PAMR1 is a favorable diagnostic and prognostic biomarker in hepatocellular carcinoma

PAMR1 is a favorable diagnostic and prognostic biomarker in hepatocellular carcinoma | 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 PAMR1 is a favorable diagnostic and prognostic biomarker in hepatocellular carcinoma Xiaoping Zhou, Teng Liu, Shihua Deng, Ting Zhang, Dongming Wu, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2114251/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Peptidase domain containing associated with muscle regeneration 1 (PAMR1) is downregulated in breast cancer and cervical cancer. This study aimed to evaluate the role of PAMR1 in hepatocellular carcinoma (HCC) and explore the underlying molecular mechanisms. Base on the analysis of datasets from the Gene Expression Omnibus and The Cancer Genome Atlas (TCGA), a lower mRNA level of PAMR1 was detected in HCC compared that in normal liver tissues. The result was also confirmed by the experiment with immunohistochemistry (IHC), and qRT-PCR. The area under the curve(AUC) was 0.918 through receiver operating characteristic (ROC) curve analysis. The Kaplan-Meier analysis revealed that lower PAMR1 expression predicted prognostic outcome. Then, the genes closely associated with PAMR1 were screened and enriched by Gene Ontology (GO) analysis, showing their role on extracellular matrix organization, cell adhesion, and blood vessel development. Moreover, PAMR1 expression was positively correlated with immune cells infiltration. In addition, Gene Set Enrichment Analysis (GSEA) showed that the downregulated genes in the low- PAMR1 subgroup were significantly enriched in an inflammatory response, hypoxia, epithelial-mesenchymal transition, KRAS signaling, and TNF-α signaling via NF-κB signaling pathway. Collectively, PAMR1 shows lower level in HCC,and represents a favorable diagnostic and prognostic factor for HCC. Biological sciences/Computational biology and bioinformatics Health sciences/Biomarkers Biological sciences/Cancer Biological sciences/Cancer/Tumour biomarkers Biological sciences/Biological techniques Biological sciences/Biological techniques/Bioinformatics Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Hepatocellular carcinoma (HCC), the most common type of malignancy, is the third leading cause of cancer-related death worldwide in 2020 1 . Amount of risk factors are considered to contribute to HCC, such as chronic viral hepatitis, alcohol abuse, excess body weight, toxic exposure and HCC-driver genes mutation 2 , 3 . Because of aflatoxin exposure and chronic HBV infection, over half of HCC patients are prevalent in Asia 4 , and China is estimated to be with the most highest HCC incidence by 2030 5 .Surgery is the best approach in the treatment of HCC patients, but unfortunately, most cases are unresectable, as they are already at late stages at diagnosis. Other strategies, such as percutaneous ablation, radiotherapy, and liver transplantation, also have limited efficiency with a high postoperative recurrence rate and distant metastasis 6 , 7 .Therefore, identification of biomarkers of diagnosis and prognosis is crucial for treatment efficacy improvment and clinical outcome prediction. Peptidase domain-containing protein associated with muscle regeneration 1 (PAMR1) was first reported to be downregulated in muscle cell lines from five patients with Duchenne muscular dystrophy 8 . It was later found to be also expressed in normal cervix and endometrium tissue, and was correlated with psoriasis and type 2 diabetes 9 , 10 . The injury mouse model showed that PAMR1 was upregulated in the regenerating area of injured skeletal muscle, while it was detected with downregulation in breast and cervical cancer cells. PAMR1 inactivation was induced by hypermethylation at promoter in breast cancer cells 11 . The protein also inhibits the proliferation, migration, and invasion of cervical cancer cells 12 . Therefore, PAMR1 appears to be characterized as a tumor suppressor, but its role in HCC is still unclear. In this study, we evaluated PAMR1 expression level in HCC base on the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases, followed by the experimental confirmation with qRT-PCR and immunohistochemistry. Additionally, its potential role as a prognostic factor for HCC was also investigated. Finally, Gene Set Enrichment Analysis (GSEA) was performed to clarify the underlying mechanism of PAMR1 on HCC pathogenesis. Results Identification of DEGs in HCC. Base on the analysis of datasets from GEO (GSE45267, GSE101685, GSE121248, and GSE14520), we selected top 100 DEGs in each of the microarray dataset, and identified 15 overlapping genes that composed of 3 upregulated and 12 downregulated genes (Fig. 1 A). Notably, PAMR1 was one of most obvious DEGs in HCC patients that was worth further investigation (adjusted P = 2.84e-25, logFC = − 1.51, Fig. 1 B). PAMR1 was downregulated in HCC and its expression predicted a favorable prognosis. It was showed the PAMR1 mRNA levels was obviously lower in HCC tissues than that in healthy liver tissue in the above four GEO datasets (P < 0.05; Fig. 2 A-D), which was further confirmed by the cohort from TCGA(Fig. 2 E, S1A). Although PAMR1 mRNA level was lower in HCC, its promoter methylation level was upregulated in liver cancer tissue (Fig. 2 F). Additionally, the PAMR1 expression level was negatively correlated to HCC progression (Fig. 2 G). and tumor grade of cervical cancer (Fig. S1B). In addition, PAMR1 expression is suppressed in multiple cancer types, including prostate adenocarcinoma, uterine corpus endometrial carcinoma, and skin cutaneous melanoma (Fig. S1C). After bioinformatical identification of lower PAMR1 level in HCC, we used IHC to validate the result in clinical samples comprising 2 normal liver tissues and 29 HCC plus adjacent tissue pairs. The results showed obvious lower PAMR1 level in HCC compared with that in both normal and adjacent tissues (Fig. 3 A), which were statistically quantified as the PAMR1 score (Fig. 3 B). Moreover, the same result was obtained by qPCR from cDNA chip which contained 2 cDNA of HCC cell lines and 14 pairs cDNA of HCC plus adjacent tissues (Fig. 3 C). In addition, the expresstion of PAMR1 in HCC cell lines was detected by qRT-PCR. Obviously, The results revealed that mRNA level of PAMR1 were lower in the HCC cell lines(HepG2, Hep3B, SMMC7721, Huh7) than that in normal liver line QSG7701(Fig. 3 D).We evaluated the diagnostic value of PAMR1 in liver cancer by ROC curve, and the AUC was up to 0.918(95% CI 0.890–0.946). Furthermore, the sensitivity and specificity were 94.0% and 84.5%, indicating that PAMR1 has higher diagnostic efficiency for HCC detection (Fig. 3 E). Next, Kaplan-Meier analysis of a HCC cohort containing 364 cases was performed to evaluate the posibility of PAMR1 as a prognostic marker. As shown in Fig. 4 , the HCC patients with low PAMR1 level were with lower 5-year overall survival (P < 0.05; Fig. 4 A), progression-free survival (P < 0.05; Fig. 4 B), relapsefree survival (P < 0.05; Fig. 4 C), as well as diseasespecific survival (P < 0.05; Fig. 4 D), suggesting its potential as a favorable prognostic biomarker. GO enrichment analysis of PAMR1-related genes in HCC. We next used cBioPortal to identify PAMR1 -related genes in HCC from TCGA transcriptome data (The top 20 genes were listed in Table 1 ). The genes with Spearman correlation coefficients over 0.5 were considered with high correlation to PAMR1 in LIHC. Then, top 100 co-expressed genes were analysed by GO on the DAVID platform and 20 hits were identified (Table 2 ), which were mainly enriched in the regulation of extracellular matrix organization, plasma membrane, and calcium ion binding. Table 1 cBioPortal analysis of the 20 genes most closelyrelated to PAMR1. Correlated Gene Cytoband Spearman's Correlation p-Value q-Value SFRP1 8p11.21 0.682 2.23E-52 2.48E-48 PXDN 2p25.3 0.682 2.49E-52 2.48E-48 AFAP1L2 10q25.3 0.675 5.28E-51 3.51E-47 COL4A2 13q34 0.672 3.10E-50 1.54E-46 SPARC 5q33.1 0.661 3.54E-48 1.41E-44 COL4A1 13q34 0.659 9.83E-48 2.98E-44 NID2 14q22.1 0.658 1.05E-47 2.98E-44 ADGRA2 8p11.23 0.651 2.65E-46 6.61E-43 HEPH Xq12 0.645 2.92E-45 6.47E-42 ADAMTS12 5p13.3-p13.2 0.637 6.91E-44 1.38E-40 F2R 5q13.3 0.636 1.04E-43 1.82E-40 TGFB1I1 16p11.2 0.636 1.10E-43 1.82E-40 ZFPM2 8q23 0.635 1.44E-43 2.09E-40 TCF4 18q21.2 0.635 1.47E-43 2.09E-40 COL6A3 2q37.3 0.635 1.77E-43 2.35E-40 CALHM5 6q22.1 0.635 1.99E-43 2.48E-40 FBN1 15q21.1 0.634 2.14E-43 2.52E-40 GLI3 7p14.1 0.633 3.70E-43 4.10E-40 TSHZ3 19q12 0.630 1.11E-42 1.12E-39 CD248 11q13.2 0.630 1.12E-42 1.12E-39 Table 2 Gene ontology enrichment analysis of genes related to PAMR1 in HCC. Category Term Count FDR BP GO:0030198 ~ extracellular matrix organization 19 2.13E-13 BP GO:0030199 ~ collagen fibril organization 13 3.81E-11 BP GO:0007155 ~ cell adhesion 13 0.002072 BP GO:0007507 ~ heart development 10 1.89E-04 BP GO:0030324 ~ lung development 8 6.82E-05 BP GO:0001568 ~ blood vessel development 5 0.009887 CC GO:0005886 ~ plasma membrane 38 0.034479 CC GO:0005576 ~ extracellular region 30 3.20E-06 CC GO:0005615 ~ extracellular space 24 5.99E-04 CC GO:0031012 ~ extracellular matrix 16 1.91E-10 CC GO:0005788 ~ endoplasmic reticulum lumen 13 1.33E-06 CC GO:0005581 ~ collagen trimer 9 1.19E-06 CC GO:0005604 ~ basement membrane 7 1.84E-04 MF GO:0005509 ~ calcium ion binding 13 0.007163 MF GO:0005201 ~ extracellular matrix structural constituent 12 1.33E-08 MF GO:0030020 ~ extracellular matrix structural constituent conferring tensile strength 8 9.98E-08 MF GO:0005516 ~ calmodulin binding 8 0.002623 MF GO:0008201 ~ heparin binding 7 0.006459 MF GO:0005518 ~ collagen binding 6 9.32E-04 MF GO:0048407 ~ platelet-derived growth factor binding 4 9.32E-04 Correlation analysis between PAMR1 expression and infiltrating immune cells. It was reported that tumor-infiltrating leukcyte (TIL) was closely associated with patient survival and prognosis in types of cancers. Here, we used the TIMER database to determine whether PAMR1 expression was correlated with tumor purity and TIL in HCC. As shown in Fig. 5 , the PAMR1 expression level showed negative correlation with tumor purity (r =-0.322, P = 8.25e-10) while positive correlation with B cells (r = 0.193, P = 3.20e-04), CD8 + T cells (r = 0.312, P = 3.67e-09), CD4 + T cells (r = 0.345, P = 4.70e-11), macrophages (r = 0.388, P = 1.06e-13), neutrophils (r = 0.379, P = 3.11e-13), and dendritic cells (r = 0.371, P = 1.66e-12) . GSEA identified PAMR1-related signaling pathways. We next performed GSEA to compare the enriched signaling pathways between PAMR1 -high and PAMR1 -low cases in datasets from TCGA-LIHC. The PAMR1 suppressed signaling pathways were gated with the criteria of NES > 2 and nominal P < 0.05. The six most significant enriched pathways associated with low PAMR1 level were inflammatory response, hypoxia, epithelial-mesenchymal transition, KRAS signaling, TNF-a signaling via NF-κB, and myogenesis ( (Fig. 6 and Table 3 )). Table 3 The results of gene set enrichment analysis. MSigDB collection Gene set name NES NOM p-val FDR q-val h.all.v7.5.1.symbols.gmt HALLMARK_INFLAMMATORY_RESPONSE -2.41 0.001 0.001 HALLMARK_HYPOXIA -2.23 0.001 0.001 HALLMARK_EPITHELIAL_MESENCHYMAL _TRANSITION -2.13 0.001 0.017 HALLMARK_KRAS_SIGNALING_UP -2.13 0.001 0.014 HALLMARK_TNFA_SIGNALING_VIA_NFKB -2.06 0.001 0.008 HALLMARK_MYOGENESIS -2.03 0.001 0.007 Discussion The biomarkers for diagnosis and prognosis prediction in cancers were crucial for precision medicine 13 . It has been reported that targeted drugs showed a 30% higher response rate than chemotherapy in clinical trials 14 . Moreover, several novel targeted drugs have superior secondary efficacy for liver-cancer treatment, broadening prospects for HCC patients 15 , which make the identification of prognostic biomarkers crucial for precision medicine. The gene PAMR1 was reported to show lower protein level in breast and cervical cancer tissues compared with that in normal tissues 11 , 12 . Herein, we screened DEGs between the HCC and adjacent normal tissues in GEO datasets, and identified the lower PAMR1 level in HCC, followed by validation of experimental IHC and qRT-PCR on the mRNA and protein level, respectively.Meanwhile, the ROC curve showed that AUC,sensitivity and specificity were 0.918, 94.0% and 84.5%, indicating that PAMR1 has good diagnostic efficacy. Additionally, Kaplan-Meier analysis indicated that patients with higher PAMR1 level showed higher survival rate than patients with lower PAMR1 level. Taken together, our results provide convincing evidence that PAMR1 is an appropriate biomarker for clinical prognosis in patients with HCC. We further investigated the underlying mechanism of PAMR1 on HCC pathogenesis. CBioportal was used to screen for PAMR1 associated genes, such as SFRP1, PXDN and AFAP1L2. The results of GO analysis then indicated that PAMR1-related genes were mainly enriched in the regulation of extracellular matrix organization, cell adhesion, and blood vessel development. These functions are closely associated with cancer; loss of cellular adhesion is a crucial step in tumor metastasis, whereas blood vessel development contributes to both tumor growth and spread 16 , 17 . Compellingly, our findings reveal that PAMR1 expression is positively correlated with infiltration of immune cells, including B cells, CD8 + T cells, CD4 + T cells, macrophages, dendritic cells, and neutrophils in HCC. In line with its enrichment in non-tumor cells, PAMR1 expression was also negatively correlated with tumor purity. It was well acknowledged that B cells and macrophages are important antigen-presenting cells in various cancers, while CD8 + T cells are immune effectors 18 , 19 . Therefore, PAMR1 may be important for immune responses in HCC. Supporting the role of PAMR1 in cancer immune response, GSEA of high- and low-PAMR1-expressing HCC patients identified six signaling pathways: inflammatory response, hypoxia, epithelial-mesenchymal transition, KRAS signaling, TNF-a signaling via NF-κB, and myogenesis. Epithelial-mesenchymal transition could not only promote multiple tumor metastasis 20 , 21 , but also played an important role in tumor drug resistance 22 , immunosuppression 23 , and stem cells during cancer progression 24 . KRAS is the most common oncogene in human cancer 25 , 26 . Both of TNF-α and NF-κB signaling pathways are involved in cancer proliferation, migration, and apoptosis 27 , 28 . The involvement of these pathways may clarify the pathophysiological mechanisms underlying HCC pathogenesis. Although the meaningful discovery has been uncovered, this study has several limitations. First, experiments from public databases were conducted in different laboratories, making it unavaible to control the variation in interventions or data availability. Second, we did not fully verify conclusions of the pathogenic mechanism based on database analysis with empirical experiments. Our future goals include performing cell-level and animal-level validations in the laboratory to further investigate the underlying mechanism of PAMR1 on HCC pathogenesis. Materials And Methods Data collection. Genes were screened base on GSE45267( https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE45267 ), GSE101685( https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE101685 ), GSE121248( https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE121248 ), and GSE14520( https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE14520 ) from GEO ( http://www.ncbi.nlm.nih.gov/geo ) database. Probes were converted into gene symbols based on annotations of the corresponding platform. TCGA ( https://portal.gdc.cancer.gov/ ) containing 50 normal and 371 HCC tissues was also analysed. PAMR1 expression in liver cancer was evaluated with UALCAN ( http://ualcan.path.uab.edu/ ) and GEPIA ( http://gepia.cancer-pku.cn/ ) databases 29 , 30 . Correlations between PAMR1-related genes from TCGA were analyzed using cBioPortal ( http://www.cbioportal ) 31 . Gene Ontology (GO) enrichment analysis of these genes was performed with DAVID ( https://david.ncifcrf.gov/home.jsp ) 32 . Identification of differentially expressed genes. GEO2R ( https://www.ncbi.nlm.nih.gov/geo/geo2r/ ) is a web tool for a quick identification of differentially expressed genes (DEGs) in GEO datasets. Here, it was used to identify DEGs between HCC and healthy liver samples. A log2fold change (logFC) > 1 and adjusted P < 0.05 were cut-offs for DEGs. The top 100 DEGs from each dataset (based on adjusted p-value) were selected, and overlapped by the Venn online tool ( http://bioinformatics.psb.ugent.be/webtools/Venn/ ) for further analysis. Cell Culture. The human normal liver cell line QSG7701 was purchased from Beyotime Biotechnology. The human HCC cell lines Hep3B, Huh7 were purchased from Procell Life Science&Technology company. Other HCC cell lines SMMC7721、HepG2 were donated by Ming Zhao's laboratory. Cell lines were cultured in DMEM with 10% fetal bovine serum at 37°C in a 5% CO 2 air atmosphere. All media and supplements were purchased from Invitrogen. Total Rna Extraction And Quantitative Real-time Pcr Total RNA was extracted from five cell lines (SMMC7721, HepG2, Hep3B, Huh7, QSG7701) using a total RNA extraction kit (Solarbio, Beijing, China), according to the manufacturer’s instructions. RNA was reverse-transcribed into cDNA using the PrimeScript™ RT Reagent kit. Quantitative reverse transcription PCR (qRT-PCR) was performed using SYBR Green (Bio-Rad, Hercules, CA, USA).The cDNA chip was purchased from Outdo Biotech Co., Ltd (Shanghai, China), and amplificatied in the same method. Primers for PAMR1 and GAPDH were as follows: PAMR1 forward primer: 5′-CTTCCGATGCAGGTTCAGT-3′, PAMR1 reverse primer: 5′-GCTGGCTTCTTGGTAGGG-3′; GAPDH forward primer: 5′-ACAGTCAGCCGCATCTTC-3′, GAPDH reverse primer: 5′-CTCCGACCTTCACCTTCC-3′. Immunohistochemistry(IHC) . Liver cancer tissue arrays (HLivH060CS01) were purchased from Outdo Biotech Co., Ltd(Shanghai, China). After dewaxina, antigens were retrieved under high pressure followed by rinse and endogenous peroxidases block with 3% hydrogen peroxide for 10 min. Then, tissues were incubated in normal goat serum for 15 min and anti-PAMR1 rabbit polyclonal antibody (1:50, Wuhan, China) was incubated overnight at 4°C. The next day, tissue arrays were washed and incubated with secondary antibody at 37°C for 30 min. Then, DAB was incubated for 5 min, and the tissues were coverslipped for observation under a microscope. Staining intensity was scored on the following scale: 0 (none), 1 (weak), 2 (moderate), and 3 (strong). The extent (0–100%) of reactivity was scored with five levels: 0 ( 75%). Two pathologists independently scored the staining of each slide. Tumor immune estimation resource analysis. The Tumor Immune Estimation Resource (TIMER) ( http://cistrome.org/TIMER/ ) 33 was used to evaluate the correlation between PAMR1 expression level and the abundance of infiltrating immune cells (CD8 + T cells, CD4 + T cells, B cells, dendritic cells, macrophages, and neutrophils), as well as tumor purity in HCC. Gene set enrichment analysis (GSEA). GSEA version 4.2.1 ( http://www.gsea-msigdb.org/gsea/downloads.jsp ) is a computational method based on the entire gene expression matrix 34 . The cases in TCGA-LIHC cohort was separated into PAMR1-low and PAMR1-high subgroups with median PAMR1 level as the cut-off point. Reference gene sets were H.all.v7. 2.symbols.gmt gene sets (Hallmarks) from MSigDB 35 . Each analysis required at least 100 times permutation tests. An adjusted P 2 were considered as significant enrichment. Statistical analysis. Survival curves were calculated with the Kaplan-Meier method with log-rank tests. Data from all experiments were analyzed with unpaired t-tests or one-way ANOVA in GraphPad Prism 8 (GraphPad, San Diego, CA, USA). The receiver operating characteristic (ROC) curve analysis was performed by pROC packages in R sofware, version 3.6.3. P < 0.05 was defined as significance. Conclusions In conclusion, this is the first study to demonstrate that PAMR1 level is lower in HCC compared with that in normal liver tissues, and is closely associated with the shape of hepatic immune microenvironment, and has high diagnostic efficiency, and predicts poor prognostic outcome, which makes it as a favorable diagnostic and prognostic factor for HCC. Declarations Data Availability The datasets generated and analyzed during the current study are available from public databases, TCGA (https://portal.gdc.cancer.gov/), GEO (https://www.ncbi.nlm.nih.gov/geo/), UALCAN (http://ualcan.path.uab.edu/), GEPIA (http://gepia.cancer-pku.cn/), cBioPortal(http://www.cbioportal), and DAVID (https://david.ncifcrf.gov/home.jsp)databases. Acknowledgments This work was supported by the Foundation of Health Commission of Chengdu (No.2022235), the Foundation of the First Affiliated Hospital of Chengdu Medical College (No.CYFY2020YB05). Authors Contributions Y.X. and X.P.Z. conducted the study design. X.P.Z. wrote original draft. Y.X. wrote, reviewed, and edited. X.P.Z. and T.L carried out experiments and data analysis. S.H.D.and T.Z. provided technical support and material. D.M.W.supervised. All the authors read and approved the final manuscript. Competing interests The authors declare no competing interests. References Sung, H. et al. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-2114251","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":151058159,"identity":"9cd2a03b-d60b-4c0a-b245-449560a583c9","order_by":0,"name":"Xiaoping Zhou","email":"","orcid":"","institution":"Clinical Medical College and the First Affiliated Hospital of Chengdu Medical College","correspondingAuthor":false,"prefix":"","firstName":"Xiaoping","middleName":"","lastName":"Zhou","suffix":""},{"id":151058160,"identity":"477d9401-4e99-4cf2-b034-8d17673d8211","order_by":1,"name":"Teng Liu","email":"","orcid":"","institution":"Clinical Medical College and the First Affiliated Hospital of Chengdu Medical College","correspondingAuthor":false,"prefix":"","firstName":"Teng","middleName":"","lastName":"Liu","suffix":""},{"id":151058161,"identity":"c6507a24-f010-44fa-b412-3d64a2af9796","order_by":2,"name":"Shihua Deng","email":"","orcid":"","institution":"Clinical Medical College and the First Affiliated Hospital of Chengdu Medical College","correspondingAuthor":false,"prefix":"","firstName":"Shihua","middleName":"","lastName":"Deng","suffix":""},{"id":151058162,"identity":"1058ac08-5837-42c9-8bdf-ea3e902f2e24","order_by":3,"name":"Ting Zhang","email":"","orcid":"","institution":"Clinical Medical College and the First Affiliated Hospital of Chengdu Medical College","correspondingAuthor":false,"prefix":"","firstName":"Ting","middleName":"","lastName":"Zhang","suffix":""},{"id":151058164,"identity":"5e9189f8-efca-46ca-bb13-55872f83b473","order_by":4,"name":"Dongming Wu","email":"","orcid":"","institution":"Clinical Medical College and the First Affiliated Hospital of Chengdu Medical College","correspondingAuthor":false,"prefix":"","firstName":"Dongming","middleName":"","lastName":"Wu","suffix":""},{"id":151058167,"identity":"fc580eda-53b5-437d-b6a1-f5b9a267c157","order_by":5,"name":"Ying Xu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAz0lEQVRIiWNgGAWjYDACZjBpI2ff3tj44AMJWtKMDXgONxvOIMGuw4kbJNLbpDmIUWtwnPfwa962NMbtkg8bpBkY7OR0GwhpOcyXZjmzzYbZcnZig3EBQ7Kx2QGCWnjMDD62pbEx3E5sSJ7BcCBxG1FaEtsO8zDcPNgAJInTYvzgY9thCYMbjI3NRGmRBNrCOONcmoFkT2Iz4wwDIvzCd/6M8WeeMpv6fvbjz398qLCTI6hF4QADmwSSOwkoBwH5BgZm4pLJKBgFo2AUjFwAACy1Rbxi79a4AAAAAElFTkSuQmCC","orcid":"","institution":"Clinical Medical College and the First Affiliated Hospital of Chengdu Medical College","correspondingAuthor":true,"prefix":"","firstName":"Ying","middleName":"","lastName":"Xu","suffix":""}],"badges":[],"createdAt":"2022-09-28 22:59:01","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2114251/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2114251/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":29044555,"identity":"a0a7bb22-0f5b-496a-a0d8-ddf9da3c6d5e","added_by":"auto","created_at":"2022-11-14 18:28:48","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":677550,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification of the differentially expressed genes (DEGs) between HCC and noncancerous liver tissues via a Venn diagram and Volcano plot. (A)DEGs were identified from GSE45267, GSE101685, GSE121248 and GSE14520 gene expression profiling datasets based on the top 100 adjusted p value\u0026lt;0.05 and fold change\u0026gt;1.The four datasets share 15 overlapping DEGs.(B)Volcano plot of the distribution of DEGs in GSE45267. Blue dots represent downregulated DEGs, whereas red dots are upregulated DEGs in HCC. PAMR1 gene was indicated.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-2114251/v1/57841b9d2a37762857cf6339.png"},{"id":29045416,"identity":"3db0740f-389c-4b1b-9fe5-3630fc3e63e2","added_by":"auto","created_at":"2022-11-14 18:36:48","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":242644,"visible":true,"origin":"","legend":"\u003cp\u003eThe expression of PAMR1 in liver cancer. PAMR1 mRNA expression levels in unpaired non-tumor liver and hepatocellular carcinoma tumor tissue samples were examined in the (A) GSE45267, (B) GSE101685, (C) GSE121248 and (D) GSE14520 datasets from the GEO database. (E)The expression of PAMR1 in TCGA-LIHC from the UALCAN database. (F)The promoter methylation level of PAMR1 in the UALCAN database.(G)The expression of PAMR1 in TCGA-LIHC based on individual cancer stages in the UALCAN database.*\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003ep\u003c/em\u003e \u0026lt;0.01, \u003cem\u003e***p\u003c/em\u003e\u0026lt;0.001.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-2114251/v1/6002b865ab17cde0a10016ac.png"},{"id":29044552,"identity":"55f0cb02-2755-48aa-b2f2-e27d087935e7","added_by":"auto","created_at":"2022-11-14 18:28:48","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1719854,"visible":true,"origin":"","legend":"\u003cp\u003eThe protein and mRNA level of PAMR1 in liver cancer. (A)The protein level of PAMR1 in HCC tissues detected by immunohistochemical (IHC) staining.(B)IHC scores between liver normal tissues, adjacent liver tissues, and liver cancer tissues.(C) The mRNA level of PAMR1 between adjacent liver tissues and liver cancer.(D)QRT-PCR was used to assess the mRNA levels of PAMR1 in the HepG2,Hep3B,SMMC7721,Huh7 cell lines and in the human QSG7701 normal hepatocyte line.(E)Receiver operating characteristic (ROC) curve for PAMR1 expression in normal liver tissue and liver cancer from TCGA-LIHC. *\u003cem\u003ep\u003c/em\u003e\u0026lt; 0.05, **\u003cem\u003ep\u003c/em\u003e \u0026lt;0.01, \u003cem\u003e***p\u003c/em\u003e\u0026lt;0.001.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-2114251/v1/58d3ae7b89aca5de88ce8025.png"},{"id":29044553,"identity":"8a2ea498-353c-45e2-a5d5-1fb4642171be","added_by":"auto","created_at":"2022-11-14 18:28:48","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":277016,"visible":true,"origin":"","legend":"\u003cp\u003ePAMR1 overexpression is associated with favorable prognosis. The association of PAMR1 expression with 5-year (A) OS, (B) PFS, (C) RFS and (D) DSS was determined by analyzing patients with hepatocellular carcinoma included in the KMplot™ database. OS, overall survival; PFS, progression‑free survival; RFS,relapse‑free survival; DSS, disease‑specific survival; HR, hazard ratio.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-2114251/v1/99957a04eb0cce5bbecafa62.png"},{"id":29044557,"identity":"60d23dcd-3bc2-49e0-8f6b-3fec11aaca51","added_by":"auto","created_at":"2022-11-14 18:28:49","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":703788,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation of PAMR1 expression with infiltrating immune infiltration in HCC.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-2114251/v1/6228577d3a60d5c469a8d649.png"},{"id":29044554,"identity":"efd7979a-512f-44db-807f-af6e80c6d6e7","added_by":"auto","created_at":"2022-11-14 18:28:48","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1720837,"visible":true,"origin":"","legend":"\u003cp\u003eEnrichment plots from gene set enrichment analysis (GSEA) in the low PAMR1 expression phenotype.(A) inflammatory response pathway; (B) hypoxiay pathway; (C) epithelial mesenchymal transition pathway; (D) KRASsignaling up pathway; (E) TNF-a signaling via NF-κB pathway; (F) myogenesis pathway.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-2114251/v1/ca3caa899be7bcf79d1d6814.png"},{"id":31292251,"identity":"6f8a0e54-5cc8-4652-b990-9215d3afd799","added_by":"auto","created_at":"2023-01-09 08:59:43","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2230618,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2114251/v1/abc89098-6cd1-4d4c-bec4-6613d91d66e5.pdf"},{"id":29044556,"identity":"de34996c-3d6d-4205-b240-06128d0a7e8b","added_by":"auto","created_at":"2022-11-14 18:28:48","extension":"pdf","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":377376,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigureS1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2114251/v1/55be143aa10aa1230c0bd66a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"PAMR1 is a favorable diagnostic and prognostic biomarker in hepatocellular carcinoma","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHepatocellular carcinoma (HCC), the most common type of malignancy, is the third leading cause of cancer-related death worldwide in 2020 \u003csup\u003e1\u003c/sup\u003e. Amount of risk factors are considered to contribute to HCC, such as chronic viral hepatitis, alcohol abuse, excess body weight, toxic exposure and HCC-driver genes mutation\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Because of aflatoxin exposure and chronic HBV infection, over half of HCC patients are prevalent in Asia\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e, and China is estimated to be with the most highest HCC incidence by 2030\u003csup\u003e5\u003c/sup\u003e.Surgery is the best approach in the treatment of HCC patients, but unfortunately, most cases are unresectable, as they are already at late stages at diagnosis. Other strategies, such as percutaneous ablation, radiotherapy, and liver transplantation, also have limited efficiency with a high postoperative recurrence rate and distant metastasis\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e.Therefore, identification of biomarkers of diagnosis and prognosis is crucial for treatment efficacy improvment and clinical outcome prediction.\u003c/p\u003e \u003cp\u003ePeptidase domain-containing protein associated with muscle regeneration 1 (PAMR1) was first reported to be downregulated in muscle cell lines from five patients with Duchenne muscular dystrophy\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. It was later found to be also expressed in normal cervix and endometrium tissue, and was correlated with psoriasis and type 2 diabetes\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. The injury mouse model showed that PAMR1 was upregulated in the regenerating area of injured skeletal muscle, while it was detected with downregulation in breast and cervical cancer cells. PAMR1 inactivation was induced by hypermethylation at promoter in breast cancer cells\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. The protein also inhibits the proliferation, migration, and invasion of cervical cancer cells\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Therefore, PAMR1 appears to be characterized as a tumor suppressor, but its role in HCC is still unclear.\u003c/p\u003e \u003cp\u003eIn this study, we evaluated PAMR1 expression level in HCC base on the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases, followed by the experimental confirmation with qRT-PCR and immunohistochemistry. Additionally, its potential role as a prognostic factor for HCC was also investigated. Finally, Gene Set Enrichment Analysis (GSEA) was performed to clarify the underlying mechanism of PAMR1 on HCC pathogenesis.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eIdentification of DEGs in HCC.\u003c/strong\u003e Base on the analysis of datasets from GEO (GSE45267, GSE101685, GSE121248, and GSE14520), we selected top 100 DEGs in each of the microarray dataset, and identified 15 overlapping genes that composed of 3 upregulated and 12 downregulated genes (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eA). Notably, \u003cem\u003ePAMR1\u003c/em\u003e was one of most obvious DEGs in HCC patients that was worth further investigation (adjusted P\u0026thinsp;=\u0026thinsp;2.84e-25, logFC\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;1.51, Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eB).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePAMR1 was downregulated in HCC and its expression predicted a favorable prognosis.\u003c/strong\u003e It was showed the \u003cem\u003ePAMR1\u003c/em\u003e mRNA levels was obviously lower in HCC tissues than that in healthy liver tissue in the above four GEO datasets (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eA-D), which was further confirmed by the cohort from TCGA(Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eE, S1A). Although \u003cem\u003ePAMR1\u003c/em\u003e mRNA level was lower in HCC, its promoter methylation level was upregulated in liver cancer tissue (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eF). Additionally, the \u003cem\u003ePAMR1\u003c/em\u003e expression level was negatively correlated to HCC progression (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eG). and tumor grade of cervical cancer (Fig. S1B). In addition, \u003cem\u003ePAMR1\u003c/em\u003e expression is suppressed in multiple cancer types, including prostate adenocarcinoma, uterine corpus endometrial carcinoma, and skin cutaneous melanoma (Fig. S1C).\u003c/p\u003e\n\u003cp\u003eAfter bioinformatical identification of lower PAMR1 level in HCC, we used IHC to validate the result in clinical samples comprising 2 normal liver tissues and 29 HCC plus adjacent tissue pairs. The results showed obvious lower PAMR1 level in HCC compared with that in both normal and adjacent tissues (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eA), which were statistically quantified as the PAMR1 score (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eB). Moreover, the same result was obtained by qPCR from cDNA chip which contained 2 cDNA of HCC cell lines and 14 pairs cDNA of HCC plus adjacent tissues (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eC). In addition, the expresstion of PAMR1 in HCC cell lines was detected by qRT-PCR. Obviously, The results revealed that mRNA level of PAMR1 were lower in the HCC cell lines(HepG2, Hep3B, SMMC7721, Huh7) than that in normal liver line QSG7701(Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eD).We evaluated the diagnostic value of PAMR1 in liver cancer by ROC curve, and the AUC was up to 0.918(95% CI 0.890\u0026ndash;0.946). Furthermore, the sensitivity and specificity were 94.0% and 84.5%, indicating that PAMR1 has higher diagnostic efficiency for HCC detection (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eE).\u003c/p\u003e\n\u003cp\u003eNext, Kaplan-Meier analysis of a HCC cohort containing 364 cases was performed to evaluate the posibility of \u003cem\u003ePAMR1\u003c/em\u003e as a prognostic marker. As shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e, the HCC patients with low PAMR1 level were with lower 5-year overall survival (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eA), progression-free survival (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eB), relapsefree survival (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eC), as well as diseasespecific survival (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eD), suggesting its potential as a favorable prognostic biomarker.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGO enrichment analysis of PAMR1-related genes in HCC.\u003c/strong\u003e We next used cBioPortal to identify \u003cem\u003ePAMR1\u003c/em\u003e-related genes in HCC from TCGA transcriptome data (The top 20 genes were listed in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). The genes with Spearman correlation coefficients over 0.5 were considered with high correlation to \u003cem\u003ePAMR1\u003c/em\u003e in LIHC. Then, top 100 co-expressed genes were analysed by GO on the DAVID platform and 20 hits were identified (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e), which were mainly enriched in the regulation of extracellular matrix organization, plasma membrane, and calcium ion binding.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003ecBioPortal analysis of the 20 genes most closelyrelated to PAMR1.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eCorrelated Gene\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eCytoband\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSpearman's Correlation\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ep-Value\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eq-Value\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSFRP1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8p11.21\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.682\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.23E-52\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.48E-48\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePXDN\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2p25.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.682\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.49E-52\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.48E-48\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAFAP1L2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10q25.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.675\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.28E-51\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.51E-47\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCOL4A2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13q34\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.672\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.10E-50\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.54E-46\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSPARC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5q33.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.661\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.54E-48\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.41E-44\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCOL4A1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13q34\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.659\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9.83E-48\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.98E-44\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNID2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14q22.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.658\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.05E-47\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.98E-44\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eADGRA2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8p11.23\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.651\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.65E-46\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.61E-43\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHEPH\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eXq12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.645\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.92E-45\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.47E-42\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eADAMTS12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5p13.3-p13.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.637\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.91E-44\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.38E-40\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eF2R\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5q13.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.636\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.04E-43\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.82E-40\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTGFB1I1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16p11.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.636\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.10E-43\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.82E-40\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eZFPM2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8q23\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.635\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.44E-43\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.09E-40\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTCF4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18q21.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.635\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.47E-43\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.09E-40\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCOL6A3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2q37.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.635\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.77E-43\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.35E-40\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCALHM5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6q22.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.635\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.99E-43\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.48E-40\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFBN1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15q21.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.634\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.14E-43\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.52E-40\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGLI3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7p14.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.633\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.70E-43\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.10E-40\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTSHZ3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e19q12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.630\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.11E-42\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.12E-39\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCD248\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11q13.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.630\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.12E-42\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.12E-39\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eGene ontology enrichment analysis of genes related to PAMR1 in HCC.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eCategory\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eTerm\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eCount\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eFDR\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGO:0030198\u0026thinsp;~\u0026thinsp;extracellular matrix organization\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e19\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.13E-13\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGO:0030199\u0026thinsp;~\u0026thinsp;collagen fibril organization\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.81E-11\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGO:0007155\u0026thinsp;~\u0026thinsp;cell adhesion\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.002072\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGO:0007507\u0026thinsp;~\u0026thinsp;heart development\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.89E-04\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGO:0030324\u0026thinsp;~\u0026thinsp;lung development\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.82E-05\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGO:0001568\u0026thinsp;~\u0026thinsp;blood vessel development\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.009887\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGO:0005886\u0026thinsp;~\u0026thinsp;plasma membrane\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e38\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.034479\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGO:0005576\u0026thinsp;~\u0026thinsp;extracellular region\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e30\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.20E-06\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGO:0005615\u0026thinsp;~\u0026thinsp;extracellular space\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.99E-04\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGO:0031012\u0026thinsp;~\u0026thinsp;extracellular matrix\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.91E-10\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGO:0005788\u0026thinsp;~\u0026thinsp;endoplasmic reticulum lumen\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.33E-06\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGO:0005581\u0026thinsp;~\u0026thinsp;collagen trimer\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.19E-06\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGO:0005604\u0026thinsp;~\u0026thinsp;basement membrane\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.84E-04\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMF\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGO:0005509\u0026thinsp;~\u0026thinsp;calcium ion binding\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.007163\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMF\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGO:0005201\u0026thinsp;~\u0026thinsp;extracellular matrix structural constituent\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.33E-08\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMF\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGO:0030020\u0026thinsp;~\u0026thinsp;extracellular matrix structural constituent conferring tensile strength\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9.98E-08\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMF\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGO:0005516\u0026thinsp;~\u0026thinsp;calmodulin binding\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.002623\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMF\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGO:0008201\u0026thinsp;~\u0026thinsp;heparin binding\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.006459\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMF\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGO:0005518\u0026thinsp;~\u0026thinsp;collagen binding\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9.32E-04\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMF\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGO:0048407\u0026thinsp;~\u0026thinsp;platelet-derived growth factor binding\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9.32E-04\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eCorrelation analysis between PAMR1 expression and infiltrating immune cells.\u003c/strong\u003e It was reported that tumor-infiltrating leukcyte (TIL) was closely associated with patient survival and prognosis in types of cancers. Here, we used the TIMER database to determine whether \u003cem\u003ePAMR1\u003c/em\u003e expression was correlated with tumor purity and TIL in HCC. As shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e, the \u003cem\u003ePAMR1\u003c/em\u003e expression level showed negative correlation with tumor purity (r =-0.322, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;8.25e-10) while positive correlation with B cells (r\u0026thinsp;=\u0026thinsp;0.193, P\u0026thinsp;=\u0026thinsp;3.20e-04), CD8\u0026thinsp;+\u0026thinsp;T cells (r\u0026thinsp;=\u0026thinsp;0.312, P\u0026thinsp;=\u0026thinsp;3.67e-09), CD4\u0026thinsp;+\u0026thinsp;T cells (r\u0026thinsp;=\u0026thinsp;0.345, P\u0026thinsp;=\u0026thinsp;4.70e-11), macrophages (r\u0026thinsp;=\u0026thinsp;0.388, P\u0026thinsp;=\u0026thinsp;1.06e-13), neutrophils (r\u0026thinsp;=\u0026thinsp;0.379, P\u0026thinsp;=\u0026thinsp;3.11e-13), and dendritic cells (r\u0026thinsp;=\u0026thinsp;0.371, P\u0026thinsp;=\u0026thinsp;1.66e-12) .\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGSEA identified PAMR1-related signaling pathways.\u003c/strong\u003e We next performed GSEA to compare the enriched signaling pathways between \u003cem\u003ePAMR1\u003c/em\u003e-high and \u003cem\u003ePAMR1\u003c/em\u003e-low cases in datasets from TCGA-LIHC. The PAMR1 suppressed signaling pathways were gated with the criteria of NES\u0026thinsp;\u0026gt;\u0026thinsp;2 and nominal P\u0026thinsp;\u0026lt;\u0026thinsp;0.05. The six most significant enriched pathways associated with low \u003cem\u003ePAMR1\u003c/em\u003e level were inflammatory response, hypoxia, epithelial-mesenchymal transition, KRAS signaling, TNF-a signaling via NF-\u0026kappa;B, and myogenesis ( (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e and Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e)).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab3\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eThe results of gene set enrichment analysis.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eMSigDB collection\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eGene set name\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eNES\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eNOM p-val\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eFDR q-val\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eh.all.v7.5.1.symbols.gmt\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHALLMARK_INFLAMMATORY_RESPONSE\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-2.41\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHALLMARK_HYPOXIA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-2.23\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHALLMARK_EPITHELIAL_MESENCHYMAL\u003c/p\u003e\n\u003cp\u003e_TRANSITION\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-2.13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.017\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHALLMARK_KRAS_SIGNALING_UP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-2.13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.014\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHALLMARK_TNFA_SIGNALING_VIA_NFKB\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-2.06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.008\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHALLMARK_MYOGENESIS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-2.03\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.007\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe biomarkers for diagnosis and prognosis prediction in cancers were crucial for precision medicine\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. It has been reported that targeted drugs showed a 30% higher response rate than chemotherapy in clinical trials\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Moreover, several novel targeted drugs have superior secondary efficacy for liver-cancer treatment, broadening prospects for HCC patients\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e, which make the identification of prognostic biomarkers crucial for precision medicine.\u003c/p\u003e \u003cp\u003eThe gene PAMR1 was reported to show lower protein level in breast and cervical cancer tissues compared with that in normal tissues \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Herein, we screened DEGs between the HCC and adjacent normal tissues in GEO datasets, and identified the lower PAMR1 level in HCC, followed by validation of experimental IHC and qRT-PCR on the mRNA and protein level, respectively.Meanwhile, the ROC curve showed that AUC,sensitivity and specificity were 0.918, 94.0% and 84.5%, indicating that PAMR1 has good diagnostic efficacy. Additionally, Kaplan-Meier analysis indicated that patients with higher PAMR1 level showed higher survival rate than patients with lower PAMR1 level. Taken together, our results provide convincing evidence that PAMR1 is an appropriate biomarker for clinical prognosis in patients with HCC.\u003c/p\u003e \u003cp\u003eWe further investigated the underlying mechanism of PAMR1 on HCC pathogenesis. CBioportal was used to screen for PAMR1 associated genes, such as SFRP1, PXDN and AFAP1L2. The results of GO analysis then indicated that PAMR1-related genes were mainly enriched in the regulation of extracellular matrix organization, cell adhesion, and blood vessel development. These functions are closely associated with cancer; loss of cellular adhesion is a crucial step in tumor metastasis, whereas blood vessel development contributes to both tumor growth and spread\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eCompellingly, our findings reveal that PAMR1 expression is positively correlated with infiltration of immune cells, including B cells, CD8\u0026thinsp;+\u0026thinsp;T cells, CD4\u0026thinsp;+\u0026thinsp;T cells, macrophages, dendritic cells, and neutrophils in HCC. In line with its enrichment in non-tumor cells, PAMR1 expression was also negatively correlated with tumor purity. It was well acknowledged that B cells and macrophages are important antigen-presenting cells in various cancers, while CD8\u0026thinsp;+\u0026thinsp;T cells are immune effectors\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Therefore, PAMR1 may be important for immune responses in HCC.\u003c/p\u003e \u003cp\u003eSupporting the role of PAMR1 in cancer immune response, GSEA of high- and low-PAMR1-expressing HCC patients identified six signaling pathways: inflammatory response, hypoxia, epithelial-mesenchymal transition, KRAS signaling, TNF-a signaling via NF-κB, and myogenesis. Epithelial-mesenchymal transition could not only promote multiple tumor metastasis\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e, but also played an important role in tumor drug resistance\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e, immunosuppression\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e, and stem cells during cancer progression\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. KRAS is the most common oncogene in human cancer\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Both of TNF-α and NF-κB signaling pathways are involved in cancer proliferation, migration, and apoptosis\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. The involvement of these pathways may clarify the pathophysiological mechanisms underlying HCC pathogenesis.\u003c/p\u003e \u003cp\u003eAlthough the meaningful discovery has been uncovered, this study has several limitations. First, experiments from public databases were conducted in different laboratories, making it unavaible to control the variation in interventions or data availability. Second, we did not fully verify conclusions of the pathogenic mechanism based on database analysis with empirical experiments. Our future goals include performing cell-level and animal-level validations in the laboratory to further investigate the underlying mechanism of PAMR1 on HCC pathogenesis.\u003c/p\u003e"},{"header":"Materials And Methods","content":"\u003cp\u003e \u003cb\u003eData collection.\u003c/b\u003e Genes were screened base on GSE45267(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE45267\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE45267\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), GSE101685(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE101685\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE101685\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), GSE121248(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE121248\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE121248\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), and GSE14520(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE14520\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE14520\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) from GEO (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.ncbi.nlm.nih.gov/geo\u003c/span\u003e\u003cspan address=\"http://www.ncbi.nlm.nih.gov/geo\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) database. Probes were converted into gene symbols based on annotations of the corresponding platform. TCGA (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://portal.gdc.cancer.gov/\u003c/span\u003e\u003cspan address=\"https://portal.gdc.cancer.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) containing 50 normal and 371 HCC tissues was also analysed. PAMR1 expression in liver cancer was evaluated with UALCAN (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://ualcan.path.uab.edu/\u003c/span\u003e\u003cspan address=\"http://ualcan.path.uab.edu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and GEPIA (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://gepia.cancer-pku.cn/\u003c/span\u003e\u003cspan address=\"http://gepia.cancer-pku.cn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) databases\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e,\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. Correlations between PAMR1-related genes from TCGA were analyzed using cBioPortal (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.cbioportal\u003c/span\u003e\u003cspan address=\"http://www.cbioportal\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)\u003csup\u003e31\u003c/sup\u003e. Gene Ontology (GO) enrichment analysis of these genes was performed with DAVID (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://david.ncifcrf.gov/home.jsp\u003c/span\u003e\u003cspan address=\"https://david.ncifcrf.gov/home.jsp\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)\u003csup\u003e32\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003cb\u003eIdentification of differentially expressed genes.\u003c/b\u003e GEO2R (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/geo2r/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/geo/geo2r/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) is a web tool for a quick identification of differentially expressed genes (DEGs) in GEO datasets. Here, it was used to identify DEGs between HCC and healthy liver samples. A log2fold change (logFC)\u0026thinsp;\u0026gt;\u0026thinsp;1 and adjusted P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were cut-offs for DEGs. The top 100 DEGs from each dataset (based on adjusted p-value) were selected, and overlapped by the Venn online tool (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://bioinformatics.psb.ugent.be/webtools/Venn/\u003c/span\u003e\u003cspan address=\"http://bioinformatics.psb.ugent.be/webtools/Venn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) for further analysis.\u003c/p\u003e \u003cp\u003e \u003cb\u003eCell Culture.\u003c/b\u003e The human normal liver cell line QSG7701 was purchased from Beyotime Biotechnology. The human HCC cell lines Hep3B, Huh7 were purchased from Procell Life Science\u0026amp;Technology company. Other HCC cell lines SMMC7721、HepG2 were donated by Ming Zhao's laboratory. Cell lines were cultured in DMEM with 10% fetal bovine serum at 37\u0026deg;C in a 5% CO\u003csub\u003e2\u003c/sub\u003e air atmosphere. All media and supplements were purchased from Invitrogen.\u003c/p\u003e\n\u003ch3\u003eTotal Rna Extraction And Quantitative Real-time Pcr\u003c/h3\u003e\n\u003cp\u003eTotal RNA was extracted from five cell lines (SMMC7721, HepG2, Hep3B, Huh7, QSG7701) using a total RNA extraction kit (Solarbio, Beijing, China), according to the manufacturer\u0026rsquo;s instructions. RNA was reverse-transcribed into cDNA using the PrimeScript\u0026trade; RT Reagent kit. Quantitative reverse transcription PCR (qRT-PCR) was performed using SYBR Green (Bio-Rad, Hercules, CA, USA).The cDNA chip was purchased from Outdo Biotech Co., Ltd (Shanghai, China), and amplificatied in the same method. Primers for \u003cem\u003ePAMR1\u003c/em\u003e and \u003cem\u003eGAPDH\u003c/em\u003e were as follows:\u003c/p\u003e \u003cp\u003e \u003cem\u003ePAMR1\u003c/em\u003e forward primer: 5\u0026prime;-CTTCCGATGCAGGTTCAGT-3\u0026prime;,\u003c/p\u003e \u003cp\u003e \u003cem\u003ePAMR1\u003c/em\u003e reverse primer: 5\u0026prime;-GCTGGCTTCTTGGTAGGG-3\u0026prime;;\u003c/p\u003e \u003cp\u003e \u003cem\u003eGAPDH\u003c/em\u003e forward primer: 5\u0026prime;-ACAGTCAGCCGCATCTTC-3\u0026prime;,\u003c/p\u003e \u003cp\u003e \u003cem\u003eGAPDH\u003c/em\u003e reverse primer: 5\u0026prime;-CTCCGACCTTCACCTTCC-3\u0026prime;.\u003c/p\u003e \u003cp\u003e \u003cb\u003eImmunohistochemistry(IHC)\u003c/b\u003e. Liver cancer tissue arrays (HLivH060CS01) were purchased from Outdo Biotech Co., Ltd(Shanghai, China). After dewaxina, antigens were retrieved under high pressure followed by rinse and endogenous peroxidases block with 3% hydrogen peroxide for 10 min. Then, tissues were incubated in normal goat serum for 15 min and anti-PAMR1 rabbit polyclonal antibody (1:50, Wuhan, China) was incubated overnight at 4\u0026deg;C. The next day, tissue arrays were washed and incubated with secondary antibody at 37\u0026deg;C for 30 min. Then, DAB was incubated for 5 min, and the tissues were coverslipped for observation under a microscope. Staining intensity was scored on the following scale: 0 (none), 1 (weak), 2 (moderate), and 3 (strong). The extent (0\u0026ndash;100%) of reactivity was scored with five levels: 0 (\u0026lt;\u0026thinsp;5%), 1 (5\u0026ndash;25%), 2 (25\u0026ndash;50%), 3 (50\u0026ndash;75%), and 4 (\u0026gt;\u0026thinsp;75%). Two pathologists independently scored the staining of each slide.\u003c/p\u003e \u003cp\u003e \u003cb\u003eTumor immune estimation resource analysis.\u003c/b\u003e The Tumor Immune Estimation Resource (TIMER) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://cistrome.org/TIMER/\u003c/span\u003e\u003cspan address=\"http://cistrome.org/TIMER/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)\u003csup\u003e33\u003c/sup\u003e was used to evaluate the correlation between PAMR1 expression level and the abundance of infiltrating immune cells (CD8\u0026thinsp;+\u0026thinsp;T cells, CD4\u0026thinsp;+\u0026thinsp;T cells, B cells, dendritic cells, macrophages, and neutrophils), as well as tumor purity in HCC.\u003c/p\u003e \u003cp\u003e \u003cb\u003eGene set enrichment analysis (GSEA).\u003c/b\u003e GSEA version 4.2.1 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.gsea-msigdb.org/gsea/downloads.jsp\u003c/span\u003e\u003cspan address=\"http://www.gsea-msigdb.org/gsea/downloads.jsp\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) is a computational method based on the entire gene expression matrix\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. The cases in TCGA-LIHC cohort was separated into PAMR1-low and PAMR1-high subgroups with median PAMR1 level as the cut-off point. Reference gene sets were H.all.v7. 2.symbols.gmt gene sets (Hallmarks) from MSigDB\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. Each analysis required at least 100 times permutation tests. An adjusted P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and normalized enrichment score (NES)\u0026thinsp;\u0026gt;\u0026thinsp;2 were considered as significant enrichment.\u003c/p\u003e \u003cp\u003e \u003cb\u003eStatistical analysis.\u003c/b\u003e Survival curves were calculated with the Kaplan-Meier method with log-rank tests. Data from all experiments were analyzed with unpaired t-tests or one-way ANOVA in GraphPad Prism 8 (GraphPad, San Diego, CA, USA). The receiver operating characteristic (ROC) curve analysis was performed by pROC packages in R sofware, version 3.6.3. P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was defined as significance.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn conclusion, this is the first study to demonstrate that PAMR1 level is lower in HCC compared with that in normal liver tissues, and is closely associated with the shape of hepatic immune microenvironment, and has high diagnostic efficiency, and predicts poor prognostic outcome, which makes it as a favorable diagnostic and prognostic factor for HCC.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and analyzed during the current study are available from public databases, TCGA (https://portal.gdc.cancer.gov/), GEO (https://www.ncbi.nlm.nih.gov/geo/), UALCAN (http://ualcan.path.uab.edu/), GEPIA (http://gepia.cancer-pku.cn/), cBioPortal(http://www.cbioportal), and DAVID (https://david.ncifcrf.gov/home.jsp)databases.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Foundation of Health Commission of Chengdu (No.2022235), the Foundation of the First Affiliated Hospital of Chengdu Medical College (No.CYFY2020YB05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eY.X. and X.P.Z. conducted the study design. X.P.Z. wrote original draft. Y.X. wrote, reviewed, and edited. X.P.Z. and T.L carried out experiments and data analysis. S.H.D.and T.Z. provided technical support and material. D.M.W.supervised. All the authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003einterests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSung, H. et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J. Clin. \u003cb\u003e71\u003c/b\u003e, 209\u0026ndash;249 (2021) .\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDegasperi, E. \u0026amp; Colombo, M. Distinctive features of hepatocellular carcinoma in non-alcoholic fatty liver disease. Lancet Gastroenterol. 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Cell Syst. \u003cb\u003e1\u003c/b\u003e, 417\u0026ndash;425 (2015).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-2114251/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2114251/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePeptidase domain containing associated with muscle regeneration 1 (PAMR1) is downregulated in breast cancer and cervical cancer. This study aimed to evaluate the role of PAMR1 in hepatocellular carcinoma (HCC) and explore the underlying molecular mechanisms. Base on the analysis of datasets from the Gene Expression Omnibus and The Cancer Genome Atlas (TCGA), a lower mRNA level of \u003cem\u003ePAMR1\u003c/em\u003e was detected in HCC compared that in normal liver tissues. The result was also confirmed by the experiment with immunohistochemistry (IHC), and qRT-PCR. The area under the curve(AUC) was 0.918 through receiver operating characteristic (ROC) curve analysis. The Kaplan-Meier analysis revealed that lower \u003cem\u003ePAMR1\u003c/em\u003e expression predicted prognostic outcome. Then, the genes closely associated with \u003cem\u003ePAMR1\u003c/em\u003e were screened and enriched by Gene Ontology (GO) analysis, showing their role on extracellular matrix organization, cell adhesion, and blood vessel development. Moreover, \u003cem\u003ePAMR1\u003c/em\u003e expression was positively correlated with immune cells infiltration. In addition, Gene Set Enrichment Analysis (GSEA) showed that the downregulated genes in the low-\u003cem\u003ePAMR1\u003c/em\u003e subgroup were significantly enriched in an inflammatory response, hypoxia, epithelial-mesenchymal transition, KRAS signaling, and TNF-α signaling via NF-κB signaling pathway. Collectively, PAMR1 shows lower level in HCC,and represents a favorable diagnostic and prognostic factor for HCC.\u003c/p\u003e","manuscriptTitle":"PAMR1 is a favorable diagnostic and prognostic biomarker in hepatocellular carcinoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-11-14 18:28:43","doi":"10.21203/rs.3.rs-2114251/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":"2661166f-6332-4bec-96e9-b1f793bbd3ff","owner":[],"postedDate":"November 14th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":16871843,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":16871844,"name":"Health sciences/Biomarkers"},{"id":16871845,"name":"Biological sciences/Cancer"},{"id":16871846,"name":"Biological sciences/Cancer/Tumour biomarkers"},{"id":16871847,"name":"Biological sciences/Biological techniques"},{"id":16871848,"name":"Biological sciences/Biological techniques/Bioinformatics"}],"tags":[],"updatedAt":"2023-01-09T08:59:30+00:00","versionOfRecord":[],"versionCreatedAt":"2022-11-14 18:28:43","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-2114251","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2114251","identity":"rs-2114251","version":["v1"]},"buildId":"_2-kVJe1T_tPrBINL-cwx","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}