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Table 4 Essential properties of the 11 records for the five miRNAs by data from GEO

From: A circRNA–miRNA–mRNA network identification for exploring underlying pathogenesis and therapy strategy of hepatocellular carcinoma

miRNA

First author

Year

Region

Data source

Platform

Number of case

Expression (mean ± SD)

Cancer

Normal

Cancer

Normal

hsa-miR-1303

Hou J

2010

China

GSE21279

GPL9052

4

7

4.15 ± 1.206

2.49 ± 0.554

Sato F

2011

Japan

GSE21362

GPL10312

59

61

1.59 ± 1.014

1.39 ± 1.042

Kim J

2012

South Korea

GSE39678

GPL15852

16

8

11.00 ± 0.702

11.49 ± 0.364

Diaz G

2013

USA

GSE40744

GPL14613

9

19

3.72 ± 0.774

2.78 ± 0.492

Villanueva A

2016

Spain

GSE74618

GPL14613

230

10

1.88 ± 0.658

1.51 ± 0.263

TCGA

2017

USA

TCGA

none

21

5

0.35 ± 0.225

0.17 ± 0.100

Xie Z

2017

China

GSE98269

GPL20712

3

3

5.07 ± 0.071

5.10 ± 0.115

hsa-miR-142-5p

Li W

2008

China

GSE10694

GPL6542

78

88

11.01 ± 0.605

11.19 ± 0.710

Su H

2008

China

GSE12717

GPL7274

9

6

9.11 ± 1.406

9.09 ± 0.707

Burchard J

2010

USA

GSE22058

GPL10457

96

96

0.95 ± 0.241

1.07 ± 0.135

Hou J

2010

China

GSE21279

GPL9052

4

7

7.04 ± 1.190

7.77 ± 1.112

Sato F

2011

Japan

GSE21362

GPL10312

59

61

7.06 ± 1.022

7.93 ± 0.577

Kim J

2012

South Korea

GSE39678

GPL15852

16

8

11.10 ± 0.959

11.88 ± 0.182

Morita K

2013

Japan

GSE41874

GPL7722

6

4

0.72 ± 0.412

1.34 ± 0.225

Diaz G

2013

USA

GSE40744

GPL14613

9

19

1.96 ± 0.506

1.74 ± 0.224

Villanueva A

2016

Spain

GSE74618

GPL14613

230

10

1.37 ± 0.206

1.36 ± 0.212

Xie Z

2017

China

GSE98269

GPL20712

3

3

6.76 ± 0.733

6.88 ± 0.169

TCGA

2017

USA

TCGA

None

371

49

5.45 ± 1.264

6.86 ± 0.796

hsa-miR-877-5p

Hou J

2010

China

GSE21279

GPL9052

4

6

3.64 ± 1.753

2.19 ± 0.713

Sato F

2011

Japan

GSE21362

GPL10312

59

61

2.17 ± 0.921

2.05 ± 0.961

Morita K

2013

Japan

GSE41874

GPL7722

6

4

1.08 ± 0.313

1.06 ± 0.452

Diaz G

2013

USA

GSE40744

GPL14613

9

19

5.36 ± 0.619

3.88 ± 0.797

Villanueva A

2016

Spain

GSE74618

GPL14613

230

10

3.64 ± 0.741

3.42 ± 0.636

Xie Z

2017

China

GSE98269

GPL20712

3

3

5.31 ± 0.073

5.12 ± 0.066

TCGA

2017

USA

TCGA

None

360

46

1.47 ± 0.807

0.73 ± 0.328

hsa-miR-583

Sato F

2011

Japan

GSE21362

GPL10312

59

61

1.47 ± 0.722

1.46 ± 0.837

Morita K

2013

Japan

GSE41874

GPL7722

6

4

0.91 ± 0.249

0.76 ± 0.293

Diaz G

2013

USA

GSE40744

GPL14613

9

19

1.75 ± 0.247

1.77 ± 0.344

Villanueva A

2016

Spain

GSE74618

GPL14613

230

10

1.43 ± 0.237

1.45 ± 0.294

Xie Z

2017

China

GSE98269

GPL20712

3

3

5.07 ± 0.119

5.02 ± 0.020

hsa-miR-1276

Sato F

2011

Japan

GSE21362

GPL10312

59

61

1.55 ± 0.810

1.72 ± 0.737

Diaz G

2013

USA

GSE40744

GPL14613

9

19

1.9 ± 0.254

1.70 ± 0.209

Villanueva A

2016

Spain

GSE74618

GPL14613

230

10

1.34 ± 0.164

1.38 ± 0.191

Xie Z

2017

China

GSE98269

GPL20712

3

3

5.06 ± 0.049

5.06 ± 0.027

TCGA

2017

USA

TCGA

None

233

17

0.61 ± 0.380

0.36 ± 0.233

  1. TCGA The Cancer Genome Atlas, SD standard deviation