Volume 8, Issue 1 p. 64-84
Research Article
Open Access

Diagnostic value of strand-specific miRNA-101-3p and miRNA-101-5p for hepatocellular carcinoma and a bioinformatic analysis of their possible mechanism of action

Xia Yang

Xia Yang

Department of Pathology, First Affiliated Hospital of Guangxi Medical University, Nanning, China

These authors contributed equally to this work.Search for more papers by this author
Yu-Yan Pang

Yu-Yan Pang

Department of Pathology, First Affiliated Hospital of Guangxi Medical University, Nanning, China

These authors contributed equally to this work.Search for more papers by this author
Rong-Quan He

Rong-Quan He

Department of Medical Oncology, First Affiliated Hospital of Guangxi Medical University, Nanning, China

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Peng Lin

Peng Lin

Department of Ultrasonography, First Affiliated Hospital of Guangxi Medical University, Nanning, China

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Jie-Mei Cen

Jie-Mei Cen

Department of Medical Oncology, First Affiliated Hospital of Guangxi Medical University, Nanning, China

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Hong Yang

Hong Yang

Department of Ultrasonography, First Affiliated Hospital of Guangxi Medical University, Nanning, China

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Jie Ma

Corresponding Author

Jie Ma

Department of Medical Oncology, First Affiliated Hospital of Guangxi Medical University, Nanning, China

These authors contributed equally to this work.

Correspondence

J. Ma, Department of Medical Oncology, First Affiliated Hospital of Guangxi Medical University, Shuangyong Road 6, Nanning, Guangxi, China

Tel: +86 771 5312980

E-mail: [email protected]

G. Chen, Department of Pathology, First Affiliated Hospital of Guangxi Medical University, Shuangyong Road 6, Nanning, Guangxi, China

Tel: +86 771 5356534

E-mail: [email protected]

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Gang Chen

Corresponding Author

Gang Chen

Department of Pathology, First Affiliated Hospital of Guangxi Medical University, Nanning, China

These authors contributed equally to this work.

Correspondence

J. Ma, Department of Medical Oncology, First Affiliated Hospital of Guangxi Medical University, Shuangyong Road 6, Nanning, Guangxi, China

Tel: +86 771 5312980

E-mail: [email protected]

G. Chen, Department of Pathology, First Affiliated Hospital of Guangxi Medical University, Shuangyong Road 6, Nanning, Guangxi, China

Tel: +86 771 5356534

E-mail: [email protected]

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First published: 17 November 2017
Citations: 17

Abstract

There is accumulating evidence that miRNA might serve as potential diagnostic and prognostic markers for various types of cancer. Hepatocellular carcinoma (HCC) is the most common type of malignant lesion but the significance of miRNAs in HCC remains largely unknown. The present study aimed to establish the diagnostic value of miR-101-3p/5p in HCC and then further investigate the prospective molecular mechanism via a bioinformatic analysis. First, the miR-101 expression profiles and parallel clinical parameters from 362 HCC patients and 50 adjacent non-HCC tissue samples were downloaded from The Cancer Genome Atlas (TCGA). Second, we aggregated all miR-101-3p/5p expression profiles collected from published literature and the Gene Expression Omnibus and TCGA databases. Subsequently, target genes of miR-101-3p and miR-101-5p were predicted by using the miRWalk database and then overlapped with the differentially expressed genes of HCC identified by natural language processing. Finally, bioinformatic analyses were conducted with the overlapping genes. The level of miR-101 was significantly lower in HCC tissues compared with adjacent non-HCC tissues (P < 0.001), and the area under the curve of the low miR-101 level for HCC diagnosis was 0.925 (P < 0.001). The pooled summary receiver operator characteristic (SROC) of miR-101-3p was 0.86, and the combined SROC curve of miR-101-5p was 0.80. Bioinformatic analysis showed that the target genes of both miR-101-3p and miR-101-5p are involved in several pathways that are associated with HCC. The hub genes for miR-101-3p and miR-101-5p were also found. Our results suggested that both miR-101-3p and miR-101-5p might be potential diagnostic markers in HCC, and that they exert their functions via targeting various prospective genes in the same pathways.

Abbreviations

  • AFP
  • alpha-fetoprotein
  • AUC
  • area under the curve
  • BP
  • biological process
  • CC
  • cellular component
  • CI
  • confidence interval
  • CNKI
  • Chinese National Knowledge Infrastructure
  • DAVID
  • Database for Annotation, Visualization and Integrated Discovery
  • FN
  • false negative
  • FoxO
  • forkhead box O
  • FP
  • false positive
  • GEO
  • Gene Expression Omnibus
  • GO
  • Gene Ontology
  • HBV
  • hepatitis B virus
  • HCC
  • hepatocellular carcinoma
  • KEGG
  • Kyoto Encyclopedia of Genes and Genomes
  • MAPK
  • mitogen-activated protein kinase
  • MF
  • molecular function
  • NLP
  • natural language processing
  • NLR
  • negative likelihood ratio
  • PI3K
  • phosphoinositide-3-kinase
  • PLR
  • positive likelihood ratio
  • PPI
  • protein–protein interaction
  • pre-miRNA
  • precursor miRNA
  • ROC
  • receiver operating characteristic
  • SROC
  • summary receiver operator characteristic
  • TCGA
  • The Cancer Genome Atlas
  • TN
  • true negative
  • TP
  • true positive
  • According to Cancer Statistics, 2017 [1], the incidence rates of liver cancer in the USA continue to increase rapidly (~ 3% per year in women and 4% per year in men), and the death rate rose by almost 3% per year from 2010 to 2014. In addition, the mortality rate is three times higher in men than in women. Since Asia is the area with the highest incidence rate of liver cancer, especially China, annual incidence and mortality are more than half of the global totals [2]. Among the three histological types of liver malignancy, hepatocellular carcinoma (HCC) has become the leading cause of death from cancer. Since there have been no biomarkers or common surgical techniques for the early stage of HCC, the majority of patients with HCC are diagnosed late, which directly correlates with a poor outcome and low survival rate. As with other cancers, HCC development is a multistep process with abundant genetic and epigenetic mutations. A recent study confirmed that hepatocarcinogenesis can be caused by chronic hepatitis B virus (HBV) infection [3]. Much effort towards the treatment of HBV-infected HCC has been made in the past, but with only limited success. Thus, identifying novel biochemical markers for early HCC diagnosis is a matter of the utmost urgency.

    miRNAs, ~ 20–22 nucleotides in length, are a class of small endogenous non-coding RNA molecules. They post-transcriptionally regulate mRNA expression through imperfect base paring with the 3′-untranslated region of target genes. With comprehensive study, miRNAs have become known as the star molecules of cancer research. miRNAs in human cancers are involved in several pivotal biological processes (BP), including cancer proliferation, differentiation, progression and cell apoptosis [4-6]. Although their functions remain elusive, up- and down-regulation of miRNAs have been widely reported in all kinds of cancer tissues in comparison with expression in the corresponding normal tissues [7, 8]. In particular, miRNAs have been found to be biomarkers for cancer clinical diagnosis, histological classification and prognosis [9-13].

    Accumulating evidence has clearly demonstrated that the aberrant expression of miRNAs may further influence the expression of tumor oncogenes and suppressor genes, thereby leading to the occurrence of a tumor [14-17]. Theoretically, mature miRNA generation requires a series of enzyme reactions. First, primary miRNA transcripts are cleaved in the nucleus by the Drosha enzyme to liberate the precursor miRNA (pre-miRNA) hairpin. Subsequently, the pre-miRNA is exported to the cytoplasm and further processed by the enzyme Dicer to produce two mature miRNAs (miR-5p and miR-3p) [18, 19]. Even though the two mature miRNAs are transcribed from the same pre-miRNA, they may have different target genes and biological functions. A previous study [20] reported that the expression levels of the miR-5p and miR-3p mature sequences can be altered in different tissues.

    Accumulating evidence [19, 21-25] has shown that miR-101-3p/-5p is down-regulated in multiple malignances, including HCC. For example, Hou et al. [26] explored miRNA expression profiling and revealed that miR-101 (3p and 5p were not distinguished) expression in HCC tissues was lower than in healthy controls. Wei et al. [27] also showed that miR-101 (3p and 5p were not distinguished) was down-regulated in HBV-associated HCC tissues and may have therapeutic potential in HCC. Additionally, the function of these miRNAs has also been investigated. Zhang et al. [28] revealed that enforced expression of miR-101 (3p and 5p were not distinguished) by siRNA inhibited the cell proliferation and tumorigenicity of an HCC cell line in vitro. Sheng et al. [29] investigated how miR-101-3p regulated cell proliferation, cell cycle and apoptosis in HCC and found that overexpression of miR-101-3p caused an enhanced rate of apoptosis but no obvious change in the cell cycle. Besides, several oncogenes, such as EZH2, FOS, COX-2 and SOX9, have been found to be directly regulated by miR-101-3p/5p [30-32]. Recently the potential of the miR-101 family as diagnostic indicators has also caught the eye of researchers. He et al. [33] conducted a meta-analysis that summarized miRNAs’ diagnostic value in HCC and found that miR-101-5p had great diagnostic value, though only three data sets were included and the results need to be further validated. Furthermore, in human, miR-101 precursor transcripts are encoded with two genomic loci (miR-101-1 and miR-101-2). For two mature miRNAs, miR-101-3p is generated from the 3′ ends of the precursors, and miR-101-5p from the 5′ end of pre-miR-101-1 (http://www.mirbase.org/). We speculated that miR-101-3p may also serve as a diagnostic marker for HCC. Since the seed region of miR-101-3p and miR-101-5p is unique, they are predicted to regulate unique targets. However, to the best of our knowledge, the comparative roles of miR-101-3p and miR-101-5p in HCC have not yet been fully studied.

    The present study investigated miR-101-3p and miR-101-5p expression in HCC tissues compared with that in healthy controls. Published studies, Gene Expression Omnibus (GEO) microarray chips and The Cancer Genome Atlas (TCGA) data that included miR-101-3p or miR-101-5p expression information were collected together. Additionally, previous studies have mainly focused on a single gene [34-36], and studies have rarely focused on the function of coexpressed genes in cancers. For the purpose of obtaining a full understanding of the molecular mechanisms underlying HCC, comprehensive bioinformatics methods were used to investigate the function and pathways of target genes of miR-101-3p and miR-101-5p associated with HCC. In a word, the present study aimed to analyze the expression and mechanism of miR-101-3p and miR-101-5p in the initiation and development of HCC. This exploration will provide novel insights into HCC. A flowchart for the whole study designed is shown in Fig. 1.

    Details are in the caption following the image
    Flowchart of the study design.

    Material and methods

    The clinical role of miR-101 based on the public database TCGA

    To verify the difference in the miR-101 expression levels between HCC and normal liver tissues, we downloaded relevant data from the public tumor database TCGA, in which samples from 362 HCC patients and 50 adjacent non-HCC tissues were included. Additionally, miR-101-1 and miR-101-2 levels were both calculated because the relevant sample data were provided in TCGA. miR-101-1 and miR-101-2 are two precursor hairpin structures of miR-101 miRNA that are located in the human genome on chromosome 1 (MI0000103) and 9 (MI0000739), respectively [37]. Both of them are processed by the Dicer enzyme to form the mature miRNA. All of the available clinical parameters were analyzed by spss statistics 22.0 (IBM Corp., Armonk, NY, USA).

    Data mining

    Search strategy and study selection

    Comprehensive literature searches were conducted on electronic databases PubMed, EMBASE, Web of Science, the Cochrane Library, and Chinese National Knowledge Infrastructure (CNKI) up to 29 December 2016. No language limitations were imposed. Qualifying articles were screened by combining the following keywords: ‘miR-101’ OR ‘miRNA-101’ OR ‘miRNA-101’ OR ‘miR101’ OR ‘miRNA101’ OR ‘miRNA 101’ OR ‘miR-101-5p’ OR ‘miRNA-101-5p’ OR ‘miRNA-101-5p’ OR'miR-101-3p’ OR ‘miRNA-101-3p’ OR ‘miRNA-101-3p’ AND malignan* OR cancer OR tumor OR neoplas* OR carcinoma AND hepatocellular OR liver OR hepatic OR HCC AND diagnos* OR receiver operating characteristic (ROC) OR specificity OR sensitivity OR DEGs OR DEMs OR ‘differentially expressed’. In addition, the reference lists were also manually searched to reduce article omission. The title and abstract of the obtained studies were scanned to exclude any clearly irrelevant publications. In addition to searching the literature, we also searched the GEO database for eligible microarrays with the following terms: malignan* OR cancer OR tumor OR neoplas* OR carcinoma AND hepatocellular OR liver OR hepatic OR HCC.

    Criteria for inclusion and exclusion

    Studies that met the following criteria were included: (a) investigated HCC; (b) measured the level of miR-101, miR-101-3p or miR-101-5p in HCC tissue, plasma or serum; (c) included the diagnosis of HCC or the clinical parameters; and (d) reported true positives (TPs), false positives (FPs), false negatives (FNs), and true negatives (TNs) or sensitivity and specificity of miR-101. In addition, (e) if the studies did not provide a fourfold contingency table, they were included if the original data were available; and (f) microarrays were included if they enrolled more than three patient samples and measured the miR-101 profile for HCC.

    Articles that met the following criteria were excluded: (a) studies without sufficient data, such as reviews or systematic reviews, (b) repeat reports, (c) studies conducted on cell lines or animals and (d) letters to the editor or conference abstracts.

    Data synthesis and analysis

    Studies that did not provide TPs, FPs, FNs and TNs but gave sensitivity and specificity or the original data were translated by medcalc 11.4.2.0 (MedCalc Software, Ostend, Belgium). To reduce inaccuracy in the relevant data extracted from the included studies, three independent researchers (XY, PL and JMC) performed the data extraction separately.

    Statistical analysis

    All statistical analyses were performed using spss statistics 20.0 or stata 12.0 (StataCorp, College Station, TX, USA). For the clinical parameter analysis, miR-101 expression was represented as the mean ± standard deviation. The standards for assessing the area under the curve (AUC) in the ROC curve were as follows: 0.5–0.7 represented poor evidence for diagnosis, 0.7–0.9 represented moderate evidence for diagnosis and 0.9–1.0 represented high evidence for diagnosis. The correlation between miR-101 expression and the clinicopathological parameters was investigated with Spearman's rank correlation. The significance of the difference between HCC and non-cancerous liver tissues was studied using Student's t test. The significant differences among three groups were examined by one-way ANOVA. For data mining, the pooled sensitivity, specificity, positive likelihood ratios (PLRs), negative likelihood ratios (NLRs), and diagnostic odds ratio with their corresponding 95% confidence intervals (CIs) were calculated with the bivariate regression model. Additionally, the summary receiver operator characteristic (SROC) curve with the area under the SROC curve was calculated [38]. What is more, the Q test and the I2 measure of inconsistency were used to quantify heterogeneity between studies [39]. The possibility of publication bias was finally explored by Deeks’ funnel plot, and P values < 0.1 were considered significant.

    Bioinformatic analysis

    In silico analysis of target genes of miR-101

    MiRWalk2.0 [40] (http://zmf.umm.uni-heidelberg.de/apps/zmf/mirwalk2/), which combines 12 existing miRNA-target prediction programs, was used to provide comprehensive potential targets for miR-101-3p and miR-101-5p. The genes identified by more than eight prediction software programs for miR-101-3p and more than six for miR-101-5p were selected to obtain more reliable targets.

    Natural language processing

    Natural language processing (NLP) is a novel computerized approach to analyze electronic free text to achieve ‘human-like language processing’. With this approach, programmers create software to ‘read’ text and extract key pieces of information from clinician notes, procedure/radiology/pathology reports and laboratory results [41, 42]. We performed a literature search in PubMed to obtain all related electronic records. The detailed process was described in our previous article [43, 44]. Finally, 1800 genes that were differentially expressed in HCC were identified for further analysis.

    Functional and signaling pathway analyses

    A set of condition-specific genes from the overlapping genes from the target prediction software and NLP further underwent functional and signaling pathway analyses on a public database platform, the Database for Annotation, Visualization and Integrated Discovery (DAVID; https://david.ncifcrf.gov/), which provides a functional interpretation of massive gene lists derived from genomic studies. The analyses included Gene Ontology (GO) function analysis (http://www.geneontology.org/) and Kyoto Encyclopedia of Genes and Genomes (KEGG; http://www.genome.jp/kegg/) analysis. The GO function analysis categorized selected genes into groups in accordance with three independent classification standards, BPs, cellular components (CCs), and molecular functions (MFs). The top 10 terms of each GO category and top 30 pathways of the KEGG pathways were visualized as GO maps and KEGG maps, separately, via cytoscape v3.4.0 (http://cytoscape.org/).

    Protein–protein interaction network construction

    Overlapping genes were inputted to the string v10.0 online tool (http://string-db.org/) to construct the protein–protein interaction (PPI) network. The direct (physical) and indirect (functional) associations of proteins were derived from four methods: (a) literature-reported protein interactions, (b) high-throughput experiments, (c) genome analysis and prediction and (d) coexpression studies. By scrutinizing the connectivity degrees of the nodes in the PPI networks, we determined the hub genes. A node with a high degree of connectivity is perceived as a hub node.

    Results

    Clinicopathological significance of miR-101-1/miR-101-2 in HCC tissues

    The relationship between miR-101-1/miR-101-2 and clinicopathological parameters in HCC was mined from TCGA, as shown in Tables 1 and 2. Data profiling revealed that when compared with the expression in para-non-cancerous normal tissues (16.93 ± 0.62), miR-101-1 expression was significantly reduced in HCC tissues (15.25 ± 1.05, P < 0.001). In addition, the AUC of the low miR-101-1 level for HCC diagnosis was 0.925 (95% CI: 0.896–0.953, P < 0.001, Fig. 2) with a cut-off value of 16.17 (82.6% sensitivity and 90.0% specificity). Similar results were also obtained for miR-101-2 (15.27 ± 1.05 in HCC and 16.94 ± 0.61 in para-non-cancerous liver tissues, P < 0.001). Additionally, the altered expressions of miR-101-1 and miRNA-101-2 were both associated with pathological stage, pathological T stage and histological stage. Compared with the expression in advanced stage (III and IV) HCC patients, the relative expression of miR-101 in early stage patients was notably increased (I and II, P < 0.05), and the Spearman correlation test confirmed that the correlations between miR-101 and the pathological stage, pathological T stage and histological stage were r = −0.17, P = 0.001; r = −0.17, P = 0.001 and r = −0.18, P < 0.001, respectively.

    Table 1. Relationship between miR-101-1 and clinicopathological parameters in HCC (TCGA data)
    Clinicopathological feature miR-101-1 relative expression Correlation analysis
    n Mean ± SD t P r P
    Tissue
    HCC 362 15.25 ± 1.05 −16.198 0.000 0.480 0.000
    Normal 50 16.93 ± 0.62
    Gender
    Male 247 15.23 ± 1.03 −0.542 0.588 0.015 0.774
    Female 115 15.29 ± 1.09
    Age
    < 50 years 68 15.06 ± 1.13 −1.708 0.089 0.031 0.559
    ≥ 50 years 290 15.30 ± 1.03
    HBV
    255 15.20 ± 1.08 1.347 0.179 0.079 0.133
    + 106 15.37 ± 0.98
    HCV
    308 15.22 ± 1.05 1.407 0.160 0.054 0.309
    + 54 15.44 ± 1.01
    Pathological stage
    Stage I–II 250 15.34 ± 1.01 3.276 0.001 −0.174 0.001
    Stage III–IV 88 14.92 ± 1.16
    Pathological T
    T1–T2 268 15.33 ± 1.01 2.867 0.004 −0.173 0.001
    T3–T4 92 14.97 ± 1.12
    Histological grade
    GI–II 223 15.40 ± 1.05 3.467 0.001 −0.184 0.000
    GIII–IV 135 15.00 ± 1.02
    Table 2. Relationship between miR-101-2 and clinicopathological parameters in HCC (TCGA data)
    Clinicopathological feature miR-101-2 relative expression Correlation analysis
    n Mean ± SD t P r P
    Tissue
    HCC 362 15.27 ± 1.05 −16.256 0.000 0.481 0.000
    Normal 50 16.94 ± 0.61
    Gender
    Male 247 15.25 ± 1.03 −0.533 0.594 0.016 0.760
    Female 115 15.31 ± 1.09
    Age
    < 50 years 68 15.08 ± 1.13 −1.709 0.088 0.032 0.550
    ≥ 50 years 290 15.32 ± 1.03
    HBV
    255 15.22 ± 1.07 1.347 0.179 0.079 0.133
    + 106 15.39 ± 0.98
    HCV
    308 15.24 ± 1.05 1.425 0.155 0.054 0.309
    + 54 15.46 ± 1.01
    Pathological stage
    Stage I–II 250 15.36 ± 1.01 3.309 0.001 −0.174 0.001
    Stage III–IV 88 14.93 ± 1.15
    Pathological T
    T1–T2 268 15.35 ± 1.00 2.904 0.004 −0.174 0.001
    T3–T4 92 14.99 ± 1.12
    Histological grade
    GI–II 223 15.41 ± 1.04 3.452 0.001 −0.184 0.000
    GIII–IV 135 15.03 ± 1.02
    Details are in the caption following the image
    MiR-101 expression profiles for the diagnosis of HCC. The AUC of the low miR-101 level for HCC diagnosis was 0.925 (95% CI: 0.896–0.953, P < 0.001).

    Diagnostic value of miR-101-3p and miR-101-5p

    Study selection

    Through the literature search, 341 relevant articles were identified, 339 of which were excluded for being case reports, reviews, letters, repeat publications and studies not specifically pertaining to miR-101-3p/5p. The two remaining publications were examined by three researchers and ultimately included. Moreover, GEO microarrays that detected miR-101-3p and/or miR-101-5p were identified for further analysis and were combined after assessment. Finally, 12 datasets including 315 HCC and 330 normal control samples were downloaded from the GEO database to calculate the miR-101-3p diagnostic value (GSE39678, GSE21279, GSE67882, GSE65708, GSE12717, GSE10694, GSE22058, GSE21362, GSE40744, GSE41874, GSE54751 and GSE57555); five datasets including 308 HCC and 114 normal control samples were downloaded from the GEO database to calculate the miR-101-5p diagnostic value (GSE74618, GSE21362, GSE40744, GSE41874 and GSE57555). In addition, the precursors of miR-101 identified from TCGA were also considered.

    Heterogeneity analysis

    The analysis of heterogeneity is widely used to evaluate the accuracy of statistical pooling from multiple studies [45]. Since heterogeneities may come from a threshold effect and a non-threshold effect, the threshold effect was first explored by the Spearman test to calculate the heterogeneity of miR-101-3p/5p among the included studies. In other words, the correlation coefficient and P value between the logit of sensitivity and logit of 1 – specificity were calculated. As a result, the Spearman correlation coefficients for miR-101-3p and miR-101-5p were 0.386 (P = 0.215) and −0.059 (P = 0.912), respectively, indicating that heterogeneity from the threshold effect was not found. However, the I2 values in the forest plots of sensitivity and specificity (more than 50%) revealed that we cannot ignore the non-threshold effect from the included studies.

    Diagnostic accuracy of miR-101-3p and miR-101-5p in HCC

    Evident heterogeneity for pooled sensitivity and specificity of miR-101-3p was seen in the collected data (I2 = 87.02% and 83.73%, respectively, P < 0.05); thus, a random effects model was finally selected, based on which the pooled sensitivity and specificity of miR-101-3p was 78.0% (95% CI: 65.0–88.0%) and 79.0% (95% CI: 67.0–88.0%), respectively (Fig. 3A). In addition, the summary SROC of miR-101-3p was 0.86 (95% CI: 0.82–0.89; Fig. 3B). In addition, the PLR and NLR for HCC diagnosis were 3.803 (95% CI: 2.398–6.033) and 0.272 (95% CI: 0.169–0.439), respectively. Furthermore, the pre-test probability was 20 for miR-101-3p, and the corresponding positive and negative post-test probabilities of miR-101-3p were 49 and 6, respectively, suggesting that the power of miR-101-3p to diagnose real patients as HCC was 3.8 times the normal control (Fig. 4A). In addition, the likelihood ratio scattergram disclosed that the summary point of the PLR together with the NLR lies in the right lower quadrant (PLR < 10, NLR > 0.1; Fig. 4B).

    Details are in the caption following the image
    Diagnostic accuracy of miR-101-3p in HCC. (A) Sensitivity (SENS) and specificity (SPEC) with corresponding heterogeneity statistics. (B) SROC curves for miR-101-3p with CI in the diagnosis of HCC.
    Details are in the caption following the image
    Fagan diagram and likelihood matrix for miR-101-3p to diagnose cancer or to eliminate the diagnosis of cancer. (A) Pre-test probability of the miR-101-3p assay in HCC detection. (B) Likelihood matrix showing individual (circles) and pooled (diamond) values of PLRs combined with NLRs. LLQ, left lower quadrant; LUQ, left upper quadrant; RLQ, right lower quadrant; RUQ, right upper quadrant.

    With regard to miR-101-5p, a random effects model was also selected for further analysis (I2 = 78.79% and 92.24%, for sensitivity and specificity, respectively, P < 0.01). The overall sensitivity and specificity were 79.0% (95% CI: 75.0–83.0%) and 60.0% (95% CI: 27.0–86.0%), respectively (Fig. 5A). The calculated AUC of the summary SROC was 0.80 (95% CI: 0.76–0.83; Fig. 5B). Additionally, the PLR was 1.981 (95% CI: 0.831–4.721), and the NLR was 0.349 (95% CI: 0.174–0.698); the pre-test probability of miR-101-5p was 20, and the corresponding positive and negative post-test probability of miR-101-5p was 33 and 8, respectively, suggesting that the power of miR-101-5p to diagnose real patients as HCC was 1.98 times the normal control (Fig. 6A). In addition, the summary point of the PLR combined with the NLR also lies in the right lower quadrant (PLR < 10, NLR > 0.1), which was consistent with the Fagan's nomogram result (Fig. 6B).

    Details are in the caption following the image
    Diagnostic accuracy of miR-101-5p in HCC. (A) Sensitivity (SENS) and specificity (SPEC) with corresponding heterogeneity statistics. (B) SROC curves for miR-101-5p with CI in the diagnosis of HCC.
    Details are in the caption following the image
    Fagan diagram and likelihood matrix for miR-101-5p to diagnose cancer or to eliminate the diagnosis of cancer. (A) Pre-test probability of the miR-101-5p assay in HCC detection. (B) Likelihood matrix showing individual (circles) and pooled (diamond) values of PLRs combined with NLRs. LLQ, left lower quadrant; LUQ, left upper quadrant; RLQ, right lower quadrant; RUQ, right upper quadrant.

    Publication bias

    Publication bias was conducted by using the Deeks’ funnel plot asymmetry test. According to the results, the funnel plots that represented every study were almost symmetric, suggesting that publication bias from the studies included was absent in our study. The obtained P-values of 0.718 and 0.447 for miR-101-3p and miR-101-5p, respectively, also revealed the absence of publication bias (Fig. 7).

    Details are in the caption following the image
    The Deeks’ test that assesses potential publication bias in the miR-101 assay. (A) Potential publication bias assessment of miR-101-3p. (B) Potential publication bias assessment of miR-101-5p.

    Bioinformatic analysis

    To improve understanding of the function of miR-101, the potential target genes of miR-101-3p and miR-101-5p in HCC were identified separately. Based on the prediction software and NLP, 73 target genes corresponding to miR-101-3p and 90 target genes corresponding to miR-101-5p were obtained. Subsequently, bioinformatic analyses were conducted to investigate the function and pathways of target genes of miR-101 associated with HCC. All of the target genes were inputted into DAVID for bioinformatic analysis.

    KEGG pathway enrichment analysis

    Our study revealed that 23 KEGG pathways corresponding to miR-101-3p were enriched, from which the top five pathways in which target genes were enriched were (a) the adherens junction pathway (hsa04520: P = 8.53 × 10−4), (b) the leishmaniasis pathway (hsa05140: P = 8.53 × 10−4), (c) the prolactin signaling pathway (hsa04917: P = 8.53 × 10−4), (d) pathways in cancer (hsa05200: P = 0.002) and (e) the mitogen-activated protein kinase (MAPK) signaling pathway (hsa04010: P = 0.004). For miR-101-5p, 90 KEGG pathways were enriched, and the top pathways were (a) pathways in cancer (hsa05200: P = 7.08 × 10−16), (b) the forkhead box O (FoxO) signaling pathway (hsa04068: P = 5.59 × 10−14), (c) hepatitis B (hsa05161: P = 2.82 × 10−12), (d) the phosphoinositide-3-kinase (PI3K)–Akt signaling pathway (hsa04151: P = 7.91 × 10−11) and (e) the prolactin signaling pathway (hsa04917: P = 2.43 × 10−8). The top 30 pathways associated with miR-101-3p and miR-101-5p are shown in Figs 8 and 9, respectively, and all of the pathways are displayed in Table 3.

    Details are in the caption following the image
    The KEGG pathway analysis of miR-101-3p predicted target genes in HCC. Pathway analyses were performed to identify significantly enriched pathways by using cytoscape v3.4.0. The top 30 pathways are displayed; the map node size represents the P value of targets, low values are indicated by large nodes, and the node color represents the gene count number with low values indicated by pink.
    Details are in the caption following the image
    The KEGG pathway analysis of miR-101-5p predicted target genes in HCC. Pathway analyses were performed to identify significantly enriched pathways by using cytoscape v. 3.4.0. The top 30 pathways are displayed; the map node size represents the P value of targets, low values are indicated by large nodes, and the node color represents the gene count number with low values indicated by pink.
    Table 3. KEGG functional annotation for most significantly related targets of miR-101
    KEGG ID Term Gene no. P Genes
    MiR-101-3p
    hsa04520 Adherens junction 5 8.53 × 10−4 MAP3K7, MAPK1, TGFBR1, NLK, SSX2IP
    hsa05140 Leishmaniasis 5 8.53 × 10−4 MAP3K7, MAPK1, FOS, PTGS2, JAK2
    hsa04917 Prolactin signaling pathway 5 8.53 × 10−4 MAPK1, FOS, SOCS2, GSK3B, JAK2
    hsa05200 Pathways in cancer 9 2.18 × 10−3 CEBPA, MAPK1, FOS, PTGS2, TGFBR1, GSK3B, PTCH1, CDK6, CXCL12
    hsa04010 MAPK signaling pathway 7 4.09 × 10−3 MAP3K7, MAPK1, FOS, DUSP1, TGFBR1, NLK, SRF
    hsa05210 Colorectal cancer 4 6.16 × 10−3 MAPK1, FOS, TGFBR1, GSK3B
    hsa04360 Axon guidance 5 7.10 × 10−3 MAPK1, EPHA7, NRP1, GSK3B, CXCL12
    hsa05162 Measles 5 8.34 × 10−3 MAP3K7, GSK3B, IL13, CDK6, JAK2
    hsa04068 FoxO signaling pathway 5 8.56 × 10−3 MAPK1, TGFBR1, NLK, CCNG2, BCL2L11
    hsa05166 HTLV-I infection 6 1.86 × 10−2 FOS, NRP1, ETS1, TGFBR1, GSK3B, SRF
    MiR-101-5p
    hsa05200 Pathways in cancer 35 7.08 × 10−16 XIAP, PTGS2, FOXO1, MMP1, TPM3, CCNE2, IGF1R, KRAS, CDKN2B, BCL2, SOS1, ITGAV, MYC, AKT3, PRKCA, BMP4, IL6, RALBP1, TGFBR1, CREBBP, et al.
    hsa04068 FoxO signaling pathway 21 5.59 × 10−14 IL6, SGK3, TGFBR1, CREBBP, SMAD4, SMAD3, FOXO1, SMAD2, MAPK10, IL7R, CCNG2, BCL2L11, ATM, IGF1R, NRAS, MAPK1, KRAS, CDKN2B, CCND2, SOS1, AKT3
    hsa05161 Hepatitis B 20 2.82 × 10−12 PRKCA, IL6, YWHAZ, TGFBR1, CREBBP, MAP2K4, CYCS, SMAD4, CDK6, MAPK10, STAT2, CCNE2, NRAS, MAPK1, KRAS, DDX3X, BCL2, NFATC2, MYC, AKT3
    hsa04151 PI3K–Akt signaling pathway 27 7.91 × 10−11 YWHAZ, RPS6KB1, IL7R, CCNE2, IGF1R, KRAS, SOS1, ITGAV, BCL2, ANGPT2, MYC, AKT3, GHR, PRKCA, IL6, FLT1, SGK3, MET, CDK6, BCL2L11, et al.
    hsa04917 Prolactin signaling pathway 12 2.43 × 10−8 MAPK1, NRAS, KRAS, TNFRSF11A, SOCS2, PRLR, CCND2, SOS1, ESR1, ESR2, MAPK10, AKT3
    hsa04520 Adherens junction 12 2.43 × 10−8 MAP3K7, MAPK1, IGF1R, TGFBR1, CREBBP, MET, SMAD4, SMAD3, SMAD2, WASL, YES1, CTNNA1
    hsa05220 Chronic myeloid leukemia 12 2.83 × 10−8 MAPK1, NRAS, KRAS, CRKL, HDAC2, TGFBR1, SOS1, SMAD4, CDK6, MYC, AKT3, PTPN11
    hsa05205 Proteoglycans in cancer 18 3.80 × 10−8 PRKCA, ERBB4, MET, ESR1, RPS6KB1, SDC2, FZD7, PTPN11, IGF1R, NRAS, MAPK1, KRAS, ITGAV, SOS1, MYC, FRS2, AKT3, TWIST1
    hsa05210 Colorectal cancer 11 7.32 × 10−8 MAPK1, KRAS, TGFBR1, BCL2, CYCS, SMAD4, SMAD3, SMAD2, MAPK10, MYC, AKT3
    hsa04630 Jak–STAT signaling pathway 15 1.37 × 10−7 IL6, SOCS2, IL6ST, LEPR, CREBBP, IL7R, STAT2, PTPN11, LEP, PRLR, CCND2, SOS1, MYC, AKT3, GHR

    GO enrichment analysis

    As shown in Tables 4 and 5, the GO enrichment was composed of the BP, CC and MF categories. In the BP category for miR-101-3p, we can observe that the 73 target genes were mainly enriched in (a) positive regulation of transcription from the RNA polymeraseIIpromoter (GO: 0045944, P = 1.90 × 10−8), (b) positive regulation of transcription, DNA-templated (GO: 0045893, P = 3.19 × 10−8) and (c) response to estradiol (GO: 0032355, P = 4.40 × 10−5). In addition, in the CC category, (a) nucleoplasm (GO: 0005654, P = 2.50 × 10−7), (b) nucleus (GO: 0005634, P = 1.24 × 10−5) and (c) external side of plasma membrane (GO: 0009897, P = 0.002) remained the top three enriched items. With regard to MF, the top ranking three items were protein binding (GO: 0005515, P = 1.67 × 10−9), RNA polymerase II core promoter proximal region sequence-specific DNA binding (GO: 0000978, P = 2.20 × 10−6) and transcriptional activator activity, RNA polymerase II core promoter proximal region sequence-specific binding (GO: 0001077, P = 6.46 × 10−6). In the same way, the top three enriched pathways of the 119 target genes of miR-101-5p in the BP category were (a) negative regulation of the apoptotic process (GO: 0043066, P = 2.01 × 10−13), (b) positive regulation of transcription, DNA-templated, negative regulation of transcription from the RNA polymerase II promoter (GO: 0045893, P = 2.34 × 10−11) and (c) positive regulation of transcription from the RNA polymerase II promoter (GO: 0045944, P = 5.43 × 10−11). For the CC category, (a) cytosol (GO: 0005829, P = 3.59 × 10−8), (b) nucleoplasm (GO: 0005654, P = 1.23 × 10−5) and (c) external side of the plasma membrane (GO: 0009897, P = 1.50 × 10−5) were the most enriched terms. In addition, in the MF category, the utmost significant three items were (a) protein binding (GO: 0005515, P = 5.59 × 10−14), (b) transcription factor binding (GO: 0008131, P = 1.69 × 10−8) and (c) transcriptional repressor activity, RNA polymerase II core promoter proximal region sequence-specific binding (GO: 0001078. P = 3.79 × 10−6). All of the GO enrichment items are visualized in the GO network (Figs 10 and 11).

    Table 4. GO functional annotation for most significantly related targets of miR-101-3p
    GO ID Term Gene no. P Genes
    BP
    GO: 0045944 Positive regulation of transcription from RNA polymerase II promoter 20 1.90 × 10−8 CEBPA, SOX6, ZEB1, ZIC1, TET2, SOX9, PROX1, SRF, MYCN, PGR, MEF2D, FOS, ETS1, ZNF148, GSK3B, ASH1L, NEUROD1, TCF4, BCL9, SMARCA4
    GO: 0045893 Positive regulation of transcription, DNA-templated 15 3.19 × 10−8 KLF6, RSF1, TGFBR1, ARID1A, SOX9, ZIC1, PROX1, MYCN, MAPK1, FOS, ETS1, NEUROD1, PTCH1, TCF4, SMARCA4
    GO: 0032355 Response to estradiol 6 4.40 × 10−5 DUSP1, SOCS2, PTGS2, ETS1, EZH2, PTCH1
    GO: 0008285 Negative regulation of cell proliferation 10 4.72 × 10−5 CEBPA, PTGS2, ETS1, CDK6, JAK2, ZEB1, ARID2, PROX1, SRF, CDH5
    GO: 0001764 Neuron migration 6 8.71 × 10−5 NRP1, GJA1, PAFAH1B1, TOP2B, CXCL12, SRF
    GO: 0001701 In utero embryonic development 7 1.49 × 10−4 TGFBR1, MYO1E, GJA1, PTCH1, SOX6, SRF, BCL2L11
    GO: 0000122 Negative regulation of transcription from RNA polymerase II promoter 12 2.32 × 10−4 CUL3, CEBPA, JDP2, ZNF148, EZH2, PTCH1, ARID1A, SOX6, ZEB1, TCF4, PROX1, SMARCA4
    GO: 0006366 Transcription from RNA polymerase II promoter 10 3.32 × 10−4 CEBPA, MEF2D, FOS, ZNF148, ETS1, ASH1L, NEUROD1, ZIC1, SOX9, SRF
    GO: 0018107 Peptidyl-threonine phosphorylation 4 5.73 × 10−4 MAPK1, TGFBR1, GSK3B, NLK
    GO: 0007179 Transforming growth factor beta receptor signaling pathway 5 6.53 × 10−4 MAP3K7, FOS, TGFBR1, NLK, CDH5
    CC
    GO: 0005654 Nucleoplasm 30 2.50 × 10−7 ING3, RSF1, XPO5, XPO4, EZH2, ZEB1, SOX6, ZIC1, SOX9, SRF, ARID2, LARP1, CUL3, PGR, FOS, FBXW7, ZNF148, TOP2B, BCL9, NLK, et al.
    GO: 0005634 Nucleus 40 1.24 × 10−5 ING3, JDP2, RSF1, PTGS2, XPO5, EZH2, SOX6, ZEB1, SOX9, ZIC1, TIMP3, SRF, MAP3K7, CUL3, PGR, FOS, MSI1, SSX2IP, TOP2B, TCF4, et al.
    GO: 0009897 External side of plasma membrane 6 1.54 × 10−3 CLCN3, FGA, IL13, ABCA1, CXCL12, CDH5
    GO: 0005901 Caveola 4 2.17 × 10−3 MAPK1, PTGS2, PTCH1, JAK2
    GO: 0009986 Cell surface 8 5.50 × 10−3 CLCN3, NRP1, FGA, TGFBR1, CFTR, SPARC, CDH5, SLC7A11
    GO: 0043234 Protein complex 7 5.73 × 10−3 MAPK1, FBXW7, PTGS2, CFTR, SSX2IP, SOX9, SMARCA4
    GO: 0000790 Nuclear chromatin 5 7.16 × 10−3 EZH2, ARID1A, TCF4, SRF, SMARCA4
    GO: 0005737 Cytoplasm 31 1.21 × 10−2 ING3, PTGS2, XPO5, XPO4, EZH2, IL13, ZEB1, ZIC1, CCNG2, SRF, LIN28B, LARP1, MAP3K7, FBXW7, MSI1, TOP2B, ZMYM2, SOCS2, MYO1E, CFTR, et al.
    GO: 0005769 Early endosome 5 1.28 × 10−2 MAPK1, CLCN3, NRP1, GJA1, CFTR
    GO: 0005794 Golgi apparatus 9 2.02 × 10−2 CUL3, MAPK1, CLCN3, ZNF148, ASH1L, GJA1, PTCH1, ABCA1, BCL9
    MF
    GO: 0005515 Protein binding 62 1.67 × 10−9 JDP2, NRP1, RSF1, PTGS2, XPO5, XPO4, EZH2, IL13, GJA1, ZEB1, LARP1, MAP3K7, PGR, CUL3, FOS, ZNF148, SOCS2, CFTR, ARID1A, CDK6, et al.
    GO: 0000978 RNA polymerase II core promoter proximal region sequence-specific DNA binding 11 2.20 × 10−6 PGR, CEBPA, MEF2D, FOS, JDP2, ZNF148, NEUROD1, TCF4, ZIC1, SRF, SMARCA4
    GO: 0001077 Transcriptional activator activity, RNA polymerase II core promoter proximal region sequence-specific binding 9 6.46 × 10−6 PGR, CEBPA, MEF2D, FOS, NEUROD1, TCF4, ZIC1, SOX9, SRF
    GO: 0008134 Transcription factor binding 9 2.47 × 10−5 CEBPA, MAPK1, FOS, ETS1, NLK, NEUROD1, ZEB1, SRF, SMARCA4
    GO: 0003700 Transcription factor activity, sequence-specific DNA binding 15 3.92 × 10−5 CEBPA, JDP2, SOX6, ZEB1, SOX9, ZIC1, PROX1, SRF, MYCN, PGR, MEF2D, FOS, ETS1, NEUROD1, TCF4
    GO: 0003677 DNA binding 20 4.29 × 10−5 CEBPA, KLF6, ZMYM2, EZH2, ARID1A, SOX6, ZEB1, TET2, ARID2, LIN28B, PROX1, MYCN, PGR, MAPK1, FOS, SP2, ETS1, ASH1L, TOP2B, TCF4
    GO: 0030332 Cyclin binding 4 7.72 × 10−5 CUL3, FBXW7, PTCH1, CDK6
    GO: 0046982 Protein heterodimerization activity 10 1.38 × 10−4 CUL3, MEF2D, FOS, CLCN3, JDP2, NEUROD1, SOX6, TOP2B, TCF4, SOX9
    GO: 0003682 Chromatin binding 9 2.29 × 10−4 FOS, JDP2, EZH2, ASH1L, NEUROD1, ZEB1, TOP2B, TCF4, SOX9
    GO: 0005102 Receptor binding 8 6.95 × 10−4 PGR, FGA, CADM2, GJA1, JAK2, ABCA1, CXCL12, CDH5
    Table 5. GO functional annotation for most significantly related targets of miR-101-5p
    GO ID Term Gene no. P Genes
    BP
    GO: 0043066 Negative regulation of apoptotic process 29 2.01 × 10−13 YWHAZ, MTDH, ERBB4, XIAP, IL6ST, FOXO1, PRKDC, RPS6KB1, IGF1R, DDX3X, BCL2, TPT1, GLO1, MYC, TWIST1, BMP4, IL6, TBX3, SOCS2, SMAD3, et al.
    GO: 0045893 Positive regulation of transcription, DNA-templated 28 2.34 × 10−11 RSF1, ERBB4, FOXO1, ZIC1, ASPH, NFATC2, MYC, BMP4, KLF6, IL6, TBX3, TGFBR1, CREBBP, SMAD5, SMAD4, ESR1, ATAD2, SMAD3, SMAD2, ESR2, et al.
    GO: 0045944 Positive regulation of transcription from RNA polymerase II promoter 38 5.43 × 10−11 PRKDC, FOXO1, ZEB2, NR3C1, SOX6, ZEB1, ZIC1, PGR, IL17A, BARX2, CDKN2B, DDX3X, ZNF148, NFATC2, YES1, MYC, TWIST1, CKAP2, BMP4, IL6,et al.
    GO: 0000122 Negative regulation of transcription from RNA polymerase II promoter 32 9.99 × 10−11 JDP2, MTDH, USP2, FOXO1, ZEB2, SOX6, ZEB1, BARX2, ZNF148, NFATC2, MYC, TWIST1, BMP4, DAB2IP, TBX3, YY1, CREBBP, SMAD4, ESR1, KLF17, et al.
    GO: 0008284 Positive regulation of cell proliferation 25 4.77 × 10−10 ERBB4, IL6ST, IGF1R, CD47, KRAS, TNFRSF11A, ITGAV, BCL2, MYC, IL6, FLT1, KLB, TBX3, TGFBR1, PROX1, TET1, LEP, MAPK1, HDAC2, CRKL, et al.
    GO: 0043065 Positive regulation of apoptotic process 19 7.80 × 10−9 BMP4, IL6, DAB2IP, ERBB4, PTGS2, PRKDC, FOXO1, FRZB, LATS1, BCL2L11, ATM, BAK1, TRIM35, ITGA6, DDX3X, SFRP1, ATG7, SOS1, UNC5C
    GO: 0050900 Leukocyte migration 12 1.23 × 10−7 NRAS, CD47, KRAS, ITGA6, ITGAV, SOS1, TREM1, YES1, ANGPT2, MMP1, SLC7A11, PTPN11
    GO: 0008285 Negative regulation of cell proliferation 18 2.36 × 10−6 BMP4, IL6, DAB2IP, ERBB4, PTGS2, SMAD4, SMAD2, CDK6, ZEB1, FRZB, ARID2, PROX1, SLIT3, BAK1, SPRY1, CDKN2B, SFRP1, MDM4
    GO: 0001568 Blood vessel development 7 3.73 × 10−6 MIB1, LAMA4, CRKL, TBX3, ITGAV, FOXO1, AHR
    GO: 0071498 Cellular response to fluid shear stress 5 7.02 × 10−6 MTSS1, PTGS2, CA2, NFE2L2, TFPI2
    CC
    GO: 0005829 Cytosol 67 3.59 × 10−8 RPL36A, FOXO1, RPS6KB1, LATS1, MAP3K7, CCNE2, BAK1, SPRY1, GSTM3, CDKN2B, MAT1A, ATG7, MYC, PRKCA, DAB2IP, SOCS2, SGK3, RALBP1, G3BP1, CYCS, et al.
    GO: 0005654 Nucleoplasm 53 1.23 × 10−5 RSF1, XPO4, FOXO1, RPS6KB1, ZEB1, ZIC1, PGR, CCNE2, SPRY1, CDKN2B, ZNF148, MYC, AKT3, PRKCA, DTL, ESR1, CDK6, ESR2, AHR, MCM6, et al.
    GO: 0009897 External side of plasma membrane 12 1.50 × 10−5 EPHA5, VCAM1, CLCN3, IL17A, IL6, TNFRSF11A, ITGA6, CD40LG, IL6ST, ITGAV, CD274, IL7R
    GO: 0005634 Nucleus 84 2.85 × 10−5 JDP2, RSF1, PTGS2, CPEB4, FOXO1, ZEB2, RPS6KB1, ZEB1, ZIC1, MAP3K7, CCNE2, PGR, GSTM3, BARX2, CDKN2B, TPT1, LOX, TFPI2, MYC, ANGPT2, et al.
    GO: 0005737 Cytoplasm 81 4.65 × 10−5 MTSS1, PTGS2, XPO4, CPEB4, FOXO1, RPS6KB1, ZEB1, ZIC1, MAP3K7, SPRY1, GSTM3, CDKN2B, ATG7, TPT1, FRS2, AKT3, PRKCA, DAB2IP, SOCS2, LPGAT1, et al.
    GO: 0071141 SMAD protein complex 4 6.01 × 10−5 SMAD5, SMAD4, SMAD3, SMAD2
    GO: 0005667 Transcription factor complex 10 1.97 × 10−4 BARX2, YY1, SMAD5, SMAD4, SMAD3, PRKDC, SMAD2, DACH1, ZEB1, AHR
    GO: 0043235 Receptor complex 8 3.74 × 10−4 IGF1R, FLT1, ERBB4, LEPR, TGFBR1, NTRK2, SMAD3, GHR
    GO: 0009986 Cell surface 16 5.76 × 10−4 CLCN3, TGFBR1, MET, RPS6KB1, CFTR, SDC2, SLC7A11, VCAM1, ITGA6, SULF2, PRLR, SFRP1, CD40LG, ITGAV, CNTN2, GHR
    GO: 0005622 Intracellular 27 1.45 × 10−3 PRKCA, KLB, TGFBR1, G3BP1, SMAD5, SOCS6, SMAD4, DCDC2, SMAD3, MAPK10, LATS1, SEC63, WSB1, MAPK1, RND3, NRAS, TRIM35, KRAS, SFRP1, TRIM33, et al.
    MF
    GO: 0005515 Protein binding 147 5.59 × 10−14 MTSS1, RPL36A, JDP2, PTGS2, IL6ST, XPO4, FOXO1, RPS6KB1, PGR, MAP3K7, CD47, BAK1, CDKN2B, ATG7, TPT1, ASPH, LOX, FRS2, TWIST1, DAB2IP, et al.
    GO: 0008134 Transcription factor binding 18 1.69 × 10−8 YWHAZ, CREBBP, ESR1, SMAD3, PRKDC, SMAD2, ZEB1, AHR, MAPK1, HDAC2, SP1, DDX3X, PSMD10, ATG7, BCL2, NFATC2, MYC, TWIST1
    GO: 0001078 Transcriptional repressor activity, RNA polymerase II core promoter proximal region sequence-specific binding 10 3.79 × 10−6 JDP2, TBX3, ZNF148, YY1, CREBBP, KLF17, FOXO1, DACH1, NFATC2, PROX1
    GO: 0005524 ATP binding 37 5.87 × 10−6 CLCN3, ERBB4, PFKFB2, STK17B, PRKDC, RPS6KB1, LATS1, MAP3K7, IGF1R, KRAS, DDX3X, MAT1A, HSPE1, YES1, AKT3, PRKCA, FLT1, SGK3, TGFBR1, UBE4B, et al.
    GO: 0000978 RNA polymerase II core promoter proximal region sequence-specific DNA binding 15 4.05 × 10−5 JDP2, TBX3, SMAD4, ESR1, KLF17, SMAD3, SMAD2, NR3C1, ZIC1, PGR, HDAC2, SP1, ZNF148, NFATC2, MYC
    GO: 0042802 Identical protein binding 22 8.15 × 10−5 MTSS1, YWHAZ, DAB2IP, XIAP, USP2, SMAD4, ESR1, SMAD3, CLDN10, RPS6KB1, STAT2, MCM6, PBLD, IGF1R, BAK1, MAPK1, GLUL, GSTM3, SFRP1, BCL2, CNTN2, GBP1
    GO: 0008270 Zinc ion binding 29 8.19 × 10−5 RSF1, XIAP, NR3C1, ZEB1, LIN28B, MMP1, PGR, GLO1, XAF1, RCHY1, PRKCA, ZMYM2, YY1, CREBBP, ESR1, SMAD3, WHSC1, ESR2, TET2, TET1, et al.
    GO: 0004672 Protein kinase activity 14 1.79 × 10−4 PRKCA, SGK3, TGFBR1, MET, MAP2K4, STK17B, PRKDC, RPS6KB1, PBK, MAPK10, MAP3K7, MAP4K4, HIPK2, AKT3
    GO: 0043565 Sequence-specific DNA binding 17 2.04 × 10−4 JDP2, TBX3, SMAD4, ESR1, SMAD3, FOXO1, WHSC1, NR3C1, ESR2, SOX6, PGR, HDAC2, SP1, ZNF148, BCL2, NFE2L2, MYC
    GO: 0019899 Enzyme binding 13 3.32 × 10−4 PRKCA, PTGS2, UBE4B, ESR1, PRKDC, CFTR, ESR2, PGR, GSTM3, HDAC2, MDM4, YES1, GBP1
    Details are in the caption following the image
    GO functional analysis of miR-101-3p in HCC. Top 10 terms of each category are displayed, and every node represents different BP terms; the map node size represents the P value of targets, low values are indicated by large nodes, and the node color represents the gene count number with low values indicated by pink.
    Details are in the caption following the image
    GO functional analysis of miR-101-5p in HCC. Top 10 terms of each category are displayed, and every node represents different BP terms; the map node size represents the P value of targets, low values are indicated by large nodes, and the node color represents the gene count number with low values indicated by pink.

    Protein–protein interaction network

    A PPI network was designed to screen out the hub genes according to the degree to which each of the genes appeared in the network. Here, the PPI network was constructed by using the STRING database. As shown in Figs 12 and 13, FOX, SMARCA4 and MAPK1 remained the top three utmost important genes for miR-101-3p, while ESR1, KRAS, NRAS, FOXO1, CREBBP and SMAD3 were regarded as the hub genes for miR-101-5p.

    Details are in the caption following the image
    The PPI network of miR-101-3p potential targets. Both the color and the size of the nodes reflect the connectivity degrees of two nodes; nodes with a green color are perceived as hub genes.
    Details are in the caption following the image
    The PPI network of miR-101-5p potential targets. Both the color and the size of the nodes reflect the connectivity degrees of two nodes; nodes with a blue color are perceived as hub nodes.

    Discussion

    In the present study, we investigated the relationship between miR-101 expression and clinicopathological parameters. TCGA data showed that the miR-101 level was significantly lower in HCC than in para-non-cancerous liver tissues, and great diagnostic value of miR-101 in HCC was found. Additionally, miR-101 was negatively correlated with pathological stage, pathological T stage and histological grade. Since these clinicopathological parameters are indicators of tumor deterioration and progression, monitoring the level of miR-101 may have a certain significance in the progression of HCC.

    Accumulating studies have indicated that dysregulation of circulating miRNAs could be a biomarker of tumorigenesis, development and invasion in various cancers including prostate cancer, gastric cancer, ovarian cancer, breast cancer and lung cancer [30, 46-49]. A diagnostic value for circulating miR-101-3p/5p in HCC has also been reported [50, 51]. Both of these studies validated that a lower miR-101-3p/5p level had diagnostic potential for HCC. However, due to the limited number of available publications, the exact diagnostic value of miR-101 and the difference between miR-101-3p and miR-101-5p are still unclear. Alpha-fetoprotein (AFP), as the traditional marker of liver diseases, has been used for HCC diagnosis in the clinic. Recently, He et al. [33] conducted a meta-analysis with 10 data sets (879 HCC patients and 1028 controls) assessing AFP for HCC diagnosis and revealed that the AUC–SROC of pooled AFP was 0.82 (95% CI: 0.78–0.85), with sensitivity of 0.631 (95% CI: 0.552–0.703) and specificity of 0.943 (95% CI: 0.875–0.975). Here, we first combined gene expression microarray datasets from the GEO database and RNA-seq from TCGA database, as well as two studies, to further confirm the diagnostic efficacy of miR-101-3p and miR-101-5p and then discover the difference between the two mature mRNAs. Our findings suggested that the pooled diagnostic accuracy of miR-101-3p for HCC (SROC: 0.86 (95% CI: 0.82–0.89); sensitivity and specificity were 78.0% (95% CI: 65.0–88.0%) and 79.0% (95% CI: 0.67–88.0%), respectively), which showed a slightly higher diagnostic value than AFP. As for miR-101-5p, the SROC was 0.80 (95% CI: 0.76–0.83), a little bit lower than AFP, but it also showed a moderate value for HCC diagnosis, which is comparable to AFP's diagnostic value.

    Even though miR-101-3p/5p expression showed a high diagnostic value for HCC, the heterogeneity among the studies must be considered. Since our study indicated that heterogeneity from the threshold effect was absent, we deduced that the heterogeneity may be caused by the different data platforms and the large gaps between each study. Considering that the number of studies was small, we did not conduct a subgroup analysis.

    Subsequently, bioinformatic analysis was performed to determine the molecular mechanism of miR-101-3p/5p in HCC. In the past, researchers exploring the molecular mechanism of miRNAs only concentrated on one or two target genes. For example, Varambally et al. [52] first reported that EZH2 was the target gene of miR-101 (3p and 5p were not distinguished) several years ago. Another study confirmed that miR-101 (3p and 5p were not distinguished) inhibits HCC progression and metastasis through EZH2 down-regulation [53]. Liu et al. [54] identified another target gene of miR 101 (3p and 5p were not distinguished), VEGF C, which promotes invasion and migration. MCL-1 and COX-2, which play a role in tumorigenesis, have also been identified as the target genes of miR-101 (3p and 5p were not distinguished) [31, 55]. In addition, metastasis of HCC has been shown to be affected by different target genes of miR-101 (3p and 5p were not distinguished), such as STMN1 [56] and PTEN [57]. Since a single miRNA can target multiple genes to achieve its biological and clinical functions, the exploration of the relevant gene network can reveal the widespread molecular mechanism of miR-101-3p/5p. Hence, we identified potential target genes of miR-101-3p/5p in silico. Moreover, we further narrowed the list by analyzing the genes that overlapped with the differentially expressed genes of HCC identified via NLP. Next, these target genes were subjected to KEGG pathway annotation and GO enrichment analysis by using the DAVID. The target genes of both miR-101-3p and miR-101-5p are involved in pathways in cancer, hepatitis B and the MAPK signaling pathway. These results reveal that miR-101 probably contributes to the tumorigenesis and metastasis of HCC. Previous studies have reported the role of these pathways in liver cancer [58, 59]. The GO term analysis indicated that these potential target genes of miR-101-3p/5p were significantly involved in the regulation of the cell cycle and cell proliferation, which are associated with tumor occurrence or stepwise development.

    Furthermore, we constructed the PPI network with potential target genes, showing that miR-101-3p probably targets FOS, SMARCA4, MAPK1, GSK3B and JAK2 to exert its function in HCC. Li et al. [60] reported that FOS acts as a regulator of cell proliferation, differentiation and transformation, and miR-101 inhibits cell invasion and migration via down-regulation of FOS. MAPK1 has also been reported to be involved in a variety of cellular processes, such as differentiation, proliferation and development through the MAPK pathway [61]. JAK2 is a protein tyrosine kinase, and recent evidence has demonstrated that miR-101 (3p and 5p were not distinguished) inhibits breast cancer cell proliferation and promotes apoptosis by targeting JAK2 [49]. Previous studies reveal that miR-101-3p might regulate the occurrence and development of HCC by targeting various genes, and tumorigenesis probably results from the abnormality of multiple genes. Of course, the correlation of those potential key genes of miR-101-3p needs further experimental validation. Next, a functional analysis of these target genes in vitro and in vivo will need to be conducted, such as by RNA interference and cellular transfection, luciferase reporter assay, western blot and so on. miR-101-5p possibly targets ESR1, KRAS, CREBBP, FOXO1 and SMAD3 through different pathways. Among them, KRAS, a Kirsten ras oncogene homolog, was reported to have functional synergy with HBx in HCC initiation and progression [62], and FOXO1 has been proposed to inhibit EMT transcriptional activators in HCC [63, 64]. Additionally, Hishida et al. [65] indicated that ESR1 is a tumor suppressor gene in HCC. Taken together, the hub genes identified may perform key roles in HCC. Further investigation appears to be necessary to confirm their exact function in HCC.

    Taken together, the present study validated the down-regulation of the two opposing strands, miR-101-3p and miR-101-5p, in HCC clinical specimens; however, miR-101-3p held a greater value for HCC diagnosis. Bioinformatic analysis revealed that miR-101-3p and miR-101-5p are involved in the same or similar signaling pathways through regulating a different set of target genes. The fact that miR-101-3p and miR-101-5p are involved in these signaling pathways suggests that the expression of miR-101-3p and miR-101-5p is close in HCC tissues, and they may function cooperatively with each other in the differentiation, proliferation and development of HCC.

    In conclusion, we provide a comprehensive analysis of miR-101-3p/5p and evaluated the value of miR-101-3p and miR-101-5p as biomarkers for the early diagnosis of HCC. In addition, we investigated the prospective molecular mechanisms of these two opposing strands in silico. Our results provide a deeper understanding of the role of miR-101-3p/5p in HCC and facilitate the possible development of a miRNA-based targeted therapy of HCC. However, several limitations should be considered in this study. First, the total number of studies included was limited; second, further experiments in vitro and in vivo are still required to confirm the function of the target genes.

    Acknowledgements

    The study was supported by the Fund of Youth Science Foundation of Guangxi Medical University (GXMUYSF201624). The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

      Author contributions

      XY and Y-YP contributed equally in this study. XY, Y-YP and PL collected data and wrote the paper draft; R-QH and J-MC contributed to the analysis; JM, GC and HY designed the study, supervised the report and corrected the manuscript.