OL Oncology Letters 1792-1074 1792-1082 D.A. Spandidos 10.3892/ol.2016.5497 OL-0-0-5497 Articles Identification of genes associated with tongue cancer in patients with a history of tobacco and/or alcohol use ZhaoYin 1 FuDongna 2 XuChengbi 1 YangJingpu 1 WangZonggui 1 Department of Otolaryngology, Head and Neck Surgery, The Second Hospital of Jilin University, Changchun, Jilin 130041, P.R. China Department of Otolaryngology, Changchun People's Hospital, Changchun, Jilin 130041, P.R. China Correspondence to: Dr Zonggui Wang, Department of Otolaryngology, Head and Neck Surgery, The Second Hospital of Jilin University, 218 Ziqiang Street, Changchun, Jilin 130041, P.R. China, E-mail: wangmidg@163.com 02 2017 14 12 2016 13 2 629 638 08072015 14102016 Copyright: © Zhao et al. 2017 This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

The present study aimed to identify genes associated with tongue cancer in patients with a history of tobacco and/or alcohol use. Microarray dataset GSE42023, including 10 tissue samples of tongue cancer from patients with a history of tobacco and/or alcohol use (habit group) and 11 tissue samples of non-habit-associated tongue cancer (non-habit group), were downloaded from the Gene Expression Omnibus database. Differentially-expressed genes (DEGs) between the habit and non-habit groups were identified using the Linear Models for Microarray Data software package. The enrichment functions and pathways of these genes were subsequently predicted using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analysis. Transcription factors (TFs) and tumor-associated genes (TAGs) were selected from the DEGs using the Encyclopedia of DNA Elements database and the TAG database, respectively. Protein-protein interaction (PPI) networks for DEGs were constructed using Cytoscape. In addition, functional module analysis was performed using BioNet. This analysis identified 642 DEGs between the habit and non-habit groups, including 200 upregulated and 442 downregulated genes. The majority of upregulated DEGs were functionally enriched in the regulation of apoptosis and the calcium signaling pathway. The majority of downregulated DEGs were functionally enriched in fat cell differentiation and the adipocytokine signaling pathway. In addition, 31 TFs and 42 TAGs were identified from the DEGs. Furthermore, this analysis demonstrated that certain DEGs, including AKT serine/threonine kinase 1 (AKT1), E1A binding protein p300 (EP300), erb-b2 receptor tyrosine kinase 2 (ERBB2) and epiregulin (EREG), had high connectivity degrees in the PPI networks and/or functional modules. Overall, DEGs in a functional module, such as AKT1, EP300, ERBB2 and EREG, may serve important roles in the development of tongue cancer in patients with a history of tobacco and/or alcohol use. These DEGs are potential therapeutic targets for the treatment of tongue cancer in these groups.

 tongue cancer differentially-expressed genes protein-protein interaction functional module Introduction

Oral cancer is the most frequently observed cancer of the head and neck region worldwide, with ~363,000 new cases reported annually and a mortality rate of ~50% (1,2). The tongue is a vital organ that serves an essential role in speech and swallowing. Tongue cancer, a form of oral cancer, has become one of the greatest challenges in the head and neck cancer field (3). It has been reported that tongue cancer comprises between 22 and 49% of all oral cancer (4). Tongue cancer begins as a small lump and may spread throughout the tongue and to the gums (5). It has been estimated that 6–7% of tongue cancer occurs in patients <40 years old (6).

Tongue cancer may be caused by numerous factors, including old age, geographical location, family history, nutritional deficiencies, infectious agents, and chronic alcohol and tobacco use (7), however, the exact cause is unknown. A previous study demonstrated that cyclin D1 was overexpressed in patients with anterior tongue cancer with no history of tobacco and alcohol use, and postulated that it may contribute to the development of this cancer. A total of 18 DEGs were identified in this study (8). Another study reported that tumor protein p53, BCL2 associated X apoptosis regulator and BCL2 apoptosis regulator were associated with squamous cell cancer of the tongue (9). Therefore, although previous studies have identified a number of genes and proteins associated with tongue cancer, the exact pathogenesis of the disease remains unknown.

The present study investigated gene expression profiles to identify differentially-expressed genes (DEGs) between tongue cancer from patients with a history of tobacco and/or alcohol use (habit group) and tongue cancer from non-habit-associated patients (non-habit group). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were then performed to analyze the DEGs. Several key genes associated with habit-associated tongue cancer were identified through protein-protein interaction (PPI) network and functional module analysis. These results provide insight into the molecular mechanisms underlying habit-associated tongue cancer. In addition, the key DEGs identified are potential therapeutic targets for the treatment of tongue cancer in patients with a history of tobacco and/or alcohol use.

 Materials and methods <sec> <title>Microarray data

The gene expression profile of microarray dataset GSE42023 was obtained from the Gene Expression Omnibus (GEO) database (www.ncbi.nlm.nih.gov/geo). This dataset was originally produced using the HumanHT-8 v3.0 Gene Expression BeadChip Array (Illumina, Inc., San Diego, CA, USA) (8). Gene expression data from 22 human anterior tongue cancer tissue samples were analyzed in this study, including 10 habit-associated samples, which were obtained from patients who had a long history (>10 years) of tobacco and/or alcohol use, in addition to 12 non-habit associated samples, which were taken from patients who had no prior history of tobacco and/or alcohol use. Details of the patients included in this dataset are listed in Table I. The gene expression data of all samples was pre-processed through background correction, quantile normalization, probe summarization and probe ID to gene symbol using the Robust Multi-array Average algorithm (10) in the affy software package (version 1.8.31) of Bioconductor (http://www.bioconductor.org/packages/release/bioc/html).

 DEG analysis

The Linear Models for Microarray Data software package (version 3.16.8) (11) from Bioconductor (version 2.12; http://www.bioconductor.org/packages/release/bioc/html/limma.html) was used to identify DEGs between the habit and non-habit groups. DEGs with a cutoff criteria of P<0.05 and |log2 fold-change| value ≥1 were used for screening.

 GO and KEGG pathway enrichment analysis

The Database for Annotation, Visualization and Integrated Discovery (DAVID; version 6.7; https://david.ncifcrf.gov) (12) was used to identify the GO (13) biological process associated with the DEGs identified. KEGG (14) pathway enrichment analysis was subsequently used to identify the primary signaling pathways the DEGs functioned in. P<0.05 calculated by Fisher's exact test was used as the cutoff criterion for statistically significant GO and KEGG enrichment analysis.

 Screening for transcription factors (TFs) and tumor-associated genes (TAGs)

TFs and TAGs were identified from the DEGs using the Encyclopedia of DNA Elements database (https://www.encodeproject.org) (15) and the TAG database (http://www.binfo.ncku.edu.tw/TAG) (16), respectively.

 PPI network construction

The Search Tool for the Retrieval of Interacting Genes (STRING; version 9.05; http://string-db.org) database, which provides experimental and predicted PPI information (17), was used to analyze the PPI network for the DEGs. A confidence score >0.4 was chosen as the threshold for a significant interaction. Finally, the PPI network for the remaining DEGs was visualized using Cytoscape software (version 3.0.0; www.cytoscape.org) (18).

 Screening and analysis of the functional module

The BioNet Package (version 1.8.0; http://bionet.bioapps.biozentrum.uni-wuerzburg.de) provides a set of statistics for the analysis of gene expression data and biological networks (19). The functional module for DEGs was obtained based on BioNet analysis of the PPI network. A false discovery rate <0.005 was used as the cutoff criterion for functional module screening. GO and KEGG enrichment analysis of functional modules was performed using DAVID, with a statistically significant cutoff criterion of P<0.05.

 Results <sec> <title>Identification of DEGs

The microarray dataset GSE42023 was obtained from the GEO database in order to identify the DEGs between the habit and non-habits groups. In total, 642 DEGs were identified in the habit group compared with the non-habit group, including 200 upregulated and 442 downregulated DEGs.

 GO and KEGG functional enrichment analysis

GO enrichment analysis demonstrated that the upregulated DEGs were enriched in 29 biological processes, including regulation of apoptosis (P=0.00531), skeletal muscle tissue development (P=0.00773) and positive regulation of nuclear factor-kB transcription factor activity (P=0.00485) (Table II). The downregulated DEGs were identified to be enriched in 39 biological processes, including fat cell differentiation (P=0.00074), response to ultraviolet light (P=0.00118) and embryonic pattern specification (P=0.01686) (Table II).

KEGG enrichment analysis identified that the upregulated DEGs were significantly enriched in 9 signaling pathways, including that of calcium (P=0.01949), long-term potentiation (P=0.00326) and the spliceosome (P=0.02543) (Table III). The downregulated DEGs were significantly enriched in 5 signaling pathways, such as the adipocytokine signaling pathway (P=0.00374), the B cell receptor signaling pathway (P=0.00607) and the non-small cell lung cancer-associated signaling pathway (P=0.03098) (Table III).

 Screening for TFs and TAGs

A total of 31 DEGs were identified as TFs, including 10 upregulated DEGs [e.g., E1A binding protein p300 (EP300), RARG and HOXB13] and 21 downregulated DEGs (e.g. CTBP1, GTF2A1 and RORB) (Table IV). Furthermore, 41 DEGs were identified as TAGs, including 13 upregulated DEGs [e.g., erb-b2 receptor tyrosine kinase 2 (ERBB2), FGF8 and MLLT11] and 28 downregulated DEGs [e.g., AKT serine/threonine kinase 1 (AKT1), MLF1 and FGF20] (Table IV).

 Construction of the PPI network

The DEG PPI network was constructed using STRING. The resulting PPI network contained 330 nodes and 462 PPIs (Fig. 1). The top 10% of nodes were classified as having a high degree of connectivity in the PPI network, these included AKT1, EP300, CALM2 and PIK3R1 (Table V). AKT1 was identified to interact with ERBB2, epiregulin (EREG) and EP300 (Fig. 1).

 Construction and analysis of the functional module

Based on the PPI network created, a functional module was constructed by BioNet. The functional module contained 33 nodes and 35 PPIs (Fig. 2). The connectivity degree of AKT1, CALM2, GNB2L1 and ERBB2 was >4 in the functional module (data not shown). DEGs in the functional module were enriched in 18 biological processes defined by GO, including protein autophosphorylation (P=0.0000425), female pregnancy (P=0.00034), positive regulation of GTPase activity (P=0.00037) and cytokine-mediated signaling (P=0.00586) (Table VI). KEGG enrichment analysis demonstrated that the DEGs in the functional module were enriched in 16 signaling pathways, such as the ErbB signaling pathway (P=0.00126; e.g., EREG, ERBB2 and AKT1), the focal adhesion pathway (P=0.01310; e.g., RASGRF1, ERBB2 and AKT1) and cancer-associated pathways (P=0.04696; e.g., DAPK3, ERBB2 and ATK1) (Table VII).

 Discussion

Tongue cancer has been associated with a number of factors, including old age, geographical location and family history (7). In addition, tongue cancer is associated with certain habits, such as chewing betel nuts, smoking and alcohol abuse (3). Although knowledge of tongue cancer has progressed, the complex pathogenesis of the cancer remains unclear. Therefore, there is a requirement to investigate the molecular mechanisms underlying the pathogenesis of tongue cancer and to screen for novel markers of the disease. In the present study, the gene expression profiles of habit- and non-habit-associated tongue cancer samples were analyzed using bioinformatics methods. A total of 642 DEGs were identified between the habit and non-habit groups. Through analysis of the biological functions and pathways of the DEGs, a set of genes and signaling pathways was identified to be associated with habit-associated tongue cancer.

In the PPI network constructed, EP300 and AKT1 exhibited a high degree of connectivity. EP300, also known as p300, is a global transcriptional coactivator that regulates the activity of numerous DNA-binding transcription factors that are associated with a wide array of cellular activities, such as cell growth and differentiation (20,21), which are increased in uncontrolled malignant tumors (22). EP300 has been found to be involved in DNA repair synthesis through its interaction with proliferating cell nuclear antigen, which is essential for DNA replication (23,24). To the best of our knowledge, there is no evidence that EP300 is associated with habit-associated tongue cancer currently. However, EP300 has been found to promote cancer progression in the prostate (25) and colon (26). Thus, EP300 may serve a role in the development of habit-associated tongue cancer, likely through regulating cell growth.

AKT1 belongs to the Akt/protein kinase B subfamily of serine/threonine kinases, which is frequently hyperactivated in human cancer (27). The AKT family (AKT1-3) has been found to integrate extracellular signals in several cellular processes, including growth, proliferation, differentiation, migration and survival (28). Numerous studies have demonstrated that the phosphoinositide 3-kinase (PI3K)/AKT/mammalian target of rapamycin pathway serves an essential role in apoptosis and is frequently activated in numerous types of human cancer, such as head and neck squamous cell carcinoma (29,30), prostate cancer (31), breast cancer (32) and colorectal cancer (33). Cancer cells have a higher proliferation rate compared with wild-type cells and frequently lose the ability to undergo apoptosis (18). A previous study reported that activated AKT regulates its downstream targets to increase proliferation and decrease apoptosis in cells (34). AKT activation has been described as an early cellular response to carcinogen exposure and may be a key step in environmental carcinogenesis (35). In the current study, AKT1 was identified to be significantly functionally enriched in cancer-associated signaling pathways. The overexpression of AKT has been detected in a variety of cancer types, including tongue cancer (36), head and neck squamous cell carcinoma (37), ovarian cancer (38) and prostate cancer (39). There is no evidence, to the best of our knowledge, that AKT1 is associated with habit-associated tongue cancer at present. However, AKT1 may be associated with the development of habit-associated tongue cancer via the regulation of cell proliferation and differentiation.

In the current study, AKT1, ERBB2 and EREG were demonstrated to be significantly functionally enriched in the ErbB signaling pathway. The ErbB signaling pathway regulates cell migration and invasion in normal and tumor mammary epithelial cells (40). The ErbB family, which consists of four members [epidermal growth factor receptor (EGFR), ERBB2, ERBB3 and ERBB4], plays an important role in cell proliferation and survival in numerous epithelial malignancies (41). ERBB2 was predicted to be a TAG in the current study. The overexpression of ERBB2 particularly occurs with a high frequency in breast cancer (42). In addition, Silva et al (43) reported that ERBB2 expression is associated with the 10-year survival of patients with tongue cancer (43), indicating that ERBB2 serves an important role in tongue cancer development and progression. However, to the best of our knowledge, there have been no reports of an association between ERBB2 and habit-associated tongue cancer thus far. EGFR regulates cell motility, invasion and proliferation (44). EGFR mutations have been identified to activate anti-apoptotic signaling pathways, such as PI3K/AKT/mTOR and mitogen-activated protein kinase (45). EREG, as a ligand of EGFR, stimulates the EGFR signaling pathway, which promotes the metastasis of breast cancer cells (46). The results of the current study identified that AKT1 interacts with EREG in the PPI network. In addition, AKT1 and ERBB2 were classified as oncogenes using the TAG database. These results indicate that AKT1, ERBB2 and EREG are associated with the tumorigenesis of habit-associated tongue cancer and are potential therapeutic targets for the treatment of this cancer.

The grade of the tumor samples in the microarray dataset used in the present study was different between the habit and non-habit groups (Table I). The grade of the tumor may impact gene expression, so this should be taken into consideration when interpreting the results. In future, a more accurate comparison could be made if tumor samples of different grades were divided into subgroups. In addition, the results of the present study will be validated experimentally.

In conclusion, the present study identified key DEGs in habit-associated tongue cancer. These DEGs, such as AKT1, EP300, ERBB2 and EREG, may serve important roles in the tumorigenesis of habit-associated tongue cancer and could be used as therapeutic targets for the treatment of this cancer. However, further experiments are required to verify the results of the current study and increase our understanding of the pathogenesis of habit-associated tongue cancer.

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Search Tool for the Retrieval of Interacting Genes PPI network of the DEGs. Red and green nodes indicate upregulated and downregulated DEGs, respectively, in the habit group compared with the non-habit group. Darker shades represent higher |log2 fold-change| values. Lines indicate predicted or experimental PPIs. PPI network visualized using Cytoscape software. PPI, protein-protein interaction; DEG, differentially-expressed gene.

BioNet functional module within the Search Tool for the Retrieval of Interacting Genes protein-protein interaction network. Red and green nodes indicate upregulated and downregulated differentially-expressed genes, respectively, in the habit group compared with the non-habit group. Darker shades represent higher higher |log2 fold-change| values. Lines indicate predicted or experimental interactions between proteins.

Details of patients with habit and non-habit associated tongue cancer in the GSE42023 microarray dataset.

Category Sample number Gender (M/F) Age (years) Tumor grade
Habit-associated tongue cancer samples   1 M 37 Moderately-differentiated
  2 M 45 Moderately-differentiated
  3 M 52 Moderately-differentiated
  4 M 42 Moderately-differentiated
  5 M 45 Well-differentiated
  6 M 42 Well-differentiated
  7 M 41 Well-differentiated
  8 M 52 Well-differentiated
  9 M 67 Well-differentiated
10 F 48 Moderately-differentiated
Non-habit-associated tongue cancer samples   1 M 30 Moderately-differentiated
  2 M 36 Moderately-differentiated
  3 M 37 Well-differentiated
  4 F 50 Well-differentiated
  5 F 80 Well-differentiated
  6 F 40 Moderately-differentiated
  7 F 25 Well-differentiated
  8 F 46 Moderately-differentiated
  9 F 50 Moderately-differentiated
  10 F 63 Well-differentiated
11 F 70 Well-differentiated
12 F 56 Well-differentiated

Microarray dataset GSE42023 was obtained from the Gene Expression Omnibus database and was originally produced by Sebastian et al (8). M, male; F, female.

Top 10 enriched GO functions for upregulated and downregulated DEGs.

Type of DEG GO no. GO function No. of DEGs enriched P-valuea Genes
Upregulated GO:0042981 Regulation of apoptotic process 21 0.00531 TEX11, MST4, FGF8, FHL2, RARG, DNAJC5, APAF1, PRKCZ, TNFRSF10B, IFI27, INSL3, CTH, PSMB10, MLLT11, KDM2B, DAPK3, SCG2, MTDH, CLU, SHQ1, GRM4
GO:0007519 Skeletal muscle tissue development   7 0.00773 RCAN1, ERBB2, TCF21, CHRNA1, ADAM12, MYEF2, EP300
GO:0051092 Positive regulation of NF-kB transcription factor activity   5 0.00485 TLR9, PRKCZ, CTH, MTDH, CLU
GO:0021532 Neural tube patterning   3 0.00499 FGF8, FOXA2, KDM2B
GO:0060425 Lung morphogenesis   3 0.01026 FGF8, FOXA2, TCF21
GO:0060337 Type I interferon-mediated signaling pathway   3 0.04164 OAS2, IFI27, IFNA13
  GO:0045945 Positive regulation of transcription from RNA polymerase III promoter   2 0.00212 ERBB2, FOXA2
GO:0006853 Carnitine shuttle   2 0.00358 CPT1A, PRKAB2
GO:0050860 Negative regulation of T cell receptor signaling pathway   2 0.00643 PTPN22, ELF1
GO:0071542 Dopaminergic neuron differentiation   2 0.00755 FGF8, FOXA2
Downregulated GO:0045444 Fat cell differentiation   11 0.00074 CTBP1, LRP6, RETN, WNT5B, TGFB1I1, ALDH6A1, SLC2A4, AKT1, INHBB, CREB5, CREBL2
GO:0009411 Response to UV   9 0.00118 TGFB1I1, ALDH6A1, SLC2A4
GO:0009880 Embryonic pattern specification   5 0.01686 AKT1, INHBB, CREB5, CREBL2
GO:0002089 Lens morphogenesis in camera-type eye   4 0.00275 MSH6, ZRANB3, XPA, AKT1
GO:0043044 ATP-dependent chromatin remodeling   4 0.00985 N4BP1, PIK3R1, SPRTN, REV1, HYAL1
GO:0035690 Cellular response to drug F   4 0.02375 LRP6, TDGF1, TFAP2A, COBL, PCSK6
GO:0071364 Cellular response to epidermal growth factor stimulus   3 0.00459 PVRL3, TFAP2A, PROX1, CITED2
GO:0010765 Positive regulation of sodium ion transport   3 0.01115 RBBP7, RSF1, NASP, SMARCAD1
GO:0021516 Dorsal spinal cord development   3 0.01475 CAD, PPM1F, TXN, KCNH2
GO:0086091 Regulation of heart rate by cardiac conduction   3 0.01475 CAD, AKT1, TDGF1

Fisher's exact test. GO, gene ontology; DEG, differentially-expressed genes; NF, nuclear factor; UV, ultraviolet; ATP, adenosine triphosphate.

Enriched KEGG signaling pathways for DEGs.

Type of DEG KEGG no. KEGG signaling pathway No. of DEGs enriched P-valuea Genes
Upregulated 4020 Calcium signaling pathway 5 0.01949 CHP2, AVPR1B, CAMK2D, ERBB2, CALM2
4720 Long-term potentiation 4 0.00326 CHP2, CAMK2D, EP300, CALM2
3040 Spliceosome 4 0.02543 SNRPD1, SRSF4, TXNL4A, CCDC12
4650 Natural killer cell mediated cytotoxicity 4 0.03167 RAET1E, CHP2, TNFRSF10B, IFNA13
4012 ErbB signaling pathway 3 0.04129 CAMK2D, ERBB2, GAB1
4210 Apoptosis 3 0.04129 CHP2, APAF1, TNFRSF10B
511 Other glycan degradation 2 0.00962 GLB1, GBA
4744 Phototransduction 2 0.02683 GUCA1B, CALM2
600 Sphingolipid metabolism 2 0.04847 GLB1, GBA
Downregulated 4920 Adipocytokine signaling pathway 6 0.00374 RXRB, SLC2A4, LEPR, AKT1, CAMKK2, MAPK10
4662 B-cell receptor signaling pathway 6 0.00607 RASGRP3, PIK3AP1, AKT1, BTK, PIK3R1, NFATC3
5223 Non-small cell lung cancer 4 0.03098 RXRB, AKT1, PIK3R1, RASSF1
4012 ErbB signaling pathway 5 0.04306 AKT1, EREG, PIK3R1, ELK1, MAPK10
5210 Colorectal cancer 4 0.04781 MSH6, AKT1, PIK3R1, MAPK10

Fisher's exact test. KEGG, Kyoto Encyclopedia of Genes and Genomes; DEG, differentially-expressed genes.

TFs and TAGs among the DEGs.

TFs TAGs
Type of DEG No. of genes Genes No. of genes Genes
Upregulated 10 EP300, RARG, HOXB13, RORA, FOXA2, TCF21, ELF, SIX5, CCNT2, SUPT4H1 13 ERBB2, FGF8, MLLT11, MT1G, APAF1, HOXB13, TNFRSF10B, PDLIM4, FOXA2, DAPK3, CLU, MTUS1, FHL2
Downregulated 21 CTBP1, GTF2A1, RORB, RXRB, HOXB4, TGFB1I1, LMX1B, NR2F6, KCNIP3, CDX4, SKIL, GABPB2, MEIS3, NKX6-1, PAX3, ELK1, NFATC3, SIM1, FOXD1, PROX1, RFX5 28 MLF1, FGF20, CTTN, SKIL, AKT1, EWSR1, ELK1, DCUN1D1, CTBP1, PTPN2, RBBP7, FBXO32, TGFBI, SIRT3, NEO1, RARRES1, PER1, SPINK7, CBFA2T3, ENC1, CREBL2, PROX1, RASSF1, MSH6, PAX3, DDX6, TFAP2A, DHX16

TF, transcription factor; TAG, tumor-associated gene; DEG, differentially-expressed genes.

DEGs in the top 10% of nodes with a high connectivity degree in the PPI.

DEG STRING degree of connectivity
AKT1 37
EP300 25
CALM2 23
PIK3R1 18
ATN1 11
PRKCZ 11
SNRPD1 10
EEF2 10
MSH6 10
ERBB2   9
RBBP7   9
GNB2L1   9
TLR9   9
RRM1   9
BTK   8
SLC2A4   8
TFIP11   8
GRP   8
DYNLT3   8
ADRA1D   7
NASP   7
ARHGEF7   7
TXNL4A   7
TXN   7
GRB10   7

DEG, differentially-expressed gene; STRING, Search Tool for the Retrieval of Interacting Genes.

Top 10 enriched GO functions for DEGs in the functional module.

GO no. GO function No. of DEGs enriched P-valuea Genes
GO:0046777 Protein autophosphorylation 5     0.0000425 CAD, DAPK3, CAMKK2, ERBB2, AKT1
GO:0007565 Female pregnancy 4 0.00034 UPRT, CAD, CITED2, AKT1
GO:0043547 Positive regulation of GTPase activity 4 0.00037 RASGRF1, ERBB2, ARHGEF7, GNB2L1
GO:0019221 Cytokine-mediated signaling pathway 4 0.00586 IFNAR2, EIF4A1, EREG, PTPN2
GO:0042059 Negative regulation of epidermal growth factor receptor signaling pathway 3     0.0000918 TFAP2A, PTPN2, ARHGEF7
GO:0042593 Glucose homeostasis 3 0.00340 PTPN2, TXN, AKT1
GO:0051151 Negative regulation of smooth muscle cell differentiation 2 0.00016 RCAN1, EREG
GO:0006222 UMP biosynthetic process 2 0.00025 UPRT, CAD
GO:0010765 Positive regulation of sodium ion transport 2 0.00075 AKT1, SCN3B
GO:0021602 Cranial nerve morphogenesis 2 0.00092 TFAP2A, CITED2

Fisher's exact test. GO, gene ontology; DEG, differentially-expressed gene; GTPase, guanosine triphosphatase; UMP, uracil monophosphate.

Enriched KEGG signaling pathways for DEGs in the functional module.

KEGG no. KEGG signaling pathway No. of DEGs enriched P-valuea Genes
4012 ErbB signaling pathway 3 0.00126 EREG, ERBB2, AKT1
4510 Focal adhesion 3 0.01310 RASGRF1, ERBB2, AKT1
5200 Pathways in cancer 3 0.04696 DAPK3, ERBB2, AKT1
5219 Bladder cancer 2 0.00495 DAPK3, ERBB2
5213 Endometrial cancer 2 0.00751 ERBB2, AKT1
5223 Non-small cell lung cancer 2 0.00808 ERBB2, AKT1
5214 Glioma 2 0.01155 AKT1, CALM2
4920 Adipocytokine signaling pathway 2 0.01260 CAMKK2, AKT1
5212 Pancreatic cancer 2 0.01332 ERBB2, AKT1
5215 Prostate cancer 2 0.02100 ERBB2, AKT1

Fisher's exact test. KEGG, Kyoto Encyclopedia of Genes and Genomes; DEG, differentially-expressed genes.