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. 2017 Nov 1;8(65):108810–108824. doi: 10.18632/oncotarget.22224

Serum microRNA signatures and metabolomics have high diagnostic value in hepatocellular carcinoma

Hai-Ning Liu 1,*, Hao Wu 1,*, Yan-Jie Chen 1,*, Yu-Jen Tseng 1, Enkhnaran Bilegsaikhan 1, Ling Dong 1, Xi-Zhong Shen 1,2, Tao-Tao Liu 1
PMCID: PMC5752483  PMID: 29312570

Abstract

Background

Many new diagnostic biomarkers have been developed for hepatocellular carcinoma (HCC). We selected two methods with high diagnostic value, the detection of serum microRNAs and metabolomics based on gas chromatography/mass spectrometry (GC/MS), and attempted to establish appropriate models.

Methods

We reviewed the diagnostic efficiencies of all microRNAs identified by previous diagnostic tests. Then we chose appropriate microRNAs to validate the diagnostic efficiencies, and determined the optimal combination. We included 66 patients with HCC and 82 healthy controls (HCs) and detected the expression of the microRNAs. GC/MS analysis was performed, and we used three multivariate statistical methods to establish diagnostic models. The concentration of alpha feto-protein (AFP) was determined for comparison with the novel models.

Results

82 published studies and 92 microRNAs were ultimately included in this systematic review. Seven microRNAs were selected for further validation of their diagnostic efficiencies. Among which, miR-21, miR-106b, miR-125b, miR-182 and miR-224 had a significantly different expression in HCC patients. The combination of miR-21, miR-106b and miR-224 had the highest area under the curve (AUC) at 0.950 with a sensitivity of 80.3% and a specificity of 92.7%. The GC/MS analysis exhibited an excellent diagnostic value and the AUC reached 1.0. In comparison, the AUC of the traditional biomarker, AFP, was 0.755.

Conclusion

MicroRNAs and metabolomics shows promising potential as new diagnostic methods due to their high diagnostic value compared with traditional biomarkers.

Keywords: liver neoplasms, diagnostic test, meta-analysis, microRNA, metabolomics

INTRODUCTION

Hepatocellular carcinoma (HCC) has the sixth highest cancer morbidity and the second highest mortality rate worldwide. The ratio of deaths to new cases for liver cancer is 0.95 each year, while colorectal cancer, which has a better prognosis, is 0.51 [1]. Currently, the diagnosis of HCC relies on biopsy, imaging reports (ultrasound B, CT or MRI) and alpha feto-protein (AFP), according to the American Association for the Study of Liver Diseases (AASLD) Practice Guidelines. However, the sensitivity and specificity of AFP is barely satisfactory [2], necessitating the discovery of circulating biomarkers with a higher diagnostic value. After screening a host of novel biomarkers, including DNAs, RNAs, proteins and low-molecular-weight metabolites [2, 3], we selected two methodology: the detection of serum microRNAs and metabolomics based on gas chromatography/mass spectrometry (GC/MS), validated their diagnostic value and established appropriate models.

MicroRNAs are small, endogenous, non-coding RNAs that can regulate the expression of genes at the post-transcriptional level [4]. MicroRNAs can be released into peripheral blood when liver cell damage occurs [5]. During the past ten years, decades of studies have shown that diverse microRNAs possess great potential for the diagnosis of HCC. Therefore, it is essential to summarize the diagnostic efficiencies of these microRNAs via a systematic review. It is a pity that there are deficiencies in the published systematic reviews and meta-analyses. Some of these studies reviewed only one microRNA [6–9], while others conducted a meta-analysis including the whole diagnostic tests, but lacked the information on each microRNA [10–13]. We tried to overcome these disadvantages by selecting seven microRNAs with high Youden indexes and area under the curve (AUC) values of the receiver operating curve (ROC) to develop a diagnostic panel.

Metabolomics is defined as the quantitative measurement of all small molecule metabolites in an organism at a specified time under specific environmental conditions [14]. Rapid development in metabolomics has made it a promising technology in disease diagnosis and biomarker generation [15]. Compared with other metabolomic techniques, such as nuclear magnetic resonance (NMR) and liquid chromatography/mass spectrometry (LC/MS), GC/MS has a more robust result and is widely used in metabolite identification based on its high sensitivity, peak resolution, and reproducibility [16]. Several studies have reported the diagnostic value of metabolomics in HCC [17]. We further validated the diagnostic accuracy of GC/MS analysis and compared the most frequently used statistical methods.

RESULTS

Study selection and literature characteristics

The initial search from the databases and other sources returned a total of 590 articles, of which, 226 were from PubMed, 271 were from Embase, and 93 were from the Chinese Biomedical Literature Database (CBM). After removing 131 duplicates, 372 irrelevant studies and five articles that failed to provide enough diagnostic information, 82 published studies were enrolled into this systematic review (Supplementary Table 1). A total of 6035 HCC patients and 8181 healthy control (HCs) were included. The characteristics of the 82 studies are displayed in Supplementary Table 2.

Diagnostic value of microRNAs in the literature

92 microRNAs were mentioned in the included articles, of which, 65 were studied in a single article. We conducted the meta-analyses to represent the diagnostic accuracy of the other 27 microRNAs. The details of their corresponding diagnostic value are shown in Table 1.

Table 1. Characteristics of the microRNAs mentioned in the literature.

MicroRNA Expression HCC sample size Control sample size Sensitivity (%) Specificity (%) AUC Number of included articles
miR-223 Upregulated & Downregulated 253 235 93.3 84.2 0.950 4
miR-146a Upregulated 112 167 96.4 67.1 0.940 1
miR-30c Downregulated 55 110 81.8 71.8 0.932 1
miR-152 Downregulated 112 145 88.6 87.8 0.930 2
miR-186 Upregulated 55 110 78.2 63.6 0.927 1
miR-595 Upregulated 87 31 81.7 93.2 0.920 1
miR-130b Upregulated 57 59 87.7 81.4 0.913 1
miR-130a Upregulated 112 42 96.4 78.4 0.910 1
miR-338 Upregulated & Downregulated 156 257 87.1 86.8 0.910 3
miR-34a Upregulated 112 167 98.6 73.3 0.910 1
miR-182 Upregulated 203 315 83.1 86.5 0.910 3
miR-30e Downregulated 39 31 92.3 71.0 0.910 1
miR-96 Upregulated 60 180 83.3 80.8 0.902 1
miR-224 Upregulated 347 545 87.6 82.5 0.900 4
miR-7 Downregulated 30 60 76.7 85.0 0.898 1
miR-145 Upregulated & Downregulated 332 483 96.3 75.8 0.890 2
miR-331-3p Upregulated 103 95 79.6 89.5 0.890 1
miR-21 Upregulated & Downregulated 943 1176 84.5 80.6 0.890 13
miR-125b Downregulated 443 602 84.3 80.8 0.890 4
miR-214-5p Downregulated 224 334 81.8 83.3 0.890 1
miR-16-2 Upregulated & Downregulated 233 158 84.6 79.9 0.890 3
miR-3126-5p Downregulated 115 40 87.0 78.4 0.881 1
miR-301 Upregulated 42 38 88.1 70.3 0.880 1
miR-19a Downregulated 112 167 81.5 82.1 0.870 1
miR-150 Downregulated 120 230 80.8 80.0 0.870 1
miR-143 Upregulated & Downregulated 401 428 76.4 81.3 0.860 4
miR-29b Downregulated 87 96 75.9 89.5 0.855 1
miR-4651 Upregulated 279 662 78.1 92.1 0.850 1
miR-106b Upregulated & Downregulated 206 595 76.7 80.0 0.850 5
miR-574-3p Upregulated 90 90 78.9 77.8 0.850 1
miR-26b Downregulated 50 50 86.0 90.0 0.843 1
miR-1269 Upregulated 224 334 90.7 69.7 0.840 1
miR-939 Upregulated 87 31 85.8 73.7 0.840 1
miR-122 (miR-122a) Upregulated & Downregulated 683 682 77.1 77.4 0.840 8
miR-10b Upregulated 27 81 77.8 76.5 0.840 1
miR-101 Upregulated & Downregulated 156 333 76.7 75.7 0.820 3
miR-519d Upregulated 87 31 72.4 78.4 0.820 1
miR-215 Upregulated 95 127 80.0 91.0 0.816 1
miR-27b-3p Upregulated 91 91 63.0 89.0 0.812 1
miR-27a Downregulated 90 60 86.7 65.0 0.811 1
miR-138b Downregulated 224 334 88.3 69.1 0.810 1
miR-18a Upregulated 101 90 81.8 73.1 0.810 1
miR-192 Upregulated & Downregulated 492 722 77.6 74.6 0.810 5
miR-203 Upregulated & Downregulated 107 158 72.5 76.5 0.810 2
miR-221 Upregulated & Downregulated 192 328 77.6 70.8 0.810 4
miR-4281 Upregulated 45 45 84.4 73.3 0.805 1
miR-15b Upregulated 133 176 85.2 58.3 0.800 3
miR-4429 Upregulated 69 87 75.0 70.0 0.798 1
miR-764 Upregulated 37 60 74.5 77.0 0.791 1
miR-29a Upregulated & Downregulated 138 209 77.3 82.3 0.790 2
miR-183 Upregulated 95 111 68.5 75.3 0.790 2
miR-185-5p Upregulated 67 82 91.0 39.0 0.788 1
miR-6086 Upregulated 45 45 71.1 71.1 0.782 1
miR-195 Downregulated 112 167 83.4 65.9 0.780 1
miR-494 Upregulated 224 334 76.8 65.9 0.780 1
miR-296 Downregulated 112 167 76.8 64.6 0.780 1
miR-451a Downregulated 66 40 95.0 81.8 0.770 1
miR-20a-5p Upregulated 67 82 86.6 57.3 0.770 1
miR-92a-3p Upregulated 182 122 69.0 73.6 0.770 2
miR-205 Downregulated 98 175 89.9 66.9 0.760 2
miR-483-5p Upregulated 161 190 74.8 79.1 0.760 2
miR-199 (miR-199a) Downregulated 266 455 67.6 80.8 0.760 4
miR-181a Downregulated 27 81 74.2 67.7 0.760 1
miR-26a Upregulated & Downregulated 277 367 68.6 72.8 0.760 3
miR-141 Upregulated & Downregulated 157 259 60.3 78.8 0.760 2
miR-335 Downregulated 50 40 78.0 70.0 0.750 1
miR-505 Upregulated 108 149 90.7 56.4 0.736 1
miR-218 Downregulated 156 162 66.7 69.1 0.734 1
miR-133a Upregulated 108 149 64.8 81.9 0.733 1
miR-375 Downregulated 302 490 90.4 68.7 0.730 2
miR-107 Upregulated 115 40 75.4 62.5 0.730 1
miR-202 Downregulated 70 30 91.6 65.0 0.723 1
miR-132-3p Upregulated 67 82 91.0 36.6 0.722 1
miR-25-3p Upregulated 67 82 55.3 79.3 0.718 1
miR-148b Downregulated 76 117 48.0 80.3 0.710 1
miR-29c Upregulated 108 149 77.8 63.1 0.704 1
miR-129 Downregulated 23 55 81.8 69.7 0.700 1
miR-30a-5p Upregulated 67 82 64.2 68.3 0.681 1
miR-155 Downregulated 23 55 86.2 62.3 0.680 1
miR-148a Downregulated 76 117 55.5 85.6 0.680 1
miR-320a Upregulated 67 82 38.8 87.8 0.678 1
miR-200a Downregulated 22 37 56.9 92.6 0.670 1
miR-206 Upregulated 135 222 85.2 52.3 0.665 1
miR-500a Upregulated 112 141 47.2 81.9 0.660 1
miR-324-3p Upregulated 67 82 74.6 50.0 0.656 1
miR-24-3p Upregulated 72 31 57.9 79.5 0.636 1
miR-let-7b Upregulated 120 30 82.5 46.7 0.633 1
miR-433-5p Upregulated 135 222 83.0 39.2 0.607 1
miR-126 Upregulated & Downregulated 82 103 83.7 51.8 0.600 2
miR-142-3p Upregulated 59 48 32.0 91.0 0.553 1
miR-222 Upregulated 60 40 55.0 50.0 0.541 1
miR-1228-5p Upregulated 135 222 66.7 43.7 0.534 1
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The upregulated or downregulated expression trend in the HCC patients versus the control group. The data on the sensitivity, specificity and AUC were obtained via the meta-analysis when the number of included articles was more than one.

Abbreviations: HCC, hepatocellular carcinoma; AUC, area under the curve.

Publication bias

A Deeks’ funnel plot was used to evaluate publication bias (Figure 1), and the P values of Deeks’ tests was 0.08, which indicated no significant publication bias was observed in this analysis.

Figure 1. Deeks’ funnel plot for the assessment of publication bias.

Figure 1

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Study population

The clinical and pathological characteristics of the study participants are presented in Table 2. The age and gender ratio were significantly different between HCC patients and HCs, thus, a covariance analyses were conducted. The results suggested that age and gender ratio was unrelated to the expression of the microRNAs, scores of the components and concentration of AFP.

Table 2. Clinical characteristics of the study population.

Variable Patients (n=66) Control subjects (n=82)
Age (year) 57.9 ± 9.9 34.7 ± 7.3
Gender
 Male 53 49
 Female 13 33
ALT (U/L) 34.0 ± 29.6 20.5 ± 13.2
 <45 56 78
 ≥45 10 4
Tumor size (cm) 5.21 ± 3.81
 <5 39
 ≥5 27
TNM stage
 I 25
 II 16
 III 19
 IV 6
Histological grade
 II 22
 III 10
 II∼III 15
 Unknown 19
Etiology
 HBV 52
 HCV 2
 Fatty 4
 Unknown 8
Liver cirrhosis
 Yes 52
 No 14
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Abbreviation: ALT, alanine aminotransferase; TNM, tumor-node-metastasis; HBV, hepatitis B virus; HCV, hepatitis C virus.

Expression of microRNAs

MiR-21, miR-106b, miR-125b, miR-130b, miR-182, miR-224 and miR-338 were selected through the systematic review. The results of the quantitative reverse-transcription polymerase chain reaction (qRT-PCR) indicated that the serum levels of miR-21, miR-106b and miR-125b in the HCC patients were significantly higher than those in HCs, while those of miR-182 and miR-224 were significantly lower. As for miR-130b and miR-338, no significant difference was observed between HCC patients and HCs (Supplementary Table 3 and Figure 2). The expression of all of the seven microRNAs had no significant differences among four TNM stages (Kruskal-Wallis test, P > 0.05).

Figure 2. Box plots for the expression of the seven microRNAs.

Figure 2

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The P values of miR-21, miR-106b, miR-125b, miR-130b, miR-182, miR-224 and miR-338 were < 0.001, 0.008, < 0.001, 0.224, 0.028, <0.001 and 0.070, respectively. The lines within the boxes represent the median values, and the edges of the boxes demonstrate the interquartile ranges. The lines outside the boxes demonstrate the 95% ranges. The points outside the boxes represent the values beyond the 95% ranges.

Abbreviations: HCC, hepatocellular carcinoma; HC, healthy control.

Diagnostic models established using microRNAs

Table 3 presents the cut-off value, sensitivity, specificity, Youden index and AUC of each microRNA and their combinations. The combination of miR-21, miR-106b and miR-224 had the highest AUC value at 0.950, with a sensitivity of 80.3% and a specificity of 92.7%. The cut-off value of the model was -8.99, according to the formula miR-21 × 2.271 + miR-106b × 1.647 + miR-224 × (-3.306).

Table 3. Diagnostic value of five single microRNAs and their combinations.

MicroRNA(s) Sensitivity (%) Specificity (%) AUC (95% CI) Cut-off value Youden index
miR-21 98.5 59.8 0.872 (0.818, 0.925) 2.700 0.582
miR-106b 56.1 69.5 0.628 (0.538, 0.718) 3.008 0.256
miR-125b 77.3 58.5 0.693 (0.608, 0.779) 1.910 0.358
miR-182 56.1 65.2 0.605 (0.513, 0.697) 2.870 0.212
miR-224 43.9 90.9 0.715 (0.631, 0.799) 6.995 0.348
miR-21+miR-106b 98.5 61.0 0.872 (0.818, 0.926) 4.985 0.595
miR-21+miR-125b 90.9 64.6 0.873 (0.819, 0.926) 5.384 0.555
miR-21+miR-182 72.7 90.2 0.905 (0.860, 0.950) 4.917 0.630
miR-21+miR-224 75.8 95.1 0.924 (0.883, 0.964) -4.642 0.709
miR-106b+miR-125b 72.7 59.8 0.684 (0.598, 0.770) 1.370 0.325
miR-106b+miR-182 72.7 70.7 0.751 (0.674, 0.829) 0.809 0.435
miR-106b+miR-224 68.2 91.5 0.872 (0.817, 0.927) -11.789 0.596
miR-125b+miR-182 86.4 65.9 0.777 (0.701, 0.853) -0.546 0.522
miR-125b+miR-224 71.2 85.4 0.846 (0.785, 0.908) -9.422 0.566
miR-182+miR-224 80.3 58.5 0.741 (0.660, 0.821) -9.163 0.388
miR-21+miR-106b+miR-125b 95.5 59.8 0.873 (0.819, 0.926) 4.859 0.552
miR-21+miR-106b+miR-182 95.5 74.4 0.914 (0.872, 0.956) 4.858 0.698
miR-21+miR-106b+miR-224 80.3 92.7 0.950 (0.920, 0.980) -8.986 0.730
miR-21+miR-125b+miR-182 90.9 75.6 0.913 (0.870, 0.955) 3.811 0.665
miR-21+miR-125b+miR-224 81.8 92.7 0.938 (0.902, 0.974) -8.325 0.745
miR-21+miR-182+miR-224 75.8 95.1 0.924 (0.883, 0.964) -5.105 0.709
miR-106b+miR-125b+miR-182 90.9 58.5 0.789 (0.716, 0.862) 0.168 0.494
miR-106b+miR-125b+miR-224 89.4 78.0 0.897 (0.848, 0.946) -14.671 0.674
miR-106b+miR-182+miR-224 74.2 85.4 0.873 (0.818, 0.928) -13.746 0.596
miR-125b+miR-182+miR-224 74.2 81.7 0.854 (0.794, 0.915) -12.424 0.559
miR-21+miR-106b+miR-125b+miR-182 95.5 74.4 0.915 (0.873, 0.957) 4.703 0.698
miR-21+miR-106b+miR-125b+miR-224 80.3 96.3 0.953 (0.923, 0.982) -10.320 0.766
miR-21+miR-106b+miR-182+miR-224 80.3 92.7 0.950 (0.920, 0.980) -8.553 0.730
miR-21+miR-125b+miR-182+miR-224 81.8 92.7 0.936 (0.900, 0.973) -10.653 0.745
miR-106b+miR-125b+miR-182+miR-224 81.8 80.5 0.896 (0.846, 0.946) -16.120 0.623
miR-21+miR-106b+miR-125b+miR-182+miR-224 78.8 96.3 0.952 (0.923, 0.981) -10.813 0.751
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The bold font indicates that the P value of each microRNA in the combination was less than 0.05 in the logistic regression.

Abbreviation: AUC, area under the curve; CI, confidence interval.

Discrepant metabolites and total ion chromatogram

A total of 1118 features were extracted in this experiment. Seventeen significantly different metabolites are presented in Supplementary Table 4. The retention time (RT) in the total ion chromatograms was stable with no drift in all of the peaks, which indicated that the results were reliable.

Diagnostic models established using metabolomics

First, we performed the multivariate statistical analyses in all 1118 metabolites. In the principal component analysis (PCA) model, we extracted ten principal components, seven of whose eigenvalue were more than 1.0. We calculated the diagnostic parameters when fitting into one to ten principal components (Table 4). As shown, the AUC was higher as the number of the principal components fitted into the model were increased. We extracted one component in partial least squares-discriminate analysis (PLS-DA) and orthogonal partial least squares-discriminant analysis (OPLS-DA) model, respectively, and the AUC reached 0.89 and 1.0.

Table 4. Diagnostic value of the gas chromatography/mass spectrometry analysis with multivariate statistical analysis methods.

Source of components Statistical method Number of components Sensitivity (%) Specificity (%) AUC (95% CI) Youden index Cumulative variance
All metabolites PCA 10 100.0 100.0 1.000 (1.000, 1.000) 1.000 0.783
9 100.0 100.0 1.000 (1.000, 1.000) 1.000 0.777
8 75.0 96.7 0.924 (0.857, 0.990) 0.717 0.767
7 75.0 96.7 0.921 (0.852, 0.990) 0.717 0.745
6 58.3 100.0 0.876 (0.788, 0.965) 0.583 0.725
5 62.5 100.0 0.883 (0.797, 0.970) 0.625 0.694
4 95.8 60.0 0.857 (0.761, 0.953) 0.558 0.635
3 95.8 60.0 0.854 (0.757, 0.951) 0.558 0.589
2 95.8 60.0 0.854 (0.757, 0.951) 0.558 0.543
1 70.8 50.0 0.564 (0.409, 0.719) 0.208 0.329
PLS-DA 1 83.3 76.7 0.894 (0.815, 0.974) 0.600 0.341
OPLS-DA 1 100.0 100.0 1.000 (1.000, 1.000) 1.000 0.704
Significantly different metabolites PCA 5 100.0 100.0 1.000 (1.000, 1.000) 1.000 0.744
4 100.0 100.0 1.000 (1.000, 1.000) 1.000 0.734
3 95.8 96.7 0.994 (0.983, 1.000) 0.925 0.713
2 95.8 100.0 0.994 (0.982, 1.000) 0.958 0.673
1 100.0 93.3 0.989 (0.970, 1.000) 0.933 0.413
PLS-DA 1 100.0 93.3 0.996 (0.986, 1.000) 0.933 0.628
OPLS-DA 1 100.0 93.3 0.996 (0.986, 1.000) 0.933 0.628
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Abbreviations: AUC, area under the curve; CI, confidence interval; PCA, principal component analysis; PLS-DA, partial least squares-discriminate analysis; OPLS-DA, orthogonal partial least squares-discriminant analysis.

When the seventeen significantly different metabolites were used to diagnose HCC, the AUC reached 1.0. Further multivariate statistical analyses also displayed promising results. In the PCA model, we extracted five principal components, three of whose eigenvalue was more than 1.0. The AUC reached 1.0 when more than four principal components were included. Only one component was extracted in both of PLS-DA and OPLS-DA model, and the AUC both reached 0.996.

More diagnostic information regarding the multivariate statistical analyses is shown in Table 4 and Figure 3.

Figure 3. Score plots of the GC/MS analysis in the hepatocellular carcinoma patients and healthy controls.

Figure 3

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○ represents the hepatocellular carcinoma group. ▲ represents the healthy control group. The scatter plots of the principal component analysis (PCA) with two principal components for all metabolites (1A) and significantly different metabolites (1B). The line within the plot represents the optimal cut-off line. The strip charts of the partial least squares-discriminate analysis (PLS-DA) with the only component for all metabolites (2A) and significantly different metabolites (2B). The strip charts of the orthogonal partial least squares-discriminant analysis (OPLS-DA) with the only component for all metabolites (3A) and significantly different metabolites (3B).

Diagnostic value of traditional tumor biomarkers

The AFP concentration was significantly different between HCC patients and HCs (Mann-Whitney U-test, P < 0.001). The median concentrations in the patients and HCs were 42.2 (range, 1.2 - > 60500) and 3.6 (range, 0.9 – 10.3) μg/L, respectively. The AUC of AFP was 0.755 (95% CI, 0.666 - 0.843; sensitivity = 59.1%, specificity = 100.0%) when the cut-off value was 12.3 μg/L. When the cut-off value was 20 μg/L, which is the upper bound of 95% of healthy individuals, the sensitivity was 54.5%, and the specificity was still 100.0%.

The ROC curves of AFP, metabolomics and the combination of microRNAs are displayed in Figure 4.

Figure 4. Receiver operating characteristic (ROC) curve.

Figure 4

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ROC curve of the combination of miR-21, miR-106b and miR-224, GC/MS analysis with three statistical methods for all metabolites and AFP for discriminating hepatocellular carcinoma patients from control subjects. The curve of PCA model was performed when including two principal components.

Abbreviations: PCA, principal component analysis; PLS-DA, partial least squares-discriminate analysis; OPLS-DA, orthogonal partial least squares-discriminant analysis; AFP, alpha feto-protein.

DISCUSSION

Early diagnosis and treatment of HCC can improve patient survival is a well-established consensus. Thus, looking for new biomarkers is in the ascendant. Novel diagnostic biomarkers almost belong to gene mutations, single nucleotide polymorphisms (SNP), epigenetics, mRNAs, non-coding RNAs and proteins including GPC3, GP73, DKK1 [18, 19]. Screening via proteomics or metabolomics is also a feasible way to discover new biomarkers. After investigating the diagnostic efficiencies and limitations of biomarkers, we selected serum microRNAs and GC/MS to validate their diagnostic value and establish appropriate models.

Among the thousands of microRNAs that have been discovered, many have been testified for their diagnostic value in HCC [10]. A general research routine is through screening microRNA microarray in a small sample size, then validating the results via qRT-PCR in a larger sample size. We reviewed the diagnostic value of each microRNA. Meta-analyses made the statistical power increase through the expansion of the included articles and sample sizes.

Based on the result of systematic review, we selected seven microRNAs with high AUC values or Youden indexes that were included in various articles. MiR-21, miR-106b, miR-125b, miR-182 and miR-224 had significantly different expression in HCC patients versus HCs. AUC higher than 0.7, miR-21 and miR-224 had potential to become independent diagnostic biomarkers of HCC. The combination of microRNAs further raised the diagnostic value and the combination of miR-21, miR-106 and miR-224 allowed the AUC to exceed 0.950. With miR-21, miR-106b, miR-125b, miR-182 and miR-224 combined, the AUC was 0.952. However, there was no significant difference between the above two combinations.

As circulating diagnostic biomarkers, microRNAs have advantages and disadvantages. Different from mRNAs, microRNAs are stable at room temperature and remains so after repeated freeze-thawing [20]. In addition, compared with liver puncture, blood examination is non-invasive. Nevertheless, the choice of internal/external reference RNA, the dosage of reagents and the operating process lacks standardization, therefore the cut-off value cannot be unified, and even the variation trend of the expression for some microRNAs are distinct. On the other hand, the etiology, such as hepatitis B virus or hepatitis C virus, may affect the expression of microRNAs.

As expected, the diagnostic efficiency of metabolomics is satisfactory, whether all detected metabolites or significantly different metabolites were included. As shown in Table 4 , when a PCA, PLS-DA or OPLS-DA model includes the same number of components, the OPLS-DA model had the highest AUC, and the PCA model ranks last. This conclusion can be explained from a mathematics perspective. PCA is non-supervisory, while PLS-DA and OPLS-DA are supervisory analysis methods. Based on PLS, OPLS further separates the orthogonal variables by an orthogonal signal correction and expands the differences between the two data matrices [21, 22]. Although the diagnostic value of the PCA model was not superior to that of the PLD-DA and OPLS-DA model when including the same number of components, the PCA can extract more principal components to increase the AUC.

The advantage of serum GC/MS analysis are high diagnostic value and non-invasive examination process. The statistical models, which are established by PCA, PLA-DA and OPLS-DA, are stable when the variables are numerous and the observations are little. Nevertheless, same as detecting the expression of microRNAs, the pretreatment process is not standardized, including the choice of the derivatization reagents and internal standard, the time of each step and the operating order.

In terms of the price, new biomarker detections are more expensive than traditional AFP, which costs only 5.2 dollars in China. Each sample detection for three microRNAs and the metabolic spectra costs approximately 20 and 72.5 dollars, respectively. Moreover, an abdominal enhancement CT and enhancement MRI are priced around 100 and 135 dollars, respectively. A liver puncture costs 44 dollars, excluding test-related room and nursing care charges.

In conclusion, the diagnostic value of the new models are higher than that of the traditional biomarker, AFP, without doubt. We suggest GC/MS analysis and a combination of microRNAs applied to the diagnosis of HCC, especially after the position diagnosis is made via imaging examination.

MATERIALS AND METHODS

Study design

First, an electronic search of PubMed, Embase and the CBM databases was performed to identify relevant articles published up to July 6, 2017. The search strategy was (miRNA OR microRNA OR miR) AND (“liver neoplasms”[Mesh] OR “hepatocellular carcinoma” OR “liver cancer”) AND (blood OR serum OR plasma OR circulating) AND (diagnosis OR diagnostic OR diagnose). In addition, we examined the reference lists in identified articles to included additional relevant studies. No language restrictions were imposed.

Secondly, we chose microRNAs with high AUC values and is included in numerous studies to establish a diagnostic model. The serum specimens from 66 HCC patients and 82 HCs were collected to detect the expression of microRNAs through qRT-PCR.

Next, we randomly selected 24 patients and 30 HCs from the cohort mentioned above and profiled their metabolomic signatures via GC/MS analysis.

Finally, we detected the serum concentration of the traditional tumor biomarker, AFP. The diagnostic efficiency was calculated and compared to the new models. The flow-process diagram for the study is shown in Figure 5.

Figure 5. Study design.

Figure 5

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Abbreviations: HCC, hepatocellular carcinoma; HC, healthy control; RT-PCR, reverse-transcription polymerase chain reaction; GC/MS, gas chromatography/mass spectrometry; PCA, principal component analysis; PLS-DA, partial least squares-discriminate analysis; OPLS-DA, orthogonal partial least squares-discriminant analysis; AFP, alpha feto-protein.

Inclusion and exclusion criteria of the literature

The inclusion criteria for the systematic review were as follows: (1) studies regarding microRNAs comparing HCC patients with HCs; (2) studies that employed blood specimens, including serum and plasma; and (3) qRT- PCR techniques. The exclusion criteria included: (1) failure to provide sufficient diagnostic information; (2) duplicate data from identical authorities; and (3) cell or animal studies, reviews and letters.

Data extraction

Two reviewers were independently responsible for study selection and data extraction. Data were retrieved from all included studies: (1) basic characteristics of the studies, including the first author, year of publication, country, ethnicity, sample size, mean age, gender, type of specimens, target microRNAs, and reference control; and (2) diagnostic parameters of the microRNAs, including expression variation, sensitivity, specificity, and AUC.

Patients and specimens

In this study, we included patients and HCs from Zhongshan Hospital, Fudan University between May 2015 and July 2015. The HCC patients were all definitively diagnosed in accordance with the AASLD Practice Guidelines. The patients were excluded if they had history of other malignant tumors or had received surgical operation, interventional therapy, radiotherapy or chemotherapy. Healthy individuals were identified by clinical manifestations, histories of illness and normal liver function. The serum samples were centrifuged for 10 min at 820 g and 4°C to remove cell debris, and the supernatants were immediately stored at −80°C until analysis. The concentration of serum AFP was measured via an electro-chemiluminescence immunoassay.

The protocol was approved by the Ethics Committee of Zhongshan Hospital of Fudan University, Shanghai. All participants provided a written informed consent.

RNA extraction and reverse transcription

2 μl of 25 fmol cel-miR-39 (Tiangen, Beijing, China) was added to 200 μl of serum samples as external reference. Total RNA was isolated simultaneously using the miRcute microRNA Isolation Kit (Tiangen, Beijing, China) abiding by the manufacturer’s protocol [23]. The optical density of the extracted total RNA was determined at 260 and 280 nm on a NanoDrop spectrophotometer (NanoDrop, Wilmington, DE, USA) to assess for concentrations and purities.

The extracted microRNA was polyadenylated with poly (A) polymerase in a 20-μl volume, and 6 μl of the poly (A) reaction solution was reversely transcribed to cDNA in another 20 μl with miRcute microRNA The First-strand cDNA Synthesis Kit (Tiangen, Beijing, China) according to the manufacturer’s protocol. All procedures were carried out in triplicates to remove outliers.

Quantitative real-time PCR

The qPCR reaction was conducted with the miRcute microRNA qPCR Detection Kit (Tiangen, Beijing, China) on ABI PRISM 7500 Sequence Detection System (Applied Biosystems, Foster City, CA, USA). Each 20-μl qPCR reaction solution contained cDNA, 2× miRcute microRNA premix (with SYBR and ROX), the manufacturer-provided universal reverse primer, and a microRNA-specific forward primer (Tiangen, Beijing, China). The real-time PCR cycling conditions: 94°C for 2 min, 45 cycles at 94°C for 20 s, annealing at 60°C for 34 s, and extension at 72°C for 30 s. At the end of the real-time PCR reaction, a melting curve analysis was accomplished to ensure specific amplification of the expected PCR product.

The relative expression of the microRNAs was calculated from the equation log10 (2−ΔCt) with cel-miR-39. The ΔCT was calculated by subtracting the CT values of the cel-miR-39 from those of the microRNAs of interest [23].

Specimen processing for metabolomics

200 μl of the serum samples were transferred into glass centrifuge tubes for GC/MS analysis. 200 μl of 2-chloro-phenylalanine (0.3 g/L) served as internal standard. 600 μl of methanol was added into each sample. The mixture was vortexed for 30 s, followed by incubation at -20°C for 10 min. The samples were then centrifuged for at 12000 × g and 4°C for 15 min. 800 μl of the supernatant was collected individually from each sample into an ampoule bottle and evaporated to dryness under a stream of nitrogen gas at 50°C for approximately 30 min. 200 μl of a methoxyamine pyridine solution (15 g/L) was subsequently added into the ampoule bottle. The mixture was vortexed for 2 min and incubated at 37°C for 1 hour. Then, 200 μl of bis-(trimethylsilyl)-trifluoroacetamide (BSTFA) plus 1% trimethylchlorosilane (TMCS) was added, and the mixture was again vortexed for 2 min and incubated at 100°C for 30min. The methanol, 2-chloro-phenylalanine, methoxyamine and pyridine were obtained from Aladdin (Shanghai, China). BSTFA with 1% TMCS was purchased from Sigma-Aldrich (St. Louis, MO, USA). Each reaction sample was performed in duplicates.

GC/MS analysis

The GC/MS analysis was performed on an Agilent 6980 GC system equipped with a fused-silica capillary column (internal diameter: 30 m × 0.25 mm) and a 0.25-μm HP-5MS stationary phase (Agilent, Shanghai, China). We used the same operational methods as our previous studies [24].

Statistical analyses

The statistical analyses were carried out using R software 3.3.3 (R Foundation for Statistical Computing, Vienna, Austria), Stata 12.0 (StataCorp LP, College Station, TX, USA) and SIMCA-P 13.0 (Umetrics AB, Umea, Vasterbotten, Sweden). P values < 0.05 were considered statistically significant.

Meta-analyses were used to assess the accuracy of individual microRNAs for HCC diagnosis, based on its sensitivity, specificity and AUC of the summary receiver operator characteristic (SROC). Deeks’ funnel plot was selected to evaluated publication bias.

A power analysis was used to calculate the number of cases and HCs in the microRNA validation phase. A Mann-Whitney U-test was used to compare the expression of microRNAs and concentration of AFP in HCC patients and HCs. A Kruskal-Wallis test was used to calculate the relationship between the expression of microRNAs and TNM stage. The diagnostic efficiencies of the microRNAs were determined by assessing the sensitivity, specificity and the AUC. A stepwise logistic regression was used to include microRNAs into the diagnostic model.

The metabolomic data were normalized with “XCMS” package in R software and then stored in a two-dimensional matrix, including the RT, mass-to-charge ratio (MZ) and peak intensity. The metabolites were identified based on the National Institute of Standards and Technology (NIST) mass spectra library through RT and MZ [24]. Significantly different metabolites were screened via the variable importance in the projection (VIP) value of the OPLS-DA model (> 1) and the P value of t-test (≤ 0.001). Multivariate statistical analyses, including the PCA, PLS-DA and OPLS-DA, were carried out via SIMCA-P in all metabolites and significantly different metabolites, respectively. A logistic regression was used to investigate the better diagnostic models by combinations of the components when more than one component was extracted.

SUPPLEMENTARY MATERIALS TABLES

Acknowledgments

The authors would like to thank the members of Prof. Xi-Zhong Shen’s laboratory for helpful discussions and critical reading of the manuscript.

Abbreviations

AASLD

American Association for the Study of Liver Diseases

AFP

alpha feto-protein

AUC

area under the curve

BSTFA

bis-(trimethylsilyl)-trifluoroacetamide

CBM

Chinese Biomedical Literature Database

CT

cycle threshold or computed tomography

DKK1

dickkopf-related protein 1

GC/MS

gas chromatography/mass spectrometry

GP73

Golgi glycoprotein 73

GPC3

glypican 3

HC

healthy control

HCC

hepatocellular carcinoma

LC/MS

liquid chromatography/mass spectrometry

MRI

magnetic resonance imaging

MZ

mass-to-charge ratio

NIST

National Institute of Standards and Technology

NMR

nuclear magnetic resonance

OPLS-DA

orthogonal partial least squares-discriminant analysis

PCA

principal component analysis

PLS-DA

partial least squares-discriminate analysis

qRT-PCR

quantitative reverse-transcription polymerase chain reaction

ROC

receiver operating curve

RT

retention time

SNP

single nucleotide polymorphisms

SROC

summary receiver operator characteristic

TMCS

trimethylchlorosilane

VIP

variable importance in the projection

Author contributions

HNL, TTL and XZS conceived and designed the experiments. YJT drafted the manuscript. HNL, YJC and EB extracted the data of systematic review and meta-analysis. HNL, HW and YJC performed the experiments of microRNA and metabolomics. LHN and HW performed the statistical analyses. LD and XZS revised the article. All authors finished reading and approving the final manuscript of this study.

CONFLICTS OF INTEREST

The authors have stated that they have no conflicts of interest.

FUNDING

This study was supported by the National Nature Science Foundation of China (No. 81000968; No. 81101540; No. 81101637; No. 81172273; No. 81272388; No. 81301820; No. 81472673), Doctoral Fund of Ministry of Education of China (20120071110058), and The National Clinical Key Special Subject of China.

REFERENCES

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

oncotarget-08-108810-s001.pdf (1MB, pdf)
oncotarget-08-108810-s002.docx (30.3KB, docx)
oncotarget-08-108810-s003.docx (32.9KB, docx)

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