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  • Primary research
  • Open Access

Identification of candidate genes or microRNAs associated with the lymph node metastasis of SCLC

Cancer Cell International201818:161

https://doi.org/10.1186/s12935-018-0653-5

©  The Author(s) 2018

  • Received: 23 May 2018
  • Accepted: 1 October 2018
  • Published:

Abstract

Background

Small cell lung cancer (SCLC) is a highly malignant cancer, and over 70% of patients with SCLC present with the metastatic disease. We aimed to explore some novel differentially expressed genes (DEGs) or microRNAs (miRNAs) associated with the lymph node metastasis of SCLC.

Methods

The DEGs between the metastasis and cancer groups were identified, and GO functional and KEGG pathway enrichment analyses for these DEGs were implemented. Subsequently, the protein–protein interaction network and subnetwork of module were constructed. Then the regulatory networks based on miRNAs, transcription factors (TFs) and target DEGs were constructed. Ultimately, the survival analysis for DEGs was performed to obtain the DEGs related to the survival of SCLC.

Results

Here, 186 upregulated (e.g., GSR, HCP5) and 144 downregulated DEGs (e.g., MET, GRM8, and DACH1) were identified between the SCLC patients with lymph node metastasis and without lymph node metastasis. GRM8 was attracted to the G-protein coupled receptor signaling pathway. Besides, miR-126 was identified in the miRNAs-TFs-target regulatory network. GRM8 and DACH1 were all regulated by miR-126. In particular, GSR and HCP5 were correlated with survival of SCLC patients.

Conclusion

MiR-126, DACH1, GRM8, MET, GSR, and HCP5 were implicated in the lymph node metastasis process of SCLC.

Keywords

  • Metastasis
  • Lymph node
  • Small cell lung cancer
  • Survival

Background

Lung cancer (LC) is a malignant lung tumor characterized by unbounded cell growth in the lung tissues [1]. It is estimated that there are approximately 4,291,600 new cancer cases in China in 2015, and LC is still the main factor for cancer-associated death [2, 3]. Currently, LCs are frequently divided into small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC), and 10–15% of LCs are SCLC [4, 5]. SCLC, a poorly differentiated and aggressive type of LC, presents an early metastases, fleetly growth rate, and poor prognosis with a lower overall 5-year survival rate [6–8]. However, the molecular determinants of SCLC metastasis are unclear. Thus, it is essential to explore the determinants to prevent SCLC metastasis.

Lymph nodes, the central trafficking hubs for recirculating immune cells, are widely present throughout the body [9]. Conceivably, tumor cells could migrate into the lymph nodes and rapidly spread to other organs [10]. Activator protein-1 (AP-1), a transcription factor (TF), regulates the gene expression in response to various stimulus [11]. It has been reported that the overexpression of AP-1 is related to the lymphatic metastasis of LC [12]. Intriguingly, the abnormal expression of genes regulated by AP-1 was also involved in the process of lymphatic metastasis. For example, previous studies found that the overexpressions of urokinase type plasminogen activator (u-PA) and u-PA receptor (u-PAR) were correlated with the lymphatic metastasis of LC [13, 14]. In particular, the overexpression of AP-1 contributes to the overexpressions of u-PA and u-PAR. Recently, other studies demonstrated that cyclooxygenase-2 (COX-2) overexpression is well related to the lymphatic metastasis of LC [15, 16]. In the promoter regions of COX-2 genes, there is a binding site of AP-1 [17]. Furthermore, in metastatic lymph nodes, the vascular endothelial growth factor C (VEGF-C) overexpression is closely correlated with the lymph node metastasis of NSCLC [18]. These all findings revealed that the abnormal expression of TFs or genes were associated with the lymphatic metastasis of LC, especially for the NSCLC. However, yet little is known about the processes of tumor cell migration and lymph node metastasis in SCLC [19]. Therefore, we aimed to explore some novel differentially expressed genes (DEGs) related to the lymph node metastasis process of SCLC, and the potential mechanism would be elucidated.

The bioinformatics analysis methods were carried out for screening DEGs correlated with the lymph node metastasis process of SCLC. Firstly, the DEGs between the metastasis and cancer groups were screened. Afterwards, Gene Ontology (GO) functional and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses for the DEGs were implemented to obtain the potential functions of DEGs. Afterwards, the protein–protein interaction (PPI) network and subnetwork of module were established. Then the regulatory networks based on microRNA (miRNAs), TFs and target DEGs were constructed. Ultimately, the survival analysis for DEGs was performed to obtain the DEGs related to the survival of SCLC.

Materials and methods

Microarray data

The gene expression profiling GSE40275 was obtained from the Gene Expression Omnibus (GEO) database (http://www.ncbi.nlm.nih.gov/geo/) [20], which included 4 SCLC samples with the lymph node metastasis (metastasis group, GSM990225, 226, 227, 247) and 6 SCLC samples without the lymph node metastasis (cancer group, GSM990214, 215, 216, 217, 218, 246). All samples were collected from the SCLC patients and detected through the GPL15974 Human Exon 1.0 ST Array [CDF: Brainarray Version 9.0.1, HsEx10stv2_Hs_REFSEQ] platform.

Data preprocessing and DEGs screening

We downloaded the raw CEL data and used the Oligo package (ver.1.38.0) (http://bioconductor.org/help/search/index.html?q=oligo/) [21] in R language to pre-process all the data by performing background correction, conversion of original data and quartile data normalization. In order to remove the probes that cannot match the gene symbol, probes were annotated by the annotations file. The average value of different probes would serve as the final expression level of gene if different probes were mapped to the same gene symbol. DEGs were screened via the classical Bayesian method provided by limma package (ver. 3.30.13, http://www.bioconductor.org/packages/2.9/bioc/html/limma.html) [22]. The setting of thresholds was p value < 0.05 and |log fold change (FC)| ≥ 1.5.

Functional and pathways enrichment analyses

GO [23] and KEGG pathway [24] analyses for DEGs were implemented utilizing the Database for Annotation, Visualization and Integration Discovery (DAVID) (ver. 6.8, https://david-d.ncifcrf.gov/) [25] tool. The number of enrichment genes (count number) ≥ 2 and p value < 0.05 were regarded as the thresholds criteria.

PPI network and subnetwork of module analyses

The Search Tool for the Retrieval of Interacting Genes (STRING) (ver. 10.5, http://www.string-db.org/) [26] database was carried out to analyze the protein–protein interactions of DEGs. The DEGs acted as the input gene set, while the homo sapiens served as species. The PPI score was set as 0.4. Thereafter, the Cytoscape (ver. 3.6.0, http://www.cytoscape.org/) software was applied to construct the PPI network. The significant clustering modules were analyzed using Molecular Complex Detection (MCODE) (ver. 1.5.1, http://apps.cytoscape.org/apps/MCODE) [27] plugin. The threshold value was set as score ≥ 5.

MiRNAs-TFs-target regulatory network analyses

The iRegulon (ver. 1.3, http://apps.cytoscape.org/apps/iRegulon) [28] plugin in Cytoscape was performed to predict and analyze the interaction pairs of TF-target gene in the PPI network. The parameters were set as follows: the minimum identity value among orthologous genes was set as 0.05, and the maximum false discovery rate on motif similarity was set as 0.001. The higher score of Normalized Enrichment Score (NES) in output results presented the more reliable results. The TF-target interaction pairs whose NES > 4 were selected for further study. Afterwards, the miRNAs-target were predicted on the basis of WebGestal (http://www.webgestalt.org/option.php) using the Overrepresentation Enrichment Analysis (ORA) method. The setting of threshold was count number ≥ 2 and p value < 0.05. Ultimately, the miRNAs-TFs-target regulatory network was constructed utilizing the Cytoscape software.

Survival analysis

GSE29016 gene expression profiling data including 20 SCLC samples and clinical data were obtained from the GEO database (http://www.ncbi.nlm.nih.gov/geo/). The common samples between the SCLC samples and clinical data were screened and removed the samples with survival time less than 1 month. Here, a total of 14 samples were enrolled in the present study.

The sample informations were filtered, and the samples were deleted if the survival time was < 1 month. The samples corresponding to DEGs were screened. The survival package (ver. 2.41-3) in R language and median grouping method were used to conduct the survival analysis. Finally, DEGs under p value < 0.05 were selected to generate the survival curve.

Results

Identification of DEGs

We obtained 330 DEGs between the metastasis and cancer groups, of these, 186 were upregulated and 144 were downregulated. The volcano map and heatmap of DEGs were presented in Fig. 1. Here, our results presented that the expressions of MET proto-oncogene, receptor tyrosine kinase (MET), glutamate metabotropic receptor 8 (GRM8), cholinergic receptor nicotinic alpha 5 subunit (CHRNA5), and dachshund family transcription factor 1 (DACH1) were reduced in the SCLC patients with lymph node metastasis compared with those patients without lymph node metastasis. Besides, glutathione-disulfide reductase (GSR), human leukocyte antigen complex P5 (HCP5), and achaete-scute family bHLH transcription factor 1 (ASCL1) were upregulated in the SCLC patients with lymph node metastasis.
Fig. 1
Fig. 1

The heatmap and volcano plot of DEGs. a The heatmap of DEGs. Green colour corresponds to lowest and red to the highest level of gene expression. By unsupervised clustering, the samples all correctly segregate themselves. b Volcano plots for all the genes. The green dots indicate that up- and down-regulated DEGs were significant at p values less than 0.05

Functional and pathways enrichment analyses

The enriched functions for upregulated DEGs were listed in Table 1a, such as kidney development (GO_BP; p value = 1.89 × 10−4), extracellular region (GO_CC; p value = 1.72 × 10−4), and calcium ion binding (GO_MF; p value = 2.70 × 10−4). The enriched functions for downregulated DEGs were presented in Table 1b, such as nervous system development (GO_BP; p value = 2.57 × 10−6), integral component of plasma membrane (GO_CC; p value = 4.14 × 10−4), and calcium ion binding (GO_MF; p value = 6.10 × 10−3). Here, the upregulated DEGs were not enriched in any pathway. However, downregulated DEGs were attracted to 5 pathways (Table 1c), such as transcriptional misregulation in cancer (pathway; p value = 1.64 × 10−3), axon guidance (pathway; p value = 4.07 × 10−3), and alcoholism (pathway; p value = 1.29 × 10−2). Moreover, a total 6 DEGs were attracted to the pathway of transcriptional misregulation in cancer, such as MET.
Table 1

Enrichment analyses for DEGs

Category

Term

Description

P value

Genes

(a) GO functional analysis for upregulated DEGs

 BP

GO:0001822

Kidney development

1.89 × 10− 4

SULF1, ITGA8, etc

 BP

GO:0043066

Negative regulation of apoptotic process

4.92 × 10−3

GCLC, CD38, etc

 BP

GO:0045779

Negative regulation of bone resorption

5.80 × 10−3

CALCA, CD38, etc

 BP

GO:0000302

Response to reactive oxygen species

6.35 × 10−3

GPX2, GSR, etc

 BP

GO:0038083

Peptidyl-tyrosine autophosphorylation

6.82 × 10−3

FRK, LYN, etc

 BP

GO:0009725

Response to hormone

7.81 × 10−3

GCLC, LYN, etc

 BP

GO:0045892

Negative regulation of transcription, DNA-templated

9.56 × 10−3

CD38, GCLC, etc

 BP

GO:0007169

Transmembrane receptor protein tyrosine kinase signaling pathway

1.43 × 10−2

FRK, LYN, etc

 BP

GO:0002250

Adaptive immune response

1.48 × 10−2

LYN, LAX1, etc

 BP

GO:0050853

B cell receptor signaling pathway

1.55 × 10−2

CD38, LYN, etc

 CC

GO:0005576

Extracellular region

1.72 × 10−4

CER1, C3, etc

 CC

GO:0005615

Extracellular space

5.81 × 10−3

CER1, SELP, etc

 CC

GO:0048471

Perinuclear region of cytoplasm

7.73 × 10−3

SYT4, LYN, etc

 CC

GO:0005886

Plasma membrane

9.00 × 10−3

SYT4, CDCP1, etc

 CC

GO:0070062

Extracellular exosome

1.21 × 10−2

FRK, TSPAN1, etc

 CC

GO:0031234

Extrinsic component of cytoplasmic side of plasma membrane

2.85 × 10−2

FRK, LYN, etc.

 CC

GO:0016942

Insulin-like growth factor binding protein complex

2.89 × 10−2

IGF1, IGFBP5

 CC

GO:0042567

Insulin-like growth factor ternary complex

3.83 × 10−2

IGF1, IGFBP5

 CC

GO:0005604

Basement membrane

4.16 × 10−2

MATN2, CCDC80, etc

 CC

GO:0005578

Proteinaceous extracellular matrix

4.69 × 10−2

P3H1, OGN, etc

 MF

GO:0005509

Calcium ion binding

2.70 × 10−4

ME3, SYT4, etc

 MF

GO:0008201

Heparin binding

1.08 × 10−3

OGN, SELP, etc.

 MF

GO:0032403

Protein complex binding

4.45 × 10−3

CALCA, FYB, etc

 MF

GO:0004715

Non-membrane spanning protein tyrosine kinase activity

1.06 × 10−2

FRK, LYN, etc

 MF

GO:0033040

Sour taste receptor activity

1.97 × 10−2

PKD2L1, PKD1L3

 MF

GO:0004222

Metalloendopeptidase activity

2.59 × 10−2

ADAM28, MME, etc

 MF

GO:0016668

Oxidoreductase activity, acting on a sulfur group of donors, NAD (P) as acceptor

3.90 × 10−2

GSR, TXNRD1

 MF

GO:0043208

Glycosphingolipid binding

3.90 × 10−2

SELP, LYN

 MF

GO:0008237

Metallopeptidase activity

4.63 × 10−2

ADAM28, MME, etc.

 MF

GO:0000988

Transcription factor activity, protein binding

4.85 × 10−2

HEY1, SMAD3

(b) GO analysis for downregulated DEGs

 BP

GO:0007399

Nervous system development

2.57 × 10−6

ZC4H2, PCDHB6, etc

 BP

GO:0007156

Homophilic cell adhesion via plasma membrane adhesion molecules

4.09 × 10−6

CDH7, FAT1, etc

 BP

GO:0007268

Chemical synaptic transmission

1.12 × 10−4

CBLN1, PCDHB6, etc

 BP

GO:0007155

Cell adhesion

2.53 × 10−4

EFNB2, SPOCK1, etc

 BP

GO:0001764

Neuron migration

1.39 × 10−3

ASTN1, RELN, etc

 BP

GO:0007411

Axon guidance

8.18 × 10−3

NEO1, CDH4, etc

 BP

GO:0007416

Synapse assembly

1.20 × 10−2

ADGRL3, PCDHB10, etc

 BP

GO:0051965

Positive regulation of synapse assembly

1.26 × 10−2

LRRN3, LRRN1,

 BP

GO:0016339

Calcium-dependent cell–cell adhesion via plasma membrane cell adhesion molecules

2.00 × 10−2

PCDHB6, PCDHB11, etc

 BP

GO:0045666

Positive regulation of neuron differentiation

2.31 × 10−2

SOX11, MMD, etc

 CC

GO:0005887

Integral component of plasma membrane

4.14 × 10−4

GRIK2, MET, etc

 CC

GO:0045211

Postsynaptic membrane

4.08 × 10−3

CBLN1, ZC4H2, etc

 CC

GO:0005886

Plasma membrane

1.17 × 10−2

GRIK2, FHL1, etc

 CC

GO:0031941

Filamentous actin

2.05 × 10−2

MYO6, FSCN1, etc

 CC

GO:0030424

Axon

2.15 × 10−2

STMN2, CNR1, etc

 CC

GO:0030425

Dendrite

3.31 × 10−2

RELN, GNG3, etc

 CC

GO:0000788

Nuclear nucleosome

3.93 × 10−2

HIST1H2BB, HIST1H3C, etc

 CC

GO:0043204

Perikaryon

4.02 × 10−2

ASTN1, KCNK1, etc

 CC

GO:0034705

Potassium channel complex

4.21 × 10−2

KCNA6, KCNK1

 CC

GO:0030054

Cell junction

4.64 × 10−2

ZC4H2, GRIK2, etc

 MF

GO:0005509

Calcium ion binding

6.10 × 10−3

CDH7, DGKB, etc

 MF

GO:0032051

Clathrin light chain binding

3.60 × 10−2

NSG1, HMP19

 MF

GO:0009931

Calcium-dependent protein serine/threonine kinase activity

5.00 × 10−2

CAMK4, DCX

Term

Description

Count

P value

Key genes

(c) KEGG pathway analysis for downregulated DEGs

 hsa05202

Transcriptional misregulation in cancer

6

1.64 × 10−3

HIST1H3J, EYA1, MET, ETV1, HIST1H3C, MEIS1

 hsa04360

Axon guidance

5

4.07 × 10−3

EPHA4, PAK3, EFNB2, MET, DPYSL5

 hsa05034

Alcoholism

5

1.29 × 10−2

HIST1H3J, HIST1H2BB, CAMK4, HIST1H3C, GNG3

 hsa04723

Retrograde endocannabinoid signaling

4

1.53 × 10−2

SLC32A1, GABRG2, CNR1, GNG3

 hsa05322

Systemic lupus erythematosus

4

3.20 × 10−2

HIST1H3J, HIST1H2BB, HIST1H3C, HLA-DQA1

DEGs, differentially expressed genes; GO, Gene Ontology; BP, biological process; CC, cellular component; MF, molecular function; KEGG, Kyoto Encyclopedia of Genes and Genomes

PPI network and module analyses

There were 178 nodes and 237 interaction pairs in the PPI network (Fig. 2). Afterwards, one subnetwork module a (score = 5) with 5 nodes and 10 interaction pairs was obtained through the MCODE (score ≥ 5) plugin in Cytoscape software. According to the degree of DEGs in the PPI network, the top 10 DEGs were selected, then the GO-BP analysis for the top 10 DEGs and module a DEGs were implemented. The top 10 DEGs in the PPI network and DEGs in the module a are presented in Table 2. The enriched functions for the DEGs in the PPI network were shown in Table 3, such as homeostatic process (GO_BP; p value = 5.44 × 10−5), regulation of phosphorylation (GO_BP; p value = 1.53 × 10−4), and regulation of phosphate metabolic process (GO_BP; p value = 1.78 × 10−4). Meanwhile, the enriched functions for module a DEGs were listed in Table 3, such as G-protein coupled receptor protein signaling pathway (GO_BP; p value = 2.14 × 10−3), cell surface receptor linked signal transduction (GO_BP; p value = 9.26 × 10−3), and regulation of inflammatory response (GO_BP; p value = 2.23 × 10−2). Here, our results showed that GRM8 was attracted to the G-protein coupled receptor signaling pathway.
Fig. 2
Fig. 2

PPI network and subnetwork module of the DEGs. The orange roundness stands for the upregulated DEGs and the green rhombus stands for the downregulated DEGs. PPI, protein–protein interaction; DEGs, differentially expressed genes

Table 2

The top 10 DEGs in the PPI network and module a

Gene

Degree

Regulate

PPI

 HIST1H2BB

11

Down

 IGF1

10

Up

 PRKAR2B

9

Down

 GNG3

9

Down

 GSR

8

Up

 LYN

8

Up

 MGP

8

Up

 CALCA

7

Up

 TXNRD1

7

Up

Module a

 GNG3

9

Down

 C3

6

Up

 GRM8

5

Down

 SAA1

5

Up

 CNR1

5

Down

DEGs, differentially expressed genes; PPI, protein–protein interaction

Table 3

Enrichment analyses for DEGs in the PPI network and subnetwork module a

Term

Description

Count

P value

Key genes

PPI

 GO:0042592

Homeostatic process

6

5.44 × 10−5

CALCA, GSR, LYN, IGF1, TXNRD1, GNG3

 GO:0042325

Regulation of phosphorylation

5

1.53 × 10−4

CALCA, PRKAR2B, LYN, IGF1, GNG3

 GO:0019220

Regulation of phosphate metabolic process

5

1.78 × 10−4

CALCA, PRKAR2B, LYN, IGF1, GNG3

 GO:0051174

Regulation of phosphorus metabolic process

5

1.78 × 10−4

CALCA, PRKAR2B, LYN, IGF1, GNG3

 GO:0009725

Response to hormone stimulus

4

1.47 × 10−3

PRKAR2B, LYN, MGP, GNG3

 GO:0009719

Response to endogenous stimulus

4

1.96 × 10−3

PRKAR2B, LYN, MGP, GNG3

 GO:0019725

Cellular homeostasis

4

2.92 × 10−3

CALCA, GSR, TXNRD1, GNG3

 GO:0043085

Positive regulation of catalytic activity

4

3.99 × 10−3

CALCA, PRKAR2B, GNG3, CAP1

 GO:0001932

Regulation of protein amino acid phosphorylation

3

5.52 × 10−3

PRKAR2B, LYN, IGF1

 GO:0044093

Positive regulation of molecular function

4

5.58 × 10−3

CALCA, PRKAR2B, GNG3, CAP1

Module a

 GO:0007186

G-protein coupled receptor protein signaling pathway

4

2.14 × 10−3

C3, GRM8, CNR1, GNG3

 GO:0007166

Cell surface receptor linked signal transduction

4

9.26 × 10−3

C3, GRM8, CNR1, GNG3

 GO:0050727

Regulation of inflammatory response

2

2.23 × 10−2

C3, SAA1

 GO:0002526

Acute inflammatory response

2

2.87 × 10−2

C3, SAA1

 GO:0007204

Elevation of cytosolic calcium ion concentration

2

3.21 × 10−2

SAA1, GNG3

 GO:0051480

Cytosolic calcium ion homeostasis

2

3.44 × 10−2

SAA1, GNG3

 GO:0032101

Regulation of response to external stimulus

2

4.62 × 10−2

C3, SAA1

DEGs, differentially expressed genes; PPI, protein–protein interaction

MiRNAs-TFs-target regulatory network analyses

The miRNAs-TFs-target regulatory network was established with 8 TFs, 10 miRNAs and 187 DEGs through the Cytoscape software (Fig. 3). MiR-126 was identified in the miRNAs-TFs-target regulatory network. A total of 11 genes were regulated by miR-126 in our study, such as GRM8 and DACH1 (Table 4).
Fig. 3
Fig. 3

The miRNAs-TFs -target regulatory network. The orange roundness presents the upregulated DEGs, and the green rhombus presents the downregulated DEGs. The light blue triangle stands for miRNAs, and the yellow hexagon stands for TFs. miRNA, microRNA; TFs, transcription factors; DEGs, differentially expressed genes

Table 4

Genes associated with miR-126 in miRNAs-TFs-target regulatory network

miRNAs

Genes

miR-126

ZMPSTE24

miR-126

DACH1

miR-126

EYA1

miR-126

RNF152

miR-126

GRM8

miR-126

ZNF354C

miR-126

MYT1

miR-126

PCSK2

miR-126

JPH1

miR-126

TMEM47

miR-126

XPR1

Survival analysis

Total 164 DEGs have the corresponding sample information. Hence, the 164 DEGs were used for generating the survival curve. There were 2 DEGs correlated with the survival of SCLC, such as GSR and HCP5 (Fig. 4).
Fig. 4
Fig. 4

The survival curve for the GSR (a) and HCP5 (b). The horizontal axis represents the survival time (months), and the vertical axis presents the survival rate. The red curve stands for the group of upregulated gene expression (high expression), and the black curve represents the group of downregulated gene expression (low expression). A p value of < 0.05 was condidered statistically significant between upregulated and downregulated gene expression. GSR, glutathione-disulfide reductase; HCP5, human leukocyte antigen complex P5

Discussion

SCLC is a highly malignant cancer, and over 70% of patients with SCLC present with the metastatic disease [5]. But the molecular determinants of SCLC metastasis are unknown. In the current study, some novel DEGs and miRNA associated with the lymph node metastasis of SCLC were obtained via the comprehensive bioinformatical analyses, such as miR-126, DACH1, GRM8, MET, RSD and HCP5.

In SCLC, more than 95% of patients have a smoking history and their 5-year survival rates are under 2% [29]. As known, a variety of addictive compound nicotine was contained in tobacco, which begins with the binding of nicotine to the nicotinic acetylcholine receptor (nAChR). In especial, the nAChR gene cluster encoding the α3, α5 and β4 nAChR subunits such as CHRNA5/A3/B4 was differentially expressed in SCLC [30]. In addition, ASCL1 which is a transcription factor implicated in the pathogenesis of SCLC is overexpressed in SCLC [31]. Similarly, CHRNA5 and ASCL1 were differentially expressed in SCLC with the metastatic disease. Interestingly, ASCL1 might regulate the expression of the clustered nAChR genes. Therefore, positive control genes have validated that the analysis pipeline is doable.

In general, lymph node metastasis of LC is positively related to lymphangiogenesis [32]. Currently, there is no direct proofs present that miR-126 is significantly associated with the lymph node metastasis of SCLC. However, Sasahira et al. found that the downregulated miR-126 was correlated with the induction of lymphangiogenesis in the OSCC [33]. It has been uncovered that miR-126 is downregulated in primary SCLC tumor samples [34]. In addition, miR-126 has an negative effect on the growth and proliferation of SCLC cells [35]. Meanwhile, miR-126 is also a crucial regulator for the vessel development [36]. Here, miR-126 was identified in the miRNAs-TFs-target regulatory network. These findings all indicated that miR-126 is likely to participate in the lymph node metastasis process of SCLC through inducing the lymphangiogenesis.

A total of 11 genes were regulated by miR-126 in our study, such as GRM8 and DACH1. DACH1, implicated in the suppression of tumor growth, is downregulated in human malignancies, such as LC [37]. It is reported that the LC invasion and tumor growth can be inhibited by DACH1 through suppressing the CXCL5 signaling [38]. Here, our results presented that DACH1 expression was downregulated in the SCLC patients with lymph node metastasis. Probably, DACH1 decrease inhibits the lymph node metastasis process of SCLC. GRM8, encoded by the GRM8 gene, are a family of G protein-coupled receptors. Here, our results showed that GRM8 was downregulated in the SCLC patients with lymph node metastasis and was attracted to the G-protein coupled receptor signaling pathway. However, the role of GRM8 in the lymph node metastasis process of SCLC remains unclear. Previous studies indicated that signaling pathways controlled by GPCRs facilitate proliferation, cell migration, angiogenesis, and inflammation [39]. Namely, GPCRs are likely to promote the migration of LC cells into the lymph node. Therefore, we speculated that GRM8 was likely to participate in the lymph node metastasis process of SCLC.

The gene expression programs are controlled by a variety of TFs, and its misregulation result in various diseases [40]. Briefly, the transcriptional misregulation can cause thousands of diseases. Here, a total of 6 DEGs were attracted to the pathway of transcriptional misregulation in cancer, such as MET. It has been revealed that MET regulates the remodeling and morphogenesis of tissues, and its dysregulation is implied in the oncogenic signaling and metastasis [41, 42]. Here, MET was regulated in the SCLC patients with lymph node metastasis. Thus, MET dysregulation may participate in the lymph node metastasis process of SCLC through the transcriptional misregulation in cancer pathway.

GSR encoded a member of the class-I pyridine nucleotide-disulfide oxidoreductase family, which played a key role in cellular antioxidant defense. GSR reduced oxidized glutathione disulfide (GSSG) to the sulfhydryl form glutathione (GSH). Whereas, GSH played complex roles in cancer, including the protective and pathogenic roles [43]. In a previous study, GSR expression level was significantly increased in tumor tissue from patients with LC [44]. Although there was no direct proofs to indicate GSR association with the lymph node metastasis process of SCLC, the glioblastoma multiforme patients with high GSR expression showed poor survival [45]. Similarly, GSR was up-regulated in the patients with lymph node metastasis process of SCLC and displayed a poor survival results. Therefore, we speculated that GSR might have a central role in the lymph node metastasis process of SCLC.

In addition, HCP5 was significantly down-regulated in patients with ovarian cancer [46]. Similarly, HCP5 was also down-expressed in patients with lung adenocarcinoma. However, Teng et al. reported that HCP5 was up-regulated in glioma tissues as well as in U87 and U251 cells [47]. In addition, they found that HCP5 regulated the glioma cells malignant proliferation through binding to miR-139 by up-regulating RUNX1. Probably, HCP5 expression was different in various cancers. In the present study, HCP5 was upregulated in the lymph node metastasis process of SCLC. Hence, we speculated that GSR and HCP5 may involve in the lymph node metastasis process of SCLC. However, the predicted results cannot be verified by laboratory data due to the limitation of sample extraction. In further studies, we will confirm the expressions of the above discussed DEGs and miRNA once we collected the sufficient samples.

Conclusion

In summary, our results suggested that miR-126 and its target gene DACH1 may implicate in the lymph node metastasis process of SCLC. Additionally, GRM8, MET, RSD and HCP5 were implicated in the lymph node metastasis process of SCLC.

Abbreviations

SCLC: 

small cell lung cancer

DEGs: 

differentially expressed genes

LC: 

lung cancer

KEGG: 

Kyoto Encyclopedia of Genes and Genomes

PPI: 

protein–protein interaction

STRING: 

Search Tool for the Retrieval of Interacting Genes

GRM8: 

glutamate metabotropic receptor 8

DACH1: 

dachshund family transcription factor 1

GSR: 

glutathione-disulfide reductase

HCP5: 

human leukocyte antigen complex P5

Declarations

Authors’ contributions

Conception of the research and Drafting the manuscript: ZW. Acquisition of data: BL. Analysis and interpretation of data: LS. Statistical analysis: XY. Revision of manuscript for important intellectual content: JX. All authors read and approved the final manuscript.

Acknowledgements

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Availability of data and materials

Not applicable.

Consent for publication

Not applicable.

Ethics approval and consent to participate

This study was approved by Ethics Committee of The Fourth Affiliated Hospital of Harbin Medical University.

Funding

This work was supported by National Natural Science Foundation of China (Program No. 81571736).

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Authors’ Affiliations

(1)
Department of Thoracic Surgery, The Fourth Affiliated Hospital of Harbin Medical University, No. 37, Yiyuan Street, Nangang District, Harbin, 150001, Heilongjiang, China

References

  1. Siegel R, Naishadham D, Jemal A. Cancer statistics, 2012. CA Cancer J Clin. 2012;63(1):11.View ArticleGoogle Scholar
  2. Ferlay J, Shin HR, Bray F, Forman D, Mathers C, Parkin DM. Estimates of worldwide burden of cancer in 2008: GLOBOCAN 2008. Int J Cancer. 2010;127(12):2893–917.View ArticleGoogle Scholar
  3. Chen W, Zheng R, Baade PD, Zhang S, Zeng H, Bray F, Jemal A, Yu XQ, He J. Cancer statistics in China, 2015. CA Cancer J Clin. 2016;66(2):115–32.View ArticleGoogle Scholar
  4. Govindan R, Page N, Morgensztern D, Read W, Tierney R, Vlahiotis A, Spitznagel EL, Piccirillo J. Changing epidemiology of small-cell lung cancer in the United States over the last 30 years: analysis of the surveillance, epidemiologic, and end results database. J Clin Oncol. 2006;24(28):4539–44.View ArticleGoogle Scholar
  5. Devesa SS, Bray F, Vizcaino AP, Parkin DM. International lung cancer trends by histologic type: male:female differences diminishing and adenocarcinoma rates rising. Int J Cancer. 2005;117(2):294–9.View ArticleGoogle Scholar
  6. Neal JW, Gubens MA, Wakelee HA. Current management of small cell lung cancer. Clin Chest Med. 2011;32(4):853–63.View ArticleGoogle Scholar
  7. Demedts IK, Vermaelen KY, van Meerbeeck JP. Treatment of extensive-stage small cell lung carcinoma: current status and future prospects. Eur Respir J. 2010;35(1):202.View ArticleGoogle Scholar
  8. Schneider BJ, Kalemkerian GP. Personalized therapy of small cell lung cancer. Berlin: Springer International Publishing; 2016.View ArticleGoogle Scholar
  9. Girard JP, Moussion C, Förster R. HEVs, lymphatics and homeostatic immune cell trafficking in lymph nodes. Nat Rev Immunol. 2012;12(11):762–73.View ArticleGoogle Scholar
  10. Hellman S. Karnofsky Memorial Lecture. Natural history of small breast cancers. J Clin Oncol. 1994;12(10):2229–34.View ArticleGoogle Scholar
  11. Hess J, Angel P, Schorpp-Kistner M. AP-1 subunits: quarrel and harmony among siblings. J Cell Sci. 2004;117(Pt 25):5965–73.View ArticleGoogle Scholar
  12. Volm M, Van KG, Mattern J. Analysis of c-fos, c-jun, c-erbB1, c-erbB2 and c-myc in primary lung carcinomas and their lymph node metastases. Clin Exp Metas. 1994;12(4):329–34.View ArticleGoogle Scholar
  13. Oka T, Ishida T, Nishino T, Sugimachi K. Immunohistochemical evidence of urokinase-type plasminogen activator in primary and metastatic tumors of pulmonary adenocarcinoma. Can Res. 1991;51(13):3522.Google Scholar
  14. Nagayama M, Sato A, Hayakawa H, Urano T, Takada Y, Takada A. Plasminogen activators and their inhibitors in non-small cell lung cancer. Low content of type 2 plasminogen activator inhibitor associated with tumor dissemination. Cancer. 1994;73(5):1398–405.View ArticleGoogle Scholar
  15. Hida T, Yatabe Y, Achiwa H, Muramatsu H, Kozaki K, Nakamura S, Ogawa M, Mitsudomi T, Sugiura T, Takahashi T. Increased expression of cyclooxygenase 2 occurs frequently in human lung cancers, specifically in adenocarcinomas. Cancer Res. 1998;58(17):3761–4.PubMedGoogle Scholar
  16. Wolff H, Saukkonen K, Anttila S, Karjalainen A, Vainio H, RistimãKi A. Expression of cyclooxygenase-2 in human lung carcinoma. Cancer Res. 1998;58(22):4997–5001.PubMedGoogle Scholar
  17. Zhang F, Altorki NK, Mestre JR, Subbaramaiah K, Dannenberg AJ. Curcumin inhibits cyclooxygenase-2 transcription in bile acid- and phorbol ester-treated human gastrointestinal epithelial cells. Carcinogenesis. 1999;20(3):445.View ArticleGoogle Scholar
  18. Bi MM, Shang B, Wang Z, Chen G. Expression of CXCR4 and VEGF-C is correlated with lymph node metastasis in non-small cell lung cancer. Thoracic Cancer. 2017;8(6):634–41.View ArticleGoogle Scholar
  19. Takanami I. Overexpression of CCR7 mRNA in nonsmall cell lung cancer: correlation with lymph node metastasis. Int J Cancer. 2003;105(2):186–9.View ArticleGoogle Scholar
  20. Barrett T, Suzek TO, Troup DB, Wilhite SE, Ngau W-C, Ledoux P, Rudnev D, Lash AE, Fujibuchi W, Edgar R. NCBI GEO: mining millions of expression profiles—database and tools. Nucleic Acids Res. 2005;33(suppl 1):D562–6.PubMedGoogle Scholar
  21. Carvalho BS, Irizarry RA. A framework for oligonucleotide microarray preprocessing. Bioinformatics. 2010;26(19):2363–7.View ArticleGoogle Scholar
  22. Smyth GK. Limma: linear models for microarray data. In: Gentleman R, Carey VJ, Huber W, Irizarry RA, Dudoit S, editors. Bioinformatics and computational biology solutions using R and bioconductor. New York: Springer; 2005. p. 397–420.View ArticleGoogle Scholar
  23. Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT, et al. Gene ontology: tool for the unification of biology. Nat Genet. 2000;25(1):25–9.View ArticleGoogle Scholar
  24. Kanehisa M, Goto S. KEGG: Kyoto Encyclopedia of genes and genomes. Nucleic Acids Res. 2000;28(1):27–30.View ArticleGoogle Scholar
  25. Huang DW, Sherman BT, Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protocols. 2008;4(1):44–57.View ArticleGoogle Scholar
  26. Szklarczyk D, Franceschini A, Wyder S, Forslund K, Heller D, Huerta-Cepas J, Simonovic M, Roth A, Santos A, Tsafou KP. STRING v10: protein–protein interaction networks, integrated over the tree of life. Nucleic Acids Res. 2014;43:D447–52.View ArticleGoogle Scholar
  27. Bandettini WP, Kellman P, Mancini C, Booker OJ, Vasu S, Leung SW, Wilson JR, Shanbhag SM, Chen MY, Arai AE. MultiContrast Delayed Enhancement (MCODE) improves detection of subendocardial myocardial infarction by late gadolinium enhancement cardiovascular magnetic resonance: a clinical validation study. J Cardiovasc Magn Reson. 2012;14:83.View ArticleGoogle Scholar
  28. Janky RS, Verfaillie A, Imrichová H, Van de Sande B, Standaert L, Christiaens V, Hulselmans G, Herten K, Naval Sanchez M, Potier D, et al. iRegulon: from a gene list to a gene regulatory network using large motif and track collections. PLoS Comput Biol. 2014;10(7):1003731.View ArticleGoogle Scholar
  29. Jackman DM, Johnson BE. Small-cell lung cancer : the lancet. Lancet. 2005;366(9494):1385–96.View ArticleGoogle Scholar
  30. Improgo MR, Scofield MD, Tapper AR, Gardner PD. From smoking to lung cancer: the CHRNA5/A3/B4 connection. Oncogene. 2010;29(35):4874–84.View ArticleGoogle Scholar
  31. Westerman BA, Neijenhuis S, Poutsma A, Steenbergen RD, Breuer RH, Egging M, van Wijk IJ, Oudejans CB. Quantitative reverse transcription-polymerase chain reaction measurement of HASH1 (ASCL1), a marker for small cell lung carcinomas with neuroendocrine features. Clin Cancer Res. 2002;8(4):1082–6.PubMedGoogle Scholar
  32. Jacobson O, Weiss ID. CXCR4 chemokine receptor overview: biology, pathology and applications in imaging and therapy. Theranostics. 2013;3(1):1.View ArticleGoogle Scholar
  33. Sasahira T, Kurihara M, Bhawal UK, Ueda N, Shimomoto T, Yamamoto K, Kirita T, Kuniyasu H. Downregulation of miR-126 induces angiogenesis and lymphangiogenesis by activation of VEGF-A in oral cancer. Br J Cancer. 2012;107(4):700–6.View ArticleGoogle Scholar
  34. Miko E, Czimmerer Z, Csánky E, Boros G, Buslig J, Dezső B, Scholtz B. Differentially expressed micrornas in small cell lung cancer, experimental lung research, Informa Healthcare. Exp Lung Res. 2009;35(8):646–64.View ArticleGoogle Scholar
  35. Miko E, Margitai Z, Czimmerer Z, Várkonyi I, Dezső B, Lányi Á, Bacsó Z, Scholtz B. miR-126 inhibits proliferation of small cell lung cancer cells by targeting SLC7A5. FEBS Lett. 2011;585(8):1191–6.View ArticleGoogle Scholar
  36. Fish JE, Santoro MM, Morton SU, Yu S, Yeh RF, Wythe JD, Bruneau BG, Stainier DYR, Srivastava D. miR-126 regulates angiogenic signaling and vascular integrity. Dev Cell. 2008;15(2):272–84.View ArticleGoogle Scholar
  37. Chen K, Wu K, Cai S, Zhang W, Zhou J, Wang J, Ertel A, Li Z, Rui H, Quong A, et al. Dachshund binds p53 to block the growth of lung adenocarcinoma cells. Cancer Res. 2013;73(11):3262–74.View ArticleGoogle Scholar
  38. Han N, Yuan X, Wu H, Xu H, Chu Q, Guo M, Yu S, Chen Y, Wu K. DACH1 inhibits lung adenocarcinoma invasion and tumor growth by repressing CXCL5 signaling. Oncotarget. 2015;6(8):5877–88.View ArticleGoogle Scholar
  39. Dorsam RT, Gutkind JS. G-protein-coupled receptors and cancer. Nat Rev Cancer. 2007;7(2):79–94.View ArticleGoogle Scholar
  40. Lee TI, Young RA. Transcriptional regulation and its misregulation in disease. Cell. 2013;152(6):1237–51.View ArticleGoogle Scholar
  41. Maroun C, Rowlands T. The Met receptor tyrosine kinase: a key player in oncogenesis and drug resistance. Pharmacol Ther. 2014;142(3):316–38.View ArticleGoogle Scholar
  42. Drilon A, Cappuzzo F, Ou S, Camidge D. Targeting MET in lung cancer: will expectations finally be MET? J Thorac Oncol. 2017;12(1):15–26.View ArticleGoogle Scholar
  43. Balendiran GK, Dabur R, Fraser D. The role of glutathione in cancer. Cell Biochem Funct. 2004;22(6):343–52.View ArticleGoogle Scholar
  44. Saydam N, Kirb A, Demir O, Hazan E, Oto O, Saydam O, Güner G. Determination of glutathione, glutathione reductase, glutathione peroxidase and glutathione S-transferase levels in human lung cancer tissues. Cancer Lett. 1997;119(1):13.View ArticleGoogle Scholar
  45. Zhu Z, Du S, Du Y, Jing R, Ying G, Zhao Y. Glutathione reductase mediates drug resistance in glioblastoma cells by regulating redox homeostasis. J Neurochem. 2018;144(1):93.View ArticleGoogle Scholar
  46. Liu N, Zhang R, Zhao X, Jiaming SU, Bian X, Jinsong NI, Yue Y, Cai Y, Jin J. A potential diagnostic marker for ovarian cancer: involvement of the histone acetyltransferase, human males absent on the first. Oncol Lett. 2013;6(2):393–400.View ArticleGoogle Scholar
  47. Teng H, Wang P, Xue Y, Liu X, Ma J, Cai H, Xi Z, Li Z, Liu Y. Role of HCP5-miR-139-RUNX1 feedback loop in regulating malignant behavior of glioma cells. Mol Ther J Am Soc Gene Ther. 2016;24(10):1806.View ArticleGoogle Scholar

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