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Research Papers:

Signature miRNAs in colorectal cancers were revealed using a bias reduction small RNA deep sequencing protocol

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Oncotarget. 2016; 7:3857-3872. https://doi.org/10.18632/oncotarget.6460

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Guihua Sun, Ya-Wen Cheng, Lily Lai, Tsui-Chin Huang, Jinhui Wang, Xiwei Wu, Yafan Wang, Yasheng Huang, Jinghan Wang, Keqiang Zhang, Shuya Hu, Ji-Rui Yang and Yun Yen _

Abstract

Guihua Sun1, Ya-Wen Cheng4, Lily Lai2, Tsui-Chin Huang4, Jinhui Wang3, Xiwei Wu3, Yafan Wang1, Yasheng Huang1, Jinghan Wang1, Keqiang Zhang1, Shuya Hu1, Ji-Rui Yang4 and Yun Yen1,4

1 Department of Molecular Pharmacology, Beckman Research Institute of the City of Hope, Duarte, CA, USA

2 Department of Surgery, Beckman Research Institute of the City of Hope, Duarte, CA, USA

3 Integrated Genome Core, Beckman Research Institute of the City of Hope, Duarte, CA, USA

4 Graduate Institute of Cancer Biology and Drug Discovery, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan

Correspondence to:

Guihua Sun, email:

Yun Yen, email:

Keywords: microRNA, colorectal cancer, small RNA deep sequencing, miR-21, miR-143

Received: August 19, 2015 Accepted: November 16, 2015 Published: December 04, 2015

Abstract

To explore the role of miRNAs in colorectal cancers (CRC), we have deep sequenced 48 pairs of frozen CRC samples, of which 44 pairs produced high quality sequencing data. By using a combined approach of our bias reduction small RNA (smRNA) deep sequencing protocol and Illumina small RNA TruSeq method for sample bar coding, we have obtained data from samples of relatively large size with bias reduced digital profile results. This novel approach allowed us to validate many previously published results using various techniques to profile miRNAs in CRC tissues or cell lines and to characterize ‘true’ miRNA signatures highly expressed in colon/rectum (CR) or CRC tissues. According to our results, miR-21, a miRNA that is up-regulated in CRC, and miR-143, a miRNA that is down-regulated in CRC, are the two miRNAs that dominated the miRNA population in CR tissues, and probably are also the most important miRNAs in CRCs. These two miRNAs, together with the other eight miRNAs, miR-148a, -194, -192, 200b, -200c, -10b, -26a, and -145, with descending expressing levels, constituted the top 10 highly expressed miRNAs in CR/CRC. Using TaqMan miRNA qPCR, we detected the relative expression of some of the CRC miRNAs in 10 CRC cell lines, validated their dysregulation under cancer condition, and provided possible explanation for their dysregulation, which could be caused by APC, KRAS, or TP53 mutations. We believe these results will provide a new direction in future miRNA-related CRC development studies, and application of miRNAs in CRC diagnosis/prognosis, and therapy.


INTRODUCTION

CRC includes two major entities: colon cancer (CC) and rectal cancer (RC). It ranks the 3rd in all cancer cases worldwide and represents one of the cancers with high mortality [1]. CRC rates are much higher in populations consuming low fiber, high fat, and high protein diet, compared with populations consuming high fiber, low fat, and low protein diet [2]. It is conceivable that as more populations in the world are moving towards high standard of living, which allows consuming varieties of foods, more CRC cases will be inevitably increasing worldwide. One supporting evidence for the above conclusion is that CRC already becomes the number one cancer cases in some rich areas that allows consuming large amount of high protein, high fat foods. CRC can be cured if it is diagnosed early. But because of its location, it is not easily spotted early and currently is usually diagnosed through unpleasant colonoscopy and biopsy (most in advanced stages already). It is conceivable researches that address novel mechanisms in CRC development may be able to provide noninvasive, cost-effective approaches to diagnose CRC in early stages and provide new avenues for treatment.

MicroRNA (miRNA) is a family of conserved small RNAs (smRNAs) that can regulate target gene expression through mediating mRNA degradation or translational repression. It was proposed that miRNAs will become new frontier players in cancer biology [3]. Reduced accumulation of specific miRNAs in colorectal neoplasia was reported early on [4]. The milestone miRNA-cancer paper that reported the profile of miRNAs in cancers to be correlated with the origin, progression, and metastasis of many cancers have attracted the attention of more cancer biologists in this new field. Since then, more and more miRNA-cancer studies have advanced basic cancer research, the development of biomarkers for diagnosis/prognosis, and the identification of miRNAs as targets for cancer therapy [5].

There are thousands of human miRNAs and their abundance is varied among different tissues [6, 7]. It is more practical to use a panel of miRNAs that are specifically expressed in certain cancer to study their role in carcinogenesis and to use them as biomarkers in cancer clinical practice. Therefore, it is critical to profile miRNAs that are highly or specifically expressed in certain cancer. While both microarray and deep sequencing are excellent platforms for global miRNA profiling, they are labor intensive, costly, have relatively low detecting range when compared to RT-qPCR, and the turnaround time is in days. In clinical setting, a small number of highly expressed miRNAs in the panel that can be detected by qRT-PCR within a few hours will be ideal. So the ideal approach is to screen a panel of miRNA using global miRNA profiling approaches, such as deep sequencing and microarray, then detect this panel of miRNAs by qRT-PCR that has high specificity, sensitivity, and fast turnaround time.

To explore the role of miRNAs in CRCs, Northern blotting, miRNA microarray, miRNA qRT-PCR, and high-throughput small RNA sequencing methods have been applied in miRNA-CRC studies using CRC cell lines or patient samples in the past several years (See latest review [8]). Although results from these published studies are encouraging, they are limited by the techniques available, sample size, and the difficulty in sampling CRCs at different stages [8-10]. We are still in the early stage of collecting CRC-miRNA data and much more of this kind of data are necessary to advance this field and achieve our goal of using miRNAs in CRC diagnosis/prognosis, and therapy. To achieve this goal, CR/CRC miRNAs profiling will be the first step. Among all the miRNA profiling platforms, deep sequencing is the best technique to identify signature/marker miRNAs because they can detect thousands of annotated and novel miRNAs, as well as other small RNAs simultaneously.

Here we report our finding of a pilot study on miRNA deep sequencing using 48 paired frozen biopsy CRC samples, of which 44 paired samples produced high sequencing quality data and were used for further analysis. This study allowed us to validate many previously published results. Moreover, by using a bias reduction protocol for smRNA deep sequencing, we were able to provide novel digital miRNA profile data of CRC samples. The bias reduced digital profile results allowed us to characterize a ‘true’ miRNA signature that is highly expressed in CR or CRC tissues. Using TaqMan miRNA qPCR, we detected the relative expression of some of the CRC miRNAs obtained from deep sequencing in 10 CRC cell lines, which validated their dysregulation under cancer condition. We also provide possible explanation for their dysregulation, which could be caused by APC, KRAS, or TP53 mutations. We believe these results will provide a new direction in future miRNA-CRC development studies, and the application of miRNAs in CRC diagnosis/prognosis, and therapy.

RESULTS

Deep sequencing data summary and signature miRNAs in CR or CRC

Today, the bias results in ligation-based deep sequencing gene profiling remains as one major technical problem for its application to quantity gene expression after its discovery and publication of our bias reduction workaround solution [11-13]. In the current study, CRC samples were sequenced using a combined approach of our bias reduction smRNA deep sequencing method with the protocol provided in the Illumina TRUE smRNA sequencing kit for both bias reduction and sample bar coding [11]. Total 48 paired samples were used for this pilot experiment (one and half runs, each run using twelve lanes on an Illumina HighSeq2000 machine with eight bar coded samples per lane). After data validation, we were able to get 46 paired samples (46 samples set) that produced high quality sequencing data.

After filtering out low quality reads, we obtained about 7 to 10 M smRNA reads per sample for most of the samples (Figures 1a, S1, Table S1). Analysis of reads composition of smRNA fragments revealed that majority of them are mature miRNAs, ranging from 60 to 80 % for most of the samples (Figures 1b, S2). Total reads agreed well with mature miRNA reads in each sample (Figure 1c). Summarized composition of smRNAs in reads from all sequenced samples showed that miRNAs represent the largest group, comprising of 72% of the total population. The second largest population is tRNA-derived smRNAs, which consist of 16% of the total population (Figure 1d). This data showed that we have high quality RNA samples and small RNA libraries, and our smRNA deep sequencing produced highly reliable data. Next, we performed miRNA qRT-PCR for five miRNAs to validate our smRNA deep sequencing results. The qRT-PCR data agrees well with the deep sequencing reads (Figure 1e, 1f). Two pairs of samples were later reclassified as none cancer samples by pathologist and were removed from further analysis (44 samples set in Table 1; most data analysis refers to this set hereafter unless specified).

Table 1: Summary information of CRC samples used in this study

Samples

 

All

Colon

Rectal

Size

Normal

44

29

15

 

Tumor

44

29

15

Stage

0

1

1

0

 

1

7

5

2

 

2

7

5

2

 

3

33

22

11

 

4

1

1

0

Sex

Male

13

7

6

 

Female

31

23

8

Race

Caucasian

32

19

13

 

African

4

3

1

 

Asian

7

6

1

 

Unknown

1

1

0

Age

30-39

2

1

1

 

40-49

2

1

1

 

50-59

16

10

6

 

60-69

9

7

2

 

70-79

10

6

4

 

80-89

5

4

1

Summary of smRNA deep sequencing reads.

Figure 1: Summary of smRNA deep sequencing reads. a. Histogram of smRNA deep sequencing reads for all samples. Majority of samples have smRNA reads between 7 to 10 millions. b. Histogram of miRNA percentage for all samples. Most of the samples have miRNA percentage ranging from 60% to 80%. c. Paired x-y plot of total smRNA reads per samples versus % of miRNA population per sample. d. Summary of smRNA composition for all samples. e. Box plot for log transformed T to N ratio of deep sequencing reads of miR-21, -31, -135b, -143, and -484 in the 44 pairs sample set. f. Box plot for ΔΔCt value of miRNA qRT-PCR detected miR-21, -31, -135b, -143, and -484 in the 44 pairs sample set.

Our bias reduction approach showed miR-21 and miR-143 together represent over 60% of all miRNAs, and the rest of the eight miRNAs from the top 10 highly expressed miRNAs covers 17% of total miRNAs (Figure 2a). These data implied CR tissues are mainly controlled by miR-21 and miR-143. While miR-21 is up-regulated, miR-143 is down-regulated in majority of the tumor samples, which results as the rank of their abundance was switched in tumor samples versus normal samples (Figure 2b). Only the rank of miR-148a remains unchanged (by %, Tumor versus Normal) in the top 10 miRNAs that are highly expressed in CR/CRC (Tables 2, S2).

Table 2: Top 25 miRNAs in samples by reads or percentage

 

By Reads ( in 1000, Sorted by All)

By % in all reads (Sorted by All)

Rank

miRNA

Normal

Tumor

All

p-value

miRNA

Normal

Tumor

All

1

143

1831.29

1058.94

1445.11

0.0028

143

38.13

21.72

29.86

2

21

817.00

1993.57

1405.28

0.0000

21

17.01

40.89

29.04

3

148a

232.12

233.52

232.82

0.8591

148a

4.83

4.79

4.81

4

194

165.30

103.07

134.18

0.0048

194

3.44

2.11

2.77

5

192

146.86

96.18

121.52

0.0075

192

3.06

1.97

2.51

6

200b

110.35

109.81

110.08

0.6898

200b

2.30

2.25

2.27

7

200c

80.28

72.69

76.49

0.4658

10b

1.93

1.00

1.46

8

10b

92.64

48.62

70.63

0.0000

26a

1.88

1.04

1.46

9

26a

90.08

50.85

70.46

0.0000

145

1.70

0.67

1.18

10

145

81.46

32.82

57.14

0.0495

200c

1.67

1.49

1.58

11

10a

55.01

55.68

55.34

0.8860

10a

1.15

1.14

1.14

12

101

53.57

41.33

47.45

0.0038

101

1.12

0.85

0.98

13

let-7f

38.86

35.08

36.97

0.0774

let-7f

0.81

0.72

0.76

14

20a

23.45

46.18

34.82

0.0032

1

0.76

0.32

0.54

15

27b

34.50

26.95

30.73

0.0002

27b

0.72

0.55

0.64

16

200a

30.00

29.18

29.59

0.6801

let-7g

0.67

0.49

0.58

17

23a

27.68

28.95

28.31

0.6455

215

0.66

0.20

0.43

18

let-7g

32.19

23.93

28.06

0.0001

378

0.64

0.25

0.44

19

199a-3p

24.32

29.85

27.09

0.1416

200a

0.62

0.60

0.61

20

199b-3p

24.32

29.85

27.09

0.1416

30d

0.59

0.41

0.50

21

1

36.33

15.68

26.00

0.0001

23a

0.58

0.59

0.59

22

30d

28.36

20.12

24.24

0.0023

let-7a

0.54

0.46

0.50

23

let-7a

25.71

22.37

24.04

0.0439

199a-3p

0.51

0.61

0.56

24

146b-5p

20.28

27.00

23.64

0.1105

199b-3p

0.51

0.61

0.56

25

378

30.54

12.25

21.40

0.0000

20a

0.49

0.95

0.72

Highly expressed miRNAs in CR/CRC.

Figure 2: Highly expressed miRNAs in CR/CRC. a. Box plot of top 25 miRNAs by total reads in 44 pairs sample set. b. Circular plot of top 10 miRNA in normal tissue versus tumor tissue (44 pairs sample set).

Compare published profile data with results in the current study

The advantage for miRNA deep sequencing is that the status (up/down in expression level) of miRNA dysregulation can be directly linked with the expression level of miRNAs in all CR/CRC miRNAs and the result can be digitalized. We compared our profile data with published CRC-miRNA results (Data was summarized in reference [8-10]). These top 10 miRNAs (by %) we identified in this study are also among the highly expressed miRNAs in CR/CRC reported before albeit in a different rank [14-16]. Overall, the status of miRNA dysregulation in published data agreed well with the results in our study (Tables 3, S3). However, several of dysregulated miRNAs that were reported previously are expressed at very low level according to our data, with only few reads per sample on average. This comparison showed the limitation of previous profiling methods. Therefore, some of the previously reported miRNAs may not play a critical physiological role in CR/CRC and will be difficult for detection and clinical applications due to their low abundance (Table 3). Furthermore, many of the reported dysregulated miRNAs are up/down less than 50%, implying that they will not be good candidates to be used as biomarkers (Table S3).

Table 3: Dysregulated CRC miRNAs: published list versus this study (changes by > 50%)

Rank

miRNA

Normal

Tumor

All (T+N)

Ref

This

T/N ratio

161

135b

94

733

414

Up

Up

7.80

596

663

1

4

3

Down

Up

4.00

149

224

239

815

527

Up

Up

3.41

101

183

667

2174

1421

Up

Up

3.26

79

182

1641

5197

3419

Up

Up

3.17

2

21

817001

1993567

1405284

Up

Up

2.44

228

96

76

168

122

Up

Up

2.21

34

17

10882

23553

17218

Up

Up

2.16

39

7

8042

17185

12614

Up

Up

2.14

14

20a

23452

46183

34818

Up

Up

1.97

53

203

6086

10185

8136

Up

Up

1.67

93

106a

1513

2322

1918

Up

Up

1.53

5

192

146863

96181

121522

Down

Down

0.65

73

99b

5486

3549

4518

Down

Down

0.65

37

125a-5p

17052

10790

13921

Down

Down

0.63

4

194

165296

103069

134183

Down

Down

0.62

52

30c

11117

6625

8871

Down

Down

0.60

84

195

3654

2123

2889

Down

Down

0.58

1

143

1831286

1058943

1445115

Down

Down

0.58

80

133a

4218

2340

3279

Down

Down

0.55

42

451

15459

8048

11754

Down

Down

0.52

115

29c

1449

754

1102

Down

Down

0.52

43

30a

15825

7575

11700

Down

Down

0.48

63

375

7856

3399

5628

Down

Down

0.43

21

1

36329

15680

26005

Down

Down

0.43

423

129-3p

17

7

12

Down

Down

0.41

10

145

81457

32816

57137

Down

Down

0.40

25

378

30542

12252

21397

Down

Down

0.40

411

139-3p

20

8

14

Down

Down

0.40

147

9

765

294

530

Down

Down

0.38

318

135a

60

21

41

Up

Down

0.35

26

215

31876

9542

20709

Down

Down

0.30

410

124

23

5

14

Down

Down

0.22

378

137

31

6

19

Down

Down

0.19

The power of paired samples and miRNAs pool to distinguish tumor tissues from normal tissues

There are hundreds of miRNAs detected in our samples and the variation of reads is large, it is unlikely all of them can be used to classify tumor versus normal tissues. Unsupervised hierarchical cluster analysis using reads count of all miRNAs to distinguish tumor versus normal tissues failed to give a clear results. Instead, normal tissues were mainly clustered into two groups (Figure S3). To reduce the variation among samples, we applied the power of paired sample by using tumor to normal reads count ratio (TN ratio) in each paired sample and selected a list of miRNAs using the criteria of p-value < 0.05. Using this approach, we were able to differentiate most tumor samples from normal samples and these differentially expressed miRNAs are classified into two groups that are up-regulated or down-regulated in tumor samples, compared with normal samples (Figure 3).

Hierarchical cluster analysis classifies tumor versus normal tissues.

Figure 3: Hierarchical cluster analysis classifies tumor versus normal tissues. Dendrogram of miRNAs with p<0.05 cluster normal tissue versus tumor tissue (44 pairs sample set). miRNAs are clustered into two groups that are either correlated with normal tissue or tumor tissue. There are several cases of tumor tissues are classified into normal tissues.

Detect the CRC miRNAs in CRC cell lines

Cancer cell lines have advantages over tissues for many cancer-related studies and research, such as drug testing, to study cancer causes by gene mutations, and using tumor mouse model for cancer studies. Therefore, we detected some CRC miRNAs from our deep sequencing results in 10 CRC cell lines using miRNA Taqman qPCR and correlated their expression status with APC, KRAS and TP53 mutations. Expression levels were measured by relative –delta Ct value (miRNA to U6sn). The expression level of miR-21, miR-200 family, miR-194, miR -192, miR-148a, and miR-26a, was relatively high, consistent with their high expression in CRC tissues; the expression level of miR-143-3p, miR-10b, and miR-145 was relative low, agreeing with their down-regulation in CRC tissues. Heatmap analysis revealed that mutation in either TP53 (HCT116 and LoVo are wild type) or APC (HCT116 is wild type) may be responsible for the down-regulated miRNAs (Figures 4, S4, S5). These miRNAs may also be used to separate k-ras mutation from wt (HT-29 and WiDr are wild type) (Figures 4, S6). These data provide the opportunity to manipulate these miRNAs in CRC cell lines for loss or gain of miRNA function studies using cell lines and tumor mouse model.

Relative expression level of miRNAs in CRC cell lines.

Figure 4: Relative expression level of miRNAs in CRC cell lines. Total of 18 miRNAs expression were measured by Taqman miRNA qPCR. ΔCt value of each miRNA was calculated as average value of ΔCtmiRNA = Ctcell line-miRNA - Ctcell line-U6sn from three independent reactions with duplicates in each reaction, and -ΔCtmiRNA values were used for hierarchical clustering.

Functional enrichment analysis of miR-21, miR-200c, and miR-26a target genes and pathways

To explore the possible role of oncogenic miRNAs in CRC, we performed enrichment analysis of experimentally validated target genes of miRNAs in oncogenic signatures and biological pathways listed in MSigDB. We listed the top 5 enriched terms in each databases in Table 4. The results demonstrated that miR-21-regulated genes are involved in RAS/RAF activation, including mitogen-activated signaling and TBK1-regulated pathways, miR-200 family-targeted genes are suppressed by VEGF signaling, and miR-26a-modulated genes are correlated with cell cycle progression.

Table 4: Top 5 gene sets in oncogenic signature, Reactome, and Biocarta overlapped with experimental validated miR target genes

miR

Gene Set Name

# Genes in Gene Set (K)

# Genes in Overlap (k)

k/K

p-valuea

FDR q-valueb

miR-21

TBK1.DF_DN

287

32

0.1115

2.41E-23

4.56E-21

P53_DN.V1_UP

194

18

0.0928

2.51E-12

1.58E-10

RAF_UP.V1_DN

194

18

0.0928

2.51E-12

1.58E-10

TBK1.DF_UP

290

19

0.0655

2.66E-10

1.26E-08

PIGF_UP.V1_UP

191

15

0.0785

1.74E-09

6.23E-08

REACTOME_METABOLISM_OF_LIPIDS_AND_LIPOPROTEINS

478

22

0.046

7.82E-09

2.72E-06

REACTOME_DEVELOPMENTAL_BIOLOGY

396

20

0.0505

8.06E-09

2.72E-06

REACTOME_FATTY_ACID_TRIACYLGLYCEROL_AND_KETONE_BODY_METABOLISM

168

12

0.0714

2.09E-07

4.69E-05

REACTOME_SIGNALING_BY_TGF_BETA_RECEPTOR_COMPLEX

63

8

0.127

2.85E-07

4.80E-05

REACTOME_HEMOSTASIS

466

19

0.0408

5.23E-07

5.92E-05

BIOCARTA_MAPK_PATHWAY

87

13

0.1494

6.50E-12

1.41E-09

BIOCARTA_HIVNEF_PATHWAY

58

10

0.1724

4.16E-10

4.51E-08

BIOCARTA_KERATINOCYTE_PATHWAY

46

9

0.1957

9.62E-10

6.96E-08

BIOCARTA_CTCF_PATHWAY

23

7

0.3043

2.53E-09

1.37E-07

BIOCARTA_ALK_PATHWAY

37

8

0.2162

3.59E-09

1.56E-07

miR-200c

TBK1.DF_DN

287

4

0.0139

2.53E-04

2.71E-02

BMI1_DN_MEL18_DN.V1_UP

145

3

0.0207

5.10E-04

2.71E-02

ESC_V6.5_UP_LATE.V1_DN

186

3

0.0161

1.05E-03

2.71E-02

P53_DN.V1_DN

192

3

0.0156

1.15E-03

2.71E-02

VEGF_A_UP.V1_DN

193

3

0.0155

1.17E-03

2.71E-02

REACTOME_INTRINSIC_PATHWAY_FOR_APOPTOSIS

30

3

0.1

4.53E-06

3.05E-03

REACTOME_HEMOSTASIS

466

6

0.0129

1.02E-05

3.43E-03

REACTOME_VEGF_LIGAND_RECEPTOR_INTERACTIONS

10

2

0.2

4.98E-05

9.68E-03

REACTOME_RIG_I_MDA5_MEDIATED_INDUCTION_OF_IFN_ALPHA_BETA_PATHWAYS

73

3

0.0411

6.72E-05

9.68E-03

REACTOME_PLATELET_ACTIVATION_SIGNALING_AND_AGGREGATION

208

4

0.0192

7.36E-05

9.68E-03

BIOCARTA_VEGF_PATHWAY

29

3

0.1034

4.08E-06

8.86E-04

BIOCARTA_AKAP13_PATHWAY

12

2

0.1667

7.30E-05

5.97E-03

BIOCARTA_HIF_PATHWAY

15

2

0.1333

1.16E-04

5.97E-03

BIOCARTA_RELA_PATHWAY

16

2

0.125

1.32E-04

5.97E-03

BIOCARTA_IL7_PATHWAY

17

2

0.1176

1.50E-04

5.97E-03

miR-26a

RB_DN.V1_UP

137

8

0.0584

6.20E-08

1.17E-05

E2F1_UP.V1_UP

189

7

0.037

8.79E-06

5.73E-04

CYCLIN_D1_KE_.V1_UP

190

7

0.0368

9.09E-06

5.73E-04

CAMP_UP.V1_UP

200

7

0.035

1.27E-05

5.99E-04

MTOR_UP.N4.V1_UP

196

6

0.0306

1.12E-04

4.24E-03

REACTOME_CELL_CYCLE

421

18

0.0428

5.79E-14

3.90E-11

REACTOME_CELL_CYCLE_MITOTIC

325

14

0.0431

3.62E-11

1.22E-08

REACTOME_MITOTIC_G1_G1_S_PHASES

137

10

0.073

1.50E-10

3.36E-08

REACTOME_G1_S_TRANSITION

112

8

0.0714

1.28E-08

2.15E-06

REACTOME_CYCLIN_E_ASSOCIATED_EVENTS_DURING_G1_S_TRANSITION

65

6

0.0923

1.92E-07

2.48E-05

BIOCARTA_G1_PATHWAY

28

5

0.1786

6.92E-08

1.50E-05

BIOCARTA_CELLCYCLE_PATHWAY

23

4

0.1739

1.70E-06

1.23E-04

BIOCARTA_CTCF_PATHWAY

23

4

0.1739

1.70E-06

1.23E-04

BIOCARTA_WNT_PATHWAY

26

4

0.1538

2.84E-06

1.54E-04

BIOCARTA_ALK_PATHWAY

37

4

0.1081

1.22E-05

5.28E-04

aP-value from the hypergeometric distribution for (k-1, K, N - K, n) where k is the number of genes in the intersection of the query set with a set from MSigDB, K is the number of genes in the set from MSigDB, N is the total number of all known human gene symbols, and n is the number of genes in the query set.

bFalse discovery rate analog of hypergeometric p-value after correction for multiple hypothesis testing according to Benjamini and Hochberg.

Furthermore, we analyzed the co-expressed genes and miRNAs in CRC patients from an independent dataset in terms of oncogenic signature and signaling transduction pathway using gene set enrichment analysis (GSEA, Table 5). Of interest, our results revealed that genes involving EGFR, TBK1, KRAS signaling, as well as cell surface interactions at the vascular wall were positively correlated with miR-21 expression (Figure 5), supporting its oncogenic roles with integrated oncogenic gene expression in CRC progression.

Table 5: Top 3 enriched terms of miR correlated gene expression in oncogenic signatures and signaling pathway database Reactome

miR Name Size ES NES p-val FDR q-val
miR-21 Oncogenic signature          
EGFR_UP.V1_UP* 177 0.578 1.935 0.002 0.061
TBK1.DN.48HRS_DN* 49 0.519 1.666 0.024 0.210
KRAS.DF.V1_UP* 174 0.444 1.663 0.029 0.180
YAP1_DN 40 -0.480 -1.741 0.006 0.448
KRAS.600_UP.V1_DN 266 -0.379 -1.469 0.048 1.000
KRAS.AMP.LUNG_UP.V1_UP 128 -0.418 -1.462 0.066 1.000
Pathway          
REACTOME_CELL_SURFACE_INTERACTIONS_AT_THE_VASCULAR_WALL* 84 0.534 1.921 0.002 0.247
REACTOME_IL1_SIGNALING 32 0.628 1.864 0.002 0.275
REACTOME_SIGNALING_BY_ILS 98 0.505 1.748 0.015 0.785
REACTOME_DEFENSINS* 26 -0.636 -1.913 0.004 0.242
REACTOME_ACYL_CHAIN_REMODELLING_OF_PC 16 -0.604 -1.769 0.011 0.690
REACTOME_ACYL_CHAIN_REMODELLING_OF_PE 17 -0.594 -1.768 0.010 0.467
miR-200c Oncogenic signature          
JAK2_DN.V1_DN 132 0.410 1.518 0.079 1.000
SNF5_DN.V1_DN 146 0.332 1.408 0.057 1.000
CAHOY_OLIGODENDROCUTIC 89 0.291 1.216 0.176 1.000
RPS14_DN.V1_UP 179 -0.528 -1.619 0.067 1.000
SNF5_DN.V1_UP 163 -0.440 -1.612 0.048 1.000
KRAS.LUNG.BREAST_UP.V1_UP 134 -0.389 -1.420 0.092 1.000
Pathway          
REACTOME_CHOLESTEROL_BIOSYNTHESIS 21 0.688 1.545 0.076 1.000
REACTOME_TRANSPORT_OF_VITAMINS_NUCLEOSIDES_AND_RELATED_MOLECULES 31 0.410 1.454 0.030 1.000
REACTOME_BIOLOGICAL_OXIDATIONS 120 0.323 1.314 0.119 1.000
REACTOME_GPVI_MEDIATED_ACTIVATION_CASCADE 30 -0.593 -1.807 0.004 1.000
REACTOME_GENERATION_OF_SECOND_MESSENGER_MOLECULES 24 -0.754 -1.801 0.014 0.780
REACTOME_COSTIMULATION_BY_THE_CD28_FAMILY 57 -0.552 -1.791 0.012 0.576
miR-26a Oncogenic signature          
TBK1.DN.48HRS_DN* 49 0.602 1.920 0.000 0.056
BCAT_BILD_ET_AL_UP 44 0.465 1.520 0.046 1.000
BCAT_GDS748_DN 40 0.411 1.511 0.020 1.000
KRAS.AMP.LUNG_UP.V1_UP 128 -0.434 -1.536 0.035 1.000
KRAS.LUNG.BREAST_UP.V1_UP 134 -0.394 -1.526 0.055 1.000
KRAS.600.LUNG.BREAST_UP.V1_UP 260 -0.376 -1.510 0.048 0.809
Pathway          
REACTOME_TRANSCRIPTIONAL_REGULATION_OF_WHITE_ADIPOCYTE_DIFFERENTIATION 53 0.478 1.750 0.006 1.000
REACTOME_ARMS_MEDIATED_ACTIVATION 15 0.650 1.688 0.006 1.000
REACTOME_PROLONGED_ERK_ACTIVATION_EVENTS 17 0.610 1.660 0.013 1.000
REACTOME_OLFACTORY_SIGNALING_PATHWAY 78 -0.636 -1.816 0.012 0.507
REACTOME_LIGAND_GATED_ION_CHANNEL_TRANSPORT 21 -0.661 -1.691 0.022 0.979
REACTOME_DEFENSINS 26 -0.575 -1.647 0.018 0.946

* Bold indicates significantly enriched terms with FDR less than 0.25.

miR-21 expression is positive correlated with cancer-associated pathways.

Figure 5: miR-21 expression is positive correlated with cancer-associated pathways. Top ranked enriched terms of gene set enrichment analysis (GSEA) on miR-21 correlated gene expression. a. Oncogenic signature (NES = 1.935, FDR = 0.061). b. The expression levels of leading edge genes in oncogenic signature and miR-21 with descending levels from red to blue and orange to green, respectively. c. Reactome pathway database (NES = 1.921, FDR = 0.247). d. The expression levels of leading edge genes in reactome pathway database and miR-21 with descending levels from red to blue and orange to green, respectively.

DISCUSSION

The role of miRNAs in CRC has attracted attention from researchers as well from physicians. MiRNAs in CRC tissues may correlate with it disease progression and disease stages and serve as biomarkers for diagnosis, prognosis, or suitable targets for treatments. Because miRNAs can also be transported through exosome and circulate exosomally, miRNAs have become very attractive noninvasive diagnosis biomarkers for cancers [17, 18]. In the past 10 years, many data have been generated using a variety of techniques and CRC samples [8-10]. Among all the techniques, deep sequencing is the most advanced and cost effective method to deal thousands of miRNAs at the same time, the ability to detect samples in a high dynamic range, and the potential to provide digital signature of miRNAs in profiled samples. It can also simultaneously detect novel miRNAs, others smRNAs, such as tRNA-derived smRNA fragments. But, due to the technical and bioinformatics challenges, and the high cost per run, only a few publications have employed deep sequencing technology in CRC-miRNA studies [14, 16].

In the current report, we performed a pilot study using deep sequencing technology to profile smRNAs from paired frozen CRC tissue samples. We also applied sample bar-coding to reduce cost and increase efficiency. Compared with published results, we get more reads (about 5 fold) and may have produced a list of ‘true’ signature miRNAs in CR/CRC. These signature miRNAs are critical candidate miRNAs that can be used in future miRNAs-CRC studies, CRC diagnosis/prognosis, and therapy. Their high expression level supports the notion that they could be biologically significant targets. The high level expression of these miRNAs may also reduce the technique challenge for detecting them to facilitate their application in clinical setting.

According to our results, miR-21, a miRNA that is up-regulated in CRC, and miR-143, a miRNA that is down-regulated in CRC, are the two miRNAs that dominated the miRNA population in CR tissue, and probably are also the most important miRNAs in CRC. The next eight miRNAs, from high to low in relative expression level, are miR-148a, -194, -192, 200b, -200c, -10b, -26a, and -145, which constituted the top 10 highly expressed miRNAs in CR/CRC. According to the changes in expression level, it seems miR-21 could act as an oncogene and miR-143 could serve a tumor suppressor role, but their exactly roles need to be defined by identifying their bona fide targets that can play direct physiology roles in CRC, and the cause of their dysregulation. The function of miR-21 has been well established because it is up-regulated in nearly all types of cancers and target tumor suppressor genes PTEN and PDCD4 [19-21]. The role of miR-143 in CRC is complicated despite many earlier reports indicating that miR-143 is a critical CRC miRNA [22-28], a recent careful study put this into question [29]. This previous study demonstrated that miR-143 and miR-145 are highly expressed in mesenchymal cells and are undetectable in colonic epithelial cells by various techniques, including the same deep sequencing platform we have used. Therefore, the role of miR-143 in CRC may be questionable. This result reminds us of a limitation of our study: the heterogeneity of tissue samples used in our study, especially the adjacent normal tissues that were resected during surgery. This may also have reflected in our clustering data (Figure S3). The current study can be further refined by detecting the list of identified candidate miRNAs by miRNA qRT-PCR using RNA samples from FFPE samples.

It also needs to be noted that some miRNAs with relatively low expression level, such as miR-31, miR-9, miR-135b, have been shown to play critical roles in CRC by several previous reports [22, 27, 30-35]. It is possible that these miRNAs may not need high expression level to be physiologically relevant. Alternatively, it could be technical difficulties that have prevented the detection of these miRNAs by ligation-based deep sequencing technology. One example is miR-31, which was detected as a low abundant miRNA using deep sequencing but can be detected relatively easy by qRT-PCR in this study and another independent study [16].

CRCs origins in the lining of the bowel and can invade the muscle layers underneath, and then grow through the bowel wall, and some will eventually develop into cancer. Our results showed that some muscle-specific-miRNAs (miR-1, miR-133a, ranked #21 and 80 respectively, Table S2) were detected to be highly expressed in CRC and could be involved in the progress of CRCs.

Another limitation of this study is that most of our samples are at stages 3, therefore it is impossible to draw a conclusion on how their expressions are related to CRC progression. However, by comparing the normal tissues to CRC tissues, our data support the conclusion that dysregulation of miRNA expression could contribute to diseases development. More stage 1, 2, and 4 samples will help to correlate miRNA expression to CRC development process and it can provide physicians treatment options in addition to tradition method.

Our Taqman miRNA qPCR expression data in CRC cell lines validated the some CRC miRNAs identified by deep sequencing. It will provide a basis for functional studies of miRNAs in CRC in the future.

MATERIALS AND METHODS

Patient cohort and samples collection

Paired samples (CRC and adjunct normal tissues) were collected during surgery at City of Hope using IRB protocol #COH05130. The biopsies were immediate frozen in liquid nitrogen after surgery and stored at -80 °C until RNA isolation. Total 48 pairs of samples (CRC and adjunct region normal tissues) were used (Table 1). CRCs were staged according to American Joint Committee on Cancer (AJCC) staging criteria. Samples are most at stage 3, with age from 50 to 80, and ratio of colon to rectal about 2:1, and female to male ratio about 3:1.

RNA isolation

Total RNA was isolated from frozen tissues or cell lines using Trizol (Life Technologies, Carlsbad, CA). RNA quality was checked and was quantified using a Nanodrop and an Agilent Bioanalyzer.

Small RNA deep sequencing

One µg of total RNA was used to construct small RNA deep sequencing libraries as described in our previous publication [11] with the following modifications. Briefly, we mixed 64 equal molar oligos (adding three nt to the 3’ end of Illumina default 5’ adaptor) to produce a mixture of small RNA library 5’ ligation adaptor. We chose eight 3’ adaptors from a panel of 3’ adaptor in smRNA TruSeq kit (Illumina, San Diego, CA) as 3’ ligation adaptor and also bar-coding samples. Each lane was loaded with eight bar-coded samples on a HiSeq2000 (Illumina, San Diego, CA) machine using all eight lanes for the first run (64 samples) and four lanes for the second run (32 samples).

Deep sequencing data analysis

Deep sequencing data analysis was performed as previously reported [11] with the following modification: 1) Reads were aligned to human genome hg19; 2) The mapping table was created using the human miRNA mature sequences from miRBase release16 and aligned back to human hg19 genome afterward [11]; 3) MiRNAs with less than 10 reads per sample in both normal and tumor samples are removed for further data analyzed. Deep sequencing data were analyzed, summarized, and plotted using R or Excel. Partek genomic suite (Partek Incorporated, St. Louis, Missouri) was used for hierarchical cluster analysis.

SmRNA qRT-PCR for tissue samples

We followed the S-Poly(T) smRNA qRT-PCR detection protocol as previously reported [36]. Briefly, 100 ng total RNA was poly-A tailed using the poly-A tailing kit from Epicentre (Madison, WI). U47 snoRNA was chosen as RNA sample control. ΔΔCt value of each paired samples was calculated as (CtTumor-miRNA-CtTumor-U47) – (CtNormal-miRNA-CtNormal-U47).

Taqman miRNA qPCR for cell lines

Taqman miRNA assay kits were purchased from Life Technologies (Grand Island, New York, USA). We followed the protocol from the manufacture. U6 snRNA was used as RNA sample control. ΔCtmiRNA values were used as their relative expression to U6 snRNA for data analysis.

Functional gene set enrichment analysis

For miRNA-targeted genes, we obtained experimentally validated miRNA-target gene pairs (474, 49, and 175 experimentally validated target genes of miR-21-5p, miR-200c-3p, and miR-26a-5p, respectively) from miRTarBase [37] and assigned them as the query set for oncogenic signatures (C6), Reactome (CP:REACTOME of C2), and Biocarta (CP:BIOCARTA of C2) in MSigDB [38]. There are 189 and 674 gene sets collection in the oncogenic, and the Reactome category, respectively. For overlaps analysis, we used hypergeometric test to estimate the probability that the number of miR targeted genes overlapped with the genes from a given collection from the number of input genes randomly selected genes in 45956 human genes. For instance, the top term of miR-21 in oncogenic signature results in a probability of 4.56E-21 to draw 32 TBK1.DF_DN-associated genes or more from 474 randomly selected genes in the list. Significance is indicated by p-value from the hypergeometric test and adjusted by false discovery rate (FDR) after correction for multiple hypothesis testing according to Benjamini and Hochberg. For gene set enrichment analysis (GSEA), gene expression dataset was downloaded from GEO (GSE29623 [39]) with corresponding miRNA expression profiles. We permutated phenotype labels 1000 times and performed GSEA on the permutated data to obtain a random ES distribution. For the GSEA test, a p-value is calculated on the original data, and the resulting enrichment score is compared to the distribution of the values obtained from the permuted data. Distinct GSEA on miR-21, miR-26a, and miR-200c expression levels were performed on gene set in oncogenic signatures and Reactome using Pearson matrix. FDR < 0.25 was used to define significant enriched gene sets as suggested by GSEA documentation [40].

ACKNOWLEDGMENTS

Authors would like to thanks Dr. David Ann and Ms Mansze Kong for their helps in this project. Deep sequencing and raw data processing were conducted by City of Hope integrated genomic core.

CONFLICTS OF INTEREST

All authors declared there is no conflict of interest.

GRANT SUPPORT

Part of this work was supported by grants from the Ministry of Science and Technology (MOST 103-2320-B-038 -053) and Ministry of Health and Welfare (MOHW103-TD-B-111-01, MOHW103-TDU-B-212-113001 and MOHW104-TDU-B-212-124-001) of Taiwan.

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