Open Access

For robust big data analyses: a collection of 150 important pro-metastatic genes

Chinese Journal of Cancer201736:16

https://doi.org/10.1186/s40880-016-0178-z

Received: 19 October 2016

Accepted: 3 November 2016

Published: 21 January 2017

Abstract

Metastasis is the greatest contributor to cancer-related death. In the era of precision medicine, it is essential to predict and to prevent the spread of cancer cells to significantly improve patient survival. Thanks to the application of a variety of high-throughput technologies, accumulating big data enables researchers and clinicians to identify aggressive tumors as well as patients with a high risk of cancer metastasis. However, there have been few large-scale gene collection studies to enable metastasis-related analyses. In the last several years, emerging efforts have identified pro-metastatic genes in a variety of cancers, providing us the ability to generate a pro-metastatic gene cluster for big data analyses. We carefully selected 285 genes with in vivo evidence of promoting metastasis reported in the literature. These genes have been investigated in different tumor types. We used two datasets downloaded from The Cancer Genome Atlas database, specifically, datasets of clear cell renal cell carcinoma and hepatocellular carcinoma, for validation tests, and excluded any genes for which elevated expression level correlated with longer overall survival in any of the datasets. Ultimately, 150 pro-metastatic genes remained in our analyses. We believe this collection of pro-metastatic genes will be helpful for big data analyses, and eventually will accelerate anti-metastasis research and clinical intervention.

Keywords

Pro-metastatic geneBig data analysisRenal cancerLiver cancer

Background

Cancer metastasis is the greatest cause of death in almost all types of malignancies [1]. Multiple factors from the tumor and the host contribute to the formation and progression of distant secondary tumors [1, 2], and most of the mechanistic studies to date have mainly focused on the metastatic potential of tumor cells. It is believed that the metastasis of single cancer cells begins with the cells gaining the ability to migrate and invade. The cancer cells can gain motility in several ways, including epithelial-mesenchymal transition (EMT) and fusion of cancer cells to highly mobile bone marrow-derived cells [3, 4]. In the metastases formed by clusters of tumor cells, EMT may not be necessary [5]; however, the layer of endothelial cells enveloping the entire tumor cluster/embolus seems critical for the survival of tumor clusters [6].

The ability to identify cancer patients with a high risk of metastasis is essential in the era of precision medicine. In addition to applying clinicopathologic parameter combination, also known as clinical prognostic classifiers in some circumstances, molecular profiling based on high-throughput technologies is expected to allow for a more accurate and robust prognostic prediction of metastatic potential in patients. How to effectively analyze big data generated from high-throughput screening is an emerging issue for many bioinformaticians. We hypothesize that, with optimal weighting on the impact of each individual gene, a collection of key pro-metastatic genes could be useful to generate a prognostic tool to identify the metastatic potential of a specific tumor and novel signaling pathways underlying metastasis.

Main text

The increased investigation of cancer metastasis in recent years has identified over 200 pro-metastatic genes. In this review, we aim to identify a group of key pro-metastatic genes with in vivo functional evidence and reasonable clinical relevance for application to big data analyses.

Figure 1 summarizes the analytic procedure of this review. First, we carefully selected 285 genes from the literature through searching PubMed based on the following criteria: (1) author-provided evidence of promoting migration and/or invasion of cancer cells; (2) author-provided evidence of promoting metastasis in vivo using animal models; (3) when a gene has been reported as pro-metastatic in several articles, all articles reporting the link were reviewed, and the most convincing studies are listed as the key references in Table 1. In addition, we applied survival analyses as validation tests using the publicly available TCGA datasets (threshold = 0.05). For analyses of clear cell renal cell carcinoma (ccRCC), the mRNA expression data of 72 non-cancerous kidney tissues and 539 tumors [clear cell kidney carcinoma (KIRC) in the TCGA database] were downloaded. For analyses of hepatocellular carcinoma (HCC), the mRNA expression data of 50 non-cancerous liver tissues and 374 tumors [liver hepatocellular carcinoma (LIHC) in the TCGA database] were used. Normalization was performed using the DESeq method (Version 1.26.0). For each individual gene, the median expression level was used as a cut-off value to separate the patients into high and low expression groups. Genes were excluded if their elevated expression significantly associated with better patient prognosis in any patient cohort. Finally, 150 genes passed the tests and are listed in Table 1. Among them, 79 genes have significant prognostic values in the ccRCC patient cohort, 35 genes have significant prognostic values in the HCC cohort, and 23 genes have significant prognostic values in both cohorts.
Fig. 1

A schematic illustration of the study design and findings

Table 1

The list of 150 pro-metastatic genes with clinical relevance and key references

Number

Gene name

Clinical relevance validation (P value of overall survival analysis)

Reference

ccRCC cohort

HCC cohort

1

ADAM9

NS

0.001

[10]

2

ADORA2B

0.006

NS

[11]

3

AGR2

<0.001

NS

[12]

4

AKT1

NS

NS

[13]

5

ANXA1

NS

NS

[14]

6

APOBEC3G

0.045

NS

[15]

7

ATF4

0.001

0.031

[16]

8

AXL

0.005

NS

[17]

9

BACH1

NS

NS

[18]

10

BCL2L1

NS

NS

[19]

11

BCL3

<0.001

NS

[20]

12

BIRC5

<0.001

<0.001

[21]

13

BSG

NS

0.004

[22]

14

C5AR1

NS

NS

[23]

15

CAV1

NS

NS

[24]

16

CCL2

NS

NS

[25]

17

CCR7

NS

0.002

[26]

18

CD24

NS

NS

[27]

19

CD44

0.016

NS

[28]

20

CDCP1

NS

NS

[29]

21

CEACAM6

0.004

NS

[30]

22

CEBPD

0.022

NS

[31]

23

CENPF

<0.001

0.008

[32]

24

CHD1L

<0.001

0.007

[33]

25

CHI3L1

NS

NS

[34]

26

CLDN9

0.039

NS

[35]

27

COL6A1

<0.001

NS

[36]

28

COMP

0.040

NS

[37]

29

CSNK2A2

NS

NS

[38]

30

CTSB

NS

NS

[38]

31

CTSZ

<0.001

NS

[39]

32

CXCL1

<0.001

0.001

[40]

33

CXCL10

NS

NS

[41]

34

CXCL8

0.002

<0.001

[42]

35

CXCR4

NS

NS

[43]

36

E2F1

0.001

0.005

[44]

37

EIF5A

<0.001

NS

[45]

38

ELF5

NS

NS

[46]

39

ENAH

NS

0.012

[47]

40

ENPP2

NS

NS

[48]

41

ETV4

0.003

0.001

[49]

42

EZH2

<0.001

<0.001

[50]

43

FGFR1

NS

NS

[51]

44

FLOT2

NS

NS

[52]

45

FOSL1

<0.001

NS

[53]

46

FOXC1

NS

NS

[54]

47

FOXM1

<0.001

0.009

[55]

48

FOXQ1

NS

NS

[56]

49

FZD2

<0.001

NS

[57]

50

GABRA3

NS

0.004

[58]

51

GDF15

NS

NS

[59]

52

GHRL

<0.001

NS

[60]

53

GLI2

<0.001

NS

[61]

54

GOLM1

NS

0.049

[62]

55

GRK3

NS

NS

[63]

56

HMGB1

NS

NS

[64]

57

HMMR

0.003

<0.001

[65]

58

HOXB13

<0.001

NS

[66]

59

HOXB7

NS

NS

[67]

60

HOXB9

<0.001

NS

[68]

61

ID1

NS

NS

[69]

62

IDO1

NS

NS

[70]

63

IGFBP2

NS

NS

[71]

64

IL32

NS

NS

[72]

65

IL5

NS

NS

[73]

66

IL6

<0.001

NS

[74]

67

IP6K2

0.001

NS

[75]

68

ITGA3

NS

NS

[76]

69

ITGA5

0.018

0.011

[77]

70

ITGBL1

NS

NS

[78]

71

KISS1R

NS

NS

[79]

72

KLF8

NS

NS

[80]

73

L1CAM

0.007

NS

[81]

74

LAMB3

0.001

NS

[67]

75

LEF1

0.007

NS

[82]

76

LGALS1

<0.001

0.048

[83]

77

LGALS3

NS

NS

[84]

78

LOX

NS

0.047

[85]

79

LOXL2

0.033

NS

[86]

80

MBD4

NS

NS

[87]

81

MCAM

NS

NS

[88]

82

MET

NS

NS

[89]

83

MMP1

0.030

0.002

[90]

84

MMP16

NS

NS

[91]

85

MMP9

0.001

0.009

[92]

86

MTA1

0.015

NS

[93]

87

MTA2

0.001

NS

[94]

88

MYB

0.031

0.021

[95]

89

NFATC2

NS

NS

[96]

90

NRP2

NS

NS

[97]

91

NTRK3

NS

0.044

[98]

92

PARP1

NS

NS

[99]

93

PCDH7

NS

NS

[100]

94

PDGFRB

NS

NS

[101]

95

PDPN

0.034

NS

[102]

96

PELP1

0.011

NS

[103]

97

PHGDH

NS

NS

[104]

98

PHIP

NS

NS

[105]

99

PLAUR

<0.001

NS

[35]

100

PLOD2

0.004

0.008

[106]

101

POSTN

NS

NS

[107]

102

PPIA

0.015

0.038

[108]

103

PRRX1

0.045

NS

[109]

104

PRSS50

<0.001

NS

[89]

105

PTGS2

0.040

NS

[110]

106

PTTG1

<0.001

0.004

[111]

107

PXN

0.001

NS

[112]

108

RAB22A

0.024

NS

[113]

109

RAC1

NS

NS

[97]

110

RAF1

0.025

NS

[23]

111

RHOC

0.030

NS

[114]

112

ROR2

0.001

NS

[115]

113

RRAS

<0.001

NS

[116]

114

RUNX3

NS

0.032

[117]

115

S100A4

NS

NS

[118]

116

S100P

NS

NS

[119]

117

SEMA3E

<0.001

NS

[120]

118

SFRP2

0.020

NS

[121]

119

SIX2

0.001

0.036

[122]

120

SNAI1

0.045

NS

[123]

121

SNAI2

NS

NS

[124]

122

SOX12

<0.001

0.045

[125]

123

SOX4

NS

0.018

[126]

124

SPINK1

<0.001

NS

[127]

125

SPON2

<0.001

NS

[128]

126

SPP1

NS

0.000

[129]

127

SRC

<0.001

0.037

[130]

128

SRGN

NS

NS

[131]

129

SRPK1

NS

NS

[132]

130

TACSTD2

NS

NS

[133]

131

TDO2

0.020

NS

[134]

132

TF

<0.001

NS

[135]

133

TGFB1

0.008

NS

[73]

134

TGM2

0.003

NS

[136]

135

TNC

NS

NS

[137]

136

TNFSF10

NS

NS

[138]

137

TNK2

0.016

NS

[139]

138

TP73

0.016

NS

[140]

139

TPO

0.043

NS

[141]

140

TRIM28

NS

0.00

[142]

141

TWIST1

0.002

NS

[143]

142

UBE2 N

NS

NS

[144]

143

VAV1

0.038

NS

[145]

144

VEGFB

NS

NS

[146]

145

VIM

0.014

NS

[147]

146

WASF3

NS

NS

[148]

147

WNT5A

0.008

NS

[149]

148

WSB1

<0.001

NS

[150]

149

YBX1

0.038

<0.001

[151]

150

ZEB2

NS

NS

[152]

NS not significant

Although different tumor types are believed to rely on different molecular mechanisms for metastasis, 23 common pro-metastatic genes have been identified in our analyses, associating with poor prognosis in both cancer types. Among them, we are most interested in 11 genes that are not only statistically significant in terms of prognostic impact but also associated with distinct overall survival curves in both cohorts, suggesting the genes’ profound biological impacts on tumor progression. For the other 12 genes, although their biological impact on tumor progression were found to be significant in log-rank tests in both cohorts, the survival curves of high versus low expression groups crossed at some time points. The 11 most interesting genes are BIRC5 (Survivin), CXCL1, CXCL8 (IL8), E2F1, ETV4, EZH2, MMP1, MMP9, MYB, PTTG1, and YBX1. Figure 2 shows the survival curves of patients with either ccRCC or HCC expressing these 11 genes. Our findings suggest that different tumor types may partially share some common metastatic mechanisms, therefore strengthening the rationale of applying the list of 150 pro-metastatic genes to big data analyses. Interestingly, 4 of these 11 genes encode secreted proteins, namely, CXCL1, CXCL8, MMP1, and MMP9, which are ideal pharmaceutical targets for blocking cancer metastasis.
Fig. 2

The survival curves of two cohorts of cancer patients comparing the mRNA expression levels of 11 genes. The data were retrieved from The Cancer Genome Atlas (TCGA) database. The survival curves were plotted using the Kaplan–Meier method and compared using the log-rank test. Consistently, among all 11 genes presented in this figure, elevated gene expression levels significantly associate with shorter overall patient survival (P < 0.05) in both tumor types. ccRCC clear cell renal cell carcinoma, HCC hepatocellular carcinoma

Although not covered in this review article, emerging data regarding the regulatory roles of non-coding RNA in metastasis have linked different pro-metastatic genes to forming signaling cascades [79]. Further investigation into the roles of non-coding RNA in metastasis is warranted.

Conclusions

In summary, we present here a collection of 150 important pro-metastatic genes for big data analyses. We expect more key molecules to be identified and validated in the near future to be included in the list, thereby accelerating the efforts in preventing and treating cancer metastasis.

Declarations

Authors’ contributions

Study conception and design: CNQ; acquisition of data: YM and JPY; analysis and interpretation of data: YM, JPY and CNQ; drafting of manuscript: YM and CNQ. All authors read and approved the final manuscript.

Acknowledgements

This work was supported by grants from the National Natural Science Foundation of China (No. 81272340, No. 81472386, No. 81672872), the National High Technology Research and Development Program of China (863 Program) (No. 2012AA02A501), the Science and Technology Planning Project of Guangdong Province, China (No. 2014B020212017, No. 2014B050504004 and No. 2015B050501005), and the Natural Science Foundation of Guangdong Province, China (No. 2016A030311011).

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)
State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center
(2)
Department of Nasopharyngeal Carcinoma, Sun Yat-sen University Cancer Center

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