Urinary biomarkers for hepatocellular carcinoma: current knowledge for clinicians

Abstract

Hepatocellular carcinoma (HCC) is the most predominant primary liver cancer, causing many illnesses and deaths worldwide. The insidious clinical presentation, difficulty in early diagnosis, and the highly malignant nature make the prognosis of HCC extremely poor. The complex and heterogeneous pathogenesis of HCC poses significant challenges to developing therapies. Urine-based biomarkers for HCC, including diagnostic, prognostic, and monitoring markers, may be valuable supplements to current tools such as serum α-fetoprotein (AFP) and seem promising for progress in precision medicine. Herein, we reviewed the major urinary biomarkers for HCC and assessed their potential for clinical application. Molecular types, testing platforms, and methods for building multimolecule models in the included studies have shown great diversity, thus providing abundant novel tools for future clinical transformation and applications.

Keywords: Biomarkers; Diagnosis; Hepatocellular carcinoma; Multi-omics; Prognosis; Urine testing.

Fig. 1

Urinary biomarkers for HCC: the origins, testing platforms, and applications. The components of the urine samples, including proteins, nucleic acids, and metabolites, are tested and screened for biomarkers of HCC with indicative value in the diagnosis, prognosis, and treatment monitoring of HCC

Fig. 2

Representative multi-metabolite models in HCC. (A) Typical original results from testing platforms [1] H-NMR analysis of urine samples, Reprinted from Shariff et al., 2010. (B) Typical original results from testing platforms GC-MS/MS analysis of urine samples. Reprinted from Osman et al., 2017. (C) Alterations in urinary metabolic profiles from non-cirrhosis liver disease to liver cirrhosis and HCC (left) Distinct metabolomic profiles of HCC, cirrhosis, liver disease, and normal control illustrated by the PCA score plot. (right) Correlation between levels of urinary metabolites and disease categories and clinical stages of HCC. Reprinted from Ladep et al., 2014. (D) Differential metabolites and altered metabolic pathways between HCC and normal control. (left) Metabolomic alterations in HCC compared to normal controls illustrated by heatmap. (right) Major dysregulated pathways in HCC are illustrated by pathway-associated metabolite set enrichment analysis. Reprinted from Liang et al., 2016

Fig. 3

A combined analysis of urinary proteomics and tissue IHC. (A) Diagnostic power of a urinary proteomic model including 31 peptide markers for HCC, illustrated by ROC. (B) Tissue IHC confirmed the dysregulation of KLK6 and MEP1A, two proteases potentially involved in HCC progression, deduced by the N- and C-terminals of 31 differential peptides. (A–B) Reprinted from Bannaga et al., 2017

Fig. 4

Representative urinary ctDNA biomarkers for HCC. (A) Diagnostic performance of multi-ctDNA marker panel for HCC. Reprinted from Su et al., 2014. (B) A two-stage model combining ctDNAs and serum AFP in the diagnosis of HCC. Reprinted from Kim et al., 2022. (C) Improving the specificity of urinary ctDNA marker mRASSF1A by detecting the methylation at different sites. (a) Different methylation sites in the promoter and first exon of RASSF1A gene. (b) Methylation of P1 is the most specific HCC marker among the three types of mRASSF1A, with the highest AUROC. Reprinted from Jain et al., 2015

Fig. 5

Representative urinary protein biomarkers for HCC. (A) Prognostic value of urinary protein TGF-β1 in HCC patients illustrated by Kaplan–Meier plots. Reprinted from Tsai et al., 1997. (B) Prognostic value of urinary protein MMP-2 in HCC patients as illustrated by Kaplan-Meier plots. Reprinted from Suh et al., 2014. (C). Prognostic value of urinary multiprotein models in HCC patients. (left) Co-expression of S100A9 and GRN mRNA in tumor tissues. (middle) Associated elevations in urinary S100A9 and GRN proteins. (right) Prognostic value of both S100A9 and GRN amplification/gain in HCC patients. Reprinted from Huang et al., 2015

Fig. 6

Representative urinary microRNA marker for HCC. (A) The consistent upregulation of miR-93-5p in tissue with HBV-related HCC. (B) The consistent upregulation of miR-93-5p in plasma in HBV-related HCC. (C) The consistent upregulation of miR-93-5p in urine in HBV-related HCC. (D) Application of urinary miR-93-5p in the detection of HBV-related HCC. (E) Application of urinary miR-93-5p in the treatment monitoring of HBV-related HCC. (F) Application of urinary miR-93-5p in the prognosis of HBV-related HCC. (A–F) Reprinted from Zhou et al., 2022