Open Access

Diagnostic significance and potential function of miR-338-5p in hepatocellular carcinoma: A bioinformatics study with microarray and RNA sequencing data

  • Authors:
    • Liang Liang
    • Li Gao
    • Xiao‑Ping Zou
    • Meng‑Lan Huang
    • Gang Chen
    • Jian‑Jun Li
    • Xiao‑Yong Cai
  • View Affiliations

  • Published online on: November 21, 2017     https://doi.org/10.3892/mmr.2017.8125
  • Pages: 2297-2312
  • Copyright: © Liang et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

MicroRNA (miR)-338-5p has been studied in hepatocellular carcinoma (HCC); however, the diagnostic value and molecular mechanism underlying its actions remains to be elucidated. The present study aimed to validate the diagnostic ability of miR‑338‑5p and further explore the underlying molecular mechanism. Data from eligible studies, Gene Expression Omnibus (GEO) chips and The Cancer Genome Atlas (TCGA) datasets were gathered in the data mining and the integrated meta‑analysis, to evaluate the significance of miR‑338‑5p in diagnosing HCC comprehensively. The potential target genes of miR‑338‑5p were achieved from the intersection of the deregulated targets of miR‑338‑5p from GEO and TCGA in addition to the predicted target genes from 12 online software. A protein‑protein‑interaction (PPI) network was drawn to illustrate the interaction between target genes and to define the hub genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed to investigate the function of the target genes. From the results, miR‑338‑5p exhibited favorable value in diagnosing HCC. Types of sample and experiment were defined as the possible sources of heterogeneity in meta‑analysis. A total of 423 genes were selected as the potential target genes of miR‑338‑5p, and five genes were defined as the hub genes from the PPI network. The GO and KEGG analyses indicated that the target genes were significantly assembled in the pathways of metabolic process and cell cycle. miR‑338‑5p may function as a novel diagnostic target for HCC through regulating certain target genes and signaling pathways.

Introduction

Hepatocellular carcinoma (HCC) ranked as the 5th most frequent cancer and was also one of the lethal cancers, particularly in People's Republic of China where liver cancer was the most commonly diagnosed cancer and the most prevalent cause of cancer-related deaths followed by lung, stomach, and esophageal cancers (based on the statistics in 2015) (1,2). Because HCC was often diagnosed at an advanced stage, the prognosis of HCC patients was not optimistic (3). Therefore, a better understanding of the pathogenesis of HCC and a novel target for the early screening of HCC might improve the survival of HCC patients (4).

MicroRNAs (miRNAs) are small non-coding RNAs (18–25 nucleotides in length) that regulate the expression of multiple mRNAs at the post-transcriptional level by suppressing the stability and the translation of mRNAs (5,6). The aberrant expressions of miRNAs was observed in various human cancers, and extensive studies suggested that these deregulated miRNAs had the capacity to distinguish malignant tumors of liver, breast, lung, pancreas and leukemia from adjacent non-tumorous tissue (712). miR-338-3p and miR-338-5p originate from an intron of the gene encoding apoptosis-associated tyrosine kinase (AATK). Both miR-338-3p and miR-338-5p are co-expressed because they enjoyed the same promoter together (13). miR-338 was the prvious ID of miR-338-3p, which had been reported in variety of diseases (1316). miR-338* is one of the members of miR-338 family and usually represented as miR-338-5p (17). As a member of the miRNA family, miR-338-5p was reported to be correlated with the carcinogenesis and progression of several human cancers including gastric cancer (18), colorectal cancer (19), and glioblastoma (20). However, there were limited studies on the clinical significance of miR-338-5p in HCC. Chen et al reported the overexpression of miR-338-5p in tumor tissues of the liver and preoperative plasma by miRNA array in Asian patients (21). Whether miR-338-5p is indeed a qualified diagnostic biomarker for HCC and the underlying molecular mechanism of miR-338-5p in HCC remained unclarified.

Therefore, this study aimed to investigate the diagnostic significance of miR-338-5p in HCC tissues and the molecular mechanism of miR-338-5p in HCC with a combination of meta-analysis and bioinformatics analysis. Our study confirmed the significance of miR-338-5p for the diagnosis of HCC and might promote the understanding of the molecular mechanism underlying it. The framework of this article was displayed in Fig. 1.

Materials and methods

The process of study selection

In order to obtain the comprehensive data of the diagnostic value of miR-338-5p in HCC, a thorough search for the related studies was conducted in Gene Expression Omnibus (GEO) dataset and other database including PubMed, Embase, Cochrane, Web of Science, Sinomed, Chinese VIP, Wanfang database and China National Knowledge Infrastructure (CNKI) until December 15, 2016 with the searching strategies: (miR-338 or miRNA-338 or microRNA-338 or miR338 or miRNA338 or microRNA338 or ‘miR 338’ or ‘miRNA 338’ or ‘microRNA 338’) and (malignan* or cancer or tumor or tumour or neoplas* OR carcinoma) AND (hepatocellular or liver or hepatic or HCC). Studies that meet the following criteria were eligible for the meta-analysis: i) Studies evaluated the expression of miR-338-5p for the diagnosis of HCC; ii) the disease of the patients were validated with golden standard; iii) the number of cases were reported in the study; and iv) the sensitivity and specificity of the diagnostic test were available directly or indirectly from the study. The exclusion criteria of the studies were as follows: i) The content of the studies were irrelevant with HCC; ii) the subjects of experiment were not human beings; iii) there was no sufficient data for researchers to directly acquire or calculate sensitivity or specificity of the diagnostic test; and iv) studies were classified as review, meta-analysis, case study or conference note. Moreover, data of the diagnostic value of miR-338-5p in HCC was also downloaded from the cancer genome atlas (TCGA) (https://cancergenome.nih.gov/).

Data extraction and statistical analysis

The following information and data were extracted from the included studies: The ID no. of each GSE chip, first author, year of publication, country, experiment type, platform of each GSE chip, sample number for the experiment group and control group, tissue types, true positivity (TP), false positivity (FP), false negativity (FN) and true negativity (TN).

MetaDiSc1.4 and STATA12.0 were applied for all the statistical analysis. To explore expression of miR-338-5p, the continuous outcomes of GEO and TCGA datasets were calculated with standard mean difference (SMD). The sensitivity (SEN), specificity (SPE), positive likelihood ratio (PLR), negative likelihood ratio (NLR) and diagnostic odds ratio (DOR) of the included studies were pooled with the bivariate meta-analysis model (22,23). The summary receiver operator characteristic (SROC) curve was plotted according to the sensitivity and specificity from each study. The area under the SROC curve (AUC) calculated from the SROC reflected the capacity of miR-338-5p to differentiate HCC patients from non-cancer patients accurately. An AUC value of 0.5 or 1.0 represents a poor or perfect diagnostic value, respectively (24). Additionally, Q test and I2 statistics were employed to assess the heterogeneity between studies. The random-effects model would be used to pool the results if an I2 value was more than 50% with a P-value <0.10; otherwise, a fixed-effects model would be applied (25,26). To identify the source of heterogeneity, the subgroup analysis was conducted based on the number and features of the included studies. With regard to the publication bias, the Deeks' funnel plot asymmetry test was carried out to detect the publication bias, and P-value <0.05 was indicative of significance.

The target genes of miR-338-5p

The potential target genes of miR-338-5p came from three sources: The differentially expressed genes from GEO, TCGA and the predicted genes from 12 online software (miRWalk, MicroT4, miRanda, mirBridge, miRDB, miRMap, miRNAMap, PicTar2, PITA, RNA22, RNAhybrid and TargetScan). We firstly searched Gene Expression Omnibus (GEO) datasets for deregulated target genes of miR-338-5p from the mRNA profiling data of HCC samples on December 15, 2016. All the GSE chips shared the same platform: GPL570 (Affymetrix Human Genome U133 Plus 2.0 Array). After preliminary screening, 54 studies remained for further selection. Among the 54 studies, Homo sapiens tissue samples instead of cell lines samples were included for further analysis. Finally, 10 HCC datasets (GSE29721, GSE45436, GSE55092, GSE62232, GSE9843, GSE41804, GSE6764, GSE33006, GSE6222 and GSE19665) comprising 431 HCC samples and 198 control samples were chosen for further analysis. Differentially expressed genes (DEGs) between cancer and normal samples of 10 datasets were acquired via GCBI online tool (https://www.gcbi.com.cn/gclib/html/index). Fold-change >1.5, and a P-value <0.05 was set as the threshold for the DEGs. Another database containing high-throughput data: TCGA was also searched. Publicly available miRNA-seq and RNA-seq data of liver HCC was downloaded from the TCGA data portal (December 2016, https://gdc-portal.nci.nih.gov/). Since the TCGA data were a community resource project, additional approval by an ethics committee of our hospital was not mandatory. And the present study adhered to the TCGA publication guidelines and data access policies. From the downloaded data of 377 HCC samples and 50 normal liver samples. R language package DESeq was subsequently used for the calculation of DEGs (Padj <0.05 and the absolute log2 fold-change >1). As for the predicted genes, selection was based on the condition that they were recorded in more than 4 of the 12 prediction software. The selected qualified target genes from the online software and the validated target genes from miRWalk were considered as the potential target genes of miR-338-5p.

The protein-protein-interaction (PPI) network and validation of target genes

To illustrate the interaction between the targets of miR-338-5p, a PPI network was drawn by Cytoskape v.5.3.0. The nodes and edges represented target genes and the interactions between target genes, respectively. Hub genes were identified according to the value of degrees of each node. Protein expression of hub gene was validated by The Human Protein Atlas (HPA), an immunohistochemisty (IHC) database (27). Each antibody in the database has been used for IHC staining of both normal and HCC tissues.

The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of the target genes

The GO and KEGG pathway analysis were performed by the BiNGO and ClueGO plug-in unit in Cytoscape v.3.5.0 for the functional annotation of the target genes. Three GO terms including biological process (BP), cellular component (CC) and molecular function (MF) were utilized to identify the enrichment of target genes. P-value <0.05 was significant.

Results

Eligible studies for the meta-analysis

As shown in Fig. 2, the flowchart exhibited the selection and retrieval process of the qualified studies. A total of 69 studies were identified as the initial records, and 30 studies remained after the removal of duplicate records. Then, 14 records were excluded in the preliminary screening of the titles and abstracts of the articles. As a consequence, 16 studies were reviewed in the full text. Among the 16 studies, 14 studies were ineligible due to insufficient data of the diagnostic parameters or duplicate data. Eventually, two studies were enrolled for the meta-analysis. Though two included studies were conducted by the same authors, we failed to validate that the two studies shared the same patient cohorts. Thus, we regard the two studies of Chen et al as two different studies (21,28).

Assessment of the diagnostic value and the integrated meta-analysis

To comprehensively evaluate the diagnostic value of miR-338-5p, we supplemented the literature analysis with GEO data and TCGA data. We searched the GEO dataset with the same searching strategies in literature meta-analysis. Finally, a total of eligible 11 GSE chips were included in our meta-analysis (2938) (Table I), and the expression level of each study were showed in Fig. 3. With the random-effects model, the forest-plot represented that no significant difference expression was observed between HCC tissue and normal tissue. The pooled SMD (0.11, 95% CI: −0.13, 0.34) was showed in Fig. 4.

Table I.

Basic information and clinical data of the included studies.

Table I.

Basic information and clinical data of the included studies.

GEO accessionAuthor, yearPublic yearCountryExperiment typePlatformHCCControlSample typeTPFPFNTN(Refs.)
GSE6857Budhu et al, 20082008USANon-coding RNA profiling by arrayGPL4700240241Tissue  9162149179(29)
GSE12717Su et al, 20092008USANon-coding RNA profiling by arrayGPL7274  10     6Tissue     7  1     3     5(30)
GSE22058Burchard et al, 20102010USANon-coding RNA profiling by arrayGPL10457  96  96Tissue  36  8  60  88(31)
GSE40744Diaz et al, 20132013USANon-coding RNA profiling by arrayGPL14613  26  19Tissue     8  4  18  26(32)
GSE50013Shen et al, 20132013USANon-coding RNA profiling by arrayGPL15497  18  18Plasma  1610     2     8(33)
GSE41874Morita (unpublished)2013JapanNon-coding RNA profiling by arrayGPL7722     6     4Tissue     4  1     2     3
GSE57555Murakami et al, 20152015JapanNon-coding RNA profiling by arrayGPL16699     5  16Tissue     4  6     1  10(34)
GSE74618Martinez-Quetglas et al, 20162016SpainNon-coding RNA profiling by arrayGPL14613223  20Tissue13110  92  10(35)
GSE36915Shih et al, 20122012TaiwanNon-coding RNA profiling by arrayGPL8179  68  21Tissue  37  6  31  15(36)
GSE39678Noh et al, 20132013South KoreaNon-coding RNA profiling by arrayGPL15852  16     8Tissue  14  2     2     6(37)
GSE21362Sato et al, 20112011JapanNon-coding RNA profiling by arrayGPL10312  73  73Tissue  2716  46  57(38)
TCGA miRNA-SeqIllumina375  50Tissue156  8218  42
LiteraureChen et al, 20152015ChinaqRT-PCR   37  31Plasma  27  0  10  31(21)
LiteraureChen et al, 20152015ChinaqRT-PCR   39  25Plasma  35  4     4  21(28)

[i] TP, true positivity; FP, false positivity; FN, false negativity; TN, true negativity.

From the chi-square test and I2 test, significant heterogeneity existed in all the pooled effects (SE, SP, PLR, NLR and DOR) between studies (All I2>50%; P<0.05). Therefore, random effects model were employed to estimate the overall SE, SP, PLR, NLR and DOR of all the data. As shown in Figs. 59, the SE, SP, PLR, NLR and DOR of all the studies were 0.51 (95% CI: 0.48–0.54), 0.69 (95% CI: 0.65–0.73), 1.76 (95% CI: 1.17–2.66), 0.64 (95% CI: 0.52–0.80) and 3.17 (95% CI: 1.83–5.47). As for the result of SROC, the AUC value of miR-338-5p was 0.691 (Fig. 10). Moreover, the Deeks funnel plot asymmetry test was carried out with Stata 12.0, and no publication bias was detected (P>0.05) (Fig. 11).

Now that significant heterogeneity existed between the studies, the subgroup analysis was performed to seek the potential sources of heterogeneity. In the subgroup of sample types, the heterogeneity decreased substantially in the pooling estimates of NLR (49.2%) and DOR (5.6%) in the group of tissue. The value of SE, SP, PLR and DOR were obviously higher in the plasma group (0.83, 0.74–0.90; 0.86, 0.77–0.93; 14.02, 0.08–2,395.96; 61.30, 3.61–1,040.31) than in the tissue group (0.48, 0.45–0.51; 0.67, 0.63–0.71; 1.51, 1.08–2.11; 2.05, 1.51–2.77) and the value of NLR was notably lower in the plasma group (0.21, 0.11–0.38) than in the tissue group (0.81, 0.71–0.92).

With regard to the subgroup of experiment, the heterogeneity decreased substantially in the pooling estimate of DOR (10.3%) in the microarray group, and declined heterogeneity of SE (0%), PLR (0%) and DOR (0.0%) were also observed in the group of qRT-PCR. The value of SE, SP, PLR and DOR were obviously higher in the qRT-PCR group (0.82, 0.71–0.90; 1.00, 0.94–1.00; 46.23, 6.60–323.68; 254.42, 32.2–2,010.45) than in the microarray group (0.49, 0.46–0.52; 0.66, 0.62–0.70; 1.51, 1.11–2.06; 2.15, 1.56–2.97) and the value of NLR was notably lower in the qRT-PCR group (0.19, 0.08–0.47) than in the microarray group (0.79, 0.69–0.91). This result confirmed that types of sample and experiment were the possible sources of heterogeneity in this study.

Bioinformatics study of the target genes of miR338-5p

According to the results, a total of 1,698 and 1,798 genes were identified as DEGs targeted by miR-338-5p from TCGA and GEO, respectively. Additionally, a total of 3,610 predicted target genes that appeared in more than four times of the 12 online software were obtained. Taking the intersection of the DEGs from GEO and TCGA as well as the qualified predicted targets genes, we selected 423 genes for the following bioinformatics analyses (Fig. 12). The PPI network shown in Fig. 13 illustrated the interactions between the target genes of miR-338-5p. There were 147 nodes and 248 edges in the network. Hub genes with a degree values of more than 11 including NCOR1, IGF1, FOXO1, FOS, CDCA8, BUB1B, PCNA, ESR1, BIRC5, MYC and CDK1 were emphasized in red while the remaining were colored in green. To verify that these hub genes are targeted by miR-338-5p, we obtained the immunohistochemical staining of several of the hub genes including NCOR1 and FOXO1 in HCC tissues and normal tissues. As shown in Fig. 14, NCOR1 and FOXO1 were found to have medium staining and moderate intensity in cytoplasmic/menbranous of normal tissues, while a lower staining and weaker intensity of these genes were observed in HCC tissues.

According to the results of GO analysis in cytoskape, the target genes were found to enrich most significantly in the following biological pathways: response to organic substance, response to chemical stimulus and oxoacid metabolic process. As for cellular component and molecular function, target genes mainly assembled in extracellular region part and binding, respectively (Table II; Fig. 15). Moreover, a total of 5 significant pathways were recorded from the KEGG pathway analysis such as valine, leucine and isoleucine degradation, pathways in cancer and cell cycle (Table III; Fig. 16) were the most significant.

Table II.

GO functional annotation of the target genes of miR-338-5p from Cytoskape.

Table II.

GO functional annotation of the target genes of miR-338-5p from Cytoskape.

IDCategoryGO termP-valueCount
GO:0006082GO_Biological processOrganic acid metabolic process 3.87×10−9  37
GO:0016054GO_Biological processOrganic acid catabolic process 6.26×10−9  16
GO:0008152GO_Biological processMetabolic process 1.29×10−3159
GO:0009056GO_Biological processCatabolic process 1.15×10−1  28
GO:0007275GO_Biological processMulticellular organismal development 1.11×10−6102
GO:0048731GO_Biological processSystem development 9.71×10−8  91
GO:0048519GO_Biological processNegative regulation of biological process 1.82×10−5  72
GO:0048523GO_Biological processNegative regulation of cellular process 3.81×10−5  66
GO:0050789GO_Biological processRegulation of biological process 3.62×10−4176
GO:0065007GO_Biological processBiological regulation 6.72×10−5189
GO:0008150GO_Biological process Biological_process 1.15×10−3336
GO:0032501GO_Biological processMulticellular organismal process 1.15×10−5133
GO:0044237GO_Biological processCellular metabolic process 6.22×10−3132
GO:0034641GO_Biological processCellular nitrogen compound metabolic process 2.19×10−2  59
GO:0044238GO_Biological processPrimary metabolic process 4.35×10−3140
GO:0050896GO_Biological processResponse to stimulus 4.59×10−5112
GO:0009719GO_Biological processResponse to endogenous stimulus 3.77×10−9  34
GO:0050794GO_Biological processRegulation of cellular process 9.14×10−4166
GO:0023052GO_Biological processSignaling 5.63×10−5  99
GO:0044281GO_Biological processSmall molecule metabolic process 3.29×10−8  62
GO:0006519GO_Biological processCellular amino acid and derivative metabolic process 5.92×10−5  21
GO:0043436GO_Biological processOxoacid metabolic process 2.77×10−9  37
GO:0019752GO_Biological processCarboxylic acid metabolic process 2.77×10−9  37
GO:0009987GO_Biological processCellular process 2.40×10−8258
GO:0046395GO_Biological processCarboxylic acid catabolic process 6.26×10−9  16
GO:0009063GO_Biological processCellular amino acid catabolic process 2.78×10−6  10
GO:0044248GO_Biological processCellular catabolic process 3.54×10−2  25
GO:0009725GO_Biological processResponse to hormone stimulus 4.39×10−8  30
GO:0048545GO_Biological processResponse to steroid hormone stimulus 2.46×10−6  18
GO:0006807GO_Biological processNitrogen compound metabolic process 2.95×10−3  67
GO:0006520GO_Biological processCellular amino acid metabolic process 4.99×10−5  16
GO:0009605GO_Biological processResponse to external stimulus 1.81×10−8  35
GO:0009308GO_Biological processAmine metabolic process 4.15×10−6  25
GO:0009310GO_Biological processAmine catabolic process 9.50×10−6  10
GO:0032502GO_Biological processDevelopmental process 5.43×10−7110
GO:0048856GO_Biological processAnatomical structure development 8.77×10−7  94
GO:0009653GO_Biological processAnatomical structure morphogenesis 1.19×10−6  53
GO:0042221GO_Biological processResponse to chemical stimulus 3.51×10−10  70
GO:0010033GO_Biological processResponse to organic substance 4.09×10−11  52
GO:0010646GO_Biological processRegulation of cell communication 1.30×10−5  48
GO:0008283GO_Biological processCell proliferation 3.68×10−6  26
GO:0044282GO_Biological processSmall molecule catabolic process 2.35×10−5  18
GO:0023046GO_Biological processSignaling process 8.53×10−6  77
GO:0023060GO_Biological processSignal transmission 8.53×10−6  77
GO:0042127GO_Biological processRegulation of cell proliferation 1.66×10−6  41
GO:0048513GO_Biological processOrgan development 5.37×10−8  74
GO:0009888GO_Biological processTissue development 2.37×10−8  42
GO:0044106GO_Biological processCellular amine metabolic process 1.70×10−4  18
GO:0032787GO_Biological processMonocarboxylic acid metabolic process 3.81×10−6  21
GO:0007165GO_Biological processSignal transduction 2.88×10−6  71
GO:0010648GO_Biological processNegative regulation of cell communication 3.44×10−6  22
GO:0051239GO_Biological processRegulation of multicellular organismal process 1.60×10−6  48
GO:0042180GO_Biological processCellular ketone metabolic process 5.36×10−9  37
GO:0050793GO_Biological processRegulation of developmental process 1.88×10−6  39
GO:0005515GO_Molecular functionProtein binding 2.06×10−6223
GO:0003674GO_Molecular function Molecular_function 4.03×10−4359
GO:0005488GO_Molecular functionBinding 8.63×10−8316
GO:0048037GO_Molecular functionCofactor binding 3.31×10−6  19
GO:0005102GO_Molecular functionReceptor binding 1.45×10−7  46
GO:0005575GO_Cellular component Cellular_component 1.14×10−2370
GO:0005615GO_Cellular componentExtracellular space 1.96×10−8  42
GO:0005576GO_Cellular componentExtracellular region 1.10×10−5  73
GO:0044421GO_Cellular componentExtracellular region part 3.08×10−9  52

[i] GO, Gene Ontology.

Table III.

KEGG pathway analysis of the target genes of miR-338-5p from Cytoskape.

Table III.

KEGG pathway analysis of the target genes of miR-338-5p from Cytoskape.

IDNameCategoryTerm P-valueCount
GO:0000280Valine, leucine and isoleucine degradationKEGG 5.1×10−6  9
GO:0005200Pathways in cancerKEGG 9.5×10−525
GO:0004110Cell cycleKEGG 1.4×10−412
GO:0000071Fatty acid degradationKEGG 1.7×10−4  7
GO:0000640Propanoate metabolismKEGG 2.0×10−4  6

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

Discussion

Considerable attention has been attracted to miRNAs as promising diagnostic targets for the early screening of human cancers. Prior to our study, several researches have reported some miRNAs had diagnostic value in HCC. A 3-miRNA panel: miR-92-3p, miR-107, and miR-3126-5p discovered by Zhang et al were claimed to distinguish HCC patients in early stage and HCC patients with low-level AFP from their corresponding controls with high accuracy (39). Additionally, some single miRNAs including miR-21 and miR-224 also exhibited prominent diagnostic potential for HCC (40,41) and so far, only one study referred to the diagnostic value of miR-338-5p in HCC with the method of miRNA array. Chen et al (21) reported a moderate ability of miR-338-5p to differentiate HCC from liver cirrhosis with the AUC of 0.799. Furthermore, an extremely strong diagnostic value of miR-338-5p (AUC=0.909) was observed when diagnosing HCC from healthy controls. Despite some advances has been made in exploring the diagnostic capacity of miRNAs for HCC, the diagnostic significance of miR-338-5p in HCC was indefinite, and the relative molecular mechanism has not been elucidated in these studies; therefore our study was the first one to comprehensively assess the diagnostic value of miR-338-5p in HCC with the data from GEO, TCGA and literature as well as to investigate the underlying molecular mechanism through bioinformatics study.

From the meta-analysis result from our collected literature and the integrated meta-analysis, miR-338-5p may serve as a possible diagnostic target for HCC with fair sensitivity and specificity, which enlightened us that miR-338-5p might play an essential role in the occurrence and progression. Previous studies have pointed out that miR-338-5p exerted a tumor suppressive function in a wide range of cancers. In glioblastoma, miR-338-5p was discovered to inhibit the proliferation, invasion and promote apoptosis by targeting EFEMP1 (42); similarly, miR-338-5p significantly attenuated the malignant potential of gastric cancer cells through regulating BMI1 (13). In contrast, miR-338-5p was increased in both blood and tissue of coloreactal cancer (CRC), and represented high area under ROC curve (AUC) of 0.871. The performance of miR-338-5p indicated that it could be a potential biomarker in CRC (19). A similar trend was observed in CRC compared with HCC. Furthermore, according to the subgroup analysis in the integrated meta-analysis, studies with samples from plasma and the method of qRT-PCR were more precise in diagnosing HCC than studies with the controlled conditions, which hinted that the sample type and experiment type may also influence the accuracy of the diagnosis. Although the overall diagnostic ability of miR-338-5p in HCC was the same, which was reflected by the integrated meta-analysis and the meta-analysis from our literature there were still some differences between them. The sensitivity, specificity, diagnostic odds ratio and the area under SROC of the result from the literature meta-analysis were higher than those from the integrated meta-analysis, especially in the evaluation of sensitivity; miR-338-5p showed a poor sensitivity of only 0.51 in the integrated meta-analysis. This might be attributed to the difference in sample type and experiment type as well as the sources of the data. The integrated meta-analysis included GSE datasets from different platforms and TCGA data based on the literature meta-analysis, the samples of which were different. Moreover, due to the limited number of literature, we failed to trace the heterogeneity by carrying out subgroup analysis for our literature meta-analysis. Expanding the sample size was necessary for a more reliable assessment of the diagnostic value of miR-338-5p in HCC.

The results from meta-analysis only provided a superficial hint that miR-338-5p possessed significant diagnostic capacity in HCC and the molecular mechanism underlying it needed further exploration. Thus, we emphasized on the network and functional analysis of the target genes.

We firstly identified the potential target genes of miR-338-5p and further defined the hub genes from PPI network. The 11 hub genes were assumed to correlate closely with miR-338-5p and play essential roles in the miR-338-5p relevant pathogenesis of HCC. Among the hub genes, CDK1 was important protein for the regulation of cell cycles belonging to the cyclin-dependent kinases family (43). The overexpression of CDK1 was detected in various cancers, and a poor prognosis of renal cell carcinoma patients was associated with the high expression of CDK1 and CDK2 (4448). We hypothesized that CDK1 deregulated by miR-338-5p might promote the deterioration of HCC by affecting the cell cycle of HCC cells. Apart from CDK1, several hub genes such as MYC, BIRC5, IGF1, NCOR1 and FOXO1 participate in the regulation of a wide range of biological processes including cell proliferation, apoptosis and migration (4957). These genes were reported to be aberrantly expressed in various cancers (5862) and they were also involved in the malignant progression of HCC (49,50,52,63). It was conceived that miR-338-5p might interact with these molecules through potential signaling pathways to influence the development of HCC. PCNA, a protein that acted as DNA sliding clamp, was found to engage in DNA duplication and repair with its overexpressed in HCC. Further study was necessary to probe into the association between PCNA and miR-338-5p in HCC. In this study, we analyzed 11 hub genes protein expression by HPA database. The result indicated that the expression of FOXO1 and NCOR1 was downregulated in HCC and most likely regulated by miR-338-5p.

GO enrichment analysis was indicative of the possible functions of the target genes in HCC and the results from three GO terms hinted that the target genes were mainly assembled in response to organic substance. Most of the potential functions of the target genes from the GO analysis were accomplished in signaling pathways. Therefore, it is of great importance to investigate the signaling pathways gathered by the target genes of miR-338-5p. From the results of the KEGG pathway analysis, the most significant ten pathways such as pathways in cancer and cell cycle were closely associated with cancer.

Despite the valuable findings acquired from the meta-analysis and bioinformatics study, there were still some limitations in our study. The sample size of our literature was too small for further analysis to identify heterogeneity, which weakened the reliability of our results. Since the samples are from different types, including tissue and plasma, a bias and sensitivity problems might originate from sample types in analysis. Additionally, we only included studies published in Chinese or English, which might cause bias of selection to the meta-analysis. A plausible way to address these issues is to conduct future studies with larger samples and fewer language restrictions to further verify the diagnostic value of miR-338-5p for HCC.

In conclusion, we anticipated that miR-338-5p may serve as a promising diagnostic marker for HCC and miR-338-5p could affect the development of HCC by targeting certain downstream genes and pathways. The future research will be concentrated on validating the target genes of miR-338-5p and its function in the significant signaling pathways mentioned before.

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February-2018
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Liang L, Gao L, Zou XP, Huang ML, Chen G, Li JJ and Cai XY: Diagnostic significance and potential function of miR-338-5p in hepatocellular carcinoma: A bioinformatics study with microarray and RNA sequencing data. Mol Med Rep 17: 2297-2312, 2018
APA
Liang, L., Gao, L., Zou, X., Huang, M., Chen, G., Li, J., & Cai, X. (2018). Diagnostic significance and potential function of miR-338-5p in hepatocellular carcinoma: A bioinformatics study with microarray and RNA sequencing data. Molecular Medicine Reports, 17, 2297-2312. https://doi.org/10.3892/mmr.2017.8125
MLA
Liang, L., Gao, L., Zou, X., Huang, M., Chen, G., Li, J., Cai, X."Diagnostic significance and potential function of miR-338-5p in hepatocellular carcinoma: A bioinformatics study with microarray and RNA sequencing data". Molecular Medicine Reports 17.2 (2018): 2297-2312.
Chicago
Liang, L., Gao, L., Zou, X., Huang, M., Chen, G., Li, J., Cai, X."Diagnostic significance and potential function of miR-338-5p in hepatocellular carcinoma: A bioinformatics study with microarray and RNA sequencing data". Molecular Medicine Reports 17, no. 2 (2018): 2297-2312. https://doi.org/10.3892/mmr.2017.8125