Low expression of WW domain-containing oxidoreductase associates with hepatocellular carcinoma aggressiveness and recurrence after curative resection.
Chenhao ZhouWanyong ChenJialei SunManar AtyahYirui YinWentao ZhangLei GuoQinghai YeQiongzhu DongYi ShiNing RenPublished in: Cancer medicine (2018)
WW domain-containing oxidoreductase (WWOX), which has a protein-interaction domain and is regarded to be a tumor suppressor, has been known to play an important role in anti-angiogenesis and cancer progression. This study aimed to investigate prognostic values of WWOX expression in hepatocellular carcinoma (HCC) patients after hepatectomy. Additionally, we intended to formulate a valuable prognostic nomogram for HCCs. 182 HCC patients who underwent hepatectomy from January 2009 to January 2010 were enrolled in our study. qRT-PCR, Western blot, and immunohistochemistry on tissue microarrays were then used to determine the expression levels of WWOX. An evaluation of the role of WWOX expression levels in the prognosis and outcome of patients was established. A decrease in the expression of WWOX was found when compared to adjacent tumor-free tissues, which led to worse overall survival (OS) and recurrence-free survival (RFS) and, therefore, was considered as an independent negative factor in the prognosis of HCC. Two nomograms, comprising WWOX, alpha-fetoprotein (AFP), tumor size, and γ-glutamyltransferase (γ-GT), were constructed to obtain superior discriminatory abilities than conventional staging systems in terms of C-index and clinical net benefit on decision curve analysis (DCA) for OS and RFS. Our data suggest that WWOX expression is strongly related to HCC post-resection aggressiveness and recurrence. Additional advanced and accurate predictive model through the incorporation of WWOX into nomogram could help predict OS or RFS for HCC patients.
Keyphrases
- poor prognosis
- end stage renal disease
- chronic kidney disease
- newly diagnosed
- ejection fraction
- free survival
- prognostic factors
- squamous cell carcinoma
- machine learning
- endothelial cells
- long non coding rna
- young adults
- patient reported outcomes
- lymph node
- vascular endothelial growth factor
- electronic health record
- big data
- patient reported
- artificial intelligence
- squamous cell