Open Access

Oncogenic value of microRNA‑15b‑5p in hepatocellular carcinoma and a bioinformatics investigation

  • Authors:
    • Wen‑Ya Pan
    • Jiang‑Hui Zeng
    • Dong‑Yue Wen
    • Jie‑Yu Wang
    • Peng‑Peng Wang
    • Gang Chen
    • Zhen‑Bo Feng
  • View Affiliations

  • Published online on: November 22, 2018     https://doi.org/10.3892/ol.2018.9748
  • Pages: 1695-1713
  • Copyright: © Pan et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

miR‑15b‑5p has frequently been reported to function as a biomarker in some malignancies; however, the function of miR‑15b‑5p in hepatocellular carcinoma (HCC) and its molecular mechanism are still not well understood. The present study was designed to confirm the clinical value of miR‑15b‑5p and further explore its underlying molecular mechanism. A comprehensive investigation of the clinical value of miR‑15b‑5p in HCC was investigated by data mining The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets as well as literature. In addition, intersected target genes of miR‑15b‑5p were predicted using the miRWalk database and differentially expressed genes of HCC from TCGA. Furthermore, gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were carried out. Then, a protein‑protein interaction network (PPI) was constructed to reveal the interactions between some hub target genes of miR‑15b‑5p. The miR‑15b‑5p expression level in HCC was predominantly overexpressed compared with non‑HCC tissues samples (SMD=0.618, 95% CI: 0.207, 1.029; P<0.0001) based on 991 HCC and 456 adjacent non‑HCC tissue samples. The pooled summary receiver operator characteristic (SROC) of miR‑15b‑5p was 0.81 (Q*=0.74), and the pooled sensitivity and specificity of miR‑15b‑5p in HCC were 72% (95% CI: 69‑75%) and 68% (95% CI: 65‑72%), respectively. Bioinformatically, 225 overlapping genes were selected as prospective target genes of miR‑15b‑5p in HCC, and profoundly enriched GO terms and KEGG pathway investigation in silico demonstrated that the target genes were associated with prostate cancer, proximal tubule bicarbonate reclamation, heart trabecula formation, extracellular space, and interleukin‑1 receptor activity. Five genes (ACACB, RIPK4, MAP2K1, TLR4 and IGF1) were defined as hub genes from the PPI network. The high expression of miR‑15b‑5p could play an essential part in hepatocarcinogenesis through diverse regulation approaches.

Introduction

Hepatocellular carcinoma (HCC), which represents an overwhelming majority of liver cancer, is the sixth most widespread cancer all over the world and the third most common cause of cancer-related deaths (1). As a health threat to people worldwide and, in particular, to developing countries, its overall 5-year survival rate is 5–9% (2). However, with early diagnosis and curative resection, the 5-year survival rate can be increased to 69% (3). Hence, a diagnostic biomarker with high efficacy is urgently needed. In current clinical work, HCC detection often hinges on a-fetoprotein (AFP), which is the most general biomarker for HCC detection. An aberrant high AFP expression level is frequently observed in HCC patients, with a sensitivity of 39–65% and a specificity of 76–94% (4). In addition, some researchers have reported that the ability of AFP to identify and differentiate HCC from non-cancerous hepatopathy is unsatisfying (57).

MicroRNAs (miRNAs) are a sequence of short noncoding RNAs that are ~20–22 nucleotides in length. They post-transcriptionally regulate the gene level by combining with their target mRNAs and play important administrative roles in a variety of biological processes (812). During the past few years, aberrant expression of miRNAs for the early discovery of HCC has been widely verified with inconsistent results. Since then, the significant high expression of miR-15b-5p in HCC patients has been demonstrated by multiple studies (13,14). Liu et al (15) detected miR-15b-5p in HCC with an AUC of 0.485 (98.25% sensitivity, 15.25% specificity). In another study, Chen et al (16) revealed that the AUC value of miR-15b-5p for HCC detection was 0.654 (68.1% sensitivity, 79.0% specificity), 0.871 (87.2% sensitivity, 74.2% specificity), and 0.765 (68.1% sensitivity, 80.0% specificity), respectively, in subgroups of HCC vs. liver cirrhosis patients, HCC vs. healthy controls, as well as HCC vs. liver cirrhosis and healthy controls. In fact, the clinical effects of miR-15b-5p on HCC have been reported by only three research groups: i) Hung et al (14) first analyzed the role of miR-15b-5p in the early diagnosis of HCC. They found that when dysplastic nodules (DN) progressed to HCC (n=10), miR-15b-5p levels significantly increased and the serum level of miR-15b-5p in 120 patients with early HCC was also upregulated as compared to that of 30 patients with chronic hepatitis B using reverse transcription-quantitative polymerase chain reaction (RT-qPCR). ii) Liu et al (15) detected the serum levels of 29 hepatitis B carriers, 57 patients with HCC and 30 healthy controls also using RT-qPCR. They revealed that the expression of miR-15b-5p was significantly higher in all HCC samples. iii) Chen et al (16) detected the expression level of miR-15b-5p in 37 patients with HCC, 29 patients with cirrhosis, and 31 healthy controls by RT-qPCR, and the results revealed that the plasma levels of miR-15b-5p in HCC patients were higher than in the other 2 groups (P<0.05). However, these studies were inconsistent their miR-15b-5p multiple ability indexes. To improve this issue, a meta-analysis of miR-15b-5p in patients with HCC grounded in data gathered from the Gene Expression Omnibus (GEO; http://www.ncbi.nlm.nih.gov/geo/) and The Cancer Genome Atlas (TCGA; http://cancergenome.nih.gov/) were utilized to evaluate the clinical effectiveness of miR-15b-5p. Bioinformatics analyses were also utilized to investigate the mechanism of miR-15b-5p in HCC. The framework of this study is displayed in Fig. 1.

Materials and methods

Excavation of TCGA and GEO

In TCGA, HCC-related resources with entries named liver hepatocellular carcinoma (LIHC) were downloaded, correlated miRNA-Seq profiles were provided and miR-15b-5p levels were extracted. To normalize the expression level of miR-15b-5p in different trials, data were log2-scaled afterwards. Expression data sets of miR-15b-5p were attained from the GEO database, and the expression levels of miR-15b-5p in other types of hepatic tissues were also gathered as the control group as well as miR-15b-5p in HCC. Aimed at more precisely assessing the potential value of miR-15b-5p, the inclusion criteria extended to various non-cancerous samples. Microarrays concerning cell lines and other species were excluded because they did not conform to our study. Based on the expression levels of miR-15b-5p in GEO and TCGA, GraphPad Prism 6.0 (GraphPad Software, Inc., La Jolla, CA, USA) was used to generate the scatter plots and ROC. Additionally, HCC patients obtained from TCGA were utilized to analyze the corresponding clinical information.

Literature search

Relevant literature was read and gathered from PubMed (https://www.ncbi.nlm.nih.gov/pubmed/), Web of Science (https://clarivate.com/products/web-of-science/), Ovid (http://www.ovid.com/site/index.jsp), EBSCO (https://www.ebsco.com/products/research-databases), Embase (https://www.elsevier.com/solutions/embase-biomedical-research), Cochrane Library (https://www.cochranelibrary.com/), Chinese CNKI (http://www.cnki.net/), China Biology Medicine disc, the Chinese Chong Qing VIP (http://en.cqvip.com/), and the Chinese Wan Fang (http://eng.med.wanfangdata.com.cn/). No language limitations were imposed. The following combination of terms with two sets of keywords were screened by combining the underlying searching strategies: (miR-15b OR miRNA-15b OR micrORNA-15b OR miR15b OR miRNA15b OR microRNA15b OR ‘miR 15b’ OR ‘miRNA 15b’ OR ‘microRNA 15b’ OR miR-15b-5p OR miRNA-15b-5p OR micrORNA-15b-5p OR micrORNA-15b OR miR-15b-5p) AND (malignan* OR cancer OR tumor OR tumour OR neoplas* OR carcinoma) AND (hepatocellular cancer OR hepatocellular tumor OR hepatocellular carcinoma OR hepatocellular neoplasm OR liver cancer OR liver tumor OR liver carcinoma OR liver neoplasm OR HCC). Human studies were limited to our literature searches. To confirm the qualified studies, the relevant studies and other references of the review papers were included to avoid missing related studies. Sufficient HCC data and control groups were required to obtain true positives (TPs), false positives (FPs), false negatives (FNs), and true negatives (TNs).

Potential target gene collection and bioinformatics analyses

To further investigate the regulatory mechanism of miR-15b-5p in HCC, MiRWalk2.0 (http://zmf.umm.uni-heidelberg.de/apps/zmf/mirwalk2/), which combines 12 online prediction programs, was used to provide comprehensive potential targets for miR-15b-5p. Genes identified by >4 prediction software programs for miR-15b-5p were selected to obtain more reliable targets. The selected predicted target genes were further intersected with TCGA differentially expressed genes. The overlapping genes were considered to be potential target genes of miR-15b-5p. We combined the two parts of the target genes of miR-15b-5p for further gene functional enrichment analyses. In addition, the genes were input to the STRING version 10.0 online tool (http://string-db.org/) to construct the protein-protein interaction (PPI) network.

DAVID 6.8 (https://david.ncifcrf.gov/) was applied for gene ontology (GO; http://geneontology.org/) and Kyoto Encyclopedia of Genes and Genomes (KEGG; http://www.genome.jp/kegg/) pathway analysis. GO was composed of three sections: Molecular function (MF), cellular component (CC) and biological process (BP). The top ten terms of each GO category and marked KEGG pathways were visualized as GO maps and KEGG maps (17,18). Protein expression of hub genes was validated by The Human Protein Atlas (HPA; www.proteinatlas.org), an immunohistochemistry (IHC) database. The IHC images are publicly available.

Diagnostic test and statistical analysis

Diagnostic tests were conducted to assess the efficacy of miR-15b-5p in HCC. To thoroughly determine its clinical potential, analyses were carried out between HCC patients and controls, including healthy controls, non-cancerous controls, adjacent non-neoplastic hepatic tissues, HBV+/HCV+ controls and liver cirrhosis controls. The efficiency of miR-15b-5p in the serum/plasma and tissues was also examined. Stata 12.0 (https://www.stata.com/stata12/) was used to detect publication bias, and the standard mean difference (SMD) was used to calculate the outcome from GEO and TCGA. The remaining analyses were accomplished by Meta-DiSc 1.4. P<0.05 was recognized as statistically significant. The summary receiver operator characteristic (SROC) curve was plotted according to the included studies. A meta-analysis was carried out using a random effects model, including SEN, SEP, diagnostic odds ratio (DOR), positive likelihood ratio (PLR), and negative likelihood ratio (NLR). In addition, the I2 index and χ2 test were used to evaluate the heterogeneity in the study. If the I2 value was over 50% or P-values of the χ2 test were <0.05, heterogeneity would be shown. Finally, Deek's funnel plot was displayed to assess the publication bias.

Results

Qualified studies and dataset

A total of 3,310 relevant articles were acquired from the aforementioned online databases by a primary search. After removing the duplicated articles as well as screening titles, abstracts and full texts, 2 studies were eventually included, and both were published in English (15,19). The chosen studies provided data from 114 HCC patients and 119 people as controls.

According to our criteria, 11 microarray datasets from GEO were evaluated as eligible, consisting of 512 HCC tissue samples and 287 control samples. Sequencing data in TCGA were based upon 425 samples, with 375 diagnosed HCC samples and 50 control samples, as shown in Table I (2027).

Table I.

Basic information and clinical data of the included studies.

Table I.

Basic information and clinical data of the included studies.

AccessionAuthorYearCountryExperiment typePlatformHCC numbersControl numbersSample typeTPFPFNTN(Refs.)
GSE57555Murakami et al2015JapanNon-coding RNA profiling by arrayGPL18044516Tissue21315(20)
GSE69580Hung et al2015TaiwanNon-coding RNA profiling by arrayGPL1085055Tissue2035Citation missing
GSE67882Ghosh et al2015IndiaNon-coding RNA profiling by arrayGPL1085048Tissue5304Citation missing
GSE54751Shen et al2015USAExpression profiling by RT-PCRGPL182621010Tissue4169(21)
GSE21362Sato et al2011JapanNon-coding RNA profiling by arrayGPL103127373Tissue49242647(22)
GSE22058Burchard et al2010USAExpression profiling byGPL104579696Tissue7292487(23)
GSE10694Li et al2008ChinaNon-coding RNA profiling by arrayGPL65427810Tissue4453483(24)
GSE12717Su et al2008ChinaNon-coding RNA profiling by arrayGPL727453Tissue7125(25)
GSE40744Diaz G et al2013USANon-coding RNA profiling by arrayGPL146131033Tissue730237(26)
GSE74618Martinez-Quetglas I et al2016SpainNon-coding RNA profiling by arrayGPL1461321820Tissue13907920(27)
GSE41874Morita et al2013JapanNon-coding RNA profiling by arrayGPL772264Tissue5014Citation missing
TCGA 2018USAmiRNA-SeqIllumina37550Tissue28546904
Liu et al2012ChinaRT-qPCR/5759Serum2701031(15)
Chen et al2015ChinaRT-qPCR/4760Plasma354421(16)

[i] HCC, hepatocellular carcinoma; TP, true positivity; FP, false positivity; FN, false negativity; TN, true negativity; RT-qPCR, reverse transcription-quantitative polymerase chain reaction.

Overall assessment of the diagnostic value and diagnostic meta-analysis

For a more comprehensive understanding of the efficiency of miR-15b-5p in HCC, the eligible 11 GSE chips that we searched were included in our meta-analysis. The expression levels that correlated with miR-15b-5p in GEO and TCGA were also displayed in Fig. 2. TCGA and 11 GEO profiles with an AUC were presented in Fig. 3. To further explore the clinicopathological features of miR-15b-5p in TCGA, all of the clinicopathological features mentioned in the chips were collected to investigate their correlation with the miR-15b-5p expression level, the results of which are provided in Table II. No noteworthy relationships were observed between miR-15b-5p expression and the clinicopathological characteristics. Compared with the non-neoplastic controls, the miR-15b-5p levels in HCC revealed that the pooled AUC, SEN, SPE, PLR, NLR and DOR were 0.81 (Q*=0.74), 0.72 (95% CI: 0.69–0.75), 0.68 (95% CI: 0.65–0.72), 3.18 (95% CI: 1.83–5.51), 0.43 (95% CI: 0.35–0.54), and 8.98 (95% CI: 4.30–18.76) (Fig. 4), Furthermore, subgroup analyses with both SMD and sROC methods were performed. The subgroups included sample sources (tissues and serum/plasma) and control types (healthy controls, adjacent non-cancerous hepatic tissues, HBV+ or HCV+ tissues, liver cirrhosis tissues, and those combining HBV+/HCV+ and cirrhosis). The results were presented from Fig. 5 to Fig. 12. The control types in healthy people and HBV+ or HCV+ patients had a favorable diagnostic accuracy with AUC-SROC over 0.9, respectively, when in tissues, serum/plasma, adjacent non-cancerous hepatic tissues, liver cirrhosis and combined HBV+/HCV+ and cirrhosis were used as the control. The AUC in the ROC curve was over 0.7.

Table II.

Relationship between the levels of miR-15b-5p and clinicopathological variables in HCC from the TCGA database.

Table II.

Relationship between the levels of miR-15b-5p and clinicopathological variables in HCC from the TCGA database.

ParametersNMean valueT-valueP-value
Group
  HCC37510.128±0.796−4.331<0.001
  Normal  509.823±0.402
Sex
  Male25410.072±0.9731.0690.009
  Female1229.886±1.802
Tumor status
  With tumor15210.207±0.818−1.6730.527
  Tumor-free20110.066±0.764
Age (years)
  <6017010.100±0.778−0.4240.711
  ≥6020110.134±0.799
Race
  Caucasian18210.074±0.819−1.1990.562
  Asian16110.176±0.742
TNM Stage
  I–II25810.119±0.7670.3460.203
  III–IV  9010.085±0.859
Pathological Stage
  G1-223110.054±0.754−2.0370.922
  G3-413710.226±0.832
T stage
  T1-227610.111±0.775−0.4710.477
  T3-4  9310.156±0.833

[i] HCC, hepatocellular carcinoma; TCGA, The Cancer Genome Atlas; TNM, tumor-node-metastasis.

With the random-effects model, forest-plots were generated to represent significant differences in expression between HCC and non-neoplastic control tissues. The pooled SMD (0.62, 95% CI: 0.21, 1.03) is presented in Fig. 12A. The results of pooled SMD between HCC and tissues, healthy subjects, adjacent non-cancerous hepatic tissues, HBV+ or HCV+ patients, liver cirrhosis, and HBV+/HCV+ combined with cirrhosis were displayed in Fig. 12. The expression level of miR-15b-5p in HCC samples was markedly overexpressed than in non-HCC tissues samples. Moreover, the Deek's funnel plot asymmetry test was carried out with STATA 12.0, and no publication bias was detected apart from healthy people and HBV+ or HCV+ patients. (P<0.05) (Fig. 13).

Potential target genes and bioinformatics annotation

Nine thousand seven-hundred-eighty target genes appearing ≥4 times in 12 prediction methods were regarded as probable target genes of miR-15b-5p from miRWalk. Furthermore, downregulated expressed genes assembled from TCGA were integrated to generate the intersection of target genes, which had more potential to be the real targets of miR-15b-5p in HCC. After 9,780 potential target genes and 1,123 TCGA differentially expressed genes were analyzed for intersection, 225 co-predicted genes from the following bioinformatics analyses were established. In respect to the bioinformatics analyses, ‘Prostate cancer’ (P=1.56×10−3), ‘Proximal tubule bicarbonate reclamation’ (P=4.10×10−3), ‘Complement and coagulation cascades’ (P=1.68×10−2), ‘Metabolic pathways’ (P=2.18×10−2), ‘Valine, leucine and isoleucine degradation’ (P=2.94×10−2), ‘Rap1 signaling pathway’ (P=3.06×10−2), ‘Arginine biosynthesis’ (P=3.26×10−2) and ‘FoxO signaling pathway’ (P=4.28×10−2) (Table III, Fig. 14A) were recognized as the most enriched KEGG pathways. For the results of GO pathway analysis in DAVID, the potential targets of miR-15b-5p were notably associated with ‘heart trabecula formation’ (P=5.26×10−4), ‘extracellular space’ (P=4.20×10−3) and ‘interleukin-1 receptor activity’ (P=2.79×10−3) (Table IV; Figs. 14B and 15). A PPI network of the 225 genes was constructed in the present study with 224 nodes and 221 edges. In the network, ACACB, RIPK4, MAP2K1, TLR4, and IGF1 were identified as the hub target genes of miR-15b-5p due to the highest significance (Figs. 1618). In the respect to the bioinformatics analyses with these hub genes, the ‘activation of MAPK activity’ (P=2.39×10−4), ‘ATP binding’ (P=4.17×10−2) were regarded as the most significant GO categories. The pathways of ‘HIF-1 signaling pathway’ (P=5.92×10−4), ‘Proteoglycans in cancer’ (P=2.45×10−3) and ‘PI3K-Akt signaling pathway’ (P=7.21×10−3) were considered to be the most significant pathways as assessed by KEGG (Tables V and VI). We also ascertained the downregulation of ACACB, RIPK4, MAP2K1, TLR4 and IGF1 in HCC tissues via TCGA data. To verify the possibility that these hub genes were targeted by miR-15b-5p, we further revealed the protein levels of ACACB, RIPK4, MAP2K1, TLR4 and IGF1 in HCC tissues and normal tissues. As revealed in Fig. 19, ACACB had medium staining and moderate intensity in cytoplasmic/membranous normal liver tissues. MAP2K1, TLR4 and IGF1 exhibited low staining and weaker intensity in cytoplasmic/membranous normal liver tissues. In addition, all these hub genes had a lower staining and weaker intensity in HCC tissues. These findings warrant further validation, as limited sample size is provided by the HPA.

Table III.

KEGG functional annotation for most significantly related targets of miR-15b-5p.

Table III.

KEGG functional annotation for most significantly related targets of miR-15b-5p.

CategoryTermCountP-value
KEGG_PATHWAYhsa05215: Prostate cancer7 1.56×10−3
hsa04964: Proximal tubule bicarbonate reclamation4 4.10×10−3
hsa04610: Complement and coagulation cascades5 1.68×10−2
hsa01100: Metabolic pathways27 2.18×10−2
hsa00280: Valine, leucine and isoleucine degradation4 2.94×10−2
hsa04015: Rap1 signaling pathway8 3.06×10−2
hsa00220: Arginine biosynthesis3 3.26×10−2
hsa04068: FoxO signaling pathway6 4.28×10−2

[i] KEGG, Kyoto Encyclopedia of Genes and Genomes.

Table IV.

GO functional annotation of the target genes of miR-15b-5p.

Table IV.

GO functional annotation of the target genes of miR-15b-5p.

GO IDCategoryGO termP-valueCount
GO:0060347GO_Biological processHeart trabecula formation 5.26×10−44
GO:0001657GO_Biological processUreteric bud development 9.93×10−45
GO:0006520GO_Biological processCellular amino acid metabolic process 1.21×10−35
GO:0032354GO_Biological processResponse to follicle-stimulating hormone 1.34×10−33
GO:0000187GO_Biological processActivation of MAPK activity 1.64×10−37
GO:0032754GO_Biological processPositive regulation of interleukin-5 production 1.99×10−33
GO:0006814GO_Biological processSodium ion transport 2.63×10−36
GO:0010951GO_Biological processNegative regulation of endopeptidase activity 3.04×10−37
GO:0008652GO_Biological processCellular amino acid biosynthetic process 3.39×10−34
GO:0019221GO_Biological processCytokine-mediated signaling pathway 4.49×10−37
GO:0005615GO_Cellular componentExtracellular space 4.20×10−328
GO:0005759GO_Cellular componentMitochondrial matrix 5.27×10−311
GO:0005886GO_Cellular componentPlasma membrane 6.97×10−365
GO:0005887GO_Cellular componentIntegral component of plasma membrane 7.97×10−328
GO:0005578GO_Cellular componentProteinaceous extracellular matrix 1.37×10−29
GO:0005576GO_Cellular componentExtracellular region 2.18×10−229
GO:0009986GO_Cellular componentCell surface 2.59×10−213
GO:0045211GO_Cellular componentPostsynaptic membrane 3.79×10−27
GO:0005739GO_Cellular componentMitochondrion 3.85×10−224
GO:0009897GO_Cellular componentExternal side of plasma membrane 3.93×10−27
GO:0004908GO_Molecular functionInterleukin-1 receptor activity 2.79×10−33
GO:0008201GO_Molecular functionHeparin binding 2.91×10−38
GO:0004867GO_Molecular functionSerine-type endopeptidase inhibitor activity 5.81×10−36
GO:0042803GO_Molecular functionProtein homodimerization activity 5.89×10−318
GO:0001078GO_Molecular functionTranscriptional repressor activity, RNA polymerase II core promoter proximal region sequence-specific binding 1.01×10−26
GO:0005088GO_Molecular functionRas guanyl-nucleotide exchange factor activity 1.17×10−26
GO:0004860GO_Molecular functionProtein kinase inhibitor activity 2.34×10−24
GO:0002114GO_Molecular functionInterleukin-33 receptor activity 2.34×10−22
GO:0004854GO_Molecular functionXanthine dehydrogenase activity 2.34×10−22
GO:0051537GO_Molecular function2 iron, 2 sulfur cluster binding 3.47×10−23

[i] GO, gene ontology.

Table V.

GO functional annotation of the hub genes of miR-15b-5p in HCC.

Table V.

GO functional annotation of the hub genes of miR-15b-5p in HCC.

GO IDCategoryTermP-valueCount
GO:0000187GOTERM_BPActivation of MAPK activity 2.39×10−43
GO:0070371GOTERM_BPERK1 and ERK2 cascade 5.71×10−32
GO:0006928GOTERM_BPMovement of cell or subcellular component 2.03×10−22
GO:0051092GOTERM_BPPositive regulation of NF-κB transcription factor activity 3.13×10−22
GO:0010629GOTERM_BPNegative regulation of gene expression 3.22×10−22
GO:0070374GOTERM_BPPositive regulation of ERK1 and ERK2 cascade 4.10×10−22
GO:0010628GOTERM_BPPositive regulation of gene expression 6.10×10−22
GO:0005524GOTERM_MFATP binding 4.17×10−23
GO:0005515GOTERM_MFProtein binding 7.33×10−25
GO:0004672GOTERM_MFProtein kinase activity 8.24×10−22
GO:0004674GOTERM_MFProtein serine/threonine kinase activity 8.62×10−22

[i] GO, gene ontology; HCC, hepatocellular carcinoma; BP, biological process; MF, molecular function; CC, cellular component.

Table VI.

KEGG pathway analysis of the hub genes of miR-15b-5p in HCC.

Table VI.

KEGG pathway analysis of the hub genes of miR-15b-5p in HCC.

CategoryTermCountP-valueGenes
KEGG_PATHWAYhsa04066: HIF-1 signaling pathway3 5.92×10−4MAP2K1, IGF1, TLR4
KEGG_PATHWAYhsa05205: Proteoglycans in cancer3 2.45×10−3MAP2K1, IGF1, TLR4
KEGG_PATHWAYhsa04151: PI3K-Akt signaling pathway3 7.21×10−3MAP2K1, IGF1, TLR4
KEGG_PATHWAYhsa04730: Long-term depression2 2.58×10−2MAP2K1, IGF1
KEGG_PATHWAYhsa05214: Glioma2 2.80×10−2MAP2K1, IGF1
KEGG_PATHWAYhsa05218: Melanoma2 3.05×10−2MAP2K1, IGF1
KEGG_PATHWAYhsa04914: Progesterone-mediated oocyte maturation2 3.73×10−2MAP2K1, IGF1
KEGG_PATHWAYhsa05215: Prostate cancer2 3.77×10−2MAP2K1, IGF1
KEGG_PATHWAYhsa04620: Toll-like receptor signaling pathway2 4.53×10−2MAP2K1, TLR4
KEGG_PATHWAYhsa04114: Oocyte meiosis2 4.66×10−2MAP2K1, IGF1

[i] KEGG, Kyoto Encyclopedia of Genes and Genomes; HCC, hepatocellular carcinoma.

Discussion

In the present study, the results of the investigation demonstrated an overall moderate test performance of miR-15b-5p with respect to its clinical value. The summary sensitivity of the plasma/serum miR-15b-5p (81%) revealed superiority compared to AFP despite an overall sensitivity of less than 60%. The AUC value was 0.83 in serum and 0.82 in tissue. Data comparing HCC with non-HCC tissue in the same liver or others controls indicated different factors. In our study, detection of miR-15b-5p in serum/plasma sample had a more favorable diagnostic accuracy than that in tissues. The results also revealed via SMD analysis, that the miR-15b-5p expression level in HCC was markedly overexpressed when compared to non-HCC tissues samples. In detail, the random-effects model was used for the pooled SMD of miR-15b-5p to resolve the problem of heterogeneity. Furthermore, to decrease the heterogeneity, we also performed subgroup analyses with both SMD and sROC methods. The subgroups included sample sources (tissues and serum/plasma) and control types (healthy controls, adjacent non-cancerous hepatic tissues, HBV+ or HCV+ tissues, liver cirrhosis tissues, and those combining HBV+/HCV+ and cirrhosis), which revealed that miR-15b-5p may be a prospective biomarker to distinguish HCC patients from healthy people. Numerous HCC patients have a background of liver cirrhosis and chronic HBV and/or HCV. Whether circulating miR-15b-5p can be used to differentiate HCC from benign hepatic lesions has been studied (16). Furthermore, our analysis of the studies indicated that the summary sensitivity and specificity of miR-15b-5p for distinguishing HCC from chronic HBV and/or HCV as well as liver cirrhosis were 60 and 80%, respectively, indicating a potential value that was worth exploring. It is regrettable that there is no evidence that miR-15b-5p is related to the progression of HCC, which is worthy of further study.

Some studies have revealed that circulating miRNAs can be potential markers for HCC determination. These miRNAs have been reported to be deregulated in cirrhosis and during development of hepatic malignancy. Abdalla et al summarized the tested diagnostic accuracy of miR-618 and miR-650 in HCC which were revealed to be 0.71 and 0.70, and may be of great value for the early diagnosis of HCC (28). Tan et al reported that serum miR-206, miR-141-3p, miR-433-3p, miR-1228-5p, miR-199a-5p, miR-122-5p, miR-192-5p and miR-26a-5p were potential circulating markers for the diagnosis of HCC with AUC from 0.53–0.73 (29). In the current study, compared with the non-neoplastic controls, the miR-15b-5p levels in HCC revealed a favorable diagnostic value with a pooled AUC of 0.81 which indicated that miR-15b-5p may be a prospective biomarker to distinguish HCC patients from non-HCC controls. Our studies on miR-15b-5p in HCC are limited and the role of miR-15b-5p remains largely unknown. The main limitation of this study was that the sample size was small and present findings should be validated in trials with more cases.

With regards to the existing related literature, there were 3 studies that addressed the clinical potential of miR-15b-5p. Hung et al (14) first indicated that miR-15b-5p could be utilized for the early detection of HCC. However, no explicit diagnostic test was conducted in this study. The study of Liu et al (15) noted that binding of miR-130b and miR-15b-5p could improve the accuracy of HCC diagnosis based on expression data collected from HCC patients, HBV carriers and healthy controls. Chen et al (16) designed three subgroups including HCC patients, cirrhosis patients and healthy individuals to evaluate the clinical potential of miR-15b-5p. Only two genes: OIP5 and Rab1A have been revealed to be the direct targets of miR-15b-5p in HCC, however, the existing studies solely concentrated on the performance of circulating miR-15b-5p. Therefore, we collected expression profiles in hepatic tissues to broaden the spectrum of research and carried out a comprehensive analysis.

In bioinformatics analyses, KEGG analysis highlighted the insulin signaling pathway, which was connected with other enriched pathways in our analysis. Insulin can induce phosphorylation of the insulin receptor substrate (IRS), thus allowing IRS to interact with the PI3K/Akt signaling pathway and MAPK signaling pathway, which are involved in biological mechanisms, such as glycogen synthesis, cell glucose intake, protein synthesis and gene transcription (30). In HCC, aberrantly elevated insulin receptor and IRS-1 were demonstrated to have a correlation with tumorigenesis contributing to precancerous liver glycogenosis and hepatocellular proliferation (3133). Tanaka et al also reported that IRS-1 could impede transforming growth factor β1-induced cell apoptosis, which may increase the risk of HCC (34). In association with IRS-1, the PI3K/Akt signaling pathway and MAPK signaling pathway were also considered to play roles in the molecular mechanism of HCC, and the joint effect of these pathways was also investigated by researchers. Liang et al (35) reported that aconitum coreanum polysaccharide inhibited the expression of pituitary tumor transforming gene 1, an oncogene, by suppressing the PI3K/Akt signaling pathway and upregulating the MAPK signaling pathway. A study from Gedaly et al (36) also discovered that targeting the PI3K/Akt signaling pathway and MAPK signaling pathway could attenuate the proliferation of HCC cells. Moreover, these two signaling pathways were also revealed to have independent correlations with HCC cell growth (37,38) apoptosis (39,40) migration, and invasion, as well as adhesion (4143), which affects the tumorigenesis and progression of HCC (44,45) via various genes. Therefore, we concluded that miR-15b-5p may participate in hepatocellular carcinogenesis via diverse pathways.

However, certain limitations still exist in our study. Significant heterogeneity was considered in our meta-analysis. The sample types, study design, population, sample size and some of the clinical characteristics may contribute to the increased heterogeneity. Furthermore, overexpression of miR-15b-5p has been previously reported in other malignancies, such as colorectal cancer (46), endometrial endometrioid adenocarcinoma (47) and non-small cell lung cancer (48). In summary, we confirmed that increased miR-15b-5p may not be particularly connected with HCC itself, however it affects the progression of different cancers. For this reason, in daily clinical practice, miR-15b-5p alone may not play an essential role for HCC, however if it is used in combination with other markers, this biomarker may improve test performance.

To conclude, a high miR-15b-5p level may be one the probable causes of HCC tumorigenesis, however, it could also be the consequence after HCC has already occurred, which warrants further investigation. Based on the marked upregulation of the level of miR-15b-5p in HCC, its potential clinical value is anticipated, which also requires practical validation. Notably, the prospective role and signaling pathways of miR-15b-5p have been revealed by in silico methods only, and thus, the specific mechanism of miR-15b-5p in HCC requires further study.

Acknowledgements

Not applicable.

Funding

The present study was supported by a fund from the National Natural Science Foundation of China (NSFC81560386).

Availability of data and materials

The datasets used during the present study are available from the corresponding author upon reasonable request.

Authors' contributions

WYP collected the public data and drafted the present study. JHZ and PPW performed the statistical analysis and constructed enrichment pathways. DYW and JYW re-analyzed the data of the results and drafted the section of the results, including writing and modifying tables and figures. GC and ZBF participated in the design and revision of the study. All authors read and approved the final manuscript

Ethics approval and consent to participate

Not applicable.

Patient consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Glossary

Abbreviations

Abbreviations:

miR-15b-5p

microRNA-15b-5p

HCC

hepatocellular carcinoma

GEO

gene expression omnibus

TCGA

The cancer genome atlas

ROC

receiver operating characteristic

AUC

area under curves

GO

gene ontology

KEGG

Kyoto encyclopedia of genes and genomes

PPI

protein-protein interactions

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February-2019
Volume 17 Issue 2

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Spandidos Publications style
Pan WY, Zeng JH, Wen DY, Wang JY, Wang PP, Chen G and Feng ZB: Oncogenic value of microRNA‑15b‑5p in hepatocellular carcinoma and a bioinformatics investigation. Oncol Lett 17: 1695-1713, 2019
APA
Pan, W., Zeng, J., Wen, D., Wang, J., Wang, P., Chen, G., & Feng, Z. (2019). Oncogenic value of microRNA‑15b‑5p in hepatocellular carcinoma and a bioinformatics investigation. Oncology Letters, 17, 1695-1713. https://doi.org/10.3892/ol.2018.9748
MLA
Pan, W., Zeng, J., Wen, D., Wang, J., Wang, P., Chen, G., Feng, Z."Oncogenic value of microRNA‑15b‑5p in hepatocellular carcinoma and a bioinformatics investigation". Oncology Letters 17.2 (2019): 1695-1713.
Chicago
Pan, W., Zeng, J., Wen, D., Wang, J., Wang, P., Chen, G., Feng, Z."Oncogenic value of microRNA‑15b‑5p in hepatocellular carcinoma and a bioinformatics investigation". Oncology Letters 17, no. 2 (2019): 1695-1713. https://doi.org/10.3892/ol.2018.9748