Long Non-Coding RNAs Roles in Tumorigenesis of Esophageal Carcinoma

DOI: https://doi.org/10.21203/rs.3.rs-1381569/v1

Abstract

Background:Esophageal carcinoma (ESCA) is one of the most common types of cancer. ESCA is accounted for the sixth leading cause of cancer-related deaths globally. The majority of the patients are diagnosed at late stages of ESCA, with distance metastasis and/or chemoresistance, which lead to a poor prognosis. Previous studies demonstrated lncRNA presentation and roles in ESCA cells and patients tissue. It has been proposed that lncRNAs can be considered as a new prognostic and diagnostic biomarker in ESCA. This study comprehensively explored the interaction of lncRNAs with miRNAs and mRNAs of TCGA database and proposed novel promising biomarker with favorable diagnostic and prognostic values.

Methods: The public data of RNA-seq, miR-seq and related clinical data were downloaded from TCGA database. Differential expression analysis was conducted by “limma” in R. GO and KEGG signaling pathway were used for enrichments. STRING database was used for PPI analysis. CE-network was constructed by STAR database in R. Kaplan-Meier survival analysis (log-rank test) and ROC curve analysis to indicate the biomarkers' diagnostic and prognostic values.

Results: Differentially expressed data illustrated that 1332 mRNA including 610 upregulated and 722 down-regulated were differentially expressed in ESCA.The GO and KEGG pathway analysis showed that the differentially expressed mRNAs were enriched in critical biological processes. The PPI showed that IGFBP5, ACAN, ADAMTS12, MMP13, and CDH2 were the important PPI hubs. The ceRNA network data demonstrated critical lncRNAs including TMEM16B-AS1, AC093010.3, SNHG3, and PVT1 which have an essential role in ESCA development.The data revealed that the lncRNA WDFY3-AS2, AC108449.2,DLEU2, AC007128.1, and AP003356.1 are potential diagnostic prognostic biomarker in the ESCA patients.

Conclusion: Altogether, in our study, we demonstrated lncRNA, miRNA, and mRNA interaction and mentioned regulatory networks, which can be considered as a therapeutic option in ESCA. In addition, we proposed potential diagnostic and prognostic biomarkers for the patients.

Background

Esophageal carcinoma (ESCA) is one of the most common types of gastrointestinal cancer. According to pathological features, ESCA is mainly classified into esophageal adenocarcinoma and esophageal squamous cell carcinoma(1). ESCA is accounted for the sixth leading cause of malignancy-related deaths in the world(2). The majority of the patients are diagnosed at late stages of ESCA, with distance metastasis and/or chemoresistance, which lead to a poor prognosis (3). Based on previous reports, the overall 5-year survival rate is so frustrating, around 15-25% in the ESCA patients (4). Currently, radical surgery is a favorable option for early-ESCA treatment but is not conclusive in the advanced stages of the disease (5). Furthermore, standard chemotherapies have been implicated in advanced stages of the patients, but treatment outcomes still remain dismal in ESCA patients (6). Therefore, there is an urgent need to find out novel biomarkers for early diagnosis of ESCA patients to promote therapeutic approaches efficacy and outcomes in the patients.

Recently, it has been demonstrated that the main part of the human genome is transcribed to RNA and not capable of coding proteins,which is attributed to non-coding RNAs (7). Non-non coding RNA is a class of RNA that includes different types of RNA such as transfer RNAs (tRNAs), ribosomal RNAs (rRNAs), microRNAs (miRNAs), long ncRNAs (lncRNAs), and circular RNAs (circRNAs)(8). Numerous investigations highlighted the crucial role of LncRNAs in cancer development and progression. LncRNAs are a group of non-coding RNAs with more than 200 nt in length and with no or little capability of coding proteins (9). LncRNA has been explained that plays different canonical roles in diverse biological processes such as cell proliferation, differentiation, and cellular development and in carcinogenesis and metastasis through regulating corner stone genes expression (10). Previous studies demonstrated lncRNA presentation and roles in ESCA cells and patients tissue. For instance, it has been illustrated that lncRNA ZFAS1 drives tumorigenesis and invasion by regulating the STAT3 signaling pathway through sponging miRNA-124 in the esophageal squamous cell carcinoma cell(11).

Furthermore, lncRNAs can confer chemoresistance to the ESCA cell by modulating signaling pathways. For instance, lncRNA TUSC7 overexpression suppressed cell proliferation and chemoresistance by miR-224/DESC1/EGFR/AKT axis in the ESCA cells(12). However, the exact mechanisms of lncRNAs function in ESCA are not yet well understood.

In this study, we comprehensively retrieved and explored RNA-seq data of the TCGA (The Cancer Genome Atlas) database to illustrate the interaction of lncRNAs with miRNAs and mRNAs and find a novel promising biomarker favorable diagnostic and prognostic values.

Materials And Methods

Sample and data collection

The ESCA data of the patients were retrieved from the TCGA database (https://portal.gdc.cancer.gov/repository). The inclusion criteria were: (1) the histopathological diagnosis was ESCA; (2) having complete demographic data including age, vital status, race, ethnicity, pathological stage, TNM classification, and overall survival time. Totally, 185ESCA were enrolled in this study. Eighty-nine participants had age > 61 years and 96 patients had age ≤ 61 and 158 and 27 patients were male and female, respectively. Among 185 patients, only 5 patients were Black or African American, 46 were Asian, and 114 were white. Pathological stages of I, II, III, and IV were 18, 79, 56, and 9, respectively. The clinical characteristics are presented in Table 1.

Table 1. Clinicopathological characteristics of ESCA patients.

Characteristics

N

(%)

Age (year) (mean ± SD)

62.45±11.90

 

Age > 61

89

48.10

Age ≤ 61

96

51.89

Sex

 

 

Male

158

85.41

Female

27

14.59

Race

 

 

Asian

46

24.86

Black or African American

5

2.70

White

114

61.62

NA

20

10.81

Vital status

 

 

Alive

108

58.38

Dead

77

41.62

Pathologic (stage)

 

 

Stage I

18

9.73

Stage II

79

42.70

Stage III

56

30.27

Stage IV

9

4.86

NA

23

12.43

Pathologic (T)

 

 

T0

1

0.54

T1

31

16.76

T2

43

23.24

T3

88

47.57

T4

5

2.70

NA

17

9.19

Pathologic (M)

 

 

M0

136

73.51

M1

9

4.86

MX

18

9.73

NA

22

11.89

Pathologic (N)

 

 

N0

77

41.62

N1

69

37.30

N2

12

6.49

N3

8

4.32

NX

2

1.08

NA

17

9.19

NA: Not Available.

RNA-seq and miR-seq data analysis

The molecular data (RNA-Seq and miR-Seq Level 3) of ESCA were downloaded from the TCGA database. The raw count of the reads of RNA-Seq and miR-Seq data was normalized by Voom and TMM normalization methods. The “limma” package was used to indicate the differentially expressed mRNAs (DEmRNAs), lncRNAs (DElncRNAs), and miRNAs (DEmiRNAs) between normal solid tissues and primary tumors. The concluded data were filtered based on the |log2 fold change (FC)| > 1 for DEmRNA, DElncRNA, and DEmiRNA. P-value < 0.05 and false discovery rate (FDR) < 0.05 were considered as significant thresholds. All the analyses were accomplished in R software.

In Silico functional enrichment analysis and protein-protein interaction (PPI) network

Gene ontology (GO) in three domains, including biological processes, cellular components, and molecular functions, and Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathways were used for functional enrichment analysis. The GO and KEGG outputs were visualized by R software (ggplot2 package). The PPI network was constructed based on the STRING online database by Cytoscape 3.7.2. Molecular Complex Detection (MCODE) was used to analyze and predict the interactions (score value > 0.4).

LncRNA-miRNA-mRNA ceRNA network construction

LncRNA-miRNA-mRNA ceRNA network was constructed by “GDCRNATools” (http://bioconductor.org/packages/devel/bioc/html/GDCRNATools.html) package in R software based on starbase database (10). The nodes and edges were virtualized by Cytoscape 3.7.2.

Statistical Analysis

All the differentially expressed data were analyzed by using R software (3.5.2) through the “GDCRNATools” package. Kaplan-Meier survival analysis (log-rank test) was utilized to indicate the relationship between over or downregulation of the RNA, based on median expression with patient’s survival time. ROC curve analysis was conducted by SPSS v21. P-value < 0.05 was considered as a significant threshold.

Results

Differentially Expressed Genes

Differentially expressed data illustrated that 1332 mRNA including 610 upregulated and 722 down-regulated were differentially expressed in ESCA. Furthermore, 98 lncRNAs including 49 upregulated and 49 down-regulated were indicated as deferentially expressed lncRNA in the patients. One hundred and one miRNAs including 62 up-regulated and 39 down-regulated demonstrated differential expression in the ESCA samples. The data are shown in Figs. 1, and Tables 2, 3.

 

Table 2. Top 20 up-regulated mRNAs, lncRNAs, and miRNAs

mRNA

 

 

 

 

 

 

 

 

symbol

logFC

AveExpr

t

PValue

FDR

B

ENSG00000128422

KRT17

4.77

8.62

3.69

0.00

0.00

-0.08

ENSG00000060718

COL11A1

4.66

2.33

3.45

0.00

0.01

-0.60

ENSG00000136231

IGF2BP3

4.63

3.32

4.22

0.00

0.00

1.99

ENSG00000123388

HOXC11

4.52

1.29

6.73

0.00

0.00

13.01

ENSG00000137745

MMP13

4.35

1.30

3.62

0.00

0.00

-0.08

ENSG00000149968

MMP3

4.31

3.62

3.69

0.00

0.00

0.16

ENSG00000180818

HOXC10

4.31

2.80

4.63

0.00

0.00

3.52

ENSG00000123500

COL10A1

4.30

2.90

3.45

0.00

0.01

-0.59

ENSG00000099953

MMP11

4.29

5.35

3.86

0.00

0.00

0.68

ENSG00000180806

HOXC9

4.29

1.10

8.60

0.00

0.00

22.99

ENSG00000262406

MMP12

4.24

4.08

4.16

0.00

0.00

1.75

ENSG00000037965

HOXC8

4.18

1.04

7.85

0.00

0.00

18.85

ENSG00000169429

CXCL8

4.11

4.99

4.04

0.00

0.00

1.30

ENSG00000123364

HOXC13

4.05

0.99

4.19

0.00

0.00

1.86

ENSG00000170373

CST1

4.01

3.24

3.22

0.00

0.01

-1.29

ENSG00000131015

ULBP2

3.95

2.24

5.91

0.00

0.00

9.01

ENSG00000127928

GNGT1

3.94

0.11

3.66

0.00

0.00

0.06

ENSG00000206075

SERPINB5

3.83

7.05

4.11

0.00

0.00

1.46

ENSG00000115008

IL1A

3.81

1.98

3.57

0.00

0.00

-0.23

ENSG00000164283

ESM1

3.80

1.81

5.99

0.00

0.00

9.38

LncRNA

 

 

 

 

 

 

 

 

symbol

logFC

AveExpr

t

PValue

FDR

B

ENSG00000228742

AC002384.1

4.21

0.43

5.23

0.00

0.00

6.00

ENSG00000268621

IGFL2-AS1

3.94

0.15

3.69

0.00

0.00

0.15

ENSG00000276850

AC245041.2

3.76

1.55

4.17

0.00

0.00

1.79

ENSG00000229970

AC007128.1

3.58

-0.70

4.76

0.00

0.00

4.05

ENSG00000281406

BLACAT1

3.39

1.85

4.77

0.00

0.00

4.10

ENSG00000204949

FAM83A-AS1

3.33

0.27

3.35

0.00

0.01

-0.90

ENSG00000273760

AC245041.1

3.31

0.49

3.41

0.00

0.01

-0.71

ENSG00000226476

LINC01748

3.24

0.38

4.18

0.00

0.00

1.85

ENSG00000249395

CASC9

3.12

2.04

3.37

0.00

0.01

-0.84

ENSG00000206195

DUXAP8

3.02

2.03

4.05

0.00

0.00

1.39

ENSG00000230061

TRPM2-AS

2.59

0.86

3.23

0.00

0.01

-1.25

ENSG00000259230

LINC02323

2.43

0.30

4.02

0.00

0.00

1.27

ENSG00000265415

AC099850.3

2.42

1.63

6.74

0.00

0.00

13.04

ENSG00000254560

BBOX1-AS1

2.38

1.52

3.94

0.00

0.00

1.00

ENSG00000172965

MIR4435-2HG

2.23

4.11

6.84

0.00

0.00

13.72

ENSG00000253669

AP003356.1

2.18

0.72

5.20

0.00

0.00

5.88

ENSG00000227403

LINC01806

2.17

1.49

3.31

0.00

0.01

-1.01

ENSG00000249859

PVT1

2.03

3.97

4.89

0.00

0.00

4.57

ENSG00000222041

CYTOR

2.03

3.11

5.99

0.00

0.00

9.49

ENSG00000261116

AL049555.1

1.92

4.31

3.45

0.00

0.01

-0.73

miRNA

 

 

 

 

 

 

 

 

 

logFC

AveExpr

t

PValue

FDR

B

hsa-miR-196a-5p

5.14

6.47

7.40

0.00

0.00

17.19

hsa-miR-196b-5p

4.16

7.36

7.62

0.00

0.00

18.42

hsa-miR-767-5p

3.94

2.12

2.70

0.01

0.02

-3.02

hsa-miR-944

3.75

3.80

2.46

0.01

0.04

-3.61

hsa-miR-105-5p

3.62

2.31

2.48

0.01

0.03

-3.55

hsa-miR-205-5p

3.40

9.25

2.46

0.01

0.04

-3.95

hsa-miR-1269a

3.21

2.45

2.11

0.04

0.07

-4.37

hsa-miR-135b-5p

2.96

5.08

5.31

0.00

0.00

6.33

hsa-miR-4652-5p

2.89

0.62

4.46

0.00

0.00

2.81

hsa-miR-224-5p

2.48

5.58

3.67

0.00

0.00

-0.37

hsa-miR-615-3p

2.25

0.78

4.69

0.00

0.00

3.72

hsa-miR-205-3p

2.18

0.01

2.83

0.01

0.02

-2.67

hsa-miR-452-3p

2.09

1.75

3.09

0.00

0.01

-1.94

hsa-miR-937-3p

2.03

0.70

4.38

0.00

0.00

2.50

hsa-miR-431-5p

1.99

0.17

4.85

0.00

0.00

4.40

hsa-miR-181b-3p

1.98

2.55

5.92

0.00

0.00

9.32

hsa-miR-4746-5p

1.97

1.96

5.57

0.00

0.00

7.62

hsa-miR-135b-3p

1.94

0.32

4.21

0.00

0.00

1.82

hsa-miR-452-5p

1.79

6.23

3.46

0.00

0.00

-1.08

hsa-miR-675-3p

1.71

3.24

2.34

0.02

0.05

-3.98

 

Table 3. Top 20 down-regulated mRNAs, lncRNAs, and miRNAs

mRNA

             

 

symbol

logFC

AveExpr

t

PValue

FDR

B

ENSG00000096088

PGC

-9.90

1.58

-7.95

0.00

0.00

19.92

ENSG00000168631

DPCR1

-6.40

1.02

-7.60

0.00

0.00

17.91

ENSG00000184956

MUC6

-6.08

2.13

-6.17

0.00

0.00

10.30

ENSG00000167653

PSCA

-5.90

3.12

-8.23

0.00

0.00

21.56

ENSG00000019102

VSIG2

-5.25

1.84

-8.64

0.00

0.00

23.99

ENSG00000196188

CTSE

-5.07

2.69

-4.47

0.00

0.00

2.60

ENSG00000215182

MUC5AC

-5.03

2.86

-4.65

0.00

0.00

3.34

ENSG00000115386

REG1A

-4.81

1.67

-4.14

0.00

0.00

1.35

ENSG00000160182

TFF1

-4.81

1.65

-4.73

0.00

0.00

3.70

ENSG00000134240

HMGCS2

-4.80

0.95

-5.25

0.00

0.00

5.97

ENSG00000112936

C7

-4.54

1.11

-7.29

0.00

0.00

16.22

ENSG00000109906

ZBTB16

-4.45

0.54

-8.59

0.00

0.00

23.63

ENSG00000174514

MFSD4A

-4.43

2.79

-8.94

0.00

0.00

25.79

ENSG00000168079

SCARA5

-4.30

0.20

-7.44

0.00

0.00

17.04

ENSG00000066405

CLDN18

-4.24

3.86

-3.84

0.00

0.00

0.24

ENSG00000125144

MT1G

-4.23

3.14

-8.73

0.00

0.00

24.52

ENSG00000163884

KLF15

-4.18

0.38

-9.84

0.00

0.00

31.17

ENSG00000170011

MYRIP

-4.16

0.14

-9.12

0.00

0.00

26.77

ENSG00000180875

GREM2

-4.14

0.10

-7.52

0.00

0.00

17.47

ENSG00000139874

SSTR1

-4.07

0.26

-4.82

0.00

0.00

4.19

LncRNA

             

 

symbol

logFC

AveExpr

t

PValue

FDR

B

ENSG00000241388

HNF1A-AS1

-3.02

1.24

-3.24

0.00

0.01

-1.51

ENSG00000254343

AC091563.1

-2.97

0.09

-6.23

0.00

0.00

10.67

ENSG00000259291

ZNF710-AS1

-2.97

2.31

-11.47

0.00

0.00

41.54

ENSG00000203709

C1orf132

-2.93

1.77

-9.43

0.00

0.00

28.67

ENSG00000250742

LINC02381

-2.68

2.18

-6.90

0.00

0.00

14.14

ENSG00000260912

AL158206.1

-2.24

2.63

-6.94

0.00

0.00

14.31

ENSG00000268388

FENDRR

-2.17

2.25

-4.87

0.00

0.00

4.40

ENSG00000272894

AC004982.2

-2.12

0.66

-4.56

0.00

0.00

3.21

ENSG00000227218

AL157935.1

-2.05

0.28

-4.92

0.00

0.00

4.69

ENSG00000196167

COLCA1

-1.98

2.77

-3.42

0.00

0.01

-1.03

ENSG00000224078

SNHG14

-1.96

3.00

-4.53

0.00

0.00

2.95

ENSG00000249669

CARMN

-1.76

1.78

-3.75

0.00

0.00

0.22

ENSG00000180769

WDFY3-AS2

-1.70

0.29

-6.59

0.00

0.00

12.44

ENSG00000188242

PP7080

-1.64

4.99

-3.81

0.00

0.00

0.12

ENSG00000277496

AL357033.4

-1.59

1.04

-3.73

0.00

0.00

0.20

ENSG00000260461

AL133355.1

-1.54

1.14

-6.53

0.00

0.00

12.16

ENSG00000180139

ACTA2-AS1

-1.49

0.34

-3.72

0.00

0.00

0.19

ENSG00000251615

AC104825.2

-1.48

1.56

-4.48

0.00

0.00

2.91

ENSG00000261338

AC021016.2

-1.48

0.14

-6.76

0.00

0.00

13.28

ENSG00000225302

AC023283.1

-1.48

1.22

-3.57

0.00

0.00

-0.34

miRNA

             

 

 

logFC

AveExpr

t

PValue

FDR

B

hsa-miR-204-5p

-3.91

0.51

-8.58

0.00

0.00

24.23

hsa-miR-375

-3.71

11.09

-3.90

0.00

0.00

0.35

hsa-miR-133a-3p

-2.98

4.07

-5.24

0.00

0.00

5.86

hsa-miR-1-3p

-2.63

4.72

-4.30

0.00

0.00

1.87

hsa-miR-133b

-2.39

0.64

-4.59

0.00

0.00

3.17

hsa-miR-129-5p

-2.38

1.30

-5.62

0.00

0.00

7.72

hsa-miR-1468-5p

-2.25

1.67

-7.11

0.00

0.00

15.49

hsa-miR-139-5p

-2.03

5.22

-7.48

0.00

0.00

17.60

hsa-miR-29b-2-5p

-2.02

4.15

-8.54

0.00

0.00

23.98

hsa-miR-148a-3p

-1.95

14.33

-6.52

0.00

0.00

12.20

hsa-miR-30a-3p

-1.93

10.62

-6.16

0.00

0.00

10.35

hsa-miR-29c-3p

-1.89

10.36

-6.05

0.00

0.00

9.79

hsa-miR-30a-5p

-1.87

13.23

-5.98

0.00

0.00

9.41

hsa-miR-145-5p

-1.78

11.00

-4.35

0.00

0.00

2.06

hsa-miR-338-5p

-1.75

2.04

-4.92

0.00

0.00

4.52

hsa-miR-378c

-1.74

3.04

-6.49

0.00

0.00

12.09

hsa-miR-145-3p

-1.62

5.36

-4.17

0.00

0.00

1.36

hsa-miR-338-3p

-1.56

9.42

-4.19

0.00

0.00

1.43

hsa-miR-29c-5p

-1.55

3.70

-7.52

0.00

0.00

17.83

hsa-miR-139-3p

-1.47

3.00

-4.79

0.00

0.00

3.90

 

GO enrichment and KEGG pathway analysis

Thereby GO enrichment analysis, we indicated several prominent roles of the DEmRNAs, Biological process of GO illustrated that the DEmRNAs are majorly assigned to DNA replication, mitotic nuclear division, organelle fission, chromosome segregation, and sister chromatid segregation.Also, the cellular component of GO depicted that the genes were significantly classified inthe chromosomal region, condensed chromosome, spindle, collagen-containing, and extracellular matrix. Moreover, the GO molecular function part showed that the DEmRNAs dominantly enriched in extracellular matrix structural constituent, DNA helicase activity, catalytic activity, acting on DNA, single-stranded DNA-dependent ATP-dependent DNA helicase activity, and DNA replication origin binding (Fig 2).Furthermore, KEGG pathway analysis showed that the DEmRNAs remarkably attributed to Cell cycle, DNA replication, p53 signaling pathway, AGE-RAGE signaling pathway in diabetic, and PPAR signaling pathway (fig 3).

 

protein-protein interaction (PPI) network construction

For better understanding of the protein-protein interactions, we constructed a PPI network of the DEmRNAs via the STRING database. The data showed that IGFBP5, ACAN, ADAMTS12, MMP13, and CDH2 were the important PPI hubs (Fig 4)

 

LncRNA-miRNA-mRNA ceRNA network construction

Based on the competing endogens RNA (ceRNA) hypothesis, which explains that lncRNAs regulate mRNA expression level by competing the shared miRNAs in cells, a ceRNA network was built based on the differentially expressed genes data via starbase database in R software. The nodes and edges were visualizedbyCytoscape 3.7.2. The ceRNA network data demonstrated critical lncRNAs includingTMEM16B-AS1, AC093010.3, SNHG3, and PVT1 which have an important role in the development of ESCA(Fig. 5).

Kaplan-Meier survival analysis of differentially expressed genes

To explore the association of differential expression of the genes and the ESCApatient’s prognosis, Kaplan-Meier survival analysis was conducted over the differentially expressed genes. The data indicated that 41 mRNAs, 5 lncRNAs, and 23 miRNAs were associated with the overall survival rate in the patients. The top 20 hits of each group are presented in Table 4.

Table 4. Top 20 mRNAs, lncRNAs, and miRNAs that were associated with overall survival.

mRNA

         

 

symbol

HR

lower95

upper95

pValue

ENSG00000091879

ANGPT2

2.10

1.28

3.46

0.00

ENSG00000146386

ABRACL

2.10

1.27

3.45

0.00

ENSG00000168298

HIST1H1E

1.90

1.15

3.13

0.01

ENSG00000130208

APOC1

1.89

1.15

3.11

0.01

ENSG00000121769

FABP3

1.76

1.08

2.88

0.02

ENSG00000164283

ESM1

1.72

1.05

2.82

0.03

ENSG00000130826

DKC1

1.66

1.01

2.72

0.04

ENSG00000180818

HOXC10

1.66

1.02

2.71

0.04

ENSG00000040275

SPDL1

1.64

1.00

2.69

0.04

ENSG00000105486

LIG1

1.64

1.00

2.70

0.04

ENSG00000153310

FAM49B

1.64

1.00

2.68

0.04

ENSG00000124731

TREM1

1.61

0.97

2.67

0.05

ENSG00000126709

IFI6

0.62

0.38

1.01

0.05

ENSG00000148180

GSN

0.61

0.37

1.00

0.05

ENSG00000175287

PHYHD1

0.61

0.37

1.00

0.05

ENSG00000149582

TMEM25

0.61

0.37

0.99

0.05

ENSG00000128340

RAC2

0.61

0.37

0.99

0.05

ENSG00000137198

GMPR

0.61

0.37

0.99

0.04

ENSG00000182568

SATB1

0.60

0.36

1.00

0.04

ENSG00000090006

LTBP4

0.60

0.37

0.98

0.04

LncRNA

         

 

symbol

HR

lower95

upper95

pValue

ENSG00000180769

WDFY3-AS2

0.51

0.31

0.85

0.01

ENSG00000253669

AP003356.1

1.66

1.01

2.74

0.03

ENSG00000229970

AC007128.1

1.65

1.01

2.70

0.05

ENSG00000259366

AC108449.2

0.52

0.32

0.86

0.01

ENSG00000231607

DLEU2

1.70

1.04

2.80

0.03

miRNA

         

 

symbol

HR

lower95

upper95

pValue

hsa-miR-29c-3p

0.56

0.36

0.88

0.01

hsa-miR-181b-3p

1.61

1.03

2.51

0.04

hsa-miR-550a-3p

1.73

1.10

2.70

0.02

hsa-miR-3682-3p

1.71

1.09

2.68

0.02

hsa-miR-101-3p

0.61

0.39

0.97

0.03

hsa-miR-27a-3p

0.59

0.38

0.92

0.02

hsa-miR-23a-3p

0.59

0.38

0.92

0.02

hsa-miR-99a-5p

0.58

0.37

0.91

0.02

hsa-miR-1249-3p

0.64

0.41

1.00

0.05

hsa-miR-425-5p

1.96

1.25

3.08

0.00

hsa-miR-323b-3p

1.72

1.09

2.69

0.02

hsa-miR-1269a

1.56

0.99

2.47

0.04

hsa-miR-6842-3p

0.62

0.40

0.97

0.04

hsa-miR-151a-3p

0.63

0.40

0.98

0.04

hsa-let-7b-3p

0.56

0.36

0.88

0.01

hsa-let-7a-5p

0.55

0.35

0.87

0.01

hsa-miR-412-5p

0.60

0.39

0.95

0.03

hsa-let-7a-3p

0.57

0.37

0.89

0.01

hsa-miR-33a-3p

0.58

0.37

0.91

0.02

hsa-miR-31-3p

0.63

0.40

0.99

0.04

Diagnostic value analysisof differentially expressed lncRNAs

For demonstrating the diagnostic value of each DElncRNAs, AUC curve analysis was accomplished in the ESCA samples. All 98 DElncRNAs indicated remarkable diagnostic values in the patients. The top 30 hits of the lncRNAs are presented in Table 5.

Table 5. Top 20 lncRNAs that had remarkable diagnostic value.

lncRNA

AUC

SE

p-value

Lower (95%CI)

Upper (95%CI)

expression

MIR4435-2HG

0.99

0.007

0

0.977

1

Up

CYTOR

0.977

0.013

0

0.951

1

Up

AP003356.1

0.955

0.033

0

0.891

1

Up

PVT1

0.951

0.024

0

0.905

0.997

Up

C1orf132

0.941

0.031

0

0

0.12

Down

MAFG-AS1

0.94

0.035

0

0.872

1

Up

AL158212.3

0.936

0.046

0

0

0.155

Down

DLEU2

0.928

0.026

0

0.877

0.98

Up

ZNF710-AS1

0.926

0.039

0

0

0.151

Down

AC021016.2

0.924

0.04

0

0

0.155

Down

AL133355.1

0.919

0.039

0

0.004

0.159

Down

MELTF-AS1

0.916

0.035

0

0.848

0.984

Up

BLACAT1

0.911

0.029

0

0.854

0.969

Up

AC002384.1

0.909

0.035

0

0.84

0.978

Up

AC099850.3

0.906

0.053

0

0.802

1

Up

AC092718.4

0.903

0.052

0

0.8

1

Up

TYMSOS

0.901

0.041

0

0.82

0.981

Up

AC091563.1

0.901

0.044

0

0.014

0.185

Down

TMPO-AS1

0.899

0.031

0

0.839

0.96

Up

AC026401.3

0.898

0.04

0

0.82

0.977

Up

 

Potential diagnostic and prognostic lncRNA

Thereby merging the diagnostic (AUC value) and prognostic (HR) values of the LncRNAs in the ESCA patients, potential novel lncRNA biomarkers were retrieved. The summary of the data is presented in table 6. The data demonstrated that the lncRNA WDFY3-AS2, AC108449.2,DLEU2, AC007128.1,and AP003356.1 as potential diagnostic and prognostic biomarker in the ESCApatients (Figure 6).

 

Table 6. Merge diagnostic and prognostic data of the LncRNAs.

symbol

HR

lower95

upper95

pValue

AUC

SE

p-value

Lower (95%CI)

Upper (95%CI)

expression

WDFY3-AS2

0.514

0.313

0.846

0.006

0.885

0.062

0.000

0.000

0.236

Down

AC108449.2

0.524

0.318

0.863

0.007

0.842

0.051

0.000

0.058

0.257

Down

DLEU2

1.702

1.035

2.799

0.029

0.928

0.026

0.000

0.877

0.980

Up

AP003356.1

1.661

1.005

2.744

0.034

0.955

0.033

0.000

0.891

1.000

Up

AC007128.1

1.654

1.013

2.700

0.046

0.854

0.063

0.000

0.731

0.977

Up

UGDH-AS1

0.645

0.395

1.053

0.077

0.827

0.083

0.000

0.011

0.335

Down

TMEM161B-AS1

1.478

0.905

2.416

0.114

0.862

0.061

0.000

0.019

0.257

Down

CD44-AS1

1.412

0.853

2.337

0.153

0.804

0.067

0.001

0.672

0.936

Up

AGAP2-AS1

0.707

0.433

1.154

0.164

0.853

0.060

0.000

0.734

0.971

Up

LINC00511

1.396

0.852

2.290

0.173

0.843

0.064

0.000

0.717

0.969

Up

AC122129.1

0.721

0.440

1.179

0.182

0.824

0.060

0.000

0.058

0.294

Down

AL357033.4

1.387

0.849

2.266

0.193

0.804

0.059

0.001

0.081

0.311

Down

AL133355.1

0.731

0.447

1.196

0.202

0.919

0.039

0.000

0.004

0.159

Down

FOXD2-AS1

1.374

0.841

2.243

0.205

0.827

0.091

0.000

0.648

1.000

Up

AC004803.1

0.728

0.446

1.190

0.208

0.780

0.070

0.002

0.084

0.357

Down

AC099850.3

0.735

0.450

1.201

0.210

0.906

0.053

0.000

0.802

1.000

Up

AC022211.2

0.735

0.450

1.203

0.214

0.863

0.048

0.000

0.769

0.956

Up

CASC9

1.338

0.818

2.187

0.239

0.822

0.064

0.000

0.695

0.948

Up

LINC01572

1.325

0.811

2.164

0.255

0.785

0.097

0.002

0.595

0.974

Up

TSC22D1-AS1

0.754

0.462

1.231

0.255

0.862

0.044

0.000

0.053

0.224

Down

Discussion

Esophageal cancer is one of the most aggressive types of cancer with an increasing rate of death and dismal prognosis. Previous investigations highlighted non-coding RNA particularly lncRNA's roles in cancer development, progression, and clinicopathological features of the patients (13-15). A large body of studies considered lncRNAs as a major contributor to ESCA development and showed the lncRNAs prognosticand diagnostic values for the ESCA patients (16). Our study comprehensively considered the expression and interaction of protein-coding RNAs (mRNAs), miRNAs, and lncRNAs. Furthermore, our data presented the CE-network of lncRNA-miRNA-mRNA in ESCA patients specimens. GO and KEGG pathway analysis demonstrated that several crucial signaling pathways such as cell cycle and replication, p53, AGE-RAGE, and PPAR (peroxisome proliferator-activated receptor) signaling pathways have the main contribution to tumorigenesis of ESCA patients. Accumulating evidence illustrated that cell cycle regulatory proteins dysregulation such ascyclin-dependent kinase inhibitor 3 (CDKN3) can drive tumorigenesis and chemoresistance of ESCA cells (17). Furthermore, it has been shown that PRDX2 develops ESCA by instigating Wnt/β-catenin and AKT pathways in the cells (18). P53 is one of the well-known tumor suppressor genes, which is dysregulated in the number of malignancies. There are many examples that depicted lncRNAs role in p53 regulation indifferent types of cancers. For instance, it has been demonstrated that lncRNA AK001796 had an invention in ESCA tumorigenesis by regulating MDM2 to suppress p53 in the cells (19). LncRNA SNHG1 increases liver cancer progression by recruiting DNMT1 to epigenetically suppress p53 expression (20). 

Recently, the cross-talk between metabolism and cancer are vastly explained in various cancer. It has been demonstrated that PPAR Signaling Pathway is one of the most important signaling hubs between lipid metabolism and carcinogenesis (21). LncRNA Ftx has been shown that promotes tumorigenesis, by increasing glucose uptake,lactate production, and relative glycolytic enzyme through controlling the PPARγ pathway in hepatocellular carcinoma (HCC) (22).

Our protein-protein interaction data demonstrated that IGFBP5, ACAN, ADAMTS12, MMP13, and CDH2 had a main role in the signaling hubs through the PPI network. IGFBP5 has been discovered to act as an oncogene in the cells and drive tumorigenesis in different cancer types. LncRNA UCA1 promotes carcinogenesis by upregulating IGFBP5 through sponging miR-204 in papillary thyroid carcinoma (PTC) cells (23). ADAMTS12 has been reported that had an anti-tumorigenic effect in various cancer. LncRNA AK001058 can regulate tumor development, progression, and invasion by suppressing ADAMTS12 expression via methylation of its promoter (24). The previous investigation depicted that MMP13 (Matrix Metalloproteases 13) had key roles in embryogenic development and cancerogenesis such asproliferation, migration(25). LncRNA LINC00511 promotes tumor growth, migration and invasion by directly binding to miR-150 to upregulate MMP13 in the breast cancer cells (26). Cadherin-2 (CDH2) is a member of the cadherin family that regulates crucial biological functions and tumorigenesis in a variety of cancers (27). Overexpression of lncRNA JPX has been reported that elevates cell proliferation and tumor growth by upregulating CDH2 through sponging miR944 in Oral squamous cell carcinoma (OSCC) cells (28).

Furthermore, we demonstrated that lncRNA TMEM16B-AS1, AC093010.3, SNHG3, and PVT1 participated in CE-networks and regulate several mRNAs expression by sponging various miRNAs.

A large body of evidence indicated that overexpression of lncRNA SNHG3 is associated with tumorigenesis, invasion and metastasis, and poor prognosis in patients. It can promote tumorigeneses by epigenetically suppressing MED18 through recruiting EZH2 to methylate the MED18 neighboring region in gastric cancer (29). Recently, in a study, lncRNA SNHG3 has been shown that elevated the m6A level by binding tomiR-186-5p to increase METTL3 expression in the ESCA cells (30).

LncRNA PVT1 has presented oncogenic effects in various tumor types. The last investigation demonstrated that overexpression of PVT1 is associated with poor clinicopathological characteristics and overall survival rate in ESCA patients (31). Furthermore, in vitro studies showed that PVT1could induce invasion and metastasis by instigating epithelial‑to-mesenchymal transition (EMT) in ESCA cells (32). Interestingly, PVT1 has been indicated that induced tumorigenesis through sponging miR-203 and LASP1 which have tumor suppressive impact in the ESCA cells (33).

Finally, in the last part of our work, our results proposed potential diagnostic and prognostic lncRNAs, including WDFY3-AS2, AC108449.2,DLEU2, AC007128.1, and AP003356.1 which showed promising outcomes. To the best of our knowledge, lncRNA AC108449.2, AC007128.1, and AP003356.1 were presented for the first time reviewed in the studies as new novel biomarkers in ESCA. While, lncRNA WDFY3-AS2 and DLEU2 have been considered different types of cancer as well as ESCA. Overexpression of lncRNA WDFY3-AS2 has been demonstrated remarkably associated with clinical and molecular characteristics of glioma in the patients and involved in the TNF signaling pathway (34). Furthermore, WDFY3-AS2 expression was showed that significantly associated with a dismal overall survival rate in patients with triple-negative breast cancer (TNBC), which is consistent with our results (35).Previous studies have illustrated that lncRNADLEU2 expression correlates to poor prognosis in ESCA patients (36). Furthermore, it has been shown that DLEU2 can induce tumor growth, cell proliferation, invasion, and metastasis by upregulating E2F7 through directly inhibiting miR-30e-5p in ESCA cells (37).

Conclusion

Numerousreports explained lncRNA roles in ESCA, but in this work, we thoroughly presented lncRNA, miRNA, and mRNA networks. Altogether, in our study, we demonstrated lncRNA, miRNA, and mRNA interaction and mentioned regulatory networks, which can be considered as a therapeutic option in ESCA. In addition, we proposed potential diagnostic and prognostic biomarkers for the patients.

Declarations

Acknowledgments

Not applicable.

Author’s contributions

S.T. M.K. M.J. and M.G.conducted the research; S.T. M.K. M.J. and M.G. analyzed the data; S.T. M.K. M.J. and M.G.wrote the paper; M.G.had primary responsibility for the final content. All authors have reviewed and approved the final manuscript.

Funding

Not applicable.

Availability of data and materials

The authors declare that the datasets on which the conclusions of this manuscript rely are deposited in publicly available repositories.

Ethics approval and consent to participate

The authors declare that there is no conflict of interest.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

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