Meta-analysis of mRNA expression profiles to identify differentially expressed genes in lung adenocarcinoma tissue from smokers and non-smokers.

Compared to other types of lung cancer, lung adenocarcinoma patients with a history of smoking have a poor prognosis during the treatment of lung cancer. How lung adenocarcinoma-related genes are differentially expressed between smoker and non-smoker patients has yet to be fully elucidated. We performed a meta-analysis of four publicly available microarray datasets related to lung adenocarcinoma tissue in patients with a history of smoking using R statistical software. The top 50 differentially expressed genes (DEGs) in smoking vs. non‑smoking patients are shown using heat maps. Additionally, we conducted KEGG and GO analyses. In addition, we performed a PPI network analysis for 8 genes that were selected during a previous analysis. We identified a total of 2,932 DEGs (1,806 upregulated, 1,126 downregulated) and five genes (CDC45, CDC20, ANAPC7, CDC6, ESPL1) that may link lung adenocarcinoma to smoking history. Our study may provide new insights into the complex mechanisms of lung adenocarcinoma in smoking patients, and our novel gene expression signatures will be useful for future clinical studies.

Introduction

Lung cancer is one of the most common types of cancer and is the leading cause of cancer-related mortality wordwide. Small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC) are the most common types of lung cancer, of which NSCLC accounts for approximately 85% of all cases (1). Lung adenocarcinoma is the most common subtype of NSCLC (40%) in many countries (2,3). To date, many genetic factors have been proposed to be involved in lung adenocarcinoma, including several tumour-suppressor genes (TP53, CDKN2A, STK11, NF1, ATM, RB1, and APC) (4,5). Several new targeted therapies have resulted in considerable clinical benefits for cancer patients in recent years, as well as a deeper understanding of lung adenocarcinoma at the molecular level. One example of a new targeted therapy is epidermal growth factor receptor (EGFR) and KRAS targeted gene therapy (6,7). However, targeted gene therapy is mainly used when patients have special characteristics. EGFR mutations occur more frequently in female lung adenocarcinoma patients with a non-smoking history (8). HER2 mutations tend to occur in non-smoking males (9). In contrast, KRAS mutations occur during the early development of smoking-related lung adenocarcinoma (10). Based on these observations, there is a need to develop individualized treatment programs for patients with unique clinical characteristics. Lung adenocarcinoma is caused by a combination of genetic and environmental effects (11).

More recently, the incidence of lung adenocarcinoma has increased in smokers (12). Tobacco smoke contains a mixture of harmful compounds and carcinogens (13). Therefore, smoking plays an important role in the development of lung adenocarcinoma. Although the correlation between smoking and lung adenocarcinoma has been demonstrated in previous studies, a meta-analysis of the gene mutations in a large number of tissue samples that considers the smoking history in lung adenocarcinoma has not yet been conducted (14). This large scale analysis can reduce the differences caused by different research conditions and can integrate the results from previous studies to evaluate the issue from another point of view. The development of microarray methods for large scale analysis of gene expression makes it possible to perform a more comprehensive analysis for potential genes and molecular pathways associated with lung adenocarcinoma in smoking patients (15). DNA microarray analysis has been applied to investigate whole genomic expression profiles and physiological mechanisms in health and disease (16,17). Therefore, a high-throughput microarray experiment was designed to analyse the genetic expression patterns and identify potential genes to target for lung adenocarcinoma (18). Meta-analysis provides a powerful tool for analysing microarray experiments by combining data from multiple studies (19). Genes identified by meta-analysis tend to overlap with genes identified in other studies, suggesting increased reliability (20). In addition to providing a new perspective, this research topic will further the understanding of the relationship between smoking and lung adenocarcinoma.

The aim of this study was to identify possible candidate genes for personalized treatment for lung adenocarcinoma patients with a history of smoking to provide patients with better treatment options and ensure a good prognosis. Therefore, we conducted a meta-analysis using the same platform of gene expression profile data that associated smoking with lung adenocarcinoma tissue.

Materials and methods

Selection of microarray datasets for meta-analysis

According to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines published in 2009, we performed a detailed and comprehensive search of microarray datasets in the Gene Expression Omnibus (GEO) database of the National Center for Biotechnology Information (NCBI) (http://www.ncbi.nlm.nih.gov/geo/).

Meta-analysis data

To maintain objectivity, the data were simultaneously extracted by two independent reviewers from the original search. Any discrepancies that arose between the two reviewers were resolved by consultation with a third reviewer. The terms ‘lung neoplasms’ and ‘lung cancer’ were considered keywords during our search for this study. In addition, studies that reported non-human data were excluded in the selection process for microarray datasets. Finally, 583 datasets were obtained from searching the Gene Expression Omnibus (GEO) database. Datasets with >20,288 samples were elected for the study. We included a dataset in the meta-analysis if it contained i) all samples on the Affymetrix Human Genome U133 Plus 2.0 Array platform, ii) samples from lung adenocarcinoma tissue and iii) samples with valid smoking statuses. According to the criteria, the four datasets that were selected from the 288 datasets included 477 lung adenocarcinoma tissues with valid smoking statuses. Then, we downloaded the lung adenocarcinoma tissue files (CEL) of the four microarray datasets from the GEO database with accession numbers GSE12667, GSE31210, GSE40791, and GSE50081. The four datasets included 477 lung adenocarcinoma patients; 327 of which were smokers, and 150 were non-smokers; the smokers included former smokers, current smokers and ex-smokers.

Meta-analysis of microarray datasets using the same platform

We conducted the meta-analysis of gene expression profiles of the selected four microarray datasets by using R statistical software (http://www.r-project.org/) with the same platform. Prior to the meta-analysis, we performed data normalization of the four datasets using R statistical software. Then, we processed the meta-analysis using the MAMA, mataMA, affyPLM and CLL packages in R statistical software according to the t-test and z-score methods. During the meta-analysis with R statistical software, a list of differentially expressed genes (DEGs) (upregulated or downregulated) were identified based on the P-values (where the threshold was <0.005) and z-scores (where the threshold was an absolute value >3).

Enrichment analysis of the GO function and KEGG pathway

It is important to understand the biological implications of the identified DEGs in lung adenocarcinoma tissue. According to the meta-analysis results, the most significant 200 DEGs (100 upregulated and 100 downregulated) were selected for enrichment analysis. Then, we conducted the functional enrichment analysis of the gene ontology (GO) function and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway using the WEB-based GEne SeT AnaLysis Toolkit (http://bioinfo.vanderbilt.edu/webgestalt/login.php) under a significance threshold of P<0.05.

PPI network analysis

To further understand and predict the biological activity of the identified DEGs that were based on the results of the GO function and KEGG pathway enrichment analyses, we conducted a protein-protein interaction (PPI) network using the Cytoscape software. Prior to this analysis, we imported the DEG-encoding proteins into a protein-protein interaction (PPI) network, which was downloaded from the Biological General Repository for Interaction Datasets (BioGRID, http://thebiogrid.org/).

Results

Selection of microarray datasets related to lung adenocarcinoma for meta-analysis

From the microarray datasets retrieved from the GEO database of NCBI, we extracted 477 GEO lung adenocarcinoma samples that belonged to four microarray datasets, which met our criteria for meta-analysis (see Materials and methods, and Fig. 1). All four GEO series (GSEs) were microarray datasets that used only lung adenocarcinoma tissue with valid smoking statuses. The GEO Platform Files (GPLs) from the four datasets (GSE12667, GSE31210, GSE40791 and GSE50081) were obtained using the Affymetrix ‘Gene Chip’ (Table I).

Figure 1.

Selection process of the microarray datasets for meta-analysis of lung adenocarcinoma tissue with smoking status.

Table I.

Characteristic of individual studies retrieved from Gene Expression Omnibus for meta-analysis.

	Sample
Dataset	Smoking status	Non-smoking status	Tissue	Platform
GSE12667	40	8	Lung adenocarcinoma	Affymetrix Human Genome U133 Plus 2.0 Array
GSE31210	111	115	Lung adenocarcinoma	Affymetrix Human Genome U133 Plus 2.0 Array
GSE40791	82	4	Lung adenocarcinoma	Affymetrix Human Genome U133 Plus 2.0 Array
GSE50081	94	23	Lung adenocarcinoma	Affymetrix Human Genome U133 Plus 2.0 Array

Identification of upregulated or downregulated DEGs through meta-analysis

We performed the meta-analysis of gene expression profiles according to t-test and z-score methods using MAMA, mataMA, affyPLM and CLL packages in R statistical software on the same platform. According to the P-value (where the threshold was <0.005) and z-score (where the threshold was an absolute value >3), we were able to identify a total of 2,932 DEGs, including 1,806 upregulated and 1,126 downregulated genes using Venny 2.0 (http://bioinfogp.cnb.csic.es/tools/venny/index.html). The 200 genes that showed maximum upregulation and downregulation are shown in Tables II and III, and the overlapping DEGs based on P-values and z-scores are shown in Fig. 2. A subset of the top 50 DEGs (25 upregulated and 25 downregulated) in the four microarray datasets were visualized with heat maps using the Mev software and are shown in Figs. 3–6.

Table II.

The 100 upregulated genes.

Probe ID	Gene	P-value	z-score
218670_at	PUS1	1.26565E-14	−3.364765896
202856_s_at	SLC16A3	1.31006E-14	−3.005755138
1553984_s_at	DTYMK	2.73115E-14	−3.77721059
210052_s_at	TPX2	3.28626E-14	−3.156028484
225620_at	RAB35	6.72795E-14	−3.977400883
201710_at	MYBL2	1.13465E-13	−3.753904206
200896_x_at	HDGF	1.32117E-13	−6.606272774
233986_s_at	PLEKHG2	1.34559E-13	−4.721664344
209186_at	ATP2A2	1.52767E-13	−3.331133151
202954_at	UBE2C	1.96732E-13	−3.433957425
234992_x_at	ECT2	2.22933E-13	−3.540186205
218468_s_at	GREM1	2.91323E-13	−3.421989473
221591_s_at	FAM64A	3.1064E-13	−3.645233189
223308_s_at	WDR5	3.71925E-13	−3.441383479
204092_s_at	AURKA	4.20552E-13	−4.669115008
218593_at	RBM28	5.6688E-13	−3.725504934
204962_s_at	SLC35F6	6.05294E-13	−3.16224673
218726_at	HJURP	9.13047E-13	−3.516355847
206364_at	KIF14	1.22724E-12	−3.097688744
202870_s_at	CDC20	1.31761E-12	−3.025537109
212680_x_at	PPP1R14B	1.41753E-12	−3.30292041
220651_s_at	MCM10	1.66711E-12	−3.962832885
222441_x_at	SLMO2	1.88827E-12	−3.580783528
212541_at	FLAD1	2.68452E-12	−4.335857984
223931_s_at	CHFR	2.91989E-12	−5.133807637
203612_at	BYSL	2.94276E-12	−3.332540528
219874_at	SLC12A8	3.14992E-12	−4.228880162
229538_s_at	IQGAP3	3.39373E-12	−4.67663851
38158_at	ESPL1	3.52074E-12	−4.330276826
224753_at	CDCA5	3.8165E-12	−3.102794749
200044_at	SRSF9	5.19895E-12	−4.335016805
234915_s_at	DENR	6.64646E-12	−3.045464333
206316_s_at	KNTC1	7.17115E-12	−3.034017863
225468_at	PATL1	7.18048E-12	−4.555045317
200756_x_at	CALU	7.89546E-12	−3.573314992
202095_s_at	BIRC5	8.23586E-12	−3.071731969
209464_at	AURKB	8.59246E-12	−5.290213575
204430_s_at	SLC2A5	9.54348E-12	−3.999406252
219918_s_at	ASPM	9.98956E-12	−3.385882475
218512_at	WDR12	1.10383E-11	−3.127647757
203702_s_at	TTLL4	1.10745E-11	−3.222581427
242944_at	FAM83A	1.14144E-11	−6.56980268
206205_at	MPHOSPH9	1.17426E-11	−3.286743793
221520_s_at	CDCA8	1.222E-11	−3.189226567
220011_at	AUNIP	1.32323E-11	−5.645650742
203004_s_at	MEF2D	1.41975E-11	−6.628593875
204005_s_at	PAWR	1.44695E-11	−4.589047842
200744_s_at	GNB1	1.57292E-11	−3.309783419
202580_x_at	FOXM1	1.92268E-11	−3.156340828
201761_at	MTHFD2	2.141E-11	−3.158744955
204603_at	EXO1	2.21381E-11	−3.093222948
225401_at	C1orf85	2.37168E-11	−4.583223012
228703_at	P4HA3	2.44789E-11	−4.354770166
204709_s_at	KIF23	2.78617E-11	−3.130038648
212322_at	SGPL1	3.15128E-11	−3.303129755
202779_s_at	UBE2S	3.25431E-11	−3.246262139
210386_s_at	MTX1	3.28946E-11	−3.499628552
205733_at	BLM	3.44063E-11	−3.183717987
223307_at	CDCA3	3.49276E-11	−3.223011207
1555943_at	PGAM5	3.49287E-11	−4.908658645
219493_at	SHCBP1	3.69571E-11	−3.171551777
223785_at	FANCI	4.13012E-11	−3.72118368
212021_s_at	MKI67	4.16123E-11	−3.291213712
200750_s_at	RAN	4.22222E-11	−3.060882727
229892_at	EP400NL	4.39129E-11	−4.569469931
204126_s_at	CDC45	4.39451E-11	−3.107729352
226949_at	GOLGA3	4.51967E-11	−3.569550938
205895_s_at	NOLC1	4.80713E-11	−3.479055682
205691_at	SYNGR3	4.92397E-11	−6.345274404
204641_at	NEK2	4.94367E-11	−3.260850411
223365_at	DHX37	5.08806E-11	−6.413792983
229610_at	CKAP2L	5.22091E-11	−3.506800101
207590_s_at	CENPI	5.60811E-11	−3.706888048
224742_at	ABHD12	6.35478E-11	−3.351775356
209052_s_at	WHSC1	6.63429E-11	−3.610265902
206074_s_at	HMGA1	6.86768E-11	−3.035687751
225554_s_at	ANAPC7	7.7532E-11	−4.210797517
204649_at	TROAP	8.73972E-11	−3.344919358
212871_at	MAPKAPK5	9.64493E-11	−6.062517519
201954_at	ARPC1B	1.04984E-10	−3.29272791
203967_at	CDC6	1.15562E-10	−3.032999971
205024_s_at	RAD51	1.27276E-10	−3.317013997
201127_s_at	ACLY	1.40898E-10	−3.598775099
201292_at	TOP2A	1.69439E-10	−3.586121076
1555274_a_at	EPT1	1.82091E-10	−3.107139925
222077_s_at	RACGAP1	1.98689E-10	−3.463568797
212949_at	NCAPH	2.04934E-10	−3.123094613
214866_at	PLAUR	2.8521E-10	−6.066208054
209836_x_at	BOLA2B	3.03036E-10	−3.581736948
236957_at	CDCA2	3.37438E-10	−3.267349523
204318_s_at	GTSE1	3.6192E-10	−3.165321627
222622_at	PGP	3.89473E-10	−3.166188967
218497_s_at	RNASEH1	4.25561E-10	−3.276072648
218984_at	PUS7	4.45897E-10	−4.331098443
205394_at	CHEK1	4.6472E-10	−3.071160119
210821_x_at	CENPA	4.95303E-10	−3.345790152
223484_at	C15orf48	6.08452E-10	−3.301630777
213523_at	CCNE1	6.55394E-10	−4.360746545
209642_at	BUB1	7.26076E-10	−3.325492652
202240_at	PLK1	8.52925E-10	−3.537560833

Table III.

The 100 downregulated genes.

Probe ID	Gene	P-value	z-score
225956_at	CREBRF	0	3.056084
209740_s_at	PNPLA4	0	8.750866
204754_at	HLF	0	3.370263
230163_at	GFRA1	0	3.162875
242496_at	ART4	0	3.160279
221518_s_at	USP47	0	4.047036
235830_at	NT5DC1	0	3.951365
235155_at	BDH2	0	3.138416
208741_at	SAP18	0	3.588813
228692_at	PREX2	0	3.033953
211999_at	MIR4738	0	3.297597
227562_at	LAMTOR3	0	3.340261
229573_at	USP9X	2.22E-16	4.870675
205756_s_at	F8	2.22E-16	3.20333
229319_at	BC022047	2.22E-16	3.024973
228411_at	PARD3B	4.44E-16	3.454669
212425_at	SCAMP1	4.44E-16	3.064577
213876_x_at	ZRSR2	4.44E-16	5.174619
239252_at	COX7B	4.44E-16	3.999039
200933_x_at	RPS4X	4.44E-16	5.299386
210829_s_at	SSBP2	4.44E-16	3.082665
206767_at	RBMS3	6.66E-16	3.71459
226709_at	ROBO2	6.66E-16	3.615428
203991_s_at	KDM6A	8.88E-16	5.796073
227274_at	SYNJ2BP-COX16	1.11E-15	3.517758
228504_at	SCN7A	1.78E-15	3.16819
225998_at	GAB1	2E-15	3.00431
218346_s_at	SESN1	2.44E-15	3.055691
224976_at	NFIA	3.11E-15	3.007387
205857_at	SLC18A2	4.22E-15	3.457499
225352_at	SEC62	6.88E-15	3.26132
200810_s_at	CIRBP	1.49E-14	3.072028
200983_x_at	CD59	2.22E-14	3.24769
212249_at	PIK3R1	2.44E-14	4.98666
241689_at	METTL14	3.42E-14	3.311901
228716_at	THRB	4.88E-14	3.021776
205259_at	NR3C2	5E-14	3.392261
223588_at	THAP2	5.44E-14	6.445672
201427_s_at	SEPP1	6.02E-14	3.146142
219427_at	FAT4	7.7E-14	3.056389
209807_s_at	NFIX	7.97E-14	3.105386
201498_at	USP7	8.55E-14	3.827248
228243_at	RP11-5C23.1	8.84E-14	3.43588
238786_at	ANK3	1.58E-13	3.075604
233249_at	LOC100507073	1.61E-13	3.069721
208633_s_at	MACF1	1.79E-13	3.260397
226816_s_at	KIAA1143	1.94E-13	3.431996
208792_s_at	CLU	2.46E-13	3.627978
210426_x_at	RORA	2.51E-13	3.077789
229969_at	SEC63	2.86E-13	3.019815
225811_at	C11orf58	2.90212E-13	3.095344537
227847_at	EPM2AIP1	3.27738E-13	3.460553723
201019_s_at	EIF1AX	3.35065E-13	4.257274339
223695_s_at	ARSD	3.475E-13	5.635180257
228905_at	PCM1	3.53051E-13	3.340750721
217707_x_at	SMARCA2	3.67262E-13	4.020194349
225093_at	UTRN	6.21503E-13	3.138806562
227425_at	REPS2	7.33413E-13	3.055352168
211734_s_at	FCER1A	8.45324E-13	3.411503985
244007_at	ZNF462	9.36362E-13	3.786986943
212675_s_at	CEP68	1.00742E-12	3.307657084
238454_at	ZNF540	1.13221E-12	3.186059238
224889_at	FOXO3	1.14175E-12	3.853408162
1558512_at	RP11-819C21.1	1.37579E-12	3.144887286
213802_at	PRSS12	1.47216E-12	4.357472705
225465_at	MAGI1	1.47393E-12	4.208157151
223126_s_at	C1orf21	1.56142E-12	3.186640389
230479_at	EIF3F	1.58984E-12	3.299359045
228448_at	MAP6	1.66223E-12	3.143593284
217779_s_at	PNRC2	1.91847E-12	3.246325539
1560648_s_at	TSPYL1	1.9309E-12	3.760805629
212936_at	FAM172A	2.19358E-12	4.299840018
227091_at	CCDC146	2.29194E-12	3.206298087
221564_at	PRMT2	2.38565E-12	3.547995663
43427_at	ACACB	2.44649E-12	3.004593504
229384_at	CTC-429P9.3	2.57394E-12	3.228782722
222663_at	RIOK2	2.69118E-12	3.35934368
238472_at	FBXO9	2.69273E-12	3.562133246
222533_at	CRBN	2.82396E-12	3.004216036
228751_at	CLK4	3.30425E-12	3.359190366
208832_at	ATXN10	3.36042E-12	3.408974266
238043_at	ARID1B	3.38618E-12	3.280003422
1559412_at	LINC00478	3.50475E-12	4.041998876
238081_at	WDFY3-AS2	3.68106E-12	3.077236586
228760_at	SRSF8	4.13358E-12	3.538832842
235240_at	ATXN3	4.47198E-12	3.59474854
240806_at	RPL15	5.22404E-12	3.229351616
228027_at	GPRASP2	5.30198E-12	3.191435286
209815_at	PTCH1	5.63194E-12	3.080285017
208760_at	UBE2I	6.31295E-12	3.075043093
229317_at	KPNA5	6.53722E-12	3.749106743
228420_at	PDCD2	7.1736E-12	3.442288871
227520_at	TXLNG	7.54685E-12	5.386988658
244294_at	GTF2H5	7.70273E-12	4.035395557
204011_at	SPRY2	7.75358E-12	3.811245705
209614_at	ADH1B	7.83396E-12	3.188622844
226774_at	FAM120B	8.43059E-12	3.286960689
235612_at	PRPF38A	1.023E-11	3.636955078
232122_s_at	VEPH1	1.20886E-11	3.052642894
216342_x_at	RPS4XP2	1.22578E-11	6.967247025

Figure 2.

The 2932 overlapping differentially expressed genes (DEGs) based on P-value (where the threshold was <0.005) and z-score (where the threshold was an absolute value >3) were detected using Venny 2.1.0.

Figure 3.

Heat-map representation of the expression profiles for the top 25 upregulated and downregulated genes in the GSE12667 dataset. The clustering of the selected genes on the heat-map was performed by using a hierarchical clustering algorithm that uses an average linkage method and Pearson's correlation coefficient.

Figure 6.

Heat-map representation of the expression profiles for the top 25 upregulated and downregulated differentially expressed genes (DEGs) in the GSE50081 dataset. The clustering of the selected genes on the heat-map was performed using a hierarchical clustering algorithm that uses an average linkage method and Pearson's correlation coefficient.

Enrichment analysis of the GO function and KEGG pathway for the top 100 upregulated and downregulated DEGs

We classified the 200 DEGs that were identified through meta-analysis according to the GO hierarchy into functional categories (biological process, molecular function, and cellular component) and based on the KEGG pathway, with a significance threshold of <0.05. The most significant GO terms under the biological processes category were enriched in the following descending order: ‘cell cycle phase’ (GO:0022403), ‘M phase of mitotic cell cycle’ (GO:0000087) and ‘mitotic cell cycle’ (GO:0000278). The most enriched GO terms under the molecular functions and cellular components categories were ‘protein binding’ (GO:0005515) and ‘nuclear part’ (GO:0044428). The most enriched KEGG pathway terms were (in descending order): ‘Cell cycle’ (kegg:04110), ‘Oocyte meiosis’ (kegg:04114) and ‘Ubiquitin mediated proteolysis’ (kegg:04120) (Tables IV and V).

Table IV.

The enrichment based on the top 10 GO functions shows the top 100 upregulated and downregulated DEGs.

GO ID	GO term	No. of Genes	P-value
GO:0022403	Cell cycle phase	48	3.26E-18
GO:0000087	M phase of mitotic cell cycle	33	6.78E-18
GO:0022402	Cell cycle process	52	6.78E-18
GO:0000278	Mitotic cell cycle	45	6.78E-18
GO:0044428	Nuclear part	70	6.12E-10
GO:0031981	Nuclear lumen	64	1.48E-09
GO:0044422	Organelle part	112	1.63E-09
GO:0005515	Protein binding	112	1.27E-05
GO:0042975	Peroxisome proliferator activated receptor binding	3	0.0097
GO:0019899	Enzyme binding	25	0.0135

GO, gene ontology; DEGs, differentially expressed genes.

Table V.

The enrichment based on the top KEGG pathway shows the top 100 upregulated and downregulated DEGs.

KEGG ID	KEGG pathway	No. of Genes	P-value
kegg:04110	Cell cycle	8	2.45E-06
kegg:04114	Oocyte meiosis	7	9.76E-06
kegg:04120	Ubiquitin mediated proteolysis	5	0.0032
kegg:03013	RNA transport	5	0.0036
kegg:04610	Complement and coagulation cascades	3	0.013
kegg:04115	p53 signalling pathway	3	0.013
kegg:05200	Pathways in cancer	6	0.013
kegg:03060	Protein export	2	0.0144
kegg:03008	Ribosome biogenesis in eukaryotes	3	0.0152
kegg:03440	Homologous recombination	2	0.0168

KEGG, Kyoto Encyclopedia of Genes and Genomes; DEGs, differentially expressed genes.

PPI network analysis of the DEGs

To understand the biological meaning of the 8 upregulated DEGs identified by the KEGG pathway under the cell cycle pathway at the protein level, we constructed a PPI network for the proteins encoded by the 8 DEGs with interactions that included 541 nodes and 671 edges as shown in Fig. 7.

Figure 7.

Protein-protein interaction (PPI) network of the 8 upregulated differentially expressed genes (DEGs).

Discussion

In the present study, we showed that genes are differentially expressed in lung adenocarcinoma in smoking and non-smoking patients. Some genes that showed the highest expression levels were found in lung adenocarcinoma patients who had a smoking history. Smoking consistently plays an important role in the development of lung adenocarcinoma. Cigarette smoke contains over 400 identified chemicals, at least 250 of which are implicated in tumour initiation and promotion (21). It is estimated that more than 50 chemicals in tobacco smoke cause cancers (22). Cigarette smoke is by far the most widespread link between exposure to known carcinogens and death from lung cancer (23). Lung adenocarcinoma is one of the main types of lung cancer in smokers and cannot be successfully treated with traditional treatments. Therefore, the effects of cigarette smoke on the genes that are implicated in lung adenocarcinoma are critical to increase our understanding of the carcinogenesis and in finding targeted genes. In our study, we found that the cell cycle pathway was significantly altered in lung adenocarcinoma tissues from patients with a smoking history.

Using several perspectives would allow us to characterise the underlying mechanisms of lung adenocarcinoma in smokers. Thus, we performed a meta-analysis of four independent microarray datasets using the same platform. The large number of DEGs identified in our study implies that our approach produces more reliable results in identifying differences in gene expression levels among lung adenocarcinoma patients who either had a smoking or a non-smoking history. In this study, the microarray expression datasets derived from lung adenocarcinoma tissue with patients with either a smoking or non-smoking history were publicly available. A number of previous studies have molecularly characterised the genetic profiles in lung cancer patients with or without a smoking history. The present investigation focused on a relatively larger cohort with 477 lung adenocarcinoma tissues from 327 smoking patients and 150 non-smoking patients, thereby providing a more powerful analysis. Our study results were highly consistent with previous DEG analyses, supporting the utility and validity of this analytical approach. Additionally, it also revealed that multiple biological processes and pathways, including cell cycle phase and the cell cycle pathway, were significantly affected in lung adenocarcinoma tissues from smoking patients compared to the non-smoking patients. Consistently, many previous studies have revealed that cigarette smoke extract accelerated premature gene mutations in the cell cycle pathway. Cigarette smoke extract alters the cell cycle via the phospholipid transfer protein/transforming growth factor-β1/cyclinD1/CDK4 pathway (24). Cigarette smoking is a major factor for many cancers including, pancreatic cancer, human ovarian cancer and colon cancer (25–27). This study identified the 8 overexpressed genes in the cell cycle pathway as CDC45, PLK1, CDC20, ANAPC7, CDC6, CHEK1, CCNE1 and ESPL1. According to the P-values in the meta-analysis, we identified a few significant DEGs including CDC45, CDC20, ANAPC7, CDC6, and ESPL1. Based on our meta-analysis results, these five genes may be potential target genes for the treatment of this disease.

CDC45 is a member of the highly conserved multiprotein complex including Cdc6/Cdc18. The replication factor CDC45 has essential functions in the initiation and plays an important role in the intra-S-phase checkpoint (28). CDC45 has been found to be upregulated in many neoplasms, such as breast neoplasms, colorectal neoplasms, lung neoplasms and haematological neoplasms (29).

CDC20 appears to act as a regulatory protein by interacting with several other proteins at multiple points in the cell cycle (30). The CDC20 gene might play an important role in the malignancy of NSCLC. Additionally, CDC20 has been found to be upregulated in lung cancer patients with a smoking history (31). In addition, through this analysis, we identified the overexpression of the CDC20 gene in lung adenocarcinoma patients who had a smoking history compared to the non-smoking patients. Combined with previous research, our analysis demonstrates that the CDC20 gene might play an important role in the treatment of lung adenocarcinoma in smoking patients.

ANAPC7 is an E3 ligase enzyme that ubiquinates various proteins involved in the cell cycle (32). This protein complex may have a pivotal role in the cell cycle control affecting pathological conditions such as cancer (33). ANAPC mutations have been reported in lung squamous cell carcinoma and small cell lung carcinoma.

CDC6, a cell cycle regulatory gene, is an essential regulator of DNA replication and plays important roles in the activation and maintenance of the checkpoint mechanism in the cell cycle (34). CDC6 has been associated with the oncogenic activities in human cancers, such as ovarian cancer, lung cancer and prostate cancer (35,36). However, the biological function and clinical significance of CDC6 in lung adenocarcinoma remain unclear. A previous study suggests that CDC6 is associated with the decline in lung function of ex-smoking in COPD (37). Our study also revealed CDC6 overexpression in lung adenocarcinoma patients with a smoking history compared to non-smoking patients.

ESPL1 is a protein-coding gene, and its overexpression has been found in a variety of human cancers such as rectum adenocarcinoma, prostate carcinoma, breast carcinoma and lung carcinoma (38,39). Consistent with earlier results, our study revealed that ESPL is overexpressed in lung adenocarcinoma in patients with a smoking history compared to those who had a non-smoking history.

Overall, the present study identified that a few genes are differentially expressed in lung adenocarcinoma samples between smoker and non-smoker patients. This observation supports previous studies; however, our analysis provides new insights that enable better understanding of the molecular mechanisms of lung adenocarcinoma in smokers, which may provide potential targets for the therapeutic design of individualized treatments for lung adenocarcinoma patients who have a smoking history.