Analysis of gene expression profiles of non-small cell lung cancer at different stages reveals significantly altered biological functions and candidate genes

  • Authors:
    • Jin Wang
    • Jianxiang Song
    • Zhengya Gao
    • Xudong Huo
    • Yajun Zhang
    • Wencai Wang
    • Jianwei Qi
    • Shiying Zheng
  • View Affiliations

  • Published online on: January 17, 2017     https://doi.org/10.3892/or.2017.5380
  • Pages: 1736-1746
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Abstract

We attempt to dissect the pathology of non-small cell lung cancer (NSCLC) patients at different stages and discover the novel candidate genes. Microarray data (GSE21933) were retrieved from the Gene Expression Omnibus database. The differential expression profiles of lung tumor tissues during different stages were analyzed. The significantly altered functions and pathways were assessed and the key nodes in a protein-protein interaction (PPI) network were screened out. Then, the coexpression gene pairs and tumor-related genes were assessed. RT-PCR analysis was performed to validate the candidate gene, natural killer-tumor recognition sequence (NKTR). The number of differentially expressed genes (DEGs) for stage IB, IIB, IIIA and IV tumors were 499, 602, 592 and 457, respectively. Most of the DEGs were NSCLC-related genes identified through literature research. A few genes were commonly downregulated in all the 4 stages of tumors, such as CNTN6 and LBX2. The DEGs in early‑stage tumors were closely related with the negative regulation of signal transduction, the apoptosis pathway and the p53 signaling pathway. DEGs in late-stage tumors were significantly enriched in transcription, response to organic substances and synapse regulation-related biological processes. A total of 16 genes (including NKTR) made up the significant coexpression network. NKTR was a key node in the PPI network and was significantly upregulated in lung cancer cells. The mechanism of NSCLC progression in different tumor stages may be different. NKTR may be the target candidate for NSCLC prevention and treatment.

Introduction

Non-small cell lung cancer (NSCLC) is the most common type of lung cancer, accounting for about 85% of all lung cancers (1). NSCLC is the main cause of lung cancer-related deaths. Most patients are diagnosed with late-stage tumors and only 15% of patients are found in the early stage (2). The survival rate of patients with early-stage NSCLC after complete tumor resection is more favourable. The current therapeutic strategies for NSCLC cases mainly include surgery, chemotherapy and radiotherapy. The combination of two or more treatments is often used for NSCLC cases, which is usually influenced by NSCLC staging. It is reported that the combinational treatment of adjuvant carboplatin plus paclitaxel reduces the mortality rate of patients with stage IB NSCLC (3). The overall survival for stage IB-II NSCLC cases was found to improve after vinorelbine plus cisplatin treatment (4).

Microarray technology is a novel tool to analyze the global gene expression and decipher biological pathways (5). Advances in microarray technology have facilitated the development of novel therapies and the understanding of potential mechanisms of diseases. Based on gene expression arrays, Lu et al found a group of genes related with lung cancer metastasis such as APC, CDH8 and IL8RB, which may be used for predicting the survival of patients with stage I NSCLC and preparing a treatment plan (6). Jiang et al employed tissue lung microarray technology and suggested that aldehyde dehydrogenase 1, a marker for lung tumor stem cells, may be a therapeutic target for lung cancer (7).

The gene expression array of GSE21933 was developed from Asian lung cancer patients. With the microarray data, Lo et al have established that ARHGAP19, FRAT2g, PAFAH1B1 and ZNF322A involved in Rho activity, Wnt signaling, motility control and MAPK signaling are the candidate genes for lung cancer (8). Currently, the integrity analysis of altered gene expression patterns for lung cancer during different stages is rare. In the present study, we further analyzed the expression array data of lung tumor tissues to disclose the difference and similarity in the gene expression profile of lung cancer cases during different stages. We expect that our findings are helpful in understanding the mechanism of lung cancer during different stages and discovering novel candidate genes.

Materials and methods

Microarray data

The gene expression dataset of a previous human NSCLC study was retrieved from the public National Center for Biotechnology Information Gene Expression Omnibus (NCBI GEO) with accession no. GSE21933. The gene expression data of tumors and matched normal tissues from 21 NSCLC patients were contributed by a previously public study (8). The NSCLC patients included 10 early-stage cases (6 in stage IB, 3 in stage IIB and 1 in stage IA) and 11 late-stage patients (5 in stage IIIA, 5 in stage IV and 1 in stage IIIB). Due to the small number of samples at stage IA and IIIB, we only analyzed the remaining 19 tumor samples compared with the corresponding controls. All the DNA microarray data were analyzed by the platform Phalanx Human OneArray.

Data preprocessing

The raw data (gpr format files) were downloaded and assessed by limma software (9,10). The preprocessing procedure involved a background correction, gene expression normalization and microarray data condensing. Then, the probe IDs were translated into gene symbols by eliminating the probes with no gene symbols. When there were multiple probes for the same gene symbol, the mean value of the probes was calculated as the gene expression value.

DEG screening

The differentially expressed genes (DEGs) were analyzed in NSCLC tumors during different stages. The differential expression patterns were analyzed in stages IB, IIB, IIIA and IV of lung tumors, compared with the normal controls. The significant P-values for DEGs were calculated by a no-paired t-test with the application of a limma package. P<0.05 and log2FC (fold-change) ≥0.4 were defined as the cut-off values. Then the significant DEGs screened were subjected to hierarchical clustering analysis by a gplots package (11).

Function and pathway analysis

The significantly altered biological processes and pathways were identified using Database for Annotation, Visualization and Integrated Discovery (DAVID) (12). The statistical significances of the overrepresented Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were calculated based on hypergeometric distribution. A P-value <0.05 and a count ≥2 were used as the cut-off values.

PPI network

The protein interaction pairs corresponding to DEGs were predicted by integrating the information deposited in STRING [protein-protein interaction (PPI) networks, with increased coverage and integration] (13). The DEG lists in tumors in stages IB, IIB, IIIA and IV were analyzed, respectively. The PPI network was constructed of the protein pairs with a PPI score (medium confidence) ≥0.4 using Cytoscape software (14).

Key nodes in the PPI network

The role of nodes in large complex networks is commonly assessed based on network centralities such as degree centrality (15), betweenness centrality (16) and subgraph centrality (17). The network centralities of nodes were calculated by cytoscape plugin CytoNCA (18). The key nodes were first selected according to a high degree, subgraph and betweenness value. Then the DEGs corresponding to key nodes underwent cluster analysis to evaluate the effect on sample clustering. Finally, DEGs that could distinguish tumor and normal samples were defined as key nodes.

Venn diagram of DEGs in tumors of different stages

VennPlex (19) (https://www.irp.nia.nih.gov/bioinformatics/vennplex.html) has been widely used to separate common DEGs and uniquely altered genes between different gene lists. The Venn diagram of DEGs in the NSCLC tumors of different stages was constructed by VennPlex software. The four DEG lists and log2FC values were uploaded to identify the difference of differential expression patterns among stages IB, IIB, IIIA and IV of lung tumors.

Gene coexpression analysis

The DEGs identified in the four different tumor stages were pooled together. The coexpression coefficient was calculated by Pearson's correlation coefficient. P-values were obtained based on Z-scores and corrected by Benjamini-Hochberg (BH) method to control the false discovery rate (20). A |coexpression coefficient| >0.8 and an adjusted P-value <0.05 were set as the cut-off values for gene coexpression pairs.

Analysis of cancer-related genes

The Comparative Toxicogenomics Database (CTD) (21) is a collection of disease-related genes documented in previously published literature. The genes related with NSCLC were selected among DEGs of tumors in 4 tumor stages based on the information from the CTD.

Cell culture

Human lung epithelial cells (BEAS-2B), NSCLC cell lines such as A549 (adenocarcinoma), NCI-H1299 (large cell carcinoma) and NCI-H1650 (adenocarcinoma) were purchased from the Cell Bank of the Chinese Academy of Sciences (Shanghai, China). BEAS-2B and A549 cells were maintained in Dulbecco's modified Eagle's medium (DMEM) supplemented with 10% fetal bovine serum (FBS), 1% 100 U/ml penicillin and 0.1 mg/ml streptomycin. NCI-H1299 and NCI-H1650 cells were cultured in RPMI-1640 medium supplemented with 10% FBS and 1% 100 U/ml penicillin, and 0.1 mg/ml streptomycin. All the cells were incubated in an atmosphere of 95% air and 5% CO2 at 37°C.

Reverse transcription real-time quantitative polymerase chain reaction (RT-qPCR)

The total RNA of the BEAS-2B, A549, NCI-H1299 and NCI-H1650 cells was extracted using the RNAiso Plus reagent (Takara Bio) according to the manufacturer's instructions. cDNA was reversely transcribed by PrimeScript (according to Takara Bio) at 37°C for 15 min, followed by 5 sec at 85°C. In order to determine the DNA copy number variations of the candidate natural killer-tumor recognition sequence (NKTR) gene, PCR amplifications were performed with 10 µl SYBR Premix Ex Taq (2X), 1 µl of 10 µM forward primers, 1 µl of 10 µM reverse primers and 8 µl cDNA, in a final volume of 20 µl. The relative quantities of NKTR expression were analyzed using the comparative Ct method and were normalized to glyceraldehyde-3-phosphate dehydrogenase (GAPDH) expression. The primer sequences for NKTR and GAPDH are listed in Table I.

Table I.

The primer sequences for NKTR and GAPDH.

Table I.

The primer sequences for NKTR and GAPDH.

GenePrimer sequence (5′-3′)
GAPDHF: TGACAACTTTGGTATCGTGGAAGG
R: AGGCAGGGATGATGTTCTGGAGAG
NKTRF: ACAGTTACCACCGAGGCAGA
R: TCGGTCACTTTCACTGTCAGAGC

[i] NKTR, natural killer-tumor recognition sequence; GAPDH, glyceraldehyde-3-phosphate dehydrogenase; F, forward; R, reverse.

Results

DEG identification

Compared with normal tissues, the genes with altered expression in NSCLC tumors were identified (Table II). The total number of DEGs in tumor tissues of stage IB, IIB, IIIA and IV were 499, 602, 592 and 457, respectively. Then, the DEGs for different tumor stages were subjected to hierarchical clustering analysis based on the gene expression patterns. The heat maps illustrated that all the DEGs were able to distinguish tumor and normal tissues clearly, which suggested that the DEGs identified in the present study were significant (Fig. 1).

Table II.

The top 10 DEGs identified in tumor tissues of different stages.

Table II.

The top 10 DEGs identified in tumor tissues of different stages.

IB vs. normal (n=499)IIB vs. normal (n=602)IIIA vs. normal (n=592)IV vs. normal (n=457)




GeneslogFCP-valueGeneslogFCP-valueGeneslogFCP-valueGeneslogFCP-value
AMDHD1−0.9802532.03E-07EVI5L2.79557476.31E-06LANCL1−1.3152864.62E-08LAPTM4B−0.7559815.68E-05
OR5L2−1.5264924.07E-06DPPA22.23096043.24E-05RNF121−3.6295478.91E-08CEP135−1.065843.22E-05
POLR2D−0.8083975.45E-05FXYD62.18743475.23E-05MKKS−0.9391457.80E-06 C19orf561.11285440.0006198
LRIT3−0.8157373.93E-05EIF2B31.51353334.10E-05GPR177−0.7063763.25E-05COX11−0.5821350.0006956
SLCO3A1−0.9499876.49E-05PERLD1−0.9897654.45E-05CISH−0.7490593.32E-05DOK4−0.58330.0002439
C3orf1−1.0378345.75E-05CCDC65−1.0241914.69E-05VGF−0.9936127.68E-05CDX4−0.5981010.00057
PKM2−0.7853170.000128NUP155−1.5450526.07E-05ENO1−2.2261838.59E-05ANK2−0.676690.0004388
RAB23−0.6748760.0001587 ATP6V0A1−1.0359110.0001129PSME1−0.7582680.000161GEMIN7−0.6864580.0006848
LANCL2−0.6242050.0003193PCDH18−1.3097490.0001096JMJD2D−0.7975160.0001714CNTN6−0.7842640.0005795
CHRNA10−1.2246620.0003353BST22.36639330.000138RAMP1−0.8021020.0001188EIF5−0.8957990.0007118

[i] The DEGs were ranked according to the P-values. DEGs, differentially expressed genes; n, the total number of DEGs; FC, fold-change.

Significant GO function and pathways

The overrepresented GO terms and pathways for DEGs varied according to tumor stage. The DEGs in tumors of IB tumor stage were closely related with negative regulation of signal transduction and cellular di- and trivalent inorganic cation homeostasis biological process and the apoptosis pathway. The genes altered in stage IIB were significantly enriched in genitalia development, the monosaccharide metabolic process and the p53 signaling pathway. The upregulated DEGs in tumors of stages IIIA and IV were mainly related with transcription and synapse regulation-related biological process (Table III).

Table III.

The overrepresented GO terms and pathways for the DEGs.

Table III.

The overrepresented GO terms and pathways for the DEGs.

StagesRegulatedCategoryTermCountP-value
IBUpregulatedBPGO:0009968~negative regulation of signal transduction91.25E-03
GO:0010648~negative regulation of cell communication92.58E-03
GO:0007128~meiotic prophase I34.69E-03
Pathway hsa04210:Apoptosis44.57E-02
DownregulatedBPGO:0030005~cellular di- and trivalent inorganic cation homeostasis126.93E-04
GO:0055066~di- and trivalent inorganic cation homeostasis121.05E-03
GO:0007204~elevation of cytosolic calcium ion concentration81.41E-03
IIBUpregulatedBP GO:0048806~genitalia development33.52E-02
GO:0051054~positive regulation of the DNA metabolic process43.58E-02
Pathwayhsa04115:p53 signaling pathway44.75E-02
DownregulatedBP GO:0005996~monosaccharide metabolic process117.91E-03
GO:0019318~hexose metabolic process108.99E-03
GO:0006006~glucose metabolic process82.27E-02
IIIAUpregulatedBPGO:0010552~positive regulation of specific transcription from RNA polymerase II promoter54.89E-03
GO:0006350~transcription395.68E-03
GO:0016477~cell migration106.27E-03
DownregulatedBPGO:0019320~hexose catabolic process83.74E-04
GO:0010033~response to organic substances293.78E-04
GO:0006091~generation of precursor metabolites and energy174.20E-04
IVUpregulatedBP GO:0050807~regulation of synapse organization41.81E-03
GO:0050803~regulation of synapse structure and activity42.69E-03
GO:0010033~response to organic substances184.82E-03
DownregulatedBP GO:0050892~intestinal absorption31.41E-02
GO:0035036~sperm-egg recognition31.58E-02
GO:0007339~binding of sperm to zona pellucida31.58E-02

[i] DEGs, differentially expressed genes; GO, Gene Ontology; BP, biological process.

PPI network construction

The PPI network for DEGs in tumors of different stages was constructed respectively. The PPI network for stage IB was composed of 276 nodes and 454 pairs. The network for stage IIB included 349 nodes and 615 protein pairs. In addition, there were 366 nodes and 944 pairs for stage IIIA tumors and 252 nodes and 392 pairs for stage IV (Fig. 2).

Identification of the key nodes in the PPI network

Key nodes in the PPI network were identified by integration analysis of the scores calculated based on network centrality and the results of the clustering analysis. There were 29 key nodes in the stage IB group, 33 in the IIB group, 35 in the IIIA group and 54 in the IV group (Fig. 2). All the key nodes identified in the present study were differentially expressed and through their expression patterns we were able to distinguish normal and tumor tissues (Fig. 3). The key nodes were significantly enriched in cancer-related pathways, chronic myeloid leukemia, the chemokine signaling pathway, gap junctions and the insulin signaling pathway (Table IV).

Table IV.

The significant pathways for key nodes in the PPI network.

Table IV.

The significant pathways for key nodes in the PPI network.

StagesTermP-valueGenes
IBhsa05220:Chronic myeloid leukemia2.93E-03STAT5A, NFKBIA, RAF1, MDM2
hsa05215:Prostate cancer4.76E-03PDGFRA, NFKBIA, RAF1, MDM2
hsa04060:Cytokine-cytokine receptor interaction1.74E-02CCR9, PDGFRA, CXCL9, PRL, IL2
hsa05214:Glioma2.49E-02PDGFRA, RAF1, MDM2
hsa05218:Melanoma3.11E-02PDGFRA, RAF1, MDM2
hsa04062:Chemokine signaling pathway3.52E-02CCR9, CXCL9, NFKBIA, RAF1
hsa05200:Pathways in cancer3.62E-02STAT5A, PDGFRA, NFKBIA, RAF1, MDM2
hsa04540:Gap junction4.69E-02CSNK1D, PDGFRA, RAF1
IIBhsa04270:Vascular smooth muscle contraction1.50E-02PPP1CA, MAPK3, ADCY6, PRKCE
hsa04930:Type II diabetes mellitus2.03E-02MAPK3, HK1, PRKCE
hsa04910:Insulin signaling pathway2.46E-02PRKAR2B, PPP1CA, MAPK3, HK1
IVhsa04540:Gap junction7.13E-03EGFR, ADCY2, TUBB2A, PRKG2

[i] PPI, protein-protein interaction.

Venn diagram of DEGs

As shown in Fig. 4, the number of genes that were differentially expressed at each tumor stage was assessed and the diagrams also indicated the common DEGs in tumors of stages IB, IIB, IIIA and IV. Most DEGs were uniquely identified in a single tumor stage and a few genes were commonly differentially expressed in the 4 tumor stages, such as downregulated CNTN6 and LBX2.

Identification of the coexpression gene pairs

A total of 1,984 genes were obtained by pooling the DEGs from different tumor stages, among which the coexpression gene pairs were analyzed. The coexpression network was constructed with 102 nodes and 179 gene interaction pairs (Fig. 5). Among the 102 nodes, 16 genes were involved in 65 gene pairs, such as SEMA4C, C19orf56, MYBBP1A, SPTA1, NKTR, WHSC1L1, FARP2, CDH18, C5orf33, ZNF124, ESRRG, ARHGEF10L, RABGAP1, ZMYM1, PIP5K1B and PGBD3.

NSCLC-related gene identification

According to the CTD database, most of the genes were related with NSCLC during different stages. There were 372 genes (74.55%) among all the DEGs in the IB stage group, 465 (77.24%) for the IIB group, 466 (78.72%) in the IIIA group and 353 (77.24%) for the IV group (Table V).

Table V.

NSCLC-related genes in tumors of different stages.

Table V.

NSCLC-related genes in tumors of different stages.

GroupsTotal DEG countDisease related gene countPercentage
IB49937274.55
IIB60246577.24
IIIA59246678.72
IV45735377.24

[i] NSCLC, non-small cell lung cancer; DEGs, differentially expressed genes.

mRNA expression of NKTR

RT-PCR analysis of NKTR in A549, NCI-H1299 and NCI-H1650 cells was conducted to establish the differential expression in NSCLC, compared with the BEAS-2B cells. As shown in Fig. 6, the mean mRNA expression levels of NKTR in A549, NCI-H1299 and NCI-H1650 cells were significantly higher than those in the BEAS-2B cells (P<0.01), which corresponded to the DEG analysis and further confirmed its significance in NSCLC.

Discussion

In the present study, the gene expression profile analysis of NSCLC tissues of different stages revealed the significantly altered biological functions and pathways and proposed some candidate genes for lung cancer treatment.

The data (GSE21933) analyzed in the study were the cDNA microarray data. Both DNA and RNA microarray data can be used to analyze the expression levels of genes by limma software (1,2). The DEG analysis showed that there were 499, 602, 592 and 457 genes differentially expressed in tumors of stages IB, IIB, IIIA and IV, respectively. Clustering analysis revealed that based on the expression level of DEGs, the tumor and normal samples were clearly distinguished. In addition, literature research analysis confirmed that most of the DEGs identified in our study were NSCLC-related genes. These findings suggested that the DEGs established in our study were significant.

Function analysis showed that the significantly altered biological process in the early lung cancer stage mainly included negative regulation of signal transduction, apoptosis, cellular di- and trivalent inorganic cation homeostasis (stage IB) and positive regulation of the DNA metabolic process and the p53 signaling pathway (stage IIB). Previous evidence has shown that lung cancer-related candidate genes are mainly related with apoptosis, signal transduction and DNA repair. The ion transport is a significantly altered biological process for both the Asian and Caucasian population, which may play a key role in tumorigenesis (8). p53 gene is a tumor-suppressor gene and is reported to be frequently mutated in lung cancer patients (22). Non-disruptive p53 mutations may decrease the survival rate in patients with advanced NSCLC (23). Signal transduction induced by focal adhesion kinase is found to inhibit the p53-mediated apoptosis in malignant cells (24).

In the late-stage NSCLC tumors, the DEGs were closely related with cell migration, response to organic substances (stage IIIA) and regulation of synapse organization, structure and activity (stage IV). Cell migration has been reported to be one of the significantly altered biological processes in NSCLC cancer patients (8), which is consistent with our findings. The biological process of response to organic substances was significantly altered in stage IIIA and IV NSCLC tumors, indicating that this biological process may play a dominant role in the late stages of lung cancer. The role of the synapse regulation-related biological process has not been clarified in lung cancer progression. Thus, the altered biological processes warrant further study. Above all, the biological processes and pathways in NSCLC progression may be altered in a stage-dependent manner. The DEG enriched biological processes and pathways were different in early-stage and late-stage NSCLC, which was determined by Venn diagram analysis. The Venn diagram illustrated that a few genes had similar expression patterns in NSCLC at different stages, which revealed that NSCLC at different stages induced differential gene expression patterns leading to the dysfunction of various biological processes.

Additionally, after removing the overlapped genes, a total of 1,984 DEGs were obtained in tumors of stages IB, IIB, IIIA and IV. The coexpression network composed of 16 genes and 65 coexpression pairs was the most significant. The protein interaction pairs corresponding to DEGs can be predicted by integrating the information deposited in STRING (13). Cytoscape is a free software package and widely used in analyzing the results of mRNA expression profiling, functional genomic and proteomic experiments in the context of an interaction network obtained for genes of interest (14). Thus, protein interactions were able to be obtained based on the microarray data in the study. NKTR was one of the genes among the 16 genes that showed multiple coexpression interactions with others. Simultaneously, PPI analysis showed that NKTR was a key node in stage IV tumor tissues according to degree, betweenness and subgraph centrality. In addition, according to expression patterns of NKTR, tumor and normal tissues could be clearly distinguished. RT-PCR of gene validation analysis showed that NKTR was significantly upregulated in NSCLC cells, compared with normal epithelial cells.

All these findings indicated that NKTR is a significant DEG that plays a critical role in NSCLC progression. NKTR has been found to be differentially expressed in human lung squamous cell carcinoma and is located in chromosome 3p23-p21 according to the cDNA library (25). NKTR as a component of a spliceosome, has functions in protein folding. NKTR-102 is considered as a novel PEGylated-irinotecan conjugate, which functions in the inhibition of the growth of human colorectal and lung tumors (26). Although the role of NKTR in NSCLC has not been clarified, NKTR and its coexpressed genes may play a key role in NSCLC progression.

In this study, the microarray data of GSE21933 were developed from NSCLC patients including 10 early-stage patients (6 stage IB, 3 stage IIB and 1 stage IA) and 11 late-stage patients (5 stage IIIA, 5 stage IV and 1 stage IIIB). Due to the small number of samples at stage IA and IIIB, we only analyzed the remaining 19 tumor samples compared with the corresponding controls. In the original study, the microarray data of GSE21276 were produced from tumor and normal tissues of 40 Asian and 20 Caucasian NSLCL patients and was aimed to characterize the gene expression profiles in different racial groups. However, in the present study, we attempted to analyze the changes in gene expression profiles during the different tumor stages. The detailed information of the 19 patients was not provided in the original study, thus we cannot provide information on the type of tumors. In our present study, we performed gene validation experiments in A549 (adenocarcinoma), NCI-H1299 (large-cell carcinoma) and NCI-H1650 (adenocarcinoma) cells, and determined that NKTR was a candidate gene in NSLCL prevention and treatment.

Despite some previous microarray data that were also developed from a small number of samples (27,28), the small sample size used may be a limitation in our study. Further studies with a larger size of samples are warranted in the near future.

In conclusion, the gene expression patterns are different at various stages of NSCLC tumors, from early-stage to late-stage, which lead to the dysfunction of different biological processes. Different mechanisms may be involved in the progression of NSCLC tumors from early-stage to late-stage. An adequate understanding of the pathogenesis underlying different tumor stages may be helpful in new therapy development. NKTR is commonly differentially expressed in stage IB, IIB, IIIA and IV tumors. NKTR and its coexpressed gene pairs may be candidate genes for the prevention and treatment of NSCLC.

Glossary

Abbreviations

Abbreviations:

NSCLC

non-small cell lung cancer

PPI

protein-protein interaction

NCBI GEO

National Center for Biotechnology Information Gene Expression Omnibus

DAVID

Database for Annotation, Visualization and Integrated Discovery

GO

Gene Ontology

KEGG

Kyoto Encyclopedia of Genes and Genomes

CTD

Comparative Toxicogenomics Database

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March-2017
Volume 37 Issue 3

Print ISSN: 1021-335X
Online ISSN:1791-2431

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Copy and paste a formatted citation
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Spandidos Publications style
Wang J, Song J, Gao Z, Huo X, Zhang Y, Wang W, Qi J and Zheng S: Analysis of gene expression profiles of non-small cell lung cancer at different stages reveals significantly altered biological functions and candidate genes. Oncol Rep 37: 1736-1746, 2017
APA
Wang, J., Song, J., Gao, Z., Huo, X., Zhang, Y., Wang, W. ... Zheng, S. (2017). Analysis of gene expression profiles of non-small cell lung cancer at different stages reveals significantly altered biological functions and candidate genes. Oncology Reports, 37, 1736-1746. https://doi.org/10.3892/or.2017.5380
MLA
Wang, J., Song, J., Gao, Z., Huo, X., Zhang, Y., Wang, W., Qi, J., Zheng, S."Analysis of gene expression profiles of non-small cell lung cancer at different stages reveals significantly altered biological functions and candidate genes". Oncology Reports 37.3 (2017): 1736-1746.
Chicago
Wang, J., Song, J., Gao, Z., Huo, X., Zhang, Y., Wang, W., Qi, J., Zheng, S."Analysis of gene expression profiles of non-small cell lung cancer at different stages reveals significantly altered biological functions and candidate genes". Oncology Reports 37, no. 3 (2017): 1736-1746. https://doi.org/10.3892/or.2017.5380