TOP2A Serves as a Prognostic Marker Associated with Immune Infiltration in Hepatocellular Carcinoma

TOP2A Serves as a Prognostic Marker Associated with Immune Infiltration 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 Research Article TOP2A Serves as a Prognostic Marker Associated with Immune Infiltration in Hepatocellular Carcinoma Qiuming Su, Shengning Zhang, Jianghua Ran This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1690716/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background TOP2A is a key enzyme that controls the topological state of DNA during DNA replication and transcription. We aim to explore the role of TOP2A in hepatocellular carcinoma (HCC). Methods The expression and prognostic value of TOP2A in HCC were analyzed by TIMER, TCGA, GEO, HPA, GEPIA and Kaplan-Meier plotter databases. The potential molecular mechanism of TOP2A in HCC was researched by GSEA software. The construction of the TOP2A gene co-expression network was completed by the cBioPortal database. The relationship between TOP2A and immune cell infiltration in HCC was explored through the CIBERSORT and TIMER databases. Results TOP2A was overexpressed in HCC and was associated with a poor prognosis. High expression of TOP2A was associated with worse pathological grade, deeper invasion depth, and advanced TNM stage, which was an independent unfavorable prognostic factor for HCC (HR = 1.863, P = 0.004). The GSEA results indicated that TOP2A was closely related to tumorigenesis and cellular metabolism. Furthermore, TOP2A and its co-expressed genes were positively associated with tumor-infiltrating immune cells. These co-expressed genes were independent prognostic factors, and the expression of these genes combined with macrophage levels helped to predict prognosis of HCC. In terms of immune infiltration, HCC patients with high TOP2A expression that the proportions of resting memory CD4 + T cells, activated CD4 + T cells, follicular helper T cells, regulatory T cells, macrophages, and neutrophils were significantly increased. The copy number variations of TOP2A affected the level of tumor-infiltrating immune cells. The expression of TOP2A was positively correlated with immune gene markers of T cell exhaustion in the HCC microenvironment. Conclusion TOP2A is a prognostic molecular marker of HCC, which is related to immune cell infiltration. TOP2A hepatocellular carcinoma prognosis immune infiltration molecular marker Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Figure 14 Figure 15 Introduction Liver cancer is a global health problem with increasing morbidity and mortality[ 1 ]. According to statistics, there are approximately 850,000 new cases of liver cancer per year, which is 5.6% of all human tumors[ 2 , 3 ]. Hepatocellular carcinoma (HCC) is the most common type of liver cancer, accounting for more than 90% of liver cancers[ 2 – 4 ]. The high incidences of HCC are mainly attributed to Hepatitis B and Hepatitis C Infections, chemical carcinogens, alcoholic and nonalcoholic fatty liver disease, and some genetic metabolic diseases, such as hemochromatosis and alpha-1-antitrypsin deficiency[ 1 , 2 , 5 ]. Hepatocarcinogenesis is a complex multi-step process, which may be closely related to the abnormal expression and mutation of certain genes. Therefore, a better understanding of the molecular mechanisms of HCC can provide more specific biomarkers for tumor diagnosis and treatment. Topoisomerase II alpha (TOP2A) is a key enzyme responsible for solving various topological problems in the process of DNA metabolism[ 6 ]. It is mainly distributed in the nucleus and the encoding gene was located at 17q12-21[ 7 ]. The main function of TOP2A is to regulate DNA topology during recombination, replication and other processes by breaking and reconnecting DNA strands[ 8 ]. TOP2A is a special molecular marker of cell replication, which is involved in the cell cycle. At the same time, it can regulate cell proliferation and apoptosis. In addition, the expression of TOP2A is specific during cell division, starting to increase in the S phase of mitosis, reaching the maximized level in the G2/M phase, and declining after the end of division[ 9 ]. Because of its biological properties, TOP2A has been extensively studied in the field of oncology. Depowski et al . found that TOP2A was a candidate molecular marker for breast cancer cell proliferation and poor prognosis, which was preferentially expressed in more aggressive subsets of breast cancer (HER-2/neu overexpression)[ 10 ]. Pei et al . found that TOP2A was abnormally highly expressed in pancreatic cancer tissues, proving that TOP2A was a co-activator of β-catenin, which promoted the invasion and metastasis of pancreatic cancer by activating the EMT process[ 11 ]. Zhang et al . found that the expression of TOP2A was significantly elevated in colon cancer tissues, and played a major role in colon cancer invasion and metastasis via the AKT and ERK signaling pathways[ 12 ]. In addition to human breast, colon, and pancreatic cancers, high expression of TOP2A was also found in oral, nasopharyngeal, esophageal, lung, gallbladder and prostate cancers, in which TOP2A overexpression was associated with aggressiveness phenotype, advanced stage, recurrence and decreased overall survival[ 10 – 13 ]. However, the underlying functions and mechanisms of TOP2A in HCC remains unclear. In this study, we revealed that TOP2A predicted adverse prognosis in HCC and can be served as a potential therapeutic target for attenuating anti-tumor immunity. First, we downloaded the HCC data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, and analyzed the expression difference of TOP2A between HCC and normal hepatic tissues. Next, we further explored the association among TOP2A, the prognosis of HCC patients and the related signaling pathways. At the same time, we used bioinformatics tools to construct TOP2A co-expression gene network and analyzed their functions. Finally, we discussed the relationship between TOP2A and tumor immune infiltration from multiple dimensions. This study aims to supply a new perspective on the possible mechanism of hepatocellular carcinogenesis and to help discover new therapeutic targets for HCC. Materials And Methods Expression analysis of TOP2A in HCC We used The Tumor Immune Estimation Resource (TIMER) database[14] to assess the mRNA levels of TOP2A in different tumors. Subsequently, we downloaded the RNA sequence expression data of HCC in TCGA from the UCSC Xena browser[15]. Five microarray datasets (GSE102079[16], GSE25097[17], GSE87630[18], GSE84006 and GSE64041[19]) containing HCC and normal hepatic tissues were obtained from the GEO database. With the above data, we analyzed the differences in TOP2A expression among HCC, para-cancerous and normal hepatic tissues, the results were plotted by GraphPad Prism version 9 software. Furthermore, we observed the protein expression levels of TOP2A in HCC through the online immunohistochemistry (IHC) analysis of Human Protein Atlas (HPA) database. Correlation analysis between TOP2A and clinicopathological characteristics in HCC Clinical data with primary HCC patients in TCGA were downloaded from the UCSC Xena browser. The baseline clinicopathological data including age, gender, grade, invasion depth, lymph node metastasis, distant metastasis, TNM stage, and survival status were enrolled in this study. After missing and incomplete data were eliminated, 337 HCC samples were retained for subsequent analysis. Based on the median mRNA expression of TOP2A, 169 patients were divided into the high expression group and 168 patients into the low expression group. The chi-square test was used to explore the correlation between TOP2A expression and clinicopathological parameters. Prognostic analysis of TOP2A expression in HCC We used Gene Expression Profiling Interactive Analysis (GEPIA)[20] and Kaplan-Meier (KM) plotter[21] databases to analyze the relationship between TOP2A expression and prognosis in HCC. The log-rank P-value, hazard ratio (HR) and 95% confidence intervals (CI) were calculated. Subsequently, we downloaded the follow-up data of HCC patients in TCGA from the UCSC Xena browser. Univariate and multivariate Cox regression analysis were used to identify independent prognostic factors using SPSS 25 software. The results were presented as HR, 95% CI and P values. Gene Set Enrichment Analysis (GSEA) Here, HCC patients in TCGA were divided into high and low TOP2A expression groups according to the median expression of TOP2A. The KEGG pathway analysis of the two groups was performed by GSEA software. In the GSEA process, the number of genes parameter was 1000. The nominal (NOM) P value, false disclosure rate (FDR) and normalized enrichment score (NES) were used to evaluate the enriched signaling pathways in each phenotype. Construction and analysis of TOP2A gene co-expression network in HCC To better comprehend the molecular mechanism of TOP2A in HCC, we used the cBioPortal database[22] to identify the top 6 genes (KIF18B, BUB1B, KIF23, CKAP2L, ANLN and TPX2) most related to TOP2A. The interrelationship was examined using the TIMER database. Moreover, we analyzed the expression level of TOP2A-related genes using UALCAN[23] and TNMplot databases[24]. GEPIA and KM plotter databases were used to investigate the prognosis of TOP2A co-expressed genes. Correlation analysis of TOP2A, TOP2A co-expressed genes, and immune infiltrating cells We used the TIMER database to analyze the relationship among TOP2A, TOP2A co-expressed genes and tumor-infiltrating immune cells (TIICs), including CD8+ T cells, CD4+ T cells, B cells, macrophages, neutrophils, and dendritic cells. Next, we explored the correlation between the level of TIICs and the prognosis of HCC patients. Subsequently, we further analyzed the prognostic value of macrophage combined with TOP2A-related genes expression. Furthermore, we constructed seven multivariate Cox proportional hazards models, each model with seven variables, including age, tumor stage, gender, race, tumor purity, macrophage level, and expression of the single genes. Immune infiltration analysis of TOP2A in HCC We used CIBERSORT software to perform LM22 gene labeling (including 22 immune cell types) in HCC samples from the TCGA database, and the tumor microenvironment (TME) changed were evaluated. At the same time, we further explored the differences of TIICs in high and low TOP2A expression groups. The results were drawn using the R package “ggplot2”, “vioplot”, “pheatmap”, and “corrplot”. Furthermore, we applied TIMER and cBioportal databases to analyze the relationships among TOP2A copy number alterations, TIICs levels and TOP2A expression in HCC. Ultimately, the correlation between TOP2A and gene markers of TIICs were explored using the TIMER and GEPIA databases, Statistic significant differences were considered when P < 0.05 (*** P < 0.001, ** P < 0.01, * P < 0.05). Statistical Analysis Independent samples t test and paired sample t test were used to compare the difference of TOP2A mRNA levels between HCC and normal hepatic tissues. Chi-square test was used to research the relationship between TOP2A expression and the clinicopathological characteristics of HCC. The Cox proportional hazards regression model was used to analyze the relationship between the clinicopathological parameters and prognosis of HCC. The values are presented as mean ± SD. If not specifically stated, P values < 0.05 were considered statistically significant. SPSS software (Version 25.0) was used for all analysis. Results TOP2A was overexpressed in HCC Pan-cancer analysis of TOP2A in TCGA-HCC dataset was performed by the TIMER database. We found the expression of TOP2A in bladder urothelial carcinoma (BLCA), breast invasive carcinoma (BRCA), cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC), cholangiocarcinoma (CHOL), colon adenocarcinoma (COAD), esophageal carcinoma (ESCA), glioblastoma multiforme (GBM), head and neck squamous cell carcinoma (HNSC), kidney chromophobe (KICH), kidney renal clear cell carcinoma (KIRC), kidney renal papillary cell carcinoma (KIRP), liver hepatocellular carcinoma (LIHC), lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), pheochromocytoma and paraganglioma (PCPG), pancreatic adenocarcinoma (PRAD), rectum adenocarcinoma (READ), stomach adenocarcinoma (STAD), thyroid carcinoma (THCA), and endometrioid cancer (UCEC) was significantly increased compared to the corresponding normal tissues (Figure 1a). Meanwhile, we used the TCGA and the GEO databases to evaluate the differential expression of TOP2A in HCC and normal hepatic tissues. The results showed that the mRNA level of TOP2A was significantly upregulated in HCC samples of TCGA, GSE102079, GSE25097, GSE87630, GSE84006, and GSE64041 datasets (Figure 1b-1g). Furthermore, according to the IHC results in the HPA database, we found that TOP2A was mainly expressed in the nucleus of hepatoma cells, and the staining intensity of TOP2A in HCC was significantly higher than normal hepatic tissues (Figure 2). These findings indicate that TOP2A expression is significantly upregulated in HCC. The relationship between TOP2A expression and clinicopathological characteristics of HCC patients We investigated the relationship between TOP2A and clinicopathological characteristics of HCC patients by using TCGA-HCC dataset. The expression of TOP2A mRNA was significantly associated with the age ( P = 0.01), grade ( P < 0.001), invasion depth ( P = 0.003), TNM stage ( P < 0.001) and survival status ( P = 0.042) of HCC patients, but there were no significant differences in gender ( P = 0.051), lymph node metastasis ( P = 0.619), and distant metastasis ( P = 0.244). The results were shown in Table 1. Table 1 Relationship between TOP2A expression level and clinicopathological variables in HCC patients. Classification Total CD97 expression Χ 2 P High Low Age 6.598 0.010 <60 158 91 67 ≥60 179 78 101 S ex 3.811 0.051 Male 230 107 123 Female 107 62 45 Grade 26.858 <0.001 G1 45 13 32 G2 166 71 95 G3 114 77 37 G4 12 8 4 Invasion depth 14.159 0.003 T1 170 69 101 T2 83 47 36 T3 74 48 26 T4 10 5 5 Lymph node metastasis 0.247 0.619 N0-Nx 333 166 167 N1 4 3 1 Distant metastasis 1.357 0.244 M0-Mx 334 169 165 M1 3 0 3 TNM stage 21.842 <0.001 I 168 68 100 II 82 46 36 III 83 55 28 IV 4 0 4 Status 4.135 0.042 Alive 223 103 120 Dead 114 66 48 P < 0.05, statistically significant. The prognostic value of TOP2A in HCC We analyzed the prognostic value of TOP2A in HCC via the GEPIA and the KM plotter databases. In GEPIA database, HCC patients with high TOP2A expression had shorter overall survival (HR = 1.7, P = 0.003; Figure 3a) and disease-free survival (HR = 1.7, P = 0.000059; Figure 3b). The same results were observed in the KM plotter database (Figure 3c-3f), high expression of TOP2A in HCC predicted worse overall survival (HR = 1.82, P = 0.00071), disease-free Survival (HR = 1.66, P = 0.0024), progression-free survival (HR = 1.75, P = 0.00018), and disease-specific survival (HR = 2.17, P = 0.00062). These results suggest that high TOP2A expression is associated with poor prognosis in HCC patients. In addition, we performed univariate and multivariate Cox survival analysis on HCC samples in the TCGA database. The results of univariate Cox survival analysis showed invasion depth ( P < 0.001), distant metastasis ( P = 0.025), TNM stage ( P < 0.001), and TOP2A expression ( P = 0.002) were significantly associated with the prognosis of HCC patients. The multivariate Cox survival analysis revealed that TOP2A expression ( P = 0.004) was an independent risk factor for poor prognosis of HCC (Table 2). Summarily, we consider that TOP2A is a powerful prognostic biomarker in HCC. Table 2 Univariate and multivaruate analysis of the prognostic factors in HCC patients using a Cox regression model. Parameters Univariate analysis M ultivaruate analysis Hazard ratio 95% CI P value Hazard ratio 95% CI P value Ages, year (≥60 vs. < 60) 1.268 0.873-1.840 0.212 1.465 0.985-2.179 0.060 Sex (female vs. male) 1.289 0.883-1.881 0.189 1.105 0.743-1.644 0.621 Grade (G3-G4 vs. G1-G2) 1.149 0.790-1.673 0.467 1.035 0.692-1.546 0.867 Invasion depth (T3/T4 vs. T1/T2) 2.494 1.714-3.627 <0.001 9.664 0.568-164.302 0.117 Lymph node metastasis (N1 vs. N0-Nx) 1.934 0.476-7.856 0.356 3.745 0.497-28.190 0.200 Distant metastasis (M1 vs. M0-Mx) 3.734 1.181-11.800 0 .025 2.513 0.737-8.566 0.141 TNM stage (III-IV vs. I-II) 2.471 1.701-3.589 <0.001 0.232 0.013-4.030 0.316 TOP2A expression (High vs. Low) 1.802 1.240-2.619 0.002 1.863 1.226-2.831 0.004 CI confidence interval. GSEA identified TOP2A-related signaling pathways in HCC We analyzed the molecular mechanism of TOP2A in hepatocarcinogenesis by the GSEA software according to TCGA-HCC database. We found that 90 KEGG signaling pathways were related to HCC samples with high TOP2A expression, of which 19 signaling pathways were significantly enriched in TOP2A high expression group (NOM P < 0.05, FDR 1.9; Table 3). These KEGG signaling pathways were mainly related to tumorigenesis and cell metabolism, including “cell cycle”, “pyrimidine metabolism”, “homologous recombination”, “inositol phosphate metabolism”, “purine metabolism”, “ubiquitin mediated proteolysis”, “base excision repair”, “RNA degradation”, “spliceosome”, “DNA replication”, “P53 signaling pathway”, “n-glycan biosynthesis”, “erbb signaling pathway”, “nucleotide excision repair”, and “glycosylphosphatidylinositol GRI anchor biosynthesis” (Figure 4). Table 3 GSEA pathways upregulated due to high expression of TOP2A. GS follow link to MSigDB SIZE NES NOM p-val FDR q-val KEGG_CELL_CYCLE 124 2.162 0.000 0.005 KEGG_OOCYTE_MEIOSIS 111 2.101 0.000 0.007 KEGG_PYRIMIDINE_METABOLISM 97 2.068 0.000 0.006 KEGG_PROGESTERONE_MEDIATED_OOCYTE_MATURATION 85 2.030 0.000 0.007 KEGG_HOMOLOGOUS_RECOMBINATION 26 1.978 0.000 0.012 KEGG_INOSITOL_PHOSPHATE_METABOLISM 54 1.965 0.000 0.012 KEGG_ENDOCYTOSIS 179 1.959 0.000 0.011 KEGG_PURINE_METABOLISM 153 1.957 0.000 0.009 KEGG_UBIQUITIN_MEDIATED_PROTEOLYSIS 130 1.945 0.000 0.011 KEGG_BASE_EXCISION_REPAIR 34 1.936 0.002 0.011 KEGG_RNA_DEGRADATION 56 1.926 0.000 0.012 KEGG_SPLICEOSOME 126 1.923 0.000 0.012 KEGG_DNA_REPLICATION 36 1.922 0.000 0.011 KEGG_P53_SIGNALING_PATHWAY 66 1.914 0.000 0.012 KEGG_N_GLYCAN_BIOSYNTHESIS 46 1.912 0.000 0.011 KEGG_ERBB_SIGNALING_PATHWAY 86 1.907 0.000 0.011 KEGG_GLYCOSYLPHOSPHATIDYLINOSITOL_GPI_ANCHOR_BIOSYNTHESIS 25 1.906 0.002 0.011 KEGG_NUCLEOTIDE_EXCISION_REPAIR 44 1.905 0.002 0.010 KEGG_CHRONIC_MYELOID_LEUKEMIA 73 1.902 0.000 0.010 NES: normalized enrichment score; NOM: nominal; FDR: false discovery rate. Gene sets with NOM p-value < 0.05 and FDR q-value < 0.1 are considered as significant. Co-expression network analysis of TOP2A in HCC To further investigate the effect of TOP2A in hepatocarcinogenesis, we used the TCGA-HCC dataset to explore TOP2A positive co-expressed genes via the cBioPortal database. Among them, the TOP6 gene associated with TOP2A was selected (absolute Spearman Correlation coefficient ≥ 0.93; Figure 5a-5f). Meanwhile, the relationship between TOP2A and these genes were verified by TIMER database. The results showed that TOP2A expression was positively related to KIF18B (correlation coefficient (COR) = 0.96, P = 0e+00), BUB1B (COR = 0.956, P = 0e+00), KIF23 (COR = 0.95, P = 0e+00), CKAP2L (COR = 0.95, P = 0e+00), ANLN (COR = 0.951, P = 0e+00), and TPX2 (COR = 0.951, P = 0e+00) (Figure 5g-5l). Following, we evaluated the expression of these genes in HCC patients from the TCGA database via the UALCAN analysis tool. We observed that the TOP2A co-expressed genes were overexpressed in HCC (Figure 6a-6f). Similar changes were found in the TNMplot database (Figure 6g-6l). Furthermore, we also explored the association between these genes and the prognosis of HCC in the GEPIA database. The results showed that the prognosis of HCC patients with the high expression of KIF18B, BUB1B, KIF23, CKAP2L, ANLN, or TPX2 was significantly worse (Figure 7a-7f). We obtained the same conclusion in the KM plotter database, which HCC patients with the high KIF18B (HR = 1.86, P = 0.00048), BUB1B (HR = 1.82, P = 0.00071), KIF23 (HR = 1.66, P = 0.00071) 0.0043), CKAP2L (HR = 1.76, P = 0.0014), ANLN (HR = 1.8, P = 0.00084), or TPX2 (HR = 2.05, P = 5.7e-05) expression had a poor prognosis (Figure 7g- 7l). These results suggest that TOP2A and TOP2A co-expressed genes may contribute to the progression of HCC. TOP2A and TOP2A co-expressed genes were significantly associated with TIICs in HCC We used the TIMER database to analyze the relationship among TOP2A, TOP2A co-expressed genes and infiltrating immune cells in HCC. As shown in Figure 8, after correcting for the confounding factor of tumor purity, we identified that TOP2A, KIF18B, BUB1B, KIF23, CKAP2L, ANLN, and TPX2 were significantly positively correlated with TIICs, including B cells (COR, 0.44 to 0.5, P < 0.01), CD8+ T cell (COR, 0.282 to 0.355, P < 0.01), CD4+ T cell (COR, 0.33 to 0.412, P < 0.01), macrophage (COR, 0.442 to 0.522, P < 0.01), neutrophil (COR, 0.376 to 0.443, P < 0.01), and dendritic cell (COR, 0.45 to 0.502, P < 0.01). The above results indicate that TOP2A and TOP2A co-expressed genes are positively correlated with tumor-associated B cells, CD8+ T cells, CD4+ T cells, macrophages, neutrophils, and dendritic cells in HCC. Overexpression of TOP2A and TOP2A co-expressed genes with high levels of infiltratedvmacrophages predicted poor survival in HCC We used the TIMER database to explore the relationship between different TIICs and prognosis in HCC patients. After adjusting for five confounding factors, including age, stage, gender, race, and tumor purity, we found that macrophages (HR = 1.29, P = 0.0181) and Neutrophil cells (HR = 1.28, P = 0.0264) were significantly associated with the prognosis of HCC (Figure 9). In the present study, we combined the tumor-associated macrophages (TAMs) levels and the single gene expression patterns to assess the prognosis of HCC. The results were shown in Figure 10, there was no significant correlation between the TAMs and prognosis under the low expression level of TOP2A/BUB1B/CKAP2L/ANLN/TPX2, while the high levels of macrophages had a poor outcome in HCC under the low expression of KIF18B (HR = 1.61, P = 0.0361) and KIF23 (HR = 1.57, P = 0.044). TAMs with the high expression of TOP2A/KIF18B/BUB1B/KIF23/CKAP2L/ANLN had no significant relationship with HCC prognosis. However, under the high TPX2 expression (HR = 1.58, P = 0.0404), the higher macrophages levels had a worse survival in HCC. Furthermore, we constructed seven multivariate Cox regression analysis models. Each model contained the following variables: macrophage levels, age, stage, gender, race, tumor purity, and single gene expression (Figure 11). The results identified that TAMs with the low expression of TOP2A/KIF18B/BUB1B/KIF23/CKAP2L/ANLN/TPX2 had no statistical association with survival in HCC after adjusting these variables. However, under the high TOP2A (HR = 2.1, P = 0.00773)/KIF18B (HR = 1.87, P = 0.0193)/BUB1B (HR = 1.83, P = 0.0318)/KIF23 (HR = 1.83, P = 0.0269)/CKAP2L (HR = 2.06, P = 0.0085)/ANLN (HR = 1.95, P = 0.0171)/TPX2 (HR = 2.38, P = 0.00202) expression, the low TAM levels predicted better prognosis in HCC (Figure 12). Summarily, TOP2A and TOP2A co-expressed genes were independent adverse prognostic biomarkers and that combining these genes expression with the TAM levels may contribute to predict an adverse prognosis of HCC. TOP2A was an immune-related gene in HCC In this study, we used the CIBERSORT algorithm to evaluate the immune infiltration model of 337 HCC samples in the TCGA database (Figure 13a). Subsequently, the patients were divided into two groups according to the median value of TOP2A expression, and we observed that the proportions of diverse types of TIICs in the TME were apparently different between the two groups (Figure 13b-13c). The proportion of resting memory CD4+ T cells ( P = 0.012), activated CD4+ T cells ( P = 0.001), follicular helper T cells (P<0.001), regulatory T cells ( P = 0.009), M0 macrophages ( P < 0.001), resting dendritic cells ( P < 0.001), or neutrophils ( P = 0.026) was increased in TOP2A high expression group, while the proportion of resting NK cells ( P = 0.046), monocytes ( P = 0.027), M2 macrophages ( P = 0.003), and resting Mast cells ( P < 0.001) was decreased in TOP2A high expression group. A correlation heatmap showed that the proportions of different TIICs in HCC patients had weak to strong correlation (Figure 13d). There was a significant negative correlation between CD8+ T cells and resting memory CD4+ T cells (COR = −0.37), follicular helper T cells and resting memory CD4+ T cells were significantly negatively correlated (COR = −0.37). A significant positive correlation between CD8+ T cells and activated memory CD4+ T cells (COR = 0.51), CD8+ T cells and follicular helper T cells were significantly positive correlated (COR = 0.43). In addition, the potential association between TOP2A copy number variations (CNV) and TIICs were analyzed via the TIMER database. We observed that TOP2A CNV had a major impact on TOP2A mRNA expression (Figure 14a-14c), which were closely related to the infiltration degree of B cells, CD8+ T cells, CD4+ T cells, macrophages, neutrophils, or dendritic cells (Figure 14d). These results suggest that TOP2A may control the level of TIICs by altering TOP2A CNV, thus affecting the prognosis of HCC. What more, to further verify the relationship between TOP2A and TIICs, we explored the association between TOP2A expression and immune marker genes of TIICs in HCC via the TIMER and GEPIA databases. We found that the expression of TOP2A was positively associated with marker genes of T cell, B cell, and T cell exhaustion, including CD8A, CD8B, CD3D, CD3E, CD2, CD19, CD79A, CD20/KRT20, CD38, PD-1/PDCD1, CTLA4, LAG3, and TIM- 3/HAVCR2 (Table 4). Figure 15 showed that the correlation between TOP2A and immune marker genes of B cells, T cells, or T cell exhaustion. These results reveal that TOP2A is closely related to TIICs in HCC. Table 4 Correlation analysis between TOP2A and relate genes and markers of immune cells in TIMER and GEPIA. Description Gene markers TIMER GEPIA None Purity Tumor Rho p Rho p Rho p CD8+ T cell CD8A 0.188 *** 0.307 *** 0.14 ** CD8B 0.147 ** 0.255 *** 0.18 ** T cell (general) CD3D 0.225 *** 0.348 *** 0.32 *** CD3E 0.18 *** 0.348 *** 0.16 ** CD2 0.184 *** 0.338 *** 0.19 *** B cell CD19 0.262 *** 0.348 *** 0.27 *** CD79A 0.156 ** 0.287 *** 0.12 * CD20/KRT20 0.196 *** 0.222 *** 0.17 ** CD38 0.241 *** 0.362 *** 0.16 ** Monocyte CD86 0.299 *** 0.472 *** 0.21 *** CD115/CSF1R 0.15 ** 0.308 *** 0.064 0.22 TAM CCL2 0.068 1.9e-01 0.191 *** -0.027 0.6 CD68 0.224 *** 0.331 *** 0.091 0.08 IL10 0.24 *** 0.368 *** 0.037 0.47 M1 Macrophage iNOS/NOS2 0.077 1.39e-01 0.089 9.79e-02 -0.13 * IRF5 0.441 *** 0.449 *** 0.29 *** COX2/PTGS2 0.124 * 0.274 *** -0.031 0.56 M2 Macrophage CD163 0.102 5.01e-02 0.228 *** 0.0029 0.96 VSIG4 0.098 5.93e-02 0.225 *** 0.0039 0.94 MS4A4A 0.104 * 0.25 *** -0.013 0.81 Neutrophils CD66b/ CEACAM8 0.083 1.09e-01 0.113 * 0.073 0.16 CD11b/ITGAM 0.31 *** 0.419 *** 0.21 *** CCR7 0.111 * 0.271 *** 0.0063 0.9 Natural killer cell KIR2DL1 -0.009 8.58e-01 -0.024 6.51e-01 -0.048 0.35 KIR2DL3 0.173 ** 0.226 *** 0.084 0.11 KIR2DL4 0.191 *** 0.226 *** 0.2 *** KIR3DL1 0.047 3.64e-01 0.069 2.04e-01 -0.069 0.19 KIR3DL2 0.087 9.61e-02 0.137 * 0.075 0.15 KIR3DL3 0.06 2.47e-01 0.061 2.60e-01 0.11 * KIR2DS4 0.065 2.12e-01 0.059 2.74e-01 0.0081 0.88 Dendritic cell HLA-DPB1 0.159 ** 0.293 *** 0.088 0.092 HLA-DQB1 0.116 * 0.232 *** 0.12 * HLA-DRA 0.193 *** 0.332 *** 0.078 0.13 HLA-DPA1 0.164 ** 0.309 *** 0.043 0.41 BDCA-1/CD1C 0.142 ** 0.255 *** 0.048 0.36 BDCA-4/NRP1 0.3 *** 0.342 *** 0.055 0.29 CD11c/ITGAX 0.342 *** 0.49 *** 0.2 *** Th1 T-bet/TBX21 0.075 1.51e-01 0.191 *** 0.0028 0.96 STAT4 0.257 *** 0.332 *** 0.24 *** STAT1 0.426 *** 0.486 *** 0.23 *** IFN-g/IFNG 0.254 *** 0.343 *** 0.24 *** TNF-a/TNF 0.265 *** 0.406 *** 0.15 ** Th2 GATA3 0.191 *** 0.345 *** 0.12 * STAT6 0.183 *** 0.178 *** -0.029 0.58 STAT5A 0.311 *** 0.378 *** 0.2 *** IL13 0.111 * 0.123 * 0.093 0.075 Tfh BCL6 0.235 *** 0.242 *** 0.03 0.56 CXCR5 0.206 *** 0.336 *** 0.37 *** ICOS 0.299 *** 0.432 *** 0.27 *** Th17 STAT3 0.24 *** 0.295 *** -0.042 0.42 IL17A 0.116 * 0.133 * 0.044 0.4 Treg FOXP3 0.214 *** 0.299 *** 0.043 0.41 CCR8 0.441 *** 0.554 *** 0.22 *** STAT5B 0.42 *** 0.416 *** 0.085 0.1 TGFb/TGFB1 0.263 *** 0.376 *** 0.22 *** T cell exhaustion PD-1/PDCD1 0.283 *** 0.385 *** 0.3 *** CTLA4 0.306 *** 0.427 *** 0.34 *** LAG3 0.27 *** 0.32 *** 0.3 *** TIM-3/HAVCR2 0.301 *** 0.476 *** 0.23 *** HCC: hepatocellular carcinoma; TAM: tumor-associated macrophage; Th: T helper cell; Tfh: Follicular helper T cell; Treg, regulatory T cell; Rho, R value of Spearman’s correlation; None, correlation without adjustment. Purity, correlation adjusted by purity. * p < 0.05, ** p < 0.01, *** p < 0.001. Discussion HCC is a highly heterogeneous malignant tumor, and the vast majority of patients are diagnosed at the advanced stage[ 2 ]. Surgical resection is still the most important treatment measure for HCC, but its therapeutic effect is not always satisfactory[ 25 ]. Therefore, it is necessary to seek for new tumor markers and therapeutic targets. TOP2A is a key enzyme involved in DNA recombination and replication[ 6 ]. In recent years, the focus of research on the function of TOP2A has been extended to the field of cancer, which indicates the potential ability of TOP2A to attenuate tumor immunity and as a potential therapeutic target[ 26 ]. Currently, the role of TOP2A in HCC is limited. The purpose of our study is to deeply analyze the biological function and related regulatory pathways of TOP2A in HCC. Previous studies had reported the critical role of TOP2A in tumor angiogenesis and the aberrant expression in a variety of tumors[ 10 – 12 ]. In the present study, we found that TOP2A was overexpressed in 20 types of tumors by analyzing the TIMER database, which was consistent with previous findings. According to the TCGA, GEO and HPA databases, the expression of TOP2A in HCC was significantly higher than normal hepatic tissues, and the analysis outcomes were also consistent with the previous results[ 27 ]. Meanwhile, high TOP2A expression of HCC patients had high-grade malignancy and advanced stage. Furthermore, we also found that TOP2A overexpression in HCC predicted poor survival, which implied that the prognosis of HCC patients might be improved by regulating the expression of TOP2A. In conclusion, TOP2A can be used as a biomarker for the diagnosis and prognosis of HCC. Current research on the role of TOP2A in tumors mainly focuses on gene instability and chromosomal instability (CIN)[ 28 ]. However, other biological functions of TOP2A in HCC are rarely studied. In this study, the potential molecular mechanism of TOP2A in HCC were explored by the GSEA tool, we found that the KEGG signaling pathways were significantly enriched in the high expression of TOP2A, including “cell cycle”, “pyrimidine metabolism”, “homologous recombination”, “inositol phosphate metabolism”, “purine metabolism”, “ubiquitin mediated proteolysis”, “base excision repair”, “RNA degradation”, “spliceosome”, “DNA replication”, “P53 signaling pathway”, “n-glycan biosynthesis”, “erbb signaling pathway”, “nucleotide excision repair”, and “glycosylphosphatidylinositol GRI anchor biosynthesis”. Previous studies have confirmed that these signaling pathways were closely related to the tumorigenesis and the progression of HCC[ 2 , 29 , 30 ]. Cyclin D1 is an important regulator factor of cell cycle[ 31 ]. Nishida et al . found that Cyclin D1 was abnormally overexpressed in HCC, which was associated with the property of invasive tumor growth[ 29 ]. P53 is an important tumor suppressor gene that plays a critical role in tumor occurrence and development[ 32 ]. Llovet et al . found that mutation p53 was considered to be a tumor promoting factor in HCC by affecting tumor cell proliferation, survival, invasion and immune evasion[ 2 ]. In our study, the high expression of TOP2A in HCC predicted adverse prognosis. Therefore, TOP2A may play a vital role in hepatocarcinogenesis by affecting these signaling pathways and lead to a poor survival of HCC. To deep understand the potential molecular mechanism of TOP2A in hepatocarcinogenesis, we constructed a co-expression network of TOP2A using the cBioPortal database. We identified that KIF18B, BUB1B, KIF23, CKAP2L, ANLN and TPX2 were strongly positively associated with TOP2A in HCC. TOP2A co-expressed genes were significantly elevated in HCC, which were correlated with adverse prognosis of patients. Yang et al . found that KIF18B promoted HCC progression by activating the Wnt/β-catenin signaling pathway[ 33 ]. Qiu et al . confirmed that BUB1B upregulated the activity of mTORC1 signaling pathway to promote HCC development[ 34 ]. Cheng et al . identified that KIF23 and KIF14 promoted the proliferation, invasion and chemotherapy resistance of HCC, but the specific molecular mechanism had not been elucidated[ 35 ]. Wang et al . considered that CKAP2L was associated with poor prognosis and promoted the malignancy of HCC[ 36 ]. Li et al . demonstrated that the CDK1-PLK1/SGOL2/ANLN pathway promoted the aggressiveness of HCC by mediating abnormal cell division in the cell cycle. Wang et al . found that the Hh-FOXM1-TPX2 signaling pathway played a key role in the replication of HCC[ 37 ]. Summarily, TOP2A co-expressed genes may be involved in hepatocarcinogenesis and lead to a poor prognosis of patients. TIICs are closely related to the progression and prognosis of HCC[ 38 ]. The analysis results of the TIMER database showed that the expression of TOP2A and TOP2A-related genes were significantly positively correlated with TIICs, including B cell, CD8 + T cell, CD4 + T cell, Neutrophil, Dendritic cell, and Macrophage. Furthermore, survival analysis identified that the prognosis of HCC was significantly worse with the high levels of macrophages and neutrophil cells. Based on survival curves and multivariate Cox regression analysis, we found that TOP2A and TOP2A co-expressed genes were independent adverse prognosis in HCC. These genes had strong predictive ability for the prognosis of HCC. Previous studies demonstrated that TAMs promote angiogenesis, invasion, and metastasis of HCC, which leaded to a poor prognosis[ 39 ]. Abundant studies have confirmed that targeting TAMs was a potential tumor monotherapy or combined therapy[ 40 ]. Furthermore, the identification of TAM-related genes will help to provide more potential targets for individualized and precise treatment of HCC, thus improve prognosis of patients. Therefore, we evaluated the prognostic value of combining TAM levels and the expression of TOP2A, TOP2A-related genes. The results showed that under the high expression of TOP2A/KIF18B/BUB1B/KIF23/CKAP2L/ANLN/TPX2, the high levels of TAM predicted poor prognosis in HCC. Collectively, it can be speculated that combining the expression of these genes and the levels of TAMs play a more effective role in the prognosis prediction of HCC. TIICs are important components of the TME, which played a key role in tumorigenesis and tumor progression[ 41 ]. The analysis of 22 types of TIICs in HCC was performed by CIBERSORT. We found that the proportion of 11 immune cells were apparently different at TOP2A levels, including resting memory CD4 + T cells, activated CD4 + T cells, follicular helper T cells, regulatory T cells, resting NK cells, monocytes, M0 macrophages, M2 macrophages, resting Dendritic cells, resting Mast cells, and neutrophils. Furthermore, the expression of TOP2A and CNV were significantly correlated with the infiltration degree of B cells, CD8 + T cells, CD4 + T cells, macrophages, neutrophils, and dendritic cells. Therefore, we infered that TOP2A was closely related to TIICs in HCC and might be involved in immune responses of TME. Meanwhile, we also found that the expression of TOP2A was positively correlated with immune gene markers of CD8 + T cell, T cell, B cell, and T cell exhaustion by analyzing the TIMER and the GEPIA databases. B cells, T cells, and CD8 + T cells are important immune cells in the human body, and played an important role in anti-tumor immunity[ 42 , 43 ]. T cells and B cells are significantly upregulated in tumor tissues, and CD8 + T cells are abundant in tumor and para-cancerous tissues[ 42 – 44 ]. T cell exhaustion is an important feature of tumors, which manifested by desensitization of T cells and loss of response to tumor antigens. With the persistent stimulation of tumor antigens and multiple cell surface inhibitory receptors, such as PD-1, LAG3, and CTLA-4, thus causing T cells impaired cytotoxicity and reduced cytokine production, and ultimately leading to unrestricted proliferation and escape of tumor cells[ 45 ]. Exhaustion of infiltrating T cells had been observed in HCC, which is associated with poor clinical outcomes[ 46 ]. Hung et al . demonstrated that tumor methionine metabolism drived T cell exhaustion in an HCC cohort of 675 patients[ 47 ]. We speculate that TOP2A overexpression accelerates T cell exhaustion in HCC and ultimately promotes tumor progression. We infer that HCC patients with high expression of TOP2A may attenuate anti-tumor immune responses. Therefore, TOP2A plays an important role in the immune regulation of HCC. However, more experiments are needed to further validate our hypothesis, especially the relationship between TOP2A and T cell exhaustion. Our study had some limitations. First of all, the results that we obtained were mainly from open public databases, and had not been verified by vivo and vitro experiments. Our research only stayed at the transcriptome level, and the results were not accurate enough. The conclusions should be tested by experimental methods such as qPCR, WB, and IHC. Furthermore, the HCC samples that we obtained were limited. Therefore, larger HCC samples are needed to eliminate the interference of the high heterogeneity of HCC. Conclusion In conclusion, our study demonstrated that TOP2A was overexpressed in HCC, which was correlated with the clinicopathological characteristics and prognosis. The expression of TOP2A was related to the degree of immune cell infiltration and attenuated anti-tumor immunity by accelerating the exhaustion of infiltrating T cells. Therefore, TOP2A can be used as a biomarker for HCC diagnosis, treatment and prognosis. Abbreviations HCC Hepatocellular carcinoma TOP2A Topoisomerase II alpha TCGA The Cancer Genome Atlas GEO Gene Expression Omnibus TIMER Tumor Immune Estimation Resource IHC immunohistochemistry HPA Human Protein Atlas GEPIA Gene Expression Profiling Interactive Analysis KM Kaplan-Meier HR hazard ratio CI confidence intervals GSEA Gene Set Enrichment Analysis NES normalized enrichment score TIICs tumor infiltrating immune cells TME tumor microenvironment TAMs tumor associated macrophages CNV copy number variations. Declarations Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Availability of data and materials The raw data of this study are derived from the GEO database (https://www.ncbi.nlm.nih.gov/geo/) and the UCSC Xena database (http://xena.ucsc.edu), which are publicly available databases. Competing interests The authors declare that there is no conflict of interest regarding the publication of this paper. Funding Statement This research was supported by the National Natural Science Foundation of China [Grant No. NSF82060127]; and the Yunnan Provincial Organ Transplantation Clinical Medical Center [Grant No. 2020SYZ-Z-044]. Authors’ contributions QS and JR conceived and designed the study. QS, SN and JR performed the data curation and analysis. QS, SN and JR analyzed and interpreted the results. QS and JR drafted and reviewed the manuscript. All authors reviewed and approved the final manuscript. 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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-1690716","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":108659925,"identity":"c37dce05-3aaa-4325-a10b-994caa6c61ab","order_by":0,"name":"Qiuming Su","email":"","orcid":"","institution":"The Affiliated Calmette Hospital of Kunming Medical University","correspondingAuthor":false,"prefix":"","firstName":"Qiuming","middleName":"","lastName":"Su","suffix":""},{"id":108659926,"identity":"50df5a7b-5dcc-4be0-94eb-72c83f1a6796","order_by":1,"name":"Shengning Zhang","email":"","orcid":"","institution":"The Affiliated Calmette Hospital of Kunming Medical University","correspondingAuthor":false,"prefix":"","firstName":"Shengning","middleName":"","lastName":"Zhang","suffix":""},{"id":108659927,"identity":"38790f2b-a506-403c-8d30-3d23ed3f2530","order_by":2,"name":"Jianghua Ran","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAApUlEQVRIiWNgGAWjYFCCM4wPGCok5ORJ0cJswHDGwtiwgXgtPGwSjG0ViQwHiNVg3nj2gDTvPIkExgbmh49uEKNF5sC5BGPebRJ57AxsxsY5xGiRYDhjkAzUUszYwMMmTbSWw7xzJBIbDpCgxbCZt4FELcaMc45JGBs2E+0XiTPmP97U1MnJszc/fEyUFgaJA1AGM1HKQYC/gWilo2AUjIJRMFIBADrxLJ6/0KwHAAAAAElFTkSuQmCC","orcid":"","institution":"The Affiliated Calmette Hospital of Kunming Medical University","correspondingAuthor":true,"prefix":"","firstName":"Jianghua","middleName":"","lastName":"Ran","suffix":""}],"badges":[],"createdAt":"2022-05-25 03:14:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1690716/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1690716/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":22373450,"identity":"ced63d66-e04f-493a-9eba-0821833fe935","added_by":"auto","created_at":"2022-06-07 18:07:24","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1176903,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe expression of TOP2A in HCC and pan-carcinoma.\u003c/strong\u003e \u003cstrong\u003ea\u003c/strong\u003e TOP2A mRNA levels in pan-cancer were analyzed using TIMER. TOP2A mRNA levels in HCC and normal hepatic tissues in the TCGA (\u003cstrong\u003eb\u003c/strong\u003e), GSE102079 (\u003cstrong\u003ec\u003c/strong\u003e), GSE25097 (\u003cstrong\u003ed\u003c/strong\u003e), GSE87630 (\u003cstrong\u003ee\u003c/strong\u003e), GSE84006 (\u003cstrong\u003ef\u003c/strong\u003e), and GSE64041 (\u003cstrong\u003eg\u003c/strong\u003e) datasets. *\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01, ***\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001, and ****\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.0001.\u003c/p\u003e","description":"","filename":"figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-1690716/v1/26e78cd1b97fc175ec0b2a49.png"},{"id":22373458,"identity":"7d3d8593-8bde-40ee-b625-7873f6e42674","added_by":"auto","created_at":"2022-06-07 18:07:25","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1919304,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRepresentative IHC staining with TOP2A antibody in HCC and normal hepatic tissues derived from the HPA database.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-1690716/v1/f8bd431b943c0f548f9bff10.png"},{"id":22373922,"identity":"17c3ad1c-fde6-4226-b748-2a619c9778ca","added_by":"auto","created_at":"2022-06-07 18:12:25","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":280109,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHigh TOP2A expression predicted poor prognosis in patients with HCC.\u003c/strong\u003e \u003cstrong\u003ea-b\u003c/strong\u003e The GEPIA database was used to construct the survival curves of OS and RFS based on the TOP2A expression in HCC. \u003cstrong\u003ec-f\u003c/strong\u003e The KM plotter database was used to analysis the relationship among TOP2A expression, OS, RFS, PFS, and DSS in HCC. Log-rank \u003cem\u003ep\u003c/em\u003e value \u0026lt; 0.05 was considered statistically significant. OS, overall survival; RFS, disease-free survival; PFS, progression-free survival; DSS, disease-specific survival.\u003c/p\u003e","description":"","filename":"figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-1690716/v1/6140cd81c38ea08b8e30d2dc.png"},{"id":22373451,"identity":"7adbb643-2b49-4dd3-97da-03e5478f7428","added_by":"auto","created_at":"2022-06-07 18:07:25","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1209938,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGene Set Enrichment Analysis.\u003c/strong\u003e Pathway enriched in HCC patients with high TOP2A expression included “cell cycle” (\u003cstrong\u003ea\u003c/strong\u003e), “pyrimidine metabolism” (\u003cstrong\u003eb\u003c/strong\u003e), “homologous recombination” (\u003cstrong\u003ec\u003c/strong\u003e), “inositol phosphate metabolism” (\u003cstrong\u003ed\u003c/strong\u003e), “purine metabolism” (\u003cstrong\u003ee\u003c/strong\u003e), “ubiquitin mediated proteolysis” (\u003cstrong\u003ef\u003c/strong\u003e), “base excision repair” (\u003cstrong\u003eg\u003c/strong\u003e), “RNA degradation” (\u003cstrong\u003eh\u003c/strong\u003e), “spliceosome” (\u003cstrong\u003ei\u003c/strong\u003e), “DNA replication” (\u003cstrong\u003ej\u003c/strong\u003e), “P53 signaling pathway” (\u003cstrong\u003ek\u003c/strong\u003e), “n-glycan biosynthesis” (\u003cstrong\u003el\u003c/strong\u003e), “erbb signaling pathway” (\u003cstrong\u003em\u003c/strong\u003e), “nucleotide excision repair” (\u003cstrong\u003en\u003c/strong\u003e), and “glycosylphosphatidylinositol GRI anchor biosynthesis” (\u003cstrong\u003eo\u003c/strong\u003e). NES, normalized enrichment score; FDR, false disclosure rate.\u003c/p\u003e","description":"","filename":"figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-1690716/v1/ae79592a09208cc5c553fd6e.png"},{"id":22374497,"identity":"20428b56-8cfc-4069-99ef-ae73647d3d8e","added_by":"auto","created_at":"2022-06-07 18:17:25","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1038480,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation between TOP2A and TOP2A co-expressed genes in HCC.\u003c/strong\u003e \u003cstrong\u003ea\u003c/strong\u003e-\u003cstrong\u003ef\u003c/strong\u003e The genes co-expressed with TOP2A in HCC (COR ≥ 0.93) were assessed using the cBioPortal database. \u003cstrong\u003eg\u003c/strong\u003e-\u003cstrong\u003el\u003c/strong\u003e TOP2A was positively correlated with KIF18B (COR = 0.96, \u003cem\u003eP\u003c/em\u003e = 0e+00), BUB1B (COR = 0.956, \u003cem\u003eP\u003c/em\u003e = 0e+00), KIF23 (COR = 0.95, \u003cem\u003eP\u003c/em\u003e = 0e+00), CKAP2L (COR = 0.95, \u003cem\u003eP\u003c/em\u003e = 0e+00), ANLN (COR = 0.951, \u003cem\u003eP\u003c/em\u003e = 0e+00), and TPX2 (COR = 0.951, \u003cem\u003eP\u003c/em\u003e = 0e+00) in HCC in the TIMER database. COR, correlation coefficient.\u003c/p\u003e","description":"","filename":"figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-1690716/v1/da9bd84b856a9bfe6e33e936.png"},{"id":22373454,"identity":"a184cf99-753a-400e-8720-3ffa3c7bea52","added_by":"auto","created_at":"2022-06-07 18:07:25","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1191080,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe expression of TOP2A co-expressed genes in HCC were analyzed.\u003c/strong\u003e \u003cstrong\u003ea\u003c/strong\u003e-\u003cstrong\u003ef\u003c/strong\u003e The mRNA expression levels of KIF18B, BUB1B, KIF23, CKAP2L, ANLN, and TPX2 in HCC in the UALCAN database. \u003cstrong\u003eg\u003c/strong\u003e-\u003cstrong\u003el \u003c/strong\u003ethe expression of TOP2A co-expressed genes in HCC and normal hepatic tissues in the TNMplot database.\u003c/p\u003e","description":"","filename":"figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-1690716/v1/cd03f69b49d77bde172c0a4f.png"},{"id":22374499,"identity":"a9703a80-c0fa-4b4d-b6ea-c3a18d8c4421","added_by":"auto","created_at":"2022-06-07 18:17:25","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":642248,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe prognostic value of TOP2A co-expressed genes in HCC.\u003c/strong\u003e \u003cstrong\u003ea\u003c/strong\u003e-\u003cstrong\u003ef\u003c/strong\u003e The survival curve of KIF18B, BUB1B, KIF23, CKAP2L, ANLN, and TPX2 in HCC in the GEPIA database. \u003cstrong\u003eg\u003c/strong\u003e-\u003cstrong\u003el \u003c/strong\u003eCorrelations between OS and the mRNA levels of TOP2A co-expressed genes in HCC in the UALCAN database. Log-rank \u003cem\u003ep\u003c/em\u003e value \u0026lt; 0.05 was considered statistically significant.\u003c/p\u003e","description":"","filename":"figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-1690716/v1/8a0a9a4b55e7996081d70920.png"},{"id":22374805,"identity":"fcc62608-1a0e-4d9b-b8b3-827735195234","added_by":"auto","created_at":"2022-06-07 18:22:25","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":1506080,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelations of TOP2A and its co-expressed genes with immune cell filtration levels in HCC.\u003c/strong\u003e Correlations of TOP2A (\u003cstrong\u003ea\u003c/strong\u003e), KIF18B (\u003cstrong\u003eb\u003c/strong\u003e), BUB1B (\u003cstrong\u003ec\u003c/strong\u003e), KIF23 (\u003cstrong\u003ed\u003c/strong\u003e), CKAP2L (\u003cstrong\u003ee\u003c/strong\u003e), ANLN (\u003cstrong\u003ef\u003c/strong\u003e), and TPX2 (\u003cstrong\u003eg\u003c/strong\u003e) expression with TIICs in HCC in the TIMER database.\u003c/p\u003e","description":"","filename":"figure8.png","url":"https://assets-eu.researchsquare.com/files/rs-1690716/v1/21dbb35d200476dffd7191ae.png"},{"id":22373924,"identity":"f83b844e-3dd3-4a0d-8ba7-917502d0cb9c","added_by":"auto","created_at":"2022-06-07 18:12:25","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":262195,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation between TIICs and prognosis in HCC.\u003c/strong\u003e Correlations between OS and the levels of B cell (\u003cstrong\u003ea\u003c/strong\u003e), CD8+ T cell (\u003cstrong\u003eb\u003c/strong\u003e), CD4+ T cell (\u003cstrong\u003ec\u003c/strong\u003e), macrophage (\u003cstrong\u003ed\u003c/strong\u003e), neutrophil (\u003cstrong\u003ee\u003c/strong\u003e), and dendritic cell (\u003cstrong\u003ef\u003c/strong\u003e) in HCC in the TIMER database.\u003c/p\u003e","description":"","filename":"figure9.png","url":"https://assets-eu.researchsquare.com/files/rs-1690716/v1/dd8224f089b1afd349f9cff0.png"},{"id":22373457,"identity":"71d5241c-435e-4b1b-9361-1bf828325ab4","added_by":"auto","created_at":"2022-06-07 18:07:25","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":464403,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOS analysis for combining the expression of TOP2A, TOP2A co-expressed genes and macrophage in HCC.\u003c/strong\u003e The survival curve of TOP2A (\u003cstrong\u003ea\u003c/strong\u003e), KIF18B (\u003cstrong\u003eb\u003c/strong\u003e), BUB1B (\u003cstrong\u003ec\u003c/strong\u003e), KIF23 (\u003cstrong\u003ed\u003c/strong\u003e), CKAP2L (\u003cstrong\u003ee\u003c/strong\u003e), ANLN (\u003cstrong\u003ef\u003c/strong\u003e), and TPX2 (\u003cstrong\u003eg\u003c/strong\u003e). \u003cem\u003eP\u003c/em\u003e value \u0026lt; 0.05 was considered statistically significant.\u003c/p\u003e","description":"","filename":"figure10.png","url":"https://assets-eu.researchsquare.com/files/rs-1690716/v1/4f8ce6bd7ddad5f22262b29c.png"},{"id":22373460,"identity":"8cfee168-7999-4feb-b018-8c15ccd21627","added_by":"auto","created_at":"2022-06-07 18:07:25","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":311097,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe multivariate Cox regression analysis of TOP2A and its co-expressed genes.\u003c/strong\u003e The forest plots of TOP2A (\u003cstrong\u003ea\u003c/strong\u003e), KIF18B (\u003cstrong\u003eb\u003c/strong\u003e), BUB1B (\u003cstrong\u003ec\u003c/strong\u003e), KIF23 (\u003cstrong\u003ed\u003c/strong\u003e), CKAP2L (\u003cstrong\u003ee\u003c/strong\u003e), ANLN (\u003cstrong\u003ef\u003c/strong\u003e), and TPX2 (\u003cstrong\u003eg\u003c/strong\u003e).\u003c/p\u003e","description":"","filename":"figure11.png","url":"https://assets-eu.researchsquare.com/files/rs-1690716/v1/0012e9eb349ff2e68c3e50d8.png"},{"id":22373464,"identity":"6377f071-4a62-44c5-bb2b-3b067fca4292","added_by":"auto","created_at":"2022-06-07 18:07:25","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":471655,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOS analysis for combining the expression of TOP2A, TOP2A co-expressed genes and macrophage in HCC after adjusting the confounding factors.\u003c/strong\u003e The survival curve of TOP2A (\u003cstrong\u003ea\u003c/strong\u003e), KIF18B (\u003cstrong\u003eb\u003c/strong\u003e), BUB1B (\u003cstrong\u003ec\u003c/strong\u003e), KIF23 (\u003cstrong\u003ed\u003c/strong\u003e), CKAP2L (\u003cstrong\u003ee\u003c/strong\u003e), ANLN (\u003cstrong\u003ef\u003c/strong\u003e), and TPX2 (\u003cstrong\u003eg\u003c/strong\u003e). \u003cem\u003eP\u003c/em\u003e value \u0026lt; 0.05 was considered statistically significant.\u003c/p\u003e","description":"","filename":"figure12.png","url":"https://assets-eu.researchsquare.com/files/rs-1690716/v1/c9ad521776e7ec0e80063c37.png"},{"id":22373928,"identity":"b8ae545e-4913-4f31-a25a-337403867a44","added_by":"auto","created_at":"2022-06-07 18:12:25","extension":"png","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":748682,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLandscape of TIICs in HCC by CIBERSORT.\u003c/strong\u003e \u003cstrong\u003ea\u003c/strong\u003e The composition of 22 types of TIICs in HCC tissues. \u003cstrong\u003eb\u003c/strong\u003e Violin plots visualized the distributions of TIICs according to TOP2A expression. \u003cstrong\u003ec\u003c/strong\u003e Heatmap showed the differences in the infiltration levels of 22 types of TIICs between high and low TOP2A expression in HCC. \u003cstrong\u003ed\u003c/strong\u003e Correlation heatmap visualized the correlations among 22 types of TIICs in HCC.\u003c/p\u003e","description":"","filename":"figure13.png","url":"https://assets-eu.researchsquare.com/files/rs-1690716/v1/9fa24d9d04b8a3badae3d7d1.png"},{"id":22373925,"identity":"79327525-2313-4682-b32d-1e0250e7b23e","added_by":"auto","created_at":"2022-06-07 18:12:25","extension":"png","order_by":14,"title":"Figure 14","display":"","copyAsset":false,"role":"figure","size":628926,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe levels of TOP2A and TIICs were associated with TOP2A CNV.\u003c/strong\u003e \u003cstrong\u003ea\u003c/strong\u003e-\u003cstrong\u003ec\u003c/strong\u003e The relationship between TOP2A CNV and its mRNA level. \u003cstrong\u003ed\u003c/strong\u003e The relationships between TOP2A CNV and immune cell infiltration levels, * \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01, and ***\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001. CNV, copy number variations.\u003c/p\u003e","description":"","filename":"figure14.png","url":"https://assets-eu.researchsquare.com/files/rs-1690716/v1/578121221789858c52ca1e57.png"},{"id":22373462,"identity":"8966a859-c684-449c-b5d4-36e807cd82fc","added_by":"auto","created_at":"2022-06-07 18:07:25","extension":"png","order_by":15,"title":"Figure 15","display":"","copyAsset":false,"role":"figure","size":1101121,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTOP2A expression correlated with CD8+ T cell, T cell, B cell, and T cell exhaustion in HCC. \u003c/strong\u003eImmune marker genes include CD8A, and CD8B of CD8+ T cell (\u003cstrong\u003ea\u003c/strong\u003e), CD3D, CD3E, and CD2 of T cell (\u003cstrong\u003eb\u003c/strong\u003e) CD19, CD79A, CD20/KRT20, and CD38 of B cell (\u003cstrong\u003ec\u003c/strong\u003e) PD-1/PDCD1, CTLA4, LAG3, and TIM- 3/HAVCR2 of T cell exhaustion (\u003cstrong\u003ed\u003c/strong\u003e).\u003c/p\u003e","description":"","filename":"figure15.png","url":"https://assets-eu.researchsquare.com/files/rs-1690716/v1/69e4bc8ed08c9de4584756be.png"},{"id":23109008,"identity":"3b58ba9d-57b3-4875-8207-625b701b2ada","added_by":"auto","created_at":"2022-06-27 06:44:41","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":8178780,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1690716/v1/ddf3d4b8-9a56-4109-b83b-897a25641962.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"TOP2A Serves as a Prognostic Marker Associated with Immune Infiltration in Hepatocellular Carcinoma","fulltext":[{"header":"Introduction","content":"\u003cp\u003eLiver cancer is a global health problem with increasing morbidity and mortality[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. According to statistics, there are approximately 850,000 new cases of liver cancer per year, which is 5.6% of all human tumors[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Hepatocellular carcinoma (HCC) is the most common type of liver cancer, accounting for more than 90% of liver cancers[\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The high incidences of HCC are mainly attributed to Hepatitis B and Hepatitis C Infections, chemical carcinogens, alcoholic and nonalcoholic fatty liver disease, and some genetic metabolic diseases, such as hemochromatosis and alpha-1-antitrypsin deficiency[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Hepatocarcinogenesis is a complex multi-step process, which may be closely related to the abnormal expression and mutation of certain genes. Therefore, a better understanding of the molecular mechanisms of HCC can provide more specific biomarkers for tumor diagnosis and treatment.\u003c/p\u003e \u003cp\u003eTopoisomerase II alpha (TOP2A) is a key enzyme responsible for solving various topological problems in the process of DNA metabolism[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. It is mainly distributed in the nucleus and the encoding gene was located at 17q12-21[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. The main function of TOP2A is to regulate DNA topology during recombination, replication and other processes by breaking and reconnecting DNA strands[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. TOP2A is a special molecular marker of cell replication, which is involved in the cell cycle. At the same time, it can regulate cell proliferation and apoptosis. In addition, the expression of TOP2A is specific during cell division, starting to increase in the S phase of mitosis, reaching the maximized level in the G2/M phase, and declining after the end of division[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Because of its biological properties, TOP2A has been extensively studied in the field of oncology. Depowski \u003cem\u003eet al\u003c/em\u003e. found that TOP2A was a candidate molecular marker for breast cancer cell proliferation and poor prognosis, which was preferentially expressed in more aggressive subsets of breast cancer (HER-2/neu overexpression)[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Pei \u003cem\u003eet al\u003c/em\u003e. found that TOP2A was abnormally highly expressed in pancreatic cancer tissues, proving that TOP2A was a co-activator of β-catenin, which promoted the invasion and metastasis of pancreatic cancer by activating the EMT process[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Zhang \u003cem\u003eet al\u003c/em\u003e. found that the expression of TOP2A was significantly elevated in colon cancer tissues, and played a major role in colon cancer invasion and metastasis via the AKT and ERK signaling pathways[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. In addition to human breast, colon, and pancreatic cancers, high expression of TOP2A was also found in oral, nasopharyngeal, esophageal, lung, gallbladder and prostate cancers, in which TOP2A overexpression was associated with aggressiveness phenotype, advanced stage, recurrence and decreased overall survival[\u003cspan additionalcitationids=\"CR11 CR12\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. However, the underlying functions and mechanisms of TOP2A in HCC remains unclear.\u003c/p\u003e \u003cp\u003eIn this study, we revealed that TOP2A predicted adverse prognosis in HCC and can be served as a potential therapeutic target for attenuating anti-tumor immunity. First, we downloaded the HCC data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, and analyzed the expression difference of TOP2A between HCC and normal hepatic tissues. Next, we further explored the association among TOP2A, the prognosis of HCC patients and the related signaling pathways. At the same time, we used bioinformatics tools to construct TOP2A co-expression gene network and analyzed their functions. Finally, we discussed the relationship between TOP2A and tumor immune infiltration from multiple dimensions. This study aims to supply a new perspective on the possible mechanism of hepatocellular carcinogenesis and to help discover new therapeutic targets for HCC.\u003c/p\u003e"},{"header":"Materials And Methods","content":"\u003cp\u003e\u003cstrong\u003eExpression analysis of TOP2A in HCC\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe used The Tumor Immune Estimation Resource (TIMER) database[14]\u0026nbsp;to assess the mRNA levels of TOP2A in different tumors. Subsequently, we downloaded the RNA sequence expression data of HCC in TCGA from the UCSC Xena browser[15]. Five microarray datasets (GSE102079[16], GSE25097[17], GSE87630[18], GSE84006 and GSE64041[19]) containing HCC and normal hepatic tissues were obtained from the GEO database. With the above data, we analyzed the differences in TOP2A expression among HCC, para-cancerous and normal hepatic tissues, the results were plotted by GraphPad Prism version 9 software. Furthermore, we observed the protein expression levels of TOP2A in HCC through the online immunohistochemistry (IHC) analysis of Human Protein Atlas (HPA) database.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorrelation analysis between TOP2A and clinicopathological characteristics in HCC\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eClinical data with primary HCC patients in TCGA were downloaded from the UCSC Xena browser. The baseline clinicopathological data including age, gender, grade, invasion depth, lymph node metastasis, distant metastasis, TNM stage, and survival status were enrolled in this study. After missing and incomplete data were eliminated, 337 HCC samples were retained for subsequent analysis. Based on the median mRNA expression of TOP2A, 169 patients were divided into the high expression group and 168 patients into the low expression group. The chi-square test was used to explore the correlation between TOP2A expression and clinicopathological parameters.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePrognostic analysis of TOP2A expression in HCC\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe used Gene Expression Profiling Interactive Analysis (GEPIA)[20]\u0026nbsp;and Kaplan-Meier (KM) plotter[21]\u0026nbsp;databases to analyze the relationship between TOP2A expression and prognosis in HCC. The log-rank P-value, hazard ratio (HR) and 95% confidence intervals (CI) were calculated. Subsequently, we downloaded the follow-up data of HCC patients in TCGA from the UCSC Xena browser. Univariate and multivariate Cox regression analysis were used to identify independent prognostic factors using SPSS 25 software. The results were presented as HR, 95% CI and \u003cem\u003eP\u003c/em\u003e values.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGene Set Enrichment Analysis (GSEA)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHere, HCC patients in TCGA were divided into high and low TOP2A expression groups according to the median expression of TOP2A. The KEGG pathway analysis of the two groups was performed by GSEA software. In the GSEA process, the number of genes parameter was 1000. The nominal (NOM) P value, false disclosure rate (FDR) and normalized enrichment score (NES) were used to evaluate the enriched signaling pathways in each phenotype.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConstruction and analysis of TOP2A gene co-expression network in HCC\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo better comprehend the molecular mechanism of TOP2A in HCC, we used the cBioPortal database[22]\u0026nbsp;to identify the top 6 genes (KIF18B, BUB1B, KIF23, CKAP2L, ANLN and TPX2) most related to TOP2A. The interrelationship was examined using the TIMER database. Moreover, we analyzed the expression level of TOP2A-related genes using UALCAN[23]\u0026nbsp;and TNMplot databases[24]. GEPIA and KM plotter databases were used to investigate the prognosis of TOP2A co-expressed genes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorrelation analysis of TOP2A, TOP2A co-expressed genes, and immune infiltrating cells\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe used the TIMER database to analyze the relationship among TOP2A, TOP2A co-expressed genes and tumor-infiltrating immune cells (TIICs), including CD8+ T cells, CD4+ T cells, B cells, macrophages, neutrophils, and dendritic cells. Next, we explored the correlation between the level of TIICs and the prognosis of HCC patients. Subsequently, we further analyzed the prognostic value of macrophage combined with TOP2A-related genes expression. Furthermore, we constructed seven multivariate Cox proportional hazards models, each model with seven variables, including age, tumor stage, gender, race, tumor purity, macrophage level, and expression of the single genes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImmune infiltration analysis of TOP2A in HCC\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe used CIBERSORT software to perform LM22 gene labeling (including 22 immune cell types) in HCC samples from the TCGA database, and the tumor microenvironment (TME) changed were evaluated. At the same time, we further explored the differences of TIICs in high and low TOP2A expression groups. The results were drawn using the R package \u0026ldquo;ggplot2\u0026rdquo;, \u0026ldquo;vioplot\u0026rdquo;, \u0026ldquo;pheatmap\u0026rdquo;, and \u0026ldquo;corrplot\u0026rdquo;. Furthermore, we applied TIMER and cBioportal databases to analyze the relationships among TOP2A copy number alterations, TIICs levels and TOP2A expression in HCC. Ultimately, the correlation between TOP2A and gene markers of TIICs were explored using the TIMER and GEPIA databases, Statistic significant differences were considered when \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05 (***\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001, **\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01, *\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical Analysis\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIndependent samples t test and paired sample t test were used to compare the difference of TOP2A mRNA levels between HCC and normal hepatic tissues. Chi-square test was used to research the relationship between TOP2A expression and the clinicopathological characteristics of HCC. The Cox proportional hazards regression model was used to analyze the relationship between the clinicopathological parameters and prognosis of HCC. The values are presented as mean \u0026plusmn; SD. If not specifically stated, \u003cem\u003eP\u0026nbsp;\u003c/em\u003evalues \u0026lt; 0.05 were considered statistically significant. SPSS software (Version 25.0) was used for all analysis.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eTOP2A was overexpressed in HCC\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePan-cancer analysis of TOP2A in TCGA-HCC dataset was performed by the TIMER database. We found the expression of TOP2A in bladder urothelial carcinoma (BLCA), breast invasive carcinoma (BRCA), cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC), cholangiocarcinoma (CHOL), colon adenocarcinoma (COAD), esophageal carcinoma (ESCA), glioblastoma multiforme (GBM), head and neck squamous cell carcinoma (HNSC), kidney chromophobe (KICH), kidney renal clear cell carcinoma (KIRC), kidney renal papillary cell carcinoma (KIRP), liver hepatocellular carcinoma (LIHC), lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), pheochromocytoma and paraganglioma (PCPG), pancreatic adenocarcinoma (PRAD), rectum adenocarcinoma (READ), stomach adenocarcinoma (STAD), thyroid carcinoma (THCA), and endometrioid cancer (UCEC) was significantly increased compared to the corresponding normal tissues (Figure 1a). Meanwhile, we used the TCGA and the GEO databases to evaluate the differential expression of TOP2A in HCC and normal hepatic tissues. The results showed that the mRNA level of TOP2A was significantly upregulated in HCC samples of TCGA, GSE102079, GSE25097, GSE87630, GSE84006, and GSE64041 datasets (Figure 1b-1g). Furthermore, according to the IHC results in the HPA database, we found that TOP2A was mainly expressed in the nucleus of hepatoma cells, and the staining intensity of TOP2A in HCC was significantly higher than normal hepatic tissues (Figure 2). These findings indicate that TOP2A expression is significantly upregulated in HCC.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe relationship between TOP2A expression and clinicopathological characteristics of HCC patients\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe investigated the relationship between TOP2A and clinicopathological characteristics of HCC patients by using TCGA-HCC dataset. The expression of TOP2A mRNA was significantly associated with the age (\u003cem\u003eP\u003c/em\u003e = 0.01), grade (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001), invasion depth (\u003cem\u003eP\u003c/em\u003e = 0.003), TNM stage (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001) and survival status (\u003cem\u003eP\u003c/em\u003e = 0.042) of HCC patients, but there were no significant differences in gender (\u003cem\u003eP\u003c/em\u003e = 0.051), lymph node metastasis (\u003cem\u003eP\u003c/em\u003e = 0.619), and distant metastasis (\u003cem\u003eP\u003c/em\u003e= 0.244). The results were shown in Table 1.\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"top\" width=\"100%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 1\u0026nbsp;\u003c/strong\u003eRelationship between TOP2A expression level and clinicopathological variables in HCC patients.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" width=\"27.835051546391753%\"\u003e\n \u003cp\u003e\u003cstrong\u003eClassification\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" width=\"21.649484536082475%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"34.02061855670103%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCD97\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eexpression\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" width=\"8.24742268041237%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026Chi;\u003csup\u003e2\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" width=\"8.24742268041237%\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"50%\"\u003e\n \u003cp\u003e\u003cstrong\u003eHigh\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"50%\"\u003e\n \u003cp\u003e\u003cstrong\u003eLow\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"28.125%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.875%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e6.598\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.010\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"28.125%\"\u003e\n \u003cp\u003e\u0026lt;60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.875%\"\u003e\n \u003cp\u003e158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"28.125%\"\u003e\n \u003cp\u003e\u0026ge;60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.875%\"\u003e\n \u003cp\u003e179\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"28.125%\"\u003e\n \u003cp\u003e\u003cstrong\u003eS\u003c/strong\u003e\u003cstrong\u003eex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.875%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e3.811\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"28.125%\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.875%\"\u003e\n \u003cp\u003e230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"28.125%\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.875%\"\u003e\n \u003cp\u003e107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"28.125%\"\u003e\n \u003cp\u003e\u003cstrong\u003eGrade\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.875%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e26.858\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"28.125%\"\u003e\n \u003cp\u003eG1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.875%\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"28.125%\"\u003e\n \u003cp\u003eG2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.875%\"\u003e\n \u003cp\u003e166\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"28.125%\"\u003e\n \u003cp\u003eG3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.875%\"\u003e\n \u003cp\u003e114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"28.125%\"\u003e\n \u003cp\u003eG4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.875%\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"28.125%\"\u003e\n \u003cp\u003e\u003cstrong\u003eInvasion depth\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.875%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e14.159\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.003\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"28.125%\"\u003e\n \u003cp\u003eT1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.875%\"\u003e\n \u003cp\u003e170\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"28.125%\"\u003e\n \u003cp\u003eT2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.875%\"\u003e\n \u003cp\u003e83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"28.125%\"\u003e\n \u003cp\u003eT3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.875%\"\u003e\n \u003cp\u003e74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"28.125%\"\u003e\n \u003cp\u003eT4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.875%\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"28.125%\"\u003e\n \u003cp\u003e\u003cstrong\u003eLymph node metastasis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.875%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e0.247\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e0.619\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"28.125%\"\u003e\n \u003cp\u003eN0-Nx\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.875%\"\u003e\n \u003cp\u003e333\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e166\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e167\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"28.125%\"\u003e\n \u003cp\u003eN1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.875%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"28.125%\"\u003e\n \u003cp\u003e\u003cstrong\u003eDistant metastasis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.875%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e1.357\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e0.244\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"28.125%\"\u003e\n \u003cp\u003eM0-Mx\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.875%\"\u003e\n \u003cp\u003e334\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e169\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e165\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"28.125%\"\u003e\n \u003cp\u003eM1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.875%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"28.125%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTNM stage\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.875%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e21.842\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"28.125%\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.875%\"\u003e\n \u003cp\u003e168\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"28.125%\"\u003e\n \u003cp\u003eII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.875%\"\u003e\n \u003cp\u003e82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"28.125%\"\u003e\n \u003cp\u003eIII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.875%\"\u003e\n \u003cp\u003e83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"28.125%\"\u003e\n \u003cp\u003eIV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.875%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"28.125%\"\u003e\n \u003cp\u003e\u003cstrong\u003eStatus\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.875%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e4.135\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.042\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"28.125%\"\u003e\n \u003cp\u003eAlive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.875%\"\u003e\n \u003cp\u003e223\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"28.125%\"\u003e\n \u003cp\u003eDead\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.875%\"\u003e\n \u003cp\u003e114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"top\" width=\"100%\"\u003e\n \u003cp\u003eP \u0026lt; 0.05, statistically significant.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe prognostic value of TOP2A in HCC\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe analyzed the prognostic value of TOP2A in HCC via the GEPIA and the KM plotter databases. In GEPIA database, HCC patients with high TOP2A expression had shorter overall survival (HR = 1.7, \u003cem\u003eP\u003c/em\u003e = 0.003; Figure 3a) and disease-free survival (HR = 1.7, \u003cem\u003eP\u003c/em\u003e = 0.000059; Figure 3b). The same results were observed in the KM plotter database (Figure 3c-3f), high expression of TOP2A in HCC predicted worse overall survival (HR = 1.82, \u003cem\u003eP\u003c/em\u003e = 0.00071), disease-free Survival (HR = 1.66, \u003cem\u003eP\u003c/em\u003e = 0.0024), progression-free survival (HR = 1.75, \u003cem\u003eP\u003c/em\u003e = 0.00018), and disease-specific survival (HR = 2.17, \u003cem\u003eP\u003c/em\u003e = 0.00062). These results suggest that high TOP2A expression is associated with poor prognosis in HCC patients. In addition, we performed univariate and multivariate Cox survival analysis on HCC samples in the TCGA database. The results of univariate Cox survival analysis showed invasion depth (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001), distant metastasis (\u003cem\u003eP\u003c/em\u003e = 0.025), TNM stage (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001), and TOP2A expression (\u003cem\u003eP\u003c/em\u003e = 0.002) were significantly associated with the prognosis of HCC patients. The multivariate Cox survival analysis revealed that TOP2A expression (\u003cem\u003eP\u003c/em\u003e = 0.004) was an independent risk factor for poor prognosis of HCC (Table 2). Summarily, we consider that TOP2A is a powerful prognostic biomarker in HCC.\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e Univariate and multivaruate analysis of the prognostic factors in HCC patients using a Cox regression model.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eParameters\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnivariate analysis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eM\u003c/strong\u003e\u003cstrong\u003eultivaruate analysis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHazard ratio\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;P value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHazard ratio\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;P value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAges, year (\u0026ge;60 vs. \u0026lt; 60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.268\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.873-1.840\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.212\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.465\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.985-2.179\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.060\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSex (female vs. male)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.289\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.883-1.881\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.189\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.743-1.644\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.621\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGrade (G3-G4 vs. G1-G2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.149\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.790-1.673\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.467\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.692-1.546\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.867\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eInvasion depth (T3/T4 vs. T1/T2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.494\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.714-3.627\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9.664\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.568-164.302\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.117\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLymph node metastasis (N1 vs. N0-Nx)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.934\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.476-7.856\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.356\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.745\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.497-28.190\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.200\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDistant metastasis (M1 vs. M0-Mx)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.734\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.181-11.800\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0\u003c/strong\u003e\u003cstrong\u003e.025\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.513\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.737-8.566\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.141\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTNM stage (III-IV vs. I-II)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.471\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.701-3.589\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.232\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.013-4.030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.316\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTOP2A expression (High vs. Low)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.802\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.240-2.619\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.863\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.226-2.831\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.004\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\"\u003e\n \u003cp\u003eCI confidence interval.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eGSEA identified TOP2A-related signaling pathways in HCC\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe analyzed the molecular mechanism of TOP2A in hepatocarcinogenesis by the GSEA software according to TCGA-HCC database. We found that 90 KEGG signaling pathways were related to HCC samples with high TOP2A expression, of which 19 signaling pathways were significantly enriched in TOP2A high expression group (NOM \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, FDR \u0026lt; 0.1 and NES \u0026gt; 1.9; Table 3). These KEGG signaling pathways were mainly related to tumorigenesis and cell metabolism, including \u0026ldquo;cell cycle\u0026rdquo;, \u0026ldquo;pyrimidine metabolism\u0026rdquo;, \u0026ldquo;homologous recombination\u0026rdquo;, \u0026ldquo;inositol phosphate metabolism\u0026rdquo;, \u0026ldquo;purine metabolism\u0026rdquo;, \u0026ldquo;ubiquitin mediated proteolysis\u0026rdquo;, \u0026ldquo;base excision repair\u0026rdquo;, \u0026ldquo;RNA degradation\u0026rdquo;, \u0026ldquo;spliceosome\u0026rdquo;, \u0026ldquo;DNA replication\u0026rdquo;, \u0026ldquo;P53 signaling pathway\u0026rdquo;, \u0026ldquo;n-glycan biosynthesis\u0026rdquo;, \u0026ldquo;erbb signaling pathway\u0026rdquo;, \u0026ldquo;nucleotide excision repair\u0026rdquo;, and \u0026ldquo;glycosylphosphatidylinositol GRI anchor biosynthesis\u0026rdquo; (Figure 4).\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" width=\"100%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e GSEA pathways upregulated due to high expression of TOP2A.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"66.3265306122449%\"\u003e\n \u003cp\u003e\u003cstrong\u003eGS \u0026lt; br \u0026gt; follow link to MSigDB\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.16326530612245%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSIZE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.3281%;\" valign=\"top\" width=\"8.16326530612245%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNES\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.4219%;\" valign=\"top\" width=\"8.16326530612245%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNOM p-val\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.183673469387756%\"\u003e\n \u003cp\u003e\u003cstrong\u003eFDR q-val\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003eKEGG_CELL_CYCLE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"3.076923076923077%\"\u003e\n \u003cp\u003e124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.3281%;\" valign=\"top\" width=\"24.23076923076923%\"\u003e\n \u003cp\u003e2.162\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.4219%;\" valign=\"top\" width=\"23.076923076923077%\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.615384615384617%\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003eKEGG_OOCYTE_MEIOSIS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"3.076923076923077%\"\u003e\n \u003cp\u003e111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.3281%;\" valign=\"top\" width=\"24.23076923076923%\"\u003e\n \u003cp\u003e2.101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.4219%;\" valign=\"top\" width=\"23.076923076923077%\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.615384615384617%\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003eKEGG_PYRIMIDINE_METABOLISM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"3.076923076923077%\"\u003e\n \u003cp\u003e97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.3281%;\" valign=\"top\" width=\"24.23076923076923%\"\u003e\n \u003cp\u003e2.068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.4219%;\" valign=\"top\" width=\"23.076923076923077%\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.615384615384617%\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003eKEGG_PROGESTERONE_MEDIATED_OOCYTE_MATURATION\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"3.076923076923077%\"\u003e\n \u003cp\u003e85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.3281%;\" valign=\"top\" width=\"24.23076923076923%\"\u003e\n \u003cp\u003e2.030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.4219%;\" valign=\"top\" width=\"23.076923076923077%\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.615384615384617%\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003eKEGG_HOMOLOGOUS_RECOMBINATION\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"3.076923076923077%\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.3281%;\" valign=\"top\" width=\"24.23076923076923%\"\u003e\n \u003cp\u003e1.978\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.4219%;\" valign=\"top\" width=\"23.076923076923077%\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.615384615384617%\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003eKEGG_INOSITOL_PHOSPHATE_METABOLISM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"3.076923076923077%\"\u003e\n \u003cp\u003e54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.3281%;\" valign=\"top\" width=\"24.23076923076923%\"\u003e\n \u003cp\u003e1.965\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.4219%;\" valign=\"top\" width=\"23.076923076923077%\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.615384615384617%\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003eKEGG_ENDOCYTOSIS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"3.076923076923077%\"\u003e\n \u003cp\u003e179\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.3281%;\" valign=\"top\" width=\"24.23076923076923%\"\u003e\n \u003cp\u003e1.959\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.4219%;\" valign=\"top\" width=\"23.076923076923077%\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.615384615384617%\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003eKEGG_PURINE_METABOLISM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"3.076923076923077%\"\u003e\n \u003cp\u003e153\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.3281%;\" valign=\"top\" width=\"24.23076923076923%\"\u003e\n \u003cp\u003e1.957\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.4219%;\" valign=\"top\" width=\"23.076923076923077%\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.615384615384617%\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003eKEGG_UBIQUITIN_MEDIATED_PROTEOLYSIS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"3.076923076923077%\"\u003e\n \u003cp\u003e130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.3281%;\" valign=\"top\" width=\"24.23076923076923%\"\u003e\n \u003cp\u003e1.945\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.4219%;\" valign=\"top\" width=\"23.076923076923077%\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.615384615384617%\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003eKEGG_BASE_EXCISION_REPAIR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"3.076923076923077%\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.3281%;\" valign=\"top\" width=\"24.23076923076923%\"\u003e\n \u003cp\u003e1.936\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.4219%;\" valign=\"top\" width=\"23.076923076923077%\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.615384615384617%\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003eKEGG_RNA_DEGRADATION\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"3.076923076923077%\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.3281%;\" valign=\"top\" width=\"24.23076923076923%\"\u003e\n \u003cp\u003e1.926\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.4219%;\" valign=\"top\" width=\"23.076923076923077%\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.615384615384617%\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003eKEGG_SPLICEOSOME\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"3.076923076923077%\"\u003e\n \u003cp\u003e126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.3281%;\" valign=\"top\" width=\"24.23076923076923%\"\u003e\n \u003cp\u003e1.923\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.4219%;\" valign=\"top\" width=\"23.076923076923077%\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.615384615384617%\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003eKEGG_DNA_REPLICATION\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"3.076923076923077%\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.3281%;\" valign=\"top\" width=\"24.23076923076923%\"\u003e\n \u003cp\u003e1.922\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.4219%;\" valign=\"top\" width=\"23.076923076923077%\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.615384615384617%\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003eKEGG_P53_SIGNALING_PATHWAY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"3.076923076923077%\"\u003e\n \u003cp\u003e66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.3281%;\" valign=\"top\" width=\"24.23076923076923%\"\u003e\n \u003cp\u003e1.914\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.4219%;\" valign=\"top\" width=\"23.076923076923077%\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.615384615384617%\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003eKEGG_N_GLYCAN_BIOSYNTHESIS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"3.076923076923077%\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.3281%;\" valign=\"top\" width=\"24.23076923076923%\"\u003e\n \u003cp\u003e1.912\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.4219%;\" valign=\"top\" width=\"23.076923076923077%\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.615384615384617%\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003eKEGG_ERBB_SIGNALING_PATHWAY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"3.076923076923077%\"\u003e\n \u003cp\u003e86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.3281%;\" valign=\"top\" width=\"24.23076923076923%\"\u003e\n \u003cp\u003e1.907\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.4219%;\" valign=\"top\" width=\"23.076923076923077%\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.615384615384617%\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003eKEGG_GLYCOSYLPHOSPHATIDYLINOSITOL_GPI_ANCHOR_BIOSYNTHESIS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"3.076923076923077%\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.3281%;\" valign=\"top\" width=\"24.23076923076923%\"\u003e\n \u003cp\u003e1.906\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.4219%;\" valign=\"top\" width=\"23.076923076923077%\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.615384615384617%\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003eKEGG_NUCLEOTIDE_EXCISION_REPAIR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"3.076923076923077%\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.3281%;\" valign=\"top\" width=\"24.23076923076923%\"\u003e\n \u003cp\u003e1.905\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.4219%;\" valign=\"top\" width=\"23.076923076923077%\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.615384615384617%\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003eKEGG_CHRONIC_MYELOID_LEUKEMIA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"3.076923076923077%\"\u003e\n \u003cp\u003e73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.3281%;\" valign=\"top\" width=\"24.23076923076923%\"\u003e\n \u003cp\u003e1.902\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.4219%;\" valign=\"top\" width=\"23.076923076923077%\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.615384615384617%\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" width=\"100%\"\u003e\n \u003cp\u003eNES: normalized enrichment score; NOM: nominal; FDR: false discovery rate. Gene sets with NOM p-value \u0026lt; 0.05 and FDR q-value \u0026lt; 0.1 are considered as significant.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eCo-expression network analysis of TOP2A in HCC\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo further investigate the effect of TOP2A in hepatocarcinogenesis, we used the TCGA-HCC dataset to explore TOP2A positive co-expressed genes via the cBioPortal database. Among them, the TOP6 gene associated with TOP2A was selected (absolute Spearman Correlation coefficient \u0026ge; 0.93; Figure 5a-5f). Meanwhile, the relationship between TOP2A and these genes were verified by TIMER database. The results showed that TOP2A expression was positively related to KIF18B (correlation coefficient (COR) = 0.96, \u003cem\u003eP\u0026nbsp;\u003c/em\u003e= 0e+00), BUB1B (COR = 0.956, \u003cem\u003eP\u0026nbsp;\u003c/em\u003e= 0e+00), KIF23 (COR = 0.95, \u003cem\u003eP\u0026nbsp;\u003c/em\u003e= 0e+00), CKAP2L (COR = 0.95, \u003cem\u003eP\u0026nbsp;\u003c/em\u003e= 0e+00), ANLN (COR = 0.951, \u003cem\u003eP\u0026nbsp;\u003c/em\u003e= 0e+00), and TPX2 (COR = 0.951, \u003cem\u003eP\u0026nbsp;\u003c/em\u003e= 0e+00) (Figure 5g-5l). Following, we evaluated the expression of these genes in HCC patients from the TCGA database via the UALCAN analysis tool. We observed that the TOP2A co-expressed genes were overexpressed in HCC (Figure 6a-6f). Similar changes were found in the TNMplot database (Figure 6g-6l). Furthermore, we also explored the association between these genes and the prognosis of HCC in the GEPIA database. The results showed that the prognosis of HCC patients with the high expression of KIF18B, BUB1B, KIF23, CKAP2L, ANLN, or TPX2 was significantly worse (Figure 7a-7f). We obtained the same conclusion in the KM plotter database, which HCC patients with the high KIF18B (HR = 1.86, \u003cem\u003eP\u003c/em\u003e = 0.00048), BUB1B (HR = 1.82, \u003cem\u003eP\u003c/em\u003e = 0.00071), KIF23 (HR = 1.66, \u003cem\u003eP\u003c/em\u003e = 0.00071) 0.0043), CKAP2L (HR = 1.76, \u003cem\u003eP\u003c/em\u003e = 0.0014), ANLN (HR = 1.8, \u003cem\u003eP\u003c/em\u003e = 0.00084), or TPX2 (HR = 2.05, \u003cem\u003eP\u003c/em\u003e = 5.7e-05) expression had a poor prognosis (Figure 7g- 7l). These results suggest that TOP2A and TOP2A co-expressed genes may contribute to the progression of HCC.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTOP2A and TOP2A co-expressed genes were significantly associated with TIICs in HCC\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe used the TIMER database to analyze the relationship among TOP2A, TOP2A co-expressed genes and infiltrating immune cells in HCC. As shown in Figure 8, after correcting for the confounding factor of tumor purity, we identified that TOP2A, KIF18B, BUB1B, KIF23, CKAP2L, ANLN, and TPX2 were significantly positively correlated with TIICs, including B cells (COR, 0.44 to 0.5, \u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt; 0.01), CD8+ T cell (COR, 0.282 to 0.355, \u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt; 0.01), CD4+ T cell (COR, 0.33 to 0.412, \u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt; 0.01), macrophage (COR, 0.442 to 0.522, \u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt; 0.01), neutrophil (COR, 0.376 to 0.443, \u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt; 0.01), and dendritic cell (COR, 0.45 to 0.502,\u003cem\u003e\u0026nbsp;P\u0026nbsp;\u003c/em\u003e\u0026lt; 0.01). The above results indicate that TOP2A and TOP2A co-expressed genes are positively correlated with tumor-associated B cells, CD8+ T cells, CD4+ T cells, macrophages, neutrophils, and dendritic cells in HCC.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOverexpression of TOP2A and TOP2A co-expressed genes with high levels of infiltratedvmacrophages predicted poor survival in HCC\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe used the TIMER database to explore the relationship between different TIICs and prognosis in HCC patients. After adjusting for five confounding factors, including age, stage, gender, race, and tumor purity, we found that macrophages (HR = 1.29, \u003cem\u003eP\u003c/em\u003e = 0.0181) and Neutrophil cells (HR = 1.28, \u003cem\u003eP\u003c/em\u003e = 0.0264) were significantly associated with the prognosis of HCC (Figure 9).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn the present study, we combined the tumor-associated macrophages (TAMs) levels and the single gene expression patterns to assess the prognosis of HCC. The results were shown in Figure 10, there was no significant correlation between the TAMs and prognosis under the low expression level of TOP2A/BUB1B/CKAP2L/ANLN/TPX2, while the high levels of macrophages had a poor outcome in HCC under the low expression of KIF18B (HR = 1.61, \u003cem\u003eP\u003c/em\u003e = 0.0361) and KIF23 (HR = 1.57, \u003cem\u003eP\u003c/em\u003e = 0.044). TAMs with the high expression of TOP2A/KIF18B/BUB1B/KIF23/CKAP2L/ANLN had no significant relationship with HCC prognosis. However, under the high TPX2 expression (HR = 1.58, \u003cem\u003eP\u003c/em\u003e = 0.0404), the higher macrophages levels had a worse survival in HCC.\u003c/p\u003e\n\u003cp\u003eFurthermore, we constructed seven multivariate Cox regression analysis models. Each model contained the following variables: macrophage levels, age, stage, gender, race, tumor purity, and single gene expression (Figure 11). The results identified that TAMs with the low expression of TOP2A/KIF18B/BUB1B/KIF23/CKAP2L/ANLN/TPX2 had no statistical association with survival in HCC after adjusting these variables. However, under the high TOP2A (HR = 2.1, \u003cem\u003eP\u003c/em\u003e = 0.00773)/KIF18B (HR = 1.87, \u003cem\u003eP\u003c/em\u003e = 0.0193)/BUB1B (HR = 1.83, \u003cem\u003eP\u003c/em\u003e = 0.0318)/KIF23 (HR = 1.83, \u003cem\u003eP\u003c/em\u003e = 0.0269)/CKAP2L (HR = 2.06, \u003cem\u003eP\u003c/em\u003e = 0.0085)/ANLN (HR = 1.95, \u003cem\u003eP\u003c/em\u003e = 0.0171)/TPX2 (HR = 2.38, \u003cem\u003eP\u003c/em\u003e = 0.00202) expression, the low TAM levels predicted better prognosis in HCC (Figure 12). Summarily, TOP2A and TOP2A co-expressed genes were independent adverse prognostic biomarkers and that combining these genes expression with the TAM levels may contribute to predict an adverse prognosis of HCC.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTOP2A was an immune-related gene in HCC\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, we used the CIBERSORT algorithm to evaluate the immune infiltration model of 337 HCC samples in the TCGA database (Figure 13a). Subsequently, the patients were divided into two groups according to the median value of TOP2A expression, and we observed that the proportions of diverse types of TIICs in the TME were apparently different between the two groups (Figure 13b-13c). The proportion of resting memory CD4+ T cells (\u003cem\u003eP\u003c/em\u003e = 0.012), activated CD4+ T cells (\u003cem\u003eP\u003c/em\u003e = 0.001), follicular helper T cells (P\u0026lt;0.001), regulatory T cells (\u003cem\u003eP\u003c/em\u003e = 0.009), M0 macrophages (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001), resting dendritic cells (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001), or neutrophils (\u003cem\u003eP\u003c/em\u003e = 0.026) was increased in TOP2A high expression group, while the proportion of resting NK cells (\u003cem\u003eP\u003c/em\u003e = 0.046), monocytes (\u003cem\u003eP\u003c/em\u003e = 0.027), M2 macrophages (\u003cem\u003eP\u003c/em\u003e = 0.003), and resting Mast cells (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001) was decreased in TOP2A high expression group. A correlation heatmap showed that the proportions of different TIICs in HCC patients had weak to strong correlation (Figure 13d). There was a significant negative correlation between CD8+ T cells and resting memory CD4+ T cells (COR = \u0026minus;0.37), follicular helper T cells and resting memory CD4+ T cells were significantly negatively correlated (COR = \u0026minus;0.37). A significant positive correlation between CD8+ T cells and activated memory CD4+ T cells (COR = 0.51), CD8+ T cells and follicular helper T cells were significantly positive correlated (COR = 0.43). In addition, the potential association between TOP2A copy number variations (CNV) and TIICs were analyzed via the TIMER database. We observed that TOP2A CNV had a major impact on TOP2A mRNA expression (Figure 14a-14c), which were closely related to the infiltration degree of B cells, CD8+ T cells, CD4+ T cells, macrophages, neutrophils, or dendritic cells (Figure 14d). These results suggest that TOP2A may control the level of TIICs by altering TOP2A CNV, thus affecting the prognosis of HCC.\u003c/p\u003e\n\u003cp\u003eWhat more, to further verify the relationship between TOP2A and TIICs, we explored the association between TOP2A expression and immune marker genes of TIICs in HCC via the TIMER and GEPIA databases. We found that the expression of TOP2A was positively associated with marker genes of T cell, B cell, and T cell exhaustion, including CD8A, CD8B, CD3D, CD3E, CD2, CD19, CD79A, CD20/KRT20, CD38, PD-1/PDCD1, CTLA4, LAG3, and TIM- 3/HAVCR2 (Table 4). Figure 15 showed that the correlation between TOP2A and immune marker genes of B cells, T cells, or T cell exhaustion. These results reveal that TOP2A is closely related to TIICs in HCC.\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\" valign=\"top\" width=\"100%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 4\u003c/strong\u003e Correlation analysis between TOP2A and relate genes and markers of immune cells in TIMER and GEPIA.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" width=\"16.3265306122449%\"\u003e\n \u003cp\u003eDescription\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" width=\"21.428571428571427%\"\u003e\n \u003cp\u003eGene markers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" width=\"42.857142857142854%\"\u003e\n \u003cp\u003eTIMER\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"19.387755102040817%\"\u003e\n \u003cp\u003eGEPIA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"34.42622950819672%\"\u003e\n \u003cp\u003eNone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"34.42622950819672%\"\u003e\n \u003cp\u003ePurity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"31.147540983606557%\"\u003e\n \u003cp\u003eTumor\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"15.254237288135593%\"\u003e\n \u003cp\u003eRho\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"18.64406779661017%\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.254237288135593%\"\u003e\n \u003cp\u003eRho\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.338983050847457%\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.254237288135593%\"\u003e\n \u003cp\u003eRho\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.254237288135593%\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003eCD8+ T cell\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.875%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCD8A\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.188\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.458333333333334%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.307\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.5%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.875%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCD8B\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.458333333333334%\"\u003e\n \u003cp\u003e**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.255\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.5%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003eT cell (general)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.875%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCD3D\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.225\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.458333333333334%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.348\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.5%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.875%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCD3E\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.458333333333334%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.348\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.5%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.875%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCD2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.184\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.458333333333334%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.338\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.5%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003eB cell\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.875%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCD19\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.262\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.458333333333334%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.348\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.5%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.875%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCD79A\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.458333333333334%\"\u003e\n \u003cp\u003e**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.287\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.5%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.875%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCD20/KRT20\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.196\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.458333333333334%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.222\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.5%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.875%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCD38\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.241\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.458333333333334%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.362\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.5%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003eMonocyte\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.875%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCD86\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.299\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.458333333333334%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.472\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.5%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.875%\"\u003e\n \u003cp\u003eCD115/CSF1R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.458333333333334%\"\u003e\n \u003cp\u003e**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.308\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.5%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.064\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003eTAM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.875%\"\u003e\n \u003cp\u003eCCL2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.458333333333334%\"\u003e\n \u003cp\u003e1.9e-01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.191\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.5%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e-0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.875%\"\u003e\n \u003cp\u003eCD68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.224\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.458333333333334%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.331\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.5%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.091\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.875%\"\u003e\n \u003cp\u003eIL10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.458333333333334%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.368\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.5%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003eM1 Macrophage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.875%\"\u003e\n \u003cp\u003eiNOS/NOS2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.458333333333334%\"\u003e\n \u003cp\u003e1.39e-01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.089\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.5%\"\u003e\n \u003cp\u003e9.79e-02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e-0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.875%\"\u003e\n \u003cp\u003e\u003cstrong\u003eIRF5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.441\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.458333333333334%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.449\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.5%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.875%\"\u003e\n \u003cp\u003eCOX2/PTGS2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.458333333333334%\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.274\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.5%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e-0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003eM2 Macrophage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.875%\"\u003e\n \u003cp\u003eCD163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.458333333333334%\"\u003e\n \u003cp\u003e5.01e-02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.228\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.5%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.0029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.875%\"\u003e\n \u003cp\u003eVSIG4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.098\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.458333333333334%\"\u003e\n \u003cp\u003e5.93e-02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.225\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.5%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.0039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.875%\"\u003e\n \u003cp\u003eMS4A4A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.458333333333334%\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.5%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e-0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003eNeutrophils\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.875%\"\u003e\n \u003cp\u003eCD66b/\u003c/p\u003e\n \u003cp\u003eCEACAM8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.083\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.458333333333334%\"\u003e\n \u003cp\u003e1.09e-01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.5%\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.875%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCD11b/ITGAM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.458333333333334%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.419\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.5%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.875%\"\u003e\n \u003cp\u003eCCR7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.458333333333334%\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.271\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.5%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.0063\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003eNatural killer cell\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.875%\"\u003e\n \u003cp\u003eKIR2DL1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e-0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.458333333333334%\"\u003e\n \u003cp\u003e8.58e-01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e-0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.5%\"\u003e\n \u003cp\u003e6.51e-01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e-0.048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.875%\"\u003e\n \u003cp\u003eKIR2DL3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.458333333333334%\"\u003e\n \u003cp\u003e**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.226\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.5%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.084\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.875%\"\u003e\n \u003cp\u003e\u003cstrong\u003eKIR2DL4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.191\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.458333333333334%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.226\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.5%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.875%\"\u003e\n \u003cp\u003eKIR3DL1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.458333333333334%\"\u003e\n \u003cp\u003e3.64e-01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.069\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.5%\"\u003e\n \u003cp\u003e2.04e-01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e-0.069\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.875%\"\u003e\n \u003cp\u003eKIR3DL2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.087\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.458333333333334%\"\u003e\n \u003cp\u003e9.61e-02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.5%\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.075\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.875%\"\u003e\n \u003cp\u003eKIR3DL3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.458333333333334%\"\u003e\n \u003cp\u003e2.47e-01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.061\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.5%\"\u003e\n \u003cp\u003e2.60e-01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.875%\"\u003e\n \u003cp\u003eKIR2DS4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.458333333333334%\"\u003e\n \u003cp\u003e2.12e-01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.5%\"\u003e\n \u003cp\u003e2.74e-01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.0081\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003eDendritic cell\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.875%\"\u003e\n \u003cp\u003eHLA-DPB1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.159\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.458333333333334%\"\u003e\n \u003cp\u003e**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.293\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.5%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.088\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.092\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.875%\"\u003e\n \u003cp\u003e\u003cstrong\u003eHLA-DQB1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.458333333333334%\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.232\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.5%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.875%\"\u003e\n \u003cp\u003eHLA-DRA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.193\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.458333333333334%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.332\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.5%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.078\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.875%\"\u003e\n \u003cp\u003eHLA-DPA1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.458333333333334%\"\u003e\n \u003cp\u003e**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.309\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.5%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.875%\"\u003e\n \u003cp\u003eBDCA-1/CD1C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.458333333333334%\"\u003e\n \u003cp\u003e**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.255\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.5%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.875%\"\u003e\n \u003cp\u003eBDCA-4/NRP1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.458333333333334%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.342\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.5%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.875%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCD11c/ITGAX\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.342\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.458333333333334%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.5%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003eTh1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.875%\"\u003e\n \u003cp\u003eT-bet/TBX21\u003c/p\u003e\n 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width=\"11.458333333333334%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.486\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.5%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.875%\"\u003e\n \u003cp\u003e\u003cstrong\u003eIFN-g/IFNG\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.254\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.458333333333334%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n 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\u003cp\u003e0.406\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.5%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003eTh2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.875%\"\u003e\n \u003cp\u003e\u003cstrong\u003eGATA3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.191\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.458333333333334%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.345\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.5%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.875%\"\u003e\n \u003cp\u003eSTAT6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.183\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.458333333333334%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.5%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n 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width=\"9.375%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.875%\"\u003e\n \u003cp\u003eIL13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.458333333333334%\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.5%\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.093\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.075\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003eTfh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.875%\"\u003e\n \u003cp\u003eBCL6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.235\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.458333333333334%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.242\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.5%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.875%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCXCR5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.458333333333334%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.336\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.5%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.875%\"\u003e\n \u003cp\u003e\u003cstrong\u003eICOS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.299\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.458333333333334%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.432\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.5%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003eTh17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.875%\"\u003e\n \u003cp\u003eSTAT3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.458333333333334%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.295\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.5%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e-0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.875%\"\u003e\n \u003cp\u003e\u003cstrong\u003eIL17A\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.458333333333334%\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.5%\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003eTreg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.875%\"\u003e\n \u003cp\u003eFOXP3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.214\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.458333333333334%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.299\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.5%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.875%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCCR8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.441\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.458333333333334%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.554\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.5%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.875%\"\u003e\n \u003cp\u003eSTAT5B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.458333333333334%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.416\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.5%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.085\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.875%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTGFb/TGFB1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.263\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.458333333333334%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.376\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.5%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003eT cell exhaustion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.875%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePD-1/PDCD1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.283\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.458333333333334%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.385\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.5%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.875%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCTLA4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.306\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.458333333333334%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.427\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.5%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.875%\"\u003e\n \u003cp\u003e\u003cstrong\u003eLAG3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.458333333333334%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.5%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.875%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTIM-3/HAVCR2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.301\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.458333333333334%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.476\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.5%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\" valign=\"top\" width=\"100%\"\u003e\n \u003cp\u003eHCC: hepatocellular carcinoma; TAM: tumor-associated macrophage; Th: T helper cell; Tfh: Follicular helper T cell; Treg, regulatory T cell; Rho, R value of Spearman\u0026rsquo;s correlation; None, correlation without adjustment. Purity, correlation adjusted by purity. *\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; 0.01, ***\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eHCC is a highly heterogeneous malignant tumor, and the vast majority of patients are diagnosed at the advanced stage[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Surgical resection is still the most important treatment measure for HCC, but its therapeutic effect is not always satisfactory[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Therefore, it is necessary to seek for new tumor markers and therapeutic targets. TOP2A is a key enzyme involved in DNA recombination and replication[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. In recent years, the focus of research on the function of TOP2A has been extended to the field of cancer, which indicates the potential ability of TOP2A to attenuate tumor immunity and as a potential therapeutic target[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Currently, the role of TOP2A in HCC is limited. The purpose of our study is to deeply analyze the biological function and related regulatory pathways of TOP2A in HCC.\u003c/p\u003e \u003cp\u003ePrevious studies had reported the critical role of TOP2A in tumor angiogenesis and the aberrant expression in a variety of tumors[\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. In the present study, we found that TOP2A was overexpressed in 20 types of tumors by analyzing the TIMER database, which was consistent with previous findings. According to the TCGA, GEO and HPA databases, the expression of TOP2A in HCC was significantly higher than normal hepatic tissues, and the analysis outcomes were also consistent with the previous results[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Meanwhile, high TOP2A expression of HCC patients had high-grade malignancy and advanced stage. Furthermore, we also found that TOP2A overexpression in HCC predicted poor survival, which implied that the prognosis of HCC patients might be improved by regulating the expression of TOP2A. In conclusion, TOP2A can be used as a biomarker for the diagnosis and prognosis of HCC.\u003c/p\u003e \u003cp\u003eCurrent research on the role of TOP2A in tumors mainly focuses on gene instability and chromosomal instability (CIN)[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. However, other biological functions of TOP2A in HCC are rarely studied. In this study, the potential molecular mechanism of TOP2A in HCC were explored by the GSEA tool, we found that the KEGG signaling pathways were significantly enriched in the high expression of TOP2A, including \u0026ldquo;cell cycle\u0026rdquo;, \u0026ldquo;pyrimidine metabolism\u0026rdquo;, \u0026ldquo;homologous recombination\u0026rdquo;, \u0026ldquo;inositol phosphate metabolism\u0026rdquo;, \u0026ldquo;purine metabolism\u0026rdquo;, \u0026ldquo;ubiquitin mediated proteolysis\u0026rdquo;, \u0026ldquo;base excision repair\u0026rdquo;, \u0026ldquo;RNA degradation\u0026rdquo;, \u0026ldquo;spliceosome\u0026rdquo;, \u0026ldquo;DNA replication\u0026rdquo;, \u0026ldquo;P53 signaling pathway\u0026rdquo;, \u0026ldquo;n-glycan biosynthesis\u0026rdquo;, \u0026ldquo;erbb signaling pathway\u0026rdquo;, \u0026ldquo;nucleotide excision repair\u0026rdquo;, and \u0026ldquo;glycosylphosphatidylinositol GRI anchor biosynthesis\u0026rdquo;. Previous studies have confirmed that these signaling pathways were closely related to the tumorigenesis and the progression of HCC[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Cyclin D1 is an important regulator factor of cell cycle[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Nishida \u003cem\u003eet al\u003c/em\u003e. found that Cyclin D1 was abnormally overexpressed in HCC, which was associated with the property of invasive tumor growth[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. P53 is an important tumor suppressor gene that plays a critical role in tumor occurrence and development[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Llovet \u003cem\u003eet al\u003c/em\u003e. found that mutation p53 was considered to be a tumor promoting factor in HCC by affecting tumor cell proliferation, survival, invasion and immune evasion[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In our study, the high expression of TOP2A in HCC predicted adverse prognosis. Therefore, TOP2A may play a vital role in hepatocarcinogenesis by affecting these signaling pathways and lead to a poor survival of HCC.\u003c/p\u003e \u003cp\u003eTo deep understand the potential molecular mechanism of TOP2A in hepatocarcinogenesis, we constructed a co-expression network of TOP2A using the cBioPortal database. We identified that KIF18B, BUB1B, KIF23, CKAP2L, ANLN and TPX2 were strongly positively associated with TOP2A in HCC. TOP2A co-expressed genes were significantly elevated in HCC, which were correlated with adverse prognosis of patients. Yang \u003cem\u003eet al\u003c/em\u003e. found that KIF18B promoted HCC progression by activating the Wnt/β-catenin signaling pathway[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Qiu \u003cem\u003eet al\u003c/em\u003e. confirmed that BUB1B upregulated the activity of mTORC1 signaling pathway to promote HCC development[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Cheng \u003cem\u003eet al\u003c/em\u003e. identified that KIF23 and KIF14 promoted the proliferation, invasion and chemotherapy resistance of HCC, but the specific molecular mechanism had not been elucidated[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Wang \u003cem\u003eet al\u003c/em\u003e. considered that CKAP2L was associated with poor prognosis and promoted the malignancy of HCC[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Li \u003cem\u003eet al\u003c/em\u003e. demonstrated that the CDK1-PLK1/SGOL2/ANLN pathway promoted the aggressiveness of HCC by mediating abnormal cell division in the cell cycle. Wang \u003cem\u003eet al\u003c/em\u003e. found that the Hh-FOXM1-TPX2 signaling pathway played a key role in the replication of HCC[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Summarily, TOP2A co-expressed genes may be involved in hepatocarcinogenesis and lead to a poor prognosis of patients.\u003c/p\u003e \u003cp\u003eTIICs are closely related to the progression and prognosis of HCC[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. The analysis results of the TIMER database showed that the expression of TOP2A and TOP2A-related genes were significantly positively correlated with TIICs, including B cell, CD8\u0026thinsp;+\u0026thinsp;T cell, CD4\u0026thinsp;+\u0026thinsp;T cell, Neutrophil, Dendritic cell, and Macrophage. Furthermore, survival analysis identified that the prognosis of HCC was significantly worse with the high levels of macrophages and neutrophil cells. Based on survival curves and multivariate Cox regression analysis, we found that TOP2A and TOP2A co-expressed genes were independent adverse prognosis in HCC. These genes had strong predictive ability for the prognosis of HCC. Previous studies demonstrated that TAMs promote angiogenesis, invasion, and metastasis of HCC, which leaded to a poor prognosis[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Abundant studies have confirmed that targeting TAMs was a potential tumor monotherapy or combined therapy[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Furthermore, the identification of TAM-related genes will help to provide more potential targets for individualized and precise treatment of HCC, thus improve prognosis of patients. Therefore, we evaluated the prognostic value of combining TAM levels and the expression of TOP2A, TOP2A-related genes. The results showed that under the high expression of TOP2A/KIF18B/BUB1B/KIF23/CKAP2L/ANLN/TPX2, the high levels of TAM predicted poor prognosis in HCC. Collectively, it can be speculated that combining the expression of these genes and the levels of TAMs play a more effective role in the prognosis prediction of HCC.\u003c/p\u003e \u003cp\u003eTIICs are important components of the TME, which played a key role in tumorigenesis and tumor progression[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. The analysis of 22 types of TIICs in HCC was performed by CIBERSORT. We found that the proportion of 11 immune cells were apparently different at TOP2A levels, including resting memory CD4\u0026thinsp;+\u0026thinsp;T cells, activated CD4\u0026thinsp;+\u0026thinsp;T cells, follicular helper T cells, regulatory T cells, resting NK cells, monocytes, M0 macrophages, M2 macrophages, resting Dendritic cells, resting Mast cells, and neutrophils. Furthermore, the expression of TOP2A and CNV were significantly correlated with the infiltration degree of B cells, CD8\u0026thinsp;+\u0026thinsp;T cells, CD4\u0026thinsp;+\u0026thinsp;T cells, macrophages, neutrophils, and dendritic cells. Therefore, we infered that TOP2A was closely related to TIICs in HCC and might be involved in immune responses of TME. Meanwhile, we also found that the expression of TOP2A was positively correlated with immune gene markers of CD8\u0026thinsp;+\u0026thinsp;T cell, T cell, B cell, and T cell exhaustion by analyzing the TIMER and the GEPIA databases. B cells, T cells, and CD8\u0026thinsp;+\u0026thinsp;T cells are important immune cells in the human body, and played an important role in anti-tumor immunity[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. T cells and B cells are significantly upregulated in tumor tissues, and CD8\u0026thinsp;+\u0026thinsp;T cells are abundant in tumor and para-cancerous tissues[\u003cspan additionalcitationids=\"CR43\" citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. T cell exhaustion is an important feature of tumors, which manifested by desensitization of T cells and loss of response to tumor antigens. With the persistent stimulation of tumor antigens and multiple cell surface inhibitory receptors, such as PD-1, LAG3, and CTLA-4, thus causing T cells impaired cytotoxicity and reduced cytokine production, and ultimately leading to unrestricted proliferation and escape of tumor cells[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Exhaustion of infiltrating T cells had been observed in HCC, which is associated with poor clinical outcomes[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Hung \u003cem\u003eet al\u003c/em\u003e. demonstrated that tumor methionine metabolism drived T cell exhaustion in an HCC cohort of 675 patients[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. We speculate that TOP2A overexpression accelerates T cell exhaustion in HCC and ultimately promotes tumor progression. We infer that HCC patients with high expression of TOP2A may attenuate anti-tumor immune responses. Therefore, TOP2A plays an important role in the immune regulation of HCC. However, more experiments are needed to further validate our hypothesis, especially the relationship between TOP2A and T cell exhaustion.\u003c/p\u003e \u003cp\u003eOur study had some limitations. First of all, the results that we obtained were mainly from open public databases, and had not been verified by \u003cem\u003evivo\u003c/em\u003e and \u003cem\u003evitro\u003c/em\u003e experiments. Our research only stayed at the transcriptome level, and the results were not accurate enough. The conclusions should be tested by experimental methods such as qPCR, WB, and IHC. Furthermore, the HCC samples that we obtained were limited. Therefore, larger HCC samples are needed to eliminate the interference of the high heterogeneity of HCC.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, our study demonstrated that TOP2A was overexpressed in HCC, which was correlated with the clinicopathological characteristics and prognosis. The expression of TOP2A was related to the degree of immune cell infiltration and attenuated anti-tumor immunity by accelerating the exhaustion of infiltrating T cells. Therefore, TOP2A can be used as a biomarker for HCC diagnosis, treatment and prognosis.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHCC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHepatocellular carcinoma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTOP2A\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTopoisomerase II alpha\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTCGA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eThe Cancer Genome Atlas\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGEO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGene Expression Omnibus\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTIMER\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTumor Immune Estimation Resource\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIHC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eimmunohistochemistry\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHPA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHuman Protein Atlas\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGEPIA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGene Expression Profiling Interactive Analysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eKM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eKaplan-Meier\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ehazard ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003econfidence intervals\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGSEA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGene Set Enrichment Analysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNES\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003enormalized enrichment score\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTIICs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003etumor infiltrating immune cells\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTME\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003etumor microenvironment\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTAMs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003etumor associated macrophages\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCNV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ecopy number variations.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent 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 raw data of this study are derived from the GEO database (https://www.ncbi.nlm.nih.gov/geo/) and the UCSC Xena database (http://xena.ucsc.edu), which are publicly available databases.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that there is no conflict of interest regarding the publication of this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by the National Natural Science Foundation of China [Grant No. NSF82060127]; and the Yunnan Provincial Organ Transplantation Clinical Medical Center [Grant No. 2020SYZ-Z-044].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eQS and JR conceived and designed the study. QS, SN and JR performed the data curation and analysis. QS, SN and JR analyzed and interpreted the results. QS and JR drafted and reviewed the manuscript. All authors reviewed and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors gratefully acknowledge the TIMER, TCGA, GEO, GEPIA, Kaplan-Meier plotter, TNMplot, UALCAN, and HPA database, which made the data available.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAnwanwan D, Singh SK, Singh S, Saikam V, Singh R: \u003cstrong\u003eChallenges in liver cancer and possible treatment approaches\u003c/strong\u003e. \u003cem\u003eBiochim Biophys Acta Rev Cancer \u003c/em\u003e2020, \u003cstrong\u003e1873\u003c/strong\u003e(1):188314.\u003c/li\u003e\n\u003cli\u003eLlovet JM, Zucman-Rossi J, Pikarsky E, Sangro B, Schwartz M, Sherman M, Gores G: \u003cstrong\u003eHepatocellular carcinoma\u003c/strong\u003e. \u003cem\u003eNat Rev Dis Primers \u003c/em\u003e2016, \u003cstrong\u003e2\u003c/strong\u003e:16018.\u003c/li\u003e\n\u003cli\u003eSia D, Villanueva A, Friedman 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\u003cstrong\u003e12\u003c/strong\u003e(1):1455.\u003c/li\u003e\n\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":"TOP2A, hepatocellular carcinoma, prognosis, immune infiltration, molecular marker","lastPublishedDoi":"10.21203/rs.3.rs-1690716/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1690716/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eTOP2A is a key enzyme that controls the topological state of DNA during DNA replication and transcription. We aim to explore the role of TOP2A in hepatocellular carcinoma (HCC).\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThe expression and prognostic value of TOP2A in HCC were analyzed by TIMER, TCGA, GEO, HPA, GEPIA and Kaplan-Meier plotter databases. The potential molecular mechanism of TOP2A in HCC was researched by GSEA software. The construction of the TOP2A gene co-expression network was completed by the cBioPortal database. The relationship between TOP2A and immune cell infiltration in HCC was explored through the CIBERSORT and TIMER databases.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eTOP2A was overexpressed in HCC and was associated with a poor prognosis. High expression of TOP2A was associated with worse pathological grade, deeper invasion depth, and advanced TNM stage, which was an independent unfavorable prognostic factor for HCC (HR\u0026thinsp;=\u0026thinsp;1.863, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004). The GSEA results indicated that TOP2A was closely related to tumorigenesis and cellular metabolism. Furthermore, TOP2A and its co-expressed genes were positively associated with tumor-infiltrating immune cells. These co-expressed genes were independent prognostic factors, and the expression of these genes combined with macrophage levels helped to predict prognosis of HCC. In terms of immune infiltration, HCC patients with high TOP2A expression that the proportions of resting memory CD4\u0026thinsp;+\u0026thinsp;T cells, activated CD4\u0026thinsp;+\u0026thinsp;T cells, follicular helper T cells, regulatory T cells, macrophages, and neutrophils were significantly increased. The copy number variations of TOP2A affected the level of tumor-infiltrating immune cells. The expression of TOP2A was positively correlated with immune gene markers of T cell exhaustion in the HCC microenvironment.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eTOP2A is a prognostic molecular marker of HCC, which is related to immune cell infiltration.\u003c/p\u003e","manuscriptTitle":"TOP2A Serves as a Prognostic Marker Associated with Immune Infiltration in Hepatocellular Carcinoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-06-07 18:07:22","doi":"10.21203/rs.3.rs-1690716/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":"d73d98c9-0c8d-4042-944b-fde1f1279a5f","owner":[],"postedDate":"June 7th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2022-06-27T06:44:29+00:00","versionOfRecord":[],"versionCreatedAt":"2022-06-07 18:07:22","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-1690716","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1690716","identity":"rs-1690716","version":["v1"]},"buildId":"_2-kVJe1T_tPrBINL-cwx","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}