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Introduction

Breast cancer is a malignant tumor in the mammary gland tissue (1). Orthotopic breast cancer is not fatal, but its cells are easy to fall off, and these shedding cancer cells will be free from blood or lymph fluid and spread throughout the body to form cancer, then cancer metastasis is formed, thereby threatening life (2). The incidence of breast cancer has been on an upward trend, and in statistics of DeSantis et al (3), 1 out of 10 women in the United States has breast cancer. Although China is not a country with a high incidence of breast cancer, the growth rate in China has been approximately 2 percentage points higher than that in some high-incidence countries in recent years (4). In the recently published data (5), the incidence of breast cancer still ranks first among female malignant tumors in the cancer registration in China. The peak of onset age of breast cancer is approximately 53 years, but now it tends to be younger (6).

Methylation is an important modification of protein and nucleic acid and one of the most important research topics in epigenetics (7). In recent years, methylation has been studied in the diagnosis, efficacy and prognostic evaluation of various cancers such as ovarian cancer (8), cervical cancer (9) and hepatocellular carcinoma (10). The clinical application of methylation in breast cancer has also been systematically studied. The report of An et al (11) proposed that the methy-lation of MGMT gene that was closely related to the clinical stage, histological grade and lymph node metastasis of breast cancer played an important role in its progression. Studies of Hao et al (12) showed that the combined detection of the methy-lation degree of a variety of genes could be a good judgement of tumor stage and lymph node metastasis. However, these studies have focused on a small number of candidate genes and have not systematically screened out methylation genes that may be related to the occurrence and development of breast cancer. The Cancer Genome Atlas (TCGA) (13) and the Gene Expression Omnibus (GEO) (14) are commonly used public databases in bioinformatics analysis, but the former has complete patient data and is more conducive to the related analysis of the course and prognosis. Therefore, we hope to expand the current study on the role of methylation in the occurrence and development of breast cancer by screening out breast cancer-related methylation genes from the TCGA database, and analyzing the relationship between them and the course and prognosis of breast cancer.

Materials and methods

Data collection and preprocessing

The data were downloaded from the TCGA database. The RNA-seq data and methylation data for level 3 were downloaded from TCGA, and the selected samples were all patient tissue samples. First, the RNA-seq data files were merged into a matrix file using the merge script of the Perl language (http://www.perl.org/). Then, the gene name was converted from the Ensembl id to the matrix of the gene symbol through the Ensembl database (http://asia.ensembl.org/index.html). At the same time, the methylation data were merged into a single file through the merge script of the Perl language. In the downloaded RNA-seq data and methylation data, the data with incomplete clinical information were excluded. Only the samples that had undergone RNA sequencing and methylated chip data were retained in the remaining data to make it possible to perform a linkage analysis of transcription and methylation.

The study was approved by the Ethics Committee of The First Affiliated Hospital of China Medical University (Shenyang, China).

Screening of differentially methylated genes

All cancer tissues and normal tissues were compared and all high and low methylated genes were looked for (FDR<0.05) using the MethylMix R package (http://www.bioconductor.org/packa-ges/release/bioc/html/MethylMix.html) in the R Project for Statistical Computing software (Sax software; SAS Institute Inc., Cary, NC, USA). Bidirectional hierarchical clustering of differentially methylated genes was performed, and the differential distribution map of the genes with the most significant methylation difference screened out was plotted using pheatmap R package (https://cran.r-project.org/web/packages/pheatmap/), and the distribution of methylation degree of cancer samples relative to normal tissues was observed. The correlation between the gene methylation degree and the corresponding gene expression was calculated using Pearson's correlation test in the cor.test function of the R language (https://www.r-project.org/) (filter condition was cor <-0.3 and P<0.05).

GO enrichment analysis

The GO enrichment analysis of differentially methylated genes was performed using DAVID (Database for Annotation, Visualization and Integration Discovery). First, the DAVID database was logged in (https://david.ncifcrf.gov/), the Functional Annotation was selected, and the list of differentially expressed genes was submitted. Then, the OFFICIAL_GENE_SYMBOL in the Select Identifier was selected, and the Gene List in List Type was selected, and the Submit List was clicked finally. At the same time, the figure of enrichment results was plotted using the GOplot R package (https://cran.r-project.org/web/packages/GOplot/).

PATHWAY analysis

The differentially methylated genes were analyzed using the over-representation analysis function of ConsensusPathDB (http://cpdb.molgen.mpg.de/). The PATHWAY pathways enrichment analysis of differentially methylated genes was performed using the KEGG database. P<0.05 was the screening condition.

Single factor and multivariate Cox analysis

To determine the methylation genes related to survival, single factor Cox analysis of differentially methylated genes was performed using Survival R package (https://cran.r-project.org/web/views/Survival.html), and selection of differentially methylated genes with P<0.05 in the single factor analysis for subsequent multivariate analysis was performed. The optimal risk model was found based on the Akaike Information Criterion (AIC) (15).

Survival curve and ROC curve plotting

According to the optimal risk model obtained from the multivariate Cox analysis and the gene methylation degree of each sample, the survival score was performed, and the median value of risk score of each sample was calculated. The patients above the median value were in the high-risk group, patients below it in the low-risk group. The survival curves of the two groups were plotted using the Kaplan-Meier method, and the difference between them was tested using the log-rank method. The ROC curve was plotted to predict the value of the patient's survival time through the gene methylation degree.

Results

Clinical data of samples

According to the inclusion criteria we set, 670 samples were finally obtained as the subjects, and the clinical data statistics are shown in Table I.

Table I. Clinical data of samples.

Table I.

Clinical data of samples.

Covariates	Type	No. of patients [n (%)]
Survival status	Alive	616 (91.94%)
	Dead	54 (8.06%)
T	T1	175 (26.12%)
	T2	381 (56.87%)
	T3	93 (13.88%)
	T4	21 (3.13%)
N	N0	306 (45.67%)
	N1	233 (34.78%)
	N2	76 (11.34%)
	N3	49 (7.31%)
	NX	6 (0.90%)
M	M0	530 (79.10%)
	M1	11 (1.64%)
	MX	129 (19.25%)
Stage	Stage I	112 (16.72%)
	Stage II	380 (56.72%)
	Stage III	167 (24.93%)
	Stage IV	11 (1.64%)
Age	≤65	482 (71.94%)
	>65	188 (28.06%)

Screening of differentially methylated genes

Through a comparison of the gene methylation levels in cancer and normal tissues, 257 differentially methylated genes were screened out (FDR<0.05) and the thermal map was plotted (Fig. 1), in which there were 161 genes with higher methylation degree of cancer tissues than that of normal tissues, and 96 genes with it lower than that of normal tissues. The FDR (corrected P-value) was used as the standard and the first 10 differentially methylated genes with the smallest P-value were selected (Table II). The distribution map of methylation degree was plotted (Fig. 2A-J).

Figure 1. Thermal map analysis of differentially methylated genes. The pheatmap R package was used for bidirectional hierarchical clustering of differentially methylated genes in breast cancer and adjacent tissues. In the figure, red indicates that the gene is highly methylated in the sample, and green low methylated in the sample.

Figure 2. Distribution map of methylation degree of partially differentially methylated genes (A-J). The abscissa is the methylation degree, the ordinate is the number of methylation samples, the histogram represents the methylation distribution of cancer group, and the curve is the simulated trend curve of the methylation distribution of cancer group. The black horizontal line above the figure is the methylation level distribution of the normal sample. The distribution of methylation degree of the cancer sample relative to the normal sample can be clearly seen from the figure.

Table II. Partially differentially methylated genes.

Table II.

Partially differentially methylated genes.

Gene symbol	Normal group methylation level (A)	Cancer group methylation level (B)	Methylation degree	P-value	FDR
NKAPL	0.340837653	0.577383257	0.236545604	4.31E-54	1.22E-51
LCAT	0.664095202	0.803431309	0.139336107	1.16E-50	1.64E-48
ZNF728	0.099901799	0.333781489	0.233879691	1.24E-49	1.16E-47
COX7A1	0.498567597	0.664727174	0.166159577	2.04E-46	1.44E-44
ALS2CR11	0.334427241	0.470741226	0.136313985	4.83E-46	2.72E-44
LYPD8	0.512159389	0.335695176	−0.176464213	9.37E-46	4.40E-44
CCDC8	0.312900607	0.518293085	0.205392479	7.78E-45	3.09E-43
NAALADL1	0.467929203	0.578828343	0.110899139	8.77E-45	3.09E-43
MUC1	0.384572295	0.246245302	−0.138326993	1.13E-43	3.54E-42
USP44	0.333728357	0.532191383	0.198463026	1.97E-43	5.56E-42

[i] Methylation degree, B-A.

Correlation analysis between methylation degree and gene expression

Correlation analysis between methylation degree of 257 differentially methylated genes and their gene expression was performed, and it was found that the methylation degree of these 257 genes was negatively correlated with their expression. The higher the methylation degree was, the lower the gene expression. Based on the P-value obtained from the Pearson's correlation test, the first 10 genes with the smallest P-value were selected (Table III), and the correlation figure was plotted (Fig. 3A-J).

Figure 3. Correlation between methylation degree of partially differentially methylated genes and gene expression (A-J). Pearson's correlation analysis was used for analysis of the correlation between the methylation degree and gene expression. It can be seen from the figure that the gene methylation degree is negatively correlated with the expression. The abscissa is the beta value of the gene methylation degree, and the ordinate is the expression of the gene. Cor is the correlation coefficient and P-value is the test value of correlation.

Table III. Correlation analysis between methylation degree and gene expression.

Table III.

Correlation analysis between methylation degree and gene expression.

Gene	cor	P-value
DQX1	−0.737068974	4.83E-135
C10orf82	−0.721853363	5.52E-127
SLC39A6	−0.71121904	1.15E-121
DNALI1	−0.684796632	1.93E-109
RHOH	−0.677085189	4.09E-106
AGR3	−0.670876546	1.65E-103
TDRD1	−0.660058794	4.04E-99
RIPK3	−0.658304073	2.00E-98
ZNF502	−0.654789106	4.78E-97
MPC2	−0.64767024	2.60E-94

GO enrichment analysis

The GO enrichment analysis of 257 differentially methylated genes was performed using DAVID, and results showed that the most relevant enrichments were ‘extracellular exosome’, ‘superoxide dismutase activity’, ‘intracellular’, ‘mast cell granule’ and ‘glutathione derivative biosynthetic’, (Table IV and Fig. 4).

Figure 4. GO enrichment analysis of differentially methylated genes. The left side of the circle is the gene and the right side is the pathway. Different colors represent different pathways, and the color of each pathway is annotated below the circle. If the gene belongs to a pathway, there will be a line between the gene and the pathway.

Table IV. GO enrichment analysis of differentially methylated genes.

Table IV.

GO enrichment analysis of differentially methylated genes.

Term	Enrichments	Count	P-value	FDR
GO:0070062	Extracellular exosome	60	8.25E-05	0.10545219
GO:0004784	Superoxide dismutase activity	3	0.001692298	2.353503638
GO:0005622	Intracellular	31	0.002142855	2.7070955
GO:0042629	Mast cell granule	4	0.002400191	3.027607057
GO:1901687	Glutathione derivative biosynthetic process	4	0.002628416	4.227224422
GO:0006355	Regulation of transcription, DNA-templated	33	0.002759092	4.43294563
GO:0042178	Xenobiotic catabolic process	3	0.003254731	5.209453579
GO:0003700	Transcription factor activity, sequence-specific DNA binding	24	0.004284423	5.858842135
GO:0042476	Odontogenesis	4	0.004764259	7.538061353
GO:0043066	Negative regulation of apoptotic process	14	0.005694098	8.94558752

PATHWAY analysis

The PATHWAY pathways enrichment analysis of 257 differentially methylated genes was performed using ConsensusPathDB, and a total of 19 related PATHWAYs were found (P<0.05), among which the most relevant were ‘D-Glutamine and D-glutamate metabolism’, ‘Estrogen signaling pathway’ and ‘Fluid shear stress and atherosclerosis’ (Table V and Fig. 5).

Figure 5. PATHWAY analysis of differentially methylated genes. The circle in the figure represents the pathway, and the line represents the relationship between pathways. The size of the circle represents the number of genes in the pathway. The larger the number is, the more the number of pathway gene data is; the color of the circle represents an enriched significant P-value, and the stronger red represents a higher degree of enrichment. The bold line represents the number of genes with the same pathway and the line color represents the number of genes with the same difference before the pathway.

Table V. Partial PATHWAY enrichment analysis.

Table V.

Partial PATHWAY enrichment analysis.

Pathway ID	Pathway	P-value	Enriched genes
hsa00471	D-Glutamine and D-glutamate metabolism	0.001650165	GLS; GLUD1
hsa04915	Estrogen signaling pathway	0.002030577	SHC1; ESR1; CALML3; CALML5; KRT17; KRT14; KRT19
hsa05418	Fluid shear stress and atherosclerosis	0.00229801	BMP4; CALML3; GSTM1; GSTM2; CALML5; NQO1; GSTP1
hsa00220	Arginine biosynthesis	0.002443407	GPT; GLS; GLUD1
hsa04964	Proximal tubule bicarbonate reclamation	0.003192003	GLS; GLUD1; AQP1
hsa00480	Glutathione metabolism	0.005331216	GSTM1; GSTM2; GSTP1; GPX7
hsa04217	Necroptosis	0.005718681	H2AFY2; RIPK3; GLUD1; STAT5A; IFNGR2; TNFRSF10D; HIST3H2A
hsa00130	Ubiquinone and other terpenoid-quinone biosynthesis	0.008620151	TAT; NQO1
hsa05034	Alcoholism	0.009346881	SHC1; CALML3; GNB4; H2AFY2; CALML5; HIST1H3G; HIST3H2A
hsa00250	Alanine, aspartate and glutamate metabolism	0.010523928	GPT; GLS; GLUD1

Single factor and multivariate Cox analysis

Single factor Cox analysis of differentially methylated genes was performed using the Survival R package, the screening condition was P<0.01, and 14 genes were obtained (Table VI). At the risk ratio (HR) >1, the higher the gene expression was, the higher the risk was; at HR <1, the higher the gene expression was, the lower the risk was. Multivariate analysis of 14 selected genes significantly different from single factor was performed using Survival package. The optimal model was found according to AIC and four optimal gene models were obtained. The risk model obtained was: risk score = QRFP (Degree of methylation) × (−3.657) + S100A16 × (−3.378) + TDRD1 × (−4.001) + SMO × (3.548).

Table VI. Single factor Cox analysis.

Table VI.

Single factor Cox analysis.

Gene	HR	z	P-value
QRFP	0.047697004	−3.632634369	0.000280542
CSTA	0.062945881	−3.360829859	0.000777087
ELF5	0.058117795	−3.264669409	0.001095919
GRHL2	4.13E-06	−3.167428425	0.001537936
CCDC89	0.024575046	−2.971482358	0.002963659
S100A3	0.011686928	−2.86232633	0.004205437
S100A16	0.024716952	−2.82925571	0.00466564
TDRD1	0.017847438	−2.791779478	0.005241907
SMO	42.93347544	2.704736969	0.006835849
CALML5	0.054952261	−2.672962518	0.007518465
SCG5	0.015402449	−2.669608387	0.007593976
TAF1D	0.040537383	−2.661547052	0.007778247
SOD1	0.145404134	−2.6286443	0.008572598
KRTAP2-3	0.04690311	−2.604144621	0.009210388

Survival curve and ROC curve plotting

According to the optimal risk model obtained from the multivariate Cox analysis and the degree of gene methylation of each sample, the survival score was performed. The median value of risk score of each sample was calculated to be 0.936, and used as the cut-off value, 335 patients with a risk score >0.936 were in the high-risk group and 335 patients <0.936 in the low-risk group. Based on the high-risk and low-risk groups, the survival curve was plotted using the Kaplan-Meier method (Fig. 6). From the survival data, we could see that the five-year survival rate in the high-risk group of patients was 72.4% (95% CI, 62.7–83.6%), and that in the low-risk group of patients was 86.6% (95% CI, 78.6–95.3%), and the difference thereof between the two groups was significant (P<0.001). At the same time, the ROC curve was plotted (Fig. 7) and AUC was 0.791, indicating that our model could well predict patient survival.

Figure 6. Survival curve. The patients were divided into high-risk group (>0.936) and low-risk group (<0.936) according to the median value:-0.936 of risk score. The Kaplan-Meier method was used to plot the survival curves of the two groups, and the log-rank method was used to test the difference between them that was significant in the survival rate between the high-risk and low-risk groups (P<0.001).

Figure 7. ROC curve figure. In the ROC curve analysis, it was obtained that AUC was 0.791, indicating that our model can well predict patient survival. The larger the AUC value is, the more likely the current classification algorithm is to place the positive sample in front of the negative sample, i.e. for better classification.

Discussion

In this study, 670 samples which had undergone RNA sequencing and methylated chip data were selected by TCGA, in which a differential methylation analysis was performed, and correlation analysis, GO enrichment analysis, PATHWAY analysis, single factor analysis, multivariate analysis, prognostic model and ROC curve were performed on the differentially methylated genes.

Methylation is one of the most important studies in epigenetics. In mammals, DNA methylation mainly occurs on CpG islands, often in the promoter region or the first exon and the 3′ end of the gene (16), and about 70% of human gene promoters exist in CpG islands (17). Studies have found that almost all tumors can find abnormal DNA methylation in comparison between cancer tissues and corresponding non-tumor normal tissues (18). Therefore, methylation is a very important part in the current study on the molecular level of cancer.

In our study, a total of 257 differentially methylated genes were found by comparison between breast cancer tissues and their corresponding normal tissues, of which the methylation degree of NKAPL was the highest. In a study on 5 liver cancer cell lines and 62 pairs of primary liver cancer and its adjacent non-cancerous liver tissues, Ng et al (19) found that NKAPL was highly methylated in liver cancer, and the methylation degree was negatively correlated with its expression level. It could also inhibit the growth of cancer cells in liver cancer cells, which was a potential prognostic marker. In our study, NKAPL was also highly methyla-ted in breast cancer tissues, and the methylation degree was also negatively correlated with its expression level. It suggested that NKAPL may be involved in mechanism of cancer suppression in breast cancer tissues. At the same time, NKAPL was also enriched in ‘regulation of transcription, DNA-templated’ and we hypothesized that it may affect cancer cell changes by affecting the transcription and regulation of DNA. However, there have been no reports of NKAPL in breast cancer-related studies. In the follow-up GO enrichment analysis, it was found that ‘extracellular exosome’ and ‘superoxide dismutase activity’ were the most relevant enrichments with differentially methylated genes, which mainly affected the activity of extracellular body and superoxide dismutase (SOD).

Thirty years ago, when the first contact with the extracellular body was made, it was originally thought that the cell component was discarded or unwanted. Recently, it was realized that the extracellular body contains cell-specific proteins, lipids and genetic material that can be passed to distant tissues and cells, thereby changing their function and physiology (20). Urabe et al (21) believed that the extracellular body was expected to become a liquid biomarker for prostate cancer, kidney cancer and bladder cancer by regulating the immune system and angiogenesis that affect cancerous changes. At the same time, Chen et al (22) thought that the extracellular body induced the occurrence and recurrence of liver cancer cells through the MAPK/ERK signaling pathway. In our study, methylation genes influenced changes in the extracellular body and thus affected changes in breast cancer, but specific experiments are needed to confirm this conjecture. SOD can scavenge superoxide anion radicals, thereby inhibiting the occurrence of lipid peroxidation, autoimmune diseases and tumors (23).

In study of Kocot et al (24), it was believed that SOD was closely related to the metastasis and differentiation of colorectal cancer, and had certain application value in its treatment. According to the characteristics of SOD, we believed that its activity was related to the occurrence of cancer, and it was not comprehensive in the study on breast cancer. The results of this study again suggest the importance of SOD, and it is necessary to further discuss its influencing mechanism. In the PATHWAY analysis, the most closely related to the differentially methylated genes in breast cancer tissues was the ‘D-Glutamine and D-glutamate metabolism’. Glutamine is an important fuel for the immune system and has important immunomodulatory effects (25), which can promote the division and differentiation of lymphocytes and macrophages. Exogenous glutamine can significantly increase the number of lymphocytes, T lymphocytes and CD4/CD8 ratio in critically ill patients and enhance the body immunity (26). Many cancers are very dependent on glutamine (27), transcriptional programs of which drives its high consumption, so it is called ‘glutamine metabolism addiction’ (28). However, whether this phenomenon exists in breast cancer still needs further study.

In the follow-up risk model construction and ROC curve judgement, the best risk model was obtained, risk score = QRFP × (−3.657) + S100A16 × (−3.378) + TDRD1 × (−4.001) + SMO × (3.548); with the median value-0.936 of risk score as the cut-off value, the 5-year survival rate in high-risk group of patients (risk score >0.936) was 72.4% (95% CI, 62.7–83.6%), and that in low-risk group of patients was 86.6% (95% CI, 78.6–95.3%) and AUC was 0.791 as judged by ROC curve, having a good application value. By obtaining the methylation degree of the patient's QRFP, S100A16, TDRD1 and SMO in the clinic and it can be calculated whether the patient is in a high-risk or low-risk state and can predict its five-year survival rate, as a reminder for targeted treatment.

The main drawback of this study is that it only uses computer simulation data but fails to verify the results by specific clinical data and to perform specific cancer cell and animal model experiments. In addition, there are major differences in the regions and human races. The advantage behind these drawbacks lies in the fact that molecular bioinformatics has made it more efficient to spend time, resources and manpower on the molecular area. In future studies, we will conduct a more in-depth study on the differentially methylated genes screened out and the related GO and PATHWAY.

After long-term calculations and discussions, we finally concluded that the occurrence and development of breast cancer were closely correlated with methylation genes such as NKAPL, QRFP, S100A16, TDRD1 and SMO and related biological processes and signaling pathways such as ‘extracellular exosome’, ‘superoxide dismutase activity’ and ‘D-Glutamine and D-glutamate metabolism’. We will conduct more in-depth studies on these aspects as conditions permit. Recently, there have been few studies on the breast cancer-related gene methylation; thus, we hope that our experimental results can enrich the study in this area and provide help for clinical diagnosis and treatment in the future.

Acknowledgements

Not applicable.

Funding

No funding was received.

Availability of data and materials

The datasets used and/or analyzed during the present study are available from the corresponding author on reasonable request.

Authors' contributions

LF and FJ drafted the manuscript. LF helped with GO enrichment analysis. FJ contributed to PATHWAY analysis. Both authors have read and approved the final manuscript.

Ethics approval and consent to participate

The study was approved by the Ethics Committee of The First Affiliated Hospital of China Medical University (Shenyang, China).

Patient consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

References