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. 2018 Sep 20;4:94. doi: 10.1038/s41420-018-0096-8

The role of miR-106p-5p in cervical cancer: from expression to molecular mechanism

Yuexiong Yi 1, Yanyan Liu 1, Wanrong Wu 1, Kejia Wu 1, Wei Zhang 1,✉
PMCID: PMC6148547  PMID: 30275981

Abstract

This study aims to investigate the role of miR-106b-5p in cervical cancer by performing a comprehensive analysis on its expression and identifying its putative molecular targets and pathways based on The Cancer Genome Atlas (TCGA) dataset, Gene Expression Omnibus (GEO) dataset, and literature review. Significant upregulation of miR-106b-5p in cervical cancer is confirmed by meta-analysis with the data from TCGA, GEO, and literature. Moreover, the expression of miR-106b-5p is significantly correlated with the number of metastatic lymph nodes. Our bioinformatics analyses show that miR-106b could promote cervical cancer progression by modulating the expression of GSK3B, VEGFA, and PTK2 genes. Importantly, these three genes play a crucial role in PI3K-Akt signaling, focal adhesion, and cancer. Both the expression of miR-106b-5p and key genes are upregulated in cervical cancer. Several explanations could be implemented for this upregulation. However, the specific mechanism needs to be investigated further.

Subject terms: Oncogenes, Oncogenes

Introduction

At present, with an increase in morbidity and mortality, cancer has become the leading cause of death and a significant public health problem. There are over 500,000 novel cases and approximately 274,000 deaths estimated for cervical cancer (CC) each year all over the world1. In 2015, the number of new cases and deaths in China was 4,292,000 and 2,814,000, respectively2. CC is the fourth most common cancer in women in the world, and incidence and mortality are still rising3. Although the relationship between persistent high-risk human papillomavirus (HPV) infection and CC has been confirmed4, the specific molecular cellular network mechanism is still unclear.

Genetic mutations lead to cancer by affecting gene expression and protein function in the cells. However, the dysregulation of microRNA (miRNA) expression is detected in a variety of tumors and is considered to be a significant contributor to the development of cancer in recent years5,6. miRNAs are small single-stranded non-coding RNAs that specifically silence gene expression and alter cell or organism phenotypes. Previous studies have confirmed that miRNAs participate in proliferation, apoptosis, morphogenesis7, antiviral defense8, and tumorigenesis9.

Recently, growing evidence reveals that miR-106b-5p plays a critical role in various cancers. Huang and Hu10 showed that the upregulation of miR-106b can be observed in the endometrium and knockdown of miR-106b suppresses proliferation and promotes apoptosis. Shi et al.11 reported that upregulation of miR-106b-5p exhibited a promoting role in hepatocellular carcinoma (HCC) cell properties and migration, whereas downregulation exhibited an opposite effect. Lu et al.12 and Xiang et al.13 submitted that overexpression of miR-106b-5p could promote the proliferation and increase the number of metastatic colonies, whereas inhibition would induce cell cycle arrest, suppress cell proliferation, and promote cell apoptosis in renal cell carcinoma.

For CC, miR-106b, the pre-miRNA of miR-106b-5p, also has a pivotal role in occurrence and development. While constructing a miRNA-mRNA network for CC, Ma et al.14 found that miR-106b was one of the key nodes in the network. Overexpression of miR-106b promoted the migration of HeLa and SiHa cells significantly while inhibition displayed an opposite phenomenon15. However, few studies concern the mechanisms of miR-106b for CC at present. Cheng et al.15 found that DAB2 is identified as a direct target of miR-106b and it is inhibited by TGF-β1 partly through miR-106b and is involved in TGF-β1-induced CC cell migration. Piao et al.16 reported that miR-106 overexpression and DAB2 knockdown induced epithelial to mesenchymal transition (EMT) of CC cells cultured on substrate. As miR-106b plays an essential role in CC, its molecular mechanisms need to be further studied.

The purpose of this study is to investigate the role of miR-106b-5p in CC by performing comprehensive research on its expression and identify its putative molecular targets and pathways based on The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), and literature review.

Results

Clinical significance of miR-106b-5p

As the expression data of mature miR-106b-5p are absent in TCGA, a comparison of miR-106b between CC and healthy samples is provided. The expression level of miR-106b is higher in CC (Fig. 1) and significantly associated with the number of metastatic lymph nodes (Cor = 24.510, P = 0.006). However, there is no significant correlation in tumor purity, race, pathological M/N/T stage, number of years of birth, histological type, race, radiotherapy, or overall survival (Table 1).

Fig. 1. Expression of miR-106b-5p in cervical cancer from TCGA.

Fig. 1

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NT normal tissue, TP primary tumor. There are three samples in the NT group and 309 samples in the TP group. Student's t-test is used for the statistical analysis

Table 1.

Correlations between expression of miR-106b-5p and clinical outcomes

Item Method Cor. P value FDR
Number of lymph nodes Kruskal–Wallis Test 24.510 0.006a 0.070
Tumor purity Spearman Correlation 0.107 0.078 0.430
Race Kruskal–Wallis Test 7.214 0.125 0.458
Pathology M stage Wilcox Test 0.025 0.193 0.530
Years to birth Spearman Correlation 0.063 0.300 0.553
Histological type Kruskal–Wallis Test 6.047 0.302 0.553
Ethnicity Wilcox Test −0.015 0.386 0.606
Radiation therapy Wilcox Test 0.011 0.545 0.750
Pathology N stage Wilcox Test −0.006 0.683 0.758
Pathology T stage Kruskal–Wallis Test 2.256 0.689 0.758
Overall survival Cox Regression Test 0.045 0.792 0.792
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aSignificant difference

Meta-analysis of miR-106b-5p expression

Meta-analysis based on TCGA and GEO

A total of 1286 microarrays were obtained from GEO. After careful screening, the three microarrays, GSE86100, GSE19611, and GSE30656, meet the criteria and are included in the analysis (Fig. 2a). The forest plot presents an overall standard mean difference (SMD) of 2.85 (95% confidence interval (CI): 0.89–4.81) with P = 0.0045 and I2 = 88% (random effect used), suggesting that miR-106b-5p is upregulated in CC (Fig. 3a).

Fig. 2.

Fig. 2

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Searching workflow for the expression of miR-106b-5p between cervical cancer and non-cancerous tissue. a Searching strategy in GEO; bSearching strategy in literature review

Fig. 3. Meta-analysis of miR-106b-5p between healthy and cancerous cervical tissue based on TCGA and GEO.

Fig. 3

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a Forest plot of SMD. The expression of miR-106b-5p is significantly higher in cervical cancer tissue; b Funnel plot for four studies that are marked as circles. No significant publication bias is detected (P = 0.5187); c Influence analysis for four studies. No study had an impact on the overall SMD estimation. d Subgroup forest plot based on cancer type. As I2 value is still relatively high, the cancer subtype is not the only source of heterogeneity

A funnel plot of miR-106b-5p expression (Fig. 3b) reveals that no significant publication bias is detected by Egger’s test (P = 0.5187). Sensitivity analysis shows that similar results are obtained for the fixed effects models except for a lower difference (SMD: 2.53, 95%CI: 1.89–3.18, P < 0.0001).

The influence analysis (Fig. 3c) shows that no study had an impact on the overall SMD estimation because the point estimate for any of the studies is not outside the combined analysis CI and there is no significant statistical change.

Except for a decrease in I2 values, similar results are obtained in the subgroup analysis of cancer subtypes (Fig. 3d). The results show that the cancer subtype is not the only source of heterogeneity as the I2 value is still relatively high, but miR-106b-5p continues to be highly expressed in CC tissues.

Meta-analysis based on literature review

The workflow for searching is presented in Fig. 2b. Finally, four studies14,15,17,18 that met the criteria were selected. Consistent with the result of the meta-analysis, a common pattern of upregulation for miR-106b-5p in CC was reported across the included studies (Table 2).

Table 2.

Overview of the four studies selected in the literature review

Author Year Country Cancer (n) Normal (n) Result Detection methods
Cheng et al. 2016 China 19 19 Upregulated qRT-PCR
Gao et al. 2016 China 30 26 Upregulated qRT-PCR
Ma et al. 2012 China 8 8 Upregulated qRT-PCR
Liu et al. 2016 China 10 10 Upregulated qRT-PCR
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Bioinformatics analyses of miR-106b-5p

Screening of candidate genes

By analyzing the data from Cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) with the criterion of log|FC| > 1 and FDR < 0.05, 4857 differentially expressed genes (DEGs) were selected, including 4619 upregulated genes and 238 downregulated genes. Meanwhile, predicting using 12 databases in miRWalk, 10,073 target genes that were overlapping in at least five databases were found (Fig. 4a). After merging DEGs and the predicted target genes, 1277 candidate genes were collected (Fig. 4b).

Fig. 4. Predication of miR-106b-5p target genes and candidate genes screening.

Fig. 4

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a The number of overlapped genes across 12 databases; 10,073 target genes which overlapped at least five databases are obtained. b Venn plot for the integration between DEGs and predicted target genes of miR-106b-5p

Gene ontology enrichment analysis

The DAVID database was used for Gene Ontology (GO) analysis of the 1277 genes (Fig. 5). Using the criterion of P < 0.001, the results showed that while cellular component (CeC), target genes are mainly involved in “cytoplasm”, “cytosol”, “receptor complex”, “basolateral plasma membrane”, “perinuclear region of cytoplasm”, “ruffle membrane”, “membrane”, “lamellipodium”, “cleavage furrow”, “postsynaptic density”, “membrane raft”, “integral component of plasma membrane”, and “cell cortex”. In terms of biological process (BP), the target genes mainly participate in “protein phosphorylation”, “positive regulation of transcription from RNA polymerase II promoter”, “microtubule cytoskeleton organization”, “epithelial to mesenchymal transition”, “intracellular signal transduction”, “cell migration”, “protein autophosphorylation”, and “positive regulation of protein binding”. With regard to MF, these genes are mainly enriched in “protein binding”, “protein serine/threonine kinase activity”, “ATP binding”, “kinase activity”, “PDZ domain binding”, and “transcription factor activity, sequence-specific DNA binding”.

Fig. 5. The top 20 items of cellular component (CeC), biological process (BP) pathways, molecular function (MF), and pathways in Gene Ontology (GO) and pathway enrichment analysis for candidate target genes of miR-106b-5p in CC.

Fig. 5

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Values are expressed as −log10 (P-value)

Protein–protein interaction network analysis

The original network contains 668 nodes and 1779 edges. After cleaning the isolated genes, the main network containing 579 nodes and 1716 edges was obtained (Fig. 6a). By extracting the nodes with degree and betweenness which are higher than average, a subnetwork that contains 110 nodes and 404 edges was gained (Fig. 6b; Table 3).

Fig. 6. Protein–protein analysis (PPI) analysis of candidate genes and pathway crosstalk and gene-pathway analysis of hub genes.

Fig. 6

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a PPI network of candidate genes; b Subnetwork of PPI for main nodes extracted according to degree and betweenness being higher than average; c Pathway crosstalk analysis of hub genes. The thickness of lines between nodes are represented by the average value of Jaccard coefficient (JC) and overlapping coefficient (OC); d Subnetwork of pathway crosstalk extracted by MCODE; e Comprehensive gene-pathway network constructed by mapping the hub genes to the subnetwork. The arrow direction between gene and pathway is determined by KEGG. Red circle: genes; green square: pathway. f Subnetwork of gene-pathway collected according to the criteria that node’s degree > average

Table 3.

Characteristics of main genes

Gene Degree Betweenness
Average value 5.928 2064.408
PIK3CG 46 41488.54899
PIK3R3 41 25199.73317
APP 35 32767.23403
MAPK8 33 35071.18269
PRKACB 29 27367.34287
PTK2 28 25355.409
ITGB1 28 17088.50374
SMURF1 27 7546.059618
SMURF2 27 6748.334913
BUB1 26 11066.28571
GNG4 26 10653.80228
H2AFV 25 20305.78635
WNT5A 25 13709.55435
VEGFA 23 20760.79426
KIF23 21 10253.88184
RAD51 21 17892.93805
KIF11 21 5135.989346
CCNF 20 5540.628454
RAP1A 19 7521.72323
PPP2R1B 19 14143.0877
BRCA1 19 6029.273042
NEDD4 19 3192.727805
CLASP1 19 7496.485807
SYK 19 11386.15814
GSK3B 19 18438.62749
DNM2 19 5911.698768
ADCY2 18 7560.286406
ATM 18 9420.324388
CDKN1A 18 18240.66944
RHOT1 18 16492.35297
HIP1R 17 3517.329081
RAP1B 17 5215.72323
MCM4 17 5808.281935
UBE4A 17 2304
PARK2 17 2366.125584
CDH1 17 14376.3563
UBE2I 17 10854.31473
SYNJ2 17 5273.61775
IL10 16 11158.43666
LRP2 16 3590.998058
KIAA0319 15 3475.143506
RACGAP1 15 5861.489126
ACTR1A 15 3744.522591
PRDM10 15 14008.2127
FASLG 15 5210.263877
BMP2 15 5337.049932
LDLR 15 2191.70904
HDAC9 15 10403.97449
GRM5 14 3250.915796
CASP8 14 5874.766056
ESR1 14 11585.62988
NFATC2 14 4879.928683
PBK 14 4821.131434
VAV2 14 8716.774939
GRM1 14 3250.915796
FGFR1OP 13 6727.611655
DTL 13 2540.356126
TGFBR1 13 4022.943631
CAMK2D 13 5055.739472
CASP9 12 4919.739038
P2RY6 12 2304
YWHAZ 11 4040.292887
MAP3K7 11 6201.810194
FLT1 11 6634.16899
PPP1CB 11 2005.113067
NCOA3 11 7457.814126
FGFR2 11 3030.464668
ITGA2 10 3529.023629
NOTCH2 10 10947.19567
CENPN 10 1873.156984
FGFR1 10 8007.32537
IRF4 10 5202.998605
PTBP1 10 6730.910039
MEF2C 10 4165.497288
ARHGEF7 9 3147.468396
TIAM1 9 4058.542014
ERBB4 9 4519.484052
ARCN1 9 2088.364196
CASP7 9 5818.243304
PGR 9 4778.496029
SPTBN1 9 10172.1474
DMD 9 2456.15705
FZD3 9 2260.879218
MAPK9 9 2693.578423
DYNC1LI2 9 2495.446083
NR3C1 9 4868.398944
DVL3 9 3925.165711
HDAC8 9 2385.834093
APC 9 4483.983598
TNRC6A 8 2766.776691
RPS6KA1 8 2691.499073
TJP1 8 4534.026739
ACVR1B 8 1933.546424
TGFB2 8 2116.806735
SP100 8 2757.903141
POLR1E 7 6748.802196
ETS1 7 4087.280316
CSNK1A1 7 2157.303406
PSEN1 7 3233.020264
TAF1 7 4214.786588
ERC1 7 3537.432599
CCNE2 7 1750.370853
REL 7 2794.403409
MAPT 7 2044.275299
LIMK1 7 1750.999958
CSF2RA 7 1782.306466
E2F2 7 4585.295895
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Twelve topological algorithms were applied and the top 20 genes of each method for the subnetwork of PPI were extracted. These selected genes that appeared at least twice are conserved as hub genes (Table 4).

Table 4.

Hub genes identified from top 20 of 12 topological algorithms

Rank Gene Counts
1 APP 9
2 MAPK8 9
3 PIK3CG 9
4 PIK3R3 9
5 VEGFA 9
6 ITGB1 8
7 PRKACB 8
8 PTK2 8
9 GNG4 6
10 GSK3B 6
11 PRDM10 6
12 WNT5A 6
13 CDKN1A 5
14 RAD51 5
15 SMURF1 5
16 SMURF2 5
17 CCNF 4
18 CDH1 4
19 ESR1 4
20 H2AFV 4
21 NEDD4 4
22 TRIM36 4
23 BUB1 3
24 EHHADH 3
25 FBXL5 3
26 RAP1A 3
27 RAP1B 3
28 RHOT1 3
29 AGFG1 2
30 ASB13 2
31 FASLG 2
32 FLT1 2
33 KBTBD8 2
34 KIAA0319 2
35 KIF11 2
36 KIF23 2
37 KLHL20 2
38 KLHL5 2
39 LDLR 2
40 LRP2 2
41 MKRN1 2
42 PACSIN1 2
43 PARK2 2
44 PCYT1B 2
45 PGR 2
46 RLIM 2
47 RNF213 2
48 SPSB4 2
49 UBE4A 2
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Pathway enrichment and crosstalk analysis

Pathway enrichment analysis was also performed by the DAVID database. The results indicate that the pathways of “Signaling pathways regulating pluripotency of stem cells”, “Neurotrophin signaling pathway”, “Proteoglycans in cancer”, and “Hippo signaling pathway” are significantly enriched.

For pathway crosstalk analysis, 45 out of 47 pathways that contain more than two genes met the crosstalk analysis criteria and were selected to construct the network (Fig. 6c). The thickness of the edges indicates measurements of the average value of OC and JC. By using MCODE, a major cluster with 33 nodes and 523 edges was identified from the initial network (Fig. 6d).

Comprehensive gene-pathway analysis

After mapping the hub genes into the subnetwork of pathways guided by KEGG, a potential gene-pathway network including 33 essential pathways and 16 hub genes were constructed (Fig. 6e). This network shows that “PIK3R3” and “PIK3CG” participate in most of the pathways. Furthermore, “pathways in cancer” and “focal adhesion” and “proteoglycans in cancer” rank as the top three pathways according to the genes that they involved.

To screen the main factors (including genes and pathways) in the gene-pathway network, those nodes with degree > average were selected (Fig. 6f). It was found that 11 genes (PIK3R3, PIK3CG, MAPK8, GSK3B, ITGB1, CDKN1A, PTK2, VEGFA, PRKACB, RAP1B, and RAP1A) with five pathways (pathways in cancer, focal adhesion, proteoglycans in cancer, PI3K-Akt signaling pathway, and Ras signaling pathway) are involved and considered more likely to play a role as influential agents.

Identification of key genes and pathways

The expression of the main genes was investigated with meta-analysis through nine studies in Oncomine. The results show that all of the main genes are upregulated in CC, among which five genes (GSK3B, VEGFA, PTK2, RAP1B, and PIK3CG) gain significant differences (Fig. 7a). However, the influence analysis indicates that RAP1B and PIK3CG obtain a discrepant result with the meta-analysis for the altering of significance (Table 5). Hence, only GSK3B, VEGFA, and PTK2 are considered to be key genes.

Fig. 7.

Fig. 7

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The expression of main genes of cervical cancer across nine studies (a), Venn plot for the interaction between key genes and their pathways (b), and the binding sequence and location between miR-106b-5p and each critical gene (c). Orange square: coding sequence (CDS). Green square: binding site

Table 5.

Influence analysis of 5 key genes

Omitting study PGSK3B PVEGFA PPTK2 PRAP1B PPIK3CG
Omitting Study 1 2.16*10-6 0.023 9.22*10-4 0.067 0.165
Omitting Study 2 1.88*10-4 0.023 9.22*10-4 0.067 0.489
Omitting Study 3 2.16*10-6 7.55*10-4 9.51*10-4 0.067 0.367
Omitting Study 4 2.16*10-6 0.022 9.51*10-4 0.023 0.165
Omitting Study 5 1.88*10-4 0.023 9.22*10-4 0.067 0.367
Omitting Study 6 1.88*10-4 7.55*10-4 9.51*10-4 0.023 0.165
Omitting Study 7 1.88*10-4 7.55*10-4 2.97*10-5 0.055 0.367
Omitting Study 8 1.90*10-4 7.55*10-4 9.51*10-4 0.023 0.165
Omitting Study 9 2.16*10-6 0.023 9.22*10-4 0.023 0.367
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Study 1: Cervical Squamous Cell Carcinoma vs. Normal .Biewenga Cervix, Gynecol Oncol, 2008

Study 2: Cervical Cancer vs. Normal. Pyeon Multi-cancer, Cancer Res, 2007

Study 3: Cervical Adenocarcinoma vs. Normal. Scotto Cervix, Genes Chromosomes Cancer, 2008

Study 4: Cervical Squamous Cell Carcinoma vs. Normal. Scotto Cervix, Genes Chromosomes Cancer, 2008

Study 5: Cervical Squamous Cell Carcinoma vs. Normal. Scotto Cervix 2, Genes Chromosomes Cancer, 2008

Study 6: Cervical Keratinizing Squamous Cell Carcinoma vs. Normal. TCGA Cervix, No Associated Paper, 2012

Study 7: Cervical Non-Keratinizing Squamous Cell Carcinoma vs. Normal. TCGA Cervix, No Associated Paper, 2012

Study 8: Cervical Squamous Cell Carcinoma vs. Normal. TCGA Cervix, No Associated Paper, 2012

Study 9: Cervical Squamous Cell Carcinoma Epithelia vs. Normal. Zhai Cervix, Cancer Res, 2007.

PGene: The p-Value for a gene and is the p-Value for the median-ranked analysis

We merged the pathways that each essential gene participates in order to discover the most important pathways. Three pathways, PI3K-Akt signaling pathway, focal adhesion, and pathways in cancer, which all of the three key genes involved in, were identified (Fig. 7b).

Location and characteristic of the binding site

As presented in Fig. 7c, all of the binding sites for miR-106b-5p are located in 3′UTR of GSK3B, VEGFA, and PTK2. Furthermore, when inspecting the sequence of binding sites, adenine (A) and uracil (U) are occupied in most of the sequence.

Discussion

This study confirms that the expression of miR-106b-5p is significantly upregulated in CC and its expression is highly correlated with the number of metastatic lymph nodes. By using bioinformatics analyses, miR-106b-5p is found to be a key issue in the progression of CC by interacting with three key genes and pathways.

Our results show that miR-106b-5p promotes the progression of CC by targeting GSK3B, VEGFA, and PTK2. GSK3B is dysregulated in a variety of tumor tissues including CC19–24, and it participates in the development of CC caused by HPV16 infection by regulating Wnt signaling/β-catenin pathway23,24. In addition, GSK3B exerts antiproliferative effects by promoting APC-dependent phosphorylation and thus promotes protease-mediated degradation of β-catenin which is a transcription factor that positively regulates Myc and cyclin D1 expression21. When referring to VEGFA, its overexpression in CC can enhance the growth and invasion of tumor cells25. More importantly, VEGFA can promote the proliferation and migration of CC cells by activating the PI3K/Akt/mTOR pathway26. In addition, the expression and stability of VEGFA have a close relationship with cellular hypoxia27 and serum VEGFA level can be used as a biomarker for prognosis evaluation28. In terms of PTK2, it is a cytoplasmic protein tyrosine kinase and has an expression in various solid tumors such as ovarian cancer, gastric cancer, and bladder cancer29. It is found that PTK2 is associated with the sensitivity of colon cancer cells to DNA damage therapy30. However, there are few reports of PTK2 in CC.

The results of pathway analyses reveal that all of the three key genes are involved in the pathway of PI3K-Akt signaling pathway, focal adhesion, and Pathways in cancer. With regard to PI3K-Akt signaling pathway, its imbalance in expression is associated with a variety of tumors, including cervical, endometrial, and non-small cell lung cancers31–33. It is activated during the G1/S transition of the cell cycle and regulates several key cell cycle regulators34. Tumors associated with HPV infection are closely related to the PI3K/Akt pathway. Activation of this pathway contributes to genetic instability, dysregulation, apoptosis resistance, and altered metabolic properties ultimately leading to the malignant transformation of infected cells. Concerning focal adhesion, this pathway is involved in the invasion and metastasis of many kinds of tumors and is related to the medicine resistance of certain tumors35,36. The decreased expression of focal adhesion kinase (FAK), a key gene involved in the pathway, can inhibit the invasion and migration of CC cells37. Another study reports that targeted FAK therapy makes pancreatic cancer cells more sensitive to immunotherapy38.

It has been recognized that miRNA negatively regulates gene expression by guiding the RNA-induced silencing complex (RISC) to silence its target mRNA through degradation or translation repression39. However, it is interesting to observe that both the expression of miR-106b-5p and key genes are upregulated in CC with the binding sites located in 3′UTR of mRNAs. According to previous studies40–44, the mechanisms of miRNA-mediated gene upregulation include (1) the presence of cellular state (G0 or G0-like state) and/or specific factors (AGO1, AGO2, GW182, and FXR1); (2) miRNA directly binds to 5′UTR of RNA and increases its association with 40S and polysome formation or enhances their translation by alleviating their TOP-mediated translational repression during amino acid starvation, respectively; (3) miRNA prevents tristetraprolin (TTP) binding to the AU-rich element (ARE) sites of mRNA and inhibits its degradation by ARE-mediated mRNA decay (AMD) pathway; (4) derepression from miRNA-mediated downregulation in response to cell stresses by HuR (an RNA binding protein that interacts with ARE in 3′UTR of the mRNA). As none of the binding sites are located in 5′UTR in our results, and cells are considered active in cancer tissue, the mechanisms of upregulation for cellular state or binding site in 5′UTR may be less possible.

AREs are found in the 3′UTR of mRNAs that code for proto-oncogenes, nuclear transcription factors, and cytokines45. They can be classified into three types: (1) having dispersed AUUUA motifs within or near U-rich regions; (2) having overlapping AUUUA motifs within or near U-rich regions; (3) a much less well-defined class having a U-rich region but no AUUUA repeats46. Our results reveal that A and U are occupied in most of the binding sequence. To our knowledge, it has been verified that TTP has interactions with GSK3B47 and VEGFA48 whereas there are no reports on PTK2. It is possible that miR-106b-5p prevents TTP from binding to the mRNAs in 3′UTR and therefore regulate their expression, but the specific mechanism needs to be further investigated. In addition, it has been confirmed that HuR can bind to VEGF49 and reverse the repression effect by miR-200b50. This also could be a contributing factor to the upregulation of VEGFA and be adopted for the other key genes.

However, several limitations exist in the present study: (1) Significant heterogeneity can be observed in the meta-analysis. This high heterogeneity may result from population, race, stage, and the type of CC (squamous or adenocarcinoma). Hence, more data from large-scale clinical trials are needed to evaluate the source of the heterogeneity. (2) Parts of genes are removed due to the selection criteria. However, it is possible that these genes might also impact the progression of CC and they also need to be analyzed. (3) As this study is an in silico research, a further experiment is needed for validation. (4) Upregulations of both miR-106-5p and key genes are identified. Despite the fact that several possibilities are analyzed, the specific mechanisms still need to be studied and verified further.

Conclusions

In summary, significant upregulation of miR-106b-5p in CC is confirmed by meta-analysis with the data from TCGA and GEO, and the expression of miR-106b-5p is significantly correlated with the number of metastatic lymph nodes. Furthermore, miR-106b-5p promotes the progression of CC by targeting three key genes (GSK3B, VEGFA, PTK2) through three crucial pathways (PI3K-Akt signaling pathway, focal adhesion, and pathways in cancer). miR-106b-5p might upregulate the key genes by preventing TTP from binding to the mRNAs in 3’UTR with/without the effect of derepression of HuR. However, the specific mechanism needs to be further investigated.

Materials and methods

The workflow of this study is presented in Fig. 8 and this study is performed according to guidelines of MIAME51 and Meta-Analysis of Gene Expression Microarray Datasets52. Firstly, the clinical significance of miR-106b-5p in CC is assessed according to the CESC data from TCGA. Secondly, the expression data of miR-106b-5p from TCGA, GEO, and literature are synthesized by meta-analysis. Thirdly, DEGs from TCGA are screened and 12 databases are used to predict target genes of miR-106b-5p. The overlap genes between DEGs from TCGA and predicted target genes are explored by bioinformatic analyses.

Fig. 8. Work flow of the clinical significance evaluation and comprehensive analysis for miR-106b-5p in cervical cancer.

Fig. 8

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The modules of clinical value evaluation (green), meta-analysis (brown), and bioinformatics analyses (pink) are included

Correlations between the expression of miR-106b-5p and clinical outcomes

To identify the clinical significance of miR-106b-5p in CC, we adopt LinkedOmics53 which is constructed based on TCGA with 309 cancer samples and three normal samples as controls in CESC dataset to explore the correlations between the expression of miR-106b-5p and the clinical outcomes, including the number of metastatic lymph nodes, tumor purity, race, pathology M/N/T stage, years to birth, histological type, ethnicity, radiation therapy, and overall survival.

Meta-analysis of miR-106b-5p expression based on TCGA/GEO and literature

A systematic searching in GEO for the miR-106b-5p expression between CC and healthy tissue was performed on 30 March 2018 with the terms and eligibility criteria that are presented in Tables 5 and 6. For adjusting the data to the normal distribution to reduce variation, Log2 scale transformation was applied to all of the expression data. The meta-analysis was conducted by R Version 3.4.154 and meta package55. We pooled SMD using the Mantel–Haenszel formula (fixed-effect model) or the DerSimonian–Laird formula (random effects model) and assessed the heterogeneity using I2 statistics. Random-effect models (P < 0.05 or I2 > 50%) are used when the heterogeneity is considered significant. Otherwise a fixed-effects model would be used.

Table 6.

Searching terms used in GEO and literature review

(1) Microarray searching
 #1 microRNA OR miRNA OR micro RNA noncoding RNA OR ncRNA OR small RNA
 #2 Cervical OR cervix
 #3 Cancer OR carcinoma OR tumor OR neoplasia OR neoplasm OR malignant OR malignancy
#4 #1 AND #2 AND #3
(2) Literature search
 #1 microRNA OR miRNA OR micro RNA noncoding RNA OR ncRNA OR small RNA
 #2 106b OR 106b-5p
 #3 Cervical OR cervix
 #4 Cancer OR carcinoma OR tumor OR neoplasia OR neoplasm OR malignant OR malignancy
 #5 #1 AND #2 AND #3 AND #4
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Funnel plot with Egger’s test56 was utilized to evaluate the publication bias. P < 0.1 was considered to be significant asymmetry for the funnel plot.

To detect the robustness of the pool results, sensitivity analysis was performed by alternating analysis model. In addition, to further evaluate the impact of individual studies on the overall effect estimates, influence analysis was performed and the combined estimates were recalculated by omitting one study at a time.

Furthermore, a full-scale search of miR-106b-5p expression in eight electronic databases (PubMed, Chinese VIP, CNKI, Wanfang Database, Embase, Web of Science, Science Direct, and Wiley Online Library) accompanied with manual searching by screening the references cited in the acquired articles was conducted on 30 March 2018. The searching terms and eligible criteria are also shown in Tables 5 and 6. The data of the publications will be extracted and analyzed, including the author, year of publication, country, number of cancer and control samples, regulations, and testing methods.

Screening of candidate genes for miR-106b-5p

The CESC data were analyzed by the limma package57 to identify DEGs. We determined the significance of the difference in gene expression as Log2 |fold change (FC) | > 1 and False Discovery Rate (FDR) < 0.05. Furthermore, the miR-106b-5p targeted genes are predicted by 12 databases (Microt4, miRWalk, mir-bridge, miRanda, miRDB, miRMap, Pictar2, PITA, miRNAMap, RNAhybrid, RNA22, and Targetscan) in miRWALK version 2.058. To increase prediction accuracy, the genes that are overlapping in at least five databases were selected. Finally, the overlap genes between DEGs and predicted genes were analyzed by upsetR59 and Venn Plot.

GO and pathway enrichment analysis

GO and pathway enrichment analyses were performed by DAVID60 on the overlapping genes. The significantly enriched biological items for CeC, BP, and molecular functions (MF) were identified as P < 0.01.

Protein-protein interaction network analysis

The STRING database61 was used to construct an interaction network between the overlapped genes. To obtain precise results, those nodes in the network would be removed: (a) interaction score < 0.7; (b) not connected to the major network; (3) the value of degree and betweenness is less than average.

CytoHubba62 was used to explore the important nodes by 12 topological algorithms, including Degree, Edge Percolated Component (EPC), Maximum Neighborhood Component (MNC), Density of Maximum Neighborhood Component (DMNC), Maximal Clique Centrality (MCC), and centralities based on shortest paths, such as Bottleneck (BN), EcCentricity, Closeness, Radiality, Betweenness, ClusteringCoefficient, and Stress. The top 20 genes in each topological algorithm were extracted and the duplication of each gene was also calculated. The genes with less than 2 in repetitiveness are excluded for ensuring the genes are closely linked to CC and the rest are considered as hub genes.

Pathway enrichment and crosstalk analysis

For pathway enrichment, the predicted targets were mapped using the Kyoto Gene and Genome Encyclopedia (KEGG) database by online analysis of DAVID. Significant pathways were considered as P < 0.05.

The obtained pathways were recruited for further crosstalk analysis to explore their interactions. The method is based on the assumption that if two pathways share a proportion of genes, they are considered crosstalk63. As described in the previous study64, in order to better measure the overlap between any two pathways, JC (Jaccard coefficient) = | (A ∩ B) / (A ∪ B) | and OC (overlapping coefficient) = (|A ∩ B|) / (min(| A |, | B |)) were adopted, where A and B are the genes contained in the two pathways. As pathways with too few genes may have insufficient biological information, we excluded pathways containing fewer than three genes. Likewise, pairs of pathways with less than two overlapping genes were also removed. After obtaining the network of pathway crosstalk, the plug-in app Molecular Complex Detection (MCODE) in Cytoscape was used to screen the hub subnetwork which had the score >4.

Comprehensive gene-pathway analysis

The hub genes are mapped into the subnetwork of crosstalk to further explore the mechanism by KEGG. To screen the main genes and pathways, the nodes with degree > average are collected for constituting a subnetwork.

Identification of key genes and pathways

To further identify the key genes, we evaluated the expression of main genes between CC and healthy samples with meta-analysis in Oncomine65. P < 0.05 is considered as a significant difference. Moreover, influence analysis was also conducted to access the pool estimates. The pathways that all of the key genes participate in were determined as crucial pathways.

Possible mechanisms of regulations between miR-106b-5p and key genes

The regulation between miR-106b-5p and the key genes is recognized by their expression. To explore the possible mechanisms of the regulation, we collected the sequence of miR-106b-5p and the three key genes from miRbase66 and NCBI–nucleotide to predict their binding site by RNAhybrid67 with the criteria that mRNA has perfect nucleotide pairing between the second and eighth positions of the 5′ end of miRNA sequences. Furthermore, the character of the binding sequence was also investigated.

Electronic supplementary material

Author contribution form (228.6KB, pdf)

Conflict of interest

The authors declare that they have no conflict of interest.

Footnotes

Edited by: M.V. Niklison Chirou

Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Change history

7/10/2019

Due to a technical error, content intended for publication in Volume 4 (2018) published in Volume 5 (2019). The content has been moved into the correct volume, and the citation information was updated accordingly.

Electronic supplementary material

The online version of this article (10.1038/s41420-018-0096-8) contains supplementary material, which is available to authorized users.

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