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Involvement of anti-tumor miR-124-3p and its targets in the pathogenesis of pancreatic ductal adenocarcinoma: direct regulation of ITGA3 and ITGB1 by miR-124-3p

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Oncotarget. 2018; 9:28849-28865. https://doi.org/10.18632/oncotarget.25599

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Tetsuya Idichi, Naohiko Seki _, Hiroshi Kurahara, Haruhi Fukuhisa, Hiroko Toda, Masataka Shimonosono, Yasutaka Yamada, Takayuki Arai, Yoshiaki Kita, Yuko Kijima, Yuko Mataki, Kosei Maemura and Shoji Natsugoe

Abstract

Tetsuya Idichi1, Naohiko Seki2, Hiroshi Kurahara1, Haruhi Fukuhisa1, Hiroko Toda1, Masataka Shimonosono1, Yasutaka Yamada2, Takayuki Arai2, Yoshiaki Kita1, Yuko Kijima1, Yuko Mataki1, Kosei Maemura1 and Shoji Natsugoe1

1Department of Digestive Surgery, Breast and Thyroid Surgery, Graduate School of Medical Sciences, Kagoshima University, Kagoshima, Japan

2Department of Functional Genomics, Chiba University Graduate School of Medicine, Chiba, Japan

Correspondence to:

Naohiko Seki, email: naoseki@faculty.chiba-u.jp

Keywords: microRNA; miR-124-3p; ITGA3; ITGB1; pancreatic ductal adenocarcinoma

Received: April 07, 2018     Accepted: May 24, 2018     Published: June 22, 2018

ABSTRACT

MicroRNAs (miRNAs) are unique in that a single miRNA molecule regulates a vast number of RNA transcripts. Thus, aberrantly expressed miRNAs disrupt tightly controlled RNA networks in cancer cells. Our functional screening showed that expression of miR-124-3p was downregulated in pancreatic ductal adenocarcinoma (PDAC) tissues. Here, we aimed to investigate the anti-tumor roles of miR-124-3p in PDAC cells and to identify miR-124-3p-mediated oncogenic signaling in this disease. Ectopic expression of miR-124-3p inhibited cancer cell migration and invasion in PDAC cells. Moreover, restoration of miR-124-3p suppressed oncogenic signaling, as demonstrated by reduced phosphorylation of focal adhesion kinase, AKT, and extracellular signal-regulated kinase, in PDAC cells. Our in silico database analyses and luciferase reporter assays showed that two cell-surface matrix receptors, integrin α3 (ITGA3) and integrin β1 (ITGB1), were directly regulated by miR-124-3p in PDAC cells. Overexpression of ITGA3 and ITGB1 was confirmed in PDAC clinical specimens. Interestingly, a large number of cohort analyses from TCGA database showed that high expressions of ITGA3 and ITGB1 were significantly associated with poor prognosis of patients with PDAC. Knockdown of ITGA3 and ITGB1 by siRNAs markedly suppressed the migration and invasion abilities of PDAC cells. Moreover, downstream oncogenic signaling was inhibited by ectopic expression of miR-124-3p or knockdown of the two integrins. The discovery of anti-tumor miRNAs and miRNA-mediated oncogenic signaling may provide novel therapeutic targets for the treatment of PDAC.


Involvement of anti-tumor <i>miR-124-3p</i> and its targets in the pathogenesis of pancreatic ductal adenocarcinoma: direct regulation of <i>ITGA3</i> and <i>ITGB1</i> by <i>miR-124-3p</i> | Idichi | Oncotarget

INTRODUCTION

Among human malignant tumors, pancreatic ductal adenocarcinoma (PDAC) is the most difficult cancer to treat, and the 5-year survival rate of patients with PDAC is approximately 5% [1]. Owing to the lack of an effective diagnostic strategy, most patients with PDAC have local invasion or distant metastasis [2]. Only about 15% of patients are candidates for surgical resection, and the prognosis of patients with inoperable cancer is very poor [3]. Currently developed combination chemotherapies have a limited therapeutic impact on patients with advanced-stage disease [4]. Elucidation of the molecular mechanisms of lethal PDAC may lead to the development of novel therapeutic strategies.

MicroRNAs (miRNAs) are short noncoding RNAs that act as fine-tuners of the expression of protein-coding or noncoding genes [5]. In recent years, miRNAs have been shown to have both oncogenic and tumor-suppressive functions based on a variety of studies [6]. Currently developed bioinformatics approaches have indicated that a single miRNA can regulate many protein-coding RNAs; conversely, more than 60% of protein-coding genes in the human genome are controlled by miRNAs [7]. Therefore, aberrantly expressed miRNAs can disrupt entire networks of protein-coding or noncoding genes and affect the pathogenesis of human cancers.

Screening for dysregulated miRNAs in cancer cells is a critical first step to elucidating aberrantly expressed genes and proteins involved in cancer pathogenesis [8]. In PDAC, we have sequentially identified anti-tumor miRNAs using RNA sequencing-based miRNA expression signatures [911]. Our previous studies showed that clustered miRNAs, e.g., miR-375, miR-216a-5p, miR-216a-3p, miR-216b-5p, miR-216b-3p, and miR-217, are significantly downregulated in PDAC tissues and act as anti-tumor miRNAs [911]. Moreover, overexpression of forkhead box Q1 (FOXQ1) and actin-binding protein anilin (ANLN) promote PDAC cell aggressiveness and are involved in PDAC pathogenesis [9, 10]. These findings suggested that analysis of novel cancer pathways mediated by anti-tumor miRNAs will provide new insights into the potential mechanisms underlying the aggressive course of PDAC.

In this study, we focused on miR-124-3p because our functional screening showed that restoration of miR-124-3p markedly inhibited oncogenic signaling in PDAC cells. Here, we aimed to investigate the anti-tumor roles of miR-124-3p and to identify miR-124-3p-regulated novel oncogenic pathways in PDAC cells. The miR-124 family includes three members, miR-124-1, miR-124-2 and miR-124-3 on different human chromosomal loci, 8q23.1, 8p12.1 and 20p13.33, respectively. The mature sequences of the three miR-124 family are exactly the same. Since their sequences are identical, we define the miR-124 family as miR-124-3p in this study. The gene structure of the miR-124 family and the chromosomal loci are shown in the Supplementary Figure 1.

Our present data showed that two cell-surface matrix receptors, integrin α3 (ITGA3) and integrin β1 (ITGB1), were directly regulated by miR-124-3p in PDAC cells. Recent studies demonstrated that dysregulation of the extracellular matrix (ECM) and integrin-mediated oncogenic signaling enhances cancer cell aggressiveness [12, 13]. Thus, miR-124-3p-based approaches for PDAC can be used to identify potential targets for the development of new therapeutic strategies.

RESULTS

Expression levels of miR-124-3p in PDAC clinical specimens and cell lines

Expression levels of miR-124-3p were validated using PDAC specimens (cancer tissues: n = 30 and normal pancreatic tissues: n = 12) and three PDAC cell lines (PANC-1, SW1990, and MIApaca-2). Backgrounds and clinicopathological characteristics of clinical samples are shown in Table 1A. Normal pancreatic tissues are shown in Table 1B.

Table 1A: Characteristics of patients with PDAC

Pancreatic ductal adenocarcinoma (PDAC)

(%)

Total number

 

30

 

Average age (range), years

 

 65.8 (42-79) 

 

Sex

Male

15

 (50.0) 

 

 Female 

15

(50.0)

 

 

 

 

T category

pTis

1

(3.3)

 

pT1

1

(3.3)

 

pT2

1

(3.3)

 

pT3

27

(90.1)

 

pT4

0

(0)

 

 

 

 

N category

0

12

(40.0)

 

1

18

(60.0)

 

 

 

 

M category

0

27

(90.0)

 

1

3

(10.0)

 

 

 

 

Neoadjuvant chemotherapy

(-)

13

(43.3)

 

(+)

17

(56.7)

 

 

 

 

Recurrence

(-)

8

(26.7)

 

(+)

22

(73.3)

Table 1B: Characteristics of patients without PDAC

Normal pancreatic tissue

Total number

 

12

 

Average age (range), years

 

 65.4 (42-85) 

 

Sex

Male

5

 (41.7) 

 

 Female 

7

(58.3)

The expression levels of miR-124-3p were significantly lower in PDAC tissues than in normal pancreatic tissues (normalized to RNU48; P = 0.0029, Figure 1A). However, for clinicopathological factors (i.e., age, sex, neoadjuvant chemotherapy, and recurrence), there were no significant differences in the expression of miR-124-3p.

Anti-tumor functions of miR-124-3p in PDAC cell lines and decreased phosphorylation of the components of oncogenic signaling pathways.

Figure 1: Anti-tumor functions of miR-124-3p in PDAC cell lines and decreased phosphorylation of the components of oncogenic signaling pathways. (A) Expression levels of miR-124-3p in PDAC clinical specimens and cell lines were determined by qRT-PCR. Data were normalized to RNU48 expression. *, P < 0.0001. (B) Cell proliferation was determined by XTT assays 72 h after transfection with 10 nM miR-124-3p. *, P < 0.0001. (C) Cell migration activity was determined by migration assays. *, P < 0.0001. (D) Cell invasion activity was determined using Matrigel invasion assays. *, P < 0.0001. (E) Gain of function miR-124-3p in PDAC cells reduced the phosphorylation of FAK, AKT, and Erk1/2. GAPDH was used as a loading control.

Expression levels of three cancer cell lines were markedly low compared to normal pancreatic tissues (Figure 1A).

Effects of ectopic expression of miR-124-3p in PDAC cells

To investigate the anti-tumor roles of miR-124-3p, we performed gain-of-function studies using transfection of PANC-1, SW1990, and MIApaca-2 cells.

In vitro assays demonstrated that cell proliferation, migration, and invasion were significantly inhibited in miR-124-3p mimic transfectants compared those in with mock or miR-control transfectants (each, P < 0.0001; Figure 1B1D), with particularly remarkable effects observed in migration and invasion assays. These results indicated that miR-124-3p had anti-tumor roles in PDAC cells and could be categorized as an anti-tumor miRNA. We performed flow cytometric analyses to determine the number of apoptotic cells following restoration of miR-124-3p expression. The apoptotic cell numbers (apoptotic and early apoptotic cells) were increased in miR-124-3p expression than in mock or miR-control transfectant cells (Supplementary Figure 2A and 2B). We showed that cleaved PARP expression was detected in restoration of miR-124-3p expression (Supplementary Figure 2C).

Blocking of oncogenic signaling by ectopic expression of miR-124-3p in PDAC cells

Next, we analyzed whether oncogenic signaling pathways were affected using gain-of-function miR-124-3p in PDAC cell lines. FAK, AKT, and ERK1/2 were selected as intracellular carcinogenic signaling molecules, and the phosphorylated state of each protein was evaluated by western blotting. The levels of phospho-FAK, phospho-AKT, and phospho-Erk1/2 were blocked by miR-124-3p expression in PDAC cells (Figure 1E).

Identification of miR-124-3p-regulated oncogenic pathways and targets in PDAC cells

To identify molecular oncogenic pathways and targets regulated by miR-124-3p in PDAC cells, we applied a combination of in silico database analyses and gene expression analyses in PDAC clinical specimens. Our strategy is shown in Supplementary Figure 3.

Using the TargetScan database 7.1, we annotated 4,450 putative target genes having miR-124-3p binding sites in their 3’- untranslated regions (UTRs). Gene expression data (GEO accession number: GSE15471) revealed that 2,148 genes were upregulated in PDAC clinical specimens (fold-change log2 > 1.0). Finally, we selected 435 genes that were putative oncogenic targets regulated by miR-124-3p in PDAC cells.

We categorized 435 genes into existing molecular pathways using KEGG pathway analyses. The top 10 pathways contained 60 genes (Table 2A). We investigated the expression statuses of those 60 genes and the clinical significance of PDAC using the OncoLnc database (http://www.oncolnc.org/). Kaplan-Meier survival curves showed that high expression of 15 genes was associated with poor prognosis in PDAC (Table 2B, Figure 2).

Kaplan-Meier analysis of miR-124-3p-regulated genes related to poor prognosis in PDAC.

Figure 2: Kaplan-Meier analysis of miR-124-3p-regulated genes related to poor prognosis in PDAC. Kaplan–Meier plots of overall survival with log-rank tests between those with high and low miR-124-3p expression and expression of 15 genes in the PDAC TCGA database.

Table 2A: Enriched KEGG pathways regulated by miR-124-3p on in silico analysis

KEGG ID

Pathways

P value

No. of genes

Genes

Kegg:04512

ECM-receptor interaction

4.1783E-11

13

SDC1, ITGA1, SDC4, ITGA3, ITGA2, HSPG2, LAMC1, COL4A1, THBS2, LAMC2, ITGB1, COL5A1, COL6A3

Kegg:05200

Pathways in cancer

1.0784E-09

21

E2F3, TGFBR1, MMP2, GLI3, ITGA3, ITGA2, ETS1, AKT3, IL8, LAMC1, COL4A1, FZD2, TGFB1, MITF, PDGFRB, LAMC2, ITGB1, RALA, PLD1, CBL, CBLB

Kegg:05145

Toxoplasmosis

3.5384E-06

10

GNAI1, TLR4, AKT3, HSPA6, LAMC1, HLA-DPB1, TGFB1, IL10RA, LAMC2, ITGB1

Kegg:04060

Cytokine-cytokine receptor interaction

5.5517E-06

14

IL7R, CXCL9, TGFBR1, IL8, CCL2, TGFB1, IL10RA, PDGFRB, IFNAR2, CSF2RB, TNFRSF11B, TNFSF4, LIF, TNFSF11

Kegg:04510

Focal adhesion

7.8047E-06

12

ITGA1, ITGA3, ITGA2, AKT3, LAMC1, COL4A1, THBS2, PDGFRB, LAMC2, ITGB1, COL5A1, COL6A3

Kegg:04630

Jak-STAT signaling pathway

2.4297E-05

10

IL7R, AKT3, JAK3, SPRED1, IL10RA, IFNAR2, CSF2RB, CBL, LIF, CBLB

Kegg:04514

Cell adhesion molecules (CAMs)

2.8873E-05

9

CLDN1, SDC1, SDC4, MPZL1, HLA-DPB1, CLDN11, ITGB1, VCAN, CLDN4

Kegg:04810

Regulation of actin cytoskeleton

7.0689E-05

11

ITGA1, ARPC5, ARPC1B, ITGA3, MYH10, ITGA2, MSN, GNA13, RRAS, PDGFRB, ITGB1

Kegg:04144

Endocytosis

0.00076292

9

SH3KBP1, TGFBR1, HSPA6, TGFB1, RAB31, DAB2, PLD1, CBL, CBLB

Kegg:04010

MAPK signaling pathway

0.00596607

9

CACNA2D1, TGFBR1, AKT3, DUSP6, HSPA6, RRAS, TGFB1, PDGFRB, MAP3K8

Table 2B: Candidate target genes regulated by miR-124-3p

Entrez gene

Gene symbol

Gene name

Target sites

GEO data
FC(log2)

*TCGA_OncoLnc P-value

Conserved sites

Poorly sites

3675

ITGA3

integrin, alpha 3 (antigen CD49C, alpha 3 subunit of VLA-3 receptor)

1

0

1.329

0.0004

6237

RRAS

related RAS viral (r-ras) oncogene homolog

1

0

1.147

0.0010

3688

ITGB1

integrin, beta 1 (fibronectin receptor, beta polypeptide, antigen CD29 includes MDF2, MSK12)

2

0

1.687

0.0024

3673

ITGA2

integrin, alpha 2 (CD49B, alpha 2 subunit of VLA-2 receptor)

0

1

2.619

0.0031

5898

RALA

v-ral simian leukemia viral oncogene homolog A (ras related)

1

0

1.028

0.0036

6385

SDC4

syndecan 4

1

0

1.030

0.0058

6382

SDC1

syndecan 1

0

1

1.384

0.0079

9076

CLDN1

claudin 1

0

5

1.892

0.0092

1364

CLDN4

claudin 4

0

1

1.055

0.0097

4283

CXCL9

chemokine (C-X-C motif) ligand 9

0

1

1.294

0.0121

1848

DUSP6

dual specificity phosphatase 6

1

0

1.141

0.0137

1293

COL6A3

collagen, type VI, alpha 3

0

1

2.749

0.0154

30011

SH3KBP1

SH3-domain kinase binding protein 1

1

0

1.652

0.0264

2535

FZD2

frizzled family receptor 2

0

1

1.295

0.0349

4478

MSN

moesin

0

2

1.740

0.0389

7058

THBS2

thrombospondin 2

1

1

3.986

0.0572

7046

TGFBR1

transforming growth factor, beta receptor 1

1

0

1.567

0.0587

3918

LAMC2

laminin, gamma 2

0

1

2.761

0.0595

2113

ETS1

v-ets avian erythroblastosis virus E26 oncogene homolog 1

1

1

1.156

0.0674

161742

SPRED1

sprouty-related, EVH1 domain containing 1

1

0

1.539

0.1010

3587

IL10RA

interleukin 10 receptor, alpha

0

2

1.268

0.1380

3718

JAK3

Janus kinase 3

0

1

1.005

0.1670

11031

RAB31

RAB31, member RAS oncogene family

0

1

2.855

0.1680

4286

MITF

microphthalmia-associated transcription factor

2

0

1.117

0.2120

3976

LIF

leukemia inhibitory factor

1

2

1.161

0.2140

4628

MYH10

myosin, heavy chain 10, non-muscle

1

0

1.142

0.2170

868

CBLB

Cbl proto-oncogene B, E3 ubiquitin protein ligase

0

1

1.434

0.2380

8600

TNFSF11

tumor necrosis factor (ligand) superfamily, member 11

1

0

1.412

0.2850

6347

CCL2

chemokine (C-C motif) ligand 2

0

1

1.086

0.2990

10000

AKT3

v-akt murine thymoma viral oncogene homolog 3

2

0

1.023

0.3170

3339

HSPG2

heparan sulfate proteoglycan 2

0

1

1.080

0.3290

1289

COL5A1

collagen, type V, alpha 1

0

1

3.496

0.3300

10672

GNA13

guanine nucleotide binding protein (G protein), alpha 13

2

0

1.109

0.3300

781

CACNA2D1

calcium channel, voltage-dependent, alpha 2/delta subunit 1

1

0

1.619

0.3690

1439

CSF2RB

colony stimulating factor 2 receptor, beta, low-affinity (granulocyte-macrophage)

0

1

1.386

0.3940

1462

VCAN

versican

1

1

4.155

0.4380

4313

MMP2

matrix metallopeptidase 2 (gelatinase A, 72kDa gelatinase, 72kDa type IV collagenase)

0

2

2.531

0.4450

3575

IL7R

interleukin 7 receptor

0

1

1.444

0.4530

3115

HLA-DPB1

major histocompatibility complex, class II, DP beta 1

0

1

1.232

0.4540

7099

TLR4

toll-like receptor 4

0

3

1.095

0.5340

867

CBL

Cbl proto-oncogene, E3 ubiquitin protein ligase

3

2

1.058

0.5750

1282

COL4A1

collagen, type IV, alpha 1

1

1

2.357

0.5830

4982

TNFRSF11B

tumor necrosis factor receptor superfamily, member 11b

0

1

1.697

0.6000

3455

IFNAR2

interferon (alpha, beta and omega) receptor 2

0

1

1.072

0.6180

3672

ITGA1

integrin, alpha 1

0

2

1.561

0.6330

2770

GNAI1

guanine nucleotide binding protein (G protein), alpha inhibiting activity polypeptide 1

1

0

1.210

0.6980

10095

ARPC1B

actin related protein 2/3 complex, subunit 1B, 41kDa

1

0

1.572

0.7020

5010

CLDN11

claudin 11

0

1

2.540

0.7350

3915

LAMC1

laminin, gamma 1 (formerly LAMB2)

3

1

1.347

0.7430

5337

PLD1

phospholipase D1, phosphatidylcholine-specific

1

0

1.395

0.7630

5159

PDGFRB

platelet-derived growth factor receptor, beta polypeptide

0

1

1.799

0.7750

7292

TNFSF4

tumor necrosis factor (ligand) superfamily, member 4

0

1

1.575

0.7930

1601

DAB2

Dab, mitogen-responsive phosphoprotein, homolog 2 (Drosophila)

1

0

1.036

0.8810

7040

TGFB1

transforming growth factor, beta 1

0

1

1.423

0.8930

9019

MPZL1

myelin protein zero-like 1

1

0

1.002

0.8940

3576

IL8

interleukin 8

0

1

3.240

0.9220

1326

MAP3K8

mitogen-activated protein kinase kinase kinase 8

0

1

1.352

0.9280

10092

ARPC5

actin related protein 2/3 complex, subunit 5, 16kDa

0

1

1.038

0.9440

3310

HSPA6

heat shock 70kDa protein 6 (HSP70B')

0

1

1.347

0.9560

2737

GLI3

GLI family zinc finger 3

1

0

1.343

0.9890

*Kaplan-Meier analysis Log-rank.

P-value < 0.05 Poor prognosis with a high expression.

Furthermore, we provided a heatmap gene visualization and validated as a prognostic ability of these 15 genes (Figure 3). As shown in Figure 3, patients with high gene signature expressions (Z-score > 0) were significantly poor Overall Survival (OS) and Disease Free Survival (DFS) rate than those with low gene signature expressions (Z-score ≤ 0) (OS; P = 0.0011, DFS; P = 0.0132, Figure 3). Combination analysis of ITGA3 and ITGB1 revealed that high expression of ITGA3/ITGB1 (Z-score > 0) was predicted significantly poor OS and DFS rate than those with low expression of ITGA3/ITGB1 (Z-score ≤ 0) (OS; P = 0.0037, DFS; P = 0.0162, Supplementary Figure 4). In this study, we focused on ITGA3 and ITGB1.

Combination analysis with heatmap of 15 target genes related to poor prognosis in PDAC.

Figure 3: Combination analysis with heatmap of 15 target genes related to poor prognosis in PDAC. Heatmap was created using analysis webcite “R2: Genomics Analysis and Visualization Platform (http://r2.amc.nl)”. Z - score was evaluated by a combination of miR-124-3p final target genes based on TCGA datasets. High group (mRNA Z-score > 0) and low group (mRNA Z-score ≤ 0) are displayed as Kaplan–Meier plots with log-rank tests.

Overexpression of ITGA3 and ITGB1 in PDAC tissues and cell lines

We evaluated the expression levels of ITGA3 and ITGB1 in PDAC tissues (n = 30), normal pancreatic tissues (n = 12), and three PDAC cell lines (PANC-1, SW1990, and MIApaca-2). The expression levels of ITGA3 and ITGB1 were significantly upregulated in PDAC tissues compared with normal pancreatic tissues (normalized to GUSB: P = 0.0004, Figure 4A and P = 0.0006, Figure 5A).

Direct regulation of ITGA3 by miR-124-3p in PDAC cell lines.

Figure 4: Direct regulation of ITGA3 by miR-124-3p in PDAC cell lines. (A) Expression levels of ITGA3 in PDAC clinical specimens and cell lines were determined by qRT-PCR. Data were normalized to GUSB expression. (B) Expression levels of ITGA3 and miR-124-3p were negatively correlated. (C) ITGA3 mRNA expression in PDAC cell lines was evaluated by qRT-PCR 72 h after transfection with miR-124-3p. GUSB was used as an internal control. *, P < 0.0001. (D) ITGA3 protein expression in PDAC cell lines was evaluated by western blot analysis 96 h after transfection with miR-124-3p. GAPDH was used as a loading control. (E) miR-124-3p binding sites in the 3′-UTR of ITGA3 mRNA. Dual luciferase reporter assays using vectors encoding the putative miR-124-3p (positions 1160-1166) target site of the ITGA3 3′-UTR for both wild-type and deleted regions. Normalized data were calculated as ratios of Renilla/firefly luciferase activities. *, P < 0.0001.

Direct regulation of ITGB1 by miR-124-3p in PDAC cell lines.

Figure 5: Direct regulation of ITGB1 by miR-124-3p in PDAC cell lines. (A) Expression levels of ITGB1 in PDAC clinical specimens and cell lines were determined by qRT-PCR. Data were normalized to GUSB expression. (B) Expression levels of ITGB1 and miR-124-3p were negatively correlated. (C) ITGB1 mRNA expression in PDAC cell lines was evaluated by qRT-PCR 72 h after transfection with miR-124-3p. GUSB was used as an internal control. *, P < 0.0001. (D) ITGB1 protein expression in PDAC cell lines was evaluated by western blot analysis 96 h after transfection with miR-124-3p. GAPDH was used as a loading control. (E) miR-124-3p binding sites in the 3′-UTR of ITGB1 mRNA. Dual luciferase reporter assays using vectors encoding the putative miR-124-3p (positions 236–242 and 1095-1101) target sites of the ITGB1 3′-UTR for both wild-type and deleted regions. Normalized data were calculated as ratios of Renilla/firefly luciferase activities. *, P < 0.0001.

Negative correlations between miR-124-3p expression and ITGA3 mRNA expression were analyzed by Spearman’s rank test (R = -0.496, P < 0.0013, Figure 4B). Additionally, negative correlations between miR-124-3p expression and ITGB1 mRNA expression were analyzed by Spearman’s rank test (R = -0.543, P = 0.0004, Figure 5B).

Direct regulation of ITGA3 and ITGB1 by miR-124-3p in PDAC cells

We performed qRT-PCR to validate miR-124-3p repression of ITGA3 mRNA expression in PDAC cell lines. Our studies revealed that ITGA3 mRNA was significantly reduced in miR-124-3p mimic transfectants in comparison with mock or miR-control transfectants (P < 0.0001, Figure 4C). Expression of ITGA3 protein was also repressed in the miR-124-3p mimic transfectants (Figure 4D). In silico analysis using TargetScan database 7.1 showed that the 3'-UTR of ITGA3 harbored one binding site for miR-124-3p (Figure 4E). To determine whether ITGA3 mRNAs had functional target site, we performed dual luciferase reporter assays. Compared with the miR-control, luminescence intensity was significantly reduced by transfection with miR-124-3p at miR-124-3p target sites, position 1160-1166 in the 3’-UTR of ITGA3 (Figure 4E).

Similarly, we performed qRT-PCR to validate miR-124-3p repression of ITGB1 mRNA expression in PDAC cell lines. Our studies revealed that ITGB1 mRNA was significantly reduced in miR-124-3p mimic transfectants in comparison with mock or miR-control transfectants (P < 0.0001, Figure 5C). Expression of ITGB1 protein was also repressed in the miR-124-3p mimic transfectants (Figure 5D). In silico analysis using TargetScan database 7.1 showed that the 3'-UTR of ITGB1 harbored two binding sites for miR-124-3p (Figure 5E). To determine whether ITGB1 mRNAs had functional target sites, we performed dual luciferase reporter assays. Compared with the miR-control, luminescence intensity was significantly reduced by transfection with miR-124-3p at miR-124-3p target sites, both position-1 236-242 in the 3’-UTR of ITGB1 (Figure 5E, upper) and position-2 1095-1101 in the 3’-UTR of ITGB1 (Figure 5E, lower)

Effects of ITGA3 and ITGB1 knockdown in PDAC cells

To validate the oncogenic functions of ITGA3 and ITGB1 in PDAC cells, we performed knockdown assays using siRNAs (si-ITGA3 and si-ITGB1).

First, we evaluated the knockdown efficiency of si-ITGA3 and si-ITGB1 transfection in PDAC cell lines. In this study, we used two types of si-ITGA3 (si-ITGA3-1 and si-ITGA3-2) and si-ITGB1 (si-ITGB1-1 and si-ITGB1-2). Our data showed that all siRNAs effectively reduced ITGA3 and ITGB1 mRNA levels of expression in PDAC cell lines (Figures 6A and 7A). Furthermore, all siRNAs effectively reduced ITGA3 and ITGB1 protein levels of expression in PDAC cell lines (Figures 6B and 7B).

ITGA3 mRNA and protein expression after si-ITGA3 transfection and effects of ITGA3 silencing in PDAC cell lines.

Figure 6: ITGA3 mRNA and protein expression after si-ITGA3 transfection and effects of ITGA3 silencing in PDAC cell lines. (A) ITGA3 mRNA expression in PDAC cell lines was evaluated by qRT-PCR 72 h after transfection with si-ITGA3-1 or si-ITGA3-2. GUSB was used as an internal control. (B) ITGA3 protein expression in PDAC cell lines was evaluated by western blot analysis 96 h after transfection with si-ITGA3-1 and si-ITGA3-2. GAPDH was used as a loading control. (C) Cell proliferation was determined using XTT assays 72 h after transfection with 10 nM si-ITGA3-1 or si-ITGA3-2. *, P < 0.0001. (D) Cell migration activity was determined using migration assays. *, P < 0.0001. (E) Cell invasion activity was determined by Matrigel invasion assays. *, P < 0.0001.

ITGB1 mRNA and protein expression after si-ITGB1 transfection and effects of ITGB1 silencing in PDAC cell lines.

Figure 7: ITGB1 mRNA and protein expression after si-ITGB1 transfection and effects of ITGB1 silencing in PDAC cell lines. (A) ITGB1 mRNA expression in PDAC cell lines was evaluated by qRT-PCR 72 h after transfection with si-ITGB1-1 or si-ITGB1-2. GUSB was used as an internal control. (B) ITGB1 protein expression in PDAC cell lines was evaluated by western blot analysis 96 h after transfection with si-ITGB1-1 and si-ITGB1-2. GAPDH was used as a loading control. (C) Cell proliferation was determined with the XTT assays 72 h after transfection with 10 nM si-ITGB1-1 or si-ITGB1-2. *, P < 0.0001. (D) Cell migration activity was determined using migration assays. *, P < 0.0001. (E) Cell invasion activity was determined by Matrigel invasion assays. *, P < 0.0001.

Moreover, cancer cell proliferation, migration, and invasion abilities were suppressed by knockdown of these genes (Figures 6C-6E and 7C-7E). The apoptotic cell numbers were increased in si-ITGA3 or si-ITGB1 transfectant cells than in mock or siRNA-control transfectant cells (Supplementary Figure 2A and 2B). We showed that cleaved PARP expression was detected in transfectant cells into si-ITGA3 or si-ITGB1 (Supplementary Figure 2C).

Expression of ITGA3 and ITGB1 in PDAC clinical specimens and its clinical significance

Next, we confirmed the expression of ITGA3 and ITGB1 proteins in PDAC clinical specimens using immunohistochemistry (IHC). In total, 30 PDAC specimens were evaluated. ITGA3 showed intracellular and membrane immunoreactivity in PDAC tissue, whereas ITGB1 showed medium membrane immunoreactivity in PDAC tissue (Figure 8A). For ITGA3-IHC staining, we categorized into “Weak” and “Strong” depending on the intensity of staining. (Figure 8B), OS and DFS were evaluated by Kaplan-Meier analysis, OS was shown that strong staining has poor prognosis with significant (P = 0.0336, Figure 8C). Details of clinicopathologic factors was shown in Supplementary Table 1. High ITGA3 expression was significantly associated with increased lymph node metastasis. In contrast, there were no significant relationships between ITGB1-IHC staining.

Expression levels of ITGA3/ITGB1 as determined by immunohistochemical staining in PDAC specimens.

Figure 8: Expression levels of ITGA3/ITGB1 as determined by immunohistochemical staining in PDAC specimens. Immunohistochemical staining of ITGA3/ITGB1 in PDAC specimens. (A) All differentiated types of PDAC (well, moderately, poorly) showed immunoreactivity (left panel: hematoxylin-eosin staining, middle panel: ITGA3 staining, right panel: ITGB1 staining, original magnification, 200×). (B) Immunostaining of ITGA3 was classified by Weak (left panel) and Strong (right panel). The expression of ITGA3 was evaluated using high-power microscopy (400×). (C) Kaplan-Meier curves for OS and DFS rates based on ITGA3 expression in 30 patients with PDAC. P-values were calculated using the log-rank test.

Finally, we examined the relationships between integrin expression and clinicopathological factors using TCGA database. There were no significant differences in terms of T, N, M, and TNM stages. However, high expression of ITGA3 was significantly associated with recurrence. In addition, DFS was significantly shorter in the high expression group (Supplementary Figure 5). In contrast, there were no significant relationships between ITGB1 expression and clinicopathological factors.

DISCUSSION

PDAC is the most lethal type of gastrointestinal cancer because it often does not cause any signs or symptoms in the early stages [1]. Moreover, there are no effective treatment strategies for patients with advanced stage PDAC, and the prognosis is extremely poor. Elucidation of the aggressive nature of PDAC based on current genomic approaches will provide important insights into novel treatment strategies for this disease. We have sequentially identified novel oncogenic RNA networks based on PDAC miRNA analyses [10]. Our recent studies showed that the miR-216 family and miR-217 were downregulated in PDAC tissues and that these miRNAs acted as anti-tumor miRNAs by targeting FOXQ1 and ANLN, respectively [9, 10]. Additionally, overexpression of FOXQ1 and ANLN has been shown to promote cancer cell aggressiveness and is involved in PDAC pathogenesis [9, 10].

In this study, we focused on miR-124-3p because miRNA functional assays showed that ectopic expression of miR-124-3p dramatically inhibited cancer cell migration and invasion by blocking several oncogenic signals. Previous studies have indicated that miR-124-3p is a bona fide anti-tumor miRNA in several cancers, e.g., glioblastoma, esophageal cancer, hepatocellular carcinoma [1416]. These studies showed that overexpression of miR-124-3p inhibited cancer cells migration and invasion abilities through targeting several oncogenes, e.g., IQGAP1, LAMC1, ITGB1, STAT3, SP1 [1416]. Other studies indicated that promoter region of miR-124 genes (miR-124-1, miR-124-2 and miR-124-3) were methylated in cancer cells [17, 18]. In PDAC cells, all member of miR-124 family were highly methylated in PDAC tissues compared with in non-cancerous tissues by pyrosequencing analysis [19]. Epigenetic modification of miR-124 genes is greatly involved in silencing miR-124-3p expression in several cancer cells. Our data confirmed that miR-124-3p acted as an anti-tumor miRNA in PDAC cells, consistent with previous reports [19].

Next, we aimed to discover molecular networks controlled by anti-tumor miR-124a-3p in PDAC cells. To identify miR-124-3p-regulated networks, we applied in silico database analyses depending on a strategy we created independently [911]. Several pathways were found to be regulated by miR-124-3p in PDAC cells, including ECM-receptor interactions, pathways in cancer, cytokine-cytokine receptor interactions, focal adhesion, and actin cytoskeleton regulation. Finally, a total of 60 genes were identified as putative targets of miR-124-3p in PDAC cells. We further analyzed the genes involved in these pathways and PDAC pathogenesis using TCGA database. Importantly, among 60 putative targets, high expression of 15 genes (ITGA3, RRAS, ITGB1, ITGA2, RALA, SDC4, SDC1, CLDN1, CLDN4, CXCL9, DUSP6, COL6A3, SH3KBP1, FZD2, and MSN) was significantly associated with poor prognosis in patients with PDAC. Interestingly, many of these genes were involved in ECM/integrin signaling. The ECM is an assembly of extracellular molecules secreted by cells and provides structural and biochemical support to surrounding cells [20, 21]. Many studies have indicated that aberrant expression of ECM-related genes and activation of integrin-mediated signaling enhance cancer cell migration and invasion abilities in several cancers [22, 23]. Moreover, dysregulation of these genes is involved in the pathogenesis of PDAC [24], and detailed analyses of the functional significance of these genes in PDAC cells are necessary.

Integrins are a family of transmembrane cell adhesion receptors that are responsible for mutual cell-to-cell or cell-to-ECM, e.g., laminin, collagen, elastin, and fibronectin, communication [25]. Integrin receptors composed of specific two subunits (α and β) and up to 24 distinct integrin receptors have been reported [26]. Overexpression of integrins and activation of integrin-mediated oncogenic signaling have been reported in several cancers, affecting neighboring cancer cells or surrounding stromal cells and enhancing cancer cell aggressiveness and metastasis [27, 28]. Our recent studies have shown that anti-tumor miRNAs, e.g., the miR-29 family, miR-150s, the miR-199 family, miR-218, and miR-223, directly regulate several integrins, and these ligands and ectopic expression of these miRNAs have dramatically prevented cancer cell malignancy. [12, 13, 2931]

Our present data showed that both ITGA3 and ITGB1 were directly regulated by anti-tumor miR-124-3p in PDAC cells, and knockdown of these genes or ectopic expression of miR-124-3p significantly blocked cancer cell aggressiveness. Moreover, overexpression of ITGA3 and ITGB1 was involved in PDAC pathogenesis. ITGA3 and ITGB1 form a specific type of integrin receptor (ITGA3/ITGB1), and dysregulation of ITGA3/ITGB1-mediated signaling enhances cancer cell aggressiveness in several types of cancer [3234]. ITGB1 can form 18 receptor heterodimers with various partners (integrin α-subunits), and these receptors regulate numerous signaling pathways under both physiological and pathophysiological conditions [35]. Overexpression of ITGB1 has been frequently observed in several cancers, including PDAC [36]. Therefore, blocking ITGB1, e.g., using small molecules or antibodies, may be an effective approach for the treatment of lethal PDAC [37]. Furthermore, our findings revealed that ITGA3 affected PDAC prognosis by altering recurrence and DFS. Anti-tumor miR-124-3p regulates important intracellular pathways for pancreatic cancer via dual ITGA3/ITGB1. Identification of novel anti-tumor miRNA-mediated RNA networks may contribute to the development of new therapeutic strategies. ITGA2 is also reported as a partner subunit of ITGB1 [38]. Our analysis showed that ITGA2 was identified as miR-124-3p target and its expression was associated with poor prognosis of the patients with PDAC (Supplementary Figure 6). Previous studies showed that blocking of ITGA2 inhibited cancer cell aggressiveness [39]. Therefore, it is necessary to analyze the functional significance of ITGA2 in PDAC pathogenesis.

In conclusion, the anti-tumor roles of miR-124-3p were achieved in PDAC cells through inhibition of several oncogenic signaling pathways. Oncogenic pathways and targets regulated by miR-124-3p were involved in PDAC molecular pathogenesis. This is the first report demonstrating that the pivotal oncogenic receptors ITGA3 and ITGB1 were directly controlled by miR-124-3p in PDAC cells. Overexpression of these receptors was significantly associated with poor prognosis in patients with PDAC. Novel approaches based on anti-tumor miRNA-mediated oncogenic pathways and targets may contribute to the development of effective therapies for PDAC.

MATERIALS AND METHODS

Clinical pancreatic specimens and PDAC cell lines

In total, 30 PDAC specimens were obtained from patients who had undergone curative surgical resection at Kagoshima University Hospital from 1997 to 2016. Twelve normal pancreatic tissue specimens were collected from noncancerous regions. Frozen specimens were used for gene expression analysis, and paraffin sections were used for immunohistochemistry. The samples were staged according to the American Joint Committee on Cancer–Union Internationale Contre le Cancer (UICC) TNM classification [40].

The present study was approved by the Institutional Review Board of Kagoshima University; prior written informed consent and approval was given by each patient.

Three human PDAC cell lines (PANC-1, SW1990, and MIApaca-2) obtained from RIKEN Cell Bank (Tsukuba, Ibaraki, Japan) and the American Type Culture Collection (Manassas, VA, USA) were used for this study.

RNA containing miRNAs was extracted using ISOGEN (NIPPON GENE, Toyama, Japan), according to the manufacturer’s instructions.

Quantitative real-time polymerase chain reaction (qRT-PCR)

TaqMan qRT-PCR probes and primers were obtained from Thermo Fisher Scientific (Waltham, MA, USA) as follows: miR-124-3p (product ID: 000446; Thermo), ITGA3 (product ID: Hs01076879_m1), ITGB1 (product ID: Hs01127536_m1) and ITGA2 (product ID: Hs00158127_m1). GUSB (product ID: Hs99999908_m1) and RNU48 (product ID: 001006) were used as internal controls. The procedure for qRT-PCR was described in previous studies [30, 41].

Transient transfection of mature miRNAs and small interfering RNAs (siRNAs) into cancer cells

Pre-miR miRNA precursors and siRNAs were purchased from Thermo Fisher Scientific, as follows: miR-124-3p (product ID: PM 10245), negative control miRNA (product ID: AM 17111), two ITGA3 siRNAs (product IDs: HSS105529 and HSS179967), two ITGB1 siRNAs (product IDs: HSS105559 and HSS105561), and negative control siRNA (product ID: D-001810-10). Lipofectamine RNAiMAX and Lipofectamine 2000 (Thermo Fisher Scientific) were used for transfection into PDAC cell lines. The transfection efficiencies of miRNA in cancer cells were calculated as described in previous studies [42].

Cell proliferation, migration, and invasion assays

Cell proliferation was evaluated using a Cell Proliferation Kit II (Roche Applied Sciences, Penzberg, Germany). Cell migration was evaluated using BD Falcon Cell Culture Inserts (BD Biosciences, Franklin Lakes, NJ, USA), and cell invasion was evaluated using Corning BioCoat Matrigel Invasion Chambers (Corning, NY, USA). The protocols for functional assays were described previously [11, 13, 30].

Identification of miR-124-3p targets in PDAC cells

To identify miR-124-3p target genes, we performed a combination of genome-wide gene expression analyses and in silico database analyses as described in previous studies [9-11, 43].

We selected putative miRNA target genes using the Target Scan Human 7.1 database (http://www.targetscan.org/vert_71), GEO microarray database (https://www.ncbi.nlm.nih.gov/geo//; accession number GSE15471), to analyze gene expression levels in 36 PDAC samples versus 36 normal pancreatic tissue samples. Finally, The Cancer Genome Atlas (TCGA) database (https://cancergenome.nih.gov/) was used for analysis of gene expression in PDAC. The strategy for selecting target genes in this study is shown in Supplementary Figure 3.

TCGA database analysis of PDAC samples

To investigate the clinical significance of the expression status of putative targets of miR-124-3p in patients with PDAC, we analyzed TCGA datasets (https://gdc.nci.nih.gov/). A large amount of cohort data was retrieved from cBioPortal (http://www.cbioportal.org/) and OncoLnc (data downloaded on November 1, 2017). Detailed information on the method is described in a previous paper [4445]. The Z-scores of target genes mRNA expression data and clinical sample information corresponding to PDAC patients were collected from cBioPortal. R2: Genomics Analysis and Visualization Platform (http://r2.amc.nl) was used to create a heatmap. Furthermore, Z- score was evaluated by a combination of each genes sets. High group (mRNA Z-score > 0) and low group (mRNA Z-score ≤ 0) were analyzed by Kaplan–Meier survival curves and log-rank statistics.

Plasmid construction and dual-luciferase reporter assay

Wild-type or deletion-type sequences of the 3’-UTR of ITGA3 and ITGB1 in miR-124-3p target sites were inserted into the psiCHECK-2 vector (C8021; Promega, Madison, WI, USA). The procedure for dual luciferase reporter assays was described previously [11, 12, 42].

Western blot analyses

Antibodies used in the phosphorylation pathway were purchased from Cell Signaling Technology (Danvers, MA, USA) as follows: anti-focal adhesion kinase (FAK) antibodies (product ID: #3285), anti-phospho-FAK antibodies (product ID: #8556), anti-AKT antibodies (product ID: #4691), anti-phospho-AKT antibodies (product ID: #4060), anti-extracellular signal-regulated kinase (Erk) 1/2 antibodies (product ID: #4695), and anti-phospho-Erk1/2 antibodies (product ID: #4370).

To investigate the expression of miR-124-3p targets, anti-ITGA3 antibodies (HPA008572; Sigma-Aldrich, St. Louis, MO, USA) and anti-ITGB1 antibodies (#9699; Cell Signaling Technology) were used for western blotting, PARP antibody (product ID: 9542; Cell Signaling Technology, Danvers, USA) was used for apoptosis assay, usage was according to each data sheet. Anti-glyceraldehyde 3-phosphate dehydrogenase (GAPDH) antibodies (product ID: SAF6698; Wako, Osaka, Japan) were used as an internal loading control for western blotting. A detailed description of the western blotting procedure was published elsewhere [11].

Immunohistochemistry

Tissue sections were incubated overnight at 4˚C with anti-ITGA3 antibodies diluted 1:200 (HPA008572; Sigma-Aldrich, St. Louis, MO, USA) and anti-ITGB1 antibodies diluted 1:100 (#9699; Cell Signaling Technology). The IHC score was calculated by judging the percentage of positively stained plasma membrane in cancer cells. The scores ranged from 1 to 3 (1; <5%, 2; 5–50%, and 3; >50% immunoreactive cells). Scores 1 and 2 were considered to indicate “weak” expression, while scores 3 indicated “Strong” expression of ITGA3.

Statistical analysis

Mann-Whitney U-tests were used for analysis between two groups, and Bonferroni-adjusted Mann-Whitney U-tests were used for analysis among multiple groups. Correlations between the expression of miR-124-3p and ITGA3/ITGB1 were evaluated using Spearman's rank tests. Overall survival (OS) and disease-free survival (DFS) after surgery were analyzed using Kaplan–Meier curves and evaluated with log-rank tests. We used Expert StatView software (version 5.0 SAS Institute Inc., Cary, NC, USA) for these analyses.

ACKNOWLEDGMENTS AND FUNDING

We wish to thank the Joint Research Laboratory, Kagoshima University Graduate School of Medical and Dental Sciences, for the use of their facilities. The present study was supported by KAKENHI(C) grants 15K10801, 15K15501, 26293306, 18K09338, 18K08626, 18K08687 and 18K16322.

CONFLICTS OF INTEREST

The authors declare no conflicts of interest.

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