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. 2016 Aug 17;2016:4369431. doi: 10.1155/2016/4369431

Integrated Analysis of DNA Methylation and mRNA Expression Profiles Data to Identify Key Genes in Lung Adenocarcinoma

Xiang Jin 1, Xingang Liu 2,*, Xiaodan Li 1, Yinghui Guan 1
PMCID: PMC5005524  PMID: 27610375

Abstract

Introduction. Lung adenocarcinoma (LAC) is the most frequent type of lung cancer and has a high metastatic rate at an early stage. This study is aimed at identifying LAC-associated genes. Materials and Methods. GSE62950 downloaded from Gene Expression Omnibus included a DNA methylation dataset and an mRNA expression profiles dataset, both of which included 28 LAC tissue samples and 28 adjacent normal tissue samples. The differentially expressed genes (DEGs) were screened by Limma package in R, and their functions were predicted by enrichment analysis using TargetMine online tool. Then, protein-protein interaction (PPI) network was constructed using STRING and Cytoscape. Finally, LAC-associated methylation sites were identified by CpGassoc package in R and mapped to the DEGs to obtain LAC-associated DEGs. Results. Total 913 DEGs were identified in LAC tissues. In the PPI networks, MAD2L1, AURKB, CCNB2, CDC20, and WNT3A had higher degrees, and the first four genes might be involved in LAC through interaction. Total 8856 LAC-associated methylation sites were identified and mapped to the DEGs. And there were 29 LAC-associated methylation sites located in 27 DEGs (e.g., SH3GL2, BAI3, CDH13, JAM2, MT1A, LHX6, and IGFBP3). Conclusions. These key genes might play a role in pathogenesis of LAC.

1. Introduction

As the most common type of lung cancer and a non-small cell lung carcinoma (NSCLC) [1], lung adenocarcinoma (LAC) is characterized by gland or duct formation and massive mucus production [2]. LAC generally originates in peripheral lung tissue, and this is in contrast with squamous cell lung cancer and small cell lung cancer (SCLC), which are both apt to be located more centrally in lungs [3, 4]. In US, LAC accounts for approximately 40% of lung cancers [5]. Smoking is the main cause of LAC, and the disease has a high metastasis rate even at an early stage [4]. Therefore, it is necessary to identify key genes in LAC and develop effective therapeutic schedule.

The WNT/TCF signaling can promote osseous and brain metastasis of LAC cells through targeting HOXB9 and LEF1 which mediates chemotactic invasion and colony outgrowth [6]. Coexpression of Oct4 and Nanog, which are homeobox transcription factors important for self-renewal of stem cells, commands epithelial-mesenchymal transdifferentiation, mediates tumor-initiating ability, and contributes to metastasis of LAC [7]. As a noncoding RNA, MALAT-1 enhances motility of LAC cells via regulating motility-associated gene expression in transcriptional and posttranscriptional levels [8, 9]. BRAF mutation is frequently detected in human LAC, indicating that BRAF may serve as a therapeutic target for a subset of patients with the disease [10, 11]. Overexpression of caveolin-1 is essential for mediating filopodia formation, which may promote invasion of LAC cells [12]. In spite of the above researches, the mechanisms of LAC still remain unclear.

Recently, along with the development of chip technology, microarray data have been obtained and uploaded to Gene Expression Omnibus (GEO) for us to study [13]. Using microarray data GSE62950, we screened the differentially expressed genes (DEGs) between LAC tissue samples and adjacent normal tissue samples. And their potential functions were predicted by Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Disease Ontology (DO) enrichment analysis. Then, the interrelationships between these DEGs were analyzed by protein-protein interaction (PPI) network and module analysis. At last, LAC-associated methylation sites were identified and mapped to the DEGs to obtain LAC-associated DEGs.

2. Materials and Methods

2.1. Microarray Data

Microarray data GSE62950 was downloaded from the database of GEO (http://www.ncbi.nlm.nih.gov/geo/), which included a DNA methylation dataset and an mRNA expression profiles dataset. The DNA methylation dataset and the mRNA expression profiles dataset separately were based on the platform of GPL8432 Illumina HumanRef-8 WG-DASL v3.0 and GPL8490 Illumina HumanMethylation27 BeadChip (HumanMethylation27_270596_v.1.2), and both of them included 28 LAC tissue samples and 28 adjacent normal tissue samples.

2.2. Data Preprocessing

Normalized series matrix file of mRNA expression profiles was downloaded directly. After beta matrix of DNA methylation data was downloaded, primary methylation signals were preprocessed by Methylation Module V1.9 [14] in BeadStudio V3.1.0.0 to obtain normalized beta matrix. Those methylation sites which had no signal values in one or more samples were removed.

2.3. DEGs Screening

Using Limma package [15] in R, the DEGs between LAC tissue samples and adjacent normal tissue samples were identified. The p values of DEGs were adjusted by Benjamini-Hochberg (BH) method [16]. The adjusted p value < 0.05 and |log2⁡fold-change(FC) | ≥1 were taken as the thresholds.

2.4. GO, KEGG, and DO Enrichment Analysis

GO is utilized for predicting potential functions of gene products in three categories (biological process, BP; cellular component, CC; and molecular function, MF) [17]. The KEGG database can be used for systematic analysis of gene functions, which connects genomic information with corresponding functional information [18]. The DO database contains a comprehensive knowledge of human diseases and is applied to annotate diseases [19]. Using TargetMine online tool [20], GO, KEGG, and DO enrichment analyses were performed for the DEGs. The p values of enriched terms were corrected by Holm-Bonferroni [21]. The adjusted p value < 0.05 was used as the cut-off criterion.

2.5. PPI Network and Module Construction

The STRING [22] database was utilized to search PPI pairs among the DEGs. And combined score > 0.4 was used as the cut-off criterion. Then, the PPI network of the DEGs was visualized by Cytoscape (http://www.cytoscape.org/) [23]. Subsequently, igraph package [24] in R was used to calculate connectivity degrees of nodes (proteins) in the PPI network, and nodes with higher degrees were taken as hub nodes.

Furthermore, MCODE plugin [25] in Cytoscape was used to screen modules from the PPI network. Using BinGO plugin [26], GO functional enrichment analysis was conducted for the genes in each module.

2.6. Screening of LAC-Associated Methylation Sites

Using genefilter package [27] in R, the methylation sites which had higher beta value variations within groups compared with variations among groups were deleted. And the p value < 0.05 was used as the cut-off criterion. Then, the methylation sites located in sex chromosomes were removed. Finally, the associations between the screened methylation sites and LAC were analyzed by CpGassoc package [28] in R. The p value < 0.05 was taken as the threshold.

2.7. Correlation Analysis of Methylation Sites and DEGs

According to the annotation information of the DNA methylation profiles, the nearest genes to the LAC-associated methylation sites were obtained and then mapped to the DEGs. At last, LAC-associated methylation sites of the DEGs were gained.

2.8. Validation of Methylation Sites and DEGs in LAC Tissue Samples

The RNASeqV2 data of LAC were downloaded from The Cancer Genome Atlas (TCGA, http://cancergenome.nih.gov/) database, which included 515 LAC tissue samples and 59 adjacent normal tissue samples. Using Limma package [15] in R, the DEGs with the adjusted p value < 0.05 and |log2FC| ≥ 1 were also screened. Meanwhile, the methylation data of LAC were also downloaded from TCGA database, which included 459 LAC tissue samples and 32 adjacent normal tissue samples. Moreover, LAC-associated methylation sites were also identified and mapped to the DEGs using the same methods as the above.

3. Results

3.1. Data Preprocessing and DEGs Screening

After preprocessing, total 18626 beta values of DNA methylation data and 18626 gene expression values of mRNA expression profiles separately were obtained. Compared with adjacent normal tissue samples, there were a total of 913 DEGs (including 409 upregulated and 504 downregulated genes) in LAC tissue samples. In the heat map of the DEGs, LAC tissues could be definitely separated from adjacent normal tissues by the DEGs (Figure 1).

Figure 1.

Figure 1

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The heat map of the DEGs. Yellow and blue bars represent upregulated and downregulated genes, respectively. Two-way clustering results in the left side indicate that DEGs were clustered into three categories. Yellow and blue columns represent upregulated genes in lung adenocarcinoma tissues, while red column stands for downregulated genes.

3.2. GO, KEGG, and DO Enrichment Analysis

Using TargetMine online tool, functions of the DEGs were predicted by GO, KEGG, and DO enrichment analyses. For the upregulated genes, the enriched GO functions included multicellular organismal catabolic process (p value = 8.59E − 07) in BP category, as well as extracellular region (p value = 2.67E − 04) and extracellular space (p value = 0.002024) in CC category (Table 1(a)). The enriched KEGG pathways for upregulated genes included protein digestion and absorption (p value = 0.001244) and cell cycle (p value = 0.026338, which involved cell division cycle 20, CDC20; cyclin B2, CCNB2; and mitotic arrest deficient 2-like 1, MAD2L1) (Table 1(b)). The enriched DO terms for upregulated genes included cell type cancer (adjust. p value = 6.12E − 05), germ cell cancer (adjust. p value = 0.001326), embryoma (adjust. p value = 0.008727), and embryonal cancer (adjust. p value = 0.009481) (Table 1(c)). And all of the DO terms involved MAD2L1 and aurora B kinase (AURKB). The enriched GO functions for downregulated genes were listed in Table 1(d), including single-organism process (p value = 5.73E − 05) and single-multicellular organism process (p value = 1.93E − 04, which involved wingless-related MMTV integration site 3A, WNT3A) in BP category, as well as cell periphery in CC category (p value = 1.03E − 05, which involved WNT3A). In addition, there were nonsignificant KEGG pathways and DO terms enriched for the downregulated genes.

Table 1.

The GO functions, KEGG pathways, and DO terms separately enriched for upregulated and downregulated genes. (a) The top 5 GO functions enriched for upregulated genes. (b) The KEGG pathways enriched for upregulated genes. (c) The DO terms enriched for upregulated genes. (d) The top 5 GO functions enriched for downregulated genes.

(a).
Category ID Description Count Gene symbol p value
BP GO:0044243 Multicellular organismal catabolic process 15 ACE2, COL10A1, COL11A1,… 8.59E − 07
BP GO:0030574 Collagen catabolic process 14 FAP, MMP1, MMP10,… 4.49E − 06
BP GO:1903047 Mitotic cell cycle process 41 ANLN, AURKB, BUB1,… 1.70E − 05
BP GO:0044259 Multicellular organismal macromolecule metabolic process 15 ACE2, COL10A1, COL11A1,… 4.16E − 05
BP GO:0007067 Mitotic nuclear division 22 ANLN, AURKB, CDC20,… 4.57E − 05
CC GO:0005576 Extracellular region 122 ACE2, ACOT11, ACY3,… 2.67E − 04
CC GO:0005615 Extracellular space 46 ACE2, AGR2, BPIFA1,… 0.002024
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(b).
ID Description Count Gene symbol p value
hsa04974 Protein digestion and absorption 11 ACE2, COL10A1, COL11A1,… 0.001244
hsa04110 Cell cycle 11 CDC20, MAD2L1, CCNB2,… 0.026338
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(c).
DOID Description Count Gene symbol Adjust. p value
DOID:0050687 Cell type cancer 64 AURKB, MAD2L1, ASPM,… 6.12E − 05
DOID:2994 Germ cell cancer 41 AURKB, MAD2L1, CA9,… 0.001326
DOID:4766 Embryoma 38 AURKB, BUB1, MAD2L1,… 0.008727
DOID:688 Embryonal cancer 38 AURKB, MAD2L1, CA9,… 0.009481
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(d).
Category ID Description Count Gene symbol p value
BP GO:0044699 Single-organism process 296 AADAC, ABCB1, ABCG2,… 5.73E − 05
BP GO:0044707 Single-multicellular organism process 150 WNT3A, ACVRL1, ADCY4,… 1.93E − 04
BP GO:0003013 Circulatory system process 24 ACVRL1, ADRA1A, ADRB1,… 4.58E − 04
BP GO:0032501 Multicellular organismal process 152 WNT3A, ACVRL1, ADCY4,… 0.001393
BP GO:0008015 Blood circulation 23 ACVRL1, ADRA1A, ADRB1,… 0.001693
CC GO:0031226 Intrinsic component of plasma membrane 72 ACVRL1, ADRA1A, ADRB1,… 2.38E − 06
CC GO:0005886 Plasma membrane 139 WNT3A, ADCY8, ADGRB3,… 8.04E − 06
CC GO:0071944 Cell periphery 140 WNT3A, ANXA8, AQP10,… 1.03E − 05
CC GO:0044459 Plasma membrane part 86 AGER, AGTR1, AQP10,… 1.44E − 05
CC GO:0005887 Integral component of plasma membrane 68 FZD4, GHR, GP9,… 1.75E − 05
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3.3. PPI Network and Module Analysis

PPI networks for upregulated and downregulated genes were constructed, respectively. The PPI network for upregulated genes had 229 nodes and 725 interactions (Figure 2(a)). Particularly, MAD2L1 (degree = 36), AURKB (degree = 38), CCNB2 (degree = 40), and CDC20 (degree = 42) had higher degrees, and they can interact with each other in the PPI network. Several modules were screened from the PPI network for upregulated genes, and the largest module (module 1) is also showed in Figure 2(b). GO functional enrichment analysis showed that the genes in module 1 were involved in mitosis-associated terms.

Figure 2.

Figure 2

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The PPI network and module for upregulated genes. (a) The PPI network for upregulated genes. (b) Module 1 of the PPI network for upregulated genes. Red nodes represent upregulated genes. Thickness of edges is in direct proportion to combined scores. Node sizes are positively correlated with connectivity degrees of nodes.

The PPI network for downregulated genes had 233 nodes and 368 interactions (Figure 3). Connectivity degrees of the nodes in the PPI network were calculated, and cGMP-dependent protein kinase II (PRKG2, degree = 11), VE-cadherin (CDH5, degree = 12), WNT3A (degree = 15), adenylyl cyclase 8 (ADCY8, degree = 16), and adenylyl cyclase 4 (ADCY4, degree = 17) were the top 5 nodes which had higher degrees. What is more, nonsignificant modules were screened from the PPI network for downregulated genes.

Figure 3.

Figure 3

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The PPI network for downregulated genes. Green nodes represent downregulated genes. Thickness of edges is in direct proportion to combined scores. Node sizes are positively correlated with connectivity degrees of nodes.

3.4. Screening of LAC-Associated Methylation Sites

After screening, total 8856 methylation sites were obtained. Then, the associations between the screened methylation sites and LAC were analyzed under the threshold of p value < 0.05. As a result, 230 LAC-associated methylation sites were identified.

3.5. Correlation Analysis of Methylation Sites and DEGs

There were 29 LAC-associated methylation sites located in 27 DEGs (e.g., Src homology 3 domain GRB2-like 2, SH3GL2; brain-specific angiogenesis inhibitor 3, BAI3; H-cadherin, CDH13; junctional adhesion molecule 2, JAM2; metallothionein 1A, MT1A; LIM-homeobox containing 6, LHX6; and insulin-like growth factor binding protein-3, IGFBP3) (Table 2). And all of the methylation sites were within 2 kb from transcriptional start sites (TSSs) of the DEGs. For the 29 methylation sites, their methylation indexes in LAC tissue samples were compared with that in adjacent normal tissue samples. The methylation indexes of 19 methylation sites were significantly increased, and the downstream genes mediated by those 19 methylation sites were downregulated. Nevertheless, 1 methylation site had significantly decreased methylation index, and its downstream genes were upregulated.

Table 2.

The 29 lung adenocarcinoma-associated methylation sites located in 27 DEGs.

IllumID Chromosome Meth.pos (Genome Build 36) DEG Distance_to_TSS log2⁡FC⁡(DEG) log2⁡FC⁡(β)
cg17398595 chr9 17568725 SH3GL2 469 −2.461984643 0.419097018
cg01817029 chr12 70951777 TRHDE 953 −2.080720357 0.379725667
cg18182399 chr2 219991419 DES 76 −1.912270714 0.648411911
cg01532771 chr19 7733560 CLEC4M 521 −1.867543929 0.109407145
cg17407908 chr6 32261093 AGER 1092 −1.805516071 −0.076347441
cg21057494 chr3 45041975 CLEC3B 799 −1.784051786 0.176075101
cg08555612 chr3 71917330 PROK2 428 −1.734814286 0.599490205
cg04884908 chr2 72228348 CYP26B1 123 −1.568955357 0.541885215
cg06615154 chr1 151789177 S100A3 819 −1.430114286 0.141081243
cg10244047 chr6 69402835 BAI3 1323 −1.417121429 0.364653419
cg18343862 chr11 10546579 XLKD1 276 −1.346532857 0.053465056
cg01880569 chr16 81217829 CDH13 250 −1.329384643 0.115405772
cg08977371 chr16 81217991 CDH13 88 −1.329384643 0.396503001
cg24829483 chr4 14950739 C1QTNF7 29 −1.192036429 0.119720772
cg08448751 chr3 52454641 SEMA3G 558 −1.143811071 0.044592487
cg03382304 chr21 25934047 JAM2 587 −1.120553571 0.697781695
cg02992632 chr3 193928074 FGF12 8 −1.1105825 0.296886805
cg09137696 chr16 55229916 MT1A 163 −1.0697075 0.541279943
cg03192737 chr3 192413893 OSTN 877 −1.036060357 0.126389084
cg10031651 chr3 46596690 LRRC2 114 −1.033286429 0.101896116
cg05564657 chr3 153014065 AADAC 486 −1.018512857 −0.054630151
cg06866657 chr9 124030575 LHX6 230 −1.000721071 0.437413369
cg08831744 chr7 45927871 IGFBP3 475 1.023873571 0.456738536
cg03462055 chr6 3172555 TUBB2B 315 1.175827143 0.725177791
cg00910067 chr19 38409385 SLC7A10 837 1.289525714 0.253285976
cg05976074 chr19 38408113 SLC7A10 435 1.289525714 0.380324218
cg14546153 chr20 56523721 FLJ90166 366 1.331159286 0.43120249
cg00616129 chr12 10718281 STYK1 375 1.541281786 −0.209664965
cg23582408 chr20 61600555 EEF1A2 394 2.431418571 0.497897542
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Note: IllumID represents probe name. Meth.pos stands for the position of methylation site in genome. Distance_to_TSS indicates the distance of methylation site from transcriptional start site of downstream gene. β in log2⁡FC⁡(β) represents methylation index of methylation probe.

3.6. Validation of Methylation Sites and DEGs in LAC Tissue Samples

Total 4893 DEGs (including 2191 upregulated genes and 2702 downregulated genes) were identified in the LAC sample data downloaded from TCGA database. There were 691 common DEGs (including 310 upregulated genes and 381 downregulated genes) between the 4893 DEGs identified in the LAC sample data downloaded from TCGA database and the 913 DEGs identified in the microarray data of GSE62950. The common DEGs included genes such as WNT3A, MAD2L1, AURKB, CCNB2, CDC20, SH3GL2, BAI3, CDH13, JAM2, MT1A, and IGFBP3. In addition, the 29 LAC-associated methylation sites identified in the microarray data of GSE62950 were also detected in the methylation data downloaded from TCGA database.

4. Discussion

In this study, 913 DEGs including 409 upregulated and 504 downregulated genes were identified in LAC tissue samples compared with adjacent normal tissue samples. Total 230 LAC-associated methylation sites were identified, among which 29 LAC-associated methylation sites were located in 27 DEGs. Afterwards, the RNASeqV2 data and methylation data of LAC were downloaded from TCGA database to validate the obtained methylation sites and DEGs. There were 691 common DEGs (such as WNT3A, MAD2L1, AURKB, CCNB2, CDC20, SH3GL2, BAI3, CDH13, JAM2, MT1A, and IGFBP3) between the 913 DEGs identified in the microarray data of GSE62950 and the 4893 DEGs identified in the LAC sample data downloaded from TCGA database. In addition, the 29 LAC-associated methylation sites identified in the microarray data of GSE62950 were also detected in the methylation data downloaded from TCGA database. Functional enrichment indicated that WNT3A was involved in single-multicellular organism process and cell periphery. Overexpression of Wnt5a, which belongs to Wnt family that encodes signaling glycoproteins, promotes invasion of NSCLC during tumor progression [29, 30]. Via activating JNK pathway, Wnt-7a and Fzd-9 signaling plays role in inducing the receptor tyrosine kinase inhibitor Sprouty-4 and cadherin proteins and is essential for maintaining epithelial differentiation and inhibiting transformed cell growth in some NSCLC patients [31]. In the PPI network for downregulated genes, WNT3A had higher degrees. These suggested that WNT3A might play a role in LAC.

Besides, CDC20, CCNB2, and MAD2L1 were enriched in the pathway of cell cycle. Meanwhile, MAD2L1 and AURKB were involved in DO terms of type cancer, germ cell cancer, embryoma, and embryonal cancer. Results of immunohistochemistry suggest that CDC20 can be a negative marker in prognosis of patients with resected NSCLC, especially adenocarcinoma [32]. Overexpressed CDK5RAP3 and CCNB2, as well as suppressed RAGE, may be promising biomarkers in lung adenocarcinoma [33]. The 5-year overall survival rates of LAC patients with low CCNB2 mRNA levels were significantly higher than that with high levels, and overexpressed CCNB2 mRNA can independently predict a poor prognosis in patients with LAC [34, 35]. Immunohistochemical analysis indicates AURKB, which mediates chromosome segregation during mitosis, is frequently overexpressed in primary lung carcinomas [36, 37]. Immunohistochemistry shows that overexpression of cell division cycle associated 8 (CDCA8) and AURKB can result in bad outcome of lung cancer patients; thus, suppression of the CDCA8-AURKB pathway is a potential therapeutic strategy for lung cancer [38]. Semiquantitative RT-PCR shows that mitotic arrest defective protein 2 (MAD2) is overexpressed in a high percentage of lung cancers, and multivariate analysis suggests that high-level MAD2 can be a prognostic marker independently [39]. In the PPI network for upregulated genes, MAD2L1, AURKB, CCNB2, and CDC20 had higher degrees, and they can interact with each other. Therefore, MAD2L1, AURKB, CCNB2, and CDC20 might be implicated in LAC by interacting with each other.

Additionally, LAC-associated methylation sites were identified and mapped to the DEGs. And there were 29 LAC-associated methylation sites located in 27 DEGs (e.g., SH3GL2, BAI3, CDH13, JAM2, MT1A, LHX6, and IGFBP3). Loss of SH3GL2 is frequently detected in NSCLC and SH3GL2 can mediate cellular growth and invasion through interacting with EGFR [40]. CDX2, VIL1, and BAI3 levels have significant differences in SCLC and large-cell neuroendocrine lung carcinoma (LCNEC); therefore, they can be diagnostic markers of these tumor types [41]. Tumor suppressor gene CDH13, located on chromosome 16q24.2–3, is downregulated in lung cancer and its aberrant methylation may be a potential marker for cancer detection [42–44]. Via mediating β1 integrin subunit and ERK activation in human dermal lymphatic endothelial cells (HDLEC), junctional adhesion molecule-C (JAM-C) contributes to lymphangiogenesis and nodal metastasis, suggesting that JAM-C may be a target for treating lymphatic metastases in NSCLC [45]. Overexpression of metallothionein (MT) can be used as an independent predictor of short-term survival in SCLC patients enduring chemotherapy [46, 47]. Previous study indicates that LHX6 is a candidate tumor suppressor gene that has epigenetic silencing in patients with lung cancer [48]. In NSCLC, methylation status of IGFBP-3 before cisplatin therapy seems to be a biomarker of prognosis, helping to select appropriate therapeutic method for patients [49, 50]. These declared that SH3GL2, BAI3, CDH13, JAM2, MT1A, LHX6, and IGFBP3 might relate to LAC.

In conclusion, we carried out a comprehensive bioinformatics analysis to screen LAC-associated genes. We identified 913 DEGs and 8856 methylation sites in LAC tissue samples. Besides, LAC might correlate with several key genes (e.g., WNT3A, MAD2L1, AURKB, CCNB2, CDC20, SH3GL2, BAI3, CDH13, JAM2, MT1A, LHX6, and IGFBP3). However, these bioinformatic findings need to be validated by further researches.

Additional Points

Highlights. (1) We screened 913 DEGs and 8856 methylation sites in LAC tissue samples. (2) In the PPI networks, MAD2L1, AURKB, CCNB2, CDC20, and WNT3A had higher degrees. (3) There were 29 LAC-associated methylation sites located in 27 DEGs.

Competing Interests

All authors declare that they have no conflict of interests to state.

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