Abstract
Background
Breast cancer (BC) is one of the most prevalent cancers worldwide but
its etiology remains unclear. Obesity is recognized as a risk factor
for BC, and many obesity-related genes may be involved in its
occurrence and development. Research assessing the complex genetic
mechanisms of BC should not only consider the effect of a single gene
on the disease, but also focus on the interaction between genes. This
study sought to construct a gene interaction network to identify
potential pathogenic BC genes.
Methods
The study included 953 BC patients and 963 control individuals.
Chi-square analysis was used to assess the correlation between
demographic characteristics and BC. The joint density-based
non-parametric differential interaction network analysis and
classification (JDINAC) was used to build a BC gene interaction network
using single nucleotide polymorphisms (SNP). The odds ratio (OR) and
95% confidence interval (95% CI) of hub gene SNPs were evaluated using
a logistic regression model. To assess reliability, the hub genes were
quantified by edgeR program using BC RNA-seq data from The Cancer
Genome Atlas (TCGA) and identical edges were verified by logistic
regression using UK Biobank datasets. Go and KEGG enrichment analysis
were used to explore the biological functions of interactive genes.
Results
Body mass index (BMI) and menopause are important risk factors for BC.
After adjusting for potential confounding factors, the BC gene
interaction network was identified using JDINAC. LEP, LEPR, XRCC6, and
RETN were identified as hub genes and both hub genes and edges were
verified. LEPR genetic polymorphisms (rs1137101 and rs4655555) were
also significantly associated with BC. Enrichment analysis showed that
the identified genes were mainly involved in energy regulation and
fat-related signaling pathways.
Conclusion
We explored the interaction network of genes derived from SNP data in
BC progression. Gene interaction networks provide new insight into the
underlying mechanisms of BC.
Supplementary Information
The online version contains supplementary material available at
10.1186/s12885-022-10170-w.
Keywords: Breast cancer, Gene interaction network, Single nucleotide
polymorphism, Differential network analysis
Background
The World Health Organization (WHO)'s International Agency for Research
on Cancer (IARC) showed that the most predominant change in global
cancer data in 2020 was a rapid increase in breast cancer (BC)
incidence. BC has replaced lung cancer as the most common cancer
worldwide [[47]1]. The mortality rate of female BC is particularly high
in transitional versus developed countries [[48]2]. Obesity is a
recognized risk factor for many cancers [[49]3, [50]4]. Higher estrogen
levels resulting from the aromatization of adipose tissue, increased
production of inflammatory cytokines such as tumor necrosis factor α,
interleukin-6, and prostaglandin E2, insulin resistance, and over
activation of insulin-like growth factor signaling, adipokine
production, and oxidative stress in obese women are associated with the
development of cancer [[51]5]. Structural variants of genes associated
with BC and obesity, including LEP, LEPR, PON1, FTO, and MC4R, are
associated with a higher or lower risk of BC [[52]5].
Genome-wide association studies (GWAS) have linked many single
nucleotide polymorphisms (SNPs) with BC occurrence [[53]6–[54]9]. In
our previous studies, a potential relationship between the sequence
variations of individual gene and BC has been proposed. In the study of
11 SNPs of PTPN1, rs3787345, rs718050, rs3215684, and rs718049 were
associated with a reduction in BC risk [[55]10]. Several studies have
identified the genomic region of PTPN1 as a quantitative trait locus
(QTL) in obesity and diabetes mellitus [[56]11–[57]13]. XRCC5 and XRCC6
SNP genotyping revealed that XRCC5 rs16855458 was associated with BC,
XRCC6 rs2267437 was associated with ER-/PR- BC risk, and there may be
interactions with environmental factors [[58]14]. However, current
research has largely focused on the impact of a single SNP on disease,
and potential SNP-SNP interactions remain less well studied. Most
diseases, including cancers, follow a polygenic model, indicating that
they may involve multiple genes or SNPs [[59]9]. However, little is
known about how they interact. Understanding this issue will help to
characterize the biological mechanism of BC risk.
Differential network analysis provides information about how genes
interact. Recent studies suggest that cancer occurrence and development
are not only caused by gene mutations but also by abnormal gene
regulation [[60]15]. Thus, it is important to assess the impact of both
a single gene and gene–gene interactions on cancer onset and
progression. Network analysis can effectively capture gene–gene
interactions and genetic data can be used to establish gene regulation
networks that characterize the biological mechanisms of disease
[[61]16]. A recent study analyzed the genetic and clinical data from
gastric cancer patients using weighted gene co-expression network
analysis (WGCNA) to explore new prognostic markers and therapeutic
targets of gastric cancer [[62]17]. Jubair et al. proposed a novel
network-based method by integrating a protein–protein interaction
network with gene expression data to identify biomarkers for different
BC subtypes and predict patients ‘ survivability [[63]18]. Another
study constructed the multi-omics markers associated with BC by
high-dimensional embedding and residual neural network [[64]19]. To
date, network analysis has relied on DNA methylation and RNA-seq data
[[65]17–[66]20]. Meanwhile, genetic effects of combinations of
functionally related SNPs may affect genes in a synergistic manner,
thereby increasing BC risk [[67]21, [68]22]. Network analysis using SNP
data can provide insights into the mechanisms of disease.
The joint density-based nonparametric difference interaction network
analysis and classification (JDINAC) method [[69]23] was used to
identify the differential gene interaction network between individuals
in the BC and healthy control groups. Unlike previous studies, gene
interaction network results were based on SNP data, providing new
insight into potential pathogenic BC genes.
Methods
Participants
The study population has been described previously [[70]10]. In brief,
a hospital-based case–control study was used that included patients
diagnosed with BC by pathology between April 2012 and April 2013 in the
second hospital of Shandong University and 21 collaborative hospitals.
Non-BC patients were selected as controls using 1:1 matching on age
group (±3 years), hospital, and treatment time period (within
2 months). The subjects were 25 to 70 years of age. Patients with
clinical or pathological diagnoses of recurrence or metastasis or other
malignant tumor complications were excluded. The selection of cases and
controls was carried out in strict accordance with project research
design standards.
Data collection
The data used for this study were obtained from a key project of
clinical discipline dataset belonging to the hospitals under the
Ministry of Health (administered) of the People's Republic of China
[[71]24]. The present study collected data from a face-to-face
interview and, clinical breast and imaging examinations. The interview
included questions relating to demographics, physiology, reproductive
factors, chronic disease, and family history. Height, weight, hip and
waist circumference were also obtained, body mass index (BMI) and the
waist-hip rate (WHR) were calculated. Clinical examination results were
also collected, including visual examination, palpation, and related
diagnostic tests, including breast ultrasound, mammography, and blood
testing. Blood samples were collected using an EDTA vacuum collector.
RNA-seq expression and clinical data from BC patients, including 112
tumor tissue samples and matched normal tissue samples, were downloaded
from The Cancer Genome Atlas (TCGA; [72]https://cancergenome.nih.gov/).
SNP data from 4,030 and 3,494 women with and without BC, respectively,
were screened using UK Biobank BC data [[73]25]. These data were used
as validation datasets.
Genotyping and laboratory methods
The blood samples consisting of fasting venous whole blood were
injected into EDTA anticoagulant tubes. These were placed fully
upside-down in a 4 °C refrigerator and vertically placed in a -80 °C
refrigerator after sedimentation. DNA was extracted using the Wizard
Genomic DNA Purification Kit (a1120, Promega) and genotyped using the
Sequenom MassARRAY SNP system (CapitalBio Technology, Beijing, China).
Statistical analysis
Differential network analysis using JDINAC method
A Chi-square test was used to analyze differences in demographic and
BC-related factors between the case and control groups. BMI data from
the cases and controls was represented as the mean ± standard
deviation. First, 101 SNPs were matched to their respective genes and
the mean value of SNP for each gene was calculated for each sample. The
gene difference interaction network was obtained using the JDINAC
method. The 95% confidence interval (95% CI) and odds ratio (OR) were
also estimated for hub gene polymorphisms in the gene difference
interaction network. Significance was defined as a p-value < 0.05. All
data were statistically analyzed using R × 64 4.1.0.
The JDINAC method assumes that the network-level difference between BC
patients and healthy controls is the result of the collective effect of
differential pairwise gene–gene interactions that are characterized by
the conditional joint density of two genes [[74]23]. Formally, Y[l]
(l = 1,2,…,n) is the binary response vector and if the lth subject is
BC, Y[l] = 1, otherwise Y[l] = 0. Pr is the probability of the subject
with BC, i.e., Pr = P(Y[l] = 1), and S[i] is the ith gene risk score.
The JDINAC method based on the logistic regression is then represented
as:
[MATH:
logit(Pr)=α0+∑t=1<
/mrow>TαtZt+∑i=1p∑j>ipβij1nfij1Si,Sjfij0Si,Sj,s.t.∑i=1
p∑j>ipβij≤c,c>0, :MATH]
1
Z[t] (t = 1,…,T) denotes covariates such as BMI and age, p is the
number of genes.
[MATH: fijkk=0,1 :MATH]
denotes the group conditional joint density of S[i] and S[j] for
group k, respectively, i.e.,
[MATH: Si,SjY=1∼fij1 :MATH]
2
and
[MATH: Si,SjY=0∼fij0 :MATH]
3
which represents the strength of interaction between S[i] and S[j] for
group k [[75]23]. β[ij] indicates the dependency between specific
conditional groups.
JDINAC adopted a multiple randomly split algorithm to improve the
accuracy and robustness of the results. A Lasso penalty was added to
the logistics regression to estimate the coefficient β[ij] and a
cross-validation method was used to determine the best penalty
parameter. The importance score for each pair
[MATH:
Si,Sj<
/mi> :MATH]
was obtained by the following formula:
[MATH: ωij=∑t=1TIβ^ij,
t≠0,i<
mo>,j=1,⋯,p,j>i :MATH]
4
where
[MATH: ωij :MATH]
was the importance score,
[MATH: I· :MATH]
was an indicative function,
[MATH: β^ij,
tt=1,⋯,T :MATH]
was the tth estimation of the coefficient
[MATH: βij :MATH]
. The importance scores represented the differential dependency weight
of each pair
[MATH: Si,Sj :MATH]
between two groups [[76]23]. The difference network was inferred by
connecting pairs with high importance scores through their shared
genes.
Differential expression analysis and enrichment analysis
The edgeR package [[77]26] was utilized to identify differentially
expressed genes in TCGA breast cancer data to test the reliability of
the JDINAC results. Multiplicity correction was performed by applying
the Benjamini–Hochberg method on the p-values.
To explore the biological functions of the identified interaction
genes, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes
(KEGG) pathways in enrichment analysis were performed by the R package
"clusterProfiler" [[78]27]. Only terms with a multiple-test adjusted
p-value < 0.05 were considered significant.
Results
Participant demographic and lifestyle characteristics
There were 1,916 subjects in the study, including 953 and 963 in the BC
and control groups, respectively. There were significant differences in
BMI and menopausal status between the two groups (p-value < 0.05)
(Table [79]1). Women with BC had a higher BMI than that of healthy
women (24.36 ± 3.46 vs. 24.01 ± 3.11, respectively), indicating that
obesity may be a risk factor for BC.
Table 1.
Clinical characteristics of the study population
Variables Control n (%) BC case n (%) X^2 p value
Age, y 3.563 0.468
25- 76(7.89) 62(6.51)
35- 329(34.16) 302(31.69)
45- 352(36.55) 364(38.2)
55- 183(19) 200(20.99)
65- 23(2.39) 25(2.62)
BMI, kg/m^2 6.412 0.011
≤ 28 849(90.90) 799(87.23)
> 28 85(9.10) 117(12.77)
WHR 3.344 0.067
< 0.85 458(53.82) 389(49.30)
≥ 0.85 393(46.18) 400(50.70)
Age at menarche, y 1.036 0.596
7–11 16(1.66) 11(1.15)
12–13 231(24.01) 223(23.4)
≥ 14 715(74.32) 719(75.45)
Number of births 0.501 0.479
0 25(2.63) 20(2.13)
≥ 1 926(97.37) 918(97.87)
Diabetes mellitus history 0.094 0.759
Yes 32(3.36) 34(3.62)
No 921(96.64) 906(96.38)
Plasma glucose, mM 0.593 0.441
< 7 739(76.22) 776(95.45)
≥ 7 29(3.78) 37(4.55)
Smoking 2.406 0.121
Yes 10(1.04) 18(1.89)
No 950(98.96) 932(98.11)
Alcohol consumption 3.089 0.079
Yes 3(0.31) 9(0.95)
No 956(99.69) 939(99.05)
Menopause 6.251 0.012
Yes 260(28.11) 309(33.48)
No 665(71.89) 614(66.52)
Cholesterol, mmol/L 0.239 0.625
≤ 5.18 505(70.53) 500(69.35)
> 5.18 211(29.47) 221(30.65)
[80]Open in a new tab
Differential network of gene interaction
Twenty genes that might be related to the pathogenesis of BC and 101
SNPs associated with these genes were selected. The differential gene
interaction network was estimated based on four scenarios: no
adjustment for covariates, adjustment for BMI, adjustment for the
menopause status (Fig. [81]1), and adjustment for BMI and menopause
status simultaneously (see Additional file [82]1). The number of edges
selected under the four scenarios was 18, 14, 19 and 16, respectively.
The orange nodes in the figure represent the central genes with at
least four adjacent genes in the network. All scenarios had the three
genes, LEP, LEPR, and XRCC6 in common. Gene pairs were ranked based on
the importance scores derived from JDINAC and the top ten pairs in the
network with no covariate adjustment are summarized in Table [83]2.
Among them, six pairs had evidence of interaction in STRING database
[[84]28]. Additional data are shown in Additional files [85]2, [86]3,
[87]4 and [88]5.
Fig. 1.
[89]Fig. 1
[90]Open in a new tab
The differential interaction networks inferred by the joint
density-based nonparametric difference interaction network analysis and
classification (JDINAC). The hub genes are colored orange. A no
adjustment for covariates. B adjustment for BMI. C adjustment for the
menopause status
Table 2.
Top 10 gene interaction pairs identified by JDINAC with no covariate
Gene1 Gene2 Importance scores STRING
1 PPARD UCP2 13 Y
2 LEP XRCC6 12 N
3 LEP LEPR 11 Y
4 LEPR RETN 10 Y
4 T-cadherin XRCC6 10 N
6 IFI30 XRCC6 9 N
7 LEPR T-cadherin 8 N
7 VISFATIN XRCC6 8 N
9 GPR30 XRCC5 6 N
10 ADIPOQ LEP 5 Y
10 ADIPOR1 RETN 5 Y
10 GPR30 STAT3 5 N
10 RETN UCP2 5 Y
[91]Open in a new tab
Y indicates that the pair of genes has an interaction in the STRING
database, and N indicates not
Association between polymorphisms and BC risk
Next, the association between SNPs in the hub genes of differential
networks and BC risk was assessed (Table [92]3). Most SNPs were not
associated with BC significantly. Rs1137101 (OR = 0.728,
p-value = 0.002) and rs4655555 (OR = 0.825, p-value = 0.015) contained
in LEPR were significantly associated with BC risk, while the LEP,
XRCC6, and RETN polymorphisms were not significantly. Functional
consequences of SNPs on genes were also shown in Table [93]3. Rs4655555
is an intron variant. Rs1137101 is a missense variant and coding
sequence variant reported as benign [[94]29].
Table 3.
The association of SNPs in hub genes with breast cancer (BC) adjusted
for BMI and menopause status
SNP IDs Gene CHR Alleles OR 95% CI p value Functional consequence
rs2167270 LEP 7 G > A 1.007 0.851–1.191 0.937 5_prime_UTR_variant
rs4731426 LEP 7 C > G 0.991 0.846–1.161 0.911 intron_variant
rs10487506 LEP 7 A > G 0.970 0.829–1.135 0.702
upstream_transcript_variant,2KB_upstream_variant
rs10954173 LEP 7 G > A 0.998 0.846–1.178 0.981 intron_variant
rs3828942 LEP 7 A > G 0.985 0.843–1.151 0.854 intron_variant
rs4655555 LEPR 1 A > T 0.825 0.706–0.934 0.015 intron_variant
rs10244329 LEPR 1 A > T 0.971 0.830–1.136 0.715 intron_variant
rs1137101 LEPR 1 G > A 0.728 0.598–0.885 0.002 missense_variant,
coding_sequence_variant
rs1137100 LEPR 1 G > A 0.956 0.810–1.128 0.595 missense_variant,
coding_sequence_variant
rs3745369 RETN 19 G > C 1.085 0.945–1.247 0.246 500B_downstream_variant
rs34861192 RETN 19 G > A 0.975 0.813–1.170 0.789 2KB_upstream_variant,
upstream_transcript_variant
rs3219175 RETN 19 G > A 0.964 0.728–1.273 0.794 2KB_upstream_variant,
upstream_transcript_variant
rs3219177 RETN 19 C > T 1.011 0.716–1.428 0.949 intron_variant
rs34124816 RETN 19 A > C 1.168 0.926–1.476 0.190 2KB_upstream_variant,
upstream_transcript_variant
rs1862513 RETN 19 C > G 1.083 0.941–1.247 0.265 2KB_upstream_variant,
upstream_transcript_variant
rs3745367 RETN 19 G > A 0.969 0.844–1.113 0.657 intron_variant
rs2267437 XRCC6 22 C > G 0.985 0.843–1.151 0.851 intron_variant,
upstream_transcript_variant,2KB_upstream_variant
rs2284082 XRCC6 22 T > C 0.973 0.852–1.111 0.683 intron_variant
rs5751129 XRCC6 22 T > C 0.903 0.726–1.120 0.353 intron_variant,
upstream_transcript_variant,2KB_upstream_variant
rs5751131 XRCC6 22 A > G 0.995 0.871–1.136 0.938 intron_variant
[95]Open in a new tab
Identification of the interaction network
RNA-seq expression and clinical data from BC patients were obtained
from TCGA to analyze and verify the identified hub genes. The
validation dataset included 112 subjects for whom both tumor and
matched normal samples were available. All genes available in the TCGA
dataset were analyzed to detect differences between tumor and normal
samples, and 10 common genes in Fig. [96]1 were screened out from the
results. LEP, LEPR and XRCC6 expression was significantly different
between two groups (Table [97]4). RETN was not differentially expressed
in the TCGA data.
Table 4.
The validation results of the 10 identical genes in Fig. [98]1 using
TCGA data
Gene logFC logCPM p value p-adjust
LEPR -2.52777 5.193642 1.65 × 10^–39 8.38 × 10^–38
LEP -5.98334 7.009349 2.35 × 10^–32 5.20 × 10^–31
T-cadherin -1.17561 4.687897 7.96 × 10^–23 6.45 × 10^–22
IFI30 0.872733 -0.95925 8.69 × 10^–11 2.42 × 10^–10
UCP2 0.827575 6.632093 1.06 × 10^–9 2.71 × 10^–9
PPARD 0.328611 4.92447 1.74 × 10^–6 3.41 × 10^–6
XRCC6 0.276328 7.708723 3.52 × 10^–6 6.70 × 10^–6
GPR30 -0.79614 2.56532 0.000122 0.000203
RETN 0.10441 -3.79534 0.683576 0.714306
Visfatin -0.01691 6.395228 0.866491 0.881913
[99]Open in a new tab
logFC, log[2] fold-change; logCPM, log[2] counts-per-million
Genetic data from 4,030 BCs and 3,494 controls in the UK Biobank was
used to verify the eight identical edges of the three networks in
Fig. [100]1 using logistic regression. The data were randomly divided
into two parts, the kernel density function of the BC and control
groups were estimated, and logistic regression was used to assess the
corresponding p-value of the eight edges (Table [101]5). The results
showed that the first four edges were significantly different
(p-value < 0.05). The genes connected by these four edges were the
identified hub genes, indicating that the interaction between hub genes
in this network is more significant than it is for other genes.
Table 5.
The validation results of the 8 identical edges in Fig. [102]1 using UK
Biobank data
Gene1 Gene2 p value
LEP XRCC6 0.047
LEP LEPR 0.005
LEPR RETN 0.002
GPR30 LEPR 0.010
IFI30 XRCC6 0.206
T-cadherin XRCC6 0.052
LEPR T-cadherin 0.051
PPARD UCP2 0.318
[103]Open in a new tab
Enrichment analysis
GO analysis showed that the biological processes of the identified
genes were mainly related to glucose homeostasis and carbohydrate
homeostasis (Fig. [104]2). KEGG pathway analysis showed that these
genes were mainly enriched in adenosine-monophosphate-activated protein
kinase (AMPK) signaling pathway, adipocytokine signaling and
non-alcoholic fatty liver disease (Fig. [105]2).
Fig. 2.
[106]Fig. 2
[107]Open in a new tab
GO function and KEGG pathway enrichment analysis of the genes
identified by JDINAC. A Dot plots show the top ten enriched GO BP, CC,
and MF terms for identified genes; B Dot plots show the top ten
enriched KEGG pathways. BP, Biological Processes; CC, Cell Component;
MF, Molecular Function
Discussion
This study sought to identify potential pathogenic genes associated
with BC by constructing a BC gene interaction network. This study
extended the results of prior studies [[108]14] by not only assessing
the effect of a single gene on BC but also the gene interaction
network, providing new insight into how genetic factors impact complex
human diseases. These results suggest that BMI and menopausal status
may be risk factors for BC. The gene interaction network obtained using
the JDINAC method showed that LEPR, LEP, XRCC6, and RETN have
significant interactivity difference between BC patients and healthy
women, and are associated with higher BC risk. However, analysis of hub
gene polymorphisms indicated that only LEPR rs1137101 and rs4655555
were strongly linked to BC. Other independent datasets and
bioinformatics analysis tools were used to verify the hub genes and the
edges, increasing the reliability of the results. The expression of
LEPR, LEP and XRCC6 was significantly associated with BC in TCGA
dataset. Meanwhile, UK Biobank SNP data validated their interaction on
BC.
GO enrichment analysis showed that the interacting genes were closely
related to cell energy and cell metabolism, such as glucose
homeostasis, carbohydrate homeostasis, muscle cell proliferation and
regulation of small molecules. The results in KEGG analysis were
consistent with those by GO analysis. Studies have shown that AMPK is
the main cellular energy sensor [[109]30]. Reduced activity of AMPK is
associated with altered cellular metabolic processes that drive BC
tumor growth and progression. If AMPK is activated, it can respond to
adenosine triphosphate (ATP) depletion, glucose starvation, and
metabolic stress [[110]31]. Obesity-related factors modulate metabolic
pathways in BC, providing a molecular link between obesity and BC.
Many studies have shown that LEP and LEPR play an important role in
obesity. LEP is a hormone secreted by adipose tissue, which regulates
eating and energy consumption through the hypothalamic region of the
brain [[111]32]. Circulating leptin binds to LEPR, activating Janus
kinase 2 (JAK2), phosphorylating three tyrosine residues in LEPR, and
inducing phosphorylation of STAT transcription factors, STAT5 and
STAT3, which are involved in the development of BC [[112]32]. Leptin
may stimulate the expression of estrogen by increasing aromatase
expression, which is also involved in BC development [[113]33]. The
LEPR rs1137101 polymorphism results from a nonconservative A to G
substitution at codon 223, reducing leptin binding and impairing
signaling [[114]34]. While the effect of LEPR rs4655555 on the
development of BC has not yet been reported, one study has shown that
rs4655555 is significantly correlated with plasma soluble leptin
receptor levels and may inform diabetes prognosis [[115]35]. The
findings from the current study further support the evidence that LEP
and LEPR play an important role in BC pathogenesis.
The impact of RETN on BC has been reported previously. RETN is highly
expressed in BC tissues and may serve as a biomarker for disease stage
and the degree of inflammation [[116]36, [117]37]. Low-grade systemic
inflammation is one of the characteristics of obesity [[118]38], and
RETN is shown to exert pro-inflammatory properties by upregulating
pro-inflammatory cytokines [[119]39] through the NFκB signaling pathway
[[120]40] that lead to inflammation and tumorigenesis. Several studies
have also linked XRCC6 with an increased risk of BC [[121]14, [122]41,
[123]42]. Interaction between XRCC6 genetic polymorphisms and
reproductive risk factors is thought by some researchers to contribute
to estrogen exposure, which results in double-strand breaks on BRCA1
and BRCA2 DNA and induces BC [[124]41]. XRCC6 is also involved in the
production of proinflammatory cytokines induced by lipopolysaccharide
(LPS) in human macrophages and monocytes. Proinflammatory cytokine
production is, in turn, associated with obesity and BC [[125]42].
Recent studies have used gene expression data to explore the
pathogenesis of BC [[126]18] and other diseases [[127]17, [128]20].
However, no genetic interaction network has been constructed to
identify potential BC pathology genes using SNP data. As discussed
previously, single genetic variants often explain only a small fraction
of phenotypic variation, that is, the problem of missing heritability
[[129]43]. Gene–gene interactions are proposed as a potential source of
this problem [[130]44]. The current study built gene interaction
networks based on SNP data to explain the etiology of complex human
traits. While high-throughput SNP genotyping methods have been
developed, the computational and statistical challenges of
simultaneously analyzing large SNP datasets still exist [[131]9]. The
method used here provides ideas for handling SNP data. In addition,
because BC incidence is affected by demography [[132]45, [133]46] the
gene network was constructed adjust the influence of confounding
factors such as BMI and menopause, making the results more reliable.
This study does have some limitations, however. Only the interaction
between paired genes was assessed. For BC, the relationship between
genes may be more complicated. Future studies should assess more
complex interactions associated with this disease.
Conclusions
Potential pathogenic BC genes were investigated by constructing a gene
interaction network. LEP, LEPR, XRCC6, and RETN had significant
interactions during BC, and LEPR polymorphisms may also be associated
with BC development. Gene network analysis can provide more detailed
information about the pathogenesis of complex diseases.
Supplementary Information
[134]12885_2022_10170_MOESM1_ESM.pdf^ (50.8KB, pdf)
Additional file 1: Figure S1. The differential interaction network
inferred by JDINAC after adjusting for BMI and menopause status.
[135]12885_2022_10170_MOESM2_ESM.docx^ (16.5KB, docx)
Additional file 2: Table S1. Top 10 gene interaction pairs identified
by JDINAC after adjusting for BMI.
[136]12885_2022_10170_MOESM3_ESM.docx^ (17.2KB, docx)
Additional file 3: Table S2. Top 10 gene interaction pairs identified
by JDINAC after adjusting for menopausal status.
[137]12885_2022_10170_MOESM4_ESM.docx^ (18KB, docx)
Additional file 4: Table S3. Top 10 gene interaction pairs identified
by JDINAC after adjusting for BMI and menopause status.
[138]12885_2022_10170_MOESM5_ESM.docx^ (16.2KB, docx)
Additional file 5: Table S4. The association of IFI30 polymorphisms
with BC adjusted for BMI and menopause status.
Acknowledgements