Published online Apr 28, 2016. doi: 10.3748/wjg.v22.i16.4168
Peer-review started: December 2, 2015
First decision: January 13, 2016
Revised: February 16, 2016
Accepted: March 1, 2016
Article in press: March 2, 2016
Published online: April 28, 2016
AIM: To develop a mathematical model for the early detection of hepatocellular carcinoma (HCC) with a panel of serum proteins in combination with α-fetoprotein (AFP).
METHODS: Serum levels of interleukin (IL)-8, soluble intercellular adhesion molecule-1 (sICAM-1), soluble tumor necrosis factor receptor II (sTNF-RII), proteasome, and β-catenin were measured in 479 subjects categorized into four groups: (1) HCC concurrent with hepatitis C virus (HCV) infection (n = 192); (2) HCV related liver cirrhosis (LC) (n = 96); (3) Chronic hepatitis C (CHC) (n = 96); and (4) Healthy controls (n = 95). The R package and different modules for binary and multi-class classifiers based on generalized linear models were used to model the data. Predictive power was used to evaluate the performance of the model. Receiver operating characteristic curve analysis over pairs of groups was used to identify the best cutoffs differentiating the different groups.
RESULTS: We revealed mathematical models, based on a binary classifier, made up of a unique panel of serum proteins that improved the individual performance of AFP in discriminating HCC patients from patients with chronic liver disease either with or without cirrhosis. We discriminated the HCC group from the cirrhotic liver group using a mathematical model (-11.3 + 7.38 × Prot + 0.00108 × sICAM + 0.2574 ×β-catenin + 0.01597 × AFP) with a cutoff of 0.6552, which achieved 98.8% specificity and 89.1% sensitivity. For the discrimination of the HCC group from the CHC group, we used a mathematical model [-10.40 + 1.416 × proteasome + 0.002024 × IL + 0.004096 × sICAM-1 + (4.251 × 10-4) × sTNF + 0.02567 ×β-catenin + 0.02442 × AFP] with a cutoff 0.744 and achieved 96.8% specificity and 89.7% sensitivity. Additionally, we derived an algorithm, based on a binary classifier, for resolving the multi-class classification problem by using three successive mathematical model predictions of liver disease status.
CONCLUSION: Our proposed mathematical model may be a useful method for the early detection of different statuses of liver disease co-occurring with HCV infection.
Core tip: Hepatocellular carcinoma is one of the most common liver malignancies. We sought to create a mathematical model from a panel of serum proteins (intercellular adhesion molecules, beta catenin, interleukin-8, the proteasome, and soluble tumor necrosis factor receptor II) in combination with α-fetoprotein to aid in early detection of hepatocellular carcinoma. This panel was measured in 384 subjects infected with hepatitis C virus (HCV) as well as 95 healthy control subjects negative for HCV. Finally, we created mathematical models that may be valuable tools for the early detection of different statuses of liver disease co-occurring with HCV infection.