A Cohort Study Using Preoperative IL-6/Stat3/IL-17 And Blood Aminotransferase Levels To Predict Five-Year Survival For Patients After Liver Cancer Resection

A Cohort Study Using Preoperative IL-6/Stat3/IL-17 And Blood Aminotransferase Levels To Predict Five-Year Survival For Patients After Liver Cancer Resection | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A Cohort Study Using Preoperative IL-6/Stat3/IL-17 And Blood Aminotransferase Levels To Predict Five-Year Survival For Patients After Liver Cancer Resection Lijie Wang, Xiaoning Li, Lin Tao, Wenjie Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-261872/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 Objective: The expression/activation of p-Stat3, IL-6 and IL-17 in hepatocellular carcinoma (HCC) tissues together with the serum levels of aminotransferases ALT, AST, GGT and ALP were designed to evaluate their abilities in predicting the survival prognosis of postoperative patients with HCC. Methods: The clinicopathological data and paraffin-embedded tissues of 98 patients who underwent liver tumor resection were collected at the First Affiliated Hospital of Shihezi University School of Medicine, and the patients were followed-up after surgery. Immunohistochemistry (IHC) was used to detect the activation/expression of p-Stat3, IL-6 and IL-17 using tissue microarrays derived from these patients. Before surgery, patients’ serum levels of AST, ALT, GGT and ALP were measured using a Roche Modular DPP automated biochemical analyzer. Statistical analyses were performed using non-parametric tests, Spearman's correlation, ROC curves, Kaplan-Meier survival analysis, Cox single-factor and multifactor regression models. Results: (1) The strong positive rate of expressed IL-6 in HCC tissues (18.09%) was lower than that in paracancerous tissues (60.06%) (p<0.001); the strong positive rate of expressed IL-17 in HCC tissues (42.40%) was lower than that in paracancerous tissues (87.80%) (p<0.001). The strong positive rate of activated p-Stat3 in HCC tissues (52.20%) was higher than paracancerous tissues (18.04%) (p=0.01). (2) The higher the levels of ALT (p<0.001), AST (p=0.002), GGT (p<0.001), and ALP (p< 0.001) were, respectively, the worse the survival prognoses were observed among HCC patients. (3) High levels of ALP were an independent risk factor for postoperative prognosis in HCC patients. (4) The combination of ALT+AST+ ALP appeared to be the best survival predictor for HCC patients as indicated by AUC (0.820, 95% CI=0.714, 0.926) (p<0.001). Conclusions: The activation/expression of p-Stat3, IL-6 and IL-17 differ in HCC tissues compared with their paracancerous tissues. Using classical TNM staging system as a reference, higher levels of serum aminotransferases ALT, AST, GGT and ALP are capable of predicting poor prognosis among postoperative HCC patients. ALT+AST+ALP+p-Stat3 is an optimal combination better than the classical TNM staging system in predicting postoperative survival among HCC patients. The observations presented may provide a useful guide for clinicians to strategize individualized surgical plans for liver cancer patients before surgery. Surgery Oncology HCC Liver cancer IL-6 Stat3 IL-17 Survival prognosis Aminotransferases Figures Figure 1 Figure 2 Figure 3 Objective According to Global Cancer Statistics 2018, liver cancer (HCC) is one of the most common malignancies worldwide, with approximately 840,000 new cases and 780,000 deaths per year worldwide. In China, the incidence and the five-year survival rate of HCC are ranked third and fifth, respectively, among malignant tumors in China. 1 Most liver cancers start insidiously and lack specific clinical manifestations. Although there has been advancement in relevant medical technologies, poor prognosis and high mortality rate of liver cancer have little changes due largely to unavailability of noninvasive prescreening methodologies. Liver tissue biopsy is widely used for preoperative diagnosis of liver cancer and has a high diagnostic value, 2 but the procedure’s invasiveness has drawbacks. Postoperative pathological characteristics of HCC such as TNM clinical staging, degree of cell differentiation and depth of tumor infiltration, are indicative of patients’ prognoses, but these indicators cannot be obtained preoperatively. Therefore, search for potential indicators that can be obtained preoperatively and show significant impacts on the survival prognosis of patients with liver cancer may provide useful guidance to surgeons in choosing optimal surgical approaches and postoperative treatment strategies to improve the quality of life as well as survival after surgery. This study has, therefore, investigated several potential bio-indicators that can be tested preoperatively and show impacts on patients’ survival after surgery. These bio-indicators have included serum levels of several aminotransferases (ALT, AST, GGT, and ALP) and activational or expressed levels of the Stat3 signaling pathway (IL-6, p-Stat3 and IL-17) in tissues from patients with liver cancer. 1 Information And Methods 1.1 Subjects This study retrospectively collected 98 patients who underwent HCC resection at the First Affiliated Hospital, Shihezi University School of Medicine from January 2010 to December 2017, and the diagnoses of HCC were confirmed by two senior pathologists independently. All patients had complete demographic information and pathological data. This patient cohort included 74 males and 24 females with a mean age of 56.68 years and a median age of 57.0 years. None of the patients received adjuvant therapy such as radiotherapy or chemotherapy before surgery. Liver cancer tissues were non-necrotic tumor tissues and para-cancerous tissues were at least 2 cm away from the tumor edge. 1.2 Detection of activation/expression of p-Stat3, IL-6, and IL-17 by immunohistochemistry (IHC) IHC method was performed in HCC and para-cancerous tissues using the Envision method. Briefly, paraffin chip sections were baked at 67ºC for 30 min, de-waxed and hydrated. After antigens were thermally repaired, 3% H 2 O 2 was added to block endogenous peroxidase activity and then, specific primary antibodies (against IL-6, IL-17 and p-Stat3 purchased from Abcam Co., USA) and secondary antibodies were added respectively, and color development was achieved using DAB. 1.3 Scoring of Immunohistochemical stains Positive IHC stains were defined as yellow-brown color according to the manufacturer’s demonstrative slides (Figure. 1). IHC staining slides were read on an Olympus optical microscope over yellow-brown color stains for 12 consecutive fields and scored according to two variable factors: (1) counting the number of positively stained cells (0 = < 5%; 1 = 6%-25%; 2 = 26%-50%; 3 = 51%-75%; and 4 = 76%-100%); and (2) scoring the intensity of the staining (0 = absent; 1 = Pale Brown-yellow; 2 = Brown-yellow; 3 = Brown-yellow; 4 = Dark brown). The final score was the product of (1) multiplying (2) for individual slides (see Table S1). This scoring system was similar in principle to a published literature. 3 1.4 Serum aminotransferase measurements Upon admission to the hospital, venous blood (3ml) was collected from patients after fasting for 12 hours and levels of aminotransferases were measured using a Roche automatic biochemical analyzer. The aminotransferases tested were aspartate aminotransferase (AST), alanine aminotransferase (ALT), glutamyl transferase (GGT) and alkaline phosphatase (ALP). 1.5 Patients’ Follow-up Patients’ follow-ups were achieved via telephone calls, outpatient interviews, and home visits. Follow-up was from October 2016 to October 2018, and patients were followed-up for 1–99 months after surgery. 1.6 Diagnostic standard and reference value ranges The TNM staging was based on the eighth edition of TNM staging and cell differentiation of HCC was based on the latest edition of WHO Gastrointestinal Tumor Pathology and Genetics. Reference ranges of blood levels of aminotransferases were established by the Health Industry Standard of the People's Republic of China. 1.7 Statistical analyses Statistical analysis was performed using SPSS software package (version 23.0). Spearman grade analysis was used for correlation analyses between pathological data, IHC results and serum levels of aminotransferases. Single-factor and multifactor Cox regression risk models were used to analyze factors related to postoperative survival in patients with HCC. Kaplan-Meier analysis was used for survival curves affected by serum levels of ALT, AST, GGT and ALP. ROC (receiver operating characteristics) curve analysis generates AUC (area under the curve) which is a quantified measurement predicting the effects of clinical characteristics, aminotransferases (ALT, AST, GGT and ALP) and expression/activation of IL-6, IL-17 and p-Stat3 on patients’ survival. p < 0.05 indicates a statistically significant difference between comparisons. 2 Results 2.1 Clinical cancer staging and serum levels of aminotransferases Of 98 cases of liver cancer, 70 cases (71.4%) were defined as clinical cancer stage I, 22 cases (22.40%) as stage II, 4 cases (4.10%) as stage III, and 2 cases (2.00%) as stage IV, respectively. ROC analysis was used to calculate cut-off values for 4 aminotransferases: AST (men: 36.55U/L, women: 34.50U/L); ALT (men: 46.50U/L, women: 23.65U/L); GGT (men: 81.00U/L, women. 59.50 U/L); ALP (men: 102.50 U/L, women: 82.50 U/L) ( Table 1 ). Table 1 Clinical demographic data of preoperative and postoperative indicators of HCC patients Indicators (n) HCC patients Indicators (n) HCC patients n % n % Gender (98) ALT (U/L) (98) male 74 75.5 range 9.55–265.00 female 24 24.5 Mean ± SD 39.36 ± 39.58 Age (years) (98) Cut-off value 46.50 or 23.65 range 32–79 ≤ 46.5 or 23.65 68 69.4 Mean ± SD 56.68 ± 9.74 > 46.5 or 23.65 30 30.6 Clinical stages (98) AST (U/L) (98) I 70 71.4 range 0.65–260 II 22 22.4 Mean ± SD 39.7 ± 32.60 III 4 4.1 Cut-off value 36.50 or 34.50 IV 2 2 ≤ 36.5 or 34.5 60 61.2 Differentiation grade (98) > 36.5 or 34.5 38 38.8 high 11 11.2 GGT (U/L) (98) moderate 54 55.1 range 4.00-392.00 low 33 33.7 Mean ± SD 87.17 ± 73.73 Follow-up (months) (98) Cut-off value ≤ 81.00 or 59.50 range 1–99 ≤ 81 or 59.5 73 74.5 Mean ± SD 46.69 ± 25.59 > 81 or 59.5 25 25.5 BMI (kg/m2) (82) IL-6 (Cancer) (88) range 19.10-34.94 -/+ 62 70.5 Mean ± SD 24.93 ± 3.15 2+ 18 20.5 18.6–23.9 34 41.5 3+ཞ4+ 8 10 24-27.9 34 41.5 IL-17 (Cancer) (90) ≥ 28 14 17 -/+ 52 53.1 ALP (U/L) (98) 2+ 35 35.7 range 21.00-221.00 3+ཞ4+ 3 3.1 Mean ± SD 87.17 ± 31.70 p-Stat3 (Cancer) (90) Cut-off value 102.50 or 82.50 -/+ 43 43.9 ≤ 102.5 or 82.5 68 69.4 2+ 25 25.5 > 102.5 or 82.5 30 30.6 3+ཞ4+ 22 24.4 2.2 Expression of IL-6 and IL-17 and activation of p-Stat3 in liver cancer tissues As shown in Figure.1, when IL-6 and IL-17 were expressed, the cytoplasma of HCC and paracancerous cells was stained (from light yellow to brown). Phosphorylated Stat3 (p-Stat3) is the active form of Stat3 which enters the nuclei and, therefore, IHC showed positive staining in the nuclei of carcinoma cells and paracancerous cells (from light yellow to brownish yellow). As shown in Table 2 , the positive and strongly positive rates of IL-6 and IL-17 expression in paracancerous tissues were higher than those in HCC tissues. On the other hand, the positive and strongly positive rates of activated p-Stat3 in HCC tissues were higher than those in paracancerous tissues. Table 2 Comparisons of IHC-based indicators of IL-6, IL-17 and p-Stat3 between cancer and paracancerous tissues Grouping n - 1+ 2+ 3+ Positive rate Strong positive rate P values IL-6 liver cancer 88 12 (13.6%) 50 (56.8%) 18 (20.5%) 8 (9.1%) 86.30% 18.09% < 0.001 paracancer 79 2 (2.5%) 12 (15.2%) 60 (75.9%) 5 (6.3%) 97.47% 60.06% IL-17 liver cancer 90 5 (5.6%) 47 (52.2%) 35 (38.9%) 3 (3.3%) 94.40% 42.40% < 0.001 paracancer 82 0 (0%) 10 (12.2%) 47 (57.3%) 25 (30.5%) 100% 87.80% p-Stat3 liver cancer 90 23 (25.6%) 20 (22.2%) 25 (27.8%) 22 (24.4%) 74.44% 52.20% 0.010 paracancer 90 22 (24.4%) 46 (51.1%) 18 (20.0%) 4 (4.5%) 75.50% 18.04% Note: p < 0.05 indicates significant statistical differences. 2.3 Correlations between various bio-indicators in liver cancer tissues As shown in Table 3 , there was positive correlations between ALT, AST and GGT (p < 0.01), and between ALP and GGT (p < 0.05). There was a negative correlation between the cell differentiation of hepatocellular cancer cells and the expression of IL-6 or IL-17 (p < 0.01), i.e., the more poorly differentiated HCC cells were, the lower the levels of IL-6 and IL-17 expressed. There was also a positive correlation between IL-6 or IL-17 and p-Stat3 (p < 0.05). Table 3 Cross correlation analyses of various indices in HCC patients Index Clinical index aminotransferase Immunohistochemistry Gender Age Cell differ. TNM stage ALT AST GGT ALP IL-6 IL-17 p-Stat3 Gender 1 Age 0.112 1 Cell differ. -0.095 -0.031 1 TNM stage -0.085 0.137 0.195 1 ALT -0.284** -0.113 0.171 0.136 1 AST -0.134 -0.009 -0.113 0.013 0.261** 1 GGT -0.317** -0.027 0.015 0.074 0.522** 0.491** 1 ALP 0.013 0.057 0.001 0.098 0.056 -0.001 0.200* 1 IL-6 0.091 0.059 -0.311** -0.078 -0.061 -0.146 0.074 0.003 1 IL-17 -0.081 -0.199 -0.366** -0.024 -0.291** 0.150 -0.063 0.141 0.212* 1 p-Stat3 0.029 0.009 -0.180 0.062 -0.079 0.141 0.020 -0.092 0.310** 0.264* 1 Note: * represents p < 0.05, ** denotes p < 0.01. 2.4 Serum levels of aminotransferases are associated with survival outcomes in patients with liver cancer Patients with HCC had a maximum postoperative survival time of 99 months by October 2018 (last follow-up date), with a median survival time of 39.5 months. Survival analyses showed that TNM stages were correlated with survival prognosis among liver cancer patients (p = 0.004), validating this patient cohort. In addition as expected, cell differentiation was also correlated with the survival of liver cancer patients, i.e., the poorer the cell differentiation, the worse the patients’ survival (p = 0.023) (see Figure. 2). In terms of aminotransferases, it was obvious that the higher the levels of ALT (p < 0.001), AST (p = 0.002), GGT (p < 0.001) were, the worse the patients’ survivals were observed, respectively. Similarly, the higher the levels of ALP were, the worse the patients’ survivals were observed (p < 0.001). Therefore, higher levels of ALT, AST, GGT and ALP can predict poorer survival prognoses among liver cancer patients (Figure. 2). 2.5 Predictive values of aminotransferases for survival in liver cancer patients In ROC analysis, area under the curve (AUC) reflects the ability or power in predicting survival. The larger a factor’s AUC is, the higher its predictive ability can be. The AUCs of ALT was 0.66 (p = 0.037), GGT was 0.662 (p = 0.036) and ALP was 0.708 (p = 0.007), suggesting ALP had the greatest ability to predict postoperative survival and the quality of life among HCC patients (Figure.3). 2.6 Analyses of risk factors affecting survival prognosis of liver cancer patients COX single factor regression model analysis was performed for clinical data (sex, age, cell differentiation and TNM stages), levels of aminotransferases (ALT, AST, GGT and ALP) and expressed/activated IL-6, IL-17 and p-Stat3 among 98 liver cancer patients (Table 4 ). The results showed that risk factors for poor prognosis in HCC patients included poor cell differentiation, TNM stage III + IV, high levels of ALT, AST, GGT, ALP, and high expression levels of IL-6 and IL-17. Table 4 Cox regression model analyses of bio-indicators in HCC patients Variable COX univariate analysis COX multivariate analysis HR (95% CI) P values HR (95% CI) P values Gender (male vs. female) 1.494 (0.578,3.862) 0.407 / / Age (< 61.5 years vs. ≥61.5 years 2.018 (0.856,4.758) 0.109 / / Cell differentiation grade (low vs. high + moderate) 2.605 (1.105,6.141) 0.029 1.398 (0.408,4.783) 0.594 TNM stage (T₃+T4 vs. T1 + T2 ) 4.294 (1.434,12.862) 0.009 2.67 (0.621,11.475) 0.187 ALT (low-level vs. high-level) 7.15 (2.871,17.804) < 0.001 3.214 (0.707,14.605) 0.131 AST (low-level vs. high-level) 3.748 (1.505,9.335) 0.005 2.327 (0.676,8.007) 0.18 GGT (low-level vs. high-level) 6.094 (2.472,15.023) < 0.001 1.121 (0.291,4.325) 0.868 ALP (low-level vs. high-level) 6.063 (2.472,15.023) < 0.001 5.817 (1.87,18.088) 0.002 IL-6 (2+/3 + vs. -/1+) 0.192 (0.037,0.996) 0.049 0.697 (0.302,1.611) 0.399 IL-17 (2+/3 + vs. -/1+) 0.056 (0.004,0.837) 0.037 0.469 (0.206,1.067) 0.071 p-Stat3 (2+/3 + vs. -/1+) 0.725 (0.173,3.043) 0.660 / / Note: p < 0.05 indicates statistical differences. Multifactorial COX regression model analysis revealed that high levels of ALP were an independent risk factor for postoperative survival in patients with liver cancer (Table 4 ). 2.7 The power of combinations of risk factors in predicting patients’ survival after surgery This study attempted to investigate if combinations of multiple risk factors would be more powerful in predicting postoperative prognosis. As shown in Table 5 , AST + ALP combination showed an AUC of 0.787 (95% CI, 0.677–0.897) (p < 0.001) suggesting a better survival predicting ability than those of AST and ALP alone. The same was even better when 3 bio-indicators were combined (ALT + AST + ALP, AUC = 0.820 [95% CI, 0.714–0.926], p < 0.001), compared with ALT, AST, and ALP alone. Among these combinations, we found that the 5 factor combination of ALT + AST + ALP + IL-17 + p-Stat3 had the largest AUC value of 0.826 (95% CI, 0.708–0.945) (p < 0.001), suggesting the best predicting ability among all combinations (Table 5 ). Table 5 AUCs (area under the curves) for various bio-indicator combinations in HCC patients Combination mode Index AUC Lower limit Upper limit P value Sensitivity Specificity Youden index Aminotransferases combinations• ALT + AST 0.728 0.589 0.866 0.001 57.10% 89.60% 0.467 ALT + GGT 0.747 0.617 0.877 0.001 71.40% 70.10% 0.415 ALT + ALP 0.781 0.671 0.891 < 0.001 85.70% 64.90% 0.506 AST + GGT 0.746 0.619 0.872 0.001 57.10% 83.10% 0.402 AST + ALP 0.787 0.677 0.897 < 0.001 90.50% 51.90% 0.424 GGT + ALP 0.780 0.664 0.897 < 0.001 81.00% 67.50% 0.485 ALT + AST + GGT 0.752 0.619 0.886 < 0.001 71.40% 80.50% 0.519 ALT + AST + ALP 0.820 0.714 0.926 < 0.001 85.70% 71.40% 0.571 ALT + GGT + ALP 0.809 0.706 0.911 < 0.001 90.50% 59.70% 0.502 ALT + AST + GGT + ALP 0.825 0.720 0.931 < 0.001 90.50% 67.50% 0.580 IHC combinations IL-6 + IL-17 0.630 0.479 0.782 0.090 61.10% 62.30% 0.234 IL-6 + p-Stat3 0.612 0.468 0.756 0.074 57.90% 61.80% 0.197 IL-17 + p-Stat3 0.643 0.487 0.799 0.062 55.60% 48.20% 0.238 IL-6 + IL-17 + p-Stat3 0.646 0.485 0.808 0.057 55.60% 79.40% 0.350 Aminotransferases plus IHC combinations ALT + AST + ALP + IL-6 0.819 0.713 0.924 < 0.001 78.90% 78.30% 0.572 ALT + AST + ALP + IL-17 0.821 0.712 0.931 < 0.001 77.80% 79.20% 0.570 ALT + AST + ALP + p-Stat3 0.813 0.685 0.940 < 0.001 78.90% 83.10% 0.620 ALT + AST + ALP + IL-6 + IL-17 0.822 0.715 0.929 < 0.001 77.80% 81.20% 0.590 ALT + AST + ALP + IL-6 + p-Stat3 0.821 0.704 0.938 < 0.001 73.70% 88.20% 0.619 ALT + AST + ALP + IL-17 + p-Stat3 0.826 0.708 0.945 < 0.001 72.20% 87.30% 0.595 ALT + AST + ALP + IL-6 + IL-17 + p-Stat3 0.817 0.698 0.936 < 0.001 72.20% 88.20% 0.604 Note: Bold numbers indicate the highest AUC in that category. p < 0.05 indicates statistical difference. Discussion Liver diseases are a global healthcare burden with estimated 844 million people worldwide suffering from chronic liver diseases and an annual mortality rate of 2 million. 1 Two main epithelial cell types in the liver are hepatocytes and biliary cells. Hepatocytes make up the majority of the liver with various functions including protein synthesis, detoxification, bile production, and carbohydrate and lipid metabolism, 4 so damages to hepatocytes may severely affect liver’s functions. HCC causes massive necrosis of the hepatocytes resulting in the loss of their physiological functions. Liver cancer is a very common malignant tumor with a complicated pathogenesis and the survival prognosis and the quality of life are the result of interactions of many factors. Early detection and treatment of liver cancer can significantly improve the survival and quality of life for patients with liver cancer. If managed properly, such as surgical removal and radio-chemotherapy, patients with late stage liver cancer may enjoy a prolonged life time and reasonable quality of life. Therefore, this study has aimed at investigating potential risk factors which affect prognosis before surgery that may provide a comprehensive guidance to clinicians in their making appropriate surgical approaches and postoperative treatment strategies. Stat3 signaling pathway is a pro-inflammatory pathway that has been shown to be involved in chronic inflammation, autoimmunity, infectious diseases and cancer, and the members of the pathway are often used as diagnostic or prognostic indicators of disease activity and response to therapy. 5 IL-6 is an upstream molecule capable of activating Stat3 pathway and IL-17 is a downstream molecule that is expressed in response to the activation of Stat3. The present study has shown that IL-6 is highly expressed in paracancerous tissues (Table 2 ). Table 3 shows that the expression of IL-6 is inversely correlated with the cell differentiation of HCC. A previous study of HCC has shown that HCC tissues expressing lower IL-6 levels are correlated with a better prognosis, longer overall survival, and longer time to recurrence, 6 somewhat similar to the results in this study. On the other hand, there is growing evidence that IL-17 has an important contextual and tissue-dependent role in maintaining health in response to injury, physiological stress, and infection. 7 IL-17 is not only associated with the immunity of the body, 7 but its role in the development of tumors has also become a focus of research. Similar to IL-6, the current study shows that expressed IL-17 is higher in paracancerous tissues than in HCC tissues (Table 2 ). Again as shown in Table 3 , IL-17 expression is inversely associated with the cell differentiation of HCC. Thus far, published reports support a pathogenic role of IL-17 in cancer development including cancers of the colon, 8 the skin, 9 the pancrea, 10 the hepatocytes, 11 the lung, 12 and multiple myeloma. 13 Patients with UCC may experience a poorer survival prognosis and a higher degree of infiltration of lymphocytes if IL-17 is highly expressed. 11,14 Several reports have suggested that over-activated Stat3 is present in a variety of tumor cells and immune cells, suggesting that over-activated Stat3 may influence the body's ability to generate an effective immune response, thereby participate in tumor formation and promote tumor cell proliferation, migration, differentiation, and metastasis. 15,16 The present study shows a higher level of activated p-Stat3 in HCC tissues (Table 2 ) in keeping with the abovementioned reports. In addition, studies have shown that esophageal squamous cell carcinoma and adenocarcinoma have higher IHC scores for p-Stat3 in tumor tissues when cancer cells have higher migration and proliferation. 17 Furthermore, activated Stat3 can bind to the promoter region of the SKp2 gene which is a proto-oncogene associated with cervical carcinogenesis. 18 In summary, IL-6/Stat3/IL-17 pathway forms a cascade that has been indicated to play an important role in the development, progression and prognosis of several cancers including HCC. 19 In this context, the current study warrants further investigations as to the detailed mechanism(s) of the Stat3 pathway in the pathogenesis and prognosis of cancer. Testing of blood liver enzymes is routinely performed and changes in levels of aminotransferases are important diagnostic indicators revealing detrimental impacts to hepatocytes, therefore, liver functions. For example, elevated levels of ALT and AST may indicate hepatocyte-dominated disease, while elevated levels of ALP and GGT may suggest cholestasis-dominated disease. 20 The sources of AST can be multiple organs including liver, heart, skeletal muscles, kidneys, brain, or red blood cells. 21,22 However, ALT are more specifically derived from the liver and therefore is the preferred clinical test for diagnosing liver disease. GGT is a membrane-bound enzyme that is a key enzyme in biotransformation, nucleic acid metabolism, catalyzing the degradation of extracellular glutathione (GSH). GSH is critical in protecting cells from oxidant-induced damage produced during normal metabolism. 23 ALP is a hydrolytic enzyme that is widely present in hepatocytes and blood sinuses of the bile duct membrane and is associated with the uptake and transport of certain substances. Elevated serum levels of ALP are associated with liver diseases including HCC, cholangiocarcinoma, and biliary cirrhosis. 24 A previous case-control study has found that blood levels of GGT, ALT, and AST are elevated in approximately 90% of cases liver cancer, while half of the cases also show elevated levels of liver-specific ALP or bilirubin. 25 A retrospective study has investigated the relationship of ALT, AST, GGT and ALP with HCC among patients who underwent hepatectomy and found that ALT, AST, GGT and ALP are positively associated with the risk of HCC and, GGT and AST/ALT are independent risk factors predicting postoperative survival in patients with primary HCC. 8 These observations are consistent with our findings presented here. Due to the role of GGT in the degradation of glutathione (GSH), GGT has been associated with oxidative stress which may induce a microenvironment that promotes tumor growth in the liver. 26 There is also evidence of elevated GGT in patients with obesity or diabetes or hepatic steatosis 27 which are associated with metabolic syndrome that it is itself associated with an increased risk of HCC. 28 Having validated the patient cohort using conventional TNM clinical staging to predict survival prognosis among HCC patients, we have analyzed and found that elevated levels of all 4 enzymes are correlated with poor survival among these HCC patients (Figure. 2). To investigate individual ability of these enzymes in survival prediction, we have adopted AUC (area under the curve) for this purpose. AUC reflects the ability in predicting survival by which the larger a factor’s AUC is, the higher its predictive ability is. As shown in Figure. 3, among ALT, AST, GGT and ALP enzymes, ALP shows the highest AUC of 0.708, suggesting its greatest ability to predict postoperative survival and the quality of life in patients with HCC. The above conclusion is confirmed by multifactorial COX regression analysis which reveals elevated levels of ALP are an independent risk factor for postoperative survival in HCC patients (Table 4 ). Quantifying prediction ability for individual enzyme is advantageous because clinicians can judge the reliability of a risk factor used in their survival analyses. A novel approach in this study is the combinational analysis of multiple bio-factors that can be able to increase AUC values. For example, a 5-factor combination of ALT + AST + ALP + IL-17 + p-Stat3 shows significantly larger AUC value (0.826) than a 2-factor combination of AST + ALP (AUC = 0.787), suggesting a higher predicting power (Table 5 ). This approach is significant in that clinicians may use multiple routinely tested factors to reliably predict 5-year survival preoperatively before surgery. In summary, we have shown 3 major findings in this investigation: (1) IL-6/Stat3/IL-17 signaling pathway is involved in survival prognosis among HCC patients; (2) Liver function-related aminotransferases ALT, AST, GGT and ALP can predict survival prognosis for HCC patients before surgery; and (3) A novel approach of combining multiple risk factors can be used to better predict HCC patients’ survival prognosis and quantifying prediction power using ROC analysis (AUC values) for individual risk factors may facilitate clinicians to judge the reliability of survival analyses. Utilizing routinely tested clinical bio-indicators to predict potential survival prognosis before surgery is significant in terms of facilitating surgeons’ decision-making for personalized surgical approaches as well as appropriate radio-chemotherapy after surgery. However, to establish a clinically practical protocol, e.g., the cutoff values for serum levels of individual enzymes for survival predictions to be used as clinicians’ guiding reference, requires a much larger sample of HCC patients. Our ongoing investigation is aimed at organizing a multi-center collaboration study to accomplish the above goal. Declarations Date sharing statement All the data generated or analyzed during this study are included in this article. Acknowledgements We sincerely thank all patients participated in this investigation. We are grateful to the Ministry of Science and Technology of China, the Bureau of Science and Technology of the Xinjiang Corps, and Shihezi University for their generous financial support. Funding This work was supported by grants from the Ministry of Science and Technology of China (No. 2009BAI82B02) and the Oasis Scholar Fund of Shihezi University (No. LZXZ201023). The funder had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. Author’s contributions Planning and execution of the study: LJW, XNL, LT and WJZ. Manuscript drafting: LJW, XNL, LT. Manuscript revision: WJZ. All authors approve the final draft and agree to be accountable for all aspects of the works. Ethics approval and consent to participate Ethical approval was obtained from the Institutional Ethics Review Board (IERB) of the First Affiliated Hospital of School of Medicine, Shihezi University (No. 2018-067-01). The IERB waived the need for patient consents due to anonymous analyses of the data and confidentiality and anonymity in the handling and publication of patients’ tissues. Standard University Hospital Guidelines in accordance with the Declaration of Helsinki were followed in this study. Patient consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. References Bray F, Ferlay J, Soerjomataram I, et al. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA cancer J Clin . 2018;68:394-424. doi:10.3322/ caac.21492 Menghini G. One-second needle biopsy of the liver. Gastroenterology , 1958, 35: 190-9. Li YX, Zhang L, Simayi D, et al. Human Papillomavirus Infection Correlates with Inflammatory Stat3 Signaling Activity and IL-17 Level in Patients with Colorectal Cancer. Plos One , 2015, 10(2):e0118391. doi: 10.1371/journal.pone.0118391. eCollection 2015. Ramachandran P, Matchett KP, Dobie R, et al. Single-cell technologies in hepatology: new insights into liver biology and disease pathogenesis. Nature reviews Gastroenterology & hepatology , 2020,08;178.doi: 10.1038/s41575-020-0304-x Kishimoto T. IL-6: from its discovery to clinical applications. International immunology , 2010,22: 347-52. doi:10.1093/intimm/dxq030 Yang X, Liang L, Zhang XF, et al. MicroRNA-26a suppresses tumor growth and metastasis of human HCC by targeting interleukin-6-Stat3 pathway. Hepatology, 2013,58:158-70. doi:10.1002/ hep.26305 Mcgeachy MJ, Cua DJ, Gaffen SL. The IL-17 Family of Cytokines in Health and Disease. Immunity , 2019, 50: 892-906.doi:10.1016/j.immuni.2019.03.021 Zepp Ja, Zhao J, Liu C, et al. IL-17A-Induced PLET1 Expression Contributes to Tissue Repair and Colon Tumorigenesis. Journal of immunology , 2017, 199: 3849-57. doi:10.4049/jimmunol.1601540 Wang L, Yi T, Zhang W, et al. IL-17 enhances tumor development in carcinogen-induced skin cancer. Cancer research , 2010,70:10112-20. doi:10.1158/0008-5472.CAN-10-0775 Zhang Y, Zoltan M, Riquelme E, et al. Immune Cell Production of Interleukin 17 Induces Stem Cell Features of Pancreatic Intraepithelial Neoplasia Cells. Gastroenterology , 2018,155:210-23.e3. doi:1105 3/j.gastro.2018.03.041. Sun C, Kono H, Furuya S, et al. Interleukin-17A Plays a Pivotal Role in Chemically Induced HCC in Mice. Digestive Diseases & Sciences , 2016,61:474-88. doi:10.1007/ s10620-015-3888-1 Jin C, Lagoudas Gk, Zhao C, et al. Commensal Microbiota Promote Lung Cancer Development via γδ T Cells. Cell , 2019, 176: 998-1013.e16. doi:10.1016/j.cell.2018.12.040 Calcinotto A, Brevi A, Chesi M, et al. Microbiota-driven interleukin-17-producing cells and eosinophils synergize to accelerate multiple myeloma Nature communications , 2018,9:4832. doi:10.1038/s41467-018-07305-8 Hu Z, Luo D, Wang D, et al. IL-17 Activates the IL-6/Stat3 Signal Pathway in the Proliferation of Hepatitis B Virus-Related HCC. Cellular physiology and biochemistry , 2017,43:2379-90. doi:10.1159/00 0484390 Yu H, Lee H, Herrmann A, et al. Revisiting Stat3 signalling in cancer: new and unexpected biological functions. Nature reviews Cancer , 2014, 14:736-46. doi:10.1038/nrc3818 Yu H, Pardoll D, Jove R. STATs in cancer inflammation and immunity: a leading role for Stat3. Nature reviews Cancer , 2009, 9:798-809. doi:10.1038/nrc2734 Miao FC, Ping-Tsung Chen, et al. IL-6 expression predicts treatment response and outcome in squamous cell carcinoma of the esophagus. Molecular Cancer , 2013,12:26. doi:10.1186/1476-459 8-12-26 Huang H, Zhao W, Yang D. Stat3 induces oncogenic Skp2 expression in human cervical carcinoma cells. BBRC, 2012, 418: 186-90.doi:10.1016/j.bbrc.2012.01.004 Wang L, Yi T, Kortylewski M, et al. IL-17 can promote tumor growth through an IL-6-Stat3 signaling pathway. The Journal of experimental medicine , 2009, 206:1457-64. doi:10.1084/jem.20090207 Giannini EG, Testa R, Savarino V. Liver enzyme alteration: a guide for clinicians. CMAJ: Canadian Medical Association journal , 2005,172: 367-79. doi:10.1503/cmaj.1040752 Dufour DR, Lott JA, Nolte FS, et al.Diagnosis and monitoring of hepatic injury. II. Recommendations for use of laboratory tests in screening, diagnosis, and monitoring. Clinical chemistry , 2000, 46:2050-68. Zhang LX, Lv Y, Xu AM, et al. The prognostic significance of serum gamma-glutamyltransferase levels and AST/ALT in primary hepatic carcinoma. BMC cancer , 2019,19: 841. doi:10.1186./s12885-019-6011-8 Whitfield JB. Gamma glutamyl transferase. Critical reviews in clinical laboratory sciences , 2001,38: 263-355. doi:10.1080/20014091084227 Cai X, Chen Z , Chen J, et al. Albumin-to-Alkaline Phosphatase Ratio as an Independent Prognostic Factor for Overall Survival of Advanced HCC Patients without Receiving Standard Anti-Cancer Therapies. Journal of Cancer , 2018,9:189-97. doi:10.7150/jca.21799 Yang JG, He XF, Huang B, et al. Rule of changes in serum GGT levels and GGT/ALT and AST/ALT ratios in primary hepatic carcinoma patients with different AFP levels. Cancer biomarkers , 2018,21:743-6. doi:10.3233/CBM-170088 Zhao J, Zhao Y, Wang H, et al. Association between metabolic abnormalities and HBV related hepatocelluar carcinoma in Chinese: a cross-sectional study. Nutrition journal , 2011,10:49. doi:10.1186/1475-2891-10-49 Jiang S, Jiang D, Tao Y. Role of gamma-glutamyltransferase in cardiovascular diseases. Experimental and clinical cardiology , 2013,18: 53-6. Jinjuvadia R, Patel S, Liangpunsakul S. The association between metabolic syndrome and HCC: systemic review and meta-analysis. Journal of clinical gastroenterology , 2014,48:172-7. doi:10.1097/MCG.0b013e31 Supplementary Files 4.Supplementarytable.docx 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-261872","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":15129055,"identity":"5d9a50a0-3ff5-4f95-a381-a6a317674a71","order_by":0,"name":"Lijie Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyUlEQVRIiWNgGAWjYBACfoaDzQ8SKtjk5NkbiNQi2Xi4zeDDGT5jw54DRGoxaD7eIDmzTS6x4UYCsVrYDjYY87CZGTPOfLzxBkONTTRBLeY8Bxse8/CkybFLpxVbMBxLy20gpMVyBsgWiWPGjLNzzCQYGw4T1mJw/2GDNI/B/8SGm2eI1XLgYIPkjAQ2oPd5iNQi2XAQGMgH2ICBDPRLAjF+4Wc4/vhB4j9QVB7eeONDjQ1hLSiOlEggRTlEC6k6RsEoGAWjYGQAAPM5ROxUa9ifAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0003-4914-9781","institution":"Shihezi University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Lijie","middleName":"","lastName":"Wang","suffix":""},{"id":15129056,"identity":"142a8967-a9ab-419e-832c-1cfdc15b01ed","order_by":1,"name":"Xiaoning Li","email":"","orcid":"","institution":"Shihezi University School of Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xiaoning","middleName":"","lastName":"Li","suffix":""},{"id":15129057,"identity":"9f7e77e7-22ff-42ed-bf41-c9c910fd4dea","order_by":2,"name":"Lin Tao","email":"","orcid":"","institution":"Shihezi University School of Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Lin","middleName":"","lastName":"Tao","suffix":""},{"id":15129058,"identity":"14b7b930-922b-4ecb-b245-b951d97d04fe","order_by":3,"name":"Wenjie Zhang","email":"","orcid":"","institution":"Shihezi University School of Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Wenjie","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2021-02-20 22:34:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-261872/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-261872/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":6759955,"identity":"bbda01b7-08d6-4876-bd82-8306e1effc4c","added_by":"auto","created_at":"2021-03-09 18:18:50","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2345005,"visible":true,"origin":"","legend":"Immunochemistry (IHC) detection of expressed IL-6 and IL-17, and activated Stat3 (phosphorylated Stat3 or p-Stat3) in HCC tissues and paracancerous tissues. Panels A, E and I are positive IHC staining of IL-6, IL-17 and p-Stat3, respectively in paracancerous tissues (magnification ×200). Panels B, F and J are negative staining IL-6, IL-17 and p-Stat3, respectively in paracancerous tissues (magnification ×200). Panels C, G and K are positive staining of IL-6, IL-17 and p-Stat3, respectively in HCC tissues (magnification ×200). Panels D, H and L are positive staining of IL-6, IL-17 and p-Stat3, respectively in HCC tissues (magnification ×200). Insert image in each panel is an enlargement at magnification ×400.","description":"","filename":"2.1Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-261872/v1/4bb27c676848a23e63f265c8.png"},{"id":6759952,"identity":"8bb367a7-8567-4ce9-b58d-721fb64e3509","added_by":"auto","created_at":"2021-03-09 18:18:50","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":581470,"visible":true,"origin":"","legend":"Impacts of TNM staging, cell differentiation, ALT, AST, GGT and ALP on the survival of HCC patients. Comparisons of Kaplan-Meier survival curves for HCC patients categorized as the following: high vs. low serum levels of ALT (p\u003c0.01), AST (p=0.02), GGT (p\u003c0.01) and ALP (p\u003c0.01), respectively; clinical staging of TNM I+II vs. TNM III+IV (p=0.04); cell differentiations of well+moderate vs. poor (p=0.023).","description":"","filename":"2.2Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-261872/v1/4fd620a6ee09f80f0e53d283.png"},{"id":6760685,"identity":"96215a80-9300-4a44-a43c-dcb9540f7a63","added_by":"auto","created_at":"2021-03-09 18:24:50","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":184508,"visible":true,"origin":"","legend":"ROC curves reveal performance abilities of clinicopathological indices, IHC bio-indicators, and their combinations affecting patients’ survival. As shown, the diagonal black line is the reference line. ","description":"","filename":"2.3Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-261872/v1/9186c3de5d9c067ff37049e8.png"},{"id":13676731,"identity":"1f2c7b5e-8ac3-4571-b947-c4b2a1ef6362","added_by":"auto","created_at":"2021-09-17 11:32:02","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2325651,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-261872/v1/7a327a2a-81d3-44ea-b656-0f602a59f590.pdf"},{"id":6760348,"identity":"37fa4eb8-2dba-458b-a3dc-8e4119326cdb","added_by":"auto","created_at":"2021-03-09 18:21:50","extension":"docx","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":13822,"visible":true,"origin":"","legend":"","description":"","filename":"4.Supplementarytable.docx","url":"https://assets-eu.researchsquare.com/files/rs-261872/v1/9a55085599702d10be5c45e1.docx"}],"financialInterests":"","formattedTitle":"\u003cp\u003eA Cohort Study Using Preoperative IL-6/Stat3/IL-17 And Blood Aminotransferase Levels To Predict Five-Year Survival For Patients After Liver Cancer Resection\u003c/p\u003e","fulltext":[{"header":"Objective","content":"\u003cp\u003eAccording to Global Cancer Statistics 2018, liver cancer (HCC) is one of the most common malignancies worldwide, with approximately 840,000 new cases and 780,000 deaths per year worldwide. In China, the incidence and the five-year survival rate of HCC are ranked third and fifth, respectively, among malignant tumors in China.\u003ca href=\"#_ENREF_1\"\u003e\u003csup\u003e1\u003c/sup\u003e\u003c/a\u003e Most liver cancers start insidiously and lack specific clinical manifestations. Although there has been advancement in relevant medical technologies, poor prognosis and high mortality rate of liver cancer have little changes due largely to unavailability of noninvasive prescreening methodologies.\u003c/p\u003e\n\u003cp\u003eLiver tissue biopsy is widely used for preoperative diagnosis of liver cancer and has a high diagnostic value,\u003ca href=\"#_ENREF_2\"\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/a\u003e but the procedure\u0026rsquo;s invasiveness has drawbacks. Postoperative pathological characteristics of HCC such as TNM clinical staging, degree of cell differentiation and depth of tumor infiltration, are indicative of patients\u0026rsquo; prognoses, but these indicators cannot be obtained preoperatively. Therefore, search for potential indicators that can be obtained preoperatively and show significant impacts on the survival prognosis of patients with liver cancer may provide useful guidance to surgeons in choosing optimal surgical approaches and postoperative treatment strategies to improve the quality of life as well as survival after surgery. This study has, therefore, investigated several potential bio-indicators that can be tested preoperatively and show impacts on patients\u0026rsquo; survival after surgery. These bio-indicators have included serum levels of several aminotransferases (ALT, AST, GGT, and ALP) and activational or expressed levels of the Stat3 signaling pathway (IL-6, p-Stat3 and IL-17) in tissues from patients with liver cancer.\u003c/p\u003e"},{"header":"1 Information And Methods","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e\u003ch2\u003e1.1 Subjects\u003c/h2\u003e\u003cp\u003eThis study retrospectively collected 98 patients who underwent HCC resection at the First Affiliated Hospital, Shihezi University School of Medicine from January 2010 to December 2017, and the diagnoses of HCC were confirmed by two senior pathologists independently. All patients had complete demographic information and pathological data. This patient cohort included 74 males and 24 females with a mean age of 56.68 years and a median age of 57.0 years. None of the patients received adjuvant therapy such as radiotherapy or chemotherapy before surgery. Liver cancer tissues were non-necrotic tumor tissues and para-cancerous tissues were at least 2 cm away from the tumor edge.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e1.2 Detection of activation/expression of p-Stat3, IL-6, and IL-17 by immunohistochemistry (IHC)\u003c/h2\u003e\u003cp\u003eIHC method was performed in HCC and para-cancerous tissues using the Envision method. Briefly, paraffin chip sections were baked at 67\u0026ordm;C for 30 min, de-waxed and hydrated. After antigens were thermally repaired, 3% H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e was added to block endogenous peroxidase activity and then, specific primary antibodies (against IL-6, IL-17 and p-Stat3 purchased from Abcam Co., USA) and secondary antibodies were added respectively, and color development was achieved using DAB.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e1.3 Scoring of Immunohistochemical stains\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"Ethics-ToolTip\"\u003ePositive IHC stains were defined as yellow-brown color according to the manufacturer\u0026rsquo;s demonstrative slides (Figure.\u003c/div\u003e 1). IHC staining slides were read on an Olympus optical microscope over yellow-brown color stains for 12 consecutive fields and scored according to two variable factors: (1) counting the number of positively stained cells (0\u0026thinsp;=\u0026thinsp;\u0026lt;\u0026thinsp;5%; 1\u0026thinsp;=\u0026thinsp;6%-25%; 2\u0026thinsp;=\u0026thinsp;26%-50%; 3\u0026thinsp;=\u0026thinsp;51%-75%; and 4\u0026thinsp;=\u0026thinsp;76%-100%); and (2) scoring the intensity of the staining (0\u0026thinsp;=\u0026thinsp;absent; 1\u0026thinsp;=\u0026thinsp;Pale Brown-yellow; 2\u0026thinsp;=\u0026thinsp;Brown-yellow; 3\u0026thinsp;=\u0026thinsp;Brown-yellow; 4\u0026thinsp;=\u0026thinsp;Dark brown). The final score was the product of (1) multiplying (2) for individual slides (see Table S1). This scoring system was similar in principle to a published literature.\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e1.4 Serum aminotransferase measurements\u003c/h2\u003e\u003cp\u003eUpon admission to the hospital, venous blood (3ml) was collected from patients after fasting for 12 hours and levels of aminotransferases were measured using a Roche automatic biochemical analyzer. The aminotransferases tested were aspartate aminotransferase (AST), alanine aminotransferase (ALT), glutamyl transferase (GGT) and alkaline phosphatase (ALP).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e1.5 Patients\u0026rsquo; Follow-up\u003c/h2\u003e\u003cp\u003ePatients\u0026rsquo; follow-ups were achieved via telephone calls, outpatient interviews, and home visits. Follow-up was from October 2016 to October 2018, and patients were followed-up for 1\u0026ndash;99 months after surgery.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e1.6 Diagnostic standard and reference value ranges\u003c/h2\u003e\u003cp\u003eThe TNM staging was based on the eighth edition of TNM staging and cell differentiation of HCC was based on the latest edition of WHO Gastrointestinal Tumor Pathology and Genetics. Reference ranges of blood levels of aminotransferases were established by the Health Industry Standard of the People's Republic of China.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e1.7 Statistical analyses\u003c/h2\u003e\u003cp\u003eStatistical analysis was performed using SPSS software package (version 23.0). Spearman grade analysis was used for correlation analyses between pathological data, IHC results and serum levels of aminotransferases. Single-factor and multifactor Cox regression risk models were used to analyze factors related to postoperative survival in patients with HCC. Kaplan-Meier analysis was used for survival curves affected by serum levels of ALT, AST, GGT and ALP. ROC (receiver operating characteristics) curve analysis generates AUC (area under the curve) which is a quantified measurement predicting the effects of clinical characteristics, aminotransferases (ALT, AST, GGT and ALP) and expression/activation of IL-6, IL-17 and p-Stat3 on patients\u0026rsquo; survival. p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicates a statistically significant difference between comparisons.\u003c/p\u003e\u003c/div\u003e"},{"header":"2 Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Clinical cancer staging and serum levels of aminotransferases\u003c/h2\u003e\u003cp\u003eOf 98 cases of liver cancer, 70 cases (71.4%) were defined as clinical cancer stage I, 22 cases (22.40%) as stage II, 4 cases (4.10%) as stage III, and 2 cases (2.00%) as stage IV, respectively. ROC analysis was used to calculate cut-off values for 4 aminotransferases: AST (men: 36.55U/L, women: 34.50U/L); ALT (men: 46.50U/L, women: 23.65U/L); GGT (men: 81.00U/L, women. 59.50 U/L); ALP (men: 102.50 U/L, women: 82.50 U/L) ( Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eClinical demographic data of preoperative and postoperative indicators of HCC patients\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eIndicators (n)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eHCC patients\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eIndicators (n)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003eHCC patients\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003en\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e%\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003en\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e%\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender (98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eALT (U/L) (98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003emale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e75.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003erange\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e9.55\u0026ndash;265.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003efemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e39.36\u0026thinsp;\u0026plusmn;\u0026thinsp;39.58\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (years) (98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCut-off value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e46.50 or 23.65\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003erange\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e32\u0026ndash;79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026le;\u0026thinsp;46.5 or 23.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e69.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e56.68\u0026thinsp;\u0026plusmn;\u0026thinsp;9.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;46.5 or 23.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e30.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClinical stages (98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAST (U/L) (98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e71.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003erange\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.65\u0026ndash;260\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eII\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e39.7\u0026thinsp;\u0026plusmn;\u0026thinsp;32.60\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIII\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCut-off value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e36.50 or 34.50\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026le;\u0026thinsp;36.5 or 34.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e61.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDifferentiation grade (98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;36.5 or 34.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e38.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ehigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGGT (U/L) (98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003emoderate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e55.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003erange\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e4.00-392.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003elow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e33.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e87.17\u0026thinsp;\u0026plusmn;\u0026thinsp;73.73\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFollow-up (months) (98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCut-off value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e\u0026le;\u0026thinsp;81.00 or 59.50\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003erange\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e1\u0026ndash;99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026le;\u0026thinsp;81 or 59.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e74.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e46.69\u0026thinsp;\u0026plusmn;\u0026thinsp;25.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;81 or 59.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e25.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI (kg/m2) (82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eIL-6 (Cancer) (88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003erange\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e19.10-34.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-/+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e70.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e24.93\u0026thinsp;\u0026plusmn;\u0026thinsp;3.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e20.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e18.6\u0026ndash;23.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e41.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3+ཞ4+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e24-27.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e41.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eIL-17 (Cancer) (90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-/+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e53.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eALP (U/L) (98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e35.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003erange\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e21.00-221.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3+ཞ4+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e87.17\u0026thinsp;\u0026plusmn;\u0026thinsp;31.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep-Stat3 (Cancer) (90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCut-off value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e102.50 or 82.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-/+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e43.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026le;\u0026thinsp;102.5 or 82.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e69.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e25.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;102.5 or 82.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e30.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3+ཞ4+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e24.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Expression of IL-6 and IL-17 and activation of p-Stat3 in liver cancer tissues\u003c/h2\u003e\u003cp\u003eAs shown in Figure.1, when IL-6 and IL-17 were expressed, the cytoplasma of HCC and paracancerous cells was stained (from light yellow to brown). Phosphorylated Stat3 (p-Stat3) is the active form of Stat3 which enters the nuclei and, therefore, IHC showed positive staining in the nuclei of carcinoma cells and paracancerous cells (from light yellow to brownish yellow). As shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the positive and strongly positive rates of IL-6 and IL-17 expression in paracancerous tissues were higher than those in HCC tissues. On the other hand, the positive and strongly positive rates of activated p-Stat3 in HCC tissues were higher than those in paracancerous tissues.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e Comparisons of IHC-based indicators of IL-6, IL-17 and p-Stat3 between cancer and paracancerous tissues\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGrouping\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003en\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1+\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2+\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3+\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003ePositive rate\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eStrong positive rate\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e values\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIL-6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eliver cancer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12 (13.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e50 (56.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e18 (20.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e8 (9.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e86.30%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e18.09%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eparacancer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2 (2.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12 (15.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e60 (75.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5 (6.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e97.47%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e60.06%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIL-17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eliver cancer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5 (5.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e47 (52.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e35 (38.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3 (3.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e94.40%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e42.40%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eparacancer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10 (12.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e47 (57.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e25 (30.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e100%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e87.80%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ep-Stat3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eliver cancer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23 (25.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e20 (22.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e25 (27.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e22 (24.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e74.44%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e52.20%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.010\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eparacancer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22 (24.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e46 (51.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e18 (20.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4 (4.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e75.50%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e18.04%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e\u003cp\u003eNote: p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicates significant statistical differences.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Correlations between various bio-indicators in liver cancer tissues\u003c/h2\u003e\u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, there was positive correlations between ALT, AST and GGT (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and between ALP and GGT (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). There was a negative correlation between the cell differentiation of hepatocellular cancer cells and the expression of IL-6 or IL-17 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), i.e., the more poorly differentiated HCC cells were, the lower the levels of IL-6 and IL-17 expressed. There was also a positive correlation between IL-6 or IL-17 and p-Stat3 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e Cross correlation analyses of various indices in HCC patients\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"12\"\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eIndex\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u003cp\u003eClinical\u0026nbsp;index\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e\u003cp\u003eaminotransferase\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e\u003cp\u003eImmunohistochemistry\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCell differ.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eTNM stage\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eALT\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eAST\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eGGT\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eALP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eIL-6\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003eIL-17\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c12\"\u003e\u003cp\u003ep-Stat3\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.112\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCell differ.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.095\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.031\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTNM stage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.085\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.137\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.195\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eALT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.284**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.113\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.171\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.136\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAST\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.134\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.113\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.261**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGGT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.317**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.027\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.074\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.522**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.491**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eALP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.057\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.098\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.056\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.200*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIL-6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.091\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.059\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.311**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.078\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.061\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.146\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.074\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIL-17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.081\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.199\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.366**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.291**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.150\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.063\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.141\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.212*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ep-Stat3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.029\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.180\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.062\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.079\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.141\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.020\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-0.092\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.310**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.264*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"12\" nameend=\"c12\" namest=\"c1\"\u003e\u003cp\u003eNote: * represents p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, ** denotes p\u0026thinsp;\u0026lt;\u0026thinsp;0.01.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Serum levels of aminotransferases are associated with survival outcomes in patients with liver cancer\u003c/h2\u003e\u003cp\u003ePatients with HCC had a maximum postoperative survival time of 99 months by October 2018 (last follow-up date), with a median survival time of 39.5 months. Survival analyses showed that TNM stages were correlated with survival prognosis among liver cancer patients (p\u0026thinsp;=\u0026thinsp;0.004), validating this patient cohort. In addition as expected, cell differentiation was also correlated with the survival of liver cancer patients, i.e., the poorer the cell differentiation, the worse the patients\u0026rsquo; survival (p\u0026thinsp;=\u0026thinsp;0.023) (see Figure. 2). In terms of aminotransferases, it was obvious that the higher the levels of ALT (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), AST (p\u0026thinsp;=\u0026thinsp;0.002), GGT (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were, the worse the patients\u0026rsquo; survivals were observed, respectively. Similarly, the higher the levels of ALP were, the worse the patients\u0026rsquo; survivals were observed (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Therefore, higher levels of ALT, AST, GGT and ALP can predict poorer survival prognoses among liver cancer patients (Figure. 2).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Predictive values of aminotransferases for survival in liver cancer patients\u003c/h2\u003e\u003cp\u003eIn ROC analysis, area under the curve (AUC) reflects the ability or power in predicting survival. The larger a factor\u0026rsquo;s AUC is, the higher its predictive ability can be. The AUCs of ALT was 0.66 (p\u0026thinsp;=\u0026thinsp;0.037), GGT was 0.662 (p\u0026thinsp;=\u0026thinsp;0.036) and ALP was 0.708 (p\u0026thinsp;=\u0026thinsp;0.007), suggesting ALP had the greatest ability to predict postoperative survival and the quality of life among HCC patients (Figure.3).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e2.6 Analyses of risk factors affecting survival prognosis of liver cancer patients\u003c/h2\u003e\u003cp\u003eCOX single factor regression model analysis was performed for clinical data (sex, age, cell differentiation and TNM stages), levels of aminotransferases (ALT, AST, GGT and ALP) and expressed/activated IL-6, IL-17 and p-Stat3 among 98 liver cancer patients (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The results showed that risk factors for poor prognosis in HCC patients included poor cell differentiation, TNM stage III\u0026thinsp;+\u0026thinsp;IV, high levels of ALT, AST, GGT, ALP, and high expression levels of IL-6 and IL-17.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCox regression model analyses of bio-indicators in HCC patients\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eCOX univariate analysis\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003eCOX multivariate analysis\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHR (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e values\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHR (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e values\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender (male vs. female)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.494 (0.578,3.862)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.407\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e/\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e/\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (\u0026lt;\u0026thinsp;61.5 years vs. \u0026ge;61.5 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.018 (0.856,4.758)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.109\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e/\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e/\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCell differentiation grade\u003c/p\u003e\u003cp\u003e(low vs. high\u0026thinsp;+\u0026thinsp;moderate)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.605 (1.105,6.141)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.029\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.398 (0.408,4.783)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.594\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTNM stage (T₃+T4 vs. T1\u0026thinsp;+\u0026thinsp;T2 )\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.294 (1.434,12.862)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.67 (0.621,11.475)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.187\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eALT (low-level vs. high-level)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.15 (2.871,17.804)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.214 (0.707,14.605)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.131\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAST (low-level vs. high-level)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.748 (1.505,9.335)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.327 (0.676,8.007)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.18\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGGT (low-level vs. high-level)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.094 (2.472,15.023)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.121 (0.291,4.325)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.868\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eALP (low-level vs. high-level)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.063 (2.472,15.023)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.817 (1.87,18.088)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIL-6 (2+/3\u0026thinsp;+\u0026thinsp;vs. -/1+)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.192 (0.037,0.996)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.049\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.697 (0.302,1.611)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.399\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIL-17 (2+/3\u0026thinsp;+\u0026thinsp;vs. -/1+)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.056 (0.004,0.837)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.037\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.469 (0.206,1.067)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.071\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ep-Stat3 (2+/3\u0026thinsp;+\u0026thinsp;vs. -/1+)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.725 (0.173,3.043)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.660\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e/\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e/\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eNote: p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicates statistical differences.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eMultifactorial COX regression model analysis revealed that high levels of ALP were an independent risk factor for postoperative survival in patients with liver cancer (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e2.7 The power of combinations of risk factors in predicting patients\u0026rsquo; survival after surgery\u003c/h2\u003e\u003cp\u003eThis study attempted to investigate if combinations of multiple risk factors would be more powerful in predicting postoperative prognosis. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, AST\u0026thinsp;+\u0026thinsp;ALP combination showed an AUC of 0.787 (95% CI, 0.677\u0026ndash;0.897) (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) suggesting a better survival predicting ability than those of AST and ALP alone. The same was even better when 3 bio-indicators were combined (ALT\u0026thinsp;+\u0026thinsp;AST\u0026thinsp;+\u0026thinsp;ALP, AUC\u0026thinsp;=\u0026thinsp;0.820 [95% CI, 0.714\u0026ndash;0.926], p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), compared with ALT, AST, and ALP alone. Among these combinations, we found that the 5 factor combination of ALT\u0026thinsp;+\u0026thinsp;AST\u0026thinsp;+\u0026thinsp;ALP\u0026thinsp;+\u0026thinsp;IL-17\u0026thinsp;+\u0026thinsp;p-Stat3 had the largest AUC value of 0.826 (95% CI, 0.708\u0026ndash;0.945) (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), suggesting the best predicting ability among all combinations (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e AUCs (area under the curves) for various bio-indicator combinations in HCC patients\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCombination mode\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIndex\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAUC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLower limit\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eUpper limit\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSensitivity\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eSpecificity\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eYouden\u003c/p\u003e\u003cp\u003eindex\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"9\" rowspan=\"10\"\u003e\u003cp\u003eAminotransferases combinations\u0026bull;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eALT\u0026thinsp;+\u0026thinsp;AST\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.728\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.589\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.866\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e57.10%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e89.60%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.467\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eALT\u0026thinsp;+\u0026thinsp;GGT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.747\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.617\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.877\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e71.40%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e70.10%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.415\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eALT\u0026thinsp;+\u0026thinsp;ALP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.781\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.671\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.891\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e85.70%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e64.90%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.506\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAST\u0026thinsp;+\u0026thinsp;GGT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.746\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.619\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.872\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e57.10%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e83.10%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.402\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAST\u0026thinsp;+\u0026thinsp;ALP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.787\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.677\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.897\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e90.50%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e51.90%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.424\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGGT\u0026thinsp;+\u0026thinsp;ALP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.780\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.664\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.897\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e81.00%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e67.50%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.485\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eALT\u0026thinsp;+\u0026thinsp;AST\u0026thinsp;+\u0026thinsp;GGT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.752\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.619\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.886\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e71.40%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e80.50%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.519\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eALT\u0026thinsp;+\u0026thinsp;AST\u0026thinsp;+\u0026thinsp;ALP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.820\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.714\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.926\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e85.70%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e71.40%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.571\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eALT\u0026thinsp;+\u0026thinsp;GGT\u0026thinsp;+\u0026thinsp;ALP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.809\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.706\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.911\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e90.50%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e59.70%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.502\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eALT\u0026thinsp;+\u0026thinsp;AST\u0026thinsp;+\u0026thinsp;GGT\u0026thinsp;+\u0026thinsp;ALP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.825\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.720\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.931\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e90.50%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e67.50%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.580\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eIHC combinations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIL-6\u0026thinsp;+\u0026thinsp;IL-17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.630\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.479\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.782\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.090\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e61.10%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e62.30%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.234\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIL-6\u0026thinsp;+\u0026thinsp;p-Stat3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.612\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.468\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.756\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.074\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e57.90%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e61.80%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.197\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIL-17\u0026thinsp;+\u0026thinsp;p-Stat3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.643\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.487\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.799\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.062\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e55.60%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e48.20%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.238\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIL-6\u0026thinsp;+\u0026thinsp;IL-17\u0026thinsp;+\u0026thinsp;p-Stat3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.646\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.485\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.808\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.057\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e55.60%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e79.40%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.350\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e\u003cp\u003eAminotransferases plus IHC combinations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eALT\u0026thinsp;+\u0026thinsp;AST\u0026thinsp;+\u0026thinsp;ALP\u0026thinsp;+\u0026thinsp;IL-6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.819\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.713\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.924\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e78.90%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e78.30%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.572\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eALT\u0026thinsp;+\u0026thinsp;AST\u0026thinsp;+\u0026thinsp;ALP\u0026thinsp;+\u0026thinsp;IL-17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.821\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.712\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.931\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e77.80%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e79.20%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.570\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eALT\u0026thinsp;+\u0026thinsp;AST\u0026thinsp;+\u0026thinsp;ALP\u0026thinsp;+\u0026thinsp;p-Stat3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.813\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.685\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.940\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e78.90%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e83.10%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.620\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eALT\u0026thinsp;+\u0026thinsp;AST\u0026thinsp;+\u0026thinsp;ALP\u0026thinsp;+\u0026thinsp;IL-6\u0026thinsp;+\u0026thinsp;IL-17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.822\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.715\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.929\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e77.80%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e81.20%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.590\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eALT\u0026thinsp;+\u0026thinsp;AST\u0026thinsp;+\u0026thinsp;ALP\u0026thinsp;+\u0026thinsp;IL-6\u0026thinsp;+\u0026thinsp;p-Stat3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.821\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.704\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.938\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e73.70%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e88.20%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.619\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eALT\u0026thinsp;+\u0026thinsp;AST\u0026thinsp;+\u0026thinsp;ALP\u0026thinsp;+\u0026thinsp;IL-17\u0026thinsp;+\u0026thinsp;p-Stat3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.826\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.708\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.945\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e72.20%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e87.30%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.595\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eALT\u0026thinsp;+\u0026thinsp;AST\u0026thinsp;+\u0026thinsp;ALP\u0026thinsp;+\u0026thinsp;IL-6\u0026thinsp;+\u0026thinsp;IL-17\u0026thinsp;+\u0026thinsp;p-Stat3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.817\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.698\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.936\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e72.20%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e88.20%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.604\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e\u003cp\u003eNote: Bold numbers indicate the highest AUC in that category.\u003c/p\u003e\u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicates statistical difference.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eLiver diseases are a global healthcare burden with estimated 844\u0026nbsp;million people worldwide suffering from chronic liver diseases and an annual mortality rate of 2\u0026nbsp;million.\u003csup\u003e1\u003c/sup\u003e Two main epithelial cell types in the liver are hepatocytes and biliary cells. Hepatocytes make up the majority of the liver with various functions including protein synthesis, detoxification, bile production, and carbohydrate and lipid metabolism,\u003csup\u003e4\u003c/sup\u003e so damages to hepatocytes may severely affect liver\u0026rsquo;s functions. HCC causes massive necrosis of the hepatocytes resulting in the loss of their physiological functions. Liver cancer is a very common malignant tumor with a complicated pathogenesis and the survival prognosis and the quality of life are the result of interactions of many factors. Early detection and treatment of liver cancer can significantly improve the survival and quality of life for patients with liver cancer. If managed properly, such as surgical removal and radio-chemotherapy, patients with late stage liver cancer may enjoy a prolonged life time and reasonable quality of life. Therefore, this study has aimed at investigating potential risk factors which affect prognosis before surgery that may provide a comprehensive guidance to clinicians in their making appropriate surgical approaches and postoperative treatment strategies.\u003c/p\u003e\u003cp\u003eStat3 signaling pathway is a pro-inflammatory pathway that has been shown to be involved in chronic inflammation, autoimmunity, infectious diseases and cancer, and the members of the pathway are often used as diagnostic or prognostic indicators of disease activity and response to therapy.\u003csup\u003e5\u003c/sup\u003e IL-6 is an upstream molecule capable of activating Stat3 pathway and IL-17 is a downstream molecule that is expressed in response to the activation of Stat3.\u003c/p\u003e\u003cp\u003eThe present study has shown that IL-6 is highly expressed in paracancerous tissues (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows that the expression of IL-6 is inversely correlated with the cell differentiation of HCC. A previous study of HCC has shown that HCC tissues expressing lower IL-6 levels are correlated with a better prognosis, longer overall survival, and longer time to recurrence,\u003csup\u003e6\u003c/sup\u003e somewhat similar to the results in this study.\u003c/p\u003e\u003cp\u003eOn the other hand, there is growing evidence that IL-17 has an important contextual and tissue-dependent role in maintaining health in response to injury, physiological stress, and infection.\u003csup\u003e7\u003c/sup\u003e IL-17 is not only associated with the immunity of the body,\u003csup\u003e7\u003c/sup\u003e but its role in the development of tumors has also become a focus of research. Similar to IL-6, the current study shows that expressed IL-17 is higher in paracancerous tissues than in HCC tissues (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Again as shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, IL-17 expression is inversely associated with the cell differentiation of HCC. Thus far, published reports support a pathogenic role of IL-17 in cancer development including cancers of the colon,\u003csup\u003e8\u003c/sup\u003e the skin,\u003csup\u003e9\u003c/sup\u003e the pancrea,\u003csup\u003e10\u003c/sup\u003e the hepatocytes,\u003csup\u003e11\u003c/sup\u003e the lung,\u003csup\u003e12\u003c/sup\u003e and multiple myeloma.\u003csup\u003e13\u003c/sup\u003e Patients with UCC may experience a poorer survival prognosis and a higher degree of infiltration of lymphocytes if IL-17 is highly expressed.\u003csup\u003e11,14\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eSeveral reports have suggested that over-activated Stat3 is present in a variety of tumor cells and immune cells, suggesting that over-activated Stat3 may influence the body's ability to generate an effective immune response, thereby participate in tumor formation and promote tumor cell proliferation, migration, differentiation, and metastasis.\u003csup\u003e15,16\u003c/sup\u003e The present study shows a higher level of activated p-Stat3 in HCC tissues (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) in keeping with the abovementioned reports. In addition, studies have shown that esophageal squamous cell carcinoma and adenocarcinoma have higher IHC scores for p-Stat3 in tumor tissues when cancer cells have higher migration and proliferation.\u003csup\u003e17\u003c/sup\u003e Furthermore, activated Stat3 can bind to the promoter region of the SKp2 gene which is a proto-oncogene associated with cervical carcinogenesis.\u003csup\u003e18\u003c/sup\u003e In summary, IL-6/Stat3/IL-17 pathway forms a cascade that has been indicated to play an important role in the development, progression and prognosis of several cancers including HCC.\u003csup\u003e19\u003c/sup\u003e In this context, the current study warrants further investigations as to the detailed mechanism(s) of the Stat3 pathway in the pathogenesis and prognosis of cancer.\u003c/p\u003e\u003cp\u003eTesting of blood liver enzymes is routinely performed and changes in levels of aminotransferases are important diagnostic indicators revealing detrimental impacts to hepatocytes, therefore, liver functions. For example, elevated levels of ALT and AST may indicate hepatocyte-dominated disease, while elevated levels of ALP and GGT may suggest cholestasis-dominated disease.\u003csup\u003e20\u003c/sup\u003e The sources of AST can be multiple organs including liver, heart, skeletal muscles, kidneys, brain, or red blood cells.\u003csup\u003e21,22\u003c/sup\u003e However, ALT are more specifically derived from the liver and therefore is the preferred clinical test for diagnosing liver disease. GGT is a membrane-bound enzyme that is a key enzyme in biotransformation, nucleic acid metabolism, catalyzing the degradation of extracellular glutathione (GSH). GSH is critical in protecting cells from oxidant-induced damage produced during normal metabolism.\u003csup\u003e23\u003c/sup\u003e ALP is a hydrolytic enzyme that is widely present in hepatocytes and blood sinuses of the bile duct membrane and is associated with the uptake and transport of certain substances. Elevated serum levels of ALP are associated with liver diseases including HCC, cholangiocarcinoma, and biliary cirrhosis.\u003csup\u003e24\u003c/sup\u003e A previous case-control study has found that blood levels of GGT, ALT, and AST are elevated in approximately 90% of cases liver cancer, while half of the cases also show elevated levels of liver-specific ALP or bilirubin.\u003csup\u003e25\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eA retrospective study has investigated the relationship of ALT, AST, GGT and ALP with HCC among patients who underwent hepatectomy and found that ALT, AST, GGT and ALP are positively associated with the risk of HCC and, GGT and AST/ALT are independent risk factors predicting postoperative survival in patients with primary HCC.\u003csup\u003e8\u003c/sup\u003e These observations are consistent with our findings presented here. Due to the role of GGT in the degradation of glutathione (GSH), GGT has been associated with oxidative stress which may induce a microenvironment that promotes tumor growth in the liver.\u003csup\u003e26\u003c/sup\u003e There is also evidence of elevated GGT in patients with obesity or diabetes or hepatic steatosis\u003csup\u003e27\u003c/sup\u003e which are associated with metabolic syndrome that it is itself associated with an increased risk of HCC.\u003csup\u003e28\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eHaving validated the patient cohort using conventional TNM clinical staging to predict survival prognosis among HCC patients, we have analyzed and found that elevated levels of all 4 enzymes are correlated with poor survival among these HCC patients (Figure. 2). To investigate individual ability of these enzymes in survival prediction, we have adopted AUC (area under the curve) for this purpose. AUC reflects the ability in predicting survival by which the larger a factor\u0026rsquo;s AUC is, the higher its predictive ability is. As shown in Figure. 3, among ALT, AST, GGT and ALP enzymes, ALP shows the highest AUC of 0.708, suggesting its greatest ability to predict postoperative survival and the quality of life in patients with HCC. The above conclusion is confirmed by multifactorial COX regression analysis which reveals elevated levels of ALP are an independent risk factor for postoperative survival in HCC patients (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Quantifying prediction ability for individual enzyme is advantageous because clinicians can judge the reliability of a risk factor used in their survival analyses.\u003c/p\u003e\u003cp\u003eA novel approach in this study is the combinational analysis of multiple bio-factors that can be able to increase AUC values. For example, a 5-factor combination of ALT\u0026thinsp;+\u0026thinsp;AST\u0026thinsp;+\u0026thinsp;ALP\u0026thinsp;+\u0026thinsp;IL-17\u0026thinsp;+\u0026thinsp;p-Stat3 shows significantly larger AUC value (0.826) than a 2-factor combination of AST\u0026thinsp;+\u0026thinsp;ALP (AUC\u0026thinsp;=\u0026thinsp;0.787), suggesting a higher predicting power (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). This approach is significant in that clinicians may use multiple routinely tested factors to reliably predict 5-year survival preoperatively before surgery.\u003c/p\u003e\u003cp\u003eIn summary, we have shown 3 major findings in this investigation: (1) IL-6/Stat3/IL-17 signaling pathway is involved in survival prognosis among HCC patients; (2) Liver function-related aminotransferases ALT, AST, GGT and ALP can predict survival prognosis for HCC patients before surgery; and (3) A novel approach of combining multiple risk factors can be used to better predict HCC patients\u0026rsquo; survival prognosis and quantifying prediction power using ROC analysis (AUC values) for individual risk factors may facilitate clinicians to judge the reliability of survival analyses. Utilizing routinely tested clinical bio-indicators to predict potential survival prognosis before surgery is significant in terms of facilitating surgeons\u0026rsquo; decision-making for personalized surgical approaches as well as appropriate radio-chemotherapy after surgery. However, to establish a clinically practical protocol, e.g., the cutoff values for serum levels of individual enzymes for survival predictions to be used as clinicians\u0026rsquo; guiding reference, requires a much larger sample of HCC patients. Our ongoing investigation is aimed at organizing a multi-center collaboration study to accomplish the above goal.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eDate sharing statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the data generated or analyzed during this study are included in this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe sincerely thank all patients participated in this investigation. We are grateful to the Ministry of Science and Technology of China, the Bureau of Science and Technology of the Xinjiang Corps, and Shihezi University for their generous financial support.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by grants from the Ministry of Science and Technology of China (No. 2009BAI82B02) and the Oasis Scholar Fund of Shihezi University (No. LZXZ201023). The funder had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor\u0026rsquo;s contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePlanning and execution of the study: LJW, XNL, LT and WJZ. Manuscript drafting: LJW, XNL, LT. Manuscript revision: WJZ. All authors approve the final draft and agree to be accountable for all aspects of the works.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval was obtained from the Institutional Ethics Review Board (IERB) of the First Affiliated Hospital of School of Medicine, Shihezi University (No. 2018-067-01). The IERB waived the need for patient consents due to anonymous analyses of the data and confidentiality and anonymity in the handling and publication of patients\u0026rsquo; tissues. Standard University Hospital Guidelines in accordance with the Declaration of Helsinki were followed in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePatient consent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBray F, Ferlay J, Soerjomataram I, et al. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. \u003cem\u003eCA cancer J Clin\u003c/em\u003e. 2018;68:394-424. doi:10.3322/ caac.21492\u003c/li\u003e\n\u003cli\u003eMenghini G. One-second needle biopsy of the liver. \u003cem\u003eGastroenterology\u003c/em\u003e, 1958, 35: 190-9.\u003c/li\u003e\n\u003cli\u003eLi YX, Zhang L, Simayi D, et al. Human Papillomavirus Infection Correlates with Inflammatory Stat3 Signaling Activity and IL-17 Level in Patients with Colorectal Cancer. \u003cem\u003ePlos One\u003c/em\u003e, 2015, 10(2):e0118391. doi: 10.1371/journal.pone.0118391. eCollection 2015.\u003c/li\u003e\n\u003cli\u003eRamachandran P, Matchett KP, Dobie R, et al. Single-cell technologies in hepatology: new insights into liver biology and disease pathogenesis. \u003cem\u003eNature reviews Gastroenterology \u0026amp; hepatology\u003c/em\u003e, 2020,08;178.doi: 10.1038/s41575-020-0304-x\u003c/li\u003e\n\u003cli\u003eKishimoto T. IL-6: from its discovery to clinical applications. \u003cem\u003eInternational immunology\u003c/em\u003e, 2010,22: 347-52. doi:10.1093/intimm/dxq030\u003c/li\u003e\n\u003cli\u003eYang X, Liang L, Zhang XF, et al. MicroRNA-26a suppresses tumor growth and metastasis of human HCC by targeting interleukin-6-Stat3 pathway. \u003cem\u003eHepatology, \u003c/em\u003e2013,58:158-70. doi:10.1002/ hep.26305\u003c/li\u003e\n\u003cli\u003eMcgeachy MJ, Cua DJ, Gaffen SL. The IL-17 Family of Cytokines in Health and Disease. \u003cem\u003eImmunity\u003c/em\u003e, 2019, 50: 892-906.doi:10.1016/j.immuni.2019.03.021\u003c/li\u003e\n\u003cli\u003eZepp Ja, Zhao J, Liu C, et al. IL-17A-Induced PLET1 Expression Contributes to Tissue Repair and Colon Tumorigenesis. \u003cem\u003eJournal of immunology\u003c/em\u003e , 2017, 199: 3849-57. doi:10.4049/jimmunol.1601540\u003c/li\u003e\n\u003cli\u003eWang L, Yi T, Zhang W, et al. IL-17 enhances tumor development in carcinogen-induced skin cancer. \u003cem\u003eCancer research\u003c/em\u003e, 2010,70:10112-20. doi:10.1158/0008-5472.CAN-10-0775\u003c/li\u003e\n\u003cli\u003eZhang Y, Zoltan M, Riquelme E, et al. Immune Cell Production of Interleukin 17 Induces Stem Cell Features of Pancreatic Intraepithelial Neoplasia Cells. \u003cem\u003eGastroenterology\u003c/em\u003e, 2018,155:210-23.e3. doi:1105 3/j.gastro.2018.03.041.\u003c/li\u003e\n\u003cli\u003eSun C, Kono H, Furuya S, et al. Interleukin-17A Plays a Pivotal Role in Chemically Induced HCC in Mice. \u003cem\u003eDigestive Diseases \u0026amp; Sciences\u003c/em\u003e, 2016,61:474-88. doi:10.1007/ s10620-015-3888-1\u003c/li\u003e\n\u003cli\u003eJin C, Lagoudas Gk, Zhao C, et al. Commensal Microbiota Promote Lung Cancer Development via \u0026gamma;\u0026delta; T Cells. \u003cem\u003eCell\u003c/em\u003e, 2019, 176: 998-1013.e16. doi:10.1016/j.cell.2018.12.040\u003c/li\u003e\n\u003cli\u003eCalcinotto A, Brevi A, Chesi M, et al. Microbiota-driven interleukin-17-producing cells and eosinophils synergize to accelerate multiple myeloma \u003cem\u003eNature communications\u003c/em\u003e, 2018,9:4832. doi:10.1038/s41467-018-07305-8\u003c/li\u003e\n\u003cli\u003eHu Z, Luo D, Wang D, et al. IL-17 Activates the IL-6/Stat3 Signal Pathway in the Proliferation of Hepatitis B Virus-Related HCC. \u003cem\u003eCellular physiology and biochemistry\u003c/em\u003e, 2017,43:2379-90. doi:10.1159/00 0484390\u003c/li\u003e\n\u003cli\u003eYu H, Lee H, Herrmann A, et al. Revisiting Stat3 signalling in cancer: new and unexpected biological functions. \u003cem\u003eNature reviews Cancer\u003c/em\u003e, 2014, 14:736-46. doi:10.1038/nrc3818\u003c/li\u003e\n\u003cli\u003eYu H, Pardoll D, Jove R. STATs in cancer inflammation and immunity: a leading role for Stat3. \u003cem\u003eNature reviews Cancer\u003c/em\u003e, 2009, 9:798-809. doi:10.1038/nrc2734\u003c/li\u003e\n\u003cli\u003eMiao FC, Ping-Tsung Chen, et al. IL-6 expression predicts treatment response and outcome in squamous cell carcinoma of the esophagus.\u003cem\u003e Molecular Cancer\u003c/em\u003e, 2013,12:26. doi:10.1186/1476-459 8-12-26\u003c/li\u003e\n\u003cli\u003eHuang H, Zhao W, Yang D. Stat3 induces oncogenic Skp2 expression in human cervical carcinoma cells. BBRC, 2012, 418: 186-90.doi:10.1016/j.bbrc.2012.01.004\u003c/li\u003e\n\u003cli\u003eWang L, Yi T, Kortylewski M, et al. IL-17 can promote tumor growth through an IL-6-Stat3 signaling pathway. \u003cem\u003eThe Journal of experimental medicine\u003c/em\u003e, 2009, 206:1457-64. doi:10.1084/jem.20090207\u003c/li\u003e\n\u003cli\u003eGiannini EG, Testa R, Savarino V. Liver enzyme alteration: a guide for clinicians. CMAJ: \u003cem\u003eCanadian\u003c/em\u003e \u003cem\u003eMedical Association journal\u003c/em\u003e, 2005,172: 367-79. doi:10.1503/cmaj.1040752\u003c/li\u003e\n\u003cli\u003eDufour DR, Lott JA, Nolte FS, et al.Diagnosis and monitoring of hepatic injury. II. Recommendations for use of laboratory tests in screening, diagnosis, and monitoring. \u003cem\u003eClinical chemistry\u003c/em\u003e, 2000, 46:2050-68.\u003c/li\u003e\n\u003cli\u003eZhang LX, Lv Y, Xu AM, et al. The prognostic significance of serum gamma-glutamyltransferase levels and AST/ALT in primary hepatic carcinoma. \u003cem\u003eBMC cancer\u003c/em\u003e, 2019,19: 841. doi:10.1186./s12885-019-6011-8\u003c/li\u003e\n\u003cli\u003eWhitfield JB. Gamma glutamyl transferase. \u003cem\u003eCritical reviews in clinical laboratory sciences\u003c/em\u003e, 2001,38: 263-355. doi:10.1080/20014091084227\u003c/li\u003e\n\u003cli\u003eCai X, Chen Z , Chen J, et al. Albumin-to-Alkaline Phosphatase Ratio as an Independent Prognostic Factor for Overall Survival of Advanced HCC Patients without Receiving Standard Anti-Cancer Therapies. \u003cem\u003eJournal of Cancer\u003c/em\u003e, 2018,9:189-97. doi:10.7150/jca.21799\u003c/li\u003e\n\u003cli\u003eYang JG, He XF, Huang B, et al. Rule of changes in serum GGT levels and GGT/ALT and AST/ALT ratios in primary hepatic carcinoma patients with different AFP levels. \u003cem\u003eCancer biomarkers\u003c/em\u003e, 2018,21:743-6. doi:10.3233/CBM-170088\u003c/li\u003e\n\u003cli\u003eZhao J, Zhao Y, Wang H, et al. Association between metabolic abnormalities and HBV related hepatocelluar carcinoma in Chinese: a cross-sectional study. \u003cem\u003eNutrition journal\u003c/em\u003e, 2011,10:49. doi:10.1186/1475-2891-10-49\u003c/li\u003e\n\u003cli\u003eJiang S, Jiang D, Tao Y. Role of gamma-glutamyltransferase in cardiovascular diseases. \u003cem\u003eExperimental and clinical cardiology\u003c/em\u003e, 2013,18: 53-6.\u003c/li\u003e\n\u003cli\u003eJinjuvadia R, Patel S, Liangpunsakul S. The association between metabolic syndrome and HCC: systemic review and meta-analysis. \u003cem\u003eJournal of clinical gastroenterology\u003c/em\u003e, 2014,48:172-7. doi:10.1097/MCG.0b013e31\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"HCC, Liver cancer, IL-6, Stat3, IL-17, Survival prognosis, Aminotransferases","lastPublishedDoi":"10.21203/rs.3.rs-261872/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-261872/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective:\u003c/strong\u003e The expression/activation of p-Stat3, IL-6 and IL-17 in hepatocellular carcinoma (HCC) tissues together with the serum levels of aminotransferases ALT, AST, GGT and ALP were designed to evaluate their abilities in predicting the survival prognosis of postoperative patients with HCC.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e The clinicopathological data and paraffin-embedded tissues of 98 patients who underwent liver tumor resection were collected at the First Affiliated Hospital of Shihezi University School of Medicine, and the patients were followed-up after surgery. Immunohistochemistry (IHC) was used to detect the activation/expression of p-Stat3, IL-6 and IL-17 using tissue microarrays derived from these patients. Before surgery, patients’ serum levels of AST, ALT, GGT and ALP were measured using a Roche Modular DPP automated biochemical analyzer. Statistical analyses were performed using non-parametric tests, Spearman's correlation, ROC curves, Kaplan-Meier survival analysis, Cox single-factor and multifactor regression models.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e (1) The strong positive rate of expressed IL-6 in HCC tissues (18.09%) was lower than that in paracancerous tissues (60.06%) (p\u0026lt;0.001); the strong positive rate of expressed IL-17 in HCC tissues (42.40%) was lower than that in paracancerous tissues (87.80%) (p\u0026lt;0.001). The strong positive rate of activated p-Stat3 in HCC tissues (52.20%) was higher than paracancerous tissues (18.04%) (p=0.01). (2) The higher the levels of ALT (p\u0026lt;0.001), AST (p=0.002), GGT (p\u0026lt;0.001), and ALP (p\u0026lt; 0.001) were, respectively, the worse the survival prognoses were observed among HCC patients. (3) High levels of ALP were an independent risk factor for postoperative prognosis in HCC patients. (4) The combination of ALT+AST+ ALP appeared to be the best survival predictor for HCC patients as indicated by AUC (0.820, 95% CI=0.714, 0.926) (p\u0026lt;0.001).\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e The activation/expression of p-Stat3, IL-6 and IL-17 differ in HCC tissues compared with their paracancerous tissues. Using classical TNM staging system as a reference, higher levels of serum aminotransferases ALT, AST, GGT and ALP are capable of predicting poor prognosis among postoperative HCC patients. ALT+AST+ALP+p-Stat3 is an optimal combination better than the classical TNM staging system in predicting postoperative survival among HCC patients. The observations presented may provide a useful guide for clinicians to strategize individualized surgical plans for liver cancer patients before surgery.\u003c/p\u003e","manuscriptTitle":"A Cohort Study Using Preoperative IL-6/Stat3/IL-17 And Blood Aminotransferase Levels To Predict Five-Year Survival For Patients After Liver Cancer Resection","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2021-03-09 18:18:48","doi":"10.21203/rs.3.rs-261872/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":"1955a301-0ade-41e6-a9a1-df8c542107fa","owner":[],"postedDate":"March 9th, 2021","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":2847526,"name":"Surgery"},{"id":2847527,"name":"Oncology"}],"tags":[],"updatedAt":"2021-04-02T05:59:33+00:00","versionOfRecord":[],"versionCreatedAt":"2021-03-09 18:18:48","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-261872","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-261872","identity":"rs-261872","version":["v1"]},"buildId":"FbvkV6FR0MCFSLy54lSbu","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}