Open Access

Bioinformatics identification of potentially involved microRNAs in Tibetan with gastric cancer based on microRNA profiling

  • Yushuang Luo1, 2,
  • Chengwu Zhang3,
  • Feng Tang1,
  • Junhui Zhao2,
  • Cunfang Shen2,
  • Cheng Wang3,
  • Pengjie Yu3,
  • Miaozhou Wang2,
  • Yan Li2,
  • J. I. Di2,
  • Rong Chen2 and
  • Ge Rili1Email author
Contributed equally
Cancer Cell International201515:115

https://doi.org/10.1186/s12935-015-0266-1

Received: 16 October 2015

Accepted: 27 November 2015

Published: 12 December 2015

Abstract

Objective

The incidence of gastric cancer is high in Chinese Tibetan. This study aimed to identify the differentially expressed microRNAs (miRNAs) and further explore their potential roles in Tibetan with gastric cancer so as to predict potential therapeutic targets.

Methods

A total of 10 Tibetan patients (male:female = 6:4) with gastric cancer were enrolled for isolation of matched gastric cancer and adjacent non-cancerous tissue samples. Affymetrix GeneChip microRNA 3.0 Array was employed for detection of miRNA expression in samples. Differential expression analysis between two sample groups was analyzed using Limma package. Then, MultiMiR package was used to predict targets for miRNAs. Following, the target genes were put into DAVID (Database for Annotation, Visualization and Integrated Discovery) to identify the significant pathways of miRNAs.

Results

Using Limma package in R, a total of 27 differentially expressed miRNAs were screened out in gastric cancer, including 25 down-regulated (e.g. hsa-miR-148a-3p, hsa-miR-148b-3p and hsa-miR-363-3p) and 2 up-regulated miRNAs. According to multiMiR package, a number of 1445 target genes (e.g. Wnt1, KLF4 and S1PR1) of 13 differentially expressed miRNAs were screened out. Among those miRNAs, hsa-miR-148a-3p, hsa-miR-148b-3p and hsa-miR-363-3p were identified with the most target genes. Furthermore, three miRNAs were significantly enriched in numerous common cancer-related pathways, including “Wnt signaling pathway”, “MAPK signaling pathway” and “Jak-STAT signaling pathway”.

Conclusions

The present study identified a downregulation and enrichment in cancer-related pathways of hsa-miR-148a-3p, hsa-miR-148b-3p and hsa-miR-363-3p in Tibetan with gastric cancer, which can be suggested as therapeutic targets.

Keywords

Gastric cancermiRNA microarrayDifferentially expressed miRNAsEnrichment analysisNetwork of miRNAs-targets

Background

Gastric cancer is one of the most common malignancies worldwide. Although incidence and mortality of gastric cancer has been dramatically decreased over the last decades worldwide, the declines are becoming less remarkable in some countries [1]. The main reason may be that there are limited treatment options and patients in advanced stages could not be cured by surgical removal [2]. Moreover, 5-year survival rates of gastric cancer are decreased with gastric cancer progression [3] and metastatic stage could lead to poor outcomes [4]. In Chinese Qinghai, the Tibetan ethnic group has a higher incidence of gastric cancer than the Han ethnic group [5]. Thus, there is an urgent need to develop effective treatments to improve diagnosis and reduce burden of gastric cancer in gastric cancer-infected Tibetan.

A number of key genes with abnormal expression in the progression of gastric cancer have been screened out. For example, the over-expression of SPAG9 (sperm associated antigen 9) correlates with poor prognosis and leads to gastric cancer invasion and chemo-resistance [6]. In terms of Tibetan with gastric cancer, the expression pattern of tumor-associated antigen MG(7)-Ag is abnormal and it can be used as a reliable marker to predict gastric cancer at early stage [7]. The polymorphisms of prostate stem cell antigen gene are also associated with gastric cancer in Tibetans [8]. Therefore, the identification of key genes can improve diagnosis and management of gastric cancer in Tibetan.

MicroRNAs (miRNAs) are a group of small non-coding RNAs that have important roles in the development of numerous cancer types, through down-regulation of the target genes [9, 10]. Multiple miRNAs are expressed aberrantly and are involved in the progression and prognosis of gastric cancer [11]. Therefore, investigating role of miRNAs in gastric cancer could provide new insight into the biological mechanism of this disease. Reportedly, miR-21 is up-regulated in gastric cancer and its dysregulation can enhance cell proliferation, invasion and migration through down-regulating a set of tumor suppressor genes, such as RECK (reversion-inducing-cysteine-rich protein with kazal motifs) [12]. In addition, miR-544a could activate the Wnt signaling pathway by stabilizing the β-catenin in nucleus and its inhibition may be a therapeutic method for metastatic gastric cancer [13]. However, the research on miRNAs in gastric cancer in Tibetan is really rare and therefore, the exploration of miRNAs in the progression of gastric cancer in Tibetan is of great significance.

In the present study, the paired cancerous and adjacent non-cancerous tissue samples were collected from 10 patients with gastric cancer, and further conducted for miRNA expression profiling analysis. Differentially expressed miRNAs (DE-miRs) were screened out between two sample groups, followed by identification of target genes based on bioinformatics methods. Furthermore, functional enrichment analysis was performed for the DE-miRs so as to reveal their potential roles in progression of gastric cancer.

Methods

Sample collections

A total of 10 Tibetan patients (male:female = 6:4) with gastric cancer were enrolled in this study. They were aged between 33 and 77 years old, and the median age was 51.1. The tumor node metastasis stages (TNM) were determined basing on the International Union Against Cancer and the American Joint Committee on Cancer pathological staging systems. The patients were identified with clinical stages at T2N0M0(1/10), TisN0M0, TisN0M0IIc, TisN0M0IIc, T3N2M0, T3N0M0, T4aN1M0, T2N1M0, T3N2M0 or T3N2M0 (Table 1). Matched gastric cancer and adjacent non-cancerous tissue samples (n = 10 in each group) were obtained during surgical operation and immediately stored at −80 °C for microarray analysis. All the enrolled patients have given written informed consent and the present study was approved by Ethics Committee of Qinghai University Affiliated Hospital.
Table 1

Information on sample cases

Number

Age

Sex

Elevation (m)

Staging

HP infection

Pathological description

B1 299295

46

Male

2260

T2N0M0(1/10)

 

High-differentiated adenocarcinoma invaded submucosa, lymph node 0/10

B2 302629

38

Female

2850

TisN0M0

 

Intramucosal carcinoma in gastric antrum (0/11)

B3 WJ

57

Male

2840

T4aN1M0

 

Distal gastric carcinoma, tumor invaded full thickness and vascular nerves (+) 2/15, low and median differentiated adenocarcinoma

B5 211409

56

Male

2850

TisN0M0IIc

  

B6

49

Male

2050

TisN0M0IIc

 

Intramucosal carcinoma

A1 303135

43

Male

3280

T3N2M0

 

Low differentiated tubular adenocarcinoma invaded deep muscle layer (6/9), vascular and nerve (+)

A2 302628

33

Male

3860

T3N0M0

 

Ulcer tubular well-differentiated adenocarcinoma invaded the deep muscularis serosa, vascular nerve (+), 0/13

A3 WJ

56

Female

3800

T2N1M0

HP−, gastroscope

Distal gastric cancer

A4 WJ

77

Female

4800

T3N2M0

HP+, gastroscope

Gastric carcinoma

A6 300804

56

Male

3100

T3N2M0

 

Differentiated tubular adenocarcinoma invaded deep muscularis, vascular+, nerve+

HP Helicobacter pylori

Microarray profiling of miRNAs

Total RNA was extracted from the matched cancerous and adjacent non-cancerous tissues according to the manufacture’ s instructions using RNAiso Plus purchased from Treasure Biological Engineering (Dalian, China). Reverse transcription-quantitative polymerase chain reaction was conducted according to the manufacture’ s instructions using a PrimeScript® First Strand cDNA Synthesis kit and miRNA qPCR primer mix (Takara Bio, Inc, CA, USA). Affymetrix GeneChip microRNA 3.0 Array (Affymetrix, Inc, Santa, CA, USA) was employed for detection of miRNA expression in samples, which provides for 100 % miRBase v17 coverage (http://www.mirbase.org) by a one-color approach.

Differential expression analysis

Raw data of miRNA expression profile from cancerous and adjacent non-cancerous tissues were converted into recognizable miRNA expression data by RMA (robust multi-array analysis) method, followed by median normalization and log2 transformation using Affy package (http://www.bioconductor.org/packages/release/bioc/html/affy.html) in R [14]. During the expression conversion from probe level to miRNA level, the expression values of probes corresponding to each miRNA were averaged as the miRNA value. Differential expression analysis between two groups was analyzed using Limma package of R language (http://www.bioconductor.org/packages/release/bioc/html/limma.html) [15] based on the criteria of |log2 FC (fold change)| ≥1 and P value <0.05.

Prediction of targets for differentially expressed miRNAs

MultiMiR package (http://multimir.ucdenver.edu/) [16] was previously established to predict targets of miRNAs, which covered 14 databases including miRecords, miRTarBase, TarBase, DIANA-microT, ElMMo, MicroCosm, miRanda, miRDB, PicTar, PITA, TargetScan, miR2Disease, PharmacomiR and PhenomiR. In the present study, multiMiR package was employed to predict targets of DE-miRs with the criterion of primary score listed in top 35. Meanwhile, only the target genes predicted in at least three databases were selected for following analysis. Accordingly, the network between targets and DE-miRs was constructed using Cytoscape v3.2.1 (http://www.cytoscape.org/) [17] software.

Functional enrichment analysis for DE-miRs

Database for Annotation, Visualization and Integrated Discovery (DAVID, https://david.ncifcrf.gov/home.jsp) [18] is a powerful tool to mine functions of interested genes. As miRNAs function through down-regulating target genes, to further reveal the potential biological functions or pathways that may be changed by the DE-miRs, the target genes were put into DAVID to screen out significantly enriched Kyoto Encyclopedia of Genes and Genomes (KEGG, http://www.kegg.jp/) [19] pathways and Gene Ontology (GO, http://www.geneontology.org) [20] biological processes. The selection criterion for significant GO and KEGG pathway terms was P value <0.05.

Results

DE-miRs between gastric cancer and adjacent non-cancerous samples

By using Limma package in R with the criteria of |log2 FC| ≥1 and P value <0.05, a total of 27 DE-miRs in gastric cancer were screened out, including 25 down-regulated miRNAs (e.g. hsa-miR-148a-3p, hsa-miR-148b-3p and hsa-miR-363-3p) and 2 up-regulated miRNAs (hsa-miR-196b-3p and hsa-miR-138-1-3p) (Table 2). The heap map of DE-miRs was shown in Fig. 1.
Table 2

The 27 differentially expressed microRNAs in gastric cancer

Accession

MicroRNAs

Log fold change

P value

MIMAT0000243

hsa-miR-148a-3p

−1.8142778

0.006134223

MIMAT0027583

hsa-miR-6840-3p

−1.47970911

0.015759083

MIMAT0027464

hsa-miR-6782-5p

−1.45651738

0.018393456

MIMAT0000759

hsa-miR-148b-3p

−1.43506791

0.010053602

MIMAT0028117

hsa-miR-7110-5p

−1.43359191

0.038837699

MIMAT0000707

hsa-miR-363-3p

−1.3662862

0.049770472

MIMAT0018352

hsa-miR-3937

−1.32835296

0.023596283

MIMAT0004694

hsa-miR-342-5p

−1.27792593

0.044159324

MIMAT0022721

hsa-miR-1247-3p

−1.17878373

0.020578891

MIMAT0019077

hsa-miR-1587

−1.1697371

0.048251207

MIMAT0016882

hsa-miR-4253

−1.15418525

0.017333874

MIMAT0027474

hsa-miR-6787-5p

−1.14471871

0.040401132

MIMAT0015070

hsa-miR-3188

−1.10659872

0.042107841

MIMAT0025844

hsa-miR-6716-5p

−1.08787366

0.019653906

MIMAT0019033

hsa-miR-4498

−1.08655973

0.018678343

MIMAT0004948

hsa-miR-885-3p

−1.0692305

0.044870796

MIMAT0018986

hsa-miR-4462

−1.0690462

0.004846197

MIMAT0028211

hsa-miR-7150

−1.06220727

0.040895785

MIMAT0027408

hsa-miR-6754-5p

−1.03580421

0.022410575

MIMAT0021128

hsa-miR-5196-5p

−1.0324015

0.035802131

MIMAT0020601

hsa-miR-1273f

−1.02885192

0.031420972

MIMAT0022260

hsa-miR-5572

−1.02726895

0.033720698

MIMAT0021120

hsa-miR-5189-5p

−1.01814356

0.020114063

MIMAT0027548

hsa-miR-6824-5p

−1.01710815

0.020467938

MIMAT0031016

hsa-miR-8089

−1.01329602

0.021980497

MIMAT0009201

hsa-miR-196b-3p

1.33573282

0.048038696

MIMAT0004607

hsa-miR-138-1-3p

1.405280355

0.023694469

Log fold change >0, up-regulated; log fold change <0, down-regulated

Fig. 1

Heat map of differentially expressed microRNAs. Red represents up-regulation and green represents down-regulation

Analysis of network of miRNAs-targets

According to multiMiR package, a number of 1445 target genes of 13 DE-miRs were screened out. Based on the aforementioned criterion, the network of miRNA-target was constructed using Cytoscape (Fig. 2). In the network, hsa-miR-148a-3p, hsa-miR-148b-3p and hsa-miR-363-3p were identified with the most target genes, and furthermore, the three miRNAs targeted numerous common genes such as CAND1 (cullin-associated and neddylation-dissociated 1), KLF4 (Kruppel-like factor 4), S1PR1 (sphingosine-1-phosphate receptor 1), Wnt1 (wingless-type MMTV integration site family, member 1), CNTN4 (contactin 4) and BCL2L11 [BCL2-like 11 (apoptosis facilitator)].
Fig. 2

Network of differentially expressed microRNAs-target genes. Circle represents target genes and diamond represents microRNAs. Red represents up-regulation and green represents down-regulation

Functional and pathways of DE-miRs

KEGG pathway and GO functional enrichment analysis indicated that only three miRNAs (hsa-miR-148a-3p, hsa-miR-148b-3p and hsa-miR-363-3p) were significantly enriched in numerous common KEGG pathways and GO biological processes (P < 0.05, Additional file 1: Table S1). Among those significant pathways, some known cancer-related pathways were screened out, including “pathway in cancer”, “Wnt signaling pathway”, “MAPK signaling pathway” and “Jak-STAT signaling pathway” (Fig. 3).
Fig. 3

Network of functional enrichment results of differentially expressed microRNAs. V-shape represents microRNA and rectangle represents significant pathways enriched by target genes of microRNAs

Discussion

MiRNAs exert regulatory effects on gene expression in humans, resulting in cell growth, differentiation and apoptosis via down-regulating target genes in cancer [21]. In the present study, a total of 27 DE-miRs were screened out, including hsa-miR-148a-3p, hsa-miR-148b-3p and hsa-miR-363-3p, which targeted the most genes. Furthermore, the three miRNAs were significantly enriched in cancer-related pathways, including Wnt signaling pathway, MAPK (mitogen-activated protein kinase) signaling pathway and Jak-STAT (Janus kinase-signal transducer and activator of transcription) signaling pathway.

Wnt signaling pathway is implicated in cancer development and its hyperactivation can lead to enhanced tumorigenicity and increased metastatic potential [22]. In gastric cancer, overexpressed miR-544a is demonstrated to activate WNT signaling pathway which further contributes to the disease progression [13]. In the present study, we identified that hsa-miR-148a-3p, hsa-miR-148b-3p and hsa-miR-363-3p were remarkably enriched in Wnt signaling pathway. Reportedly, miR-148a inhibits the metastasis of hepatocellular carcinoma via acting on Wnt signaling pathway [23]. MiR-363 down-regulates the expression of myeloid cell leukemia-1 [24] whose expression correlates with phosphorylated glycogen synthase kinase-3beta, the key component of Wnt signaling pathway in breast cancer [25]. Notably, the above miRNA targets, such as Wnt1, were significantly enriched in Wnt signaling pathway. We can speculate that the miR-148a, miR-148b and miR-363 may play significant roles in gastric cancer progression via regulating the Wnt signaling pathway.

Besides, MAPK signaling pathway is dysregulated in gastric cancer, leading to abnormal cell proliferation and metastasis [26]. Moreover, the signaling pathway is implicated in drug resistance in gastric cancer by regulating the expression of apoptotic proteins Bax (BCL2-associated X protein)/Bcl-2 (B-cell CLL/lymphoma 2) [27]. Our enrichment analysis showed that hsa-miR-148a-3p, hsa-miR-148b-3p and hsa-miR-363-3p were also significantly enriched in MAPK signaling pathway. In breast cancer, miR-148a acts as a tumor suppressor via targeting IGF-IR (insulin-like growth factor-I receptor) and IRS1 (insulin receptor substrate 1) and further suppressing the downstream MAPK signaling pathway [28]. Besides, miR-363 is found to be down-regulated in gastric cancer and its down-regulation is associated with tumor differentiation, TNM stage and lymph-node metastasis [29]. Notably, the suppression of their common target KLF4 could inhibit the expression of various Erk5 (mitogen-activated protein kinase 7) targets and further affect the MAPK cascade in the regulation of endothelial integrity in cancer [30]. The enrichment of miR-148a, miR-148b and miR-363 in MAPK signaling pathway may suggest a joint contribution to gastric cancer development via involving MAPK signaling pathway.

Additionally, hsa-miR-148a-3p, hsa-miR-148b-3p and hsa-miR-363-3p were found to be dramatically enriched in Jak-STAT signaling pathway. Jak-STAT serves as a straightforward mechanism whereby cells sense environmental cures and further regulate cell growth and differentiation in cancer [31]. The inhibition of Jak-STAT signaling pathway can lead to decreased cell proliferation and enhanced cell apoptosis in gastric cancer cells [32]. Exogenous miR-363 promotes cell growth, progression and tumorsphere formation of SC-M1 gastric cancer cells, and the knockdown of miR-363 suppresses tumorigenesis of SC-M1 cells [33]. MiR-148a/b is dysregulated and their haplotype is significantly correlated with gastric cancer [34]. S1PR1, one of the common targets of miR-148a-3p, -148b-3p and -363-3p, is implicated in NFκB/IL-6/STAT3/S1PR1 amplification loop that is important for chronic colitis-related cancer and can be suggested as therapeutic option [35]. The enrichment of the three miRNAs in Jak-STAT signaling pathway implies their involvement in gastric cancer progression via Jak-STAT signaling pathway.

We should note that there were some limitations in the present study. Herein, although we demonstrated a significant enrichment of dysregulated hsa-miR-148a-3p, hsa-miR-148b-3p and hsa-miR-363-3p in cancer-related pathways in patients with gastric cancer, we did not further validate their expressions, nor demonstrate their roles in cancer-related pathways using systematically functional experiments. Moreover, as there are only 10 samples enrolled in this study, we did not further consider the DE-miRs among different stages. Besides, the present results were just obtained based on microarray analysis and bioinformatics prediction, and needed to be further validated in future. However, this study can be regarded as a preliminary study and to an extent provide some valuable directions for future studies, especially for researches on gastric cancer in Tibetan.

In summary, the present study identified a downregulation of hsa-miR-148a-3p, hsa-miR-148b-3p and hsa-miR-363-3p in gastric cancer in Tibetan using microarray analysis. What is more, we demonstrated their significant enrichment in cancer-related pathways, including Wnt signaling pathway, MAPK signaling pathway and Jak-STAT signaling pathway. These findings suggested the potential usage of hsa-miR-148a-3p, hsa-miR-148b-3p and hsa-miR-363-3p as diagnostic and therapeutic biomarkers for gastric cancer-infected Tibetan. However, further experimental validations are in urgent need to confirm these results.

Notes

Abbreviations

Bax

BCL2-associated X protein

Bcl-2

B-cell CLL/lymphoma 2

CAND1

cullin-associated and neddylation-dissociated 1

KLF4

Kruppel-like factor 4

S1PR1

sphingosine-1-phosphate receptor 1

Wnt1

wingless-type MMTV integration site family, member 1

CNTN4

contactin 4

BCL2L11

BCL2-like 11 (apoptosis facilitator)

GO: 

Gene Ontology

MicroRNAs: 

miRNAs

SPAG9

sperm associated antigen 9

RECK

reversion-inducing-cysteine-rich protein with kazal motifs

TNM: 

tumor node metastasis stages

Declarations

Authors’ contributions

YL and CZ carried out the molecular studies, FT participated in the sequence alignment, JZ, CS and CW drafted the manuscript. PY carried out the study. YL participated in the sequence alignment. MW and JD participated in the design of the study and performed the statistical analysis. RC, GR, YL and CZ conceived of the study, and participated in its design and coordination and helped to draft the manuscript. All authors read and approved the final manuscript.

Acknowledgements

This study was supported by Scientific research project funds of Qinghai department (2014-ZJ-737) and Qinghai-Utah Joint Research Key Lab for High Altitude Medicine (No.2014-ZJ-Y39). We wish to express our warm thanks to CaptialBio Corporation. Their ideas and help gave a valuable added dimension to our research.

Competing interests

The authors declare that they have no competing interests.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Research Center for High Altitude Medicine, Qinghai University of Medical School
(2)
Department of Oncology, Affiliated Hospital of Qinghai University
(3)
Department of Gastrointestinal Surgery, Affiliated Hospital of Qinghai University

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