003c/p\u003e \u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eGrant sponsor:\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eGuangdong Provincial Bureau of Traditional Chinese Medicine research project\u003c/span\u003e; Grant number:\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e20231396\u003c/span\u003e.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAuthors must provide a statement. Junyu Ke participated in the conception or design of the work; Zhikun He and Yilin Duan participated in the acquisition, analysis, or interpretation of data for the work;Yaqing Zhu, Yingjian Xu,Heng-li Zhou,Jie Lei,Haiyan Wang,Zejun Shan,Yingying Zhang,Yating Wei,Yuyin Zeng,Jiali Zhang and Yao Lu drafted the work or revised it critically for important intellectual content;Junyu Ke give final approval of the version to be published; Junyu Ke agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe thank Director YW and his colleagues at the Departmentof Surgery Il(Gaozhou Hospital of Traditional ChineseMedicine)for the processing and staining of clinical tissuesamples.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData is provided within the manuscript or supplementary information files.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSobolewski C, Sanduja S, Blanco FF, Hu L, Dixon DA. 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Signal Transduct Target therapy. 2023;8(1):198. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41392-023-01460-1\u003c/span\u003e\u003cspan address=\"10.1038/s41392-023-01460-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-4552153/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4552153/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eAngiopoietin-like protein 2(ANGPTL2)stimulates inflammatory and angiogenic pathways,promoting tumor growth and metastasis.However,research on the prognostic significance,immune infiltration,expression patterns,and underlying mechanisms of ANGPTL2 in various malignancies is sparse.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe used different online platforms and datasets to conduct a comprehensive investigation of ANGPTL2 in various human malignancies,including mutation status,methylation levels,and expression profiles.Our study looked at the impact of ANGPTL2 on survival prognosis in various tumour types,its correlation with immune checkpoint genes,immune and stromal scores in tumours,its functional relevance in different cancer types,associated signalling pathways and biological functions,validation of its expression in gastric cancer,and its effects on cell proliferation,migration,and invasion using cell models.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eANGPTL2 mutations were predominantly missense and truncation.In 31 tumour types,ANGPTL2 expression differed significantly from normal tissue(\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).Survival analysis revealed that the highest ANGPTL2 expression had worst results.Notably,patients with reduced ANGPTL2 expression showed increased overall survival(OS)in gastric adenocarcinoma,lung cancer and bladder cancer(\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).Immune infiltration analysis showed positive correlations between ANGPTL2 expression and immune infiltration in 36 tumour types(\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).Furthermore,ANGPTL2 was found to be positively associated with immune checkpoint genes in most cancers(\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).In uveal melanoma and retinoblastoma,ANGPTL2 expression was positively correlated with angiogenesis,inflammation,stemness,but negatively correlated with DNA damage,DNA repair,and cell cycle.In the AngPTL2-overexpressed cell model,the proliferation,migration and invasion of GES-1 cells were significantly enhanced.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eIncreased ANGPTL2 expression positively correlates with immune cell infiltration,immune checkpoint genes and immune scores in most tumours.In addition,ANGPTL2 has been linked to significant migration and invasion capabilities in clinical samples and in vitro experiments.\u003c/p\u003e","manuscriptTitle":"Pan-Cancer Analysis Reveals Prognostic Potential of ANGPTL2 and Its Implications in Tumor Microenvironment","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-18 20:37:02","doi":"10.21203/rs.3.rs-4552153/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b3daa6df-61d3-4f4b-b71a-30675c98f212","owner":[],"postedDate":"July 18th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-08-28T08:58:51+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-18 20:37:02","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4552153","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4552153","identity":"rs-4552153","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}