Prognostic MicroRNA Panel for HCV-Associated HCC: Integrating Computational Biology and Clinical Validation

doi:10.21203/rs.3.rs-696945/v1

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Prognostic MicroRNA Panel for HCV-Associated HCC: Integrating Computational Biology and Clinical Validation

https://doi.org/10.21203/rs.3.rs-696945/v1

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published 21 Jun, 2022

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Background

Early detection of hepatocellular carcinoma (HCC) will reduce morbidity and mortality rates of this poorly diagnosed widely-spread disease. Dysregulation in microRNA (miRNAs) expression is associated with HCC progression.

Methods

The objective is to identify a panel of differentially expressed miRNAs (DE-miRNAs) to enhance HCC early prediction in hepatitis C virus (HCV) infected patients. Candidate miRNAs were selected using bioinformatic analysis of microarray and RNA-sequencing datasets, resulting in nine DE- miRNAs (miR-142, miR-150, miR-183, miR-199a, miR-215, miR-217, miR-224, miR-424 and miR-3607). Their expressions were validated in the serum of 44 healthy individuals, 62 non-cirrhotic HCV patients, 67 cirrhotic-HCV and 72 HCV-associated HCC patients using real time PCR (qPCR).

Results

There was a significant increase in serum concentrations of the nine-candidate miRNAs in HCC and HCV patients relative to healthy individuals. MiR-424, miR-199a, miR-142, and miR-224 expressions were significantly altered in HCC compared to non-cirrhotic patients. While miR-199a and miR-183 showed differential expression in cirrhotic relative to non-cirrhotic patients. A panel of 5 miRNAs improved sensitivity and specificity of HCC detection to 100% and 95.12% relative to healthy controls. Distinguishing HCC from HCV-treated patients was achieved by 70.8% sensitivity and 61.9% specificity using the combined panel, compared to alpha-fetoprotein (51.4% sensitivity and 60.67% specificity).

Conclusion

MiR-142, miR-183, miR-199a, miR-224 and miR-424 novel panel could serve as non-invasive biomarker for HCC early prediction in chronic HCV patients.

General Biochemistry

Hepatocellular carcinoma

Hepatitis C virus

MicroRNAs

Serum biomarker

HCC prognosis

Hepatocellular carcinoma (HCC) is the most predominant type of primary liver cancers, encompasses 75% − 85% of liver cancer cases [1]. It is the sixth most prevalent cause of cancer [2, 3] and the third leading cause of cancer mortality worldwide [4, 5]. An estimate of 905,577 (4.7%) newly diagnosed HCC patients, and around 830,130 death cases (8.3%) were globally recorded in 2020 [6–9]. Egypt was ranked the third in Africa and the fifteenth globally in HCC prevalence and the recorded cases were doubled over a decade, resulting in more challenging health problem [10]. One of the most predominant risk factors for HCC development and progression is chronic infection with hepatitis C virus (HCV). Egypt has an exceptional high prevalence of HCV worldwide [11]. According to the Egyptian demographic health survey in 2015, almost 6 million Egyptians in the age group of 15–59 were chronically infected with HCV. Extensive research and huge efforts are exerted to overcome the viral spread, through national eradication programs and to explore novel anti-HCV therapies.

Diagnosis of HCC has remarkably changed over the previous decade to switch from invasive techniques as angiography and tissue biopsy to non-invasive imaging procedures as ultrasound (US), computed tomography (CT) and magnetic resonance imaging (MRI), in addition to serological testing using alpha-fetoprotein (AFP) [12–14]. Although AFP is considered the most extensively used serum biomarker for HCC, it is characterized by low sensitivity and specificity in the determination of HCC lesions even at low-level cutoffs (10–20 ng/mL) [15]. It has 25% sensitivity for the detection of small tumors (less than 3 cm), and the sensitivity could reach 50% for focal lesion (FL) larger than 3 cm [16]. AFP level increases in some benign liver diseases as liver cirrhosis (LC) and hepatitis [17], and normal serum AFP level is detected in 15–30% of advanced HCC cases [18]. Therefore, the Practice Guideline Committee of the American Association for the Study of Liver Disease (AASLD) doesn’t recommend the use of AFP in the early detection of HCC [19]. Currently, there is no sole serum biomarker; including immunoglobulin M (IgM) immunocomplexes, that could provide accurate diagnosis for HCC. Combination of multiple biomarkers could improve the efficiency and the sensitivity of detection [20].

MicroRNAs (miRNAs) are single-stranded, short (approximately 22 nucleotides), non-coding RNAs, which are associated with argonaute family protein. MiRNAs regulate gene expression through post-transcriptional gene silencing [21]. Generally, the majority of the protein-coding genes are under the influence of miRNAs [22, 23], thus deregulation in miRNAs expression is directly corelated with many physiological disorders such as cancer [24]. Numerous profiling studies have addressed miRNAs expression in HCC, and marked changes in miRNAs expression were recorded in human HCC tissues compared to non-tumorous liver tissues [25, 26]. Therefore, it is suggested that disordered miRNAs biogenesis triggers the currently existing miRNAs deregulation to further enhance HCC and metastasis [27–30]. The non-coding RNA expression levels are correlated with tumor size, tumor stage, cirrhotic state, and patients’ overall survival [31–36]. The abundance of stable miRNAs in the circulation (serum and plasma) of both healthy individuals and HCC patients with different expression patterns, suggested a promising role for these miRNAs in the prognosis of liver carcinogenesis [37–39].

In the current research, we hypothesized that a computationally assigned miRNAs possess differential expression between HCC patients and HCV-infected subjects. Through using minimally invasive serum samples, miRNA panel is capable of predicting HCC in chronic HCV patients. Thus, the current study suggested a novel miRNAs panel that potentially act as prognostic biomarker for HCC early detection.

Bioinformatic analysis

In order to identify the target miRNAs panel, bioinformatic analysis was performed on non-coding RNA microarray dataset (GSE40744) deposited on the National Center for Biotechnology Information – Gene expression omnibus (NCBI-GEO) repository [40] using 18 HCV cirrhotic samples and 9 HCV-HCC samples. MiRNA expression profiling was analyzed on R software (R x64 v.3.6.2). Further analysis of RNA sequencing data deposited on the cancer genome atlas (TCGA) was performed [41]. Data downloaded from TGCA portal represents RNA expression in HCC patients from different etiologies. HCC expression data with only HCV etiology was filtered using R software to obtain smaller dataset composed of 31 samples and was further processed for downstream analysis. Filtering criteria was applied to retain the miRNAs with expression more than 10 reads or FPKM (Fragments Per Kilobase Million) in ≥ 85% of the dataset. After that differential expression between HCV and HCC samples was performed using TCGABiolinks R package.

Patients and samples

This study included 245 individuals; 44 healthy volunteers, 62 HCV non-cirrhotic, 67 HCV cirrhotic and 72 HCC post HCV infection patients. The three diseased groups include patients chronically infected with HCV, without receiving any treatment for HCV (HCV treatment naïve) or HCV treated patients, who have achieved sustained virological response (HCV-SVR), which is guaranteed by the absence of HCV particles in the patient’s blood 12 weeks after cessation of HCV therapy. Samples were collected from the National Hepatology and Tropical Medicine Research Institute (NHTMRI) during the period from July to December 2019. Patients’ health history records were collected, with full clinical and ultrasonographic examinations.

Inclusion and exclusion criteria

Healthy individuals with normal liver functions, no history of viral hepatitis or any liver disease, general good health condition with no major disorders in kidney, heart, lungs or other vital organs were included in the study. HCV patients with positive circulating anti-HCV antibodies were classified in to two sub-groups based on the presence or the absence of cirrhosis. Diagnosis of HCV based upon ultrasonography and blood examination (complete blood count, liver function tests as aspartate aminotransferase (AST), alanine aminotransferase (ALT), albumin (ALB), total bilirubin (T. Bil) and direct bilirubin (D. Bil). Degree of fibrosis and severity of the liver condition in chronic hepatitis C (CHC) patients were assessed according to non-invasive AST to platelets ratio index (APRI), fibrosis-4 (FIB-4), AST to ALT ratio (AAR) indices and Child-Turcotte-Pugh (CTP) score. However, for HCV-HCC patients, diagnosis primarily relied on abdominal ultrasonography and triphasic CT scan or MRI for examination of the FL, in addition to alpha-fetoprotein (AFP) blood levels. Barcelona clinic liver cancer (BCLC) staging system was used for classification of HCC patients based on tumor stage, cancer-related symptoms, and serological liver function tests [42] HCC patients enrolled in the study were classified in to BCLC stages 0, A and B.

HCV patients who had other viral (e.g., hepatitis B virus) or non-viral liver disease (e.g., alcoholic liver disease or non-alcoholic fatty liver) in conjunction with HCV were excluded from the study. HCC patients with other liver disorders as hemangioma or cholangiocarcinoma were disqualified. In addition to excluding HCC patients with extrahepatic metastatic cancer, another type of cancer, or other comorbid condition such as kidney or heart disorders.

Sampling and serum preparation

Five mL of blood was withdrawn from each patient into labeled disposable serum collection tube (global roll gel and clot activator tube). For complete clotting, blood samples were kept for one hour at room temperature (15–25°C), then samples were processed for serum separation following miRNeasy serum/plasma handbook – Qiagen – 2012.

Liver function testing and HCV antibodies testing

All samples were subjected to qualitative detection of HCV antibodies in the serum using HCV rapid test cassette (ACON, San Diego, CA - Cat. No. L031-10341). Quantitative determination of liver function biomarkers was assessed for all samples. ALT, ASL and albumin were measured using (SPINREACT kit, Barcelona, Spain - Cat. No. 41283, 41273, 1001022, respectively) following the manufacturer’s protocols. While determination of serum total and direct bilirubin were done using (RANDOX kit, UK - Cat. No. BR 412) following the manufacturer’s protocol. The quantitative determination of AFP was done for diseased samples only on fully automated Cobas analyzer using Elecsys AFP (Roche, Mississauga, Canada - Cat. No. 04481798-190).

RNA isolation and cDNA synthesis

Total RNA was isolated using miRNeasy minikit (Cat. No. 217004 - Qiagen, Germantown, MD) following the manufacturer’s protocol miRNeasy serum/plasma handbook (February 2012) with minor modification. Two hundred µL of serum were used to isolated RNA. At the pre-last step, RNeasy MinElute spin columns were left with the lids opened for 5 min. on the bench top to air dry instead of centrifugation, then total RNA was eluted in 14 µL RNase free water. Reverse transcription (RT) was performed using miScript II RT kit (Qiagen, Germantown, MD - Cat. No. 218161), following the manufacturer’s protocol of miScript PCR system handbook using RNA volume equivalent to 50 ng.

Real time PCR amplification of miRNAs

qPCR of the target miRNAs was performed on applied biosystems 7500 real time PCR machine (Themo Fisher Scientific, Foster city, CA) using the miScript SYBR Green PCR kit (Qiagen, Germantown, MD - Cat. no. 218073) and miScript primer assays (SNORD 68 as a housekeeping gene and specific primers for the target miRNAs - Qiagen, Germantown, MD - Cat. no. 218300), following the manufacturer’s protocol of miScript PCR system handbook, using 1µL cDNA (complementary deoxyribonucleic acid) (concentration: 2.5 ng/µL) in 10 µL total reaction volume. Primers sequences are as the following:

hsa-miR-142-3p; 5'UGUAGUGUUUCCUACUUUAUGGA3',

hsa-miR-150-5p; 5'UCUCCCAACCCUUGUACCAGUG3',

hsa-miR-183-5p; 5'UAUGGCACUGGUAGAAUUCACU3',

hsa-miR-199a-3p; 3'ACAGUAGUCUGCACAUUGGUUA5',

hsa-miR-215-5p; 5'AUGACCUAUGAAUUGACAGAC3',

hsa-miR-217-5p, 5'UACUGCAUCAGGAACUGAUUGGA3',

hsa-miR-224-5p; 5'UCAAGUCACUAGUGGUUCCGUUUAG3',

hsa-miR-424-5p; 5'CAGCAGCAAUUCAUGUUUUGAA3' and

hsa-miR-3607-5p; 5'GCAUGUGAUGAAGCAAAUCAGU3'.

Each experiment was performed using two technical replicates. The sequences of miR-199a-3p and 199b-3p on miRbase were similar, and miR-199a-3p primer was further used in qPCR amplification.

Data analysis

Fold change of expression was calculated using the formula 2^−∆∆Ct [43]. Handling of non-detects (undetermined values) in qPCR results was done through excluding all samples that failed to have true amplification curve. However, if true amplification curve was recorded, non-detects were replaced by the maximum possible Cycle threshold (Ct) value (Ct = 40). Similarly, Ct values ≥ 40 were replaced by Ct = 40 [44–46].

Statistics

Statistical analysis was done using IBM SPSS statistical data editor - version 25 (SPSS Inc., Chicago, IL). MDCalc medical calculator software (version 15.0 for Microsoft Windows, Ostend, Belgium) was used for the calculation of fibrosis scoring indices. Analysis was done using Mann-Whitney U and Kruskal-Wallis H for non-normally distributed data and ANOVA (analysis of variance) for normally distributed quantitative data. Normality testing was performed using Anderson-Darling test and Kolmogorov-Smirnov tests using GraphPad Prism (version 8.4.3). However, analysis of qualitative data was performed using Chi-square test. Spearman’s rank correlation was used to study the correlation between studied miRNAs in different categories. Figures were designed using SPSS and GraphPad prism. For microarray dataset analysis, P-value was corrected using Bonferroni method. All statistical tests were two-tailed, and P-value ≤ 0.05 was considered statistically significant.

Microarray bioinformatic analysis

Analysis for microarray datasets was performed using R software. Filtering criterion was set to log fold change cutoff of 1.5 and adjusted P-value of 0.01 (Fig. 1). Twenty-two DE-miRNAs between cirrhotic HCV and HCC groups were generated, 4 miRNAs were upregulated and 18 were downregulated in HCC group (Log fold change and statistical significance are presented in supplementary table 1).

RNA sequencing bioinformatic analysis

MiRNAs differential expression analysis was conducted using false discovery rate (FDR) cutoff of 0.05, and fold change cutoff of 1 (Fig. 2). Nine significant DE-miRNAs were obtained, 3 were upregulated (miR-217, miR-224 and miR-183) and 6 were downregulated (miR-142, miR-199b, miR-150, miR-424, miR-215 and miR-3607) (Log fold change and FDR are shown in supplementary table 2).

Description of the study population

Demographic and clinical data records are summarized in Tables 1 and Fig. 3. No significant difference was observed in gender distribution between healthy control and HCC group (P = 0.5294), and between non-cirrhotic and cirrhotic groups (P = 0.1508). Moreover, no statistical difference was observed in age distribution among the healthy individuals, cirrhotic and HCC groups, excluding the non-cirrhotic group (P = 0.0662). Differences in serum levels of liver biomarkers; ALB, ALT, AST, T. Bil, D. Bil and AFP were statistically significant among the study groups.

Different indices as APRI, FIB-4 and AAR were used to assess the degree of fibrosis and cirrhosis in the study groups (Table 2, and Fig. 4). Significant fibrosis was calculated in 2.1%, 16.9% and 23.1% of non-cirrhotic, cirrhotic and HCC patients, respectively using APRI cutoff score > 1.5. However, applying FIB-4 cutoff score >3.27 predicated higher percentages of advanced fibrosis in 10.8%, 36%, and 49.2% of non-cirrhotic, cirrhotic and HCC patients, respectively. Moreover, AAR > 1 suggested the presence of cirrhosis in 75.4%, 73.8% and 84.3% of the three diseased groups, respectively. Results obtained from the non-invasive indices indicated the importance of combining different indices in addition to imaging techniques as US for precise determination of the degree of fibrosis and cirrhosis.

Tumor features and classification of patients among the different disease stages were assessed (Table 3). Patients were classified into Child A, B, and C in 69.7%, 24.2% and 6.1% of the cirrhotic patients, in addition to 78.3%, 20.3% and 1.4% of the HCC patients, respectively according to CTP score. While according to BCLC staging systems, the patients were classified into three groups: very early (stage 0; 17.9%), early (stage A; 64.2%), and intermediate stage (stage B; 17.9%). Furthermore, marked slight to moderate ascites was reported in 22.4% of the cirrhotic patients and 22.2% of the HCC patients. Single FL was diagnosed in 63.9% of the HCC patients, while 36.1% had multiple FLs. Additionally, 40.7% of the HCC patients has FL ≤ 3cm in diameter.

MiRNAs serum signature in the study groups

Fold changes of the DE-miRNAs among the study groups were represented in Fig. 5. The results of this study revealed that serum levels of the nine candidate miRNAs were differentially expressed in HCV and HCC patients in comparison to healthy individuals with high statistical significance (P-value < 0.0001) using Mann-Whitney U statistical test (Table 4). However, only miR-424 serum level showed statistical significance upon comparing HCV patients with those having HCC. The expression levels of miR-424, miR-199a, miR-142, and miR-224 were significantly altered in HCC patients compared to the non-cirrhotic subjects (P < 0.0001, P = 0.0001, P = 0.023, and P = 0.027, respectively). Whereas, miR-199a and miR-183 showed difference in differential expression between HCV cirrhotic and non-cirrhotic patients (P = 0.012 and P = 0.036, respectively) (Table 5).

Correlation between the studied miRNAs

Spearman’s correlation test was performed to investigate the correlation between the fold change of expression of each individual miRNA and the other miRNAs. Positive correlation was recorded between the expression of all miRNAs among the study groups, with high statistical significance (P < 0.0001) (Table 6). Moreover, the correlation between the miRNAs under study and some clinicopathological characteristics was performed. MiR-183, miR-199a and miR-215 were positively correlated with the age of the patients (P = 0.007, 0.037 and 0.026, respectively). While only miR-142 and miR-217 were positively correlated with gender (P = 0.036 and 0.001, respectively). Whereas, the whole panel of the nine miRNAs was positively correlated with the cirrhotic liver conditions and D. Bil (P < 0.0001), ALT and AST levels (P < 0.001). On the other hand, only miR-150 showed statistical significance negative correlation with T. Bil levels (P = 0.003) and with ALB levels for most of the targets (P < 0.001). Moreover, the association between the tumor characteristics and the miRNAs expression was investigated. Only miR-199a showed significant positive correlation with the AFP levels (P = 0.04). While for CTP score, miR-150 was negatively correlated (P = 0.009), while miR-217 and miR-3607 were positively correlated (P = 0.02, P = 0.032, respectively). Interestingly, no significant correlation was reported with BCLC staging.

Diagnostic potential of the DE-miRNAs in HCC patient compared to healthy individuals

Receiver operating characteristic (ROC) curves were plotted for the candidate miRNAs to discriminate HCC patients from healthy controls (Fig. 6 and Table 7). The Area under the curve (AUC) values were 0.993, 0.972, 0.968, 0.958, 0.957, 0.933, 0.928, 0.921, 0.868 corresponding to miR-424, miR-142, miR-199a, miR-215, miR-183, miR-217, miR-150, miR-224, and miR-3607, respectively with high statistical significance (P-value < 0.0001). All of the targets showed high sensitivity (ranging from 100% to 80.77%) and accuracy (ranging from 95.7% to 81.72%) for prediction of HCC patients. A combined panel of 5 miRNAs; where the whole panel is considered positive if 5 out of the 9 miRNAs tested positive, increased the sensitivity, specificity and accuracy of detection to 100%, 95.12%, and 97.85%, respectively (P-value < 0.0001).

Diagnostic potential of the DE-miRNAs in HCV patient compared to healthy individuals

To identify HCV patients from healthy individuals, ROC curves were drawn for the candidate miRNAs (Fig. 7 and Table 7). The AUC values were 0.941, 0.94, 0.927, 0.919, 0.913, 0.903, 0.882, 0.863, and 0.824 corresponding to miR-183, miR-424, miR-217, miR-199a, miR-215, miR-142, miR-150, miR-224, and miR-3607, respectively with high statistical significance (P-value < 0.0001). All of the targets showed high sensitivity (ranging from 91.58% to 80%) and accuracy (ranging from 90.44% to 79.41%) for discrimination of HCV patients. Combined panel of 5 miRNAs improved overall sensitivity, specificity and accuracy of detection to 90.53%, 85.37%, and 88.97%, respectively (P-value < 0.0001).

Diagnostic potential of the DE-miRNAs in HCC patient compared to non-HCC individuals

In a comparison between HCC patients with others without malignancy (healthy individuals, HCV non-cirrhotic and HCV cirrhotic patients), AUC were calculated and eight potential miRNAs had statistically significant values (Fig. 8 and Table 7). MiR-424, miR-199a, miR-150, miR-215, miR-224, miR-142, miR-183, and miR-3607 had AUC values of 761, 0.724, 0.706, 0.695, 0.691, 0.69, 0.664, and 0.664, respectively (P-value < 0.0001). Reasonable sensitivities (80.77% to 61.54%) and accuracies (65.96% to 57.98%) were recorded for the different targets. Using a combined panel of 5 miRNAs resulted in 80.77% sensitivity and 61.03% specificity for HCC detection (P -value < 0.0001).

Diagnostic potential of the DE-miRNAs in HCC (SVR / treatment naïve) patient compared to non-HCC (SVR / treatment naïve) patients

The overall ROC analysis measurements including AUC were improved after classifying the study groups in to SVR (Fig. 9 and Table 7) treatment naïve (Fig. 10 and Table 7). The highest AUC for both HCC (SVR) group HCC (treatment naïve) group was recorded for miR-424 (0.8, and 0.835, respectively) (P-value < 0.0001). Similarly, 5-miRNAs combined panel increased the overall sensitivity, specificity and accuracy for HCC (SVR) patients’ prognosis to (83.33%, 63.73% and 67.46%, respectively). While combining 6 miRNAs in one panel, improved the calculated measurements in HCC (treatment naïve patients) (sensitivity 89.29%, specificity 72.6% and accuracy 77.23%).

Diagnostic potential of the DE-miRNAs in HCC patients compared to patients with HCV

In order to develop potential biomarker to differentiate HCC patients from HCV subjects, ROC analysis was performed and resulted in three statistically significant targets; miR-424, miR-199a, and miR-150, with P-values 0.001, 0.018, and 0.028, respectively (Fig. 11 and Table 7). The highest calculations were obtained for miR-424. Comparison between miRNA-424 and AFP (the current HCC serum biomarker) resulted in comparable sensitivities, 63.46% for the former and 62.32% for the later. Although AFP specificity and accuracy (64.57% and 63.78%) were better than those for miR-424 (57.9% and 59.86%). However, the choice of a combined panel of 2 miRNAs with or without AFP didn’t provide significant improvement in the ROC analysis measurements.

Diagnostic potential of the DE-miRNAs in HCC (SVR) patients compared to patients with HCV (SVR)

Similarly, ROC curves were constructed to determine the best AUC for patients with HCC (SVR) compared to HCV (SVR) (Fig. 12, and Table 7). Three miRNAs had statistically significant results (miR-424, miR142, and miR-3607) with P-values 0.01, 0.018, and 0.014, respectively. The ROC measurements obtained (sensitivities: 66.67%, 66.67%, and 70.83%, accuracies: 62.1%, 68.97%, 58.62%, respectively) were remarkably higher than sensitivity and accuracy recorded for AFP (51.43% and 58.1%, respectively). Using combined panel of 2 miRNAs enhanced the sensitivity, specificity and accuracy of detection (70.83%, 61.9%, and 64.37%; respectively). Addition of AFP biomarker to the combined panel improved the overall sensitivity (70.83%), specificity (73%) and accuracy (72.4%). However, ROC analysis to discriminate patients with HCC (treatment naïve) from HCV (treatment naïve) patients didn’t show any statistically significant AUC for any of the targets.

HCC is considered one of the widely spread and most aggressive malignancies worldwide, that represents more than 80% of primary liver cancers [47]. HCV virus plays a major role in the in hepatic carcinogenicity [48] that accounts for 25% of the HCC cases worldwide [49]. Generally, poor prognosis of HCC and low survival rates drive the global efforts to identify novel biomarkers for early detection of the disease [50]. Thus, the aim of this research study was to identify miRNAs panel to serve as a non-invasive biomarker for prediction of HCC in chronic HCV patients.

The results obtained from our bioinformatics analysis highlighted four overlapping miRNAs sharing the same expression patterns between the GEO microarray and TCGA datasets. MiR-224 was upregulated, while miR-150, miR-199b and miR-424 were downregulated in liver tissues. In addition to these 4 non-coding RNAs, analysis of TGCA datasets resulted in 5 DE-miRNAs; 2 upregulated miRNAs (miR-183 and miR-217) and 3 downregulated miRNAs (miR-142, miR-215, and miR-3607) in HCC liver tissues. The 9 candidate miRNAs obtained from TCGA dataset analysis were chosen for further validation of their serum expression using qPCR.

Real time PCR results in this study showed highly significant increase in serum concentration of the nine-candidate miRNAs in HCC patients and in HCV patients relative to healthy individuals (P < 0.0001). However, the expression levels of miR-424, miR-199a, miR-142, and miR-224 were significantly altered in HCC patients upon comparison with the non-cirrhotic subjects. While miR-199a and miR-183 showed differential expression in HCV cirrhotic patients relative to non-cirrhotic subjects. The only target that showed significant difference between HCV patients with LC and HCC patients was miR-424, predicting diagnostic and prognostic biomarker powers for these miRNAs in HCC.

Serum expression of miR-183, miR-215 and miR-224 were previously studied in HCC and the results were consistent with our findings. Several research studies reported a significant increase in miR-183 concentration in the serum of cirrhotic and HCC patients relative to healthy individuals. They also concluded that the sensitivity and specificity of using miR-183 as a biomarker for HCC detection were 57.9% and 76.2% in serum respectively, proposing a diagnostic potential of miR-183 in differentiating HCC patients from those with LC without malignancy [51, 52]. Additionally, serum miR-183 level in HCC patients after surgery was significantly lower compared to the expression before surgery [53], confirming that the increase in miR-183 serum levels is positively correlated with the presence of HCC FLs. Furthermore, elevated serum expression levels of miR-224 were explained in several studies [54–56]. Serum miR-224 level was reported to be corelated with AFP levels and with other serum parameters indicating liver damage, in addition to being associated with poor survival. MiR-224 increased serum expression was also correlated with the BCLC stage progression. Higher miR-224 expression was recorded in patients with BCLC stage C compared to stage B. Therefore, miR-224 concentration could be BCLC stage dependent, in addition to being a prognostic biomarker for HCC patients’ survival [57, 58]. In a previously published study conducted on the Egyptian population, overexpression of miR-224 and miR-215 in serum of HCV-HCC Egyptian patients in comparison to healthy individuals was detected using qPCR [59, 60]. Although the increase in miR-215 serum levels failed to distinguish between HCV, HBV and HCC patients, as its expression was significantly increased in all groups relative to healthy controls [61]. However, multiple recent studies relied on the serum miR-215 expression levels to differentiate between patients with fibrosis and those with LC, and between HCC patients and other hepatic diseases’ patients. It was also observed that miR-215 expression was positively correlated with HCV viral load [62, 63] suggesting that miR-215 might act as potential prognostic biomarker for liver disease.

Furthermore, contradicting results were reported with regard to the serum expression of miR-150, miR-199a and miR-424 in HCC. In a previously published study, the increase in miR-150 serum levels in HCV-HCC patients in African Americans and Caucasians relative to healthy controls were comparable to our findings. Also, a significant increase in the serum levels was observed in HCV cirrhotic group upon comparison to healthy individuals in both ethnic groups [64]. However, analysis of miR-150 serum expression in HCV-HCC Egyptian patients in a different study recorded a significant decrease in serum miR-150 levels in HCC patients compared to healthy controls and to non-cirrhotic HCV patients, and no significant difference was found between HCC and cirrhotic HCV patients. Moreover, the expression levels were lower in HCV cirrhotic patients compared to non-cirrhotic individuals. These results contradict our findings, as miR-150 serum expression was significantly higher in HCC group in comparison to controls, non-cirrhotic and cirrhotic subjects. Whereas, no significant difference was observed neither between non-cirrhotic and cirrhotic patients or between cirrhotic and HCC individuals [65]. Although few published studies addressed the regulation of miR-424 serum expression in HCC, their results were inconsistent. Significant increase in serum miR-424-3p levels in HCC patients when compared to healthy control was reported in a previous study [66]. Although, analysis of the miR-424 serum expression using qPCR showed that its expression was reduced in HCC patients compared to healthy individuals in another. The decreased expression was also correlated with serum AFP levels, with vein invasion and with the progression of the tumor, nodes and metastasis [67]. Interestingly, qPCR results in a third study failed to find a significant difference in serum miR-424 levels in HCC patients relative to healthy controls [68]. As for miR-199a, multiple research studies performed on Egyptian patients concluded serum miR-199a overexpression in severe chronic hepatic inflammation and in HCV genotype-4 patients, especially in late-stage fibrosis compared to early fibrotic stages. This may be due to the induced inflammation triggered by HCV to the hepatocytes, concluding that members of miR-199 family are linked to liver fibrosis progression in HCV patients [60, 69, 70]. On the other hand, other studies reported a reduction in miR-199a expression in the serum of HCC patients [71–75]. It was observed that the decrease in miR-199a serum expression was inversely proportional to apoptotic markers such as programmed cell death protein 4 and cytochrome C [72]. Additionally, the antiviral activity of miR-199a against HCV was proved, and the mechanism of inhibition is attributed to the interaction between miR-199a and the step loop II region at the 5’UTR of HCV, resulting in inhibition of HCV replication. Thus, based on their findings, increased expression of miR-199a was associated with cell cycle arrest, suppression of cellular invasion, improves sensitivity to chemotherapy, and hindering HCV genome replication [76–78]. Therefore, we suggest further analysis to the differential expression of miR-150, miR-424 and miR-199a serum levels in HCV and HCC patients.

To the best of our knowledge, this research is the first to report circulatory differential expression of miR-142, miR-217 and miR-3607 in serum of HCV and HCC patients. In a previous study, miR-142-5p serum levels were found to be inversely correlated with the serum albumin levels in HCV-HCC patients, although the differential expression wasn’t reported [79]. It is worth mentioning that 20% of our qPCR signals in serum miR-217 amplification were undetectable. Thus, these samples were normalized by replacing their Ct values by the maximum allowed number of cycles = 40 [44], in order to avoid losing a significant number of samples that showed true amplification with other targets in the data analysis and to have a consistent samples number for all miRNAs. Therefore, based on the scarce data on circulatory miR-217 expression in literature, and on our findings, we suggest that miR-217 serum differential expression might not affect the regulation of HCC progression.

The polygenic nature of HCC and the complexity of serum as a detection platform favored the use of multiple biomarkers approach over a single one [80]. In the current study, we proposed a novel miRNAs study panel that could play a pivotal role in HCC detection. A combined panel of 5 miRNAs dramatically increased the sensitivity and specificity of distinguishing HCC patients from healthy individuals to 100% and 95.12% respectively, with 97.85% detection accuracy. A similar trend was obtained in identifying HCV patients to reach 90.5% sensitivity, 85.37% specificity and 89% accuracy upon relying on a 5-miRNAs-combined panel. The success chance of using multiple miRNAs in a single panel was manifested in discriminating HCC patients from non-HCC individuals in both the SVR and the treatment naïve groups. A combined 5 miRNAs panel enhanced the sensitivity and specificity of detection to 83.3% and 63.73% respectively in the SVR groups. Whereas 6-miRNAs combined panel provided better results in the treatment naïve patients (89.3% sensitivity and 72.6% specificity). Moreover, the combined panel was successfully used to assess the accuracy of distinguishing HCC from HCV patients. Only three (miRNAs 424, miR-199a and miR-150) showed statistically significant ROC AUC values. A combined panel of 2 miRNAs didn’t provide an improvement in the detection sensitivity and specificity (61.54% and 56.84%) compared to AFP (62.3% and 64.57%). However, in the comparison of HCV and HCC patients belonging to the SVR groups, the AUC of miR-424, miR-142 and miR-3607 were statistically significant. Interestingly, 2-miRNAs combined panel ameliorated the sensitivity and specificity in the SVR group to 70.83% and 61.9% respectively, in comparison to AFP results (51.43% and 60.67% respectively). Nevertheless, the inclusion of AFP to the miRNAs combined panel improved the sensitivity and specificity of detection in the HCC-SVR patients to 70.83% and 73.02%, respectively, although no remarkable difference was observed in the HCC unclassified group.

Finally, we believe that the choice of the candidate miRNAs within the panel provided a multifunctional tool for HCC early detection. MiR-199a could act as a marker for liver fibrosis progression, while miR-183 might be linked to HCC onset and progression. However, predication for HCC poor prognosis could be achieved via miR-224, whereas, miR-424 might provide a prediction tool for tumor recurrence, suggesting potential prognostic biomarker ability for both miRNAs. Moreover, the inclusion of miR-142 and miR-150 might provide information about the fibrosis and cirrhosis progression in HCV patients. Thus, the use of the miRNAs combined panel will facilitate and improve HCC prognosis than a conventional single biomarker approach.

Nearly 80% of HCC cases are untreatable owing to the presentation of the patients at their advanced stages. Consequently, the determination of a non-invasive biomarker would hasten and promote the diagnosis of HCC, decrease the risks of surgical intervention, and permit the non-invasive monitoring resulting in better opportunities of therapeutic options. In this study, the serum differential expression of nine miRNAs (miR-142, miR-150, miR-183, miR-199a, miR-215, miR-217, miR-224, miR-424 and miR-3607) were significantly overexpressed in HCC and HCV patients compared to healthy individuals. Using combined panel of five miRNAs (miR-142, miR-183, miR-199a, miR-224 and miR-424) improved the overall sensitivity and specificity of HCC detection, and could serve as an early prediction tool for HCC in chronic HCV patients.

AAR: AST to ALT ratio

AFP: Alpha fetoprotein

ALT: Alanine aminotransferase

AASLD: American Association for the Study of Liver Disease

ANOVA: Analysis of variance

APRI: Aspartate aminotransferase to platelet ratio index

AST: Aspartate aminotransferase

AUC: Area under the curve

BCLC: Barcelona clinic liver cancer

cDNA: Complementary deoxyribonucleic acid

CHC: Chronic hepatitis C

CT: Computed Tomography

Ct: Cycle threshold

CTP: Child-Turcotte-Pugh

D. Bil: Direct bilirubin

DE-miRNAs: Differentially expressed miRNAs

FIB-4: Fibrosis-4

FL: Focal lesion

FPKM: Fragments Per Kilobase Million

GEO: Gene expression omnibus

HBV: Hepatitis B virus

HCC: Hepatocellular carcinoma

HCV: Hepatitis C virus

IRB: Institutional Review Board

LC: Liver cirrhosis

miRNA: Micro ribonucleic acid

MRI: Magnetic resonance imaging

NCBI: National center for biotechnology information

NHTMRI: National Hepatology and Tropical Medicine Research Institute

NPV: Negative predictive value

PCR: Polymerase chain reaction

PS: Performance status

PPV: Positive predictive value

qPCR: Quantitative real time polymerase chain reaction

ROC: Receiver operating characteristic

RT: Reverse transcription

SEM: Standard error of mean

SVR: Sustained virological response

T. Bil: Total bilirubin

TCGA: The cancer genome atlas

US: Ultra sound.

Ethics approval and consent to participate

This study protocol was reviewed and approved the IRB of NHTMRI (serial number 25-2019) and IRB of the American University in Cairo (case number 2018-2019-060) in accordance with the Helsinki Declaration, by applying good clinical practice principles. All human participants have given written informed consent to participate in the study.

Consent for publication

Not applicable.

Availability of data and materials

The datasets analysed during the current study are available in the GEO-NCBI repository, [https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE40744], and in TCGA repository, [https://www.cancer.gov/about-nci/organization/ccg/research/structural-genomics/tcga/studied-cancers/liver].

Competing interests

The authors declare that they have no competing interests.

Funding

This research was supported by an AUC graduate research grant funding to AMD and AUC research grant funding to HA and AA.

Authors' Contributions

AMD and AA designed research studies. AMD, HMA and SS performed experiments. MA performed the computational analysis. AMD and HMA were involved in data analysis. AMD, YHM, AM, MM and MH were involved in patients’ selection, collection and preparation of samples. HE, HA and AA supervised the study. AMD wrote the manuscript. HA and AA revised the final version of the manuscript. All authors read and approved the final manuscript.

Acknowledgement

The authors are grateful to Prof. Nashwa El-Khazragy at Clinical Pathology-Hematology Department, Faculty of Medicine, Ain Shams Universityfor providing us with healthy volunteers’ samples.

Author information

Biotechnology Graduate Program, School of Science and Engineering, The American University in Cairo, New Cairo, 11835, Egypt

Areeg M. Dabbish and Moustafa Abohawya

Institute of Global Health and Human Ecology (IGHHE), The American University in Cairo, New Cairo, 11835, Egypt

Hana M. Abdelzaher, Samir Shamma, Hassan El-Fawal and Anwar Abdelnaser

Fellow of clinical pathology, National Hepatology and Tropical Medicine Research Institute (NHTMRI), Cairo, Egypt

Yosra H. Mahmoud

Tropical Medicine Department, National Hepatology and Tropical Medicine Research Institute (NHTMRI), Cairo, Egypt

Amr Maged, Mohamed Manaa and Mohamed Hassany

Department of Chemistry, The American University in Cairo, New Cairo, 11835, Egypt

Hassan Azzazy

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Table 1. Clinicopathological and demographic features of the study population

Clinico- pathological features	No. of participants [n = 245]	Groups				Statistics	P-value
Clinico- pathological features	No. of participants [n = 245]	Control [n = 44] (% within group)	HCV non-cirrhotic [n = 62] (% within group)	HCV cirrhotic [n = 67] (% within group)	HCV-HCC [n = 72] (% within group)	Statistics	P-value
Age		54.6 ± 1.93	52.6 ± 1.253	59.2 ± 1.45	61.4 ± 0.96	26.53^a	< 0.0001
Mean age (≤57)	136	25 (56.8%)	48 (77.4%)	36 (53.7%)	27 (37.5)	21.624^b	< 0.0001
Mean age (> 57)	109	19 (43.2%)	14 (22.6%)	31 (46.3%)	45 (62.5%)
Gender
Male	122	30 (68.2%)	15 (24.2%)	24 (35.8%)	53 (73.6%)	43.775^b	< 0.0001
Female	123	14 (31.8%)	47 (75.8%)	43 (64.2%)	19 (26.4%)
HCV infection
Negative	44	44 (100%)	0	0	0	245^b	NA
Positive (SVR)	126	0	48 (77.4%)	42 (62.7%)	36 (50%)
Positive (Treatment naïve)	75	0	14 (22.6%)	25 (37.3%)	36 (50%)
Cirrhosis
Negative	106	44 (100%)	62 (100%)	0	0	245^b	NA
Positive	139	0	0	67 (100%)	72 (100%)
ALT		11.84 ± 0.67	27.48 ± 2.21	33.94 ± 2.42	45.14 ± 4.91	90.3^a	< 0.0001
≤ 40 IU/L	187	42 (100%)	51 (83.6%)	50 (77%)	44 (63%)	22.75^b	NA
> 40 IU/L	51	0	10 (16.4%)	15 (23%)	26 (37%)
Missing	7	2	1	2	2
AST		16.99 ± 1.32	30.26 ± 2.95	39.95 ± 3.61	50.94 ± 4.04	90.3^a	< 0.0001
≤ 40 IU/L	167	41 (97.6%)	51 (83.6%)	39 (60%)	36 (51.4%)	35.34^b	< 0.0001
> 40 IU/L	71	1 (2.4%)	10 (16.4%)	26 (40%)	34 (48.6%)
Missing	7	2	1	2	2
ALB		4.42 ± 0.1	4.26 ± 0.046	3.658 ± 0.09	3.698 ± 0.07	23.82^c	< 0.0001
> 4 g/dL	122	28 (66.7%)	51 (83.6%)	22 (33.8%)	21 (30.4%)	49.41^b	< 0.0001
≤ 4 g/dL	115	14 (33.3%)	10 (16.4%)	43 (66.2%)	48 (69.6%)
Missing	8	2	1	2	3
T. Bil		0.98 ± 0.03	0.8 ± 0.057	1.157 ± 0.17	0.984 ± 0.0825	33.12^a	< 0.0001
≤ 1.25 mg/dL	191	41 (97.6%)	57 (93.4%)	44 (67.7%)	49 (71%)	25.19^b	< 0.0001
> 1.25 mg/dL	46	1 (2.4%)	4 (6.6%)	21 (32.3%)	20 (29%)
Missing	8	2	1	2	3
D. Bil		0.15 ± 0.01	0.41 ± 0.047	0.84 ± 0.13	0.6 ± 0.07	71.23^a	< 0.0001
≤ 0.35 mg/dL	134	42 (100%)	33 (54.1%)	27 (41.5%)	32 (46.4)	41.29^b	NA
> 0.35 mg/dL	103	0	28 (45.9%)	38 (58.5%)	37 (53.6%)
Missing	8	2	1	2	3
AFP			18.56 ± 7.42	41.75 ± 11.78	834.5 ± 156.4	19.24^a	< 0.0001
< 20 ng/ml	153	NA	56 (91.8%)	55 (83.33%)	42 (60.9%)	19.71^b	< 0.0001
20-400 ng/ml	16	NA	5 (8.2%)	10 (15.15%)	1 (1.4%)	8.45^b	0.015
> 400 ng/ml	27	NA	0	1 (1.52%)	26 (37.7%)	51.2^b	NA
Missing	5	NA	1	1	3
Hemoglobin (g/dL)		13.44 ± 0.268	12.89 ± 0.23	11.49 ± 0.22	12.79 ± 0.23	12.37^c	< 0.0001
WBCs (× 10⁹/L)		7.63 ± 0.42	4.75 ± 0.19	4.19 ± 0.09	4.614 ± 0.1	61.1^a	< 0.0001
RBCs (×10³/mm³)		4.77 ± 0.274	12.9 ± 0.226	11.491 ± 0.22	12.79 ± 0.23	106.9^a	< 0.0001
Platelets (× 10⁹/L)		300.5 ± 12.37	219.9 ± 13.7	130.1 ± 7.75	141.2 ± 9.12	84.6^a	< 0.0001

Data are expressed as mean ± SEM, statistical significance is considered as P-value ≤ 0.05.

Statistical analysis was performed using (^a) Kruskal-Wallis, (^b) Chi-square or (^c) ANOVA test.

NA: not applicable, as the number of participants in one or more groups (n=0).

Table 2. Non-invasive indices for determination of fibrosis and cirrhosis degrees

		Groups			Statistics
Parameter	No. of Participants	HCV non-cirrhotic	HCV cirrhotic	HCV-HCC	χ^2(a)	*P-value*
APRI score	213	0.424 ± 0.051	0.992 ± 0.13	01.365 ± 0.202	102.65	< 0.0001
< 0.5	113	36 (76.6%)	20 (33.9%)	16 (24.6%)	73.747	< 0.0001
0.5 - 1.5	74	10 (21.3%)	29 (49.2%)	34 (52.3%)	37.409	< 0.0001
> 1.5	26	1 (2.1%)	10 (16.9%)	15 (23.1%)	20.165	< 0.0001
Missing	32	15	8	7
FIB-4 score	192	1.684 ± 0.204	3.767 ± 0.399	4.534 ± 0.476	86.193	< 0.0001
< 1.45	67	23 (62.2%)	3 (6%)	7 (11.1%)	85.387	< 0.0001
1.45 - 3.27	71	10 (27%)	29 (58%)	25 (39.7%)	18.686	< 0.0001
> 3.27	54	4 (10.8%)	18 (36%)	31 (49.2%)	34.641	< 0.0001
Missing	53	25	17	9
AAR	238	1.14 ± 0.041	1.17 ± 0.043	1.25 ± 0.047	9.73	0.001
≤ 1	54	15 (24.6%)	17 (26.2%)	11 (15.7%)	2.805	0.423
> 1	184	46 (75.4%)	48 (73.8%)	59 (84.3%)
Missing	7	1	2	2

Data are expressed as mean ± SEM, statistical significance is considered as P-value ≤ 0.05.

^a: Statistical analysis was performed using Chi-square test.

Table 3. Characteristics and staging of HCC patients

Parameter		No. of participants	HCV cirrhotic	HCV-HCC
CTP score	A	100	46 (69.7%)	54 (78.3%)
	B	30	16 (24.2%)	14 (20.3%)
	C	5	4 (6.1%)	1 (1.4%)
	Missing	4	1	3
Ascites	Absent	117	20 (29.8%)	35 (48.6%)
	Present	31	15 (22.4%)	16 (22.2%)
	Unknown	53	32 (47.8%)	21 (29.2%)
Focal lesions number	Single		----	46 (63.9%)
	Multiple		----	26 (36.1%)
Focal lesions size (by CT)	Tumor ≤ 3cm		----	22 (40.7%)
Focal lesions size (by CT)	Tumor > 3cm		----	32 (59.2%)
	Missing		----	18
Performance status (PS)	PS = 0		----	35 (48.7%)
	PS = 1 - 2		----	31 (43%)
	PS > 2		----	6 (8.3%)
BCLC staging system	0		----	10 (17.9%)
	A		----	36 (64.2%)
	B		----	10 (17.9%)
	Missing		----	16

Table 4. Mean of ∆Ct of target miRNAs among the studied groups

	Groups					Statistics
Target	Control mean ∆Ct (n = 41)	HCV non-cirrhotic mean ∆Ct (n = 44)	HCV cirrhotic mean ∆Ct (n = 51)	HCV-HCC mean ∆Ct (n = 52)	Statistical test^(a)	P-value (cont. vs HCV and HCC) ^(b)	P-value (HCV vs HCC) ^(b)
miR-142	3.847 ± 0.42	-0.816 ± 0.47	-1.58 ± 0.5	-2.25 ± 0.32	74.022	< 0.0001	0.853^(N.S.)
miR-150	-2.402 ± 0.55	-7.42 ± 0.35	-7.37 ± 0.35	-8.49 ± 0.33	64.834	< 0.0001	0.051^(N.S.)
miR-183	4.21 ± 0.401	-0.254 ± 0.29	-1.24 ± 0.303	-1.22 ± 0.32	80.109	< 0.0001	0.689^(N.S.)
miR-199a	2.13 ± 0.49	-2.62 ± 0.35	-3.63 ± 0.32	-4.213 ± 0.26	81.468	< 0.0001	0.413^(N.S.)
miR-215	4.81 ± 0.45	0.217 ± 0.29	0.148 ± 0.27	-0.374 ± 0.21	76.745	< 0.0001	0.248^(N.S.)
mir-217	4.684 ± 0.48	-0.54 ± 0.395	-0.759 ± 0.33	-0.358 ± 0.29	72.422	< 0.0001	0.112^(N.S.)
miR-224	3.469 ± 0.46	-0.012 ± 0.41	-0.737 ± 0.3	-1.294 ± 0.25	60.309	< 0.0001	0.314^(N.S.)
miR-424	2.401 ± 0.45	-2.79 ± 0.37	-3.663 ± 0.36	-4.796 ± 0.303	90.225	< 0.0001	0.05
miR-3607	3.358 ± 0.4	0.77 ± 0.25	0.93 ± 0.17	0.317 ± 0.21	46.934	< 0.0001	0.07^(N.S.)

Data are expressed as mean ± SEM, statistical significance is considered as P-value ≤ 0.05.

(N.S.) indicates absence of statistical significance.

Statistical analysis was performed using (^a) Kruskal-Wallis or (^b) Mann-Whitney test.

Table 5. Fold change mean rank of target miRNAs among the studied groups

	Groups				Statistics
Target	Control fold change mean rank (n = 41)	HCV non-cirrhotic fold change mean rank (n = 44)	HCV cirrhotic fold change mean rank (n = 51)	HCV-HCC fold change mean rank (n = 52)	Statistical test^(a)	P-value
miR-142	31.634	98.659	115.098	120.346	74.0224	< 0.0001
miR-150	35.976	104.636	104.235	122.519	64.3728	< 0.0001
miR-183	28.879	99.705	119.971	116.856	80.1087	< 0.0001
miR-199a	30.39	92.886	116.431	124.904	81.4677	< 0.0001
miR-215	31.488	104.989	109.069	121.019	72.812	< 0.0001
mir-217	31.39	111.875	119.039	105.49	72.4221	< 0.0001
miR-224	38.146	98.091	110.235	120.462	60.2673	< 0.0001
miR-424	27.049	95.568	111.549	130.058	90.2253	< 0.0001
miR-3607	44.634	108.92	99.4314	116.779	46.9342	< 0.0001

Statistical significance is considered as P-value ≤ 0.05

Statistical analysis was performed using (^a) Kruskal-Wallis.

Table 6. Correlation between the targe miRNAs in the study groups and with other clinicopathological factors

Factor		miR-142	miR-150 2^-∆∆Ct	miR-183 2^-∆∆Ct	miR-199a 2^-∆∆Ct	miR-215 2^-∆∆Ct	miR-217 2^-∆∆Ct	miR-224 2^-∆∆Ct	miR-424 2^-∆∆Ct	miR-3607 2^-∆∆Ct
miR-142 2^-∆∆Ct	rho	1.000	0.619^**	0.403^**	0.624^**	0.371^**	0.350^**	0.579^**	0.651^**	0.359^**
miR-142 2^-∆∆Ct	P-value		< 0.0001	< 0.0001	< 0.0001	< 0.0001	< 0.0001	< 0.0001	< 0.0001	< 0.0001
miR-150 2^-∆∆Ct	rho	0.619^**	1.000	0.378^**	0.656^**	0.409^**	0.315^**	0.767^**	0.774^**	0.420^**
miR-150 2^-∆∆Ct	P-value	< 0.0001		< 0.0001	< 0.0001	< 0.0001	< 0.0001	< 0.0001	< 0.0001	< 0.0001
miR-183 2^-∆∆Ct	rho	0.403^**	0.378^**	1.000	0.617^**	0.707^**	0.725^**	0.439^**	0.495^**	0.497^**
miR-183 2^-∆∆Ct	P-value	< 0.0001	< 0.0001		< 0.0001	< 0.0001	< 0.0001	< 0.0001	< 0.0001	< 0.0001
miR-199a 2^-∆∆Ct	rho	0.624^**	0.656^**	0.617^**	1.000	0.659^**	0.541^**	0.727^**	0.828^**	0.581^**
miR-199a 2^-∆∆Ct	P-value	< 0.0001	< 0.0001	< 0.0001		< 0.0001	< 0.0001	< 0.0001	< 0.0001	< 0.0001
miR-215 2^-∆∆Ct	rho	0.371^**	0.409^**	0.707^**	0.659^**	1.000	0.681^**	0.527^**	0.582^**	0.621^**
miR-215 2^-∆∆Ct	P-value	< 0.0001	< 0.0001	< 0.0001	< 0.0001		< 0.0001	< 0.0001	< 0.0001	< 0.0001
miR-217 2^-∆∆Ct	rho	0.350^**	0.315^**	0.725^**	0.541^**	0.681^**	1.000	0.339^**	0.441^**	0.503^**
miR-217 2^-∆∆Ct	P-value	< 0.0001	< 0.0001	< 0.0001	< 0.0001	< 0.0001		< 0.0001	< 0.0001	< 0.0001
miR-224 2^-∆∆Ct	rho	0.579^**	0.767^**	0.439^**	0.727^**	0.527^**	0.339^**	1.000	0.769^**	0.569^**
miR-224 2^-∆∆Ct	P-value	< 0.0001	< 0.0001	< 0.0001	< 0.0001	< 0.0001	< 0.0001		< 0.0001	< 0.0001
miR-424 2^-∆∆Ct	rho	0.651^**	0.774^**	0.495^**	0.828^**	0.582^**	0.441^**	0.769^**	1.000	0.545^**
miR-424 2^-∆∆Ct	P-value	< 0.0001	< 0.0001	< 0.0001	< 0.0001	< 0.0001	< 0.0001	< 0.0001		< 0.0001
miR-3607 2^-∆∆Ct	rho	0.359^**	0.420^**	0.497^**	0.581^**	0.621^**	0.503^**	0.569^**	0.545^**	1.000
miR-3607 2^-∆∆Ct	P-value	< 0.0001	< 0.0001	< 0.0001	< 0.0001	< 0.0001	< 0.0001	< 0.0001	< 0.0001
Age	rho	0.021	0.049	.206^**	.160^*	.170^*	0.081	0.082	0.127	-0.047
	P-value	0.781	0.527	0.007	0.037	0.026	0.291	0.285	0.098	0.538
Gender	rho	.153^*	0.103	.168^*	0.074	0.084	.251^**	0.046	0.056	0.055
	P-value	0.036	0.159	0.021	0.315	0.252	0.001	0.530	0.442	0.450
Cirrhosis	rho	.472^**	.385^**	.485^**	.532^**	.418^**	.360^**	.424^**	.535^**	.278^**
	P-value	<0.0001	<0.0001	<0.0001	<0.0001	<0.0001	<0.0001	<0.0001	<0.000	<0.0001
ALT	rho	.452^**	.402^**	.404^**	.481^**	.422^**	.322^**	.483^**	.511^**	.268^**
	P-value	<0.0001	<0.0001	<0.0001	<0.0001	<0.0001	<0.0001	<0.0001	<0.0001	<0.0001
AST	rho	.379^**	.299^**	.323^**	.402^**	.356^**	.244^**	.387^**	.416^**	.202^**
	P-value	<0.0001	<0.0001	<0.0001	<0.0001	<0.0001	<0.0001	<0.0001	<0.0001	<0.0001
AFP	rho	0.131	-0.067	0.081	.170^*	0.082	-0.046	0.082	0.127	0.090
	P-value	0.117	0.420	0.332	0.040	0.324	0.583	0.328	0.128	0.281
T. Bil	rho	-0.064	-.219^**	-0.008	-0.006	-0.005	-0.139	-0.057	-0.059	-0.139
	P-value	0.392	0.003	0.915	0.930	0.945	0.060	0.443	0.427	0.059
D. Bil	rho	.293^**	.179^*	.423^**	.364^**	.423^**	.389^**	.312^**	.353^**	.284^**
	P-value	<0.0001	0.015	<0.0001	<0.0001	<0.0001	<0.0001	<0.0001	<0.0001	<0.0001
ALB	rho	-.211^**	-0.071	-.229^**	-.258^**	-.266^**	-.159^*	-.195^**	-.265^**	-0.132
	P-value	0.004	0.339	0.002	<0.0001	<0.0001	0.031	0.008	<0.0001	0.074
CTP score	rho	-0.037	-.259^**	0.154	0.055	0.123	.231^*	-0.057	0.045	.214^*
	P-value	0.715	0.009	0.124	0.587	0.219	0.020	0.570	0.658	0.032
BCLC	rho	-0.295	-0.110	-0.035	0.136	0.168	-0.042	-0.210	-0.115	-0.056
	P-value	0.065	0.500	0.829	0.404	0.301	0.798	0.194	0.481	0.730

Association was determined using Spearman’s correlation. Rho: Spearman’s rho coefficient.

* Correlation is significant as P-value ≤ 0.05 (two-tailed), ** correlation is significant as P-value ≤ 0.01 (two-tailed).

Table 7. Diagnostic accuracy of single and combined miRNAs between HCC relative to healthy control and non-HCC individuals

miRNA	AUC	Cut-off	Sensitivity	Specificity	PPV	NPV	Accuracy	SE	P-value
HCC vs Control
miR-424	0.993	9.046	100	90.24	92.86	100	95.7	0.005	< 0.0001
miR-199a	0.968	18.219	92.31	95.12	96	90.7	93.55	0.02	< 0.0001
miR-142	0.972	10.804	92.31	90.24	92.31	90.24	91.4	0.014	< 0.0001
miR-215	0.958	13.891	92.31	90.24	92.31	90.24	91.4	0.019	< 0.0001
miR-224	0.921	9.603	80.77	87.8	89.36	78.26	83.87	0.027	< 0.0001
miR-150	0.928	10.357	88.46	82.93	86.8	85	86.02	0.024	< 0.0001
miR-3607	0.868	3.96	82.7	80.49	84.31	78.57	81.72	0.041	< 0.0001
miR-183	0.957	9.138	94.23	90.24	92.45	92.5	92.47	0.021	< 0.0001
miR-217	0.933	9.538	86.54	85.37	88.24	83.33	86.02	0.026	< 0.0001
Combined panel^a			100	95.12	96.3	100	97.85
HCV vs Control
miR-424	0.94	7.173	85.26	85.37	93.1	71.43	85.29	0.019	< 0.0001
miR-199a	0.919	8.418	84.21	85.37	93.02	70	84.56	0.028	< 0.0001
miR-142	0.903	7.165	86.32	85.37	93.18	72.92	86.03	0.026	< 0.0001
miR-215	0.913	6.196	86.32	80.49	91.11	71.74	84.56	0.06	< 0.0001
miR-224	0.863	5.729	80	78.05	89.41	62.74	79.41	0.034	< 0.0001
miR-150	0.882	6.966	82.11	73.17	87.64	63.83	79.41	0.029	< 0.0001
miR-3607	0.824	3.271	81.05	75.61	88.5	63.27	79.41	0.044	< 0.0001
miR-183	0.941	6.266	91.58	87.8	94.57	81.82	90.44	0.022	< 0.0001
miR-217	0.927	8.496	88.42	85.37	93.33	76.1	87.5	0.023	< 0.0001
Combined panel ^a			90.53	85.37	93.48	79.55	88.97
HCC vs non-HCC
miR-424	0.761	27.943	80.77	60.29	43.75	89.13	65.96	0.035	< 0.0001
miR-199a	0.724	28.769	78.85	58.09	41.84	87.78	63.83	0.037	< 0.0001
miR-142	0.69	21.811	76.92	58.09	41.24	86.81	63.3	0.039	< 0.0001
miR-215	0.695	22.138	73.08	55.15	38.38	84.27	60.11	0.039	< 0.0001
miR-224	0.691	10.321	73.08	54.41	38	84.1	59.57	0.04	< 0.0001
miR-150	0.706	23.534	71.15	54.41	37.38	83.15	59.04	0.041	< 0.0001
miR-3607	0.664	5.797	71.15	52.94	36.63	82.76	57.98	0.042	0.001
miR-183	0.664	18.872	61.54	59.56	36.78	80.2	60.11	0.041	< 0.0001
Combined panel ^b			80.77	61.03	44.21	89.25	66.49
HCC vs non-HCC (SVR group)
miR-424	0.8	24.165	79.17	62.5	32.76	92.86	65.63	0.044	< 0.0001
miR-199a	0.74	28.769	83.33	64.42	35.1	94.37	67.97	0.045	< 0.0001
miR-142	0.794	38.561	75	70.19	36.73	92.41	71.1	0.042	< 0.0001
miR-215	0.742	24.654	75	64.42	32.73	91.78	66.41	0.045	< 0.0001
miR-224	0.703	11.042	70.83	61.54	29.82	90.14	63.28	0.051	0.002
miR-150	0.735	23.534	75	59.62	30	91.18	62.5	0.053	< 0.0001
miR-3607	0.763	8.695	75	66.35	33.96	92	67.97	0.05	< 0.0001
miR-183	0.728	18.872	62.5	65.38	29.41	88.31	64.84	0.048	0.001
miR-217	0.673	24.745	75	57.7	29.03	90.91	60.94	0.052	0.008
Combined panel ^a			83.33	63.73	35.09	94.2	67.46
HCC vs non-HCC (Naïve group)
miR-424	0.835	29.098	85.71	75.34	57.14	93.22	78.22	0.039	< 0.0001
miR-199a	0.821	22.67	89.29	68.5	52.1	94.34	74.26	0.041	< 0.0001
miR-142	0.737	12.933	85.71	64.38	48	92.16	70.3	0.049	< 0.0001
miR-215	0.772	17.087	85.71	63.01	47.06	92	69.31	0.046	< 0.0001
miR-224	0.78	9.76	82.14	61.64	45.1	90	67.33	0.046	< 0.0001
miR-150	0.791	11.152	82.14	60.27	44.23	89.8	66.34	0.046	< 0.0001
miR-3607	0.695	5.0867	71.43	61.64	41.67	84.91	64.36	0.052	0.002
miR-183	0.748	14.496	85.71	64.38	48	92.16	70.3	0.049	< 0.0001
miR-217	0.677	13.518	75	60.27	42	86.27	64.36	0.052	0.006
Combined panel ^c			89.29	72.6	55.56	94.64	77.23
HCC vs HCV (cirrhotic & non-cirrhotic)
miR-424	0.662	79.387	63.46	57.9	45.21	74.32	59.86	0.046	0.001
miR-199a	0.618	38.725	63.46	50.52	41.25	71.64	55.1	0.047	0.018
miR-150	0.61	45.166	55.77	50.53	38.16	67.61	52.38	0.05	0.028
AFP	0.688	6.25	62.32	64.57	48.86	75.93	63.78	0.043	< 0.0001
Combined panel ^d			61.54	56.84	43.84	72.97	58.5
Combined panel ^d + AFP			62.75	56.84	43.84	73.97	58.9
HCC vs HCV (cirrhotic & non-cirrhotic) (SVR)
miR-424	0.679	79.387	66.67	60.32	39.02	82.61	62.07	0.065	0.01
miR-142	0.665	112.42	66.67	69.84	45.71	84.62	68.97	0.063	0.018
miR-3607	0.672	9.325	70.83	53.97	36.96	82.93	58.62	0.067	0.014
AFP	0.615	6.05	51.43	60.67	33.96	76.06	58.065	0.059	0.047
Combined panel ^d			70.83	61.9	41.46	84.78	64.37
Combined panel^d + AFP			70.83	73.02	50	86.79	72.41

Statistical significance is considered as P-value ≤ 0.05.

Combined panel was calculated with: (^a) 5 out of 9 significantly altered candidate miRNAs, (^b) 5 out of 8 significantly altered candidate miRNAs, (^c) 6 out of 9 significantly altered candidate miRNAs and (^d) 2 out of 3 significantly altered candidate miRNAs.

NPV: negative predictive value, PPV: positive predictive value.

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published 21 Jun, 2022

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Prognostic MicroRNA Panel for HCV-Associated HCC: Integrating Computational Biology and Clinical Validation

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Abstract

Figures

Introduction

Methodology

Bioinformatic analysis

Patients and samples

Inclusion and exclusion criteria

Sampling and serum preparation

Liver function testing and HCV antibodies testing

RNA isolation and cDNA synthesis

Real time PCR amplification of miRNAs

Data analysis

Statistics

Results

Discussion

Conclusion

Abbreviations

Declarations

References

Tables

Supplementary Files

Status:

Journal Publication

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