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.
Submitted November 4, 2023; final revision received May 19, 2024; accepted for publication July 23, 2024. Published online December 27, 2024.
Z. Xie, G. Gan, and G. Zhou contributed equally to this work and are co–first authors.
Author contributions: Conceptualization: Xie, Zhou, Zeng. Data curation: Xie, Zhou, Jianhong Zhang, Zeng. Formal analysis: Xie, Zhou, Zhang, Zeng. Funding acquisition: Xie, Zhou. Methodology: Xie, Zeng. Investigation: Gan, Jiabao Zhang, Lin. Resources: Xie, Gan, Zhou, Jianhong Zhang. Software: All authors. Visualization: All authors. Supervision: Zhou, Zeng. Writing—original draft: Xie, Gan, Zhou, Zeng. Writing—review & editing: Xie, Gan, Zhou, Zeng.
Disclosures: The authors have disclosed that they have not received any financial considerations from any person or organization to support the preparation, analysis, results, or discussion of this article.
Funding: This work was supported by funding from National Natural Science Foundation of China (82203395).
Supplementary material: Supplementary material associated with this article is available online at https://doi.org/10.6004/jnccn.2024.7058. The supplementary material has been supplied by the author(s) and appears in its originally submitted form. It has not been edited or vetted by JNCCN. All contents and opinions are solely those of the author. Any comments or questions related to the supplementary materials should be directed to the corresponding author.