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. 2022 Nov 15:13:929787.
doi: 10.3389/fgene.2022.929787. eCollection 2022.

Diagnostic implication of a circulating serum-based three-microRNA signature in hepatocellular carcinoma

Affiliations

Diagnostic implication of a circulating serum-based three-microRNA signature in hepatocellular carcinoma

Tahira Yousuf et al. Front Genet. .

Abstract

Owing to the diagnostic dilemma, the prognosis of hepatocellular carcinoma (HCC) remains impoverished, contributing to the globally high mortality rate. Currently, HCC diagnosis depends on the combination of imaging modalities and the measurement of serum alpha-fetoprotein (AFP) levels. Nevertheless, these conventional modalities exhibit poor performance in detecting HCC at early stages. Thus, there is a pressing need to identify novel circulating biomarkers to promote diagnostic accuracy and surveillance. Circulating miRNAs are emerging as promising diagnostic tools in screening various cancers, including HCC. However, because of heterogenous and, at times, contradictory reports, the universality of miRNAs in clinical settings remains elusive. Consequently, we proposed to explore the diagnostic potential of ten miRNAs selected on a candidate-based approach in HCC diagnosis. The expression of ten candidate miRNAs (Let-7a, miR-15a, miR-26a, miR-124, miR-126, miR-155, miR-219, miR-221, miR-222, and miR-340) was investigated in serum and tissue of 66 subjects, including 33 HCC patients and 33 healthy controls (HC), by rt-PCR. Receiver operating characteristic curve (ROC) analysis was used to determine the diagnostic accuracy of the prospective serum miRNA panel. To anticipate the potential biological roles of a three-miRNA signature, the target genes were evaluated using the Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathway. The serum and tissue expression of miRNAs (Let-7a, miR-26a, miR-124, miR-155, miR-221, miR-222, and miR-340) were differentially expressed in HCC patients (p < 0.05). The ROC analysis revealed promising diagnostic performance of Let-7a (AUC = 0.801), miR-221 (AUC = 0.786), and miR-2 (AUC = 0.758) in discriminating HCC from HC. Furthermore, in a logistic regression equation, we identified a three-miRNA panel (Let-7a, miR-221, and miR-222; AUC = 0.932) with improved diagnostic efficiency in differentiating HCC from HC. Remarkably, the combination of AFP and a three-miRNA panel offered a higher accuracy of HCC diagnosis (AUC = 0.961) than AFP alone. The functional enrichment analysis demonstrated that target genes may contribute to pathways associated with HCC and cell-cycle regulation, indicating possible crosstalk of miRNAs with HCC development. To conclude, the combined classifier of a three-miRNA panel and AFP could be indispensable circulating biomarkers for HCC diagnosis. Furthermore, targeting predicted genes may provide new therapeutic clues for the treatment of aggressive HCC.

Keywords: biomarker; circulating miRNA; diagnosis; hepatocellular carcinoma; therapeutics.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
Differential expression of ten candidate miRNAs in sera of HCC patients (n = 33) and HC (n = 33) measured by Rt-qPCR. The statistical analysis was performed using Mann–Whitney test (GraphPad Prism 5.0). The statistically significant p-value was denoted as < 0.05.
FIGURE 2
FIGURE 2
Mean log10 expression of ten candidate miRNAs in tumor of HCC patients (n = 33) compared to paired peritumor tissue (n = 33) determined by Rt-qPCR. The statistical analysis was performed using Mann–Whitney test (GraphPad Prism 5.0). The statistically significant p-value was denoted as < 0.05. NS; non-significant.
FIGURE 3
FIGURE 3
ROC analysis for serum (A) Let-7a, (B) miR-26a, (C) miR-124, (D) miR-126, (E) miR-155, (F) miR-221, (G) miR-222, (H) miR-219, and (I) miR-340 in HCC patients. The statistical analysis was done using GraphPad Prism 5.0.
FIGURE 4
FIGURE 4
ROC analysis based on logit model for serum (A) AFP alone, (B) three-miRNA panel alone, and (C) combination of three-miRNA with AFP in discriminating HCC from. HC. The statistical analysis was done using GraphPad Prism 5.0.
FIGURE 5
FIGURE 5
Differential serum expression levels of (A) Let-7a, (B) miR-221, and (C) miR-222 in non-B/C (non-hepatitis B and non-hepatitis C), CHB (chronic hepatitis B), and CHC (chronic hepatitis C) groups of patients compared to peritumor tissue were measured by rt-qPCR. The statistical analysis was done using Mann–Whitney test (GraphPad Prism 5.0). The statistically significant p-value was denoted as < 0.05. NS; non-significant.
FIGURE 6
FIGURE 6
Differential tissue expression of (A) Let-7a, (B) miR-221, and (C) miR-222 in non-B/C (non-hepatitis B and non-hepatitis C), CHB (chronic hepatitis B), and CHC (chronic hepatitis C) groups compared to peritumor tissue were measured by rt-qPCR. The statistical analysis was done using Mann–Whitney test (GraphPad Prism 5.0). The statistically significant p-value was denoted as < 0.05. NS; non-significant.
FIGURE 7
FIGURE 7
Venn diagram for target genes prediction for miRNA signature.
FIGURE 8
FIGURE 8
Network map of miRNA target genes.
FIGURE 9
FIGURE 9
GO annotation for the predicted target genes of potential differentially expressed miRNAs. (A) Top ten enriched biological process (BP) for target genes of miRNAs. (B) Top ten enriched cellular component (CC) for target genes of miRNAs. (C) Top ten enriched molecular function (MF) for target genes of miRNAs.
FIGURE 10
FIGURE 10
Functional enrichment analysis of target genes associated with HCC. (A). Dotplot of the KEGG signal pathway showing the counts of genes. Functional enrichment analysis of target genes associated with HCC. (B) Reactome of the KEGG signal pathway showing the “pathway–gene” network.

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