To investigate the role of biomarkers in predicting postoperative liver dysfunction in patients with hepatocellular carcinoma (HCC).
A total of 200 patients operated from July 2009 to June 2010 at Zhongshan Hospital, Fudan University for pathologically confirmed HCC were retrospectively analyzed for clinical data, HBD DNA level and serum biochemical markers for liver fibrosis. The patients were followed up to observersation end point. Correlation of the monitored parameters with postoperative liver dysfunction and patient survival was statistically analyzed.
Preoperative hepatitis B virus (HBV) DNA level, serum prealbumin (PA) hyaluronic acid (HA), and laminin (LN) levels correlated with postoperative liver dysfunction. A predictive model was generated using these 4 parameters and validated in 89 HCC patients with sensitivity and specificity of 0.625 and 0.912, respectively. However, no correlation was identified between postoperative liver function and overall survival.
Citation: Shen Y, Shi G, Huang C, Zhu X, Chen S, Sun H, et al. (2015) Prediction of Post-Operative Liver Dysfunction by Serum Markers of Liver Fibrosis in Hepatocellular Carcinoma. PLoS ONE 10(10): e0140932. https://doi.org/10.1371/journal.pone.0140932
Editor: Kwan Man, The University of Hong Kong, HONG KONG
Received: July 29, 2015; Accepted: October 1, 2015; Published: October 26, 2015
Copyright: © 2015 Shen et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited
Data Availability: Due to ethical restrictions involving patient information, data are available upon request to the corresponding author at email@example.com.
Funding: This study was jointly supported by the grants from the National Major Science and Technology Project (2012ZX 10002012-004; 2013ZX 10002007). The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
Worldwide, the patients suffering from primary liver cancer are commonly complicated with cirrhosis, with a prevalence the latter of 72.1% to 82.3%. Compared to non-cirrhotic ones, the cirrhotic patients have relatively poor liver regeneration ability and impaired functional reservation. A number of extensive studies have shown that liver dysfunction or liver failure contributes to the majority of postoperative mortality of HCC. As such, accurate preoperative assessment of liver reserve function is important for not only safety of liver surgery but also post-operative survival.
A variety of methods are currently used to estimate liver function reserve, including liver enzyme tests, indole indocyanine green clearance test (ICG15), liver elasticity index detector, nuclear imaging and Child-pugh grade. However, no consensus has been reached yet regarding superiority of any one among these methods over the others.
Hepatitis B virus infection is prevalent in Asian countries. In China, the majority of patients with primary liver cancer have HBV infection leading to cirrhosis. Diagnosis criteria of liver fibrosis, the reversible precursor of cirrhosis, includes histopathology, imaging and serum markers, with pathology serving as the gold standard. Besides, emerging as a new technology for accessing liver fibrosis, liver stiffness measurement (LSM) determines transient elastography (TE). Accumulating clinical data have shown that LSM is slightly superior to Fibrotest and AST-to-Platelet Ratio Index (APRI) in terms of diagnosis efficacy. However, LSM requires highly trained operators, and is adversely affected by abdominal obesity statue/body mass index (BMI), liver inflammation activity, and abnormalities of bilirubin or transaminase as well[8, 9], and thus does not gain clinical popularity yet.
Serum markers for liver fibrosis including hyaluronic acid (HA), laminin (LN), IV collagen (IV-C) and Ⅲ procollagen N-terminal peptide (P III-NP) have been recently considered as important evaluation of disease progress, inflammatory activity, fibrosis and therapeutic effect in chronic liver diseases[10–12]. Pathophysiologically, along with the pathological process of liver fibrosis, balance between synthesis and degradation of extracellular matrix in liver tissue (ECM) is disturbed, leading to excessive proliferation and abnormal deposition of ECM, which is reflected by release into serum of ECM components/degraded products, collagenases and certain cytokines. Among the four above-mentioned serum markers of ECM components/degraded products P III-NP and IV-C represent extracellular matrix collagen, reflecting basement membrane collagen metabolism, while HA and LN mirror metabolism of basement membrane glycoproteins. Authors believe that these four markers can more accurately reflect the metabolic changes in the liver, including fiber formation, degradation, deposition, of extracellular matrix components.
Herein we reported retrospective evaluation of role of the four indicators in predicting postoperative liver dysfunction and death of liver failure in patients with primary liver cancer. We identified risk factors affecting postoperative liver function recovery in these patients, and also generated an easy-to-use mathematical model for estimating liver functional reserve preoperatively on this basis.
Materials and Methods
Clinical data of a total of 200 patients operated from July 2009 to June 2010 at Zhongshan Hospital, Fudan University for pathologically confirmed hepatocellular carcinoma were collected, with those with obstructive jaundice, biliary disease, hepatitis C and alcoholic cirrhosis and incomplete data being excluded. These patients were followed up till end of 2012. Another 89 cases meeting the above criteria and admitted between June 2010 to November 2010 were selected for validation of the mathematical model. This study was approved by The Institutional Review Board of Ethics Committee of Zhongshan Hospital, Fudan University. All participants received written and oral information prior to giving written consent, and the study was performed in accordance with the Helsinki II declaration.
The 2011 International Liver Surgery Group (ISGLS) liver function decompensation criteria was adopted in the present study.
HA, LN, P III-NP and IV-C were measured by magnetic microbead chemoluminence method following the manufacturer’s manual (Antu Bioengineering Co Ltd, Zhengzhou, China). All other tests were routine analysis done at our clinical laboratory.
Clinical data including age, sex, accompanying diabetes, HBV-DNA copy number, alpha-fetoprotein (AFP), Child-Pugh score, total bilirubin (TB), bilirubin (CB), serum albumin (Alb), alanine aminotransferase (ALT), alkaline phosphatase (ALP), γ- glutamyl GGT (γ-GT), prealbumin (PA), prothrombin time (PT), HA, LN, P III-NP, IV-C, tumor size, procedure type and total hepatic hilar occlusion time were collected and recorded.
Data with gaussian distribution were presented as mean ± standard deviation, and analyzed with independent sample t-test. Skewed variables data were presented as median (range) and analyzed with non-parametric Wilcoxon test. Attributes data were analyzed with chi-square test. Multivariate analysis was done using logistic regression with the indicators of p <0.1 in univariate analysis included, and backward method based on the likelihood ratio test of conditional parameters was used in this analysis, in which the variable was removed out of the equation when p> 0.05. The variables selected by logistic regression analysis were employed to establish an equation calculating the postoperative liver failure risk following published approach [14–16]. In this model, each dichotomous variable was multiplied by its regression coefficient. The resulting products were then added up to generate the risk score of each patient. Calculating the area under curve of the ROC was performed using non-parametric method. Survival data were analyzed by Log-rank test. Difference with P<0.05 was considered as statistically significant.
Preoperative levels of liver fibrosis markers in the patients with HCC
Preoperative HA, LN levels were analyzed using ROC curve and further stratified using Youden index selection method in which cut-off points were picked based on the largest index numbers. Specifically, HA had a cut-off value of 139 ng/ml with sensitivity of 0.557 and 1-specificity of 0.366. And LN had a cut-off value of 485 ng/ml, with sensitivity of 0.531 and specificity of 0.742 (Fig 1). Other conventional biochemical measurements were interpretated based on their reference values.
A, ROC curve of HA; B, ROC curve of LN.
Statistical analysis also showed that significant differenence existed between post-operatively liver function compensated and decompensated patients in preoperative AFP, HBV-DNA, TB, γ-GT, PT, HA, PA and LN levels, while no difference existed between these 2 groups of patient either in age, gender, diabetes, Alb, ALT, P III-NP, IV-C, tumor size, extent of resection or hilar occlusion time (Table 1).
Determinants of postoperative liver dysfunction
As shown in Table 2, Patients with higher HBV-DNA copy number had 2.96 times of risk developing postoperative liver dysfunction comparing to the patients with lower HBV viral load. Similar results were observed with HA and LN measurements. As for TB, a level greater than 20.4 umol/L imposed on the patient 4.294 times of such risk comparing those with TB<20.4 umol/L. In terms of PA, a decrease of 0.1g/L brought up odds ratio of liver function insufficiency to 3.91(1/0.256).
Mathematical model for estimating postoperative liver dysfunction
Based on logistic regression analysis results, an equation was generated for quantitatively evaluate risk of postoperative liver dysfunction:
Y (postoperative liver failure risk) = 1.2414 × (1 for HBV DNA> 103/mL, otherwise 0) + 1.667 × (1 for TB> 20.4 umol / L, otherwise 0) -1.5606 × PA + 1.0103 × (1 when HA> 139 ng / ml otherwise 0) + 1.1648 × (1 when LN> 485 ng / ml, otherwise 0).
The area under curve of Y was 0.797 (95% CI = 0.721–0.873), while Child-pugh grade had an area under curve of 0.579 (95% CI = 0.481–0.676), statistically smaller than that of Y (p <0.05), indicating that the combinatorial evaluation factor outperformed the Child-pugh classification. The cut-off point of Y values was 0.2704, with sensitivity and specificity of 0.592 and 0.874, respectively (Fig 2).
Validation of the mathematical equation
The validation data demonstrated that, 25 of the 89 cases had Y values above the threshold, and 80% (20 of 25) developed postoperative liver decompensation. Among the 64 with Y values below the threshold, 12 (18.75%) had liver decompensation with no death. The sensitivity and specificity of the model was 0.625 and 0.912, respectively (Table 3).
The patients were stratified with levels of HA and LN for survival analysis (Fig 3). The 24-month survival rates were 0.83 and 0.68, respectively. However, no correlation was identified between postoperative liver function and overall or tumor-free survival (P all >0.05).
A, Overall survival after stratification with HA level (χ2 = 0.048, p = 0.827); B. Tumor-free survival after stratification with HA level (χ2 = 0.006, p = 0.937); C, Overall survival after stratification with LN level (χ2 = 4.698, p = 0.03); D, Tumor-free survival after stratification with LN (χ2 = 1.077, p = 0.299).
Hepatectomy is treatment of choice for HCC and estimation of hepatic functional reserve of the future remnant liver is critical for liver surgery. Along with the continuous improvement in preoperative evaluation, surgical techniques and perioperative management, refinement of liver surgery brings postoperative mortality to less than 5%. However, post hepatectomy liver failure (PHLF) associated death is still the main source of mortality in the short period after surgery[17, 18]. Thus, accurate assessment of liver functional reserve and prediction of postoperative liver dysfunction is essential for hepatectomy planning. Conventionally, pre-operative assessment of liver function usually includes biochemical tests, liver function scoring systems, quantitative hepatic functional analyses, imaging evaluation, regional radiographic assessment of liver function and liver volume measurements. However, none of these methods are accurate and reliable. In fact, people tend to apply multiple methods for comprehensive assessment with hope to improve accuracy of liver reserve function evaluation. For example, Clavien et al integrated histopathology, Child-pugh score, ICGR15 and status of portal hypertension in their prediction of resection volume of liver, which yielded successful clinical outcomes. In addition, recent study reveals that besides extent of hepatectomy, preoperative HBV DNA level, high Ishak fibrosis score and HBV reactivation are important prognostic factors for postoperative liver dysfunction as well. In the present study, liver dysfunction was present in 49 cases, including ISGLS A grade in 25 cases, grade B in 22 cases, grade C in two cases. The two 2 patients in grade C died of failure, while the remaining 47 patients recovered after appropriate management. Besides, our data showed that the liver fibrosis markers, i.e., HA, and LN, were significantly shifted in post-operatively decompensated patients. Moreover, our data suggested that pre-operative LN level predicted post-operative survival in this population. Our comprehensive prediction model taking HBV DNA copy number, total bilirubin, PA and HA into consideration had sensitivity and specificity of 0.592 and 0.874, respectively. Taken together, our results demonstrated that combination of liver fibrosis markers with HBV DNA and total bilirubin could be an easy and practical approach assessing liver function for hepatectomy.
Serum total bilirubin is widely used to assess liver injury. During cirrhosis process, the regenerated hepatocytes are not connected to primary or proliferated and the conjugated bilirubin enters directly into bloodstream, causing bilirubinemia. As a matter of fact, serum total bilirubin level has been considered as a predictor of postoperative death caused by PHLF[18, 19]. In Castera’s retrospective study, preoperative serum bilirubin ≥20.4umol/L and serum prealbumin <0.14 g/L are independent risk factor for postoperative liver dysfunction for hepatectomy in HCC patients. Consistent with this finding, we found that preoperative TB>20.4 umol / L carried much higher risk for postoperative liver dysfunction than that of TB<20.4 umol /L. Similarily, Yachida S et al reported that total serum bilirubin ≥17umol/L and blood hyaluronic acid ≥120ng/mL have the same significance.
Cirrhosis is another important predictor for poor prognosis after hepatectomy in HCC. Accumulating clinical data indicate that serum liver fibrosis markers (HA, LN, IV-C and P III-NP) are efficient indicators of severity of liver fibrosis[22–26]. Although studies have shown that preoperative serum HA correlates well with postoperative liver dysfunction, but the HA thresholds were set at different levels in different studies[27–29]: It was set at 200 ng/mL in Ogata’s series but 100 ng/mL in Mima’s study, and we used HA>139 ng/ml as cutoff. Apparently, the variation in sensitivity of HA measurement necessitates further clinical studies for a more accurate reference level with satisfactory sensitivity and specificity.
LNs are 400 kD extracellular matrix glycoproteins binding to type IV collagen to form network of basal membrane for regulation of intercellular cell adhesion, migration, and cell growth/differentiation, and influencing cirrhosis and tumor spread. LNs are mainly expressed in vessel wall, bile duct and lymphatic wall, but little in normal hepatocytes. They are over-expressed in liver fibrosis and deposited in sinusoidal endothelial cell gap, which reduces permeability of endothelial cells, and consequently leading to increased portal pressure. Elevated LN level indicates fibrosis of the sinusoidal capillaries and portal triad[31–33]. Using 485 ng/ml as a cut-off, we found that high LN level was significantly associated with not only post-hepatectomy liver dysfunction but also shortened 24-month overall survival. As such, we believe that preoperative high LN level is a good predictive marker for postoperative liver dysfunction and general prognosis in HCC.
By logistic analysis, we were able to generate a mathematical model for predicting postoperative hepatic dysfunction. The model contained components of HBV-DNA, serum total bilirubin, HA and LN. It was verified with clinical data from 89 patients with sensitivity and specificity of 0.625 and 0.912, respectively. Obviously, there is room for improvement of the sensitivity of the model while the specificity was favorable. Nevertheless, comparing to that of Child-pugh grades alone, our model had improved diagnostic performance in general.
The present study has a couple of limitations. First, this was a retrospective study. Second, other clinical measurements such as indole indocyanine green clearance test (ICG15) and liver elasticity index testing were not in the scope of the study. Further prospective studies are needed to confirm our findings and improve performance of the prediction model for assessing postoperative liver dysfunction in HCC.
Conceived and designed the experiments: YHS HCS. Performed the experiments: GMS CH. Analyzed the data: XDZ SC. Contributed reagents/materials/analysis tools: JZ JF. Wrote the paper: YHS.
- 1. Yu MW, Hsu FC, Sheen IS, Chu CM, Lin DY, Chen CJ, et al. Prospective study of hepatocellular carcinoma and liver cirrhosis in asymptomatic chronic hepatitis B virus carriers. Am J Epidemiol. 1997;145(11):1039–47. pmid:9169913.
- 2. Yachida S, Wakabayashi H, Okano K, Suzuki Y. Prediction of posthepatectomy hepatic functional reserve by serum hyaluronate. The British journal of surgery. 2009;96(5):501–8. pmid:19358182.
- 3. Tanaka M, Katayama F, Kato H, Tanaka H, Wang J, Qiao YL, et al. Hepatitis B and C virus infection and hepatocellular carcinoma in China: a review of epidemiology and control measures. Journal of epidemiology / Japan Epidemiological Association. 2011;21(6):401–16. pmid:22041528; PubMed Central PMCID: PMC3899457.
- 4. Bravo AA, Sheth SG, Chopra S. Liver biopsy. The New England journal of medicine. 2001;344(7):495–500. pmid:11172192.
- 5. Sandrin L, Fourquet B, Hasquenoph JM, Yon S, Fournier C, Mal F, et al. Transient elastography: a new noninvasive method for assessment of hepatic fibrosis. Ultrasound Med Biol. 2003;29(12):1705–13. pmid:14698338.
- 6. Castera L, Vergniol J, Foucher J, Le Bail B, Chanteloup E, Haaser M, et al. Prospective comparison of transient elastography, Fibrotest, APRI, and liver biopsy for the assessment of fibrosis in chronic hepatitis C. Gastroenterology. 2005;128(2):343–50. pmid:15685546.
- 7. Castera L, Foucher J, Bernard PH, Carvalho F, Allaix D, Merrouche W, et al. Pitfalls of liver stiffness measurement: a 5-year prospective study of 13,369 examinations. Hepatology. 2010;51(3):828–35. pmid:20063276.
- 8. Arena U, Vizzutti F, Corti G, Ambu S, Stasi C, Bresci S, et al. Acute viral hepatitis increases liver stiffness values measured by transient elastography. Hepatology. 2008;47(2):380–4. pmid:18095306.
- 9. Liang XE, Chen YP, Zhang Q, Dai L, Zhu YF, Hou JL. Dynamic evaluation of liver stiffness measurement to improve diagnostic accuracy of liver cirrhosis in patients with chronic hepatitis B acute exacerbation. J Viral Hepat. 2011;18(12):884–91. pmid:21062388.
- 10. Guechot J, Loria A, Serfaty L, Giral P, Giboudeau J, Poupon R. Serum hyaluronan as a marker of liver fibrosis in chronic viral hepatitis C: effect of alpha-interferon therapy. Journal of hepatology. 1995;22(1):22–6. pmid:7751583.
- 11. Parise ER, Oliveira AC, Figueiredo-Mendes C, Lanzoni V, Martins J, Nader H, et al. Noninvasive serum markers in the diagnosis of structural liver damage in chronic hepatitis C virus infection. Liver international: official journal of the International Association for the Study of the Liver. 2006;26(9):1095–9. pmid:17032410.
- 12. Castera L, Hartmann DJ, Chapel F, Guettier C, Mall F, Lons T, et al. Serum laminin and type IV collagen are accurate markers of histologically severe alcoholic hepatitis in patients with cirrhosis. Journal of hepatology. 2000;32(3):412–8. pmid:10735610.
- 13. Rahbari NN, Garden OJ, Padbury R, Brooke-Smith M, Crawford M, Adam R, et al. Posthepatectomy liver failure: a definition and grading by the International Study Group of Liver Surgery (ISGLS). Surgery. 2011;149(5):713–24. pmid:21236455.
- 14. Peduzzi P, Concato J, Kemper E, Holford TR, Feinstein AR. A simulation study of the number of events per variable in logistic regression analysis. J Clin Epidemiol. 1996;49(12):1373–9. Epub 1996/12/01. pmid:8970487.
- 15. Nagino M, Nimura Y, Hayakawa N, Kamiya J, Kondo S, Sasaki R, et al. Logistic regression and discriminant analyses of hepatic failure after liver resection for carcinoma of the biliary tract. World J Surg. 1993;17(2):250–5. Epub 1993/03/01. pmid:8511922.
- 16. Liu AM, Yao TJ, Wang W, Wong KF, Lee NP, Fan ST, et al. Circulating miR-15b and miR-130b in serum as potential markers for detecting hepatocellular carcinoma: a retrospective cohort study. BMJ Open. 2012;2(2):e000825. Epub 2012/03/10. pmid:22403344; PubMed Central PMCID: PMC3308260.
- 17. McCormack L, Petrowsky H, Jochum W, Furrer K, Clavien PA. Hepatic steatosis is a risk factor for postoperative complications after major hepatectomy: a matched case-control study. Annals of surgery. 2007;245(6):923–30. pmid:17522518; PubMed Central PMCID: PMC1876953.
- 18. Mullen JT, Ribero D, Reddy SK, Donadon M, Zorzi D, Gautam S, et al. Hepatic insufficiency and mortality in 1,059 noncirrhotic patients undergoing major hepatectomy. Journal of the American College of Surgeons. 2007;204(5):854–62; discussion 62–4. pmid:17481498.
- 19. Balzan S, Belghiti J, Farges O, Ogata S, Sauvanet A, Delefosse D, et al. The "50–50 criteria" on postoperative day 5: an accurate predictor of liver failure and death after hepatectomy. Annals of surgery. 2005;242(6):824–8, discussion 8–9. pmid:16327492; PubMed Central PMCID: PMC1409891.
- 20. Li B, Yu Y, He TF, Fan J, Wu ZQ, Zhou J, et al. Value of the Conventional Liver Function Tests in the Assessment of Hepatic Reserve. Chinese Journal of Hepatobiliary. 2011;17(10):805–8.
- 21. Park YK, Kim BW, Wang HJ, Kim MW. Hepatic resection for hepatocellular carcinoma meeting Milan criteria in Child-Turcotte-Pugh class a patients with cirrhosis. Transplant Proc. 2009;41(5):1691–7. pmid:19545709.
- 22. Wong VS, Hughes V, Trull A, Wight DG, Petrik J, Alexander GJ. Serum hyaluronic acid is a useful marker of liver fibrosis in chronic hepatitis C virus infection. J Viral Hepat. 1998;5(3):187–92. pmid:9658372.
- 23. Tamaki S, Ueno T, Torimura T, Sata M, Tanikawa K. Evaluation of hyaluronic acid binding ability of hepatic sinusoidal endothelial cells in rats with liver cirrhosis. Gastroenterology. 1996;111(4):1049–57. pmid:8831601.
- 24. Murawaki Y, Ikuta Y, Koda M, Nishimura Y, Kawasaki H. Clinical significance of serum hyaluronan in patients with chronic viral liver disease. Journal of gastroenterology and hepatology. 1996;11(5):459–65. pmid:8743918.
- 25. Ogata T, Okuda K, Ueno T, Saito N, Aoyagi S. Serum hyaluronan as a predictor of hepatic regeneration after hepatectomy in humans. Eur J Clin Invest. 1999;29(9):780–5. pmid:10469166.
- 26. van Leeuwen DJ, Howe SC, Scheuer PJ, Sherlock S. Portal hypertension in chronic hepatitis: relationship to morphological changes. Gut. 1990;31(3):339–43. pmid:2323602; PubMed Central PMCID: PMC1378280.
- 27. Nanashima A, Yamaguchi H, Tanaka K, Shibasaki S, Tsuji T, Ide N, et al. Preoperative serum hyaluronic acid level as a good predictor of posthepatectomy complications. Surgery today. 2004;34(11):913–9. pmid:15526125.
- 28. Nanashima A, Abo T, Arai J, Matsumoto H, Kudo T, Nagayasu T. Functional liver reserve parameters predictive for posthepatectomy complications. The Journal of surgical research. 2013;185(1):127–35. pmid:23746962.
- 29. Mima K, Beppu T, Ishiko T, Chikamoto A, Nakagawa S, Hayashi H, et al. Preoperative serum hyaluronic acid level as a prognostic factor in patients undergoing hepatic resection for hepatocellular carcinoma. The British journal of surgery. 2014;101(3):269–76. pmid:24446084.
- 30. Leroy V. Other non-invasive markers of liver fibrosis. Gastroenterol Clin Biol. 2008;32(6 Suppl 1):52–7. pmid:18973846.
- 31. Halfon P, Bourliere M, Penaranda G, Deydier R, Renou C, Botta-Fridlund D, et al. Accuracy of hyaluronic acid level for predicting liver fibrosis stages in patients with hepatitis C virus. Comparative hepatology. 2005;4:6. pmid:16008833; PubMed Central PMCID: PMC1192814.
- 32. Niemela O, Risteli J, Blake JE, Risteli L, Compton KV, Orrego H. Markers of fibrogenesis and basement membrane formation in alcoholic liver disease. Relation to severity, presence of hepatitis, and alcohol intake. Gastroenterology. 1990;98(6):1612–9. pmid:1692550.
- 33. El-Mezayen HA, Habib S, Marzok HF, Saad MH. Diagnostic performance of collagen IV and laminin for the prediction of fibrosis and cirrhosis in chronic hepatitis C patients: a multicenter study. European journal of gastroenterology & hepatology. 2015;27(4):378–85. pmid:25874509.