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Journal of Thoracic Disease logoLink to Journal of Thoracic Disease
. 2017 Jan;9(1):42–53. doi: 10.21037/jtd.2017.01.04

Weighted gene co-expression network analysis in identification of metastasis-related genes of lung squamous cell carcinoma based on the Cancer Genome Atlas database

Feng Tian 1, Jinlong Zhao 2, Xinlei Fan 3,, Zhenxing Kang 4
PMCID: PMC5303106  PMID: 28203405

Abstract

Background

Lung squamous cell carcinoma (lung SCC) is a common type of malignancy. Its pathogenesis mechanism of tumor development is unclear. The aim of this study was to identify key genes for diagnosis biomarkers in lung SCC metastasis.

Methods

We searched and downloaded mRNA expression data and clinical data from The Cancer Genome Atlas (TCGA) database to identify differences in mRNA expression of primary tumor tissues from lung SCC with and without metastasis. Gene co-expression network analysis, protein-protein interaction (PPI) network, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis and quantitative real-time polymerase chain reactions (qRT-PCR) were used to explore the biological functions of the identified dysregulated genes.

Results

Four hundred and eighty-two differentially expressed genes (DEGs) were identified between lung SCC with and without metastasis. Nineteen modules were identified in lung SCC through weighted gene co-expression network analysis (WGCNA). Twenty-three DEGs and 26 DEGs were significantly enriched in the respective pink and black module. KEGG pathway analysis displayed that 26 DEGs in the black module were significantly enriched in bile secretion pathway. Forty-nine DEGs in the two gene co-expression module were used to construct PPI network. CFTR in the black module was the hub protein, had the connectivity with 182 genes. The results of qRT-PCR displayed that FIGF, SFTPD, DYNLRB2 were significantly down-regulated in the tumor samples of lung SCC with metastasis and CFTR, SCGB3A2, SSTR1, SCTR, ROPN1L had the down-regulation tendency in lung SCC with metastasis compared to lung SCC without metastasis.

Conclusions

The dysregulated genes including CFTR, SCTR and FIGF might be involved in the pathology of lung SCC metastasis and could be used as potential diagnosis biomarkers or therapeutic targets for lung SCC.

Keywords: Lung squamous cell carcinoma (lung SCC), genes expression profiling, gene regulatory network, neoplasm metastasis, biomarkers

Introduction

Lung squamous cell carcinoma (lung SCC) is a histological subtype of non-small cell lung cancer (NSCLC), which is the second most frequent type of NSCLC after lung adenocarcinoma (1). Lung SCC is a multi-step progressive disease with high rates of morbidity and mortality worldwide.

The initiation of lung SCC is divided into the following five successive stages: normal bronchial epithelium, squamous metaplasia, mild-moderate dysplasia, carcinoma in situ, and invasive carcinoma (2). The prognosis of lung cancer is unfavorable, despite significant therapeutic improvements have been made in recent years. In current, there is no specific molecular targets for therapy have been identified (3), therefore, cisplatin plus gemcitabine is still the first-line treatment for lung SCC (4).

Currently, the tumorigenesis mechanism of lung SCC remains unclear. Numerous published articles demonstrate that dysregulated genes are essential for initiation, progression and development of lung cancer. SMC4 (structural maintenance of chromosome 4) is over-expressed in lung adenocarcinoma tissues and its inhibition significantly suppresses the proliferation and invasion of A549 cells (5). Knocking down the expression of WW45 (salvador family WW domain containing protein 1) promotes cell growth and migration of lung cancer and over-expression of WW5 improves the survival of mice model of lung cancer (6). INO80 (INO80 complex subunit), the SWI/SNF ATPase in the complex, is highly expressed in NSCLC cells compared with normal lung epithelia cells and its expression level is negatively correlated with the disease prognosis of patients with lung cancer (7).

Weighted gene co-expression network analysis (WGCNA) offers an effective approach to quantitatively assess the interconnectedness of genes, investigate the expression patterns of gene co-activity and evaluate the importance of genes within the network. It benefit to provide potential malignancy diagnostic molecular and connecting them together for disease (8,9). Shi et al. identifies four co-expression modules significantly correlated with clinic trait, the hub gene of each module including RPS15A, PTGDS, CD53 and MSI2, which might play a vital role in progress of uveal melanoma (10).

To our knowledge, this is the first report of WGCNA of lung SCC expression profiles. In this study, bioinformatics methods were applied to integrate mRNA expression data of lung SCC in The Cancer Genome Atlas (TCGA) database and construct gene co-expression module for pathogenesis mechanism elucidation and identification of the diagnostic biomarkers and therapeutic targets of lung SCC metastasis.

Methods

TCGA gene expression profiles

The level 3 mRNA expression data of lung SCC and the corresponding clinical records in TCGA database (Oct 26, 2015) were obtained from the data portal (https://tcga-data.nci.nih.gov/tcga/). Total of 504 lung SCC patients were available in TCGA. The inclusion criteria of expression profiling in our study were shown as below: (I) the dataset was from primary solid tumor of patients with lung SCC; (II) patients had no other malignancy history; (III) patients received no treatment before collection of tumor samples. The mRNA expression datasets of 163 lung SCC patients with lymph node metastasis or distant metastasis, and mRNA expression datasets of 222 patients without metastasis were available in TCGA database. The datasets contained 20,531 genes and the sequencing of expression profiles was based on the platform of IlluminaHiSeq_RNASeqV2.

Identification of differentially expressed genes (DEGs)

The raw expression data of lung SCC patients in our study were downloaded. The significantly DEGs were identified between lung SCC patients with metastasis and lung SCC patients without metastasis through DESeq2 repackage in R language (11). The false discovery rate (FDR) was performed for multiple testing corrections of raw P value through the Benjamin and Hochberg method (12,13). The threshold of DEGs was set as FDR <0.05.

Construct gene co-expression network

To explore the interactions between the genes, a system biology approach, WGCNA, which converts co-expression measure into connections weight or topology overlap measure, was applied for gene co-expression network construction (14). Co-expression methodology is typically used for explore correlation between gene expression level. Genes involved in the same pathway or same functional compound tend to demonstrate a similar expression pattern (15). Therefore, the construction of a gene co-expression network facilitates the identification of genes with similar biological functions (16). In our work, all of genes of lung SCC patients with metastasis and lung SCC patients without metastasis were inputted to construct weighted co-expression modules using the WGCNA package in R language (17). The threshold of co-expression module was set as P<0.05.

Functional annotation

To obtain the biological function and signaling pathways of DEGs, the online software, GeneCodis3 was used for Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment and Gene Ontology (GO) annotation of DEGs (18). The threshold of GO function and KEGG pathway of DEGs was set as FDR <0.0001 and FDR <0.05, respectively.

Protein-protein interaction (PPI)

In order to obtain insights into the interaction between DEGs of the black module and the pink module, PPI network was constructed by BioGRID, a database of known and predicted protein interactions (19). Then, PPI was visualized by Cytoscape software (http://cytoscape.org/) (20).

The expression level of DEGs in lung SCC tumor samples were validated by quantitative real-time polymerase chain reaction (qRT-PCR)

Total RNA of fresh tumor samples from lung SCC patients with metastasis and lung SCC patients without metastasis were extracted by using TRIzol reagent (Invitrogen, CA, USA) according to the manual instructions. The SuperScript III Reverse Transcription Kit (Invitrogen, CA, USA) was used to synthesize the cDNA. qRT-PCR reactions were performed using Power SYBR Green PCR Master Mix (Applied Biosystems, Foster City, CA, USA) on the Applied Biosystems 7,500 (Applied Biosystems, Foster City, CA). 18s rRNA was used as internal control for mRNA detection. The relative expression of genes was calculated using the 2−ΔΔCT equation (21). The PCR primers used were shown as Table S1. The GraphPad Prism version 6.0 software packages (GraphPad Software, San Diego, CA, USA) was used to output figures.

Ethics statement

The study was approved by the ethics committee board of Linyi People’s Hospital (No. lyll2015N67). Written informed consent was obtained from the patient for publication of this manuscript.

Results

DEGs between lung SCC patients with metastasis and lung SCC patients without metastasis

The raw expression profiles of lung SCC patients and corresponding clinical records were downloaded from the data portal of TCGA database. All of patients were divided into lung SCC with metastasis group or lung SCC without metastasis according to AJCC pathologic tumor stage. DEGs analysis was performed to the two groups. Total of 482 DEGs were identified as the threshold of FDR <0.05 (supplementary Table S2), consisting of 312 up-regulated DEGs and 170 down-regulated DEGs. As Table 1 shown, VEG, VSIG10L and MFAP5 were the most significantly up-regulated DEGs; ZNF208, C8orf46 and TNF were the most significantly down-regulated DEGs.

Table 1. The top ten DEGs in lung SCC with metastasis.

Gene ID Gene symbol Log2FC FDR
Up-regulated DEGs
   7425 VGF 6.302272 1.90E-06
   147645 VSIG10L 5.870895 1.28E-05
   8076 MFAP5 5.335057 0.000187
   6927 HNF1A 5.184579 0.000382
   57521 RPTOR 5.125631 0.000449
   2147 F2 5.062773 0.000535
   22809 ATF5 4.959337 0.000714
   51083 GAL 4.944154 0.000714
   3706 ITPKA 4.839489 0.001009
   84057 MND1 4.835881 0.001009
Down-regulated DEGs
   7757 ZNF208 −6.99816 5.04E-08
   254778 C8orf46 −6.37244 1.81E-06
   7124 TNF −5.93751 1.28E-05
   2890 GRIA1 −5.90534 1.28E-05
   339398 LINGO4 −5.8607 1.28E-05
   338324 S100A7A −5.42097 0.000144
   4693 NDP −5.33403 0.000187
   116379 IL22RA2 −5.12306 0.000449
   25833 POU2F3 −5.09145 0.000493
   8190 MIA −5.04813 0.000542

DEG, differentially expressed genes; lung SCC, lung squamous cell carcinoma; FC, fold change; FDR, false discovery rate.

Functional annotation of DEGs between lung SCC with and without metastasis

To explore the functional significance of the identified DEGs in lung SCC metastasis, 482 DEGs were performed to unbiased GO term and KEGG pathway enrichment analyses. For DEGs related to lung SCC metastasis, transmembrane transport (FDR =8.76E-06), calcium ion binding (FDR =5.18E-06) and extracellular region (FDR =1.20E-17) were the most significant enrichment in biological process, molecular function and cellular component, respectively (Table 2); PPAR signaling pathway (KEGG ID: hsa03320, FDR =1.64E-05), cytokine-cytokine receptor interaction (KEGG ID: hsa04060, FDR =1.05E-03) and neuroactive ligand-receptor interaction (KEGG ID: hsa04080, FDR =3.95E-03) were the most significantly enriched pathways (Table 3).

Table 2. GO term enrichment analyses of DEGs.

GO ID GO term Count FDR
Biological process
   GO:0055085 Transmembrane transport 24 8.76E-06
   GO:0006810 Transport 27 9.31E-06
   GO:0050995 Negative regulation of lipid catabolic process 4 2.29E-05
   GO:0007165 Signal transduction 4 4.67E-05
   GO:0051091 Positive regulation of sequence-specific DNA binding transcription factor activity 4 4.67E-05
   GO:0070374 Positive regulation of ERK1 and ERK2 cascade 4 4.67E-05
   GO:0042493 Response to drug 4 4.67E-05
   GO:0001666 Response to hypoxia 4 4.67E-05
Molecular function
   GO:0005509 Calcium ion binding 29 5.18E-06
   GO:0005216 Ion channel activity 9 7.99E-05
Cellular component
   GO:0005576 Extracellular region 81 1.20E-17
   GO:0005615 Extracellular space 43 3.18E-12
   GO:0005576 Extracellular region 39 1.52E-11
   GO:0005737 Cytoplasm 119 5.21E-07
   GO:0016021 Integral to membrane 101 3.11E-06
   GO:0005886 Plasma membrane 87 3.69E-06
   GO:0016020 Membrane 70 7.56E-05

GO, Gene Ontology; FDR, false discovery rate; DEG, differentially expressed genes.

Table 3. KEGG pathway enrichment analyses of DEGs.

KEGG ID KEGG term Count FDR Genes
hsa03320 PPAR signaling pathway 9 1.64E-05 ACSBG1, FABP7, UCP1, APOA2, RXRG, ACSL4, APOC3, MMP1, OLR1
hsa04060 Cytokine-cytokine receptor interaction 13 1.05E-03 IL1B, KIT, CRLF2, TNFRSF19, CX3CR1, IL22RA2, CCL17, PDGFA, FIGF, LEP, PF4, IL20, CCR6
hsa04080 Neuroactive ligand-receptor interaction 12 3.95E-03 DRD2, DRD5, NTSR1, SCTR, SSTR1, AGTR2, GRIA1, GRIK4, LEP, PRSS1, F2, GRIK1
hsa04920 Adipocytokine signaling pathway 6 3.01E-03 ACSBG1, G6PC2, AGRP, LEP, RXRG, ACSL4
hsa04972 Pancreatic secretion 7 3.05E-03 CFTR, SCTR, BST1, CPB2, PRSS1, PLA2G2F, ATP2B3
hsa04976 Bile secretion 5 1.67E-02 CFTR, SCTR, AQP1, ABCC2, BAAT
hsa05160 Hepatitis C 3 1.50E-02 CLDN2, CLDN19, CLDN23
hsa04514 Cell adhesion molecules (CAMs) 3 1.53E-02 CLDN2, CLDN19, CLDN23
hsa04670 Leukocyte transendothelial migration 3 1.50E-02 CLDN2, CLDN19, CLDN23
hsa04530 Tight junction 3 1.50E-02 CLDN2, CLDN19, CLDN23
hsa04964 Proximal tubule bicarbonate reclamation 3 1.53E-02 SLC25A10, AQP1, CA4
hsa04724 Glutamatergic synapse 3 1.78E-02 GRIA1, GRIK4, GRIK1
hsa04150 mTOR signaling pathway 4 2.10E-02 RICTOR, RPS6KB1, RPTOR, FIGF
hsa05200 Pathways in cancer 3 2.18E-02 KIT, PDGFA, FIGF

KEGG, Kyoto Encyclopedia of Genes and Genomes; FDR, false discovery rate; DEGs, differentially expressed genes.

Construction of weighted gene co-expression modules

To explore the functional modules in lung SCC patients, the co-expression analysis of the 20,531 genes were performed in WGCNA. Modules for lung SCC were generated using the Scale-free Topology Criterion with a power cutoff of 12 and a minimum module size cutoff of 30. As Figure 1 and Table 4 shown, a total of 19 modules were identified. 23 DEGs and 26 DEGs between lung SCC with metastasis and lung SCC without metastasis were enriched in the respective pink and black modules through Chi-square test at the threshold of P<0.05, besides that, the expression pattern of pink modules, as well as black module, were significantly different between lung SCC with metastasis and lung SCC without metastasis through t-test at the threshold of P<0.05.

Figure 1.

Figure 1

Network analysis of gene expression in lung SCC identifies 19 distinct modules of co-expression genes. The dendrogram produced by average linkage hierarchical clustering of 20,531 genes based on WGCNA package in R. Total of 19 modules were identified. The three panels were the whole dendrogram. lung SCC, lung squamous cell carcinoma; WGCNA, weighted gene co-expression network analysis.

Table 4. The characteristics of gene co-expression modules.

Module colors consensus Number of genes Number of DEGs T.pval Chi2.pval
Black 167 26 3.95E-03 5.48E-23
Blue 1,822 61 2.06E-01 1.23E-02
Brown 1,272 44 3.45E-01 1.92E-02
Cyan 56 2 8.23E-02 8.83E-01
Green 493 6 6.68E-01 1.43E-01
Green yellow 89 0 5.25E-01 2.76E-01
Grey 1,652 52 5.38E-01 5.71E-02
Grey60 35 0 6.57E-02 7.33E-01
Light cyan 51 0 9.71E-01 5.32E-01
Light green 31 1 3.05E-01 1.00E+00
Magenta 129 1 3.95E-01 3.87E-01
Midnight blue 54 2 6.33E-02 8.49E-01
Pink 160 23 3.42E-02 1.04E-18
Purple 93 0 3.45E-01 2.59E-01
Red 441 5 7.91E-01 1.38E-01
Salmon 56 3 3.54E-01 3.21E-01
Tan 75 4 1.26E-01 2.07E-01
Turquoise 8,538 101 5.16E-01 3.12E-10
Yellow 968 13 9.01E-01 5.89E-02

DEGs, differentially expressed genes; T.pval, P value of t-test; Chi2.pval, P value of Chi-square.

The functional annotation of DEGs persevered in the black module and pink module

DEGs and biological function preserved in each module were displayed in Tables 5 and 6, respectively. For the black module, Digestion (FDR =4.66E-05) and Extracellular region (FDR =3.96E-06) were the most significant enrichment in biological process and cellular component, respectively; bile secretion (hsa04976, FDR =1.91E-05) were the one significantly enriched pathways. For the pink module, Cilium axoneme (FDR =2.38E-06) was the most significant enrichment in biological process. There was no KEGG pathway was enriched from DEGs of the pink module.

Table 5. DEGs preserved in the modules.

Module DEGs
Black [26] BAAT, BMP3, C16orf89, CFTR, CLDN2, CRTAC1, FIGF, HP, IGFALS, KCNK17, KLHDC7A, KNDC1,LRP2, MBL1P, NRGN, RASGRF1, RRAD, SCGB3A1, SCGB3A2, SCN1A, SCTR, SFTPD, SLC46A2, SLC5A9, SSTR1, WFDC2
Pink [23] EFCAB12, C1orf192, C22orf15, C5orf49,C6orf165, CCDC157, CCDC42B, CCDC65, DNAH6, DYDC2, DYNLRB2, EFCAB6, FAM166B, FAM183A, FAM92B, ROPN1L, SNTN, TEKT2, TEKT3, TMEM232, TSNAXIP1, WDR93, ZNF474

DEGs, differentially expressed genes.

Table 6. The function and pathway enrichment of DEGs of black module and pink module.

ID Terms FDR Genes
Black module-biological process
   GO:0007586 Digestion 4.66E-05 SSTR1, BAAT, SCTR
   GO:0006811 Ion transport 0.00158761 KCNK17, SCN1A, CFTR, SLC5A9
   GO:0055085 Transmembrane transport 0.00176614 SCN1A, SLC46A2, CFTR, SLC5A9
   GO:0007165 Signal transduction 0.00987753 RASGRF1, NRGN, IGFALS, RRAD
Black module-cellular component
   GO:0005576 Extracellular region 3.96E-06 C16orf89, SCGB3A2, HP, SFTPD, FIGF, WFDC2, SCGB3A1, IGFAL
   GO:0005615 Extracellular space 3.72E-05 SFTPD, FIGF, WFDC2, SCGB3A1,IGFALS,BMP3
   GO:0005886 Plasma membrane 0.000342 RASGRF1, SSTR1, KCNK17, SCN1A, SCTR, SLC46A2, CLDN2, LRP2, RRAD
   GO:0016021 Integral to membrane 0.013611 KLHDC7A, KCNK17, SCN1A, SLC46A2, CLDN2, LRP2, CFTR, SLC5A9
   GO:0016020 Membrane 0.023071 KLHDC7A, KCNK17, SCN1A, FIGF, LRP2, CFTR, SLC5A
Black-KEGG pathway
   hsa04976 Bile secretion 1.91E-05 CFTR, SCTR, BAAT
Pink-biological process
   GO:0035085 Cilium axoneme 2.38E-06 TEKT3, DNAH6, TEKT2
   GO:0005929 Cilium 1.34E-06 SNTN, TEKT3, DNAH6, TEKT2
   GO:0005737 Cytoplasm 1.31E-05 TEKT3, DNAH6, TEKT2, DYNLRB2
   GO:0005856 Cytoskeleton 1.31E-05 TEKT3, DNAH6, TEKT2, DYNLRB2
   GO:0005874 Microtubule 1.31E-05 TEKT3, DNAH6, TEKT2, DYNLRB2

FDR, false discovery rate; DEG, differentially expressed genes.

PPI network

There are respective 26 and 23 DEGs related to lung SCC metastasis in the black module and the pink module. To obtain the interaction between the DEGs in the both of modules, PPI network was explored and visualize by Cytoscape. As Figure 2 shown, the network consisted of 396 nodes, 379 edges. In the PPIs network, the nodes with high degree are defined as hub protein. The most significant hub proteins were CFTR (degree =182), LRP2 (degree =37), HP (degree =20) and TEKT2 (degree =15). CFTR, LRP2, and HP were preserved in the black module; TEKT2 was preserved in the pink module.

Figure 2.

Figure 2

The constructed PPI networks of the DEGs in black module and the pink module. Pink nodes and black nodes represent DEGs in the pink module and black module, respectively. The blue nodes denote products of genes predicted to interact with the DEGs. The solid line means PPI correlation. PPI, protein-protein interaction; DEGs, differentially expressed genes.

Verification of the expression level of DEGs through qRT-PCR

To verify our bioinformatics analyses, the expression level of DEGs were quantified by qRT-PCR in three primary tumor tissues of lung SCC patients with metastasis and three primary tumor tissues without metastasis. Seven DEGs including CFTR, FIGF, SSTR1, SFTPD, SCGB3A2, SCTR, DYNLRB2 were selected as candidate genes based on GO and KEGG annotation results from 49 genes in black and pink module, and literature review (22-26). One DEG, ROPN1L, was randomly selected as a candidate gene for qRT-PCR validation from 49 genes in black and pink module.

The biological roles of CFTR, FIGF, SSTR1, SFTPD and SCGB3A2 in lung cancer have been reported, but the biological roles of SCTR, DYNLRB2 an ROPN1L in lung SCC is unclear.

As shown in Figure 3A-C, FIGF (P<0.001), SFTPD (P<0.05), DYNLRB2 (P<0.05) were significantly down-regulated in the tumor samples of lung SCC with metastasis compared to those of lung SCC without metastasis; the expression levels of CFTR, SCGB3A2, SSTR1, SCTR and ROPN1L had no significance between lung SCC with metastasis and lung SCC without metastasis, but had the down-regulation tendency in lung SCC with metastasis (Figure 3D-H).

Figure 3.

Figure 3

The verification of mRNA expression level of DEGs between of lung SCC with and without metastasis in primary tumor tissues through qRT-PCR. (A) FIGF; (B) SFTPD; (C) DYNLRB2; (D) CFRT; (E) SCGB3A2; (F) SSTR1; (G) SCTR; (H) ROPN1L. M means patients with lung SCC metastasis; MW means patients without lung SCC metastasis. *, means P<0.05; and ***, means P<0.001. lung SCC, lung squamous cell carcinoma; DEGs, differentially expressed genes; qRT-PCR, quantitative real-time polymerase chain reactions.

Discussion

Two gene co-expression modules involved in the process of lung SCC metastasis were identified in our study. The black module and pink module contained 26 and 23 DEGs in lung SCC with metastasis compared to lung SCC without metastasis, respectively. The DEGs in the black module including CFTR, SCTR and BAAT were significantly enriched in the bile secretion pathway (hsa04976). Bile secretion is essential for digestion and absorption of fats and fat-soluble vitamins in the small intestine and the pathway is closely related to cholangiocarcinoma, gall bladder disease and familial cholestasis (27). Bile secretion pathway might be involved in the process of lung SCC metastasis.

The official name of CFTR is cystic fibrosis (CF) transmembrane conductance regulator, is a member of the ATP-binding cassette (ABC) transporter superfamily.

Mutations in this gene are associated with the autosomal recessive disorders CF, which leads to the abnormal transport of chloride and sodium across the epithelium, resulting in chronic lung obstruction, infection and inflammation. CF affects not only the physiological function of lungs, but also the pancreas, liver and intestines (22,28). In our study, CFTR was the hub protein in the PPI network, had the highest connectivity with 182 genes (Figure 2). It was down-regulated in lung SCC metastasis and its expression level was validated through qRT-PCR (Figure 3D), the result was accordance with the previous study (29). Several articles demonstrate that abnormal CFTR expression is related to the tumorigenesis and development of NSCLC. Low CFTR expression is significantly associated with advanced stage, lymph node metastasis and poor prognosis in NSCLC patients (22). The in vivo and in vitro experiments present knockdown of CFTR promotes epithelial-mesenchymal transition, invasion and migration of NSCLC cells; conversely, overexpression of CFTR suppresses cancer progression of NSCLC (22). Methylation of the CFTR gene is significantly greater in lung SCC than in lung adenocarcinomas and CFTR gene methylation is associated with significantly poorer survival in young patients, but not in elderly patients (30). Based on the above, low expression of CFTR might play pivotal roles in the process of lung SCC metastasis.

SCTR encodes secretin receptor, is a G protein-coupled receptor and belongs to the glucagon-VIP-secretin receptor family. It has been observed to be upregulated or down-regulated in several tumor types, and functions as promoting or suppressing the proliferation of tumor cells. SCTR was significantly underexpressed in primary pancreatic neuroendocrine tumors, nodal and liver metastases (31). Down-regulation of SCTR by promoter methylation promotes the cell proliferation and migration of breast cancer (32). In our study, the verification results of SCTR through qRT-PCR were accordance with our bioinformatics analysis. In addition to the bile secretion pathway, SCTR was significantly enriched in neuroactive ligand-receptor interaction and pancreatic secretion pathway (Table 3). SCTR had the connectivity with six genes in the PPI network (Figure 2). To our knowledge, this is the first reports presented SCTR was down-regulated in patients with lung SCC metastasis compared to those patients with lung SCC without metastasis. The biological function of SCTR in process of lung SCC needs to be further elucidated.

FIGF is also called VEGF-D or VEGFD. It encodes c-fos induced growth factor, a member of the platelet-derived growth factor/vascular endothelial growth factor family and is involved in angiogenesis, lymphangiogenesis and endothelial cell growth. VEGFD is a ligand for the VEGF receptor tyrosine kinases and activates it (33). The qRT-PCR shown FIGF was significantly down-regulated (P<0.001) in lung SCC metastasis compared to lung SCC without metastasis (Figure 3A). It was significantly enriched in cytokine-cytokine receptor interaction, mTOR signaling pathway and pathways in cancer. FIGF was a number of the black module and it had the connectivity with ten genes in the PPI network (Figure 2). In line with the previous article, FIGF is down-regulated in lung SCC (23). The mechanism of FIGF in lung SCC metastasis was unknown and further studies need to be investigated.

DYNLRB2 and ROPN1L were members of the pink module, encodes dynein light chain roadblock-type 2 and rhophilin associated tail protein 1 like, respectively.

ROPN1L variants were significantly associated with breast cancer risk in Korean women and Caucasian (34,35). The frequent amplification and copy number loss of DYNLRB2 is correlated with primary ductal carcinoma in situ (DCIS) and mixed DCIS, respectively (36). This is the first report displayed DYNLRB2 and ROPN1L were dysregulated in lung SCC, and might be related to the lung SCC metastasis.

SCGB3A2, SSTR1 and SFTPD were down-regulated in lung SCC metastasis compared to lung SCC without metastasis. SCGB3A2 encodes secretoglobin family 3A member 2. In lung cancer, SCGB3A2 were predominantly expressed in lung adenocarcinoma, compared with lung SCC and small cell carcinoma (24). It is a potentially useful marker for primary pulmonary tumors both in mice and humans (37). SSTR1 encodes somatostatin receptor 1, the expression level of SSTR1 mRNA was higher in both small cell lung cancer and lung SCC than in adenocarcinoma cell lines (26). SFTPD encodes surfactant protein D, is a lung-specific anti-inflammatory factor that antagonizes inflammation by inhibiting oxidative stress and stimulating innate immunity (38). In line with the previous article, SFTPD is down-regulated in NSCLC (25). The pathophysiology mechanism of dysregulated SCGB3A2, SSTR1 and SFTPD in lung SCC metastasis need further investigation.

In conclusion, we identified 482 DEGs in lung SCC without metastasis compared to lung SCC metastasis. Two gene co-expression modules including the black module and the pink module involved in the process of lung SCC metastasis were identified. Respective 26 and 23 dysregulated genes were enriched in the black module and the pink module. In the two of co-expression modules, CFTR, SCTR, FIGF might play key roles in the lung SCC metastasis. Our findings may contribute to the identification of early diagnosis biomarker for lung SCC metastasis and prognosis.

There are limitations in our study. Firstly, the biological roles of the key DEGs including SCTR and FIGF were unknown. In the future work, the in vivo and in vitro experiments were essential for exploring the function of genes mentioned above in the process of lung SCC metastasis. Secondly, additional studies with large cohorts of lung SCC with and without metastasis patients are needed to demonstrate the diagnostic value of identified genes as practical biomarkers.

Acknowledgements

Funding: This study was supported by Shandong Province Higher Educational Science and Technology Program (J11LF26).

Table S1. List of primers designed for the qRT-PCR verification.

Primer Primer sequence (5' to 3')
CFTR Forward GATGGGGGCTGTGTCCTAAG
CFTR Reverse GCATTGCTTCTATCCTGTGTTCA
FIGF Forward ATCTAATCCAGCACCCCAAAAACT
FIGF Reverse CTGGTATGAAAGGGGCATCTGTC
SCGB3A2 Forward CTCTGGACAACATTCTTCCCTTTAT
SCGB3A2 Reverse CCACCTCCGCTCTTTATCTTGA
SSTR1 Forward GGGGCTATCTGCCTGTGCTAC
SSTR1 Reverse CAAACACCATCACCACCATCAT
SCTR Forward CCGTCCTCTACTGCTTCCTCAAC
SCTR Reverse GGCTCTGCTCCAAGTGGCTG
SFTPD Forward GCTACCTGGAAGCAGAAATGAA
SFTPD Reverse AACAGAGCCATTGTCCCCTTT
DYNLRB2 Forward CACCTGACAATGAAAGCCAAAAG
DYNLRB2 Reverse TCACATGGATTCTGAATGACGAT
ROPN1L Forward GTGCCTGCCGAAGGAAAA
ROPN1L Reverse AAAACGTCTTGAAGGGGATGC
18srRNA Forward GTAACCCGTTGAACCCCATT
18srRNA Reverse CCATCCAATCGGTAGTAGCG

qRT-PCR, quantitative real-time polymerase chain reaction.

Table S2. DEGs between lung SCC with and without metastasis.

Gene Gene ID FC FDR
Up-regulated genes
   VGF 7425 6.302272 1.90E-06
   VSIG10L 147645 5.870895 1.28E-05
   MFAP5 8076 5.335057 0.000187
   HNF1A 6927 5.184579 0.000382
   RPTOR 57521 5.125631 0.000449
   F2 2147 5.062773 0.000535
   ATF5 22809 4.959337 0.000714
   GAL 51083 4.944154 0.000714
   ITPKA 3706 4.839489 0.001009
   MND1 84057 4.835881 0.001009
   TSPAN5 10098 4.772975 0.001176
   DRP2 1821 4.672904 0.001748
   DHCR7 1717 4.524834 0.003136
   C19orf76 199800 4.502532 0.003261
   KRT71 112802 4.393386 0.004713
   UPK2 7379 4.335088 0.00555
   SMARCD2 6603 4.324975 0.005592
   C4orf49 84709 4.297849 0.005983
   SRP68 6730 4.237439 0.007321
   TEX13B 56156 4.221298 0.007491
   NDUFC1 4717 4.102 0.009792
   PRB3 5544 4.098234 0.009792
   PIWIL2 55124 4.084314 0.010104
   KRT18 3875 4.064585 0.010387
   C9orf70 84850 4.058666 0.010503
   TAC3 6866 4.049676 0.010714
   NPSR1 387129 4.036762 0.011201
   SLC9A2 6549 4.011459 0.011298
   GRIK1 2897 4.008088 0.011298
   ABCC2 1244 4.007931 0.011298
   C4BPB 725 3.986718 0.011558
   TCHP 84260 3.966038 0.011919
   KCNH6 81033 3.96427 0.011919
   TRIM54 57159 3.951173 0.012284
   PROCR 10544 3.941815 0.012469
   NEB 4703 3.92073 0.013004
   ZNF787 126208 3.920271 0.013004
   FOXH1 8928 3.920242 0.013004
   ZWINT 11130 3.918314 0.013004
   GPR155 151556 3.916905 0.013004
   SYNPO2L 79933 3.904368 0.01311
   FA2H 79152 3.894921 0.01344
   NCAPG2 54892 3.873486 0.014273
   NBAS 51594 3.86964 0.014273
   POLR3G 10622 3.868808 0.014273
   COG1 9382 3.868597 0.014273
   PRR4 11272 3.856872 0.014627
   SFRS9 8683 3.839756 0.015322
   SCN3A 6328 3.826201 0.015533
   ADI1 55256 3.826146 0.015533
   DDX54 79039 3.825855 0.015533
   CPS1 1373 3.80012 0.016528
   SAPS1 22870 3.77652 0.017456
   MAPT 4137 3.766167 0.017892
   APOC3 345 3.76264 0.018047
   MIPOL1 145282 3.757985 0.018235
   HRC 3270 3.752649 0.018276
   IL1RAPL1 11141 3.749319 0.018375
   PLK4 10733 3.747044 0.018375
   SMAGP 57228 3.732997 0.018471
   DKK4 27121 3.730844 0.018471
   CADPS 8618 3.723286 0.01859
   USP32 84669 3.722682 0.01859
   NPNT 255743 3.711312 0.019352
   SYNGR4 23546 3.708512 0.019391
   PSMC5 5705 3.706852 0.019391
   USP7 7874 3.694849 0.020068
   NR2E1 7101 3.686376 0.020569
   ONECUT1 3175 3.683024 0.020731
   ASPSCR1 79058 3.669242 0.021581
   SAP30BP 29115 3.667889 0.021597
   TESK1 7016 3.651205 0.022574
   PCDHGB4 8641 3.640394 0.022895
   RAD51C 5889 3.63531 0.02311
   HES7 84667 3.632468 0.02311
   APOA2 336 3.627873 0.023268
   C19orf48 84798 3.626093 0.023293
   NADSYN1 55191 3.62274 0.023306
   NAA15 80155 3.62065 0.023398
   GALNT13 114805 3.608216 0.024172
   DCXR 51181 3.606123 0.024193
   ING2 3622 3.605638 0.024193
   C9orf82 79886 3.59792 0.024535
   MED13 9969 3.596717 0.024552
   FAM104A 84923 3.590437 0.024911
   PITPNC1 26207 3.589103 0.024911
   SMC6 79677 3.588884 0.024911
   CYP3A5 1577 3.581118 0.025177
   NTSR1 4923 3.579779 0.025198
   TRAP1 10131 3.57807 0.025198
   OR51E2 81285 3.575338 0.025228
   KPNA2 3838 3.570615 0.025532
   MELK 9833 3.565422 0.025758
   RRM2 6241 3.539854 0.027316
   C4orf41 60684 3.539614 0.027316
   FUT6 2528 3.531912 0.028026
   HIST1H3H 8357 3.525661 0.0283
   C4orf48 401115 3.524516 0.028325
   CELA1 1990 3.521018 0.028538
   COL25A1 84570 3.519272 0.028538
   TSPO2 222642 3.517829 0.028538
   MRPL38 64978 3.516225 0.028538
   DUSP13 51207 3.499272 0.029593
   CLTC 1213 3.49865 0.029593
   GAR1 54433 3.489856 0.03009
   SLC25A10 1468 3.489392 0.03009
   ABCE1 6059 3.488184 0.03009
   CCDC124 115098 3.476216 0.031069
   XRCC6BP1 91419 3.467209 0.031602
   FDXR 2232 3.465684 0.031616
   KLC2 64837 3.459955 0.032099
   C21orf45 54069 3.45586 0.032196
   ARHGDIA 396 3.433486 0.034049
   HBQ1 3049 3.428947 0.034321
   CIT 11113 3.419155 0.035096
   CENPE 1062 3.409378 0.035835
   SNX31 169166 3.407213 0.036019
   MRPL21 219927 3.404929 0.036117
   DCTD 1635 3.399858 0.03628
   KRTAP5-7 440050 3.399706 0.03628
   C17orf53 78995 3.398381 0.03628
   CLTA 1211 3.377629 0.038227
   FOXK2 3607 3.368591 0.039039
   C14orf80 283643 3.361435 0.039747
   PWP2 5822 3.36065 0.039755
   FIBCD1 84929 3.358374 0.039889
   UBTF 7343 3.356782 0.039896
   UCP1 7350 3.354004 0.040174
   LOC92659 92659 3.352846 0.040174
   FAM84B 157638 3.342063 0.041043
   DDN 23109 3.339528 0.041043
   MTL5 9633 3.336217 0.041112
   CST4 1472 3.335754 0.041112
   CENPO 79172 3.33492 0.041132
   SLC5A5 6528 3.326312 0.04147
   RPS6KB1 6198 3.325852 0.04147
   H2AFB1 474382 3.320782 0.042023
   VWA5B2 90113 3.319972 0.042044
   C16orf75 116028 3.318308 0.042194
   LOC127841 127841 3.313993 0.042443
   LSM6 11157 3.312778 0.042527
   LMBR1 64327 3.30957 0.042815
   MUC6 4588 3.307915 0.042882
   PGP 283871 3.306565 0.042882
   DDX4 54514 3.306498 0.042882
   VRK1 7443 3.303783 0.043098
   PCGF2 7703 3.302845 0.043105
   GINS2 51659 3.302439 0.043105
   RWDD4A 201965 3.300758 0.043155
   HIST1H2BM 8342 3.298546 0.043306
   C9orf140 89958 3.295731 0.043642
   C12orf43 64897 3.285002 0.044826
   ALDH1A1 216 3.283336 0.04499
   DHX8 1659 3.277351 0.045645
   UBE2O 63893 3.273996 0.045794
   HSPBP1 23640 3.27391 0.045794
   NPPC 4880 3.263528 0.047087
   THOC4 10189 3.258293 0.047551
   AKR7A3 22977 3.255861 0.047642
   GGNBP1 449520 3.253542 0.047824
   BCAT2 587 3.252302 0.047828
   LOC81691 81691 3.251006 0.047944
   TLK2 11011 3.247125 0.048489
   CLGN 1047 3.24657 0.048489
   HSD17B1 3292 3.243008 0.04858
   CKM 1158 3.237947 0.049187
   LONP1 9361 3.237664 0.049187
   GDF11 10220 3.236492 0.049227
   FGF3 2248 3.232167 0.049726
Down-regulated genes
   ZNF208 7757 −6.99816 5.04E-08
   C8orf46 254778 −6.37244 1.81E-06
   TNF 7124 −5.93751 1.28E-05
   GRIA1 2890 −5.90534 1.28E-05
   LINGO4 339398 −5.8607 1.28E-05
   S100A7A 338324 −5.42097 0.000144
   NDP 4693 −5.33403 0.000187
   IL22RA2 116379 −5.12306 0.000449
   POU2F3 25833 −5.09145 0.000493
   MIA 8190 −5.04813 0.000542
   FOXI2 399823 −5.02927 0.000562
   PCDHGA4 56111 −5.01861 0.000562
   SYCE1 93426 −4.94241 0.000714
   FCER1A 2205 −4.88237 0.000926
   C7orf16 10842 −4.82702 0.001009
   TRIM58 25893 −4.82627 0.001009
   C2orf73 129852 −4.82333 0.001009
   KLHDC7A 127707 −4.81194 0.001009
   SCG3 29106 −4.81048 0.001009
   FAM169B 283777 −4.7216 0.001467
   LOC283731 283731 −4.68577 0.001693
   CCDC42B 387885 −4.6414 0.001977
   FAIM2 23017 −4.61988 0.002131
   SLC47A2 146802 −4.56176 0.002738
   PCDHGA9 56107 −4.52173 0.003136
   UPP2 151531 −4.50633 0.003261
   PDC 5132 −4.48382 0.003474
   C1orf192 257177 −4.4712 0.003598
   HTR3A 3359 −4.44092 0.004047
   HSD17B2 3294 −4.42747 0.004129
   EMID1 129080 −4.4268 0.004129
   S100B 6285 −4.38458 0.004789
   WBSCR17 64409 −4.38066 0.004789
   DPYS 1807 −4.37219 0.004877
   CD1A 909 −4.36288 0.004988
   HP 3240 −4.32821 0.005592
   CDH20 28316 −4.30398 0.005983
   TMPRSS5 80975 −4.28326 0.006278
   NDN 4692 −4.27326 0.006453
   C5orf13 9315 −4.26551 0.006567
   OLR1 4973 −4.23143 0.007383
   MSTN 2660 −4.22817 0.007383
   C1orf186 440712 −4.20012 0.008098
   GPR17 2840 −4.18922 0.008366
   C9orf44 158314 −4.1585 0.00943
   C4orf7 260436 −4.1494 0.009666
   HEPACAM2 253012 −4.14051 0.009791
   CRLF1 9244 −4.13588 0.009791
   LOC284749 284749 −4.12985 0.009791
   IGFN1 91156 −4.1266 0.009791
   DYNLRB2 83657 −4.12242 0.009791
   ART3 419 −4.12232 0.009791
   MAGEE2 139599 −4.12076 0.009791
   CCDC129 223075 −4.11658 0.009791
   G6PC2 57818 −4.11556 0.009791
   CORO2B 10391 −4.11263 0.009791
   RBM44 375316 −4.11151 0.009791
   CNGA3 1261 −4.10112 0.009792
   MBL1P 8512 −4.09773 0.009792
   HPD 3242 −4.09711 0.009792
   OR2W3 343171 −4.08612 0.010104
   GRIK4 2900 −4.07796 0.010114
   AGTR2 186 −4.07697 0.010114
   FAM55D 54827 −4.07446 0.010114
   MOGAT2 80168 −4.07341 0.010114
   PNMA5 114824 −4.05686 0.010503
   CCDC141 285025 −4.0289 0.011298
   ACOT12 134526 −4.02808 0.011298
   WDR93 56964 −4.02511 0.011298
   TPTE2 93492 −4.02483 0.011298
   PCDHGA6 56109 −4.01945 0.011298
   C13orf15 28984 −4.01794 0.011298
   IL1B 3553 −4.01286 0.011298
   CTCFL 140690 −4.01208 0.011298
   SSTR1 6751 −4.00644 0.011298
   HEPHL1 341208 −3.99112 0.011558
   WFDC12 128488 −3.9907 0.011558
   HTR3C 170572 −3.9881 0.011558
   PADI2 11240 −3.98802 0.011558
   STAC2 342667 −3.98702 0.011558
   ACSL4 2182 −3.98589 0.011558
   CLDN2 9075 −3.98299 0.011566
   RASD2 23551 −3.98158 0.011566
   MYO1H 283446 −3.97564 0.011756
   GPIHBP1 338328 −3.97226 0.011822
   TRPC2 7221 −3.96666 0.011919
   CA4 762 −3.961 0.011984
   FOXI1 2299 −3.95747 0.012063
   CPB2 1361 −3.94544 0.012448
   TEX9 374618 −3.94412 0.012448
   SHC4 399694 −3.93515 0.012719
   FAM123A 219287 −3.9236 0.013004
   PAGE5 90737 −3.91727 0.013004
   PSAPL1 768239 −3.91368 0.013026
   C1orf150 148823 −3.91293 0.013026
   LOC646851 646851 −3.91116 0.013026
   ROPN1L 83853 −3.90859 0.013031
   CRABP1 1381 −3.90756 0.013031
   AMZ1 155185 −3.90189 0.013151
   ADAMTS8 11095 −3.88394 0.013963
   C1QL2 165257 −3.88144 0.01401
   PLD5 200150 −3.87838 0.014089
   TCTEX1D4 343521 −3.86347 0.014479
   DRGX 644168 −3.8586 0.014627
   TMPRSS3 64699 −3.85615 0.014627
   CYB561D1 284613 −3.84443 0.015243
   CLEC4C 170482 −3.83844 0.015322
   FOLR3 2352 −3.83843 0.015322
   ACRV1 56 −3.83238 0.015533
   TMEM100 55273 −3.83079 0.015533
   GPR128 84873 −3.82961 0.015533
   FNDC5 252995 −3.82395 0.015558
   CLDN23 137075 −3.81787 0.015702
   DYDC2 84332 −3.81611 0.015702
   KLK7 5650 −3.81601 0.015702
   DRD2 1813 −3.81539 0.015702
   SLC5A9 200010 −3.81427 0.015702
   FAM166B 730112 −3.7975 0.016606
   CFP 5199 −3.79313 0.016803
   COL9A2 1298 −3.78771 0.016969
   ALDH1A3 220 −3.7876 0.016969
   SERPINA3 12 −3.78639 0.016969
   RYR3 6263 −3.78106 0.017238
   IQSEC3 440073 −3.77321 0.017563
   HPR 3250 −3.7722 0.017563
   RIMS3 9783 −3.75729 0.018235
   CYP2B6 1555 −3.75556 0.018261
   RNF122 79845 −3.75386 0.018276
   DNAH6 1768 −3.74677 0.018375
   HAPLN4 404037 −3.74597 0.018375
   DENND3 22898 −3.74354 0.018391
   FAM183A 440585 −3.74312 0.018391
   TRPM5 29850 −3.7399 0.018471
   SLC6A13 6540 −3.73831 0.018471
   CHIT1 1118 −3.73584 0.018471
   FIGF 2277 −3.73417 0.018471
   RXRG 6258 −3.73254 0.018471
   DBC1 1620 −3.73111 0.018471
   MS4A8B 83661 −3.72991 0.018471
   TTTY2 60439 −3.72925 0.018471
   RHO 6010 −3.72784 0.018478
   PTX3 5806 −3.72667 0.018478
   CUX2 23316 −3.70606 0.019391
   C22orf15 150248 −3.70595 0.019391
   SPRR2G 6706 −3.70009 0.019751
   CHI3L1 1116 −3.6862 0.020569
   TNFRSF19 55504 −3.6782 0.021029
   CRTAC1 55118 −3.67011 0.021581
   C1orf175 374977 −3.66343 0.021827
   ACSBG1 23205 −3.66287 0.021827
   KRT31 3881 −3.65343 0.022543
   C6orf217 1E+08 −3.6508 0.022574
   NEDD9 4739 −3.64821 0.022701
   BST1 683 −3.64606 0.022722
   C5orf49 134121 −3.64571 0.022722
   TSNAXIP1 55815 −3.64271 0.022888
   OCA2 4948 −3.64101 0.022895
   ARHGAP6 395 −3.63432 0.02311
   SCGB3A2 117156 −3.63351 0.02311
   NANOS1 340719 −3.63266 0.02311
   PCDHB18 54660 −3.62948 0.023268
   CACNA1G 8913 −3.62744 0.023268
   DRD5 1816 −3.62405 0.023306
   BMP3 651 −3.62323 0.023306
   TRY6 154754 −3.61309 0.023993
   CELF3 11189 −3.60886 0.024172
   PSG8 440533 −3.60801 0.024172
   ERVFRDE1 405754 −3.60466 0.024193
   FBN3 84467 −3.60111 0.02435
   PTCHD2 57540 −3.60091 0.02435
   CHI3L2 1117 −3.59413 0.0247
   PRY2 442862 −3.58729 0.024968
   C16orf89 146556 −3.58584 0.025011
   FAM151A 338094 −3.58328 0.02516
   PDK4 5166 −3.58162 0.025177
   C3orf16 389161 −3.57796 0.025198
   MMP3 4314 −3.57631 0.025228
   PAK3 5063 −3.57472 0.025228
   POLB 5423 −3.56716 0.025758
   BIRC2 329 −3.56631 0.025758
   SCGB3A1 92304 −3.56338 0.025865
   TSPAN2 10100 −3.56094 0.026012
   C4A 720 −3.55936 0.026075
   COL29A1 256076 −3.55698 0.02616
   LEP 3952 −3.55605 0.02616
   TCTEX1D1 200132 −3.55567 0.02616
   TMEM217 221468 −3.55251 0.026382
   TMEM232 642987 −3.54522 0.027026
   CPAMD8 27151 −3.54242 0.027217
   MMP1 4312 −3.53001 0.028088
   LOC148824 148824 −3.52948 0.028088
   SMYD1 150572 −3.52739 0.028213
   PDGFA 5154 −3.51856 0.028538
   ADHFE1 137872 −3.51763 0.028538
   DCLK1 9201 −3.51695 0.028538
   KNDC1 85442 −3.50966 0.028964
   C19orf59 199675 −3.5085 0.028964
   CCR6 1235 −3.50812 0.028964
   NRGN 4900 −3.5079 0.028964
   CCL17 6361 −3.50786 0.028964
   SPOCK2 9806 −3.50282 0.029391
   OBP2B 29989 −3.50221 0.029391
   SCN1A 6323 −3.49743 0.029631
   PDYN 5173 −3.49134 0.03009
   FABP7 2173 −3.48896 0.03009
   LMOD3 56203 −3.48822 0.03009
   SCTR 6344 −3.4856 0.030285
   SNTN 132203 −3.48335 0.030444
   SLC25A27 9481 −3.47904 0.030841
   GNAO1 2775 −3.47505 0.031107
   KCNK17 89822 −3.47269 0.031284
   PCDHA9 9752 −3.47128 0.031351
   LRP2 4036 −3.46908 0.03151
   CCDC157 550631 −3.46663 0.031602
   C21orf62 56245 −3.46092 0.032082
   KAL1 3730 −3.45759 0.032196
   RASGEF1B 153020 −3.45638 0.032196
   NPPA 4878 −3.45595 0.032196
   LCE3E 353145 −3.45363 0.032366
   GAS2 2620 −3.45174 0.032496
   FAM92B 339145 −3.44844 0.032797
   TDRD6 221400 −3.44545 0.033063
   MEGF11 84465 −3.441 0.033388
   CX3CR1 1524 −3.44075 0.033388
   CYP2D6 1565 −3.44039 0.033388
   CLDN19 149461 −3.43628 0.033799
   CLLU1 574028 −3.42963 0.034321
   CAT 847 −3.42928 0.034321
   BOC 91653 −3.42237 0.03506
   IRX1 79192 −3.4209 0.035093
   TMEM190 147744 −3.42053 0.035093
   SLC46A2 57864 −3.41846 0.035096
   RASGRF1 5923 −3.41816 0.035096
   KANK4 163782 −3.41106 0.035835
   KCNE1L 23630 −3.41057 0.035835
   BAAT 570 −3.41002 0.035835
   KIAA1644 85352 −3.40608 0.036066
   OR4N4 283694 −3.40208 0.03628
   PCDHA10 56139 −3.40013 0.03628
   DNAH5 1767 −3.39858 0.03628
   MT1F 4494 −3.39841 0.03628
   SH2D7 646892 −3.39276 0.03693
   CFTR 1080 −3.3898 0.037229
   TAL1 6886 −3.38671 0.037548
   IGFALS 3483 −3.38561 0.037597
   EFCAB12 90288 −3.38423 0.037683
   CTSH 1512 −3.38248 0.037822
   ZNF98 148198 −3.37939 0.038146
   CCDC65 85478 −3.37732 0.038227
   C15orf37 283687 −3.3738 0.038615
   CLEC3B 7123 −3.37133 0.03886
   RGS13 6003 −3.36932 0.039039
   SFTPD 6441 −3.36378 0.03962
   TEKT3 64518 −3.36159 0.039747
   KLHDC8B 200942 −3.35827 0.039889
   ROPN1 54763 −3.35679 0.039896
   CRLF2 64109 −3.35271 0.040174
   EFCAB6 64800 −3.34864 0.040663
   RASL12 51285 −3.34363 0.041043
   IL20 50604 −3.3433 0.041043
   APOL4 80832 −3.34189 0.041043
   CAMK1G 57172 −3.34104 0.041043
   DNAI1 27019 −3.3407 0.041043
   COL14A1 7373 −3.33932 0.041043
   CNTN4 152330 −3.33921 0.041043
   PRSS1 5644 −3.339 0.041043
   BMF 90427 −3.33803 0.041084
   TMEM151B 441151 −3.33632 0.041112
   LOC283050 283050 −3.33424 0.041132
   KLF17 128209 −3.3305 0.04147
   RRAD 6236 −3.32852 0.04147
   GP5 2814 −3.32848 0.04147
   CLDN25 644672 −3.32741 0.04147
   SOX11 6664 −3.32658 0.04147
   WDR38 401551 −3.3261 0.04147
   SRD5A2 6716 −3.32582 0.04147
   C1orf228 339541 −3.32106 0.042023
   NAV3 89795 −3.31762 0.042197
   SPRR2B 6701 −3.31648 0.042268
   C14orf139 79686 −3.31408 0.042443
   TKTL1 8277 −3.31158 0.042608
   HAND1 9421 −3.30775 0.042882
   RICTOR 253260 −3.30458 0.043076
   CACNG1 786 −3.30069 0.043155
   RDH16 8608 −3.30017 0.043155
   PVALB 5816 −3.2924 0.044062
   SEC14L3 266629 −3.29154 0.044096
   HPCAL4 51440 −3.28873 0.044437
   DIRC3 729582 −3.2857 0.044817
   AGRP 181 −3.28238 0.045042
   FAM81B 153643 −3.27818 0.045613
   PLA2G2F 64600 −3.27614 0.04574
   CCDC30 728621 −3.27408 0.045794
   PF4 5196 −3.27303 0.045836
   CSDC2 27254 −3.26912 0.046371
   RNF112 7732 −3.26788 0.046472
   SLC17A8 246213 −3.26127 0.04736
   PURG 29942 −3.25821 0.047551
   TG 7038 −3.25766 0.047551
   KIT 3815 −3.25764 0.047551
   DES 1674 −3.25617 0.047642
   TEKT2 27285 −3.25453 0.047762
   ZNF474 133923 −3.25294 0.047824
   CDX1 1044 −3.24595 0.048491
   C6orf165 154313 −3.24447 0.048565
   ATP2B3 492 −3.24396 0.048565
   WFDC2 10406 −3.2437 0.048565
   LPO 4025 −3.24181 0.048682
   WDR91 29062 −3.23624 0.049227
   SGK223 157285 −3.23513 0.049315
   AQP1 358 −3.2312 0.049791
   TMEM114 283953 −3.23056 0.049798

Lung SCC, lung squamous cell carcinoma; DEG, differentially expressed genes; FC, fold change; FDR, false discovery rate; ABC, ATP-binding cassette.

Ethical Statement: The study was approved by the ethics committee board of Linyi People’s Hospital (No. lyll2015N67) and written informed consent was obtained from all patients.

Footnotes

Conflicts of Interest: The authors have no conflicts of interest to declare.

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