Open Access

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

  • Authors:
    • Xiaona He
    • Cheng Zhang
    • Chao Shi
    • Quqin Lu
  • View Affiliations

  • Published online on: January 8, 2018     https://doi.org/10.3892/or.2018.6197
  • Pages: 929-938
  • Copyright: © He et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

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).

Table I.

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

Table I.

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

Sample

DatasetSmoking statusNon-smoking statusTissuePlatform
GSE12667  40     8Lung adenocarcinomaAffymetrix Human Genome U133 Plus 2.0 Array
GSE31210111115Lung adenocarcinomaAffymetrix Human Genome U133 Plus 2.0 Array
GSE40791  82     4Lung adenocarcinomaAffymetrix Human Genome U133 Plus 2.0 Array
GSE50081  94  23Lung adenocarcinomaAffymetrix 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. 36.

Table II.

The 100 upregulated genes.

Table II.

The 100 upregulated genes.

Probe IDGeneP-valuez-score
218670_atPUS11.26565E-14−3.364765896
202856_s_atSLC16A31.31006E-14−3.005755138
1553984_s_atDTYMK2.73115E-14−3.77721059
210052_s_atTPX23.28626E-14−3.156028484
225620_atRAB356.72795E-14−3.977400883
201710_atMYBL21.13465E-13−3.753904206
200896_x_atHDGF1.32117E-13−6.606272774
233986_s_atPLEKHG21.34559E-13−4.721664344
209186_atATP2A21.52767E-13−3.331133151
202954_atUBE2C1.96732E-13−3.433957425
234992_x_atECT22.22933E-13−3.540186205
218468_s_atGREM12.91323E-13−3.421989473
221591_s_atFAM64A3.1064E-13−3.645233189
223308_s_atWDR53.71925E-13−3.441383479
204092_s_atAURKA4.20552E-13−4.669115008
218593_atRBM285.6688E-13−3.725504934
204962_s_atSLC35F66.05294E-13−3.16224673
218726_atHJURP9.13047E-13−3.516355847
206364_atKIF141.22724E-12−3.097688744
202870_s_atCDC201.31761E-12−3.025537109
212680_x_atPPP1R14B1.41753E-12−3.30292041
220651_s_atMCM101.66711E-12−3.962832885
222441_x_atSLMO21.88827E-12−3.580783528
212541_atFLAD12.68452E-12−4.335857984
223931_s_atCHFR2.91989E-12−5.133807637
203612_atBYSL2.94276E-12−3.332540528
219874_atSLC12A83.14992E-12−4.228880162
229538_s_atIQGAP33.39373E-12−4.67663851
38158_atESPL13.52074E-12−4.330276826
224753_atCDCA53.8165E-12−3.102794749
200044_atSRSF95.19895E-12−4.335016805
234915_s_atDENR6.64646E-12−3.045464333
206316_s_atKNTC17.17115E-12−3.034017863
225468_atPATL17.18048E-12−4.555045317
200756_x_atCALU7.89546E-12−3.573314992
202095_s_atBIRC58.23586E-12−3.071731969
209464_atAURKB8.59246E-12−5.290213575
204430_s_atSLC2A59.54348E-12−3.999406252
219918_s_atASPM9.98956E-12−3.385882475
218512_atWDR121.10383E-11−3.127647757
203702_s_atTTLL41.10745E-11−3.222581427
242944_atFAM83A1.14144E-11−6.56980268
206205_atMPHOSPH91.17426E-11−3.286743793
221520_s_atCDCA81.222E-11−3.189226567
220011_atAUNIP1.32323E-11−5.645650742
203004_s_atMEF2D1.41975E-11−6.628593875
204005_s_atPAWR1.44695E-11−4.589047842
200744_s_atGNB11.57292E-11−3.309783419
202580_x_atFOXM11.92268E-11−3.156340828
201761_atMTHFD22.141E-11−3.158744955
204603_atEXO12.21381E-11−3.093222948
225401_atC1orf852.37168E-11−4.583223012
228703_atP4HA32.44789E-11−4.354770166
204709_s_atKIF232.78617E-11−3.130038648
212322_atSGPL13.15128E-11−3.303129755
202779_s_atUBE2S3.25431E-11−3.246262139
210386_s_atMTX13.28946E-11−3.499628552
205733_atBLM3.44063E-11−3.183717987
223307_atCDCA33.49276E-11−3.223011207
1555943_atPGAM53.49287E-11−4.908658645
219493_atSHCBP13.69571E-11−3.171551777
223785_atFANCI4.13012E-11−3.72118368
212021_s_atMKI674.16123E-11−3.291213712
200750_s_atRAN4.22222E-11−3.060882727
229892_atEP400NL4.39129E-11−4.569469931
204126_s_atCDC454.39451E-11−3.107729352
226949_atGOLGA34.51967E-11−3.569550938
205895_s_atNOLC14.80713E-11−3.479055682
205691_atSYNGR34.92397E-11−6.345274404
204641_atNEK24.94367E-11−3.260850411
223365_atDHX375.08806E-11−6.413792983
229610_atCKAP2L5.22091E-11−3.506800101
207590_s_atCENPI5.60811E-11−3.706888048
224742_atABHD126.35478E-11−3.351775356
209052_s_atWHSC16.63429E-11−3.610265902
206074_s_atHMGA16.86768E-11−3.035687751
225554_s_atANAPC77.7532E-11−4.210797517
204649_atTROAP8.73972E-11−3.344919358
212871_atMAPKAPK59.64493E-11−6.062517519
201954_atARPC1B1.04984E-10−3.29272791
203967_atCDC61.15562E-10−3.032999971
205024_s_atRAD511.27276E-10−3.317013997
201127_s_atACLY1.40898E-10−3.598775099
201292_atTOP2A1.69439E-10−3.586121076
1555274_a_atEPT11.82091E-10−3.107139925
222077_s_atRACGAP11.98689E-10−3.463568797
212949_atNCAPH2.04934E-10−3.123094613
214866_atPLAUR2.8521E-10−6.066208054
209836_x_atBOLA2B3.03036E-10−3.581736948
236957_atCDCA23.37438E-10−3.267349523
204318_s_atGTSE13.6192E-10−3.165321627
222622_atPGP3.89473E-10−3.166188967
218497_s_atRNASEH14.25561E-10−3.276072648
218984_atPUS74.45897E-10−4.331098443
205394_atCHEK14.6472E-10−3.071160119
210821_x_atCENPA4.95303E-10−3.345790152
223484_atC15orf486.08452E-10−3.301630777
213523_atCCNE16.55394E-10−4.360746545
209642_atBUB17.26076E-10−3.325492652
202240_atPLK18.52925E-10−3.537560833

Table III.

The 100 downregulated genes.

Table III.

The 100 downregulated genes.

Probe IDGeneP-valuez-score
225956_atCREBRF03.056084
209740_s_atPNPLA408.750866
204754_atHLF03.370263
230163_atGFRA103.162875
242496_atART403.160279
221518_s_atUSP4704.047036
235830_atNT5DC103.951365
235155_atBDH203.138416
208741_atSAP1803.588813
228692_atPREX203.033953
211999_atMIR473803.297597
227562_atLAMTOR303.340261
229573_atUSP9X2.22E-164.870675
205756_s_atF82.22E-163.20333
229319_atBC0220472.22E-163.024973
228411_atPARD3B4.44E-163.454669
212425_atSCAMP14.44E-163.064577
213876_x_atZRSR24.44E-165.174619
239252_atCOX7B4.44E-163.999039
200933_x_atRPS4X4.44E-165.299386
210829_s_atSSBP24.44E-163.082665
206767_atRBMS36.66E-163.71459
226709_atROBO26.66E-163.615428
203991_s_atKDM6A8.88E-165.796073
227274_atSYNJ2BP-COX161.11E-153.517758
228504_atSCN7A1.78E-153.16819
225998_atGAB12E-153.00431
218346_s_atSESN12.44E-153.055691
224976_atNFIA3.11E-153.007387
205857_atSLC18A24.22E-153.457499
225352_atSEC626.88E-153.26132
200810_s_atCIRBP1.49E-143.072028
200983_x_atCD592.22E-143.24769
212249_atPIK3R12.44E-144.98666
241689_atMETTL143.42E-143.311901
228716_atTHRB4.88E-143.021776
205259_atNR3C25E-143.392261
223588_atTHAP25.44E-146.445672
201427_s_atSEPP16.02E-143.146142
219427_atFAT47.7E-143.056389
209807_s_atNFIX7.97E-143.105386
201498_atUSP78.55E-143.827248
228243_atRP11-5C23.18.84E-143.43588
238786_atANK31.58E-133.075604
233249_atLOC1005070731.61E-133.069721
208633_s_atMACF11.79E-133.260397
226816_s_atKIAA11431.94E-133.431996
208792_s_atCLU2.46E-133.627978
210426_x_atRORA2.51E-133.077789
229969_atSEC632.86E-133.019815
225811_atC11orf582.90212E-133.095344537
227847_atEPM2AIP13.27738E-133.460553723
201019_s_atEIF1AX3.35065E-134.257274339
223695_s_atARSD3.475E-135.635180257
228905_atPCM13.53051E-133.340750721
217707_x_atSMARCA23.67262E-134.020194349
225093_atUTRN6.21503E-133.138806562
227425_atREPS27.33413E-133.055352168
211734_s_atFCER1A8.45324E-133.411503985
244007_atZNF4629.36362E-133.786986943
212675_s_atCEP681.00742E-123.307657084
238454_atZNF5401.13221E-123.186059238
224889_atFOXO31.14175E-123.853408162
1558512_atRP11-819C21.11.37579E-123.144887286
213802_atPRSS121.47216E-124.357472705
225465_atMAGI11.47393E-124.208157151
223126_s_atC1orf211.56142E-123.186640389
230479_atEIF3F1.58984E-123.299359045
228448_atMAP61.66223E-123.143593284
217779_s_atPNRC21.91847E-123.246325539
1560648_s_atTSPYL11.9309E-123.760805629
212936_atFAM172A2.19358E-124.299840018
227091_atCCDC1462.29194E-123.206298087
221564_atPRMT22.38565E-123.547995663
43427_atACACB2.44649E-123.004593504
229384_atCTC-429P9.32.57394E-123.228782722
222663_atRIOK22.69118E-123.35934368
238472_atFBXO92.69273E-123.562133246
222533_atCRBN2.82396E-123.004216036
228751_atCLK43.30425E-123.359190366
208832_atATXN103.36042E-123.408974266
238043_atARID1B3.38618E-123.280003422
1559412_atLINC004783.50475E-124.041998876
238081_atWDFY3-AS23.68106E-123.077236586
228760_atSRSF84.13358E-123.538832842
235240_atATXN34.47198E-123.59474854
240806_atRPL155.22404E-123.229351616
228027_atGPRASP25.30198E-123.191435286
209815_atPTCH15.63194E-123.080285017
208760_atUBE2I6.31295E-123.075043093
229317_atKPNA56.53722E-123.749106743
228420_atPDCD27.1736E-123.442288871
227520_atTXLNG7.54685E-125.386988658
244294_atGTF2H57.70273E-124.035395557
204011_atSPRY27.75358E-123.811245705
209614_atADH1B7.83396E-123.188622844
226774_atFAM120B8.43059E-123.286960689
235612_atPRPF38A1.023E-113.636955078
232122_s_atVEPH11.20886E-113.052642894
216342_x_atRPS4XP21.22578E-116.967247025
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.

Table IV.

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

GO IDGO termNo. of GenesP-value
GO:0022403Cell cycle phase  483.26E-18
GO:0000087M phase of mitotic cell cycle  336.78E-18
GO:0022402Cell cycle process  526.78E-18
GO:0000278Mitotic cell cycle  456.78E-18
GO:0044428Nuclear part  706.12E-10
GO:0031981Nuclear lumen  641.48E-09
GO:0044422Organelle part1121.63E-09
GO:0005515Protein binding1121.27E-05
GO:0042975Peroxisome proliferator activated receptor binding     30.0097
GO:0019899Enzyme binding  250.0135

[i] 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.

Table V.

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

KEGG IDKEGG pathwayNo. of GenesP-value
kegg:04110Cell cycle82.45E-06
kegg:04114Oocyte meiosis79.76E-06
kegg:04120Ubiquitin mediated proteolysis50.0032
kegg:03013RNA transport50.0036
kegg:04610Complement and coagulation cascades30.013
kegg:04115p53 signalling pathway30.013
kegg:05200Pathways in cancer60.013
kegg:03060Protein export20.0144
kegg:03008Ribosome biogenesis in eukaryotes30.0152
kegg:03440Homologous recombination20.0168

[i] 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.

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 (2527). 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.

Acknowledgements

This research was supported in part by grants form the National Natural Science Foundation of China (31560314 to Q.L.) and the Natural Science Foundation of Jiangxi Province (2016BAB204168 to Q.L.).

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March 2018
Volume 39 Issue 3

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APA
He, X., Zhang, C., Shi, C., & Lu, Q. (2018). Meta-analysis of mRNA expression profiles to identify differentially expressed genes in lung adenocarcinoma tissue from smokers and non-smokers. Oncology Reports, 39, 929-938. https://doi.org/10.3892/or.2018.6197
MLA
He, X., Zhang, C., Shi, C., Lu, Q."Meta-analysis of mRNA expression profiles to identify differentially expressed genes in lung adenocarcinoma tissue from smokers and non-smokers". Oncology Reports 39.3 (2018): 929-938.
Chicago
He, X., Zhang, C., Shi, C., Lu, Q."Meta-analysis of mRNA expression profiles to identify differentially expressed genes in lung adenocarcinoma tissue from smokers and non-smokers". Oncology Reports 39, no. 3 (2018): 929-938. https://doi.org/10.3892/or.2018.6197