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Published on: February 1, 2017
Urinary Circular RNA Panels to Detect HBV-Related Hepatocellular Carcinoma: A Multicenter, Large-Scale, Case-Control
Zijun Xie1, Guangping Gan2, Guanlin Zhou1
1Department of Hepatology, The Fifth People's Hospital of Ganzhou, Ganzhou, Jiangxi Province, China.
Purpose:
More than 60% of patients with hepatocellular carcinoma (HCC) do not receive curative therapeutics due to late clinical manifestations and diagnosis. The 5-year survival rate for advanced HCC is approximately 2%. However, curative therapies for HCC detected early can improve the 5-year survival rate to >70%. We aimed to identify sensitive and noninvasive biomarkers in urine for detecting HCC.
Patients And Methods:
For this study, 4 groups of individuals (healthy controls, patients with chronic hepatitis B [CHB], patients with hepatitis B virus [HBV]-induced liver cirrhosis, and patients with HBV-related HCC) were recruited, and each group was allocated to discovery, training, and validation phases. A total of 14 circular RNAs (circRNAs) were chosen as putative biomarkers in urine due to their differential expressions in HCC tissue and blood reported in related literature. Their expression levels in urine were measured by quantitative PCR (qPCR). Logistic regression models were created using a training cohort (n=312) and then validated using an independent cohort (n=741). Area under the receiver operating characteristic curve (AUC) was used to assess the diagnostic performances.
Results:
Three circRNA panels (circ_0075792, circ_0005397, and circ_0000976) were obtained with high diagnostic performances for differentiating HCC from the 3 control groups, with sensitivity >80%, specificity >90%, and AUC >0.9.
Conclusions:
Urinary circRNA panels identified and validated based on these results show desirable diagnostic performances for detecting HCC, especially early HCC. Accordingly, using these biomarkers to detect early HCC will enable patients who would have otherwise missed the curative therapeutic window to benefit from optimal treatments.
