Sage Journals: Discover world-class research

Abstract

OBJECTIVE:

It is crucially important to discover the relationships between genes and microRNAs (miRNAs) in cancer. Thus, we proposed a combined bioinformatics method integrating Pearson’s correlation coefficient (PCC), Lasso, and causal inference method (IDA) to identify the potential miRNA targets for stomach adenocarcinoma (STAD) using Borda count election.

MATERIALS AND METHODS:

Firstly, the ensemble method integrating PCC, IDA, and Lasso was used to predict miRNA targets. Subsequently, to validate the performance ability of this ensemble method, comparisons between verified database and predicted miRNA targets were implemented. Pathway analysis for target genes in the top 1000 miRNA-mRNA interactions was implemented to discover significant pathways. Finally, the top 10 target genes were identified based on predicted times $>$ 3.

RESULTS:

The ensemble approach was confirmed to be a feasible method to predict miRNA targets The 527 target genes of the top 1000 miRNA-mRNA interactions were enriched in 21 pathways. Of note, cell adhesion molecules (CAMs) was the most significant one. The top 10 target genes were identified based on predicted times $>$ 3, such as GABRA3, CSAG1 and PTPN7. These targets were all predicted by 4 times. Moreover, GABRA3 and CSAG1 were simultaneously targeted by miRNA-105-1, miRNA-105-2, and miRNA-767. Significantly, among these top 10 targets, PTPN7 and GABRA3-miRNA interactions owned the highest correlation with 691.

CONCLUSION:

The combined bioinformatics method integrating PCC, IDA, and Lasso might be a valuable method for miRNA target prediction, and dys-regulated expression of miRNAs and their potential targets might be prominently involved in the pathogenesis of STAD.

Keywords

Stomach adenocarcinoma ensemble method miRNA targets

1. Introduction

Stomach adenocarcinoma (STAD) remains the second largest cause of cancer deaths in the world, which occupies 95% of the gastric malignant tumor [1, 2]. Sadly, STAD is characterized by poor response to chemotherapeutics with a 5-year survival below 20% [3, 4]. The poor outcomes might be attributed to lack of effective strategies for early detection, limited effect of surgery treatment in advanced stages, and weak prognosis of histological indicators. Hence, it is urgent to have a better understanding of the molecular signature of STAD as a critical step towards understanding the pathogenesis and improving our presently limited treatment approaches [5].

Among various regulations of cancer-related genes and pathways in several stages, the regulation of genes by microRNAs (miRNAs) has drawn particular attention, since many miRNAs are situated on chromosomal regions that are frequently changed in cancer [6]. MiRNAs, as a kind of small, non-coding RNA molecules, are highly conserved across species and exert important functions as regulators of gene expression, which have been estimated to regulate as much as 60% of the human protein coding genes [7]. They modulate the levels of targeted genes involved in most biological processes, including development, cell proliferation, apoptosis and differentiation [8, 9, 10]. MiRNAs are aberrantly regulated in cancers, indicating a role as a novel class of oncogenes and tumor suppressors [11]. As a consequence, identifying miRNA functions aids to elaborate the mechanisms underlying STAD and contributes to the design of drugs for effective treatments.

Recently, several computational methods have been created to identify miRNA targets using sequence data, or incorporate expression data into miRNA-mRNA regulatory network to study their relationship [12, 13, 14]. However, the results predicted using different databases or different methods are often inconsistent [15, 16]. Each method usually has a series of hypothesis on data when establishing the model. These assumptions may be suitable for some datasets but may be not fit for the others. Thus, these approaches may not perform well when the underlying relationships violate from these assumptions. It is more urgent to develop more available methods for miRNA target prediction. As documented, ensemble methods which combine different methods perform better than all the individual component methods [16]. Different miRNA target prediction approaches have their own benefits and drawbacks, and their results may be complement. Accordingly, integrating the predictions of multiple methods would improve the prediction performance. Moreover, Le and colleagues have indicated that ensemble methods receive more reliable results than existing individual methods [17].

In the current study, to enhance the understanding of miRNA-mRNA relationship, we proposed a framework that integrated Pearson’s correlation coefficient (PCC), Lasso, and causal inference method (IDA) for predicting miRNA targets based on Borda count election. Using PCC, Lasso, and IDA methods, miRNA target were respectively predicted. Next, using Borda count election, integration of the top 100 predicted targets of each miRNA generated by individual methods were obtained, following by the validation of miRNA target predictions based on the online databases such as Tarbase v6.0, miRecords v2013, miRWalk v2.0, and miRTarBase. Then, pathway enrichment analysis of target genes in the top 1000 miRNA-mRNA interactions was conducted to focus on significant KEGG pathways. These findings provide a better understanding of the mechanism of the initiation and progression of STAD.

2. Materials and methods

2.1 Data set

We collected mRNA expression and miRNA expression data sets for STAD from TCGA. Only samples which simultaneously existed in the two expression sets were reserved for downstream analysis. In the current study, a total of 405 samples were collected. Prior to analysis, the expression data of mRNAs and miRNAs were normalized and log2 transformed after replacing the values equal to zero with the minimum non-null value. After treatment, we obtained 867 miRNAs and 20,262 mRNAs. We then calculated the PCC of expression level for miRNA and mRNA in all samples. The absolute value of PCC for an miRNA-mRNA interaction was defined as $\delta$ . Only miRNA-mRNA with PCC absolute value greater than the pre-defined threshold $\delta$ (herein, $\delta=$ 0.7) were remained for subsequent analysis. Following screening, the expression profiles of 85 miRNAs and 691 mRNAs were received.

2.2 MiRNA target prediction

In the current study, an ensemble method was utilized to predict the miRNA targets, which integrated the three commonly used approaches including PCC, IDA, and Lasso, and took the advantages of the individual method. Thus, to begin with, we presented the specific information of three approaches (PCC, IDA, Lasso) for predicting miRNA target genes based on miRNA and mRNA expression data.

2.2.1 PCC method

PCC is the frequently used index for evaluating the association strength between two variables. With regards to miRNA target prediction, the PCCs in the expression levels of miRNA and mRNA were calculated. Then, miRNA-mRNA pairs were ordered according to the PCC values. Nevertheless, only linear associations are suitable for PCC algorithm, but the associations are non-linear, the effectiveness of PCC method is greatly decreased [18].

2.2.2 IDA method

In anther aspect, IDA introduced by Maathuis et al. [19], evaluates the causal effect that a variable has on the other. Excitedly, Le and colleagues [20] used IDA method to gene expression data to deduce the regulatory relationships between miRNAs and mRNAs, and found that the identified miRNA-mRNA causal regulatory relationships were observed to have a large portion of overlap with the findings of the subsequent gene knockdown experiments. Therefore, this method was covered in our study.

2.2.3 Lasso

Similar to PCC algorithm, Lasso also presented a linear expression correlation of miRNA-mRNA. In the current analysis, for each mRNA, Lasso regression was conducted on all the miRNAs by means of R package glmnet [21]. Similarly, this approach was also included in our analysis.

2.2.4 Ensemble methods of miRNA target prediction

Ensemble methods are planned to take advantage of pros of each individual methods, and to compensate for their cons. Significantly, it has been verified that Borda count election method is a simple but effective strategy to integrate the results from different individual methods, which is a method to choose candidates in a democratic election via selecting the candidate that has the best average rank. In our analysis, Borda count election method was utilized to form ensemble methods. The specific steps of the ensemble methods was shown in the following:

Step 1. Step 1.
For each miRNA, each of the individual methods (Pearson, IDA, and Lasso) was used respectively to generate the orderings of the mRNAs (as the predicted targets of the miRNA).
Step 2.
Apply Borda rank election method to the ordering from step 1 for each miRNA to form a single ranking list of selected mRNAs with respect to the miRNA. Subsequently, the top 100 ranked genes from the list were extracted as the final output, that was to say, these gene were considered as the potential target genes for the given miRNA.

Of note, Borda rank election method is a single but efficient method to combine the results from different separated approaches [22]. Borda rank election method is used to select candidates in a democratic election by choosing the candidate that has the best average rank. Thus, we calculated the average points of the candidate among all representatives, and defined it as the correlation score. The higher the correlation score was, the more significant the predictions were. We sorted the predicted miRNA targets based on their correlation scores, and then we obtained the top $\lambda$ ranked target genes for STAD.
2.3 Ground truth for validation

Because the targets of miRNAs validated by experiments still remain limited and there is no complete ground-truth to assess and compare different computational methods, it is a challenge to verify the computation findings [23]. Tarbase, miRecords, and miRTarBase contain confirmed interactions which are manually curated from the literature, and miRWalk focuses on both the predicted and the experimentally validated interactions. In our analysis, Tarbase v6.0 [24], miRecords v2013 [25], miRWalk v2.0 [26], and miRTarBase v4.5 [27] were used to verify the predictions of miRNA targets obtained from the ensemble method.

2.4 In-depth analyses of miRNA potential targets

Figure 1.

Top 50 miRNA-mRNA interactions. Red nodes were miRNAs and blue nodes denoted mRNAs. The edges stood for the interactions between any two of miRNA and mRNA.

With regard to functional analysis of miRNA potential targets in the top $\lambda$ miRNA-mRNA interactions, biological pathways defined by KEGG analysis were screened by Database for Annotation, Visualization and Integrated Discovery software (DAVID, http://david.abcc.ncifcrf.gov/tools.jsp). DAVID is an online software, and provides a set of functional annotations for a set of genes. $P$ -values of each pathway were corrected by Benjamini-Hochberg to control the false discovery rate (FDR). In our study, cellular pathways were identified with the threshold of significance being defined as FDR $<$ 0.05.

It is well known that a miRNA can regulate multiple genes [28], and a gene can be targeted by several miRNAs [13]. Changes in these relationships can alter the biological functions associated with a specific tumor [29]. Thus, the gene which was predicted by more times or which was targeted by more miRNAs, perhaps was more significant than those only was predicted by one time. For this reason, the predicted occurrence frequency for mRNAs among $\lambda$ miRNA-mRNA interactions was counted. and the targets with the predicted times $>$ 3 were identified. After the target genes were sorted according to the predicted frequency, we obtained the top 10 target genes.

3. Results

3.1 MiRNA targets prediction

Table 1
The top 50 highly-confident novel miRNA-mRNA interactions in the stomach adenocarcinoma (STAD)

Number	Gene	miRNA	Correlation
1	PTPN7	miRNA-1244-1	691
2	BLM	miRNA-1972-1	691
3	CYP1B1	miRNA-4323	691
4	GABRA3	miRNA-105-2	691
5	GABRE	MiRNA-224	691
6	GABRE	miRNA-452	691
7	GZMA	miRNA-1299	691
8	H19	miRNA-675	691
9	HOXB3	miRNA-10a	691
10	IGF2	miRNA-483	691
11	LOC642587	miRNA-205	691
12	MRC2	miRNA-127	691
13	ODZ4	miRNA-708	691
14	PDE2A	miRNA-139	691
15	PIP5K1B	miRNA-548p	691
16	MAPK10	miRNA-648	691
17	ABLIM2	miRNA-95	691
18	SH3TC2	miRNA-584	691
19	XKR6	miRNA-598	691
20	C21orf34	hsa-let-7c	518.25
21	C2orf72	miRNA-1204	518.25
22	COPZ2	miRNA-548m	518.25
23	HOXC10	miRNA-196a-1	518.25
24	SLIT2	miRNA-218-2	518.25
25	HOXC11	miRNA-196a-1	414.6
26	PKNOX2	miRNA-3924	414.6
27	TMC5	miRNA-1204	414.6
28	TRIP13	miRNA-4321	414.6
29	ZNF471	miRNA-1263	414.6
30	C2orf72	miRNA-548p	345.5
31	COPZ2	miRNA-152	345.5
32	CSAG1	miRNA-767	345.5
33	IGF2AS	miRNA-483	345.5
34	MAGEA6	miRNA-105-2	345.5
35	PABPC5	miRNA-4297	345.5
36	ZNF835	miRNA-1263	345.5
37	CDH11	miRNA-199a-1	296.1429
38	DLG2	miRNA-4324	296.1429
39	FXYD1	miRNA-195	296.1429
40	MYO1A	miRNA-215	296.1429
41	SH3BGR	miRNA-3924	296.1429
42	AURKA	miRNA-4324	259.125
43	CCL14	miRNA-139	259.125
44	FAM107A	miRNA-139	259.125
45	HAND2	miRNA-1972-1	259.125
46	ITGA7	miRN-31	259.125
47	KRT5	miRNA-205	259.125
48	MAGEA3	miRNA-767	259.125
49	ZBTB4	miRNA-648	259.125
50	PRKD1	miRNA-648	230.3333

Table 2

A total of 21 pathways of 527 target genes in top 1000 miRNA-mRNA interactions based on false discovery rate (FDR) $<$ 0.05

Pathways	Number of target genes	FDR
Cell adhesion molecules (CAMs)	18	0.0000015
Progesterone-mediated oocyte maturation	12	0.0000894
Maturity onset diabetes of the young	6	0.0004258
Cell cycle	13	0.0004258
Dilated cardiomyopathy	10	0.0001453
Oocyte meiosis	11	0.0016250
Primary immunodeficiency	6	0.0016250
Regulation of actin cytoskeleton	15	0.0047473
Vascular smooth muscle contraction	10	0.0057888
Hypertrophic cardiomyopathy (HCM)	8	0.0070315
T cell receptor signaling pathway	9	0.0085114
Salivary secretion	8	0.0085114
Melanoma	7	0.0085114
Arrhythmogenic right ventricular cardiomyopathy (ARVC)	7	0.0099182
Cytokine-cytokine receptor interaction	15	0.0189012
Epithelial cell signaling	6	0.0211565
Leukocyte transendothelial migration	8	0.0288709
Chemokine signaling pathway	11	0.0315531
Pancreatic secretion	7	0.0356091
Taurine and hypotaurine metabolism	2	0.036459
Chagas disease (American trypanosomiasis)	7	0.0369435

In the current work, based on the expression data of STAD downloaded from the TCGA database, 691 mRNAs and 85 miRNAs were reserved for further analysis after filtration treatments. An ensemble method integrating three approaches (Pearson $+$ IDA $+$ Lasso) based on Borda count election was applied to conduct miRNA targets prediction based on miRNA-mRNA interactions. In the process of prediction, each miRNA-mRNA interaction was distributed a correlation score, and all miRNA-mRNA interactions were ranked in descending order based on correlation score. The larger the correlation-score was, the more significant the miRNA targets were. Since the miRNA targets were too many, only the top $\lambda$ ( $\lambda=$ 1000) interactions were extracted as our study objects.

Table 3

The occurrence frequency of the top 10 target genes of miRNAs

Target	Frequency	MiRNAs
PTPN7	4	miRNA-1244-1, miRNA-142, miRNA-155, miRNA-577
PTGIS	4	miRNA-100, miRNA-1972-1, miRNA-218-2, miRNA-4323
GABRA3	4	miRNA-105-1, miRNA-105-2, miRNA-483, miRNA-767
CSAG1	4	miRNA-105-1, miRNA-105-2, miRNA-452, miRNA-767
ISLR	4	miRNA-10a, miRNA-125b-1, miRNA-199a-1, miRNA-708
TACR2	4	miRNA-1224, miRNA-133b, miRNA-1972-1, miRNA-490
DES	4	miRNA-1-2, miRNA-1299, miRNA-143, miRNA-145
ASPM	4	miRNA-1244-1, miRNA-196a-1, miRNA-199b, miRNA-429
DLG2	4	miRNA-1247, miRNA-23b, miRNA-4324, miRNA-548p
PRKD1	4	miRNA-125b-2, miRNA-1263, miRNA-152, miRNA-648

The top 50 predicted miRNA-mRNA interactions were listed in Table 1, and From the Table 1, we found that the top 19 miRNA-mRNA interactions had the same correlation scores and were determined as the most significant interactions. For example, PTPN7 had the correlation score of 691 and its miRNA was miRNA-1244-1, BLM (score $=$ 691) and miRNA-1972-1 were identified. In order to display the relationship between miRNAs and genes more vividly, we presented the top 50 highly-confident miRNA-mRNA interactions in the form of diagrams (Fig. 1), and in Fig. 1, red nodes stood for miRNAs and the blue node were the corresponding target genes. Interestingly, among the 50 interactions, C2orf72 was regulated by two miRNAs (miRNA-548p, miRNA-1204). Simultaneously. miRNA-139 regulated three mRNAs (CCL14, PDE2 A and FAM107 A) at the same time, and miRNA-648 also regulated three mRNAs (ZBTB4, PRKD1 and MAPK10) at the same time. Thus, a miRNA can regulate a plenty of genes, and several miRNAs coordinately control the expression of one gene.

3.2 Validation of the predicted targets

There are 20095 interactions among 228 miRNAs, 21590 interactions with 195 miRNAs, 1710 interactions with 226 miRNAs, and 37372 interactions with 576 miRNAs for Tarbase, miRecords, miRWalk, and miRTarBase databases, respectively. After eliminating the duplicates, 62,858 unique interactions were kept as background interactions to validate the predicted results in our study. Taking intersections of all predicted miRNA-mRNA interactions and background interactions, 62 intersected interactions were extracted, which further indicated that the ensemble method was a available and valuable method for predicting miRNA targets.

3.3 In-depth analyses of miRNA targets

KEGG pathway enriched analysis for 527 target genes of the top 1000 miRNA-mRNA interactions was implemented. Based on the significance set as FDR $<$ 0.05, a total of 21 pathways were detected, as shown in Table 2. Among these pathways, the top 5 pathways were cell adhesion molecules (CAMs), progesterone-mediated oocyte maturation, maturity onset diabetes of the young, cell cycle, and dilated cardiomyopathy. Of note, the pathway of cell adhesion molecules (CAMs) was the most significant one, which had the maximum number of target genes, but owned the smallest FDR value.

The gene which was predicted by more times or which was regulated by more miRNAs, perhaps was more significant than those only was predicted by one time. This might provide another way to assess the significance of one gene in a certain cancer. Thus, the prediction frequency for mRNAs was compared, and the targets with the predicted times $>$ 3 among 1000 miRNA-mRNA interactions were identified and listed in Table 3. From this Table, we found that all the 10 genes (PTPN7, PTGIS, GABRA3, CSAG1, ISLR, TACR2, DES, ASPM, DLG2, and MAPK10) were targeted by 4 miRNAs. Moreover, we observed that genes GABRA3 and CSAG1 were simultaneously targeted by miRNA-105-1, miRNA-105-2, and miRNA-767. Significantly, among the top 19 miRNA-mRNA interactions with the highest correlation scores (score $=$ 691), PTPN7 and GABRA3 had the same and the highest predicted times of 4.

4. Discussion

MiRNAs are a class of non-coding RNA molecules which are widely expressed in human tissues with significant power to mediate several biological activities [30]. In this condition, miRNAs provide a way to elaborate the complex mechanisms within the disease status, including cancer. In our study, to enhance the understanding of miRNA functions, we proposed a combined bioinformatic approach integrating PCC, Lasso, and IDA to the identification of miRNA targets based on Borda count election. The 527 target genes of the top 1000 miRNA-mRNA interactions was enriched in 21 pathways. Of note, the pathway of cell adhesion molecules (CAMs) was the most significant one, which had the maximum number of target genes, but owned the smallest FDR value. Moreover, all the top 10 target genes were targeted by 4 miRNAs, and target genes GABRA3 and CSAG1 were simultaneously targeted by miRNA-105-1, miRNA-105-2, and miRNA-767. Significantly, among the top 19 miRNA-mRNA interactions with the highest correlation scores (score $=$ 691), PTPN7 and GABRA3 had the same and the highest predicted times of 4 and the corresponding miRNA of PTPN7 was miRNA-1244-1.

As documented, cell adhesion is one important step in cancer metastasis. Syndecan-1, cadherin and integrin consist of cell adhesion molecules (CAMs) [31]. Clearly, CAMs participate in a broad range of biological processes, such as cell-cell and cell-matrix interactions, cell cycle, cell migration, and signaling during development and tissue regeneration [32]. Nevertheless, aberrant expression of CAMs disrupts normal cell-cell and cell-matrix interactions, freeing cells from normal check points and constraints, and facilitating tumor formation and metastasis [33]. Expression of the CAMs has been demonstrated to be related with many cancers, including breast cancer [34], clear cell renal carcinoma [35], gastric carcinoma [36], and colorectal cancer [37]. Accordingly, the pathway of cell adhesion molecules (CAMs) might play important roles in the progression of STAD.

Moreover, in our study, among the top 10 target genes, GABRA3 and CSAG1 were simultaneously targeted by miRNA-105-1, miRNA-105-2, and miRNA-767. GABRA is one kind of GABA receptors, and GABRA is ligand-gated chloride channels made up of five subunits which are encoded by 19 different genes that have been grouped into eight subclasses based on sequence homology ( $\alpha$ 1–6, $\beta$ 1–3, $\gamma$ 1–3, $\delta$ , $\epsilon$ , $\theta$ , $\pi$ , $\rho$ 1–3). GABRA3 is also named as GABA(A) receptor subunit alpha-3. Since GABA is involved in the proliferation of various normal cells and tissues, it is interesting to consider the potential role of GABA in cancer cells. Recently, growing evidences have indicated that GABA seem to exert critically regulative functions in many kinds of cancers [38, 39, 40, 41]. Remarkably. GABA receptor, GABRA3, has been implicated to be normally restricted to brain and testis, but its aberrant transcription has been reported in several tumor types [42, 43]. In addition, three miRNAs (miRNA-105-1, miRNA-105-2, and miRNA-767) have been reported to show over 5-fold increased expression in cancer [44]. Further, Loriot et al. [45] have suggested that Cancer/Testis-GABRA3 harbors miRNA-105 which recently is extracted as a promoter of cancer metastasis via destroying vascular endothelial barriers after exosomal secretion, and Cancer/Testis-GABRA3 also carries miRNA-767 with target sites in TET1 and TET3. Moreover, decreased TET is a characteristic of cancer [46]. CSAG1, is known as Cancer/Testis Antigen Family 24, Member 1. Cancer/Testis Antigens has been demonstrated to be frequently active in cancer cells [47, 48]. Yao and colleagues have pointed to the significance of current application of Cancer/Testis-base immunotherapy as biomarkers and immunotherapeutic targets in cancer [49]. A former study has indicated that CSAG1 is concurrently expressed in chondrosarcoma [50]. However, there were limited studies to reveal the relationship between STAD and CSAG1. Furthermore, no studies investigate the regulatory mechanisms of CSAG1 and miRNAs (miRNA-105-1, miRNA-105-2, and miRNA-767). The two genes (GABRA3 and CSAG1) are related with Cancer/Testis. Thus, we infer that miRNA-105-1, miRNA-105-2, and miRNA-767 might mediate the expression of GABRA3 and CSAG1, for the development of STAD.

In addition to GABRA3, PTPN7/miRNA-1244-1 had the highest correlation score of 691 among the top 50 highly-confident miRNA-mRNA interactions predicted by ensemble method, and PTPN7 was one of the top 10 targets in our study. PTPN7 belongs to a class of PTP family containing a group of dual-specificity phosphatases (DUSPs) which regulate MAPKs activity [51]. MAPK plays a critical role in the mediation of inflammatory, oncogenic signals and gastric adenocarcinoma [52]. Moreover, it is known that the MAPK pathway is involved in the induction of MMP-2, which has been implicated to be related with the progression and metastasis of gastric carcinoma [53]. Significantly, Wu et al. [54] have indicated that PTPN7 play important roles in STAD differentiation and progression. In our study, miRNA-1244-1 was the corresponding miRNA. MiRNA-1244-1 is one of three genomic locis encoding miRNA-1244. Significantly, miRNA-1244 has been demonstrated to play important roles in the differentiation of gastric cardia adenocarcinoma [55]. Of note, miRNA-1244-1 has been implicated to be associated with regulation of cell proliferation [56]. Moreover, regulation of cell proliferation was found to be connected with MMPs [57]. Thus, based on these results, we infer that miRNA-1244-1 expression may be not only promising therapeutic targets but also serve as valuable prognostic markers for STAD, via regulating the expression of PTPN7.

In summary, these findings indicated that the combined bioinformatics method which integrated PCC, IDA, and Lasso might be a valuable method for miRNA target prediction, and also suggested dys-regulated expression of miRNAs and their potential target mRNAs were prominently involved in the pathogenesis of STAD. However, subsequent validation of these observations in cell culture confirmed that GABRA3 and CSAG1 are both regulated by miRNA-105-1, miRNA-105-2, and miRNA-767 in STAD cells. Moreover, it remains to be investigated whether the high-confident novel miRNA targets found in our study also participate in the progression of STAD. In addition, correlation analysis of microRNA and mRNA expression in human tissue may prove useful in identifying mRNAs that are regulated by miRNAs.

References

Bertuccio

Chatenoud

Levi

Praud

Ferlay

Negri

Malvezzi

and Vecchia

C.L.

, Recent patterns in gastric cancer: A global overview, International Journal of Cancer Journal International Du Cancer 125 (2009), 666–673.

S.X.

Gan

J.H.

Wang

C.C.

Luo

E.P.

Huang

X.P.

Xie

and Huang

, Biopsy from the base of gastric ulcer may find gastric cancer earlier, Medical Hypotheses 76 (2010), 249–250.

Berger

A.C.

Farma

Scott

W.J.

Freedman

Weiner

Cheng

J.D.

Wang

and Goldberg

, Complete response to neoadjuvant chemoradiotherapy in esophageal carcinoma is associated with significantly improved survival, Journal of Clinical Oncology Official Journal of the American Society of Clinical Oncology 23 (2005), 4330–4337.

Kunz

P.L.

Gubens

Fisher

G.A.

Ford

J.M.

Lichtensztajn

D.Y.

and Clarke

C.A.

, Long-term survivors of gastric cancer: A California population-based study, Journal of Clinical Oncology Official Journal of the American Society of Clinical Oncology 30 (2012), 3507–3515.

Wang

Hong

Wei

and Cheng

, MicroRNA expression and its implication for the diagnosis and therapeutic strategies of gastric cancer, Cancer Letters 297 (2010), 137–143.

Croce

C.M.

, 27 Causes and consequences of microRNA dysregulation in cancer, Cancer Journal 18 (2012), 215–222.

Friedman

Farh

Burge

and Bartel

, Most mammalian mRNAs are conserved targets of microRNAs, Genome Research 19 (2009), 92–105.

Pan

Chu

Luo

Lin

Xiao

Shan

Wang

and Yang

, The muscle-specific microRNAs miR-1 and miR-133 produce opposing effects on apoptosis by targeting HSP60, HSP70 and caspase-9 in cardiomyocytes, Journal of Cell Science 120 (2007), 3045–3052.

Chen

J.F.

Mandel

E.M.

Thomson

J.M.

Callis

T.E.

Hammond

S.M.

Conlon

F.L.

and Wang

D.Z.

, The role of microRNA-1 and microRNA-133 in skeletal muscle proliferation and differentiation, Nature Genetics 38 (2006), 228–233.

10.

Esquela-Kerscher

and Slack

F.J.

, Oncomirs-microRNAs with a role in cancer. Nat Rev Cancer 6: 259-269, Nature Reviews Cancer 6 (2006), 259–269.

11.

Lujambio

and Lowe

S.W.

, The microcosmos of cancer, Nature 482 (2012), 347–355.

12.

Enright

A.J.

John

Gaul

Tuschl

Sander

and Marks

D.S.

, MicroRNA targets in Drosophila, Genome Biology 5 (2003), R1.

13.

Krek

Grün

Poy

M.N.

Wolf

Rosenberg

Epstein

E.J.

Macmenamin

Piedade

I.D.

Gunsalus

K.C.

and Stoffel

et al., Combinatorial microRNA target predictions, Nature Genetics 37 (2005), 495–500.

14.

Liu

Tsykin

Liu

Gaur

A.B.

and Goodall

G.J.

, Exploring complex miRNA-mRNA interactions with Bayesian networks by splitting-averaging strategy, Bmc Bioinformatics 10 (2009), 408.

15.

Rajewsky

, MicroRNA target predictions in animals, Nature Genetics 38(Suppl) (2006), 54–62.

16.

Marbach

Costello

J.C.

Küffner

Vega

N.M.

Prill

R.J.

Camacho

D.M.

Allison

K.R.

Kellis

Collins

J.J.

and Stolovitzky

, Wisdom of crowds for robust gene network inference, Nature Methods 9 (2012), 796–804.

17.

T.D.

Zhang

Liu

and Li

, Ensemble methods for MiRNA target prediction from expression data, Plos One 10 (2015).

18.

Speed

, A correlation for the 21st century, Science 334 (2011), 1502–1503.

19.

Maathuis

M.H.

Colombo

Kalisch

and Bühlmann

, Predicting causal effects in large-scale systems from observational data, Nature Methods 7 (2010), 247–248.

20.

T.D.

Liu

Tsykin

Goodall

G.J.

Liu

Sun

B.Y.

and Li

, Inferring microRNA-mRNA causal regulatory relationships from expression data, Bioinformatics 29 (2013), 765–771.

21.

Friedman

Hastie

and Tibshirani

, Regularization paths for generalized linear models via coordinate descent, Journal of Statistical Software 33 (2010), 1–22.

22.

Russell

, Complexity of control of Borda count elections, 2007.

23.

T.D.

Liu

Zhang

Liu

and Li

, From miRNA regulation to miRNA-TF co-regulation: Computational approaches and challenges, Briefings in Bioinformatics 101 (2014), 4269–4287.

24.

Vergoulis

Vlachos

I.S.

Alexiou

Georgakilas

Maragkakis

Reczko

Gerangelos

Koziris

Dalamagas

and Hatzigeorgiou

A.G.

, TarBase 6.0: Capturing the exponential growth of miRNA targets with experimental support, Nucleic Acids Research 40 (2011), D222–D229.

25.

Xiao

Zuo

Cai

Kang

Gao

and Li

, MiRecords: An integrated resource for microRNA – target interactions, Nucleic Acids Research 37 (2009), D105–D110.

26.

Dweep

Sticht

Pandey

and Gretz

, MiRWalk–database: Prediction of possible miRNA binding sites by “walking” the genes of three genomes, Journal of Biomedical Informatics 44 (2011), 839–847.

27.

Hsu

S.D.

Tseng

Y.T.

Shrestha

Lin

Y.L.

Khaleel

Chou

C.H.

Chu

C.F.

Huang

H.Y.

Lin

C.M.

and Ho

S.Y.

, MiRTarBase update 2014: An information resource for experimentally validated miRNA-target interactions, Nucleic Acids Research 42 (2014), 78–85.

28.

Lim

L.P.

Lau

N.C.

Garrett-Engele

Grimson

Schelter

J.M.

Castle

Bartel

D.P.

Linsley

P.S.

and Johnson

J.M.

, Microarray analysis shows that some microRNAs downregulate large numbers of target mRNAs, Nature 433 (2005), 769–773.

29.

Liu

Lin

Liu

Hou

Hao

Liu

Zhang

and Iwamori

, Lewis y regulate cell cycle related factors in ovarian carcinoma cell RMG-I in vitro via ERK and Akt signaling pathways, International Journal of Molecular Sciences 13 (2012), 828–839.

30.

Schonrock

Harvey

R.P.

and Mattick

J.S.

, Long noncoding RNAs in cardiac development and pathophysiology, Circulation Research 111 (2012), 1349–1362.

31.

Day

Hao

Ilyas

Daszak

Talbot

I.C.

and Forbes

, Changes in the expression of syndecan-1 in the colorectal adenoma–carcinoma sequence, Virchows Archiv 434 (1999), 121–125.

32.

Okegawa

Pong

R.-C.

and Hsieh

J.-T.

, The role of cell adhesion molecule in cancer progression and its application in cancer therapy, Acta Biochimica Polonica-English Edition- 51 (2004), 445–458.

33.

Johnson

J.P.

, Cell adhesion molecules in the development and progression of malignant melanoma, Cancer and Metastasis Reviews 18 (1999), 345–357.

34.

Spizzo

Gastl

Obrist

Went

Dirnhofer

Bischoff

Mirlacher

Sauter

Simon

and Stopatschinskaya

, High Ep-CAM expression is associated with poor prognosis in node-positive breast cancer, Breast Cancer Research and Treatment 86 (2004), 207–213.

35.

Eichelberg

Chun

F.K.

Bedke

Heuer

Adam

Moch

Terracciano

Hinrichs

Dahlem

and Fisch

, Epithelial cell adhesion molecule is an independent prognostic marker in clear cell renal carcinoma, International Journal of Cancer 132 (2013), 2948–2955.

36.

Chu

Y.-Q.

Z.-Y.

Tao

H.-Q.

Wang

Y.-Y.

and Zhao

Z.-S.

, Relationship between cell adhesion molecules expression and the biological behavior of gastric carcinoma, World Journal of Gastroenterology: WJG 14 (2008), 1990.

37.

Paschos

K.A.

Canovas

and Bird

N.C.

, The role of cell adhesion molecules in the progression of colorectal cancer and the development of liver metastasis, Cellular Signalling 21 (2009), 665–674.

38.

Azuma

Inamoto

Sakamoto

Kiyama

Ubai

Shinohara

Maemura

Tsuji

Segawa

and Masuda

, γ-Aminobutyric acid as a promoting factor of cancer metastasis; induction of matrix metalloproteinase production is potentially its underlying mechanism, Cancer Research 63 (2003), 8090–8096.

39.

Watanabe

Maemura

Oki

Shiraishi

Shibayama

and Katsu

, Gamma-aminobutyric acid GABA and cell proliferation, focus on cancer cells, 2006.

40.

Al-Wadei

H.A.

Ullah

M.F.

and Al-Wadei

, GABA (γ-aminobutyric acid), a non-protein amino acid counters the β-adrenergic cascade-activated oncogenic signaling in pancreatic cancer: A review of experimental evidence, Molecular nutrition & food research 55 (2011), 1745–1758.

41.

Schuller

H.M.

and Al-Wadei

H.A.

, Neurotransmitter receptors as central regulators of pancreatic cancer, Future Oncology 6 (2010), 221–228.

42.

Zhang

Zheng

Shen

Xiao

Shi

Huang

Tang

and Liu

, Expression of gamma-aminobutyric acid receptors on neoplastic growth and prediction of prognosis in non-small cell lung cancer, Journal of Translational Medicine 11 (2013), 102.

43.

Liu

Y.H.

Guo

F.J.

Wang

J.J.

Sun

R.L.

J.Y.

and Li

G.C.

, Gamma-aminobutyric acid promotes human hepatocellular carcinoma growth through overexpressed gamma-aminobutyric acid A receptor α3 subunit, World Journal of Gastroenterology 14 (2008), 7175–7182.

44.

Shi

Chen

Zhong

Meng

Kong

and Lu

, Identification of a six microRNA signature as a novel potential prognostic biomarker in patients with head and neck squamous cell carcinoma, Oncotarget 7 (2016), 21579–21590.

45.

Loriot

Tongelen

A.V.

Blanco

Klaessens

Cannuyer

Baren

N.V.

Decottignies

and Smet

, A novel cancer-germline transcript carrying pro-metastatic miR-105 and targeting miR-767 induced by DNA hypomethylation in tumors, Epigenetics 9 (2014), 1163–1171.

46.

Huang

and Rao

, Connections between TET proteins and aberrant DNA modification in cancer, Trends in Genetics 30 (2014), 464.

47.

Simpson

A.J.

Caballero

O.L.

Jungbluth

Chen

Y.-T.

and Old

L.J.

, Cancer/testis antigens, gametogenesis and cancer, Nature Reviews Cancer 5 (2005), 615–625.

48.

Scanlan

M.J.

Gure

A.O.

Jungbluth

A.A.

Old

L.J.

and Chen

Y.-T.

, Cancer/testis antigens: An expanding family of targets for cancer immunotherapy, Immunological Reviews 188 (2002), 22–32.

49.

Yao

Caballero

O.L.

Yung

W.A.

Weinstein

J.N.

Riggins

G.J.

Strausberg

R.L.

and Zhao

, Tumor subtype-specific cancer–testis antigens as potential biomarkers and immunotherapeutic targets for cancers, Cancer Immunology Research 2 (2014), 371–379.

50.

Lin

Mak

Meitner

P.A.

Wolf

J.M.

Bluman

E.M.

Block

J.A.

and Terek

R.M.

, Cancer/testis antigen CSAGE is concurrently expressed with MAGE in chondrosarcoma, Gene 285 (2002), 269–278.

51.

Owens

D.M.

and Keyse

S.M.

, Differential regulation of MAP kinase signalling by dual-specificity protein phosphatases, Oncogene 26 (2007), 3203–3213.

52.

Bang

Y.J.

Jin

H.K.

Kang

S.H.

Kim

J.W.

and Yang

Y.C.

, Increased MAPK activity and MKP-1 overexpression in human gastric adenocarcinoma, Biochemical & Biophysical Research Communications 250 (1998), 43–47.

53.

Mönig

S.P.

Baldus

S.E.

Hennecken

J.K.

Spiecker

D.B.

Grass

Schneider

P.M.

Thiele

Dienes

H.P.

and Hölscher

A.H.

, Expression of MMP-2 is associated with progression and lymph node metastasis of gastric carcinoma, Histopathology 39 (2001), 597–602.

54.

C.W.

Chen

J.H.

Kao

H.L.

A.F.

Lai

C.H.

Chi

C.W.

and Lin

W.C.

, PTPN3 and PTPN4 tyrosine phosphatase expression in human gastric adenocarcinoma, Anticancer Research 26 (2006), 1643–1649.

55.

Gao

Zhou

Chen

Jia

Liang

Zhang

Zhu

Zhang

and Lu

, Gastric cardia adenocarcinoma microRNA profiling in Chinese patients, Tumor Biology 37 (2016), 9411–9422.

56.

Xiong

Wei

Zhou

Mao

and Guo

, Identification and analysis of the regulatory network of Myc and microRNAs from high-throughput experimental data, Computers in Biology & Medicine 43 (2013), 1252–1260.

57.

Newby

A.C.

, Matrix metalloproteinases regulate migration, proliferation, and death of vascular smooth muscle cells by degrading matrix and non-matrix substrates, Cardiovascular Research 69 (2006), 614–624.

An ensemble method integrated with miRNA expression data for predicting miRNA targets in stomach adenocarcinoma

Abstract

OBJECTIVE:

MATERIALS AND METHODS:

RESULTS:

CONCLUSION:

Keywords

1. Introduction

2. Materials and methods

2.1 Data set

2.2 MiRNA target prediction

2.2.1 PCC method

2.2.2 IDA method

2.2.3 Lasso

2.2.4 Ensemble methods of miRNA target prediction

2.4 In-depth analyses of miRNA potential targets

3.1 MiRNA targets prediction

Table 1 The top 50 highly-confident novel miRNA-mRNA interactions in the stomach adenocarcinoma (STAD)

3.3 In-depth analyses of miRNA targets

4. Discussion

References

Table 1
The top 50 highly-confident novel miRNA-mRNA interactions in the stomach adenocarcinoma (STAD)