Expression of tumor-associated macrophages and PD-L1 in patients with hepatocellular carcinoma and construction of a prognostic model

Expression of tumor-associated macrophages and PD-L1 in patients with hepatocellular carcinoma and construction of a prognostic model | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Expression of tumor-associated macrophages and PD-L1 in patients with hepatocellular carcinoma and construction of a prognostic model Panpan kong, Huan Yang, Qing Tong, Xiaogang Dong, Mamumaimaitijiang-Abula Yi, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2579242/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: Hepatocellular carcinoma (HCC) is an inflammation-associated tumor involved in immune tolerance and evasion in the immune microenvironment. Immunotherapy can enhance the body's immune response, break immune tolerance, and then recognize and kill tumor cells. The polarization homeostasis of M1 and M2 macrophages in tumor microenvironment (TME) is involved in the occurrence and development of tumor, which is a hot topic in tumor research. Programmed cell death ligand 1 (PD-L1) plays an important role in the polarity of TAM and affects the prognosis of HCC patients as a target of immunotherapy. Therefore, we further explored the application value of PD-L1, M1 macrophages (CD86) and M2 macrophages (CD206) in the prognosis assessment of HCC, their correlation with immune cell infiltration in HCC tissues, and their bioenrichment function. Methods: The gene expression omnibus (GEO) and The Cancer Genome Atlas (TCGA) database were used to analyze the expression of PD-L1, CD86 and CD206 in different tumor tissues. The Tumor Immune Estimation Resource (TIMER) was used to analyze the correlation between the expression of PD-L1, CD86 and CD206 and the infiltration of immune cells. The tissue specimens and clinicopathological data of hepatocellular carcinoma patients who underwent surgical treatment in our hospital were collected. Immunohistochemistry was used to verify the expression of PD-L1, CD86 and CD206, and analyze the relationship with clinicopathological features and prognosis of patients. Nomogram was constructed to predict the overall survival (OS) of patients at 3 and 5 years. Finally, STRING database was used to analyze the protein-protein interaction network information, and GO analysis and KEGG (Kyoto Encyclopedia of Genes and Genomes) analysis were performed to study the biological functions of PD-L1, CD86 and CD206. Result: Bioinformatics analysis found that PD-L1, CD86 and CD206 were all under-expressed in a variety of tumor tissues including liver cancer, while our immunohistochemical analysis found the opposite result, and PD-L1, CD86 and CD206 were all over-expressed in liver cancer tissues. The expressions of PD-L1, CD86 and CD206 were positively correlated with the level of immune cell infiltration in HCC tissues; The expression of PD-L1 is positively correlated with the degree of tumor differentiation; The expression level of CD206 was positively correlated with gender and whether patients had hepatitis before operation. The prognosis of patients with low expression of PD-L1 or CD86 is poor. AJCC stage, preoperative hepatitis, and the expression level of CD206 in adjacent tissues are independent risk factors affecting the survival of patients after radical hepatectomy. KEGG pathway enrichment analysis showed that PD-L1 was significantly enriched in T cell aggregation and lymphocyte aggregation, and may be involved in the formation of T cell antigen receptor CD3 complex and cell membrane. CD86 was significantly enriched in positive regulation of cell adhesion, regulation of mononuclear cell proliferation, regulation of leukocyte proliferation and transduction of T cell receptor signaling pathway. CD206 was significantly enriched in type 2 immune response, cellular response to LPS, cellular response to LPS, and involvement in cellular response to LPS. Conclusion: In conclusion, these results suggest that PD-L1, CD86 and CD206 may not only be involved in the occurrence and development of HCC, but also in immune regulation. Therefore, PD-L1, CD86 and CD206 can be used as potential biomarkers and new therapeutic targets for HCC prognosis assessment. Biological sciences/Cancer Biological sciences/Cancer/Tumour biomarkers Hepatocellular carcinoma Programmed cell death ligand 1 M1 macrophages M2 macrophages Immune cells Prognosis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Hepatocellular carcinoma (HCC) is the seventh most common cancer in the world and the second most lethal malignant tumor in the world after lung cancer. It is heterogeneous in etiology and biology, among which hepatocellular carcinoma (HCC) is the most common histological subtype [ 1 – 2 ] . Tumor microenvironment (TME) is composed of tumor cells, a variety of immune cells and stromal cells, and is a special tissue structure dependent on the occurrence and development of solid tumors [ 3 – 4 ] . Tumor associated macrophages (TAM) plays an important role in the composition of TME, accounting for more than 50% of some TME cells. TAM is usually divided into two types according to their functions, namely, M1 type of classical activation pathway and M2 type of alternating activation pathway. The phenotype of M1 macrophages is CD86, and the phenotype of M2 macrophages is CD206. They can produce two opposite effects of anti-tumor or promoting tumor development through mutual transformation [ 5 – 8 ] . It has been reported in the literature that M2 TAM is closely related to the poor prognosis of liver cancer [ 9 ] . In recent years, the polarization homeostasis of M1 and M2 macrophages in TME has become a hot topic in tumor research, which has important significance for tumor prognosis. PD-L1, a 40kDa transmembrane protein encoded by CD274 gene, is induced to be expressed in T cells, B cells, dendritic cells, macrophages and mesenchymal stem cells, and its expression is rapidly up-regulated in tumor tissues in response to interferon and other inflammatory factors [ 10 – 12 ] . Studies have found that tumor-associated macrophages can also express Programmed cell death ligand 1 (PD-L1), which plays an important role in the regulation of TAM polarity. Genevieve et al. found that down-regulation of PD-L1 could regulate TAM polarization and activate M1 macrophages to inhibit tumor progression in melanoma [ 13 ] . This study analyzed the correlation between the expression of tumor-associated macrophages CD86, CD206 and PD-L1 in hepatocellular carcinoma and clinicopathology, as well as the clinical application value in the prognosis assessment of patients. Materials & Methods Expression and immunocorrelation analysis of pan-cancer in TIMER database The TIMER (Tumor Immune Estimation Resource) database is a data analysis platform based on the TCGA ( https://cistrome.shinyapps.io/timer/ ), The expressions of CD86, CD206 and PD-L1 in different cancers were analyzed by Diff Exp module of TIMER. Correlation modules were used to calculate the correlation between the expression level of PD-L1 and CD86 and CD206 in HCC tissues. Setting conditions: (1) Cancer Type: LIHC (Liver Hepatocellular Carcinoma); (2) Gene Symbols(Y-axis):PD-L1(CD274); (3) Gene Symbols༈X-axis༉:CD86; (4) Gene Symbols༈X-axis༉: CD206(MRC1); (5) Correlation Adjusted by: Tumor purity. TCGA database was used to analyze the expression of CD86, CD206 and PD-L1 in HCC tissues and adjacent tissues Gene Expression Profiling Inter-active Analysis (GEPIA∥gepia.Cancer-pku.cn) database is a web analysis tool based on TCGA and GTEx data, which can provide differential expression analysis, contour mapping, patient survival analysis, related gene analysis and other functions. GEPIA was used to analyze the expression differences between liver cancer tissues and normal tissues: (1) Gene: CD86, CD206 and PD-L1; (2) Datasets Selection: LIHC, Keep the default values for other filters. Patient data and specimens From January 2014 to December 2015, 60 patients with hepatocellular carcinoma who were treated for the first time in the Department of Hepatobiliary and Pancreatic Surgery, Affiliated Cancer Hospital of Xinjiang Medical University were selected as the research objects. The cancer tissues and paired adjacent tissues were collected during the operation. All 60 patients with liver cancer strictly met the exclusion criteria and inclusion criteria. Inclusion criteria: (1) First radical resection of liver tumor; (2) Hepatocellular carcinoma confirmed by pathology; (3) Complete clinical data; (4) No previous treatment for liver cancer. Exclusion criteria: (1) Patients undergoing palliative surgery or unable to complete resection; (2) Metastatic hepatocellular carcinoma or intrahepatic cholangiocarcinoma confirmed by pathology; (3) Incomplete clinical data; (4) Previously received human mediated or targeted or immune therapy. Information collected includes: The patient's gender, age, history of hepatitis B, liver cirrhosis, AFP, Carcino-embryonic antigen (CEA), Carbohydrate antigen 19−9, CA19−9), Total bilirubin (TBIL), Alanine aminotransferase (ALT), Aspartate transaminase, AST albumin level, Prothrombin time (PT), intraoperative blood loss, tumor maximum diameter, tumor differentiation, Microvascular invasion (MVI), etc. This study was approved by the Ethics Committee of the Affiliated Cancer Hospital of Xinjiang Medical University. Immunohistochemical staining The conventional paraffin samples were precooled, then sliced into 4µm sections, dewaxed three times with xylene, hydrated in ethanol gradient, and antigen repaired with methylcitric acid (PH = 6) at high temperature and pressure for 3min. After cooling to room temperature, the samples were blocked for 10min to eliminate endogenous peroxidase activity. The primary antibodies were respectively PCNP protein (1:200) and β-catenin (1:200). The primary antibodies were incubated at 4℃ overnight for about 16h. After rewarming for 1h, the secondary antibodies were flushed and incubated at 26℃ for 30min. After each of the above steps was completed, it was washed with phosphate buffer solution (PBS) for 3 times. Finally, DAB was performed for color development, restaining, differentiation, gradient ethanol dehydration, xylene transparency, and neutral gum sealing. The results were observed under light microscope and image analysis was performed. Positive control sections provided by the ordering reagent company were used for positive control, and PBS was used for negative control instead of primary antibody. All methods were performed in accordance with the relevant guidelines and regulations. Result interpretation According to the staining intensity and staining positive rate of cytoplasm and nucleus, the cancerous tissues and the adjacent tissues were interpreted respectively. PCNP is a quantitative assessment, which is mainly expressed in the nucleus. The presence of brown particles in the nucleus is considered as a positive result, and a few of the cytoplasm is brown. β-catenin was a qualitative assessment, which showed membrane staining in normal cells and cytoplasmic or nuclear staining in abnormal cells. Five fields were randomly selected under high power microscope (×200) in each tissue section to observe the staining degree of positive cells and calculate the percentage of positive cells. They were scored according to the degree of staining: 0 points for non-staining, 1 point for light yellow, 2 points for brown and 3 points for tan. Score according to the percentage of positive cells: 0 point if less than 1%, 1 point if 1%~25%; 2 points for 26%~50%; 51%~75%, 3 points; 4 points for more than 75%. The product of "staining intensity score" and "proportion of positive cells score" was used as the total score for grouping, ≤ 4 was divided into negative group, > 4 was divided into positive group. Two senior pathologists evaluated the final results in a double-blind manner. Statistical methods The data were input into SPSS 22.0 software for statistical processing. Measurement data conforming to normal distribution were expressed as x ± s. T-test was used for comparison between two groups, and one-way analysis of variance and corresponding multiple comparisons were used for comparison between multiple groups. The count data were analyzed by chi-square test and expressed as n (%). Kaplan-Meier method was used to draw the survival curve. Cox proportional hazards regression model was used for univariate and multivariate analysis of survival. All statistical results were calculated as P < 0.05 was considered statistically significant. Using the selected independent risk factors as variables, a nomogram model was established to predict the 1 -, 3 -, and 5-year specific survival rates of patients with hepatocellular carcinoma, and internal validation was performed. The discrimination and calibration ability of nomogram were evaluated by Cindex and calibration curve. Net reclassification index (NRI) and decision curve analysis (DCA) were used to evaluate the predictive ability and net benefits of nomogram. In order to narrow the bias, the above analyses were repeated 1000 times with Bootstrap. A total risk score was calculated for each patient according to the prediction model, and patients in the modeling group were stratified according to the quartile of the total risk score. Kaplan-Meier method and Log rank test were used to evaluate the significance of survival differences among risk groups. Results The expression of PD-L1, CD86 and CD206 in different tumors It has been reported that PD-L1, CD86 and CD206 are differentially expressed in a variety of tumors. This study obtained similar results through TIMER database analysis. PD-L1, also known as surface antigenin 274 (CD274), is a human protein encoded by the CD274 gene. CD206, a mannose receptor (MRC1), is considered to be a highly reliable marker of M2-type macrophages. The transcriptome levels of PD-L1 (CD274), CD86 and CD206 (MRCI) were found to be lower in many tumor tissues than in adjacent normal tissues. Such as bladder urothelial carcinoma (BLCA), breast cancer (BRCA), colon cancer (COAD), hepatocellular carcinoma (LIHC), lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), and prostate cancer (PRAD). However, for head and neck squamous cell carcinoma (HNSC), the transcriptome levels of PD-L1, CD86, and CD206 in tumor tissues were higher than those in adjacent normal tissues, as shown in Fig. 1 A, Fig. 1 C, and Fig. 1 E. Expression analysis in liver cancer tissues showed that the expression levels of PD-L1, CD86 and CD206 in liver tissues were significantly lower than those in paracancer tissues, with statistical significance ( P = 0.003, P = 0.046, P < 0.001), as shown in Fig. 1 B, Fig. 1 D, and Fig. 1 F. Relationship between PD-L1, CD86 and CD206 expression and immune cell infiltration The relationship between the expression of PD-L1, CD86, and CD206 and the regulation of the purity of immune cell infiltration (B cells, CD4 + T cells, CD8 + T cells, DC, neutrophils, and macrophages) was investigated using TIMER database. The expression level of PD-L1 was positively correlated with the infiltration level of B cells ( r =0.337, p =1.35e−10), CD8 + T cells ( r =0.378, p =4.94e−13) and macrophages ( r =0.386, p =1.43e−13). CD4 + T cells ( r =0.429, p =7.90e−17), neutrophils ( r =0.544, p =5.24e−28) and dendritic cells ( r =0.464, p =1.40e−19) were significantly increased in HCC, but negatively correlated with tumor purity ( r =−0.247, P =1.40E−19). p =3.42e−06), as shown in Figure 2A. The expression level of CD86 was positively correlated with the infiltration level of B cells ( r =0.644, p =9.61e−42), CD8 + T cells ( r =0.664, p =7.06e−45), CD4 + T cells ( r =0.429, p =7.90e−17), macrophages ( r =0.732, P =1.61e - 58). Neutrophils ( r =0.598, p =8.86e−35) and dendritic cells ( r =0.831, p =2.74e−88) were significantly increased in HCC, but negatively correlated with tumor purity ( r =−0.515, p =8.71e−25), as shown in Figure 2B. Similarly, the expression level of CD206 was positively correlated with the infiltration level of B cells ( r =0.154, p =4.11e−03), CD8 + T cells ( r =0.298, p =1.92e−08), CD4 + T cells ( r =0.069, p =2.02e−01), macrophages ( r =0.244, P = 5.18 e - 06). Neutrophils ( r =0.329, p =3.56e−10) and dendritic cells ( r =0.318, p =1.98e−09) were significantly increased in HCC, and negatively correlated with tumor purity ( r =−0.287, p =5.36e−08), as shown in Figure 2C. Expression of CD86, CD206 and PD-L1 in tumor-associated macrophages in cancer tissues and adjacent tissues of patients with hepatocellular carcinoma Immunohistochemical examination of cancer tissues and adjacent tissues from 60 patients with liver cancer showed that CD86, CD206 and PD-L1 were significantly stained in cancer tissues, as shown in Figure 3. Statistical analysis showed that the positive expression rate of PD-L1 was 57% in cancer tissues and 12% in adjacent tissues. The positive expression rate of CD86 was 50% in cancer tissues and 20% in adjacent tissues. The positive expression rate of CD206 in cancer tissues was 78.3% and that in adjacent tissues was 41.7%, showing significant differences, as shown in Figure 4. Relationship between the expression of CD86, CD206 and PD-L1 and clinicopathological features in patients with hepatocellular carcinoma The correlation analysis between the expression levels of CD86, CD206 and PD-L1 in cancer tissues of 60 patients with HCC and the clinicopathological features showed that the expression of PD-L1 was correlated with the degree of tumor differentiation ( χ2 = 7.855, P = 0.02). The expression level of CD206 was correlated with gender ( χ2 = 4.832, P = 0.028) and whether patients had hepatitis before surgery ( χ2 = 9.624, P = 0.002), as shown in Table 1 . Logistic regression analysis of PD-L1 expression and clinicopathological features in tumor tissues of patients with liver cancer showed that the degree of tumor differentiation was a risk factor for PD-L1 expression, which was positively correlated with its expression level, as shown in Table 2 . Similarly, it was found that gender and preoperative hepatitis were risk factors for CD206 expression, which were positively correlated with CD206 expression, as shown in Table 3 . Table 1 Correlation between the expression of PD-L1, CD86 and CD206 and clinicopathological features in hepatocellular carcinoma Characteristics PD-L1 Expression CD86 Expression CD206 Expression Low High χ 2 P Low High χ 2 P Low High χ 2 P Gebder Male 19 27 0.331 0.656 23 23 0.00 1.00 7 39 4.831 0.028 Female 7 7 7 7 6 8 Age <60 13 21 0.830 0.362 18 16 0.271 0.602 9 25 1.067 0.302 ≥ 60 13 13 12 14 4 22 Tumor size(cm) <5 13 16 0.051 0.821 14 15 0.067 0.796 6 23 0.032 0.859 ≥ 5 13 18 16 15 7 24 Tumor capsule Yes 13 16 0.051 0.821 14 15 0.067 0.796 6 23 0.032 0.859 N0 13 18 16 15 7 24 Number of tumors Single 22 24 1.621 0.203 22 24 0.373 0.542 9 37 0.513 0.474 Multiple 4 10 8 6 4 10 Vascular invasion Yes 5 12 1.872 0.171 9 8 0.082 0.774 5 12 0.838 0.360 NO 21 22 21 22 8 35 Portal vein tumor thrombus Yes 14 16 0.271 0.602 14 16 0.267 0.606 7 23 0.098 0.754 NO 12 18 16 14 6 24 Cirrhosis Yes 15 16 0.667 0.414 15 16 0.067 0.796 6 25 0.202 0.653 NO 11 18 15 14 7 22 AFP(ug/L) <13.4 16 11 5.287 0.071 14 13 0.085 0.959 6 21 1.852 0.396 13.4 ≤ AFP<400 7 14 10 11 6 15 ≥ 400 3 9 6 6 1 11 Hepatitis Yes 19 21 0.848 0.357 21 19 0.300 0.584 4 36 9.624 0.002 NO 7 13 9 11 9 11 Differentiation degree Low 5 12 7.855 0.02 7 10 1.744 0.418 5 12 1.075 0.584 Medium 14 21 20 15 6 29 High 7 1 3 5 2 6 AJCC stage I 14 10 3.716 0.156 11 13 1.217 0.544 3 21 2.111 0.348 II 5 11 7 7 4 12 III 7 13 12 8 6 14 *Statistically significant. The bold values were considered statistically significant. Table 2 Logistic regression analysis of the relationship between clinicopathological characteristics and the expression of PD-L1 in hepatocellular carcinoma Characteristics β SE OR 95%CI P-value Differentiation degree (Low vs Medium) 2.821 1.194 16.80 1.617 ~ 17.519 0.018 Differentiation degree (Medium vs High) 2.351 1.123 10.5 1.161 ~ 94.925 0.036 Table 3 Logistic regression analysis of the relationship between clinicopathological characteristics and the expression of CD206 in hepatocellular carcinoma Characteristics β SE OR 95%CI P Gebder (Male vs Female) 1.436 0.757 4.205 0.954 ~ 18.542 0.048 Hepatitis (Yes vs NO) 2.001 0.718 7.394 1.785 ~ 30.629 0.006 Effect of PD-L1, CD86 and CD206 expression on the overall survival and progression-free survival in patients with hepatocellular carcinoma Sixty patients with liver cancer were followed up after surgery. The shortest survival time was 1 month, and the longest survival time was 70 months. The 1 -, 2 -, and 3-year overall survival (OS) rates were 93.3%, 83.3%, and 75%, respectively, and the 3-year progression-free survival rate was 61.7%, as shown in Fig. 5 . The 3-year overall survival rate of patients with high expression of PD-L1 was 82.4%, while that of patients with low expression of PD-L1 was 76.9%, showing a significant difference ( P = 0.03), as shown in Fig. 6 A. However, there was no correlation between the expression of PD-L1 in adjacent tissues and prognosis, as shown in Fig. 6 B. Analysis of CD86 in HCC tissues showed that the 3-year overall survival rate of patients with high expression of CD86 was 83.3%, and that of patients with low expression of CD86 was 79.2%, showing a significant difference ( P = 0.04), as shown in Fig. 6 C. However, there was no correlation between the expression of CD86 in adjacent tissues and the prognosis, as shown in Fig. 6 D. Survival analysis on the expression of CD206 in cancer tissues and adjacent tissues and whether they had hepatitis before surgery showed no correlation, as shown in Fig. 6 E-G. Survival analysis of AJCC stage showed that the 3-year survival rates of patients with stage I, II and III were 100%, 81.3% and 55.2%, respectively, showing differences, as shown in Fig. 6 H. Cox multivariate analysis showed that AJCC stage ( OR = 11.841, 95% CI : 2.589–54.16, P = 0.001) and preoperative hepatitis ( OR = 5.427, 95% CI : 1.084–27.175, P = 0.04), the expression level of CD206 in paracancorous tissues ( OR = 7.172, 95% CI : 1.405–36.606, P = 0.018) was an independent risk factor affecting the survival of patients after radical resection of liver cancer, as shown in Table 4 . Table 4 Cox regression analysis of prognostic factors in patients with hepatocellular carcinoma after radical resection Characteristics β SE OR 95%CI P-value AJCC stage (I vs. II ~ III) 2.472 0.776 11.841 2.589 ~ 54.16 0.001 Hepatitis (Yes vs. No) 1.691 0.822 5.427 1.084 ~ 27.175 0.040 CD206 in adjacent tissues (High vs. Low) 1.970 0.832 7.172 1.405 ~ 36.606 0.018 Construct and validate the prognostic nomogram of patients with liver cancer According to the results of multivariate analysis, R software was used to establish the prognosis prediction model of liver cancer. In the nomogram, age ≤ 60 was 0 points, and age > 60 was 80 points. Tumor stages T1 and T2 were 0 points, T3 and T4 were 100 points. The pathological stage of the tumor was 0 for stage I and II, and 30 for stage II and IV. AFP ≤ 400 indicates a score of 0, while AFP > 400 indicates a score of 12. The low expression of PD-L1 was 0, and the high expression was 28. The high expression of CD206 was 0, and the low expression was 72. The low expression of CD86 was 0, and the high expression was 38. The higher the total score in the nomogram, the lower the OS in the corresponding 3 and 5 years, as shown in Fig. 7 A. The test results show that the C index of the line chart model is 0.742, indicating that the accuracy of the model is high. By drawing the calibration chart of the predicted value and the measured value, the consistency test was carried out. The 3-year and 5-year OS predicted by the rosette model has a good correlation with the actual 3-year and 5-year OS. Bootstrap method (repeated sampling 1000 times) was used to verify the established nomogram. The C index of inner part verification was 0.762 (95% CI : 0.744–0.780), and the C index of outer part verification was 0.787 (95% CI : 0. 767−0. 806), indicating that the nomogram has good predictive value. The predicted values of the calibration maps of the 1-year, 3-year and 5-year CSS of the two groups are in good agreement with the actual observed values, shown in Fig. 7 B. Functional prediction and protein interaction analysis of PD-L1, CD86 and CD206 Based on STRING database, the PPI networks of PD-L1, CD86 and CD206 were constructed respectively, and the top ten functionally interacting proteins with high connectivity were selected. Among them, PD-L1-related genes were CD247, CD80, CTLA4, PDC1LG2, PDCD1, HLA-DRA, CD3E, CD4, PTPN11 and HOXD13, as shown in Figure 8A. CD86-related genes were ICAM1, CD80, CD8A, CTLA4, IL-10, CTLA4, CD4, CD28, CD247, CD3E and CSF1, as shown in Figure 8C. CD206 related genes were CD68, FCGR1A, ITGAM, PLAT, ITGAX, CD86, CD163, IL10, IL4 and ARG1, as shown in Figure 8E. GO enrichment analysis included three main functions, namely biological process function, cellular component function and molecular function ( p ＜0.05). KEGG pathway and GO analysis showed that PD-L1 promoted significant enrichment in T cell aggregation, lymphocyte aggregation and other aspects of biological processes. In terms of cell components, PD-L1 participates in T cell antigen receptor CD3 complex, cell membrane and other functions, as shown in Figure 8B and Table 5. CD86 was significantly enriched in the positive regulation of cell adhesion, regulation of mononuclear cell proliferation, regulation of leukocyte proliferation, and transduction of T-cell receptor signaling pathway, as shown in Figure 8D and Table 6. Similarly, KEGG pathway analysis of CD206 showed significant enrichment in type 2 immune response, cellular response to LPS, cellular response to LPS, and cellular response to LPS involved in cellular response to LPS, as shown in Figure 8F. Table 5 GO and KEGG enrichment analyses of PD-L1 and functional partner genes in hepatocellular carcinoma. ONTOLOGY ID Description P value BP GO:0031295 T cell costimulation 1.15e-13 BP GO:0031294 lymphocyte costimulation 1.28e-13 BP GO:0050870 positive regulation of T cell activation 1.83e-12 CC GO:0009897 external side of plasma membrane 1.19e-08 CC GO:0042101 T cell receptor complex 3.03e-05 CC GO:0030136 clathrin-coated vesicle 9.75e-05 MF GO:1990782 protein tyrosine kinase binding 1.64e-05 MF GO:0015026 coreceptor activity 2.68e-04 MF GO:0001618 virus receptor activity 7.60e-04 KEGG hsa04514 Cell adhesion molecules 2.86e-09 KEGG hsa05235 PD-L1 expression and PD-1 checkpoint pathway in cancer 1.77e-08 KEGG hsa04660 T cell receptor signaling pathway 3.89e-08 Table 6 GO and KEGG enrichment analyses of CD86 and functional partner genes in hepatocellular carcinoma ONTOLOGY ID Description P value BP GO:0045785 positive regulation of cell adhesion 1.91e-12 BP GO:0032944 regulation of mononuclear cell proliferation 2.32e-12 BP GO:0070663 regulation of leukocyte proliferation 3.56e-12 CC GO:0009897 external side of plasma membrane 1.35e-10 CC GO:0042101 T cell receptor complex 3.35e-07 CC GO:0098636 protein complex involved in cell adhesion 5.58e-07 MF GO:0015026 coreceptor activity 6.90e-09 MF GO:0001618 virus receptor activity 8.25e-06 MF GO:0104005 hijacked molecular function 8.25e-06 KEGG hsa04660 T cell receptor signaling pathway 5.57e-12 KEGG hsa04514 Cell adhesion molecules 7.05e-09 KEGG hsa05323 Rheumatoid arthritis 4.38e-08 Discussion Tumor microenvironment (TME) is a dynamic system regulated by cell-to-cell communication, which is closely related to tumor development and metastasis [ 14 – 17 ] . As the main stromal cells in TME, macrophages are highly plastic and have different phenotypes under different stimuli, including M1 (tumor suppressor) and M2 (tumor promoter) [ 18 ] . TAMs are generally considered as M2-type macrophages with high expression of CD206, Arg−1, IL−10 and TGF-β [ 19 – 20 ] . According to previous studies, M1 macrophages have been considered to play an inhibitory role in tumor growth, while M2 macrophages promote tumor growth [ 21 – 22 ] . Jiang et al. showed that M1 macrophages inhibited the migration and invasion of esophageal squamous cell carcinoma cells [ 23 ] . M1 macrophages inhibit the proliferation and induce apoptosis of tumor cells in lung cancer, and play an anti-tumor role [ 24 ] . This study also found that CD86 (M1 macrophages) significantly infiltrated in cancer tissues. Prognostic analysis of the expression of CD86 in cancer tissues showed that CD86 could inhibit tumor progression and prolong the overall survival time of patients for 3 years ( P = 0.04), which further verified the effect of M1 macrophages in inhibiting tumor growth. However, in recent years, studies have shown that M1 macrophages have tumor-promoting effects. Jin et al. observed that M1 macrophages can promote the invasion of brain glioma U251 cells [ 25 ] . Zhuo Chen et al. detected that M1-type macrophages could induce EMT in breast cancer cells T47-D and MCF−7, and enhance the migration and invasion ability of the cells. Targeting M1 macrophages may inhibit EMT and limit the invasion potential of breast cancer [ 26 ] . At present, the effect of M1 macrophages on tumor promotion or inhibition is still controversial. The role of M1 macrophages in the occurrence and progression of bladder cancer is still unclear, and more basic studies are needed. Programmed death−1 (PD−1) is a type I transmembrane protein, which is mainly expressed in mature T cells, B cells, macrophages and NK cells. Its ligand PD-L1 is inductively expressed in T cells, B cells, monocytes and many types of tumor cells, such as lung cancer, liver cancer and malignant melanoma [ 27 ] . Under normal physiological conditions, PD-L1 expressed on the surface of tissue cells, endothelial cells and immune cells binds to PD−1 on the surface of activated T cells, which inhibits excessive activation of T cells and induces apoptosis of T cells, thus maintaining a certain dynamic balance of the body's immune response [ 28 ] . After carcinogenesis, tumor cells promote the up-regulation of PD-L1 expression through a variety of mechanisms. A large number of PD−1 molecules on the surface of T cells inhibit the proliferation and activation of CD4 + T cells and CD8 + T cells, and produce some activated cytokines (such as IFN-γ) to break the homeostasis of immune response. It makes tumor cells evade the surveillance of the body's immune system and promotes tumor progression [ 29 – 30 ] . Our study found that PD-L1 was significantly overexpressed in cancer tissues, which promoted tumor progression and affected the prognosis of patients, which was also consistent with the reports in the literature. Liver is a special immune tolerant organ, which can effectively evade immune response. However, immunotherapy can enhance the body's immune response, break immune tolerance, and then recognize and kill tumor cells [ 31 ] . In recent years, tumor immunotherapy represented by Immune checkpoint inhibitors (ICIs) has made breakthrough progress, which prolongates the survival of patients, and some patients can even be transformed from unresectable to radical resectable, bringing new light to the treatment of tumors [ 32 ] . In this study, the bioinformation analysis first found that PD-L1, CD86 and CD206 were differentially expressed in HCC and correlated with the immune infiltration of tumor cells. To further confirm this, our research group conducted immunohistochemical detection of PD-L1, CD86 and CD206 in cancer tissues and adjacent tissues of 60 HCC patients and found that PD-L1, CD206 and CD86 were significantly overexpressed in cancer tissues, which may be related to the suppression of immune response caused by PD-L1 overexpression in the immune microenvironment of HCC [ 33 ] . The application of PD−1 antibody can improve the immune suppression state of the body and enhance the anti-tumor immune effect of the body, which has achieved certain efficacy in some solid tumors. However, in the immunotherapy of liver cancer, the anti-PD−1 efficacy is low, only 15%−25%, which may be related to the special immune microenvironment of liver cancer, and the specific mechanism is still unclear [ 34 ] . In order to improve the immune response rate of patients with liver cancer and increase the efficacy of anti-PD−1, it is urgent to further study the immune microenvironment of liver cancer and find more effective tumor therapeutic targets, so as to achieve the purpose of precise treatment. In order to further explore the biological functions of PD-L1, CD86 and CD206, we carried out GO and KEGG enrichment analysis on them. PD-L1 is mainly involved in T cell activation, aggregation and lymphocyte activation in tumor microenvironment. CD86 is mainly involved in the positive regulation of cell adhesion and the proliferation of leukocytes and monocytes. CD206 is mainly involved in type 2 immune response and protein complex involved in cell adhesion. However, the current study had some limitations. First of all, this study only predicted the function of each gene, and the research depth is enough. The function and mechanism of tumor immune infiltration should be further explored. Secondly, in this study, we retrospectively analyzed HCC patients who underwent radical surgery in a single institution. The value of PD-L1, CD86 and CD206 expression in tumor tissues needs to be prospectively verified in multicenter studies. Conclusions In this study, we investigated the expression of PD-L1, CD86 and CD206 in hepatocellular carcinoma tissues and revealed the application value of PD-L1, CD86 and CD206 in prognosis assessment of hepatocellular carcinoma. PD-L1, CD86 and CD206 may be involved not only in the occurrence and development of HCC, but also in the immune escape of HCC. Therefore, the in-depth study of PD-L1, CD86 and CD206 can not only find biomarkers for prognosis assessment of patients with liver cancer, but also provide new therapeutic targets for patients' immunotherapy. However, the current study has certain limitations, and further mechanistic studies are needed to verify our findings and promote clinical application. Declarations Funding This work was supported by Science and Technology Department of Xinjiang Uygur Autonomous Region, Key Laboratory Open Project (2022D04030). Availability of data and materials Data supporting the conclusions of this article are included within the article. The raw datasets used and analysis for the present study are available from the corresponding author upon reasonable request. Compliance with ethical standards Conflicts of interest The authors declare no conflict of interest. Ethical approval The study was approved by the Independent Ethics Committee of Tumor Hospital Affiliated to Xinjiang Medical University in 2022. Informed consent Informed consent was obtained from all individual participants included in the study. References Kim E, Viatour P. Hepatocellular carcinoma: old friends and new tricks[J]. Exp Mol Med, 2020,52(12):1898–1907. Bray F, Ferlay J, Soerjomataram I, et al. 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Tumor-associated macrophages of the M1/M2 phenotype are involved in the regulation of malignant biological behavior of breast cancer cells through the EMT pathway[J]. Med Oncol, 2022,39(5):83. Yu Yuan, Abdalla Adam, Chen Zhao, et al. Recent Advancements in the Mechanisms Underlying Resistance to PD–1/PD-L1 Blockade Immunotherapy[J]. 2021,13(4):663. Dougan M. Checkpoint Blockade Toxicity and Immune Homeostasis in the Gastroinestinal Tract[J]. Front Immunol, 2017,8:1547. Butte MJ, Pena-Cruz V, Kim MJ, et al. Interaction of human PD-L1 and B7–1[J]. Mol Immunol, 2008,45(13):3567–3572. Zhu AX, Finn RS, Edeline J, et al. Pembmlizumab in patients with advanced hepatocellular carcinoma previously treated with sorafenib (KEYNOTE–224): A non-randomised, open–1abel phase 2 trial[J]. Lancet Oncol, 2018,19(7):940–952. Sung H, Ferlay J, Siegel RL, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries[J]. CA Cancer J Clin, 2021,71(3):209–249. Dimitri A, Herbst F, Fraietta JA. Engineering the next-generation of CAR T-cells with CRISPR-Cas9 gene editing[J]. Mol Cancer, 2020,21(1):78. Zhang J, Bu X, Wang H, et al. Cyclin D-CDK4 kinase destabilizes PD-L1 via cullin 3-SPOP to control cancer immune surveillance[J]. Nature, 2018,553(7686):91–95. Pinero F, Dirchwolf M, Pessoa MG. Biomarkers in hepatocellular carcinoma: diagnosis, prognosis and treatment response assessment[J]. Cells, 2020,9(6):1370. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-2579242","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":180618590,"identity":"6ee621f4-44f9-4ccd-b7ee-9e3bc38d1bd1","order_by":0,"name":"Panpan kong","email":"","orcid":"","institution":"Tumor Hospital of Xinjiang Medical University","correspondingAuthor":false,"prefix":"","firstName":"Panpan","middleName":"","lastName":"kong","suffix":""},{"id":180618591,"identity":"910ecc55-8392-42c2-aaf9-6f95e5def4d5","order_by":1,"name":"Huan Yang","email":"","orcid":"","institution":"Tumor Hospital of Xinjiang Medical University","correspondingAuthor":false,"prefix":"","firstName":"Huan","middleName":"","lastName":"Yang","suffix":""},{"id":180618592,"identity":"331b834e-9760-43a7-99fd-7bfcf1497652","order_by":2,"name":"Qing Tong","email":"","orcid":"","institution":"Tumor Hospital of Xinjiang Medical University","correspondingAuthor":false,"prefix":"","firstName":"Qing","middleName":"","lastName":"Tong","suffix":""},{"id":180618593,"identity":"af5fe956-d196-4ca4-8c12-f55042cef36d","order_by":3,"name":"Xiaogang Dong","email":"","orcid":"","institution":"Tumor Hospital of Xinjiang Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xiaogang","middleName":"","lastName":"Dong","suffix":""},{"id":180618595,"identity":"df0cafcc-7da2-44e7-8fb6-ce5a48092aff","order_by":4,"name":"Mamumaimaitijiang-Abula Yi","email":"","orcid":"","institution":"Tumor Hospital of Xinjiang Medical University","correspondingAuthor":false,"prefix":"","firstName":"Mamumaimaitijiang-Abula","middleName":"","lastName":"Yi","suffix":""},{"id":180618597,"identity":"06695b18-4815-41ff-979a-04f22d9c9cbf","order_by":5,"name":"Dong Yan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAsklEQVRIiWNgGAWjYDCCAxCKsZ+Z+fADYrUwNoC0zGxnSzMgTcuG8zwKEkTp4Dve/PzBxz2HZTcf5mEwYKixiSaoRfLMMcPGGc8OG287zHvgAcOxtNwGQloMbuQwNvMcOJy47TBfggFjw2EStGxu5jGQIE3LBmZitYD8MnPGgXTjGYeBgZxAjF+AIfbgw4cD1rL9/YcPP/hQY0NYCxQ0Q6gEIpWDQB0JakfBKBgFo2DEAQA8a0fyoX1E+gAAAABJRU5ErkJggg==","orcid":"","institution":"Tumor Hospital of Xinjiang Medical University","correspondingAuthor":true,"prefix":"","firstName":"Dong","middleName":"","lastName":"Yan","suffix":""}],"badges":[],"createdAt":"2023-02-12 17:14:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2579242/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2579242/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":33969783,"identity":"f9c7e39b-5614-497f-a997-3a8545f7730e","added_by":"auto","created_at":"2023-03-08 15:33:10","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":352048,"visible":true,"origin":"","legend":"\u003cp\u003eExpression levels of PD-L1, CD86 and CD206 in tumors.\u003cstrong\u003e (A) \u003c/strong\u003eBased on TCGA data analysis, the expression level of PD-L1 (CD274) in 38 tumor tissues (TIMER). \u003cstrong\u003e(B)\u003c/strong\u003ePD-L1 (CD274) expression level in liver cancer tissues and adjacent tissues (UALCAN). \u003cstrong\u003e(C) \u003c/strong\u003eCD86 expression level in 38 tumor tissues based on TCGA data analysis (TIMER). \u003cstrong\u003e(D) \u003c/strong\u003eCD86 expression level in liver cancer tissues and adjacent tissues (UALCAN).\u003cstrong\u003e (E) \u003c/strong\u003eExpression level of CD206 (MRC1) in 38 tumor tissues based on TCGA data analysis (TIMER). \u003cstrong\u003e(F)\u003c/strong\u003e CD206(MRC1) expression level in liver cancer tissues and adjacent tissues (UALCAN).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-2579242/v1/2b9669e19c7433b8f1a859ec.png"},{"id":33969252,"identity":"5e3dcd46-38a0-4137-b72e-e2f1b01bf99f","added_by":"auto","created_at":"2023-03-08 15:25:09","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":379841,"visible":true,"origin":"","legend":"\u003cp\u003eRelationship between PD-L1、CD86 and CD206 expression and immune cell infiltration in HCC. \u003cstrong\u003e(A) \u003c/strong\u003eRelationship between PD-L1 expression and immune cell infiltration in HCC. \u003cstrong\u003e(B) \u003c/strong\u003eRelationship between CD86 expression and immune cell infiltration in HCC.\u003cstrong\u003e (C) \u003c/strong\u003eRelationship between CD206 expression and immune cell infiltration in HCC.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-2579242/v1/5b6310ee95eaec0ec6543b8f.png"},{"id":33969254,"identity":"a74f03a3-1da0-4247-b774-f36c96a28df6","added_by":"auto","created_at":"2023-03-08 15:25:10","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":882428,"visible":true,"origin":"","legend":"\u003cp\u003eImmunohistochemical staining of CD86, CD206 and PD-L1 in liver cancer tissues and adjacent tissues. \u003cstrong\u003e(A-C)\u003c/strong\u003e Immunohistochemical staining of CD86, CD206 and PD-L1 in HCC tissues (SP×100). \u003cstrong\u003e(D~F)\u003c/strong\u003eImmunohistochemical staining of CD86, CD206 and PD-L1 in adjacent tissues (SP×100). \u003cstrong\u003e(G~I)\u003c/strong\u003e Immunohistochemical staining of CD86, CD206 and PD-L1 in liver cancer tissues (SP×200). \u003cstrong\u003e(G~I)\u003c/strong\u003eImmunohistochemical staining of CD86, CD206 and PD-L1 in adjacent tissues (SP×200).\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-2579242/v1/33aa636a5aad2d31fbf8adf8.png"},{"id":33969251,"identity":"a97a1a53-e30f-476a-b908-8969061acd73","added_by":"auto","created_at":"2023-03-08 15:25:09","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":103998,"visible":true,"origin":"","legend":"\u003cp\u003eThe expression statistics of PD-L1, CD86 and CD206 in liver cancer tissues and adjacent tissues. \u003cstrong\u003e(A) \u003c/strong\u003eThe differential expressions of PD-L1, CD86 and CD206 in cancer tissues and adjacent tissues.(P \u0026lt; 0.05 were statistically significant).\u003cstrong\u003e (B) \u003c/strong\u003eBar chart of PD-L1, CD86 and CD206 expression levels in cancer tissues and adjacent tissues.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-2579242/v1/6236c4eaa1ac24cf3e72c63b.png"},{"id":33968261,"identity":"d0986774-184a-4d3d-baba-1dc5c00cc990","added_by":"auto","created_at":"2023-03-08 15:17:09","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":36032,"visible":true,"origin":"","legend":"\u003cp\u003eSurvival analysis of patients with liver cancer. \u003cstrong\u003e(A) \u003c/strong\u003e5-year overall survival rate of patients with liver cancer.\u003cstrong\u003e (B) \u003c/strong\u003e3-year overall survival rate of patients with liver cancer. \u003cstrong\u003e(C) \u003c/strong\u003e1-year overall survival rate of patients with liver cancer. \u003cstrong\u003e(D) \u003c/strong\u003e3-year progression free survival rate of patients with liver cancer.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-2579242/v1/8fa8536ba42ae9ea8d387b62.png"},{"id":33968263,"identity":"a686d4ee-f2ed-4fd9-8ea0-77b8cd820c4a","added_by":"auto","created_at":"2023-03-08 15:17:10","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":108982,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier survival analysis of patients with liver cancer. \u003cstrong\u003e(A) \u003c/strong\u003eThe relationship between PD-L1 expression and 3-year overall survival in cancer tissues.\u003cstrong\u003e (B) \u003c/strong\u003eThe relationship between the expression of PD-L1 in adjacent tissues and the 3-year overall survival. \u003cstrong\u003e(C) \u003c/strong\u003eThe relationship between CD86 expression and 3-year overall survival in cancer tissues. \u003cstrong\u003e(D) \u003c/strong\u003eThe relationship between the expression of CD86 in adjacent tissues and the 3-year overall survival. \u003cstrong\u003e(E) \u003c/strong\u003eThe relationship between CD206 expression and 3-year overall survival in cancer tissues. \u003cstrong\u003e(F) \u003c/strong\u003eThe relationship between the expression of CD206 in adjacent tissues and the 3-year overall survival. \u003cstrong\u003e(G) \u003c/strong\u003eThe relationship between preoperative hepatitis and 3-year overall survival. \u003cstrong\u003e(H)\u003c/strong\u003e The relationship between between AJCC stage and three-year overall survival rate.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-2579242/v1/653d1e699b0cecd6f75d85dd.png"},{"id":33970539,"identity":"0d1c0c51-18ee-4f76-ad28-e03173a9858f","added_by":"auto","created_at":"2023-03-08 15:41:10","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":100055,"visible":true,"origin":"","legend":"\u003cp\u003ePrognostic prediction model for hepatocellular carcinoma. \u003cstrong\u003e(A) \u003c/strong\u003eIn the prediction model, the score of each factor is obtained according to the upper scale, and the total score is obtained by adding the score of each factor. From the overall score downward, the corresponding 3 - and 5-year overall survival rates were obtained. \u003cstrong\u003e(B) \u003c/strong\u003eThe comparison of 3-and 5-year overall survival plots predicted by the rotigrams with the observed 3-and 5-year overall survival.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-2579242/v1/75a6bde76389afc9c8b52efd.png"},{"id":33970538,"identity":"fe646d7c-cfb3-42e7-a1ff-71e61bc6d821","added_by":"auto","created_at":"2023-03-08 15:41:10","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":243706,"visible":true,"origin":"","legend":"\u003cp\u003eProtein-protein interaction and functional enrichment analysis of PD-L1, CD86 and CD206. \u003cstrong\u003e(A) \u003c/strong\u003eSchematic diagram of functional analysis of Protein-protein interaction between PD-L1.\u003cstrong\u003e (B) \u003c/strong\u003ePD-L Functional enrichment analysis. \u003cstrong\u003e(C) \u003c/strong\u003eSchematic diagram of functional analysis of Protein-protein interaction between CD86. \u003cstrong\u003e(D)\u003c/strong\u003eCD86 functional enrichment analysis. \u003cstrong\u003e(E) \u003c/strong\u003eSchematic diagram of functional analysis of Protein-protein interaction between CD206. \u003cstrong\u003e(F) \u003c/strong\u003eCD206 functional enrichment analysis.\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-2579242/v1/51a52c7d2f44fa98f09f480b.png"},{"id":35508131,"identity":"522b54d6-d47d-4cad-b5e8-114cf24c3a28","added_by":"auto","created_at":"2023-04-10 06:14:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2677278,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2579242/v1/e88add1e-960a-4a73-a49a-ca8aeb30ce42.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Expression of tumor-associated macrophages and PD-L1 in patients with hepatocellular carcinoma and construction of a prognostic model","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHepatocellular carcinoma (HCC) is the seventh most common cancer in the world and the second most lethal malignant tumor in the world after lung cancer. It is heterogeneous in etiology and biology, among which hepatocellular carcinoma (HCC) is the most common histological subtype \u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e–\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. Tumor microenvironment (TME) is composed of tumor cells, a variety of immune cells and stromal cells, and is a special tissue structure dependent on the occurrence and development of solid tumors \u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e–\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. Tumor associated macrophages (TAM) plays an important role in the composition of TME, accounting for more than 50% of some TME cells. TAM is usually divided into two types according to their functions, namely, M1 type of classical activation pathway and M2 type of alternating activation pathway. The phenotype of M1 macrophages is CD86, and the phenotype of M2 macrophages is CD206. They can produce two opposite effects of anti-tumor or promoting tumor development through mutual transformation\u003csup\u003e[\u003cspan additionalcitationids=\"CR6 CR7\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e–\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. It has been reported in the literature that M2 TAM is closely related to the poor prognosis of liver cancer\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. In recent years, the polarization homeostasis of M1 and M2 macrophages in TME has become a hot topic in tumor research, which has important significance for tumor prognosis. PD-L1, a 40kDa transmembrane protein encoded by CD274 gene, is induced to be expressed in T cells, B cells, dendritic cells, macrophages and mesenchymal stem cells, and its expression is rapidly up-regulated in tumor tissues in response to interferon and other inflammatory factors\u003csup\u003e[\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e–\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. Studies have found that tumor-associated macrophages can also express Programmed cell death ligand 1 (PD-L1), which plays an important role in the regulation of TAM polarity. Genevieve et al. found that down-regulation of PD-L1 could regulate TAM polarization and activate M1 macrophages to inhibit tumor progression in melanoma\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. This study analyzed the correlation between the expression of tumor-associated macrophages CD86, CD206 and PD-L1 in hepatocellular carcinoma and clinicopathology, as well as the clinical application value in the prognosis assessment of patients.\u003c/p\u003e \u003cp\u003e\u003c/p\u003e "},{"header":"Materials \u0026 Methods","content":"\u003cp\u003e \u003cb\u003eExpression and immunocorrelation analysis of pan-cancer in TIMER database\u003c/b\u003e \u003c/p\u003e\u003cp\u003eThe TIMER (Tumor Immune Estimation Resource) database is a data analysis platform based on the TCGA (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cistrome.shinyapps.io/timer/\u003c/span\u003e\u003cspan address=\"https://cistrome.shinyapps.io/timer/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), The expressions of CD86, CD206 and PD-L1 in different cancers were analyzed by Diff Exp module of TIMER. Correlation modules were used to calculate the correlation between the expression level of PD-L1 and CD86 and CD206 in HCC tissues. Setting conditions: (1) Cancer Type: LIHC (Liver Hepatocellular Carcinoma); (2) Gene Symbols(Y-axis):PD-L1(CD274); (3) Gene Symbols༈X-axis༉:CD86; (4) Gene Symbols༈X-axis༉: CD206(MRC1); (5) Correlation Adjusted by: Tumor purity.\u003c/p\u003e\u003cp\u003e \u003cb\u003eTCGA database was used to analyze the expression of CD86, CD206 and PD-L1 in HCC tissues and adjacent tissues\u003c/b\u003e \u003c/p\u003e\u003cp\u003eGene Expression Profiling Inter-active Analysis (GEPIA∥gepia.Cancer-pku.cn) database is a web analysis tool based on TCGA and GTEx data, which can provide differential expression analysis, contour mapping, patient survival analysis, related gene analysis and other functions. GEPIA was used to analyze the expression differences between liver cancer tissues and normal tissues: (1) Gene: CD86, CD206 and PD-L1; (2) Datasets Selection: LIHC, Keep the default values for other filters.\u003c/p\u003e\u003cp\u003e \u003cb\u003ePatient data and specimens\u003c/b\u003e \u003c/p\u003e\u003cp\u003eFrom January 2014 to December 2015, 60 patients with hepatocellular carcinoma who were treated for the first time in the Department of Hepatobiliary and Pancreatic Surgery, Affiliated Cancer Hospital of Xinjiang Medical University were selected as the research objects. The cancer tissues and paired adjacent tissues were collected during the operation. All 60 patients with liver cancer strictly met the exclusion criteria and inclusion criteria. Inclusion criteria: (1) First radical resection of liver tumor; (2) Hepatocellular carcinoma confirmed by pathology; (3) Complete clinical data; (4) No previous treatment for liver cancer. Exclusion criteria: (1) Patients undergoing palliative surgery or unable to complete resection; (2) Metastatic hepatocellular carcinoma or intrahepatic cholangiocarcinoma confirmed by pathology; (3) Incomplete clinical data; (4) Previously received human mediated or targeted or immune therapy. Information collected includes: The patient's gender, age, history of hepatitis B, liver cirrhosis, AFP, Carcino-embryonic antigen (CEA), Carbohydrate antigen 19−9, CA19−9), Total bilirubin (TBIL), Alanine aminotransferase (ALT), Aspartate transaminase, AST albumin level, Prothrombin time (PT), intraoperative blood loss, tumor maximum diameter, tumor differentiation, Microvascular invasion (MVI), etc. This study was approved by the Ethics Committee of the Affiliated Cancer Hospital of Xinjiang Medical University.\u003c/p\u003e\u003cp\u003e \u003cb\u003eImmunohistochemical staining\u003c/b\u003e \u003c/p\u003e\u003cp\u003eThe conventional paraffin samples were precooled, then sliced into 4µm sections, dewaxed three times with xylene, hydrated in ethanol gradient, and antigen repaired with methylcitric acid (PH = 6) at high temperature and pressure for 3min. After cooling to room temperature, the samples were blocked for 10min to eliminate endogenous peroxidase activity. The primary antibodies were respectively PCNP protein (1:200) and β-catenin (1:200). The primary antibodies were incubated at 4℃ overnight for about 16h. After rewarming for 1h, the secondary antibodies were flushed and incubated at 26℃ for 30min. After each of the above steps was completed, it was washed with phosphate buffer solution (PBS) for 3 times. Finally, DAB was performed for color development, restaining, differentiation, gradient ethanol dehydration, xylene transparency, and neutral gum sealing. The results were observed under light microscope and image analysis was performed. Positive control sections provided by the ordering reagent company were used for positive control, and PBS was used for negative control instead of primary antibody. All methods were performed in accordance with the relevant guidelines and regulations.\u003c/p\u003e\u003cp\u003e \u003cb\u003eResult interpretation\u003c/b\u003e \u003c/p\u003e\u003cp\u003eAccording to the staining intensity and staining positive rate of cytoplasm and nucleus, the cancerous tissues and the adjacent tissues were interpreted respectively. PCNP is a quantitative assessment, which is mainly expressed in the nucleus. The presence of brown particles in the nucleus is considered as a positive result, and a few of the cytoplasm is brown. β-catenin was a qualitative assessment, which showed membrane staining in normal cells and cytoplasmic or nuclear staining in abnormal cells. Five fields were randomly selected under high power microscope (×200) in each tissue section to observe the staining degree of positive cells and calculate the percentage of positive cells. They were scored according to the degree of staining: 0 points for non-staining, 1 point for light yellow, 2 points for brown and 3 points for tan. Score according to the percentage of positive cells: 0 point if less than 1%, 1 point if 1%~25%; 2 points for 26%~50%; 51%~75%, 3 points; 4 points for more than 75%. The product of \"staining intensity score\" and \"proportion of positive cells score\" was used as the total score for grouping, ≤ 4 was divided into negative group, \u0026gt; 4 was divided into positive group. Two senior pathologists evaluated the final results in a double-blind manner.\u003c/p\u003e\u003cp\u003e \u003cb\u003eStatistical methods\u003c/b\u003e \u003c/p\u003e\u003cp\u003eThe data were input into SPSS 22.0 software for statistical processing. Measurement data conforming to normal distribution were expressed as x ± s. T-test was used for comparison between two groups, and one-way analysis of variance and corresponding multiple comparisons were used for comparison between multiple groups. The count data were analyzed by chi-square test and expressed as n (%). Kaplan-Meier method was used to draw the survival curve. Cox proportional hazards regression model was used for univariate and multivariate analysis of survival. All statistical results were calculated as \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05 was considered statistically significant. Using the selected independent risk factors as variables, a nomogram model was established to predict the 1 -, 3 -, and 5-year specific survival rates of patients with hepatocellular carcinoma, and internal validation was performed. The discrimination and calibration ability of nomogram were evaluated by Cindex and calibration curve. Net reclassification index (NRI) and decision curve analysis (DCA) were used to evaluate the predictive ability and net benefits of nomogram. In order to narrow the bias, the above analyses were repeated 1000 times with Bootstrap. A total risk score was calculated for each patient according to the prediction model, and patients in the modeling group were stratified according to the quartile of the total risk score. Kaplan-Meier method and Log rank test were used to evaluate the significance of survival differences among risk groups.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eThe expression of PD-L1, CD86 and CD206 in different tumors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIt has been reported that PD-L1, CD86 and CD206 are differentially expressed in a variety of tumors. This study obtained similar results through TIMER database analysis. PD-L1, also known as surface antigenin 274 (CD274), is a human protein encoded by the CD274 gene. CD206, a mannose receptor (MRC1), is considered to be a highly reliable marker of M2-type macrophages. The transcriptome levels of PD-L1 (CD274), CD86 and CD206 (MRCI) were found to be lower in many tumor tissues than in adjacent normal tissues. Such as bladder urothelial carcinoma (BLCA), breast cancer (BRCA), colon cancer (COAD), hepatocellular carcinoma (LIHC), lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), and prostate cancer (PRAD). However, for head and neck squamous cell carcinoma (HNSC), the transcriptome levels of PD-L1, CD86, and CD206 in tumor tissues were higher than those in adjacent normal tissues, as shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eA, Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eC, and Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eE. Expression analysis in liver cancer tissues showed that the expression levels of PD-L1, CD86 and CD206 in liver tissues were significantly lower than those in paracancer tissues, with statistical significance (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.046, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), as shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eB, Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eD, and Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eF.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRelationship between PD-L1, CD86 and CD206 expression and immune cell infiltration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe relationship between the expression of PD-L1, CD86, and CD206 and the regulation of the purity of immune cell infiltration (B cells, CD4\u003csup\u003e+\u003c/sup\u003eT cells, CD8\u003csup\u003e+\u003c/sup\u003eT cells, DC, neutrophils, and macrophages) was investigated using TIMER database. The expression level of PD-L1 was positively correlated with the infiltration level of B cells (\u003cem\u003er\u003c/em\u003e=0.337, \u003cem\u003ep\u003c/em\u003e=1.35e\u0026minus;10), CD8\u003csup\u003e+\u003c/sup\u003eT cells (\u003cem\u003er\u003c/em\u003e=0.378, \u003cem\u003ep\u003c/em\u003e=4.94e\u0026minus;13) and macrophages (\u003cem\u003er\u003c/em\u003e=0.386, \u003cem\u003ep\u003c/em\u003e=1.43e\u0026minus;13). CD4\u003csup\u003e+\u003c/sup\u003eT cells (\u003cem\u003er\u003c/em\u003e=0.429, \u003cem\u003ep\u003c/em\u003e=7.90e\u0026minus;17), neutrophils (\u003cem\u003er\u003c/em\u003e=0.544, \u003cem\u003ep\u003c/em\u003e=5.24e\u0026minus;28) and dendritic cells (\u003cem\u003er\u003c/em\u003e=0.464, \u003cem\u003ep\u003c/em\u003e=1.40e\u0026minus;19) were significantly increased in HCC, but negatively correlated with tumor purity (\u003cem\u003er\u003c/em\u003e=\u0026minus;0.247, \u003cem\u003eP\u003c/em\u003e=1.40E\u0026minus;19). \u003cem\u003ep\u003c/em\u003e=3.42e\u0026minus;06), as shown in Figure 2A. The expression level of CD86 was positively correlated with the infiltration level of B cells (\u003cem\u003er\u003c/em\u003e=0.644, \u003cem\u003ep\u003c/em\u003e=9.61e\u0026minus;42), CD8\u003csup\u003e+\u003c/sup\u003eT cells (\u003cem\u003er\u003c/em\u003e=0.664, \u003cem\u003ep\u003c/em\u003e=7.06e\u0026minus;45), CD4\u003csup\u003e+\u003c/sup\u003eT cells (\u003cem\u003er\u003c/em\u003e=0.429, \u003cem\u003ep\u003c/em\u003e=7.90e\u0026minus;17), macrophages (\u003cem\u003er\u003c/em\u003e=0.732, \u003cem\u003eP\u003c/em\u003e=1.61e - 58). Neutrophils (\u003cem\u003er\u003c/em\u003e=0.598, \u003cem\u003ep\u003c/em\u003e=8.86e\u0026minus;35) and dendritic cells (\u003cem\u003er\u003c/em\u003e=0.831, \u003cem\u003ep\u003c/em\u003e=2.74e\u0026minus;88) were significantly increased in HCC, but negatively correlated with tumor purity (\u003cem\u003er\u003c/em\u003e=\u0026minus;0.515, \u003cem\u003ep\u003c/em\u003e=8.71e\u0026minus;25), as shown in Figure 2B. Similarly, the expression level of CD206 was positively correlated with the infiltration level of B cells (\u003cem\u003er\u003c/em\u003e=0.154, \u003cem\u003ep\u003c/em\u003e=4.11e\u0026minus;03), CD8\u003csup\u003e+\u003c/sup\u003eT cells (\u003cem\u003er\u003c/em\u003e=0.298, \u003cem\u003ep\u003c/em\u003e=1.92e\u0026minus;08), CD4\u003csup\u003e+\u003c/sup\u003eT cells (\u003cem\u003er\u003c/em\u003e=0.069, \u003cem\u003ep\u003c/em\u003e=2.02e\u0026minus;01), macrophages (\u003cem\u003er\u003c/em\u003e=0.244, \u003cem\u003eP\u003c/em\u003e = 5.18 e - 06). Neutrophils (\u003cem\u003er\u003c/em\u003e=0.329, \u003cem\u003ep\u003c/em\u003e=3.56e\u0026minus;10) and dendritic cells (\u003cem\u003er\u003c/em\u003e=0.318, \u003cem\u003ep\u003c/em\u003e=1.98e\u0026minus;09) were significantly increased in HCC, and negatively correlated with tumor purity (\u003cem\u003er\u003c/em\u003e=\u0026minus;0.287, \u003cem\u003ep\u003c/em\u003e=5.36e\u0026minus;08), as shown in Figure 2C.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExpression of CD86, CD206 and PD-L1 in tumor-associated macrophages in cancer tissues and adjacent tissues of patients with hepatocellular carcinoma\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eImmunohistochemical examination of cancer tissues and adjacent tissues from 60 patients with liver cancer showed that CD86, CD206 and PD-L1 were significantly stained in cancer tissues, as shown in Figure 3. Statistical analysis showed that the positive expression rate of PD-L1 was 57% in cancer tissues and 12% in adjacent tissues. The positive expression rate of CD86 was 50% in cancer tissues and 20% in adjacent tissues. The positive expression rate of CD206 in cancer tissues was 78.3% and that in adjacent tissues was 41.7%, showing significant differences, as shown in Figure 4.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRelationship between the expression of CD86, CD206 and PD-L1 and clinicopathological features in patients with hepatocellular carcinoma\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe correlation analysis between the expression levels of CD86, CD206 and PD-L1 in cancer tissues of 60 patients with HCC and the clinicopathological features showed that the expression of PD-L1 was correlated with the degree of tumor differentiation (\u003cem\u003e\u0026chi;2\u003c/em\u003e\u0026thinsp;=\u0026thinsp;7.855, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.02). The expression level of CD206 was correlated with gender (\u003cem\u003e\u0026chi;2\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4.832, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.028) and whether patients had hepatitis before surgery (\u003cem\u003e\u0026chi;2\u003c/em\u003e\u0026thinsp;=\u0026thinsp;9.624, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002), as shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. Logistic regression analysis of PD-L1 expression and clinicopathological features in tumor tissues of patients with liver cancer showed that the degree of tumor differentiation was a risk factor for PD-L1 expression, which was positively correlated with its expression level, as shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. Similarly, it was found that gender and preoperative hepatitis were risk factors for CD206 expression, which were positively correlated with CD206 expression, as shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eCorrelation between the expression of PD-L1, CD86 and CD206 and clinicopathological features in hepatocellular carcinoma\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\u003ccolgroup\u003e\u003c/colgroup\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eCharacteristics\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003ePD-L1 Expression\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003eCD86 Expression\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003eCD206 Expression\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eLow\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eHigh\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003e\u0026chi;\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eLow\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eHigh\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003e\u0026chi;\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eLow\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eHigh\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003e\u0026chi;\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth colspan=\"13\" align=\"left\"\u003e\n\u003cp\u003eGebder\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e19\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e27\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.331\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.656\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e23\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e23\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e1.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e39\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e4.831\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.028\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFemale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"13\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;60\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e21\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.830\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.362\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.271\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.602\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e25\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e1.067\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.302\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026ge;\u0026thinsp;60\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e22\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"13\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eTumor size(cm)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.051\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.821\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.067\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.796\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e23\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.032\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.859\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026ge;\u0026thinsp;5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e24\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"13\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eTumor capsule\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.051\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.821\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.067\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.796\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e23\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.032\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.859\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eN0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e24\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"13\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eNumber of tumors\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSingle\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e22\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e1.621\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.203\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e22\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.373\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.542\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e37\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.513\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.474\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMultiple\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"13\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eVascular invasion\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e1.872\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.171\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.082\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.774\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.838\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.360\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNO\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e21\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e22\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e21\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e22\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e35\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"13\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePortal vein tumor thrombus\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.271\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.602\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.267\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.606\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e23\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.098\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.754\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNO\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e24\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"13\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eCirrhosis\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.667\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.414\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.067\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.796\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e25\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.202\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.653\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNO\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e22\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"13\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eAFP(ug/L)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;13.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e5.287\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e0.071\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e0.085\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e0.959\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e21\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e1.852\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e0.396\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13.4\u0026thinsp;\u0026le;\u0026thinsp;AFP\u0026lt;400\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026ge;\u0026thinsp;400\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"13\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eHepatitis\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e19\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e21\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.848\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.357\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e21\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e19\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.300\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.584\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e36\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e9.624\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNO\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"13\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eDifferentiation degree\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLow\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e7.855\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.02\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e1.744\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e0.418\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e1.075\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e0.584\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMedium\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e21\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e29\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHigh\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"13\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eAJCC stage\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e3.716\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e0.156\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e1.217\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e0.544\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e21\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e2.111\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e0.348\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eII\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIII\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n*Statistically significant.\u003c/div\u003e\n\u003cp\u003eThe bold values were considered statistically significant.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"char\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"char\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"char\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eLogistic regression analysis of the relationship between clinicopathological characteristics and the expression of PD-L1 in hepatocellular carcinoma\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\u003ccolgroup\u003e\u003c/colgroup\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eCharacteristics\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003e\u0026beta;\u003c/em\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eSE\u003c/em\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eOR\u003c/em\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003e95%CI\u003c/em\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eP-value\u003c/em\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eDifferentiation degree\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(Low vs Medium)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.821\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.194\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e16.80\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.617\u0026thinsp;~\u0026thinsp;17.519\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.018\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eDifferentiation degree\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(Medium vs High)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.351\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.123\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e10.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.161\u0026thinsp;~\u0026thinsp;94.925\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.036\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"char\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"char\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"char\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab3\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eLogistic regression analysis of the relationship between clinicopathological characteristics and the expression of CD206 in hepatocellular carcinoma\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\u003ccolgroup\u003e\u003c/colgroup\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eCharacteristics\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003e\u0026beta;\u003c/em\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eSE\u003c/em\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eOR\u003c/em\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003e95%CI\u003c/em\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eGebder\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(Male vs Female)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.436\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.757\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4.205\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.954\u0026thinsp;~\u0026thinsp;18.542\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.048\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eHepatitis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(Yes vs NO)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.718\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e7.394\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.785\u0026thinsp;~\u0026thinsp;30.629\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.006\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEffect of PD-L1, CD86 and CD206 expression on the overall survival and progression-free survival in patients with hepatocellular carcinoma\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSixty patients with liver cancer were followed up after surgery. The shortest survival time was 1 month, and the longest survival time was 70 months. The 1 -, 2 -, and 3-year overall survival (OS) rates were 93.3%, 83.3%, and 75%, respectively, and the 3-year progression-free survival rate was 61.7%, as shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e. The 3-year overall survival rate of patients with high expression of PD-L1 was 82.4%, while that of patients with low expression of PD-L1 was 76.9%, showing a significant difference (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.03), as shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eA. However, there was no correlation between the expression of PD-L1 in adjacent tissues and prognosis, as shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eB. Analysis of CD86 in HCC tissues showed that the 3-year overall survival rate of patients with high expression of CD86 was 83.3%, and that of patients with low expression of CD86 was 79.2%, showing a significant difference (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.04), as shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eC. However, there was no correlation between the expression of CD86 in adjacent tissues and the prognosis, as shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eD. Survival analysis on the expression of CD206 in cancer tissues and adjacent tissues and whether they had hepatitis before surgery showed no correlation, as shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eE-G. Survival analysis of AJCC stage showed that the 3-year survival rates of patients with stage I, II and III were 100%, 81.3% and 55.2%, respectively, showing differences, as shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eH. Cox multivariate analysis showed that AJCC stage (\u003cem\u003eOR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;11.841, 95%\u003cem\u003eCI\u003c/em\u003e: 2.589\u0026ndash;54.16, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001) and preoperative hepatitis (\u003cem\u003eOR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5.427, 95%\u003cem\u003eCI\u003c/em\u003e: 1.084\u0026ndash;27.175, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.04), the expression level of CD206 in paracancorous tissues (\u003cem\u003eOR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;7.172, 95%\u003cem\u003eCI\u003c/em\u003e: 1.405\u0026ndash;36.606, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.018) was an independent risk factor affecting the survival of patients after radical resection of liver cancer, as shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"char\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"char\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab4\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eCox regression analysis of prognostic factors in patients with hepatocellular carcinoma after radical resection\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\u003ccolgroup\u003e\u003c/colgroup\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eCharacteristics\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003e\u0026beta;\u003c/em\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eSE\u003c/em\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eOR\u003c/em\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003e95%CI\u003c/em\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eP-value\u003c/em\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eAJCC stage\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(I\u003c/strong\u003e \u003cspan class=\"BoldItalic\"\u003evs.\u003c/span\u003e \u003cstrong\u003eII\u0026thinsp;~\u0026thinsp;III)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.472\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.776\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e11.841\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.589\u0026thinsp;~\u0026thinsp;54.16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eHepatitis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(Yes\u003c/strong\u003e \u003cspan class=\"BoldItalic\"\u003evs.\u003c/span\u003e \u003cstrong\u003eNo)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.691\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.822\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e5.427\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.084\u0026thinsp;~\u0026thinsp;27.175\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.040\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eCD206 in adjacent tissues\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(High\u003c/strong\u003e \u003cspan class=\"BoldItalic\"\u003evs.\u003c/span\u003e \u003cstrong\u003eLow)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.970\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.832\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e7.172\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.405\u0026thinsp;~\u0026thinsp;36.606\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.018\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eConstruct and validate the prognostic nomogram of patients with liver cancer\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAccording to the results of multivariate analysis, R software was used to establish the prognosis prediction model of liver cancer. In the nomogram, age\u0026thinsp;\u0026le;\u0026thinsp;60 was 0 points, and age\u0026thinsp;\u0026gt;\u0026thinsp;60 was 80 points. Tumor stages T1 and T2 were 0 points, T3 and T4 were 100 points. The pathological stage of the tumor was 0 for stage I and II, and 30 for stage II and IV. AFP\u0026thinsp;\u0026le;\u0026thinsp;400 indicates a score of 0, while AFP\u0026thinsp;\u0026gt;\u0026thinsp;400 indicates a score of 12. The low expression of PD-L1 was 0, and the high expression was 28. The high expression of CD206 was 0, and the low expression was 72. The low expression of CD86 was 0, and the high expression was 38. The higher the total score in the nomogram, the lower the OS in the corresponding 3 and 5 years, as shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eA. The test results show that the C index of the line chart model is 0.742, indicating that the accuracy of the model is high. By drawing the calibration chart of the predicted value and the measured value, the consistency test was carried out. The 3-year and 5-year OS predicted by the rosette model has a good correlation with the actual 3-year and 5-year OS. Bootstrap method (repeated sampling 1000 times) was used to verify the established nomogram. The C index of inner part verification was 0.762 (95%\u003cem\u003eCI\u003c/em\u003e: 0.744\u0026ndash;0.780), and the C index of outer part verification was 0.787 (95%\u003cem\u003eCI\u003c/em\u003e: 0. 767\u0026minus;0. 806), indicating that the nomogram has good predictive value. The predicted values of the calibration maps of the 1-year, 3-year and 5-year CSS of the two groups are in good agreement with the actual observed values, shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eB.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunctional prediction and protein interaction analysis of PD-L1, CD86 and CD206\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBased on STRING database, the PPI networks of PD-L1, CD86 and CD206 were constructed respectively, and the top ten functionally interacting proteins with high connectivity were selected. Among them, PD-L1-related genes were CD247, CD80, CTLA4, PDC1LG2, PDCD1, HLA-DRA, CD3E, CD4, PTPN11 and HOXD13, as shown in Figure 8A. CD86-related genes were ICAM1, CD80, CD8A, CTLA4, IL-10, CTLA4, CD4, CD28, CD247, CD3E and CSF1, as shown in Figure 8C. CD206 related genes were CD68, FCGR1A, ITGAM, PLAT, ITGAX, CD86, CD163, IL10, IL4 and ARG1, as shown in Figure 8E. GO enrichment analysis included three main functions, namely biological process function, cellular component function and molecular function (\u003cem\u003ep\u003c/em\u003e＜0.05). KEGG pathway and GO analysis showed that PD-L1 promoted significant enrichment in T cell aggregation, lymphocyte aggregation and other aspects of biological processes. In terms of cell components, PD-L1 participates in T cell antigen receptor CD3 complex, cell membrane and other functions, as shown in Figure 8B and Table 5. CD86 was significantly enriched in the positive regulation of cell adhesion, regulation of mononuclear cell proliferation, regulation of leukocyte proliferation, and transduction of T-cell receptor signaling pathway, as shown in Figure 8D and Table 6. Similarly, KEGG pathway analysis of CD206 showed significant enrichment in type 2 immune response, cellular response to LPS, cellular response to LPS, and cellular response to LPS involved in cellular response to LPS, as shown in Figure 8F.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab5\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eGO and KEGG enrichment analyses of PD-L1 and functional partner genes in hepatocellular carcinoma.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\u003ccolgroup\u003e\u003c/colgroup\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eONTOLOGY\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eID\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eDescription\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGO:0031295\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eT cell costimulation\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.15e-13\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGO:0031294\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003elymphocyte costimulation\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.28e-13\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGO:0050870\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003epositive regulation of T cell activation\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.83e-12\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGO:0009897\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eexternal side of plasma membrane\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.19e-08\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGO:0042101\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eT cell receptor complex\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.03e-05\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGO:0030136\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eclathrin-coated vesicle\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9.75e-05\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMF\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGO:1990782\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eprotein tyrosine kinase binding\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.64e-05\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMF\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGO:0015026\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ecoreceptor activity\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.68e-04\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMF\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGO:0001618\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003evirus receptor activity\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7.60e-04\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eKEGG\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ehsa04514\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCell adhesion molecules\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.86e-09\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eKEGG\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ehsa05235\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePD-L1 expression and PD-1 checkpoint pathway in cancer\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.77e-08\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eKEGG\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ehsa04660\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eT cell receptor signaling pathway\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.89e-08\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab6\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eGO and KEGG enrichment analyses of CD86 and functional partner genes in hepatocellular carcinoma\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\u003ccolgroup\u003e\u003c/colgroup\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eONTOLOGY\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eID\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eDescription\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGO:0045785\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003epositive regulation of cell adhesion\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.91e-12\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGO:0032944\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eregulation of mononuclear cell proliferation\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.32e-12\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGO:0070663\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eregulation of leukocyte proliferation\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.56e-12\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGO:0009897\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eexternal side of plasma membrane\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.35e-10\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGO:0042101\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eT cell receptor complex\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.35e-07\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGO:0098636\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eprotein complex involved in cell adhesion\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.58e-07\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMF\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGO:0015026\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ecoreceptor activity\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.90e-09\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMF\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGO:0001618\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003evirus receptor activity\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8.25e-06\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMF\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGO:0104005\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ehijacked molecular function\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8.25e-06\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eKEGG\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ehsa04660\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eT cell receptor signaling pathway\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.57e-12\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eKEGG\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ehsa04514\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCell adhesion molecules\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7.05e-09\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eKEGG\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ehsa05323\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRheumatoid arthritis\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.38e-08\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eTumor microenvironment (TME) is a dynamic system regulated by cell-to-cell communication, which is closely related to tumor development and metastasis\u003csup\u003e[\u003cspan additionalcitationids=\"CR15 CR16\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e–\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. As the main stromal cells in TME, macrophages are highly plastic and have different phenotypes under different stimuli, including M1 (tumor suppressor) and M2 (tumor promoter)\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. TAMs are generally considered as M2-type macrophages with high expression of CD206, Arg−1, IL−10 and TGF-β\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e–\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. According to previous studies, M1 macrophages have been considered to play an inhibitory role in tumor growth, while M2 macrophages promote tumor growth\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e–\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. Jiang et al. showed that M1 macrophages inhibited the migration and invasion of esophageal squamous cell carcinoma cells\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. M1 macrophages inhibit the proliferation and induce apoptosis of tumor cells in lung cancer, and play an anti-tumor role\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. This study also found that CD86 (M1 macrophages) significantly infiltrated in cancer tissues. Prognostic analysis of the expression of CD86 in cancer tissues showed that CD86 could inhibit tumor progression and prolong the overall survival time of patients for 3 years (\u003cem\u003eP\u003c/em\u003e = 0.04), which further verified the effect of M1 macrophages in inhibiting tumor growth. However, in recent years, studies have shown that M1 macrophages have tumor-promoting effects. Jin et al. observed that M1 macrophages can promote the invasion of brain glioma U251 cells\u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e. Zhuo Chen et al. detected that M1-type macrophages could induce EMT in breast cancer cells T47-D and MCF−7, and enhance the migration and invasion ability of the cells. Targeting M1 macrophages may inhibit EMT and limit the invasion potential of breast cancer\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. At present, the effect of M1 macrophages on tumor promotion or inhibition is still controversial. The role of M1 macrophages in the occurrence and progression of bladder cancer is still unclear, and more basic studies are needed.\u003c/p\u003e\u003cp\u003eProgrammed death−1 (PD−1) is a type I transmembrane protein, which is mainly expressed in mature T cells, B cells, macrophages and NK cells. Its ligand PD-L1 is inductively expressed in T cells, B cells, monocytes and many types of tumor cells, such as lung cancer, liver cancer and malignant melanoma\u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e. Under normal physiological conditions, PD-L1 expressed on the surface of tissue cells, endothelial cells and immune cells binds to PD−1 on the surface of activated T cells, which inhibits excessive activation of T cells and induces apoptosis of T cells, thus maintaining a certain dynamic balance of the body's immune response\u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e. After carcinogenesis, tumor cells promote the up-regulation of PD-L1 expression through a variety of mechanisms. A large number of PD−1 molecules on the surface of T cells inhibit the proliferation and activation of CD4 \u003csup\u003e+\u003c/sup\u003e T cells and CD8 \u003csup\u003e+\u003c/sup\u003e T cells, and produce some activated cytokines (such as IFN-γ) to break the homeostasis of immune response. It makes tumor cells evade the surveillance of the body's immune system and promotes tumor progression\u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e–\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e. Our study found that PD-L1 was significantly overexpressed in cancer tissues, which promoted tumor progression and affected the prognosis of patients, which was also consistent with the reports in the literature.\u003c/p\u003e\u003cp\u003eLiver is a special immune tolerant organ, which can effectively evade immune response. However, immunotherapy can enhance the body's immune response, break immune tolerance, and then recognize and kill tumor cells\u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e. In recent years, tumor immunotherapy represented by Immune checkpoint inhibitors (ICIs) has made breakthrough progress, which prolongates the survival of patients, and some patients can even be transformed from unresectable to radical resectable, bringing new light to the treatment of tumors\u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e. In this study, the bioinformation analysis first found that PD-L1, CD86 and CD206 were differentially expressed in HCC and correlated with the immune infiltration of tumor cells. To further confirm this, our research group conducted immunohistochemical detection of PD-L1, CD86 and CD206 in cancer tissues and adjacent tissues of 60 HCC patients and found that PD-L1, CD206 and CD86 were significantly overexpressed in cancer tissues, which may be related to the suppression of immune response caused by PD-L1 overexpression in the immune microenvironment of HCC\u003csup\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e. The application of PD−1 antibody can improve the immune suppression state of the body and enhance the anti-tumor immune effect of the body, which has achieved certain efficacy in some solid tumors. However, in the immunotherapy of liver cancer, the anti-PD−1 efficacy is low, only 15%−25%, which may be related to the special immune microenvironment of liver cancer, and the specific mechanism is still unclear\u003csup\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e. In order to improve the immune response rate of patients with liver cancer and increase the efficacy of anti-PD−1, it is urgent to further study the immune microenvironment of liver cancer and find more effective tumor therapeutic targets, so as to achieve the purpose of precise treatment.\u003c/p\u003e\u003cp\u003eIn order to further explore the biological functions of PD-L1, CD86 and CD206, we carried out GO and KEGG enrichment analysis on them. PD-L1 is mainly involved in T cell activation, aggregation and lymphocyte activation in tumor microenvironment. CD86 is mainly involved in the positive regulation of cell adhesion and the proliferation of leukocytes and monocytes. CD206 is mainly involved in type 2 immune response and protein complex involved in cell adhesion. However, the current study had some limitations. First of all, this study only predicted the function of each gene, and the research depth is enough. The function and mechanism of tumor immune infiltration should be further explored. Secondly, in this study, we retrospectively analyzed HCC patients who underwent radical surgery in a single institution. The value of PD-L1, CD86 and CD206 expression in tumor tissues needs to be prospectively verified in multicenter studies.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn this study, we investigated the expression of PD-L1, CD86 and CD206 in hepatocellular carcinoma tissues and revealed the application value of PD-L1, CD86 and CD206 in prognosis assessment of hepatocellular carcinoma. PD-L1, CD86 and CD206 may be involved not only in the occurrence and development of HCC, but also in the immune escape of HCC. Therefore, the in-depth study of PD-L1, CD86 and CD206 can not only find biomarkers for prognosis assessment of patients with liver cancer, but also provide new therapeutic targets for patients' immunotherapy. However, the current study has certain limitations, and further mechanistic studies are needed to verify our findings and promote clinical application.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by Science and Technology Department of Xinjiang Uygur Autonomous Region, Key Laboratory Open Project (2022D04030).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData supporting the conclusions of this article are included within the article. The raw datasets used and analysis for the present study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompliance with ethical standards\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was approved by the Independent Ethics Committee of Tumor Hospital Affiliated to Xinjiang Medical University in 2022.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed consent\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformed consent was obtained from all individual participants included in the study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eKim E, Viatour P. Hepatocellular carcinoma: old friends and new tricks[J]. 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Cyclin D-CDK4 kinase destabilizes PD-L1 via cullin 3-SPOP to control cancer immune surveillance[J]. Nature, 2018,553(7686):91\u0026ndash;95.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePinero F, Dirchwolf M, Pessoa MG. Biomarkers in hepatocellular carcinoma: diagnosis, prognosis and treatment response assessment[J]. Cells, 2020,9(6):1370.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Hepatocellular carcinoma, Programmed cell death ligand 1, M1 macrophages, M2 macrophages, Immune cells, Prognosis ","lastPublishedDoi":"10.21203/rs.3.rs-2579242/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2579242/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eHepatocellular carcinoma (HCC) is an inflammation-associated tumor involved in immune tolerance and evasion in the immune microenvironment. Immunotherapy can enhance the body's immune response, break immune tolerance, and then recognize and kill tumor cells. The polarization homeostasis of M1 and M2 macrophages in tumor microenvironment (TME) is involved in the occurrence and development of tumor, which is a hot topic in tumor research. Programmed cell death ligand 1 (PD-L1) plays an important role in the polarity of TAM and affects the prognosis of HCC patients as a target of immunotherapy. Therefore, we further explored the application value of PD-L1, M1 macrophages (CD86) and M2 macrophages (CD206) in the prognosis assessment of HCC, their correlation with immune cell infiltration in HCC tissues, and their bioenrichment function.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eThe gene expression omnibus (GEO) and The Cancer Genome Atlas (TCGA) database were used to analyze the expression of PD-L1, CD86 and CD206 in different tumor tissues. The Tumor Immune Estimation Resource (TIMER) was used to analyze the correlation between the expression of PD-L1, CD86 and CD206 and the infiltration of immune cells. The tissue specimens and clinicopathological data of hepatocellular carcinoma patients who underwent surgical treatment in our hospital were collected. Immunohistochemistry was used to verify the expression of PD-L1, CD86 and CD206, and analyze the relationship with clinicopathological features and prognosis of patients. Nomogram was constructed to predict the overall survival (OS) of patients at 3 and 5 years. Finally, STRING database was used to analyze the protein-protein interaction network information, and GO analysis and KEGG (Kyoto Encyclopedia of Genes and Genomes) analysis were performed to study the biological functions of PD-L1, CD86 and CD206.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResult:\u003c/strong\u003eBioinformatics analysis found that PD-L1, CD86 and CD206 were all under-expressed in a variety of tumor tissues including liver cancer, while our immunohistochemical analysis found the opposite result, and PD-L1, CD86 and CD206 were all over-expressed in liver cancer tissues. The expressions of PD-L1, CD86 and CD206 were positively correlated with the level of immune cell infiltration in HCC tissues; The expression of PD-L1 is positively correlated with the degree of tumor differentiation; The expression level of CD206 was positively correlated with gender and whether patients had hepatitis before operation. The prognosis of patients with low expression of PD-L1 or CD86 is poor. AJCC stage, preoperative hepatitis, and the expression level of CD206 in adjacent tissues are independent risk factors affecting the survival of patients after radical hepatectomy. KEGG pathway enrichment analysis showed that PD-L1 was significantly enriched in T cell aggregation and lymphocyte aggregation, and may be involved in the formation of T cell antigen receptor CD3 complex and cell membrane. CD86 was significantly enriched in positive regulation of cell adhesion, regulation of mononuclear cell proliferation, regulation of leukocyte proliferation and transduction of T cell receptor signaling pathway. CD206 was significantly enriched in type 2 immune response, cellular response to LPS, cellular response to LPS, and involvement in cellular response to LPS.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eIn conclusion, these results suggest that PD-L1, CD86 and CD206 may not only be involved in the occurrence and development of HCC, but also in immune regulation. Therefore, PD-L1, CD86 and CD206 can be used as potential biomarkers and new therapeutic targets for HCC prognosis assessment.\u003c/p\u003e","manuscriptTitle":"Expression of tumor-associated macrophages and PD-L1 in patients with hepatocellular carcinoma and construction of a prognostic model","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-03-08 15:17:04","doi":"10.21203/rs.3.rs-2579242/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":"2a0f3d1f-1e99-49ae-af87-138f99d531bb","owner":[],"postedDate":"March 8th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":19653537,"name":"Biological sciences/Cancer"},{"id":19653538,"name":"Biological sciences/Cancer/Tumour biomarkers"}],"tags":[],"updatedAt":"2023-05-04T05:29:24+00:00","versionOfRecord":[],"versionCreatedAt":"2023-03-08 15:17:04","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-2579242","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2579242","identity":"rs-2579242","version":["v1"]},"buildId":"_2-kVJe1T_tPrBINL-cwx","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}