BACKGROUND Epithelial-mesenchymal transition genes have prognostic influence on hepatocellular carcinoma (HCC). Previously, the following four epithelial-mesenchymal transition-related genes were considered to be significantly influential: E-cadherin (CDH1), inhibitor of DNA binding 2 (ID2), matrix metalloproteinase 9 (MMP9), and transcription factor 3 (TCF3). A prognostic prediction model, NRISK4 = (-0.333 × [CDH1] - 0.400 × [ID2] + 0.339 × [MMP9] + 0.387 × [TCF3]) was constructed, but from patients with HCC with predominantly hepatitis B virus infection. We therefore aim to validate if this model also fits patients with HCC and hepatitis C virus (HCV) infection. METHODS We collected HCC tissue samples from 67 patients with HCV infection. Discrimination of the NRISK4 was re-estimated using receiver operating curve analysis and we redefined the appropriate cutoff value. Using this cutoff value, patients were divided into two groups (high/low risk patients) and we compared their clinicopathological factors and prognosis. RESULTS Area under the curve of NRISK4 prediction was 0.70 and an appropriate cutoff value was 3.19 in this cohort. Patients were divided into high- (n = 25) and low-risk (n = 42) patients for prognosis. There were no significant differences in tumor factors between the two groups. Cancer-specific survival rates at 5 y after surgery on high- and low-risk patients were 45% and 68%, respectively (P = 0.02). At 2 y after surgery, recurrence rates were 68% and 37% among high- and low-risk patients, respectively (P = 0.01). Aggressive recurrences were highly observed in the high-risk patients (P = 0.01). CONCLUSIONS NRISK4 model could also successfully validate prognosis of patients with HCC with HCV infection similarly to in the previous report of patients with hepatitis B virus infection, especially in the early period after surgery.
ABSTRACT
PUBLICATION RECORD
- Publication year
2020
- Venue
Journal of Surgical Research
- Publication date
2020-01-01
- Fields of study
Medicine
- Identifiers
- External record
- Source metadata
Semantic Scholar, PubMed
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