PLAGL2 Is a Prognostic Biomarker in Stomach cancer：A Comprehensive Study Based on Bioinformatics and Experiments

PLAGL2 Is a Prognostic Biomarker in Stomach cancer：A Comprehensive Study Based on Bioinformatics and Experiments | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article PLAGL2 Is a Prognostic Biomarker in Stomach cancer：A Comprehensive Study Based on Bioinformatics and Experiments Lin Wang, Li Yan, Zili Zhou This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1519014/v2 This work is licensed under a CC BY 4.0 License Status: Posted Version 2 posted You are reading this latest preprint version Show more versions Abstract Background： Polymorphic adenoma-like protein 2 (PLAGL2), a zinc finger protein, has been linked to the advancement of serval-type malignancies. However, the relevance of PLAGL2 to the prognosis and regulatory networks of different cancers remains unclear. Methods： The expression of PLAGL2 was explored through Oncomine, TIMER and SangerBox websites. The relationship between PLAGL2 expression and prognosis in various cancers was analyzed through the Kaplan-Meier Plotter database and the PrognoScan databases. The expression of PLAGL2 in several immunological and molecular subtypes of human cancer was evaluated through the TISIDB database. The differentially expressed genes associated with PLAGL2 was explored through LinkedOmics database. The relationship between PLAGL2 expression and clinicopathological features in STAD was explored through the UALCAN database. The expression of PLAGL2 in STAD specimens was analyzed through western blot and IHC. The function of PLAGL2 in STAD was explored through the CCK8 test and the colony formation test. Results： In this study we found that PLAGL2 was overexpressed in most types of cancer and overexpression of PLAGL2 might predicted a poor prognosis in STAD. Next, we investigated PLAGL2 expression in several immunological and molecular subtypes and found that PLAGL2 expression differs considerably across immunological subtypes and molecular subtypes of most cancer types. Our research also shows that the expression of PLAGL2 is correlated with various immunostimulatory and immunosuppressive cytokines. We also analyzed the PLAGL2 co-expression network, and evaluated the prognostic potential of genes positive co-expression with PLAGL2 in STAD through Kaplan-Meier plotter. The results show that most of co-expression genes have a significant effect on the prognosis. We further designed experiments to explore the function of PLAGL2 in STAD. Consistent with previous studies, PLAGL2 was significantly overexpressed in STAD tissues compared with that of adjacent normal tissues. And PLAGL2 can promote the proliferation of STAD cells both in vivo and in vitro. Conclusions: Our findings showed that data mining successfully identifies PLAGL2 expression and putative regulatory networks in STAD, laying the groundwork for additional research into the function of PLAGL2 in carcinogenesis. PLAGL2 biomarker immune infiltration stomach cancer prognosis proliferation Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 7 Figure 8 Figure 9 Background Polymorphic adenoma-like protein 2 (PLAGL2), a zinc finger protein, is upregulated in several malignancies[ 1 – 5 ]. Like its related gene PLAGL1, PLAGL2 functions as an oncogene in a variety of malignancies. For example, PLAGL2 could promote the development of lung adenocarcinoma [ 6 , 7 ]. What’s more, acting as a transcription factor, PLAGL2 could promote the development of hepatocellular carcinoma through HIF-1alpha signaling pathway[ 5 ]. PLAGL2 can also promote the proliferation and migration of colorectal cancer[ 8 – 12 ]. At the same time, our studies showed that PLAGL2 suppresses cell migration and proliferation in Hirschsprung's disease[ 13 ], and it also increases colon cancer growth via binding to the MYH9 promoter[ 14 ]. PLAGL2 could also promote the development stomach cancer by promoting the deubiquitination of Snail1 protein[ 15 ]. According to previous research, we can make a conclusion that PLAGL2 is a unique proto-oncogene in cancer growth, invasion, and metastasis. Here we explored the expression and prognosis of PLAGL2 in various cancers through different databases. We also analyzed the functional network related to PLAGL2 in STAD. In addition, we also evaluated the association of PLAGL2 with tumor infiltrating immune cells. In this study we revealed for the first time the relationship between PLAG2 and tumor immune interaction. Finally, we further explored the function of PLAGL2 in STAD cells, and we found that PLAGL2 could promote the proliferation of STAD cells. In conclusion, our findings might lead to the development of new targets and techniques for the diagnosis and treatment of STAD. Materials And Method Patients and specimens In total, 57 pairs of GC primary specimens were gathered from patients who had not been treated with radiotherapy or chemotherapy before the surgery. Two pathologists confirmed the diagnosis of GC in each case. This study was following the Declaration of Helsinki and approved by the Ethics Committee of Tongji Medical College, Huazhong University of Science and Technology. The Oncomine database We analyzed the expression levels of PLAGL2 in tumors and normal tissues of various cancer types thorough the Oncomine database ( https://www.oncomine.org/resource/login.html )[16]. The TIMER database We analyzed the expression of PLAGL2 from various malignancies in TCGA through the TIMER database (https://cistrome.shinyapps.io/timer/)[17]. The association between PLAGL2 expression and tumor infiltrating immune cell gene markers was also investigated through the TIMER database[18-20]. Gene expression levels were visualized using log2 RSEM. The SangerBox website We analyzed the expression of PLAGL2 in TCGA and GTEx through the SangerBox website (http://sangerbox.com/Tool). The association between PLAGL2 expression and immune checkpoint genes or infiltrating immune cells were also investigated through the SangerBox website[21]. The Kaplan-Meier Plotter database We investigated the prognostic value of PLAGL2 in human cancers and the genes positive co-expression of PLAGL2 in STAD through the Kaplan-Meier Plotter database (http://kmplot.com/analysis/)[22]. The PrognoScan databases We investigated the prognostic value of PLAGL2 in human cancers through the PrognoScan databases ( http://dna00.bio.kyutech.ac.jp/PrognoScan/index.html )[23]. The TISIDB database We investigated the association between PLAGL2 expression and immunological or molecular subtypes of different cancer types through the TISIDB database (http://cis.hku.hk/TISIDB/index.php)[24]. The LinkedOmics database The differentially expressed genes associated with PLAGL2 was explored through LinkedOmics database (http://www.linkedomics.org/login.php). The results were statistically analyzed using Pearson correlation coefficients. Then, the analysis of GO (CC, BP and MF), KEGG pathway was measured through the WebGestalt[25]. The UALCAN database The relationship between PLAGL2 expression and clinicopathological features in STAD was explored through the UALCAN database (http://ualcan.path.uab.edu)[26]. Cell culture MKN45 and 7901 cells were cultured in RPMI-1640 supplemented with 10% FBS. All cells were maintained in a 5% CO2 humidified atmosphere at 37°C. Western blotting The western blotting was conducted as we have published elsewhere. The antibodies used included PLAGL2 (1:1000; Proteintech), GAPDH (1:1000; Proteintech)[14]. Cell proliferation assay The cell proliferation assay was conducted as we have published elsewhere[14]. Colony-formation assay The colony-formation assay was conducted as we have published elsewhere[14]. Immunohistochemical (IHC) staining The IHC staining was conducted as we have published elsewhere[14]. Xenograft subcutaneous implantation model For the xenograft subcutaneous implantation model, 7901 cells were subcutaneously injected into nude mice. After 25 days of normal feeding, all the mice were sacrificed and the tumor volume was measured every 3 days. Statistics Statistical analysis was conducted using SPSS and GraphPad. The data were expressed as means ± standard deviation. All the experiments were repeated at least three times. A P value less than 0.05 was considered statistically significant (*P < 0.05, **P < 0.01, ***P < 0.001). Results The expression of PLAGL2 in human cancers First of all, in order to analyze the expression level of PLAGL2 in various types of cancer, the Oncomine database was applied (Figure.1A). The results indicate that PLAGL2 is significantly overexpressed in various type cancers including stomach cancer. Then, we also evaluated the expression of PLAGL2 through the TIMER database which include RNA-sep data from various malignant tumors in TCGA (Figure.1B). We can find that PLAGL2 was overexpressed in most malignant tumors. Finally, the SangerBox website was used to analyzed the expression of PLAGL2 in TCGA and GTEx (Figure.1C). We can find that PLAGL2 was obviously overexpressed in most types of cancer. The above data indicate that PLAGL2 is overexpressed in most cancer types and may play an important role in the development of various types of cancer. Prognostic potential of PLAGL2 in cancers Firstly, we investigated the prognostic potential of PLAGL2 in cancers through the PrognoScan. The results indicated that PLAGL2 might participate in the prognosis of breast cancer and colorectal cancer. However, as shown in the results, high PLAGL2 expression is slightly related to a better prognosis in colorectal cancer (Figure.2A-2C), and the prognosis of PLAGL2 in breast cancer is not consistent (Figure.2D-2F). So, we further explore the prognostic value of PLAGL2 through the Kaplan-Meier plotter. We can find that high PLAGL2 expression is slightly related to a poor prognosis in stomach cancer (Figure.2G-2I). However, high PLAGL2 expression is probably related to a better prognosis in ovarian (Figure.2J-2L) and breast (Figure.2M-2O) cancer. The results above indicated that PLAGL2 might be a prognostic biomarker in stomach cancer. Then we further explored the association between the expression of PLAGL2 and the clinical characteristics of STAD through the Kaplan-Meier plotter (Table 1). We can find that high expression of PLAGL2 related to both poorer OS and PFS in female, male, HER2 negative and HER2 positive in STAD. Specifically, high expression of PLAGL2 related to both poorer OS and PFS in STAD patients belonging to stages 2, stage N1+2+3, stage N1 and stage M0. Relationship between PLAGL2 expression and immune and molecular subtypes in human cancers Next, we further analyzed the relationship between PLAGL2 expression and immune and molecular subtypes in human cancers through the TISIDB website. We can find that there was a clearly relationship between PLAGL2 and different subtypes of UCEC, BRCA, CESC, COAD, HNSC, KIRC, KIRP, LGG, LUSC, TGCT and LIHC (Figure.3). In addition, the expression of PLAGL2 is also related to different cancer molecular subtypes in various cancers (Figure.1S). We may deduce from the aforementioned findings that PLAGL2 may play an important role in the immunological and molecular subtypes of many malignancies. Relationship between PLAGL2 expression and immune checkpoint (ICP) genes in human cancers Immune cell infiltration and immunotherapy have both been shown to be significantly influenced by ICP genes[27]. The relationship between PLAGL2 expression and ICP genes in human malignancies was then investigated (Figure.4). The results indicated that PLAGL2 expression is associated to immune checkpoint genes in a range of malignancies. Relationship between PLAGL2 and immune cell infiltration in human cancers We further investigated the possible association between PLAGL2 and immune cell infiltration, we can find that there is a substantial correlation in numerous cancer types (Figure.5). PLAGL2 expression is related to dendritic cells in 19 cancers, macrophages in 14 cancers, neutrophils in 23 cancers, CD8+ T cells in 14 cancers, and B cells in 20 cancers. In 15 cancers, there is a strong correlation between CD4+ T cells. Then, we also explored the relationship between the expression of PLAGL2 and different immune marker genes in STAD (Table 2). We can find that there exist a significantly relationship between the expression level of PLAGL2 and most immune markers in various immune cells in STAD. The regulation network of PLAGL2 in stomach cancer Previous results in this study we have demonstrated that PLAGL2 was overexpressed in STAD, and predicted a poor prognosis. So, we further explored the regulation network of PLAGL2 in stomach cancer. Firstly, As shown in the volcano map (Figure.6A-6C), we explored the co-expression genes of PLAGL2 in STAD through LinkedOmics. The differentially expressed genes associated to PLAGL2 are mostly involved in cell cycle control, according to the results of GO term analysis (Figure.6D-6F). KEGG pathway analysis revealed the enrichment of cell cycle, Staphylococcus aureus infection, basic transcription factors, complement and coagulation cascade(Figure.6G). Prognostic potential of PLAGL2 co-expression genes in stomach cancers Then, we further evaluated the prognostic potential of the genes positive co-expression with PLAGL2 in STAD through Kaplan-Meier plotter (Figure.7, Figure.2S). The results showed that among the top 40 genes co-expressed with PLAGL2 in STAD, most of the genes were related to the prognosis of STAD. Relationship between PLAGL2 and different clinical subgroups in STAD. Next, the expression of PLAGL2 in STAD with different clinical characteristics was explored through UALCAN database. The results indicated that there existed an significantly difference between the expression of PLAGL2 in different STAD patients' gender (Figure.8A), age(Figure.8B), tumor grade(Figure.8C), lymph node metastasis status(Figure.8D), cancer stage(Figure.8E), and Helicobacter pylori infection status(Figure.8F), indicating that PLAGL2 may act as an important oncogene in the progress of STAD. The function of PLAGL2 in STAD cells Finally, to verify the association between PLAGL2 and STAD clinicopathological characteristics, we performed Western blot and IHC to detect PLAGL2 expression in 57 paraffin-embedded STAD specimens(Figure.9A-9C). we can find that the PLAGL2 was overexpressed in STAD tissues. More importantly, our findings reveal that the PLAGL2 is associated to lymph node metastasis and tumor size in STAD(Table 3). Then, we used a lentivirus-based system to establish stable PLAGL2 knockdown 7901 and MKN-45 cell lines (Figure.9D-9F). Next, the results of CCK8 test(Figure.9G) and the colony formation test(Figure.9H-9I) revealed that PLAGL2 could promote the proliferation ability of STAD cells. Finally, the results of the xenograft subcutaneous transplantation model showed that PLAGL2 could promote the growth of STAD cells in vivo (Figure.9J-9L). In summary, the data above supports the conclusion that PLAGL2 is an oncogene in STAD and promotes the proliferation of STAD cells in vitro and in vivo. Discussion Previous studies have demonstrated that PLAGL2, acts as a transcription factor, might participate in the progression of various cancers[ 8 , 9 , 11 , 12 , 28 ]. Our previous studies have shown that PLAGL2 suppresses cell migration and proliferation in Hirschsprung's disease[ 13 ], and it also increases colon cancer growth via binding to the MYH9 promoter[ 14 ]. PLAGL2 could promote the development stomach cancer by promoting the deubiquitination of Snail1 protein[ 15 ]. In addition, PLAGL2 could also promote the development of CRC through the Wnt signaling pathway[ 29 ]. However, the relationship between PLAGL2 and immunotherapy has not been reported. Here, we explored the expression and prognosis of PLAGL2 in various cancers, and further investigated the relationship between PLAGL2 and immune infiltration for the first time. These studies indicate that PLAGL2 might act as a prognostic biomarker and target for anti-tumor immunotherapy in human cancers. Firstly, we explored the expression of PLAGL2 through Oncomine, TIMER and SangerBox websites. Consistent with previous studies, the results showed that PLAGL2 was overexpressed in most types of cancer. These results indicate that PLAGL2 does promote the occurrence and development of human cancer. Then, we investigated the relationship between PLAGL2 expression and prognosis in various cancers. We can find that overexpression of PLAGL2 might predicted a poor prognosis in STAD, which proves that PLAGL2 may serve as a potential prognostic biomarker. Following that, we investigated PLAGL2 expression in several immunological and molecular subtypes of human cancer to evaluate its probable biochemical pathway. The findings revealed that PLAGL2 expression differs considerably across immunological subtypes and molecular subtypes of most cancer types, suggesting that PLAGL2 is a viable diagnostic pan-cancer biomarker that plays a role in immune regulation. Furthermore, we demonstrated that the expression of PLAGL2 varies significantly across clinical subgroups. PLAGL2 is differently expressed in most malignancies with various clinical features, indicating that PLAGL2 may have a role in tumor development and progression. Previous researches have shown that tumor infiltrating lymphocytes (TIL) in TME might act as an independent predictor of the prognosis of cancer patients and the effect of immunotherapy[ 30 , 31 ]. Our research shows that the expression of PLAGL2 is correlated with various immunostimulatory and immunosuppressive cytokines, which prove evidence for the potential immune function of PLAGL2. We also analyzed the PLAGL2 co-expression network, and evaluated the prognostic potential of genes positive co-expression with PLAGL2 in STAD through Kaplan-Meier plotter. The results show that most of co-expression genes have a significant effect on the prognosis, which further indicates that PLAGL2 could applied as a prognostic biomarker for STAD. Finally, we further designed experiments to explore the function of PLAGL2 in STAD. Consistent with previous studies, PLAGL2 was significantly overexpressed in STAD tissues compared with that of adjacent normal tissues. And PLAGL2 can promote the proliferation of STAD cells both in vivo and in vitro. However, this work has certain limitations, despite the fact that we did a thorough and systematic examination of PLAGL2 and used many databases for cross-validation. First, there are discrepancies between microarray and sequencing data from different databases, as well as a lack of granularity and specificity, which might contribute to system bias. Secondly, although in vivo/in vitro experiments have been carried out and proved the role of PLAGL2 in STAD, its regulatory mechanism needs to be further explored. Third, while we found that PLAGL2 expression is related to immune cell infiltration and prognosis in STAD, we don't have direct evidence that PLAGL2 affects prognosis through immune infiltration. As a result, the mechanism through which PLAGL2 contributes to immune modulation remains unknown. Further study is required to determine the specific procedure. In the future, prospective investigations on the expression of PLAGL2 and its involvement in human cancer immune infiltration will be required, as well as the effective development and testing of novel anti-tumor immunotherapy medicines for PLAGL2. Conclusions Our findings showed that data mining successfully identifies PLAGL2 expression and putative regulatory networks in STAD, laying the groundwork for additional research into the function of PLAGL2 in carcinogenesis. Abbreviations ACC Adrenocortical carcinoma BLCA Bladder Urothelial Carcinoma BRCA Breast invasive carcinoma CESC Cervical squamous cell carcinoma and endocervical adenocarcinoma CHOL Cholangiocarcinoma COAD Colon adenocarcinoma COADREAD Colon adenocarcinoma/Rectum adenocarcinoma Esophageal carcinoma DLBC Lymphoid Neoplasm Diffuse Large B-cell Lymphoma ESCA Esophageal carcinoma FPPP FFPE Pilot Phase II GBM Glioblastoma multiforme GBMLGG Glioma HNSC Head and Neck squamous cell carcinoma KICH Kidney Chromophobe KIPAN Pan-kidney cohort (KICH+KIRC+KIRP) KIRC Kidney renal clear cell carcinoma KIRP Kidney renal papillary cell carcinoma LAML Acute Myeloid Leukemia LGG Brain Lower Grade Glioma LIHC Liver hepatocellular carcinoma LUAD Lung adenocarcinoma LUSC Lung squamous cell carcinoma MESO Mesothelioma OV Ovarian serous cystadenocarcinoma PAAD Pancreatic adenocarcinoma PCPG Pheochromocytoma and Paraganglioma PRAD Prostate adenocarcinoma READ Rectum adenocarcinoma SARC Sarcoma STAD Stomach adenocarcinoma SKCM Skin Cutaneous Melanoma STES Stomach and Esophageal carcinoma TGCT Testicular Germ Cell Tumors THCA Thyroid carcinoma THYM Thymoma UCEC Uterine Corpus Endometrial Carcinoma UCS Uterine Carcinosarcoma UVM Uveal Melanoma OS Osteosarcoma ALL Acute Lymphoblastic Leukemia NB Neuroblastoma WT High-Risk Wilms Tumor Declarations Ethics approval and consent to participate Ethics approval was granted by the Ethics Committee of Tongji Medical College, Huazhong University of Science and Technology(S-082). According to the Tongji Medical College Animal Care and Use Guidelines, animal experiments were performed. All methods were carried out in accordance with relevant guidelines and regulations. The study was carried out in compliance with the ARRIVE guidelines. Patient consent for publication Not applicable. Availability of data and materials The datasets used and analyzed during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no conflict of interest. Funding Statement This study was supported by the National Natural Science Foundation of China (No. 81772581). Author’s contributions Zili Zhou and Li Yan collected the data, analyzed and interpreted the data, and wrote the manuscript. Lin Wang prepared draft figures and tables. All authors read and approved the final manuscript for publication Acknowledgements Not applicable References Yang T, Huo J, Xu R, Su Q, Tang W, Zhang D, et al. Selenium sulfide disrupts the PLAGL2/C-MET/STAT3-induced resistance against mitochondrial apoptosis in hepatocellular carcinoma. Clin Transl Med. 2021; 11: e536. Liu Q, Ran R, Song M, Li X, Wu Z, Dai G, et al. LncRNA HCP5 acts as a miR-128-3p sponge to promote the progression of multiple myeloma through activating Wnt/beta-catenin/cyclin D1 signaling via PLAGL2. Cell Biol Toxicol. 2021. Zeng Z, Teng Q, Xiao J. Long noncoding RNA ILF3-AS1 aggravates papillary thyroid carcinoma progression via regulating the miR-4306/PLAGL2 axis. Cancer Cell Int. 2021; 21: 322. Zheng S, Ni J, Li Y, Lu M, Yao Y, Guo H, et al. 2-Methoxyestradiol synergizes with Erlotinib to suppress hepatocellular carcinoma by disrupting the PLAGL2-EGFR-HIF-1/2alpha signaling loop. Pharmacol Res. 2021; 169: 105685. Wang L, Sun L, Liu R, Mo H, Niu Y, Chen T, et al. Long non-coding RNA MAPKAPK5-AS1/PLAGL2/HIF-1alpha signaling loop promotes hepatocellular carcinoma progression. J Exp Clin Cancer Res. 2021; 40: 72. Gao N, Ye B. Circ-SOX4 drives the tumorigenesis and development of lung adenocarcinoma via sponging miR-1270 and modulating PLAGL2 to activate WNT signaling pathway. Cancer Cell Int. 2020; 20: 2. Yang YS, Yang MC, Weissler JC. Pleiomorphic adenoma gene-like 2 expression is associated with the development of lung adenocarcinoma and emphysema. Lung Cancer. 2011; 74: 12-24. Wu L, Zhou Z, Han S, Chen J, Liu Z, Zhang X, et al. PLAGL2 promotes epithelial-mesenchymal transition and mediates colorectal cancer metastasis via beta-catenin-dependent regulation of ZEB1. Br J Cancer. 2020; 122: 578-89. Lv Y, Xie B, Bai B, Shan L, Zheng W, Huang X, et al. Weighted gene coexpression analysis indicates that PLAGL2 and POFUT1 are related to the differential features of proximal and distal colorectal cancer. Oncol Rep. 2019; 42: 2473-85. Germot A, Maftah A. POFUT1 and PLAGL2 gene pair linked by a bidirectional promoter: the two in one of tumour progression in colorectal cancer? EBioMedicine. 2019; 46: 25-6. Li D, Lin C, Li N, Du Y, Yang C, Bai Y, et al. PLAGL2 and POFUT1 are regulated by an evolutionarily conserved bidirectional promoter and are collaboratively involved in colorectal cancer by maintaining stemness. EBioMedicine. 2019; 45: 124-38. Li N, Li D, Du Y, Su C, Yang C, Lin C, et al. Overexpressed PLAGL2 transcriptionally activates Wnt6 and promotes cancer development in colorectal cancer. Oncol Rep. 2019; 41: 875-84. Wu L, Yuan W, Chen J, Zhou Z, Shu Y, Ji J, et al. Increased miR-214 expression suppresses cell migration and proliferation in Hirschsprung disease by interacting with PLAGL2. Pediatr Res. 2019. Zhou Z, Wu L, Liu Z, Zhang X, Han S, Zhao N, et al. MicroRNA-214-3p targets the PLAGL2-MYH9 axis to suppress tumor proliferation and metastasis in human colorectal cancer. Aging (Albany NY). 2020; 12: 9633-57. Wu L, Zhao N, Zhou Z, Chen J, Han S, Zhang X, et al. PLAGL2 promotes the proliferation and migration of gastric cancer cells via USP37-mediated deubiquitination of Snail1. Theranostics. 2021; 11: 700-14. Rhodes DR, Kalyana-Sundaram S, Mahavisno V, Varambally R, Yu J, Briggs BB, et al. Oncomine 3.0: genes, pathways, and networks in a collection of 18,000 cancer gene expression profiles. Neoplasia. 2007; 9: 166-80. Li T, Fu J, Zeng Z, Cohen D, Li J, Chen Q, et al. TIMER2.0 for analysis of tumor-infiltrating immune cells. Nucleic Acids Res. 2020; 48: W509-W14. Sousa S, Maatta J. The role of tumour-associated macrophages in bone metastasis. J Bone Oncol. 2016; 5: 135-8. Danaher P, Warren S, Dennis L, D'Amico L, White A, Disis ML, et al. Gene expression markers of Tumor Infiltrating Leukocytes. J Immunother Cancer. 2017; 5: 18. Siemers NO, Holloway JL, Chang H, Chasalow SD, Ross-MacDonald PB, Voliva CF, et al. Genome-wide association analysis identifies genetic correlates of immune infiltrates in solid tumors. PLoS One. 2017; 12: e0179726. Zhang L, Liu Z, Dong Y, Kong L. E2F2 drives glioma progression via PI3K/AKT in a PFKFB4-dependent manner. Life Sci. 2021; 276: 119412. Gyorffy B. Survival analysis across the entire transcriptome identifies biomarkers with the highest prognostic power in breast cancer. Comput Struct Biotechnol J. 2021; 19: 4101-9. Mizuno H, Kitada K, Nakai K, Sarai A. PrognoScan: a new database for meta-analysis of the prognostic value of genes. BMC Med Genomics. 2009; 2: 18. Ru B, Wong CN, Tong Y, Zhong JY, Zhong SSW, Wu WC, et al. TISIDB: an integrated repository portal for tumor-immune system interactions. Bioinformatics. 2019; 35: 4200-2. Zhou G, Soufan O, Ewald J, Hancock REW, Basu N, Xia J. NetworkAnalyst 3.0: a visual analytics platform for comprehensive gene expression profiling and meta-analysis. Nucleic Acids Res. 2019; 47: W234-W41. Chandrashekar DS, Bashel B, Balasubramanya SAH, Creighton CJ, Ponce-Rodriguez I, Chakravarthi B, et al. UALCAN: A Portal for Facilitating Tumor Subgroup Gene Expression and Survival Analyses. Neoplasia. 2017; 19: 649-58. Topalian SL, Drake CG, Pardoll DM. Immune checkpoint blockade: a common denominator approach to cancer therapy. Cancer Cell. 2015; 27: 450-61. Hu W, Zheng S, Guo H, Dai B, Ni J, Shi Y, et al. PLAGL2-EGFR-HIF-1/2alpha Signaling Loop Promotes HCC Progression and Erlotinib Insensitivity. Hepatology. 2021; 73: 674-91. Zhou J, Liu H, Zhang L, Liu X, Zhang C, Wang Y, et al. DJ-1 promotes colorectal cancer progression through activating PLAGL2/Wnt/BMP4 axis. Cell Death Dis. 2018; 9: 865. Azimi F, Scolyer RA, Rumcheva P, Moncrieff M, Murali R, McCarthy SW, et al. Tumor-infiltrating lymphocyte grade is an independent predictor of sentinel lymph node status and survival in patients with cutaneous melanoma. J Clin Oncol. 2012; 30: 2678-83. Ohtani H. Focus on TILs: prognostic significance of tumor infiltrating lymphocytes in human colorectal cancer. Cancer Immun. 2007; 7: 4. Tables Table 1 Correlation of PLAGL2 mRNA expression and prognosis in STAD with different clinicopathological factors by Kaplan-Meier plotter. Clinicopathological factors Overall survival Post progression survival N Hazard ratio P-value N Hazard ratio P-value SEX Femal 236 1.74(1.22-2.48) 0.0018 149 1.82(1.18-2.81) 0.006 Male 544 1.77(1.43-2.2) 1.50E-07 149 2.16(1.65-2.83) 9.60E-09 Stage 1 67 2.56(0.89-7.38) 0.071 31 8.33(0.99-70.06) 0.02 2 140 2.1(1.13-3.92) 0.016 105 2.09(1.02-4.28) 0.038 3 305 1.57(1.18-2.1) 0.002 142 1.53(1-2.35) 0.051 4 148 1.12(0.76-1.64) 0.57 104 1.14(0.72-1.79) 0.58 Stage T 1 14 3 2 241 1.49(0.97-2.29) 0.065 196 1.54(0.98-2.42) 0.061 3 204 1.49(1.06-2.11) 0.022 150 1.35(0.92-1.99) 0.12 4 38 1.27(0.56-2.9) 0.57 29 0.85(0.33-2.15) 0.73 Stage N 0 74 1.9（0.8-4.49 0.14 41 1.57(0.47-5.27) 0.46 1+2+3 422 1.5（1.16-1.96） 0.0022 337 1.41(1.06-1.87) 0.018 1 225 2.06（1.36-3.14） 0.00056 169 2.37(1.47-3.82) 0.00025 2 121 1.31（0.83-2.05） 0.24 105 1.24(0.77-2) 0.37 3 76 1.17（0.69-1.99） 0.56 63 1.19(0.67-2.12) 0.54 Stage M 0 444 1.45（1.1-1.91） 0.009 342 1.5(1.11-2.03) 0.0077 1 56 1.1（0.62-1.96） 0.74 36 1.09(0.53-2.25) 0.82 Differentiation poorly differentiated 165 1.1（0.74-1.64） 0.64 49 1.49(0.78-2.85) 0.23 moderately differentiated 67 1.26（0.66-2.41） 0.49 24 0.81(0.32-2.02) 0.65 well differentiated 32 0.83（0.35-1.96） 0.67 0 Treatment surgery alone 380 1.4(1.05-1.87) 0.021 277 1.35(0.98-1.85 0.061 5 FU based adjuvant 152 1.26(0.89-1.79) 0.19 135 1.49(1.04-2.14) 0.03 other adjuvant 76 0.79(0.33-1.91) 0.6 74 0.82(0.34-1.98) 0.66 HER2 status HER2 negative 532 1.69(1.35-2.12) 4.60E-06 334 1.7(1.27-2.27) 0.00029 HER2 positive 343 1.39(1.07-1.81) 0.012 164 2.24(1.55-3.23) 1.10E-05 Table 2 Correlation analysis between PLAGL2 and relate gene markers of immune cells in STAD. Description Gene markers None Core P Purity Core P CD8+ T cell CD8A -0.011 0.831 -0.22 *** CD8B 0.157 *** -0.121 ** T cell(general) CD3D -0.029 0.58 -0.315 *** CD3E 0.018 0.722 -0.335 *** CD2 0.029 0.573 -0.303 *** B cell CD19 0.051 0.32 -0.218 *** CD79A -0.051 0.318 -0.268 *** Monocyte CD86 -0.005 0.927 -0.286 *** CSF1R 0.054 0.299 -0.208 *** TAM CCL2 -0.13 ** -0.205 *** CD68 0.151 *** -0.159 *** IL10 0.032 0.534 -0.254 *** M1 Macrophage NOS2 0.284 *** -0.094 0.0663 IRF5 0.201 *** -0.111 ** PTGS2 -0.085 0.0986 -0.126 ** M2 Macrophage CD163 0.086 0.0938 -0.19 *** VSIG4 -0.04 0.44 -0.166 *** MS4A4A -0.043 0.403 -0.191 *** Natural killer cell KIR2DL1 0.026 0.615 -0.077 0.137 KIR2DL3 0.002 0.974 -0.132 ** KIR2DL4 -0.026 0.614 -0.165 *** KIR3DL1 -0.019 0.707 -0.124 ** KIR3DL2 -0.002 0.972 -0.161 *** KIR3DL3 0.072 0.16 -0.02 0.703 Dendritic cell HLA-DPB1 -0.095 0.0635 -0.293 *** HLA-DQB1 -0.075 0.142 -0.282 *** HLA-DRA -0.066 0.197 -0.276 *** HLA-DPA1 -0.061 0.235 -0.276 *** BDCA-1 -0.041 0.425 -0.285 *** BDCA-4 0.027 0.594 -0.173 *** CD11c 0.096 0.0617 -0.224 *** TH1 TBX21 0.063 0.221 -0.254 *** STAT4 0.117 0.0232 -0.245 *** STAT1 0.302 *** -0.104 0.042 TNF 0.117 0.023 -0.281 *** INF-α 0.115 ** -0.033 0.519 TH2 GATA3 -0.087 0.0925 -0.174 *** STAT6 0.316 *** 0.011 0.836 STAT5A 0.126 0.0144 -0.132 0.0101 IL13 -0.042 0.418 -0.002 0.971 Tfh BCL6 0.033 0.525 -0.135 *** TH17 STAT3 0.236 *** -0.071 0.165 IL17A 0.237 *** -0.122 0.0173 Treg FOXP3 0.214 *** -0.241 *** CCR8 0.19 *** -0.168 *** STAT5B 0.305 *** -0.023 0.664 TGFB1 0.031 0.548 -0.169 *** T cell exhaustion PD-1 0.105 ** -0.175 *** CTLA4 0.177 *** -0.197 *** LAG3 0.02 0.699 -0.227 *** TIM-3 0.022 0.665 -0.245 *** GZMB -0.001 0.988 -0.254 *** (*P < 0.05, **P < 0.01, ***P < 0.001) Table 3 Clinicopathological analysis of PLAGL2 expression in STAD. Parameters N PLAGL2 P-value High Low Age(years) ＜60 36 28 8 0.1 ≥60 21 12 9 Gender Male 33 25 8 0.28 Femal 24 15 9 Size of tumor ＜3cm 24 14 10 0.096 ≥3cm 33 26 7 Differentiation Well-moderate 26 17 9 0.469 Poor 31 23 8 T Stages T1-T2 22 12 10 0.041 T3-T4 35 28 7 N Stages N0 16 8 8 0.038 N1-2 41 32 9 M Stages M0 39 25 14 0.14 M1 18 15 3 Additional Declarations No competing interests reported. Supplementary Files FigureS1.tif Figure S1. Relationship between PLAGL2 expression and molecular subtypes in human cancers The relationship between PLAGL2 expression and pan-cancer molecular subtypes. (A) in UCEC, (B) in BRCA, (C) in COAD, (D) in HNSC, (E) in STAD, (F) in LGG, (G) in LUSC, (H) in OV, (I) in PRAD, (J) in PCPG, (K) in READ, (L) in SKCM. FigureS2.tif Figure S2. Prognostic potential of PLAGL2 co-expression genes in stomach cancersPrognostic potential of PLAGL2 co-expression genes in gastric cancers. (A)CAMKV, (B)CBFA2T2, (C)DDX27, (D)HOXD11, (E)RAE1, (F)COL2A1, (G)DHX35, (H)SALL4, (I)KNG1, (J)ZNF335, (K)SERPINA10, (L)CEP2. Cite Share Download PDF Status: Posted Version 2 posted You are reading this latest preprint version Show more versions Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-1519014","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":102987777,"identity":"df2184f7-f90d-4388-bcf9-5e6fc700bf91","order_by":0,"name":"Lin Wang","email":"","orcid":"","institution":"University of Electronic Science and Technology of China","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Lin","middleName":"","lastName":"Wang","suffix":""},{"id":102987778,"identity":"fb119148-935c-4f06-b289-98076e28d2a2","order_by":1,"name":"Li Yan","email":"","orcid":"","institution":"University of Electronic Science and Technology of China","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Li","middleName":"","lastName":"Yan","suffix":""},{"id":102987779,"identity":"f7341dcc-2f79-4fae-ab80-0e9aa1f4229d","order_by":2,"name":"Zili Zhou","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyklEQVRIiWNgGAWjYDACCRBhYMHDIH/4wIEPP4jXIsHDIMGWeHBmD9FaQKQEj/FhDjYidMjPbj4m+aNAQsbgds+HwwxA9/GLHcCvhXHOsTQJCaDDDO6c3XC4wILBcObsBPxamCVyzCQMgFokG3I3HJ7Bw5BgcJuAFjaQlgSwlpwHh3nYiNDCA9JyAKiFXyKHgTgtEhJpyZYNIC08xwyAgSxB2C/yM5IP3vzxx8aejb358YcPP2zk+aUJaMGwlTTlo2AUjIJRMAqwAwCCfzvUpiT0/QAAAABJRU5ErkJggg==","orcid":"","institution":"University of Electronic Science and Technology of China","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Zili","middleName":"","lastName":"Zhou","suffix":""}],"badges":[],"createdAt":"2022-04-03 13:44:23","currentVersionCode":2,"declarations":"","doi":"10.21203/rs.3.rs-1519014/v2","doiUrl":"https://doi.org/10.21203/rs.3.rs-1519014/v2","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":21080314,"identity":"4ec10131-5ffb-493d-bd0d-425a3bc6edcb","added_by":"auto","created_at":"2022-05-04 18:21:01","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":214235,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe expression of PLAGL2 in human cancers \u003c/strong\u003e\u003c/p\u003e\u003cp\u003e(A) The expression level of PLAGL2 in different cancers and paired normal tissue in the Oncomine database. (B) The expression level of PLAGL2 in different cancer types from the TCGA database analyzed by the TIMER database (*P \u0026lt; 0.05, **P \u0026lt; 0.01, ***P \u0026lt; 0.001). (C) The expression level of PLAGL2 in different cancer types from the TCGA and GTEx by SangerBox website (*P \u0026lt; 0.05, **P \u0026lt; 0.01, ***P \u0026lt; 0.001, ****P \u0026lt; 0.0001)\u003c/p\u003e","description":"","filename":"Online1.png","url":"https://assets-eu.researchsquare.com/files/rs-1519014/v2/15c56d8f4a0e9e74f2124b4a.png"},{"id":21079802,"identity":"0aa94a49-ea1c-43ca-be7b-aa7340510776","added_by":"auto","created_at":"2022-05-04 18:11:01","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":160673,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePrognostic potential of PLAGL2 in cancers\u003c/strong\u003e\u003c/p\u003e\u003cp\u003e(A-F) Prognostic potential of PLAGL2 in colorectal cancer(A-C) and breast cancer(D-F) analyzed by PrognoScan. (G-O) Prognostic potential of PLAGL2 in gastric cancer(G-I), ovarian cancer(J-L) and breast cancer(M-O) analyzed by Kaplan-Meier plotter.\u003c/p\u003e","description":"","filename":"Online2.png","url":"https://assets-eu.researchsquare.com/files/rs-1519014/v2/c6fd02b075a3ccea56b59fba.png"},{"id":21079606,"identity":"04192465-cd89-4797-a6ac-cf50af74b81a","added_by":"auto","created_at":"2022-05-04 18:01:01","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":170502,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRelationship between PLAGL2 expression and immune subtypes in human cancers\u003c/strong\u003e \u003c/p\u003e\u003cp\u003eThe relationship between PLAGL2 expression and pan-cancer immune subtypes. (A) in UCEC, (B) in BRCA, (C) in CESE, (D) in COAD, (E) in GBM, (F) in HNSC, (G) in KIRC, (H) in KIRP, (I) in LGG, (J) in LUSC, (K) in OV, (L) in PRAD, (M) in STAD, (N) in TCGT, (K) in THCA, (L) in LIHC. C1 (wound healing), C2 (IFN-γ dominant), C3 (inflammation), C4 (lymphocyte depletion), C5 (immune quietness) and C6 (TGF-b dominant)\u003c/p\u003e","description":"","filename":"Online3.png","url":"https://assets-eu.researchsquare.com/files/rs-1519014/v2/882ef6c8737bcdf2e36d98cf.png"},{"id":21080074,"identity":"d4df9f8f-8a87-4ee4-ab56-24293cecd37e","added_by":"auto","created_at":"2022-05-04 18:16:01","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":336534,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRelationship between PLAGL2 expression and immune checkpoint (ICP) genes in human cancers\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eThe relationship between PLAGL2 expression and pan-cancer immune checkpoint genes. *P \u0026lt; 0.05; **P \u0026lt; 0.01; ***P \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"Online4.png","url":"https://assets-eu.researchsquare.com/files/rs-1519014/v2/e2ca3cbc537e1c864439f5a0.png"},{"id":21079607,"identity":"587a882c-eb03-49bf-9136-1ec9046480bd","added_by":"auto","created_at":"2022-05-04 18:01:01","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":147779,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRelationship between PLAGL2 and immune cell infiltration in human cancers\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eThe relationship between PLAGL2 expression and immune cell infiltration in the TME. *P \u0026lt; 0.05; **P \u0026lt; 0.01; ***P \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"Online5.png","url":"https://assets-eu.researchsquare.com/files/rs-1519014/v2/45487cdcb7e34f6c49614b7b.png"},{"id":21079754,"identity":"50dbef50-d957-4f3c-b03e-2e2b7c40c554","added_by":"auto","created_at":"2022-05-04 18:06:01","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":185528,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePrognostic potential of PLAGL2 co-expression genes in stomach cancers\u003c/strong\u003e\u003c/p\u003e\u003cp\u003ePrognostic potential of PLAGL2 co-expression genes in gastric cancers. (A)POFUT1, (B)ASXL1,(C)HIC2,(D)TM9SF4,(E)KIAA0406,(F)SPATA2,(G)ZCCHC3,(H)TTLL4,(I)TAF4,(J)SLC5A6,(K)DNMT3B,(L)TRRP4AP,(M)MCM8,(N)CDK5RAP1,(O)TCP10,(P)MOCS3,(Q)SOAT2,(R)GEMB2,(S)STX3,(T)YTHDF1.\u003c/p\u003e","description":"","filename":"Online7.png","url":"https://assets-eu.researchsquare.com/files/rs-1519014/v2/6a032f278b028b9f086e4941.png"},{"id":21079616,"identity":"dbed3531-5283-4dee-ac4f-688738b2c017","added_by":"auto","created_at":"2022-05-04 18:01:02","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":63932,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRelationship between PLAGL2 and different clinical subgroups in STAD\u003c/strong\u003e\u003c/p\u003e\u003cp\u003ePLAGL2 differential expression in STAD with individual patient gender(A), age(B), tumor grade(C), lymph node metastasis status(D), cancer stages(E), and Helicobacter pylori infection status(F) (*P \u0026lt; 0.05, **P \u0026lt; 0.01, ***P \u0026lt; 0.001).\u003c/p\u003e","description":"","filename":"Online8.png","url":"https://assets-eu.researchsquare.com/files/rs-1519014/v2/230aa8945f1c59fce79035df.png"},{"id":21079614,"identity":"a5314fe2-4696-43c4-9fcd-d2cc507b91ba","added_by":"auto","created_at":"2022-05-04 18:01:01","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":1314555,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe function of PLAGL2 in STAD cells \u003c/strong\u003e\u003c/p\u003e\u003cp\u003e(A-B) The expression of PLAGL2 in STAD tissues and adjacent normal tissues detected by WB. (C) The expression of PLAGL2 in STAD tissues and adjacent normal tissues detected by IHC (scale bar: 50μm). (D-F) The expression of PLAGL2 was knocked down by sh-PLAGL2 in 7901 and MKN-45. (G) CCK8 assays revealed that PLAGL2 promotes proliferation in STAD cells. (H-I) Colony formation assays revealed that PLAGL2 promotes proliferation in STAD cells. (J-L) Subcutaneous xenograft tumors grew slower in the sh-PLAGL2 group than in the sh-NC group. Tumor weights in the sh-PLAGL2 group were lower than those in the sh-NC group (scale bar: 1 cm). (*P \u0026lt; 0.05, **P \u0026lt; 0.01, ***P \u0026lt; 0.001)\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-1519014/v2/5c6ddb03517146727715b5b1.png"},{"id":29641195,"identity":"7088686a-f9c1-4d24-88c3-aefe2330b415","added_by":"auto","created_at":"2022-11-29 09:44:41","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4670190,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1519014/v2/1a5096e0-cc1c-4377-9788-dc43d530cc33.pdf"},{"id":21079750,"identity":"90cb7908-68c7-4b71-8655-2a627b6920bc","added_by":"auto","created_at":"2022-05-04 18:06:01","extension":"tif","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1200528,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure S1. Relationship between PLAGL2 expression and molecular subtypes in human cancers\u003c/strong\u003e \u003c/p\u003e\u003cp\u003eThe relationship between PLAGL2 expression and pan-cancer molecular subtypes. (A) in UCEC, (B) in BRCA, (C) in COAD, (D) in HNSC, (E) in STAD, (F) in LGG, (G) in LUSC, (H) in OV, (I) in PRAD, (J) in PCPG, (K) in READ, (L) in SKCM.\u003c/p\u003e","description":"","filename":"FigureS1.tif","url":"https://assets-eu.researchsquare.com/files/rs-1519014/v2/9fc0cd4ed0353b3adbac8934.tif"},{"id":21079806,"identity":"ccd8461e-3701-4060-83bd-41a32afcbe25","added_by":"auto","created_at":"2022-05-04 18:11:01","extension":"tif","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":419992,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure S2. Prognostic potential of PLAGL2 co-expression genes in stomach cancers\u003c/strong\u003e\u003c/p\u003e\u003cp\u003ePrognostic potential of PLAGL2 co-expression genes in gastric cancers. (A)CAMKV, (B)CBFA2T2, (C)DDX27, (D)HOXD11, (E)RAE1, (F)COL2A1, (G)DHX35, (H)SALL4, (I)KNG1, (J)ZNF335, (K)SERPINA10, (L)CEP2.\u003c/p\u003e","description":"","filename":"FigureS2.tif","url":"https://assets-eu.researchsquare.com/files/rs-1519014/v2/c76c9dbbcb80564a70ea36a2.tif"}],"financialInterests":"No competing interests reported.","formattedTitle":"PLAGL2 Is a Prognostic Biomarker in Stomach cancer：A Comprehensive Study Based on Bioinformatics and Experiments","fulltext":[{"header":"Background","content":"\u003cp\u003ePolymorphic adenoma-like protein 2 (PLAGL2), a zinc finger protein, is upregulated in several malignancies[\u003cspan additionalcitationids=\"CR2 CR3 CR4\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Like its related gene PLAGL1, PLAGL2 functions as an oncogene in a variety of malignancies. For example, PLAGL2 could promote the development of lung adenocarcinoma [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. What\u0026rsquo;s more, acting as a transcription factor, PLAGL2 could promote the development of hepatocellular carcinoma through HIF-1alpha signaling pathway[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. PLAGL2 can also promote the proliferation and migration of colorectal cancer[\u003cspan additionalcitationids=\"CR9 CR10 CR11\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. At the same time, our studies showed that PLAGL2 suppresses cell migration and proliferation in Hirschsprung's disease[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], and it also increases colon cancer growth via binding to the MYH9 promoter[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. PLAGL2 could also promote the development stomach cancer by promoting the deubiquitination of Snail1 protein[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. According to previous research, we can make a conclusion that PLAGL2 is a unique proto-oncogene in cancer growth, invasion, and metastasis.\u003c/p\u003e \u003cp\u003eHere we explored the expression and prognosis of PLAGL2 in various cancers through different databases. We also analyzed the functional network related to PLAGL2 in STAD. In addition, we also evaluated the association of PLAGL2 with tumor infiltrating immune cells. In this study we revealed for the first time the relationship between PLAG2 and tumor immune interaction. Finally, we further explored the function of PLAGL2 in STAD cells, and we found that PLAGL2 could promote the proliferation of STAD cells. In conclusion, our findings might lead to the development of new targets and techniques for the diagnosis and treatment of STAD.\u003c/p\u003e"},{"header":"Materials And Method","content":"\u003cp\u003e\u003cstrong\u003ePatients and specimens\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn total, 57 pairs of GC primary specimens were gathered from patients who had not been treated with radiotherapy or chemotherapy before the surgery. Two pathologists confirmed the diagnosis of GC in each case. This study was following the Declaration of Helsinki and approved by the Ethics Committee of Tongji Medical College, Huazhong University of Science and Technology.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe Oncomine database\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe analyzed the expression levels of PLAGL2 in tumors and normal tissues of various cancer types thorough the Oncomine database (\u003ca href=\"https://www.oncomine.org/resource/login.html\"\u003ehttps://www.oncomine.org/resource/login.html\u003c/a\u003e)[16].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe TIMER database\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe analyzed the expression of PLAGL2 from various malignancies in TCGA through the TIMER database (https://cistrome.shinyapps.io/timer/)[17]. The association between PLAGL2 expression and tumor infiltrating immune cell gene markers was also investigated through the TIMER database[18-20]. Gene expression levels were visualized using log2 RSEM.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe SangerBox website\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe analyzed the expression of PLAGL2 in TCGA and GTEx through the SangerBox website (http://sangerbox.com/Tool). The association between PLAGL2 expression and immune checkpoint genes or infiltrating immune cells were also investigated through the SangerBox website[21].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe Kaplan-Meier Plotter database\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe investigated the prognostic value of PLAGL2 in human cancers and the genes positive co-expression of PLAGL2 in STAD through the Kaplan-Meier Plotter database (http://kmplot.com/analysis/)[22].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe PrognoScan databases\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe investigated the prognostic value of PLAGL2 in human cancers through the PrognoScan databases (\u003ca href=\"http://dna00.bio.kyutech.ac.jp/PrognoScan/index.html\"\u003ehttp://dna00.bio.kyutech.ac.jp/PrognoScan/index.html\u003c/a\u003e)[23].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe TISIDB database\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe investigated the association between PLAGL2 expression and immunological or molecular subtypes of different cancer types through the TISIDB database (http://cis.hku.hk/TISIDB/index.php)[24].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe LinkedOmics database\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe differentially expressed genes associated with PLAGL2 was explored through LinkedOmics database (http://www.linkedomics.org/login.php). The results were statistically analyzed using Pearson correlation coefficients. Then, the analysis of GO (CC, BP\u0026nbsp;and MF), KEGG pathway was measured through the WebGestalt[25].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe UALCAN database\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe relationship between PLAGL2 expression and clinicopathological features in STAD was explored through the UALCAN database (http://ualcan.path.uab.edu)[26].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCell culture\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMKN45 and 7901 cells were cultured in RPMI-1640 supplemented with 10% FBS. All cells were maintained in a 5% CO2 humidified atmosphere at 37\u0026deg;C.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWestern blotting\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe western blotting was conducted as we have published elsewhere. The antibodies used\u0026nbsp;included\u0026nbsp;PLAGL2 (1:1000; Proteintech), GAPDH (1:1000; Proteintech)[14].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCell proliferation assay\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe cell proliferation assay was conducted as we have published elsewhere[14].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eColony-formation assay\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe colony-formation assay was conducted as we have published elsewhere[14].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImmunohistochemical (IHC) staining\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe IHC staining was conducted as we have published elsewhere[14].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eXenograft subcutaneous implantation model\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor\u0026nbsp;the\u0026nbsp;xenograft subcutaneous implantation model, 7901 cells were subcutaneously injected into nude mice. After 25 days of normal feeding, all the mice were sacrificed and the tumor volume was measured every 3 days.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStatistical analysis was conducted using SPSS and GraphPad. The data were expressed as means \u0026plusmn; standard deviation. All the experiments were repeated at least three times. A P value less than 0.05 was considered statistically significant (*P \u0026lt; 0.05, **P \u0026lt; 0.01, ***P \u0026lt; 0.001).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eThe expression of PLAGL2 in human cancers\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFirst of all, in order to analyze the expression level of PLAGL2 in various types of cancer, the Oncomine database was applied (Figure.1A). The results indicate that PLAGL2 is significantly overexpressed in various type cancers including stomach cancer. Then, we also evaluated the expression of PLAGL2 through the TIMER database which include RNA-sep data from various malignant tumors in TCGA (Figure.1B). We can find that PLAGL2 was overexpressed in most malignant tumors. Finally, the SangerBox website was used to analyzed the expression of PLAGL2 in TCGA and GTEx (Figure.1C). We can find that PLAGL2 was obviously overexpressed in most types of cancer. The above data indicate that PLAGL2 is overexpressed in most cancer types and may play an important role in the development of various types of cancer.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePrognostic potential of PLAGL2 in cancers\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFirstly, we investigated the prognostic potential of PLAGL2 in cancers through the PrognoScan. The results indicated that PLAGL2 might participate in the prognosis of breast cancer and colorectal cancer. However, as shown in the results, high PLAGL2 expression is slightly related to a better prognosis in colorectal cancer (Figure.2A-2C), and the prognosis of PLAGL2 in breast cancer is not consistent (Figure.2D-2F).\u003c/p\u003e\n\u003cp\u003eSo, we further explore the prognostic value of PLAGL2 through the Kaplan-Meier plotter. We can find that high PLAGL2 expression is slightly related to a poor prognosis in stomach cancer (Figure.2G-2I). However, high PLAGL2 expression is probably related to a better prognosis in ovarian (Figure.2J-2L) and breast (Figure.2M-2O) cancer. The results above indicated that PLAGL2 might be a prognostic biomarker in stomach cancer. Then we further explored the association between the expression of PLAGL2 and the clinical characteristics of STAD through the Kaplan-Meier plotter (Table 1). We can find that high expression of PLAGL2 related to both poorer OS and PFS in female, male, HER2 negative and HER2 positive in STAD. Specifically, high expression of PLAGL2 related to both poorer OS and PFS in STAD patients belonging to stages 2, stage N1+2+3, stage N1 and stage M0.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRelationship between PLAGL2 expression and immune and molecular subtypes in human cancers\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNext, we further analyzed the relationship between PLAGL2 expression and immune and molecular subtypes in human cancers through the TISIDB website. We can find that there was a clearly relationship between PLAGL2 and different subtypes of UCEC, BRCA, CESC, COAD, HNSC, KIRC, KIRP, LGG, LUSC, TGCT and LIHC (Figure.3). In addition, the expression of PLAGL2 is also related to different cancer molecular subtypes in various cancers (Figure.1S). We may deduce from the aforementioned findings that PLAGL2 may play an important role in the immunological and molecular subtypes of many malignancies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRelationship between PLAGL2 expression and immune checkpoint (ICP) genes in human cancers\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eImmune cell infiltration and immunotherapy have both been shown to be significantly influenced by ICP genes[27]. The relationship between PLAGL2 expression and ICP genes in human malignancies was then investigated (Figure.4). The results indicated that PLAGL2 expression is associated to immune checkpoint genes in a range of malignancies.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRelationship between PLAGL2 and immune cell infiltration in human cancers\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe further investigated the possible association between PLAGL2 and immune cell infiltration, we can find that there is a substantial correlation in numerous cancer types (Figure.5). PLAGL2 expression is related to dendritic cells in 19 cancers, macrophages in 14 cancers, neutrophils in 23 cancers, CD8+ T cells in 14 cancers, and B cells in 20 cancers. In 15 cancers, there is a strong correlation between CD4+ T cells. Then, we also explored the relationship between the expression of PLAGL2 and different immune marker genes in STAD (Table 2). We can find that there exist a significantly relationship between the expression level of PLAGL2 and most immune markers in various immune cells in STAD.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe regulation network of PLAGL2 in stomach cancer\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePrevious results in this study we have demonstrated that PLAGL2 was overexpressed in STAD, and predicted a poor prognosis. So, we further explored the regulation network of PLAGL2 in stomach cancer. Firstly, As shown in the volcano map (Figure.6A-6C), we explored the co-expression genes of PLAGL2 in STAD through LinkedOmics. The differentially expressed genes associated to PLAGL2 are mostly involved in cell cycle control, according to the results of GO term analysis (Figure.6D-6F). KEGG pathway analysis revealed the enrichment of cell cycle, Staphylococcus aureus infection, basic transcription factors, complement and coagulation cascade(Figure.6G).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePrognostic potential of PLAGL2 co-expression genes in stomach cancers\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThen, we further evaluated the prognostic potential of the genes positive co-expression with PLAGL2 in STAD through Kaplan-Meier plotter (Figure.7, Figure.2S). The results showed that among the top 40 genes co-expressed with PLAGL2 in STAD, most of the genes were related to the prognosis of STAD.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRelationship between PLAGL2 and different clinical subgroups in STAD.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNext, the expression of PLAGL2 in STAD with different clinical characteristics was explored through UALCAN database. The results indicated that there existed an significantly difference between the expression of PLAGL2 in different STAD patients\u0026apos; gender (Figure.8A), age(Figure.8B), tumor grade(Figure.8C), lymph node metastasis status(Figure.8D), cancer stage(Figure.8E), and Helicobacter pylori infection status(Figure.8F), indicating that PLAGL2 may act as an important oncogene in the progress of STAD.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe function of PLAGL2 in STAD cells\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFinally, to verify the association between PLAGL2 and STAD clinicopathological characteristics, we performed Western blot and IHC to detect PLAGL2 expression in 57 paraffin-embedded STAD specimens(Figure.9A-9C). we can find that the PLAGL2 was overexpressed in STAD tissues. More importantly, our findings reveal that the PLAGL2 is associated to lymph node metastasis and tumor size in STAD(Table 3). Then, we used a lentivirus-based system to establish stable PLAGL2 knockdown 7901 and MKN-45 cell lines (Figure.9D-9F). Next, the results of CCK8 test(Figure.9G) and the colony formation test(Figure.9H-9I) revealed that PLAGL2 could promote the proliferation ability of STAD cells. Finally, the results of the xenograft subcutaneous transplantation model showed that PLAGL2 could promote the growth of STAD cells in vivo (Figure.9J-9L). In summary, the data above supports the conclusion that PLAGL2 is an oncogene in STAD and promotes the proliferation of STAD cells in vitro and in vivo.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003ePrevious studies have demonstrated that PLAGL2, acts as a transcription factor, might participate in the progression of various cancers[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Our previous studies have shown that PLAGL2 suppresses cell migration and proliferation in Hirschsprung's disease[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], and it also increases colon cancer growth via binding to the MYH9 promoter[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. PLAGL2 could promote the development stomach cancer by promoting the deubiquitination of Snail1 protein[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. In addition, PLAGL2 could also promote the development of CRC through the Wnt signaling pathway[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. However, the relationship between PLAGL2 and immunotherapy has not been reported. Here, we explored the expression and prognosis of PLAGL2 in various cancers, and further investigated the relationship between PLAGL2 and immune infiltration for the first time. These studies indicate that PLAGL2 might act as a prognostic biomarker and target for anti-tumor immunotherapy in human cancers.\u003c/p\u003e \u003cp\u003eFirstly, we explored the expression of PLAGL2 through Oncomine, TIMER and SangerBox websites. Consistent with previous studies, the results showed that PLAGL2 was overexpressed in most types of cancer. These results indicate that PLAGL2 does promote the occurrence and development of human cancer.\u003c/p\u003e \u003cp\u003eThen, we investigated the relationship between PLAGL2 expression and prognosis in various cancers. We can find that overexpression of PLAGL2 might predicted a poor prognosis in STAD, which proves that PLAGL2 may serve as a potential prognostic biomarker.\u003c/p\u003e \u003cp\u003eFollowing that, we investigated PLAGL2 expression in several immunological and molecular subtypes of human cancer to evaluate its probable biochemical pathway. The findings revealed that PLAGL2 expression differs considerably across immunological subtypes and molecular subtypes of most cancer types, suggesting that PLAGL2 is a viable diagnostic pan-cancer biomarker that plays a role in immune regulation. Furthermore, we demonstrated that the expression of PLAGL2 varies significantly across clinical subgroups. PLAGL2 is differently expressed in most malignancies with various clinical features, indicating that PLAGL2 may have a role in tumor development and progression.\u003c/p\u003e \u003cp\u003ePrevious researches have shown that tumor infiltrating lymphocytes (TIL) in TME might act as an independent predictor of the prognosis of cancer patients and the effect of immunotherapy[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Our research shows that the expression of PLAGL2 is correlated with various immunostimulatory and immunosuppressive cytokines, which prove evidence for the potential immune function of PLAGL2.\u003c/p\u003e \u003cp\u003eWe also analyzed the PLAGL2 co-expression network, and evaluated the prognostic potential of genes positive co-expression with PLAGL2 in STAD through Kaplan-Meier plotter. The results show that most of co-expression genes have a significant effect on the prognosis, which further indicates that PLAGL2 could applied as a prognostic biomarker for STAD.\u003c/p\u003e \u003cp\u003eFinally, we further designed experiments to explore the function of PLAGL2 in STAD. Consistent with previous studies, PLAGL2 was significantly overexpressed in STAD tissues compared with that of adjacent normal tissues. And PLAGL2 can promote the proliferation of STAD cells both in vivo and in vitro.\u003c/p\u003e \u003cp\u003eHowever, this work has certain limitations, despite the fact that we did a thorough and systematic examination of PLAGL2 and used many databases for cross-validation. First, there are discrepancies between microarray and sequencing data from different databases, as well as a lack of granularity and specificity, which might contribute to system bias. Secondly, although in vivo/in vitro experiments have been carried out and proved the role of PLAGL2 in STAD, its regulatory mechanism needs to be further explored. Third, while we found that PLAGL2 expression is related to immune cell infiltration and prognosis in STAD, we don't have direct evidence that PLAGL2 affects prognosis through immune infiltration. As a result, the mechanism through which PLAGL2 contributes to immune modulation remains unknown. Further study is required to determine the specific procedure. In the future, prospective investigations on the expression of PLAGL2 and its involvement in human cancer immune infiltration will be required, as well as the effective development and testing of novel anti-tumor immunotherapy medicines for PLAGL2.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eOur findings showed that data mining successfully identifies PLAGL2 expression and putative regulatory networks in STAD, laying the groundwork for additional research into the function of PLAGL2 in carcinogenesis.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"0\" cellpadding=\"0\" cellspacing=\"0\" width=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eACC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAdrenocortical carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBLCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBladder Urothelial Carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBRCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBreast invasive carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCESC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCervical squamous cell carcinoma and endocervical adenocarcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCHOL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCholangiocarcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCOAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eColon adenocarcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCOADREAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eColon adenocarcinoma/Rectum adenocarcinoma Esophageal carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDLBC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLymphoid Neoplasm Diffuse Large B-cell Lymphoma\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eESCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEsophageal carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFPPP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFFPE Pilot Phase II\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGBM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGlioblastoma multiforme\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGBMLGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGlioma\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHNSC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHead and Neck squamous cell carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eKICH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eKidney Chromophobe\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eKIPAN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePan-kidney cohort (KICH+KIRC+KIRP)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eKIRC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eKidney renal clear cell carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eKIRP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eKidney renal papillary cell carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLAML\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAcute Myeloid Leukemia\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBrain Lower Grade Glioma\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLIHC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLiver hepatocellular carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLUAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLung adenocarcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLUSC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLung squamous cell carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMESO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMesothelioma\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOvarian serous cystadenocarcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePAAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePancreatic adenocarcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePCPG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePheochromocytoma and Paraganglioma\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePRAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eProstate adenocarcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eREAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRectum adenocarcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSARC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSarcoma\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSTAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eStomach adenocarcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSKCM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSkin Cutaneous Melanoma\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSTES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eStomach and Esophageal carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTGCT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTesticular Germ Cell Tumors\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTHCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eThyroid carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTHYM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eThymoma\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUCEC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUterine Corpus Endometrial Carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUCS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUterine Carcinosarcoma\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUVM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUveal Melanoma\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOsteosarcoma\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eALL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAcute Lymphoblastic Leukemia\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNeuroblastoma\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eWT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHigh-Risk Wilms Tumor\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthics approval was granted by the Ethics Committee of Tongji Medical College, Huazhong University of Science and Technology(S-082). According to the Tongji Medical College Animal Care and Use Guidelines, animal experiments were performed. All methods were carried out in accordance with relevant guidelines and regulations. The study was carried out in compliance with the ARRIVE guidelines.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePatient consent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the National Natural Science Foundation of China (No. 81772581).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor\u0026rsquo;s contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eZili Zhou and Li Yan collected the data, analyzed and interpreted the data, and wrote the manuscript. Lin Wang prepared draft figures and tables. All authors read and approved the final manuscript for publication\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003e Yang T, Huo J, Xu R, Su Q, Tang W, Zhang D, et al. Selenium sulfide disrupts the PLAGL2/C-MET/STAT3-induced resistance against mitochondrial apoptosis in hepatocellular carcinoma. Clin Transl Med. 2021; 11: e536.\u003c/li\u003e\n \u003cli\u003e Liu Q, Ran R, Song M, Li X, Wu Z, Dai G, et al. LncRNA HCP5 acts as a miR-128-3p sponge to promote the progression of multiple myeloma through activating Wnt/beta-catenin/cyclin D1 signaling via PLAGL2. Cell Biol Toxicol. 2021.\u003c/li\u003e\n \u003cli\u003e Zeng Z, Teng Q, Xiao J. Long noncoding RNA ILF3-AS1 aggravates papillary thyroid carcinoma progression via regulating the miR-4306/PLAGL2 axis. Cancer Cell Int. 2021; 21: 322.\u003c/li\u003e\n \u003cli\u003e Zheng S, Ni J, Li Y, Lu M, Yao Y, Guo H, et al. 2-Methoxyestradiol synergizes with Erlotinib to suppress hepatocellular carcinoma by disrupting the PLAGL2-EGFR-HIF-1/2alpha signaling loop. Pharmacol Res. 2021; 169: 105685.\u003c/li\u003e\n \u003cli\u003e Wang L, Sun L, Liu R, Mo H, Niu Y, Chen T, et al. Long non-coding RNA MAPKAPK5-AS1/PLAGL2/HIF-1alpha signaling loop promotes hepatocellular carcinoma progression. J Exp Clin Cancer Res. 2021; 40: 72.\u003c/li\u003e\n \u003cli\u003e Gao N, Ye B. Circ-SOX4 drives the tumorigenesis and development of lung adenocarcinoma via sponging miR-1270 and modulating PLAGL2 to activate WNT signaling pathway. Cancer Cell Int. 2020; 20: 2.\u003c/li\u003e\n \u003cli\u003e Yang YS, Yang MC, Weissler JC. Pleiomorphic adenoma gene-like 2 expression is associated with the development of lung adenocarcinoma and emphysema. Lung Cancer. 2011; 74: 12-24.\u003c/li\u003e\n \u003cli\u003e Wu L, Zhou Z, Han S, Chen J, Liu Z, Zhang X, et al. PLAGL2 promotes epithelial-mesenchymal transition and mediates colorectal cancer metastasis via beta-catenin-dependent regulation of ZEB1. Br J Cancer. 2020; 122: 578-89.\u003c/li\u003e\n \u003cli\u003e Lv Y, Xie B, Bai B, Shan L, Zheng W, Huang X, et al. Weighted gene coexpression analysis indicates that PLAGL2 and POFUT1 are related to the differential features of proximal and distal colorectal cancer. Oncol Rep. 2019; 42: 2473-85.\u003c/li\u003e\n \u003cli\u003e Germot A, Maftah A. POFUT1 and PLAGL2 gene pair linked by a bidirectional promoter: the two in one of tumour progression in colorectal cancer? EBioMedicine. 2019; 46: 25-6.\u003c/li\u003e\n \u003cli\u003e Li D, Lin C, Li N, Du Y, Yang C, Bai Y, et al. PLAGL2 and POFUT1 are regulated by an evolutionarily conserved bidirectional promoter and are collaboratively involved in colorectal cancer by maintaining stemness. EBioMedicine. 2019; 45: 124-38.\u003c/li\u003e\n \u003cli\u003e Li N, Li D, Du Y, Su C, Yang C, Lin C, et al. Overexpressed PLAGL2 transcriptionally activates Wnt6 and promotes cancer development in colorectal cancer. Oncol Rep. 2019; 41: 875-84.\u003c/li\u003e\n \u003cli\u003e Wu L, Yuan W, Chen J, Zhou Z, Shu Y, Ji J, et al. Increased miR-214 expression suppresses cell migration and proliferation in Hirschsprung disease by interacting with PLAGL2. Pediatr Res. 2019.\u003c/li\u003e\n \u003cli\u003e Zhou Z, Wu L, Liu Z, Zhang X, Han S, Zhao N, et al. MicroRNA-214-3p targets the PLAGL2-MYH9 axis to suppress tumor proliferation and metastasis in human colorectal cancer. Aging (Albany NY). 2020; 12: 9633-57.\u003c/li\u003e\n \u003cli\u003e Wu L, Zhao N, Zhou Z, Chen J, Han S, Zhang X, et al. PLAGL2 promotes the proliferation and migration of gastric cancer cells via USP37-mediated deubiquitination of Snail1. Theranostics. 2021; 11: 700-14.\u003c/li\u003e\n \u003cli\u003e Rhodes DR, Kalyana-Sundaram S, Mahavisno V, Varambally R, Yu J, Briggs BB, et al. Oncomine 3.0: genes, pathways, and networks in a collection of 18,000 cancer gene expression profiles. Neoplasia. 2007; 9: 166-80.\u003c/li\u003e\n \u003cli\u003e Li T, Fu J, Zeng Z, Cohen D, Li J, Chen Q, et al. TIMER2.0 for analysis of tumor-infiltrating immune cells. Nucleic Acids Res. 2020; 48: W509-W14.\u003c/li\u003e\n \u003cli\u003e Sousa S, Maatta J. The role of tumour-associated macrophages in bone metastasis. J Bone Oncol. 2016; 5: 135-8.\u003c/li\u003e\n \u003cli\u003e Danaher P, Warren S, Dennis L, D\u0026apos;Amico L, White A, Disis ML, et al. Gene expression markers of Tumor Infiltrating Leukocytes. J Immunother Cancer. 2017; 5: 18.\u003c/li\u003e\n \u003cli\u003e Siemers NO, Holloway JL, Chang H, Chasalow SD, Ross-MacDonald PB, Voliva CF, et al. Genome-wide association analysis identifies genetic correlates of immune infiltrates in solid tumors. PLoS One. 2017; 12: e0179726.\u003c/li\u003e\n \u003cli\u003e Zhang L, Liu Z, Dong Y, Kong L. E2F2 drives glioma progression via PI3K/AKT in a PFKFB4-dependent manner. Life Sci. 2021; 276: 119412.\u003c/li\u003e\n \u003cli\u003e Gyorffy B. Survival analysis across the entire transcriptome identifies biomarkers with the highest prognostic power in breast cancer. Comput Struct Biotechnol J. 2021; 19: 4101-9.\u003c/li\u003e\n \u003cli\u003e Mizuno H, Kitada K, Nakai K, Sarai A. PrognoScan: a new database for meta-analysis of the prognostic value of genes. BMC Med Genomics. 2009; 2: 18.\u003c/li\u003e\n \u003cli\u003e Ru B, Wong CN, Tong Y, Zhong JY, Zhong SSW, Wu WC, et al. TISIDB: an integrated repository portal for tumor-immune system interactions. Bioinformatics. 2019; 35: 4200-2.\u003c/li\u003e\n \u003cli\u003e Zhou G, Soufan O, Ewald J, Hancock REW, Basu N, Xia J. NetworkAnalyst 3.0: a visual analytics platform for comprehensive gene expression profiling and meta-analysis. Nucleic Acids Res. 2019; 47: W234-W41.\u003c/li\u003e\n \u003cli\u003e Chandrashekar DS, Bashel B, Balasubramanya SAH, Creighton CJ, Ponce-Rodriguez I, Chakravarthi B, et al. UALCAN: A Portal for Facilitating Tumor Subgroup Gene Expression and Survival Analyses. Neoplasia. 2017; 19: 649-58.\u003c/li\u003e\n \u003cli\u003e Topalian SL, Drake CG, Pardoll DM. Immune checkpoint blockade: a common denominator approach to cancer therapy. Cancer Cell. 2015; 27: 450-61.\u003c/li\u003e\n \u003cli\u003e Hu W, Zheng S, Guo H, Dai B, Ni J, Shi Y, et al. PLAGL2-EGFR-HIF-1/2alpha Signaling Loop Promotes HCC Progression and Erlotinib Insensitivity. Hepatology. 2021; 73: 674-91.\u003c/li\u003e\n \u003cli\u003e Zhou J, Liu H, Zhang L, Liu X, Zhang C, Wang Y, et al. DJ-1 promotes colorectal cancer progression through activating PLAGL2/Wnt/BMP4 axis. Cell Death Dis. 2018; 9: 865.\u003c/li\u003e\n \u003cli\u003e Azimi F, Scolyer RA, Rumcheva P, Moncrieff M, Murali R, McCarthy SW, et al. Tumor-infiltrating lymphocyte grade is an independent predictor of sentinel lymph node status and survival in patients with cutaneous melanoma. J Clin Oncol. 2012; 30: 2678-83.\u003c/li\u003e\n \u003cli\u003e Ohtani H. Focus on TILs: prognostic significance of tumor infiltrating lymphocytes in human colorectal cancer. Cancer Immun. 2007; 7: 4.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp style=\"text-align: center;\"\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e\u003c/p\u003e\n\u003cp style=\"text-align: center;\"\u003e\u003cstrong\u003eCorrelation of PLAGL2 mRNA expression and prognosis in STAD with different clinicopathological factors by Kaplan-Meier plotter.\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" width=\"23.46089850249584%\"\u003e\n \u003cp\u003eClinicopathological factors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" width=\"38.76871880199667%\"\u003e\n \u003cp\u003eOverall survival\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" width=\"37.770382695507486%\"\u003e\n \u003cp\u003ePost progression survival\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.278867102396514%\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.36165577342048%\"\u003e\n \u003cp\u003eHazard ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.122004357298476%\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.278867102396514%\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.579520697167755%\"\u003e\n \u003cp\u003eHazard ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.379084967320262%\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.5%\"\u003e\n \u003cp\u003eSEX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.333333333333333%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"20.166666666666668%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.333333333333334%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.333333333333333%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"20.333333333333332%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.5%\"\u003e\n \u003cp\u003eFemal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.333333333333333%\"\u003e\n \u003cp\u003e236\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.166666666666668%\"\u003e\n \u003cp\u003e1.74(1.22-2.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.333333333333334%\"\u003e\n \u003cp\u003e0.0018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.333333333333333%\"\u003e\n \u003cp\u003e149\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.333333333333332%\"\u003e\n \u003cp\u003e1.82(1.18-2.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11%\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.5%\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.333333333333333%\"\u003e\n \u003cp\u003e544\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.166666666666668%\"\u003e\n \u003cp\u003e1.77(1.43-2.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.333333333333334%\"\u003e\n \u003cp\u003e1.50E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.333333333333333%\"\u003e\n \u003cp\u003e149\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.333333333333332%\"\u003e\n \u003cp\u003e2.16(1.65-2.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11%\"\u003e\n \u003cp\u003e9.60E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.5%\"\u003e\n \u003cp\u003eStage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.333333333333333%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"20.166666666666668%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.333333333333334%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.333333333333333%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"20.333333333333332%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.5%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.333333333333333%\"\u003e\n \u003cp\u003e67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.166666666666668%\"\u003e\n \u003cp\u003e2.56(0.89-7.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.333333333333334%\"\u003e\n \u003cp\u003e0.071\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.333333333333333%\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.333333333333332%\"\u003e\n \u003cp\u003e8.33(0.99-70.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11%\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.5%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.333333333333333%\"\u003e\n \u003cp\u003e140\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.166666666666668%\"\u003e\n \u003cp\u003e2.1(1.13-3.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.333333333333334%\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.333333333333333%\"\u003e\n \u003cp\u003e105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.333333333333332%\"\u003e\n \u003cp\u003e2.09(1.02-4.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11%\"\u003e\n \u003cp\u003e0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.5%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.333333333333333%\"\u003e\n \u003cp\u003e305\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.166666666666668%\"\u003e\n \u003cp\u003e1.57(1.18-2.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.333333333333334%\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.333333333333333%\"\u003e\n \u003cp\u003e142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.333333333333332%\"\u003e\n \u003cp\u003e1.53(1-2.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11%\"\u003e\n \u003cp\u003e0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.5%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.333333333333333%\"\u003e\n \u003cp\u003e148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.166666666666668%\"\u003e\n \u003cp\u003e1.12(0.76-1.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.333333333333334%\"\u003e\n \u003cp\u003e0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.333333333333333%\"\u003e\n \u003cp\u003e104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.333333333333332%\"\u003e\n \u003cp\u003e1.14(0.72-1.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11%\"\u003e\n \u003cp\u003e0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.5%\"\u003e\n \u003cp\u003eStage T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.333333333333333%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"20.166666666666668%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.333333333333334%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.333333333333333%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"20.333333333333332%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.5%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.333333333333333%\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.166666666666668%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.333333333333334%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.333333333333333%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.333333333333332%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.5%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.333333333333333%\"\u003e\n \u003cp\u003e241\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.166666666666668%\"\u003e\n \u003cp\u003e1.49(0.97-2.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.333333333333334%\"\u003e\n \u003cp\u003e0.065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.333333333333333%\"\u003e\n \u003cp\u003e196\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.333333333333332%\"\u003e\n \u003cp\u003e1.54(0.98-2.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11%\"\u003e\n \u003cp\u003e0.061\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.5%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.333333333333333%\"\u003e\n \u003cp\u003e204\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.166666666666668%\"\u003e\n \u003cp\u003e1.49(1.06-2.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.333333333333334%\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.333333333333333%\"\u003e\n \u003cp\u003e150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.333333333333332%\"\u003e\n \u003cp\u003e1.35(0.92-1.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11%\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.5%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.333333333333333%\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.166666666666668%\"\u003e\n \u003cp\u003e1.27(0.56-2.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.333333333333334%\"\u003e\n \u003cp\u003e0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.333333333333333%\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.333333333333332%\"\u003e\n \u003cp\u003e0.85(0.33-2.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11%\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.5%\"\u003e\n \u003cp\u003eStage N\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.333333333333333%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"20.166666666666668%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.333333333333334%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.333333333333333%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"20.333333333333332%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.5%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.333333333333333%\"\u003e\n \u003cp\u003e74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.166666666666668%\"\u003e\n \u003cp\u003e1.9（0.8-4.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.333333333333334%\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.333333333333333%\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.333333333333332%\"\u003e\n \u003cp\u003e1.57(0.47-5.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11%\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.5%\"\u003e\n \u003cp\u003e1+2+3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.333333333333333%\"\u003e\n \u003cp\u003e422\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.166666666666668%\"\u003e\n \u003cp\u003e1.5（1.16-1.96）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.333333333333334%\"\u003e\n \u003cp\u003e0.0022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.333333333333333%\"\u003e\n \u003cp\u003e337\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.333333333333332%\"\u003e\n \u003cp\u003e1.41(1.06-1.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11%\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.5%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.333333333333333%\"\u003e\n \u003cp\u003e225\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.166666666666668%\"\u003e\n \u003cp\u003e2.06（1.36-3.14）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.333333333333334%\"\u003e\n \u003cp\u003e0.00056\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.333333333333333%\"\u003e\n \u003cp\u003e169\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.333333333333332%\"\u003e\n \u003cp\u003e2.37(1.47-3.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11%\"\u003e\n \u003cp\u003e0.00025\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.5%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.333333333333333%\"\u003e\n \u003cp\u003e121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.166666666666668%\"\u003e\n \u003cp\u003e1.31（0.83-2.05）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.333333333333334%\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.333333333333333%\"\u003e\n \u003cp\u003e105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.333333333333332%\"\u003e\n \u003cp\u003e1.24(0.77-2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11%\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.5%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.333333333333333%\"\u003e\n \u003cp\u003e76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.166666666666668%\"\u003e\n \u003cp\u003e1.17（0.69-1.99）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.333333333333334%\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.333333333333333%\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.333333333333332%\"\u003e\n \u003cp\u003e1.19(0.67-2.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11%\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.5%\"\u003e\n \u003cp\u003eStage M\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.333333333333333%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"20.166666666666668%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.333333333333334%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.333333333333333%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"20.333333333333332%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.5%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.333333333333333%\"\u003e\n \u003cp\u003e444\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.166666666666668%\"\u003e\n \u003cp\u003e1.45（1.1-1.91）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.333333333333334%\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.333333333333333%\"\u003e\n \u003cp\u003e342\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.333333333333332%\"\u003e\n \u003cp\u003e1.5(1.11-2.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11%\"\u003e\n \u003cp\u003e0.0077\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.5%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.333333333333333%\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.166666666666668%\"\u003e\n \u003cp\u003e1.1（0.62-1.96）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.333333333333334%\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.333333333333333%\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.333333333333332%\"\u003e\n \u003cp\u003e1.09(0.53-2.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11%\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.5%\"\u003e\n \u003cp\u003eDifferentiation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.333333333333333%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"20.166666666666668%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.333333333333334%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.333333333333333%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"20.333333333333332%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.5%\"\u003e\n \u003cp\u003epoorly differentiated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.333333333333333%\"\u003e\n \u003cp\u003e165\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.166666666666668%\"\u003e\n \u003cp\u003e1.1（0.74-1.64）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.333333333333334%\"\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.333333333333333%\"\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.333333333333332%\"\u003e\n \u003cp\u003e1.49(0.78-2.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11%\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.5%\"\u003e\n \u003cp\u003emoderately differentiated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.333333333333333%\"\u003e\n \u003cp\u003e67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.166666666666668%\"\u003e\n \u003cp\u003e1.26（0.66-2.41）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.333333333333334%\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.333333333333333%\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.333333333333332%\"\u003e\n \u003cp\u003e0.81(0.32-2.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11%\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.5%\"\u003e\n \u003cp\u003ewell differentiated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.333333333333333%\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.166666666666668%\"\u003e\n \u003cp\u003e0.83（0.35-1.96）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.333333333333334%\"\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.333333333333333%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.333333333333332%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.5%\"\u003e\n \u003cp\u003eTreatment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.333333333333333%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"20.166666666666668%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.333333333333334%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.333333333333333%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"20.333333333333332%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.5%\"\u003e\n \u003cp\u003esurgery alone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.333333333333333%\"\u003e\n \u003cp\u003e380\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.166666666666668%\"\u003e\n \u003cp\u003e1.4(1.05-1.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.333333333333334%\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.333333333333333%\"\u003e\n \u003cp\u003e277\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.333333333333332%\"\u003e\n \u003cp\u003e1.35(0.98-1.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11%\"\u003e\n \u003cp\u003e0.061\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.5%\"\u003e\n \u003cp\u003e5 FU based adjuvant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.333333333333333%\"\u003e\n \u003cp\u003e152\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.166666666666668%\"\u003e\n \u003cp\u003e1.26(0.89-1.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.333333333333334%\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.333333333333333%\"\u003e\n \u003cp\u003e135\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.333333333333332%\"\u003e\n \u003cp\u003e1.49(1.04-2.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11%\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.5%\"\u003e\n \u003cp\u003eother adjuvant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.333333333333333%\"\u003e\n \u003cp\u003e76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.166666666666668%\"\u003e\n \u003cp\u003e0.79(0.33-1.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.333333333333334%\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.333333333333333%\"\u003e\n \u003cp\u003e74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.333333333333332%\"\u003e\n \u003cp\u003e0.82(0.34-1.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11%\"\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.5%\"\u003e\n \u003cp\u003eHER2 status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.333333333333333%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"20.166666666666668%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.333333333333334%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.333333333333333%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"20.333333333333332%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.5%\"\u003e\n \u003cp\u003eHER2 negative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.333333333333333%\"\u003e\n \u003cp\u003e532\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.166666666666668%\"\u003e\n \u003cp\u003e1.69(1.35-2.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.333333333333334%\"\u003e\n \u003cp\u003e4.60E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.333333333333333%\"\u003e\n \u003cp\u003e334\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.333333333333332%\"\u003e\n \u003cp\u003e1.7(1.27-2.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11%\"\u003e\n \u003cp\u003e0.00029\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.5%\"\u003e\n \u003cp\u003eHER2 positive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.333333333333333%\"\u003e\n \u003cp\u003e343\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.166666666666668%\"\u003e\n \u003cp\u003e1.39(1.07-1.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.333333333333334%\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.333333333333333%\"\u003e\n \u003cp\u003e164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.333333333333332%\"\u003e\n \u003cp\u003e2.24(1.55-3.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11%\"\u003e\n \u003cp\u003e1.10E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp style=\"text-align: center;\"\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e\u003c/p\u003e\n\u003cp style=\"text-align: center;\"\u003e\u003cstrong\u003eCorrelation analysis between PLAGL2 and relate gene markers of immune cells in STAD.\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.60483870967742%\"\u003e\n \u003cp\u003eDescription\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.782258064516128%\"\u003e\n \u003cp\u003eGene markers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.903225806451612%\"\u003e\n \u003cp\u003eNone\u003c/p\u003e\n \u003cp\u003eCore\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.903225806451612%\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.903225806451612%\"\u003e\n \u003cp\u003ePurity\u003c/p\u003e\n \u003cp\u003eCore\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.903225806451612%\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" width=\"25.60483870967742%\"\u003e\n \u003cp\u003eCD8+ T cell\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.782258064516128%\"\u003e\n \u003cp\u003eCD8A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.903225806451612%\"\u003e\n \u003cp\u003e-0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.903225806451612%\"\u003e\n \u003cp\u003e0.831\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.903225806451612%\"\u003e\n \u003cp\u003e-0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.903225806451612%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.62330623306233%\"\u003e\n \u003cp\u003eCD8B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e0.157\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e-0.121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" width=\"25.60483870967742%\"\u003e\n \u003cp\u003eT cell(general)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.782258064516128%\"\u003e\n \u003cp\u003eCD3D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.903225806451612%\"\u003e\n \u003cp\u003e-0.029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.903225806451612%\"\u003e\n \u003cp\u003e0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.903225806451612%\"\u003e\n \u003cp\u003e-0.315\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.903225806451612%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.62330623306233%\"\u003e\n \u003cp\u003eCD3E\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e0.722\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e-0.335\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.62330623306233%\"\u003e\n \u003cp\u003eCD2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e0.029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e0.573\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e-0.303\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" width=\"25.60483870967742%\"\u003e\n \u003cp\u003eB cell\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.782258064516128%\"\u003e\n \u003cp\u003eCD19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.903225806451612%\"\u003e\n \u003cp\u003e0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.903225806451612%\"\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.903225806451612%\"\u003e\n \u003cp\u003e-0.218\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.903225806451612%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.62330623306233%\"\u003e\n \u003cp\u003eCD79A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e-0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e0.318\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e-0.268\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" width=\"25.60483870967742%\"\u003e\n \u003cp\u003eMonocyte\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.782258064516128%\"\u003e\n \u003cp\u003eCD86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.903225806451612%\"\u003e\n \u003cp\u003e-0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.903225806451612%\"\u003e\n \u003cp\u003e0.927\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.903225806451612%\"\u003e\n \u003cp\u003e-0.286\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.903225806451612%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.62330623306233%\"\u003e\n \u003cp\u003eCSF1R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e0.054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e0.299\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e-0.208\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" width=\"25.60483870967742%\"\u003e\n \u003cp\u003eTAM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.782258064516128%\"\u003e\n \u003cp\u003eCCL2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.903225806451612%\"\u003e\n \u003cp\u003e-0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.903225806451612%\"\u003e\n \u003cp\u003e**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.903225806451612%\"\u003e\n \u003cp\u003e-0.205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.903225806451612%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.62330623306233%\"\u003e\n \u003cp\u003eCD68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e0.151\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e-0.159\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.62330623306233%\"\u003e\n \u003cp\u003eIL10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e0.534\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e-0.254\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" width=\"25.60483870967742%\"\u003e\n \u003cp\u003eM1 Macrophage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.782258064516128%\"\u003e\n \u003cp\u003eNOS2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.903225806451612%\"\u003e\n \u003cp\u003e0.284\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.903225806451612%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.903225806451612%\"\u003e\n \u003cp\u003e-0.094\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.903225806451612%\"\u003e\n \u003cp\u003e0.0663\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.62330623306233%\"\u003e\n \u003cp\u003eIRF5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e0.201\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e-0.111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.62330623306233%\"\u003e\n \u003cp\u003ePTGS2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e-0.085\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e0.0986\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e-0.126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" width=\"25.60483870967742%\"\u003e\n \u003cp\u003eM2 Macrophage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.782258064516128%\"\u003e\n \u003cp\u003eCD163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.903225806451612%\"\u003e\n \u003cp\u003e0.086\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.903225806451612%\"\u003e\n \u003cp\u003e0.0938\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.903225806451612%\"\u003e\n \u003cp\u003e-0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.903225806451612%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.62330623306233%\"\u003e\n \u003cp\u003eVSIG4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e-0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e-0.166\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.62330623306233%\"\u003e\n \u003cp\u003eMS4A4A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e-0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e0.403\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e-0.191\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"6\" width=\"25.60483870967742%\"\u003e\n \u003cp\u003eNatural killer cell\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.782258064516128%\"\u003e\n \u003cp\u003eKIR2DL1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.903225806451612%\"\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.903225806451612%\"\u003e\n \u003cp\u003e0.615\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.903225806451612%\"\u003e\n \u003cp\u003e-0.077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.903225806451612%\"\u003e\n \u003cp\u003e0.137\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.62330623306233%\"\u003e\n \u003cp\u003eKIR2DL3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e0.974\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e-0.132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.62330623306233%\"\u003e\n \u003cp\u003eKIR2DL4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e-0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e0.614\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e-0.165\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.62330623306233%\"\u003e\n \u003cp\u003eKIR3DL1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e-0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e0.707\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e-0.124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.62330623306233%\"\u003e\n \u003cp\u003eKIR3DL2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e-0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e0.972\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e-0.161\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.62330623306233%\"\u003e\n \u003cp\u003eKIR3DL3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e0.072\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e0.703\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"7\" width=\"25.60483870967742%\"\u003e\n \u003cp\u003eDendritic cell\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.782258064516128%\"\u003e\n \u003cp\u003eHLA-DPB1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.903225806451612%\"\u003e\n \u003cp\u003e-0.095\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.903225806451612%\"\u003e\n \u003cp\u003e0.0635\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.903225806451612%\"\u003e\n \u003cp\u003e-0.293\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.903225806451612%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.62330623306233%\"\u003e\n \u003cp\u003eHLA-DQB1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e-0.075\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e0.142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e-0.282\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.62330623306233%\"\u003e\n \u003cp\u003eHLA-DRA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e-0.066\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e0.197\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e-0.276\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.62330623306233%\"\u003e\n \u003cp\u003eHLA-DPA1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e-0.061\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e0.235\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e-0.276\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.62330623306233%\"\u003e\n \u003cp\u003eBDCA-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e-0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e0.425\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e-0.285\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.62330623306233%\"\u003e\n \u003cp\u003eBDCA-4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e0.594\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e-0.173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.62330623306233%\"\u003e\n \u003cp\u003eCD11c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e0.096\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e0.0617\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e-0.224\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" width=\"25.60483870967742%\"\u003e\n \u003cp\u003eTH1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.782258064516128%\"\u003e\n \u003cp\u003eTBX21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.903225806451612%\"\u003e\n \u003cp\u003e0.063\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.903225806451612%\"\u003e\n \u003cp\u003e0.221\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.903225806451612%\"\u003e\n \u003cp\u003e-0.254\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.903225806451612%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.62330623306233%\"\u003e\n \u003cp\u003eSTAT4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e0.117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e0.0232\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e-0.245\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.62330623306233%\"\u003e\n \u003cp\u003eSTAT1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e0.302\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e-0.104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.62330623306233%\"\u003e\n \u003cp\u003eTNF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e0.117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e-0.281\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.62330623306233%\"\u003e\n \u003cp\u003eINF-\u0026alpha;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e0.115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e-0.033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e0.519\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" width=\"25.60483870967742%\"\u003e\n \u003cp\u003eTH2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.782258064516128%\"\u003e\n \u003cp\u003eGATA3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.903225806451612%\"\u003e\n \u003cp\u003e-0.087\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.903225806451612%\"\u003e\n \u003cp\u003e0.0925\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.903225806451612%\"\u003e\n \u003cp\u003e-0.174\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.903225806451612%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.62330623306233%\"\u003e\n \u003cp\u003eSTAT6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e0.316\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e0.836\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.62330623306233%\"\u003e\n \u003cp\u003eSTAT5A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e0.126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e0.0144\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e-0.132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e0.0101\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.62330623306233%\"\u003e\n \u003cp\u003eIL13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e-0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e0.418\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e-0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e0.971\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.60483870967742%\"\u003e\n \u003cp\u003eTfh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.782258064516128%\"\u003e\n \u003cp\u003eBCL6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.903225806451612%\"\u003e\n \u003cp\u003e0.033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.903225806451612%\"\u003e\n \u003cp\u003e0.525\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.903225806451612%\"\u003e\n \u003cp\u003e-0.135\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.903225806451612%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" width=\"25.60483870967742%\"\u003e\n \u003cp\u003eTH17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.782258064516128%\"\u003e\n \u003cp\u003eSTAT3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.903225806451612%\"\u003e\n \u003cp\u003e0.236\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.903225806451612%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.903225806451612%\"\u003e\n \u003cp\u003e-0.071\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.903225806451612%\"\u003e\n \u003cp\u003e0.165\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.62330623306233%\"\u003e\n \u003cp\u003eIL17A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e0.237\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e-0.122\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e0.0173\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" width=\"25.60483870967742%\"\u003e\n \u003cp\u003eTreg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.782258064516128%\"\u003e\n \u003cp\u003eFOXP3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.903225806451612%\"\u003e\n \u003cp\u003e0.214\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.903225806451612%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.903225806451612%\"\u003e\n \u003cp\u003e-0.241\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.903225806451612%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.62330623306233%\"\u003e\n \u003cp\u003eCCR8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e-0.168\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.62330623306233%\"\u003e\n \u003cp\u003eSTAT5B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e0.305\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e-0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e0.664\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.62330623306233%\"\u003e\n \u003cp\u003eTGFB1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e0.548\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e-0.169\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" width=\"25.60483870967742%\"\u003e\n \u003cp\u003eT cell exhaustion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.782258064516128%\"\u003e\n \u003cp\u003ePD-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.903225806451612%\"\u003e\n \u003cp\u003e0.105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.903225806451612%\"\u003e\n \u003cp\u003e**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.903225806451612%\"\u003e\n \u003cp\u003e-0.175\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.903225806451612%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.62330623306233%\"\u003e\n \u003cp\u003eCTLA4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e0.177\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e-0.197\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.62330623306233%\"\u003e\n \u003cp\u003eLAG3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e0.699\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e-0.227\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.62330623306233%\"\u003e\n \u003cp\u003eTIM-3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e0.665\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e-0.245\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.62330623306233%\"\u003e\n \u003cp\u003eGZMB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e-0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e0.988\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e-0.254\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.344173441734416%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e(*P \u0026lt; 0.05, **P \u0026lt; 0.01, ***P \u0026lt; 0.001)\u003c/p\u003e\n\u003cp style=\"text-align: center;\"\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e\u003c/p\u003e\n\u003cp style=\"text-align: center;\"\u003e\u003cstrong\u003eClinicopathological analysis of PLAGL2 expression in STAD.\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eParameters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003ePLAGL2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAge(years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e＜60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026ge;60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFemal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSize of tumor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e＜3cm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e0.096\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026ge;3cm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eDifferentiation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eWell-moderate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e0.469\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePoor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eT Stages\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eT1-T2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eT3-T4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eN Stages\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eN0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eN1-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eM Stages\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eM0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eM1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\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":"PLAGL2, biomarker, immune infiltration, stomach cancer, prognosis, proliferation","lastPublishedDoi":"10.21203/rs.3.rs-1519014/v2","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1519014/v2","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\t\u003cstrong\u003eBackground：\u003c/strong\u003ePolymorphic adenoma-like protein 2 (PLAGL2), a zinc finger protein, has been linked to the advancement of serval-type malignancies. However, the relevance of PLAGL2 to the prognosis and regulatory networks of different cancers remains unclear.\u003c/p\u003e\u003cp\u003e\t\u003cstrong\u003eMethods：\u003c/strong\u003eThe expression of PLAGL2 was explored through Oncomine, TIMER and SangerBox websites. The relationship between PLAGL2 expression and prognosis in various cancers was analyzed through the Kaplan-Meier Plotter database and the PrognoScan databases. The expression of PLAGL2 in several immunological and molecular subtypes of human cancer was evaluated through the TISIDB database. The differentially expressed genes associated with PLAGL2 was explored through LinkedOmics database. The relationship between PLAGL2 expression and clinicopathological features in STAD was explored through the UALCAN database. The expression of PLAGL2 in STAD specimens was analyzed through western blot and IHC. The function of PLAGL2 in STAD was explored through the CCK8 test and the colony formation test. \u003c/p\u003e\u003cp\u003e\t\u003cstrong\u003eResults：\u003c/strong\u003eIn this study we found that PLAGL2 was overexpressed in most types of cancer and overexpression of PLAGL2 might predicted a poor prognosis in STAD. Next, we investigated PLAGL2 expression in several immunological and molecular subtypes and found that PLAGL2 expression differs considerably across immunological subtypes and molecular subtypes of most cancer types. Our research also shows that the expression of PLAGL2 is correlated with various immunostimulatory and immunosuppressive cytokines. We also analyzed the PLAGL2 co-expression network, and evaluated the prognostic potential of genes positive co-expression with PLAGL2 in STAD through Kaplan-Meier plotter. The results show that most of co-expression genes have a significant effect on the prognosis. We further designed experiments to explore the function of PLAGL2 in STAD. Consistent with previous studies, PLAGL2 was significantly overexpressed in STAD tissues compared with that of adjacent normal tissues. And PLAGL2 can promote the proliferation of STAD cells both in vivo and in vitro.\u003c/p\u003e\u003cp\u003e\t\u003cstrong\u003eConclusions:\u003c/strong\u003e Our findings showed that data mining successfully identifies PLAGL2 expression and putative regulatory networks in STAD, laying the groundwork for additional research into the function of PLAGL2 in carcinogenesis.\u003c/p\u003e","manuscriptTitle":"PLAGL2 Is a Prognostic Biomarker in Stomach cancer：A Comprehensive Study Based on Bioinformatics and Experiments","msid":"","msnumber":"","nonDraftVersions":[{"code":2,"date":"2022-05-04 18:00:59","doi":"10.21203/rs.3.rs-1519014/v2","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}},{"code":1,"date":"2022-04-07 16:01:52","doi":"10.21203/rs.3.rs-1519014/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":"9b76a35d-bcfa-45d0-8e46-0a6b3cc5be88","owner":[],"postedDate":"May 4th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2022-11-29T09:44:20+00:00","versionOfRecord":[],"versionCreatedAt":"2022-05-04 18:00:59","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v2","identity":"rs-1519014","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1519014","identity":"rs-1519014","version":["v2"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}