MIR31 host gene (MIR31HG) polymorphisms play important roles in the occurrence of osteonecrosis. However, the association of MIR31HG polymorphisms with the risk of steroid-induced osteonecrosis of the femoral head (SONFH) remains unclear. In this study, we aimed to investigate the correlation between MIR31HG polymorphisms and SONFH susceptibility in the Chinese Han population.
Methods
A total of 708 volunteers were recruited to detect the effect of seven single nucleotide polymorphisms (SNPs) in the MIR31HG gene on SONFH risk in the Chinese Han population. Genotyping of MIR31HG polymorphisms was performed using the Agena MassARRAY platform. The odds ratio (OR) and 95% confidence interval (95% CI) were used to evaluate the correlation between MIR31HG polymorphisms and SONFH risk using logistic regression model.
Results
According to the results of genetic model, rs10965059 in MIR31HG was significantly correlated with the susceptibility to SONFH (OR = 0.56, p = 0.002). Interestingly, the stratified analysis showed that rs10965059 was associated with the reduced risk of SONFH in subjects aged > 40 years (OR = 0.30, p < 0.001) and male populations (OR = 0.35, p < 0 .001). Moreover, rs10965059 was associated with the reduced risk of bilateral SONFH (OR = 0.50, p = 0.002). Finally, multi-factor dimension reduction (MDR) results showed that the combination of rs1332184, rs72703442, rs2025327, rs55683539, rs2181559, rs10965059 and rs10965064 was the best model for predicting SONFH occurrence (p < 0.0001).
Conclusion
The study indicated that rs10965059 could be involved in SONFH occurrence in the Chinese Han population, which might provide clues for investigating the role of MIR31HG in the pathogenesis of SONFH.
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Introduction
Osteonecrosis of the femoral head (ONFH) is a progressive rupture of the femoral head caused by the death of bone cells from various causes. The main characteristics of ONFH are the differentiation and the damage of bone marrow mesenchymal cells, enhanced cytotoxicity and destruction of vascular blood flow [1]. Osteonecrosis can be summarized into two categories: traumatic and non-traumatic femoral head necrosis. The steroid-induced osteonecrosis of the femoral head (SONFH), a non-traumatic femoral head necrosis, is a devastating disease, which is often result in devastating and crippling health conditions following steroid therapy [2]. In China, there were approximately 8 million patients with non-traumatic ONFH, which may be closely related to their frequent use of high-dose hormone therapy [3]. The pathogenesis is likely multifactorial, with genetic and environmental factors playing a role. Corticosteroid use, alcohol consumption, smoking, and infection and metabolic disease are all risk factors for SONFH [4]. Furthermore, genetics appears to play an important role in the development of SONFH. Previous studies have suggested that some genes play a role in SONFH occurrence including eNOS, PAI-1, VEGF, and ApoA [5]. However, there are still a large number of potential osteonecrosis-related genes and loci that have not been fully explored.
Long non-coding RNA (LncRNA) is a non-protein coding RNA molecule with a structure size of 200 nucleotides [6, 7]. Yuan and Sun’s studies showed that, lncRNA can regulate the development of immune diseases and affect immune function and autoimmunity, such as osteosarcoma and IgA nephropathy (IgAN) [8, 9]. The lncRNA MIR31 host gene (MIR31HG) is an crucial regulator of malignant tumors [10]. MIR31HG located on chromosome 9 with the length of 2166 bp, is an lncRNA that acts on the progression of cancers, such as osteosarcoma, lung cancer, breast cancer and cervical cancer [9, 11, 12]. For example, recent studies have mentioned that MIR31HG is also involved in the development and regeneration of bone, and the pathogenesis of numerous orthopaedic conditions [13]. MIR31HG was up-regulated in osteosarcoma (OS) tissues and OS cell lines. In the case of bone loss, it was usually inflamed in the defective or injured tissue. A previous research has demonstrated that knock-down of MIR31HG not only affects the enhancement of osteogenic differentiation, but also limits the inhibitory effect of osteogenic in an inflammatory environment [14]. Studies have found that the interference with MIR31HG can improve osteogenesis in bone marrow stromal cells in patients with cleidocranial dysplasia (BMSCs-CCD), possibly through by promoting osteogenic differentiation and improving the aging-related properties of BMSCs-CCD [15]. MIR31HG can regulate the tumor suppressor miR-361 and its target genes, and promote tumor progression in osteosarcoma acting as an oncogene [9]. These studies have shown that MIR31HG may play an important role in SONFH. At present, the connection between MIR31HG gene polymorphism and the susceptibility to SONFH was not reported.
Anzeige
In the case-control study, the MassARRAY platform was used to select seven single nucleotide polymorphisms (SNPs) in MIR31HG for genotyping. We further investigated the effect of MIR31HG genetic polymorphisms on SONFH risk and conducted the stratified analysis to identify the contribution of confounding factors to the association between SNPs and the risk of SONFH. Our research will provide a new perspective to study the role of MIR31HG on the susceptibility to SNOFH.
Methods
Subjects
A total of 708 unrelated participants were recruited, embracing 200 SONFH cases (41.15 ± 12.90 years) and 508 healthy controls (42.70 ± 13.01 years) geographically and ethnically matched. Exclusion criteria were as follows: (1) Patients who did not meet the diagnostic criteria of SONFH and patients with traumatic ONFH, hip dislocation and other hip diseases; and (2) Patients without major family genetic diseases. The histopathological diagnosis was based on X-rays and/or magnetic resonance imaging (MRI) examination of the hip and frog positions. The research protocol was in compliance with the Declaration of Helsinki and was approved by the ethics committee of Affiliated Hospital of Weifang Medical University and Second Affiliated Hospital of Inner Mongolia Medical University. All experimental subjects signed a written informed consent. Demographic and blood biochemical indicators of each subject were collected from standardized questionnaires and medical records by trained research staff.
Selection and genotyping for MIR31HG polymorphisms
Seven functional SNPs in MIR31HG (rs1332184, rs72703442, rs2025327, rs55683539, rs2181559, rs10965059 and rs10965064) were selected from the 1000 Genomes Project (http://www.1000genomes.org/), with the minor allele frequency (MAF) of each SNP greater than 0.05. Peripheral blood genomic DNA was extracted from all subjects according to the operating procedures of Whole Blood Genomic DNA Isolation Kit (Xi’an GoldMag Biotechnology, China). Agena MassARRAY iPLEX platform was used for genotyping. Agena Bioscience Assay was used to design PCR primers for amplification (Supplementary Table 1). Finally, Agena Bioscience TYPER application software 4.0 was performed to analyze the genetic data.
Statistical analysis
The differences in demographic or clinical characteristics between the case and the control groups were compared by χ2tests for the categorical variables and Student’s t-tests for continuous variables. PLINK software was used to detect four genetic models (co-dominant, dominant, recessive, and log-additive). Hardy-Weinberg equilibrium (HWE) of all SNPs from control individuals was evaluated by χ2 test. Multi-factor dimension reduction (MDR) is suitable for detecting the interaction between SNP-SNP and SONFH risk. Analysis of variance (ANOVA) was performed to determine differences in clinical characteristics among SNPs genotypes. By calculating the odds ratio (OR) and 95% confidence interval (CI), logistic regression results were adjusted for age and gender to assess the impact of MIR31HG polymorphism on SONFH risk. HaploView software version 4.2 and logistic regression were carried out to assess the correlation of MIR31HG haplotypes with SONFH susceptibility. All statistics were two-tailed, and a p < 0.05 was considered statistically significant. SPSS 20.0 software (Chicago, IL, USA) was used for statistical analysis in this study.
Anzeige
Results
Basic conventional characteristics
were consisted. The basic characteristics of 200 patients with SONFH and 508 healthy participants are summarized in Table 1, including age, gender, necrosis, and course. The mean age was 41.15 ± 12.90 years in the case group and 42.70 ± 13.01 years in control group. There were no significant differences in age (p = 0.152) and gender (p = 0.706) characteristics between cases and controls.
Table 1
Basic characteristic of SONFH patients and healthy subjects in this study
Characteristics
Cases (n = 200)
Controls (n = 508)
p
Age, years (mean ± SD)
41.15 ± 12.90
42.70 ± 13.01
0.152a
> 40
100 (50%)
290 (57%)
≤ 40
100 (50%)
218 (43%)
0.706b
Gender
Male
117 (59%)
425 (84%)
Female
83 (41%)
83 (16%)
Necrosis
Bilateral
143 (72%)
Missing
55 (28%)
Course (months)
> 29
61 (31%)
≤ 29
139 (69%)
SD Standard deviation
pa values were calculated from student’s t test
pb values were calculated from χ2 test
Association analysis of MIR31HG and SONFH risk
In this study, seven SNPs (rs1332184, rs72703442, rs2025327, rs55683539, rs2181559, rs10965059, and rs10965064) were successfully genotyped. The minor allele frequencies are record in Table 2. All SNP distribution of controls were in line with HWE (p > 0.05). The rs10965059-T allele frequency in the case group (0.103) was lower than that in the control group (0.169), and the reduced risk of SONFH was found (OR = 0.56, p = 0.002).
Table 2
Basic information for MIR31HG SNPs
SNP ID
Chromosome position
Role
Alleles A/B
MAF
O (HET)
E (HET)
Pa-HWE
OR (95% CI)
Pb
Case
control
rs1332184
chr9:21504203
Intron
A/G
0.245
0.264
0.383
0.389
0.732
0.90 (0.69–1.18)
0.456
rs72703442
chr9:21515795
Intron
A/C
0.143
0.163
0.286
0.273
0.328
0.86 (0.62–1.19)
0.347
rs2025327
chr9:21531629
Intron
C/T
0.108
0.122
0.217
0.214
0.999
0.87 (0.60–1.25)
0.445
rs55683539
chr9:21542134
Intron
T/C
0.230
0.244
0.341
0.369
0.093
0.92 (0.71–1.22)
0.590
rs2181559
chr9:21543938
Intron
A/T
0.332
0.359
0.443
0.460
0.387
0.89 (0.69–1.13)
0.328
rs10965059
chr9:21544062
Intron
T/C
0.103
0.169
0.299
0.281
0.204
0.56 (0.39–0.81)
0.002*
rs10965064
chr9:21553538
Intron
G/C
0.358
0.370
0.461
0.466
0.776
0.94 (0.74–1.21)
0.658
SNP Single nucleotide polymorphism, MAF Minor allele frequency, HWE Hardy-Weinberg equilibrium
Pa -values were calculated by exact test Pa < 0.05 are excluded
Pb -values were calculated by two-sided χ2. Pb < 0.05 indicates statistical significance
The correlation between the risk of SONFH and the MIR31HG polymorphisms was assessed after adjusting for age and gender in the four genetic models (co-dominant, dominant, recessive, and log-addition models). Our analysis results showed that rs10965059 was significantly related to the risk of SONFH (Table 3). In addition, rs10965059 was associated with the reduced susceptibility to SONFH in the co-dominant (T/C vs. C/C, OR = 0.50, p = 0.002), dominant (T/T-T/C vs. C/C, OR = 0.54, p = 0.004) and log-additive (OR = 0.63, p = 0.016) models.
Table 3
Association analysis between MIR31HG SNPs and SONFH risk
SNP ID
Model
Genotype
Case
Control
With Adjustment
OR (95% CI)
Pb
rs1332184
Codominant
G/G
109
276
1
A/A
7
37
0.49 (0.20–1.14)
0.099
A/G
84
194
1.05 (0.74–1.50)
0.770
Dominant
G/G
109
276
1
A/A-A/G
91
231
0.96 (0.68–1.36)
0.837
Recessive
A/G-G/G
193
470
1
A/A
7
37
0.47 (0.20–1.11)
0.084
Log-additive
–
–
–
0.88 (0.67–1.17)
0.391
rs72703442
Codominant
C/C
145
352
1
A/A
2
10
0.52 (0.11–2.49)
0.414
A/C
53
145
0.85 (0.58–1.25)
0.417
Dominant
C/C
145
352
1
A/A-A/C
55
155
0.83 (0.57–1.22)
0.342
Recessive
A/C-C/C
198
497
1
A/A
2
10
0.54 (0.11–2.59)
0.446
Log-additive
–
–
–
0.82 (0.58–1.17)
0.285
rs2025327
Codominant
T/T
159
391
1
C/C
2
7
0.61 (0.12–3.13)
0.557
C/T
39
110
0.84 (0.55–1.28)
0.407
Dominant
T/T
159
391
1
C/C-C/T
41
117
0.82 (0.54–1.25)
0.354
Recessive
C/T-T/T
198
501
1
C/C
2
7
0.64 (0.13–3.24)
0.588
Log-additive
–
–
–
0.82 (0.56–1.21)
0.324
rs55683539
Codominant
C/C
116
297
1
T/T
8
37
0.57 (0.25–1.29)
0.176
T/C
76
173
1.12 (0.78–1.60)
0.536
Dominant
C/C
116
297
1
T/T-T/C
84
210
1.02 (0.73–1.45)
0.893
Recessive
T/C-C/C
192
470
1
T/T
8
37
0.54 (0.24–1.22)
0.139
Log-additive
–
–
–
0.93 (0.71–1.24)
0.629
rs2181559
Codominant
T/T
87
213
1
A/A
20
70
0.64 (0.36–1.15)
0.135
A/T
92
225
1.05 (0.73–1.50)
0.804
Dominant
T/T
87
213
1
A/A-A/T
112
295
0.95 (0.67–1.33)
0.746
Recessive
A/T-T/T
179
438
1
A/A
20
70
0.63 (0.36–1.09)
0.097
Log-additive
–
–
–
0.87 (0.68–1.12)
0.292
rs10965059
Codominant
C/C
164
340
1
T/T
5
10
1.12 (0.36–3.49)
0.842
T/C
31
149
0.50 (0.32–0.78)
0.002*
Dominant
C/C
164
340
1
T/T-T/C
36
159
0.54 (0.35–0.82)
0.004*
Recessive
T/C-C/C
195
489
1
T/T
5
10
1.31 (0.42–4.07)
0.644
Log-additive
–
–
–
0.63 (0.43–0.92)
0.016*
rs10965064
Codominant
C/C
80
203
1
G/G
23
71
0.82 (0.47–1.42)
0.474
G/C
97
234
1.00 (0.70–1.45)
0.989
Dominant
C/C
80
203
1
G/G-G/C
120
305
0.96 (0.68–1.36)
0.818
Recessive
G/C-C/C
177
437
1
G/G
23
71
0.81 (0.48–1.37)
0.440
Log-additive
–
–
–
0.93 (0.72–1.20)
0.580
SNP Single nucleotide polymorphism, CI Confidence interval, OR Odds ratio
*indicates statistical significance (p < 0.05)
Pa-values were calculated by unconditional logistic regression analysis without adjustment for age and gender
Pb-values were calculated by unconditional logistic regression analysis with adjustment for age and gender
Stratification analyses
To further investigate the effect of confounding factors on the association of MIR31HG variants with SONFH occurrence, we conducted a stratified analysis based on age, gender, disease course, and bilateral. The age-stratified analysis of the relationship between SNPs and SONFH risk is presented in Table 4. In subjects aged > 40 years, rs10965059 was related to a reduced risk of SONFH (T allele: OR = 0.30, p < 0.001; C/T genotype: OR = 0.34, p < 0.001; C/T-T/T genotype: OR = 0.33, p < 0.001). Conversely, no significant relationship of MIR31HG variants with SONFH risk was observed in subjects aged less than 40 years.
Table 4
Correlation between MIR31HG SNPs and SONFH risk stratified by age
SNP
Allele/Genotype
Case
Control
OR (95% CI)
p
Case
Control
OR (95% CI)
p
Age
> 40
≤40
rs1332184
G
149
426
1
153
320
1
A
51
152
0.96 (0.67–1.39)
0.825
47
116
0.85 (0.58–1.25)
0.413
G/G
53
154
1
56
122
1
G/A
43
118
1.02 (0.62–1.68)
0.937
41
76
1.18 (0.72–1.93)
0.522
A/A
4
17
0.70 (0.22–2.31)
0.564
3
20
0.33 (0.09–1.15)
0.080
G/A-A/A
47
135
0.98 (0.61–1.59)
0.939
44
96
1.00 (0.62–1.61)
0.995
rs72703442
C
169
481
1
174
368
1
A
31
99
0.89 (0.57–1.38)
0.608
26
66
0.83 (0.51–1.36)
0.466
C/C
70
195
1
75
157
1
C/A
29
91
0.79 (0.47–1.34)
0.386
24
54
0.93 (0.53–1.62)
0.799
A/A
1
4
0.70 (0.07–6.83)
0.764
1
6
0.35 (0.04–2.95)
0.334
C/A-A/A
30
95
0.79 (0.47–1.33)
0.371
25
60
0.87 (0.51–1.50)
0.621
rs2025327
T
176
511
1
181
381
1
C
24
69
1.01 (0.62–1.66)
0.969
19
55
0.73 (0.42–1.26)
0.255
T/T
78
224
1
81
167
1
T/C
20
63
0.86 (0.47–1.56)
0.618
19
47
0.83 (0.46–1.51)
0.549
C/C
2
3
1.16 (0.17–7.90)
0.882
0
4
/
0.999
T/C-C/C
22
66
0.88 (0.49–1.56)
0.657
19
51
0.77 (0.43–1.39)
0.381
rs55683539
C
155
429
1
153
338
1
T
45
151
0.82 (0.56–1.21)
0.320
47
96
1.08 (0.73–1.59)
0.706
C/C
58
162
1
58
135
1
C/T
39
105
1.07 (0.65–1.76)
0.790
37
68
1.27 (0.76–2.10)
0.359
T/T
3
23
0.32 (0.09–1.17)
0.085
5
14
0.83 (0.29–2.42)
0.734
C/T-T/T
42
128
0.93 (0.57–1.50)
0.753
42
82
1.19 (0.74–1.93)
0.475
rs2181559
T
132
362
1
134
289
1
A
68
218
0.86 (0.61–1.20)
0.364
64
147
0.94 (0.67–1.33)
0.739
T/T
41
113
1
46
100
1
T/A
50
136
1.08 (0.65–1.81)
0.746
42
89
1.03 (0.62–1.70)
0.921
A/A
9
41
0.49 (0.21–1.16)
0.105
11
29
0.83 (0.38–1.79)
0.627
T/A-A/A
59
177
0.93 (0.57–1.52)
0.774
53
118
0.98 (0.61–1.57)
0.922
rs10965059
C
186
451
1
173
378
1
T
14
113
0.30 (0.17–0.54)
< 0.001*
27
56
1.05 (0.66–1.67)
0.845
C/C
86
174
1
78
166
1
C/T
14
103
0.34 (0.18–0.65)
< 0.001*
17
46
0.79 (0.42–1.46)
0.446
T/T
0
5
/
0.999
5
5
2.13 (0.60–7.57)
0.243
C/T-T/T
14
108
0.33 (0.18–0.62)
< 0.001*
22
51
0.92 (0.52–1.62)
0.768
rs10965064
C
126
350
1
131
290
1
G
74
230
0.89 (0.64–1.25)
0.507
69
146
1.05 (0.74–1.47)
0.807
C/C
37
103
1
43
100
1
C/G
52
144
0.96 (0.57–1.62)
0.884
45
90
1.16 (0.70–1.93)
0.559
G/G
11
43
0.67 (0.30–1.50)
0.334
12
28
1.00 (0.46–2.14)
0.993
C/G-G/G
63
187
0.90 (0.55–1.47)
0.662
57
118
1.12 (0.70–1.81)
0.633
SNP Single nucleotide polymorphism, CI Confidence interval, OR Odds ratio
P-Values were calculated by logistic regression adjusted by age and gender
Gender-based stratified analysis (Table 5) indicated that, rs10965059 was a protective SNP for SONFH in males (T allele: OR = 0.53, p = 0.009; C/T genotype: OR = 0.35, p < 0.001; C/T-T/T genotype: OR = 0.42, p = 0.001).
Table 5
Correlation between SNPs and SONFH susceptibility stratified by gender
SNP
Allele/Genotype
Case
Control
OR (95% CI)
p
Case
Control
OR (95% CI)
p
Gender
Male
Female
rs1332184
G
174
630
1
128
116
1
A
60
220
0.98 (0.70–1.37)
0.904
38
48
0.72 (0.44–1.18)
0.187
G/G
60
238
1
49
38
1
G/A
54
154
1.39 (0.91–2.13)
0.123
30
40
0.59 (0.31–1.11)
0.100
A/A
3
33
0.35 (0.10–1.18)
0.089
4
4
0.77 (0.18–3.30)
0.729
G/A-A/A
57
187
1.21 (0.80–1.82)
0.375
34
44
0.60 (0.33–1.12)
0.108
rs72703442
C
202
712
1
141
137
1
A
32
136
0.82 (0.54–1.26)
0.363
25
29
0.84 (0.47–1.50)
0.552
C/C
85
298
1
60
54
1
C/A
32
116
0.97 (0.61–1.54)
0.891
21
29
0.65 (0.33–1.27)
0.209
A/A
0
10
/
2
0
/
0.999
C/A-A/A
32
126
0.89 (0.56–1.41)
0.618
23
29
0.71 (0.37–1.38)
0.310
rs2025327
T
207
753
1
150
139
1
C
27
97
0.99 (0.63–1.57)
0.975
16
27
0.55 (0.28–1.06)
0.072
T/T
91
333
1
68
58
1
T/C
25
87
1.04 (0.63–1.71)
0.892
14
23
0.52 (0.24–1.10)
0.085
C/C
1
5
0.68 (0.08–5.92)
0.725
1
2
0.40 (0.03–4.63)
0.463
T/C-C/C
26
92
1.02 (0.62–1.67)
0.952
15
25
0.51 (0.24–1.05)
0.069
rs55683539
C
182
640
1
126
127
1
T
52
208
0.90 (0.64–1.26)
0.535
40
39
1.03 (0.62–1.71)
0.898
C/C
69
248
1
47
49
1
C/T
44
144
1.13 (0.73–1.74)
0.587
32
29
1.16 (0.61–2.20)
0.658
T/T
4
32
0.46 (0.16–1.34)
0.152
4
5
0.83 (0.21–3.27)
0.785
C/T-T/T
48
176
1.00 (0.66–1.53)
0.986
36
34
1.11 (0.60–2.05)
0.747
rs2181559
T
155
551
1
111
100
1
A
79
299
0.95 (0.70–1.29)
0.725
53
66
0.72 (0.46–1.14)
0.159
T/T
46
183
1
41
30
1
T/A
63
185
1.42 (0.92–2.19)
0.117
29
40
0.53 (0.27–1.04)
0.064
A/A
8
57
0.55 (0.24–1.24)
0.149
12
13
0.67 (0.27–1.68)
0.392
T/A-A/A
71
242
1.20 (0.79–1.83)
0.390
41
53
0.56 (0.30–1.05)
0.072
rs10965059
C
210
691
1
149
138
1
T
24
153
0.53 (0.33–0.85)
0.009*
17
16
0.98 (0.48–2.02)
0.965
C/C
97
277
1
67
63
1
C/T
16
137
0.35 (0.20–0.63)
< 0.001*
15
12
1.17 (0.51–2.70)
0.709
T/T
4
8
1.45 (0.42–4.94)
0.555
1
2
0.49 (0.04–5.54)
0.561
C/T-T/T
20
145
0.42 (0.25–0.71)
0.001*
16
14
1.08 (0.49–2.39)
0.857
rs10965064
C
156
536
1
101
104
1
G
78
314
0.87 (0.64–1.18)
0.370
65
62
1.08 (0.69–1.68)
0.735
C/C
52
171
1
28
32
1
C/G
52
194
0.91 (0.59–1.41)
0.669
45
40
1.28 (0.66–2.49)
0.461
G/G
13
60
0.73 (0.37–1.44)
0.359
10
11
1.03 (0.38–2.80)
0.958
C/G-G/G
65
254
0.87 (0.57–1.31)
0.496
55
51
1.23 (0.65–2.32
0.526
SNP Single nucleotide polymorphism, CI Confidence interval, OR Odds ratio
p values were calculated by logistic regression adjusted by age and gender
Additionally, the stratification analysis of the association between MIR31HG SNPs and SONFH risk by course and bilateral are presented in Table 6. We discovered that rs2025327 might contribute to prolonged SONFH course (OR = 2.14, p = 0.046). The rs10965059 was associated with the reduced risk of bilateral SONFH (T allele, OR = 0.56, p = 0.005; C/T genotype, OR = 0.43, p = 0.002; C/T-T/T genotype, OR = 0.51, p = 0.007).
Table 6
Relationships of MIR31HG SNPs with SONFH risk stratified by course and bilateral
SNP
Allele/Genotype
Case 1 (course > 29 month)
Case 2 (course ≤29 month)
OR (95% CI)
p
Cases with bilateral
Control
OR (95% CI)
p
rs1332184
G
95
207
1
216
746
1
A
27
71
0.83 (0.50–1.37)
0.466
70
268
0.90 (0.67–1.22)
0.506
G/G
35
74
1
77
276
1
G/A
25
59
0.10 (0.53–1.88)
0.987
62
194
1.10 (0.74–1.63)
0.650
A/A
1
6
0.27 (0.03–2.45)
0.246
4
37
0.38 (0.13–1.14)
0.084
G/A-A/A
26
65
0.91 (0.49–1.70)
0.764
66
231
0.98 (0.67–1.45)
0.935
rs72703442
C
106
237
1
244
849
1
A
16
41
0.87 (0.47–1.62)
0.667
42
165
0.89 (0.61–1.28)
0.517
C/C
45
100
1
102
352
1
C/A
16
37
0.98 (0.48–1.97)
0.949
40
145
0.93 (0.60–1.43)
0.727
A/A
0
2
/
1
10
0.40 (0.05–3.27)
0.393
C/A-A/A
16
39
0.90 (0.45–1.81)
0.767
41
155
0.90 (0.59–1.37)
0.610
rs2025327
T
105
252
1
259
892
1
C
17
26
1.57 (0.82–3.01)
0.173
27
124
0.75 (0.48–1.16)
0.197
T/T
44
115
1
117
391
1
T/C
17
22
2.14 (1.01–4.53)
0.046*
25
110
0.70 (0.43–1.16)
0.164
C/C
0
2
/
1
7
0.49 (0.06–4.09)
0.506
T/C-C/C
17
24
1.93 (0.92–4.02)
0.081
26
117
0.69 (0.42–1.13)
0.136
rs55683539
C
92
216
1
220
767
1
T
30
62
1.14 (0.69–1.87)
0.617
66
247
0.93 (0.68–1.27)
0.654
C/C
33
83
1
82
297
1
C/T
26
50
1.35 (0.71–2.57)
0.355
56
173
1.16 (0.77–1.73)
0.481
T/T
2
6
0.80 (0.15–4.37)
0.796
5
37
0.52 (0.19–1.41)
0.198
C/T-T/T
28
56
1.29 (0.69–2.41)
0.422
61
210
1.05 (0.71–1.55)
0.812
rs2181559
T
75
191
1
193
651
1
A
45
87
1.37 (0.84–2.06)
0.228
91
365
0.84 (0.64–1.11)
0.225
T/T
22
65
1
63
213
1
T/A
31
61
1.68 (0.85–3.31)
0.135
67
225
1.04 (0.69–1.56)
0.854
A/A
7
13
1.54 (0.53–4.48)
0.427
12
70
0.53 (0.27–1.07)
0.078
T/A-A/A
38
74
1.65 (0.87–3.15)
0.128
79
295
0.91 (0.62–1.35)
0.640
rs10965059
C
108
251
1
257
829
1
T
14
27
1.21 (0.61–2.39)
0.592
29
169
0.56 (0.36–0.84)
0.005*
C/C
48
116
1
119
340
1
C/T
12
19
1.54 (0.68–3.50)
0.301
19
149
0.43 (0.25–0.74)
0.002*
T/T
1
4
0.87 (0.09–8.29)
0.900
5
10
1.55 (0.49–4.89)
0.452
C/T-T/T
13
23
1.45 (0.66–3.16)
0.357
24
159
0.51 (0.31–0.83)
0.007*
rs10965064
C
78
179
1
184
640
1
G
44
99
1.02 (0.66–1.59)
0.931
102
376
0.94 (0.72–1.24)
0.677
C/C
21
59
1
55
203
1
C/G
36
61
1.53 (0.79–2.97)
0.212
74
234
1.11 (0.76–1.68)
0.612
G/G
4
19
0.52 (0.15–1.76)
0.290
14
71
0.73 (0.37–1.43)
0.359
C/G-G/G
40
80
1.29 (0.68–2.45)
0.440
88
305
1.03 (0.69–1.53)
0.896
SNP Single nucleotide polymorphism, CI Confidence interval, OR Odds ratio
p values were calculated by logistic regression adjusted by age and gender
*indicates statistical significance (p < 0.05)
MDR analysis of SNP-SNP interaction on SONFH
SNP-SNP interaction was determined using MDR analysis. As shown in Table 7 and Fig. 1, the analysis results indicated that the combination of rs1332184, rs72703442, rs2025327, rs55683539, rs2181559, rs10965059, and rs10965064 was the optimal model for predicting SONFH occurrence (training accuracy = 0.671, CVC = 10/10, p < 0.0001). In addition, the optimal single locus model for predicting SONFH risk was rs10965059 (training accuracy = 0.578, CVC = 10/10, p < 0.0001). Two-locus model was rs2181559 and 10,965,059. Three-locus model was consisted of rs2025327, rs2181559, and rs10965059. Four--locus model was consisted of rs1332184, rs2181559, rs10965059 and rs10965064. Five--locus model was the combination of rs1332184, rs72703442, rs55683539, rs2181559, rs10965059 and rs10965064. The results of the network diagram and the tree diagram were consistent (Fig. 1). There was a stronger redundant interaction between rs10965059 and rs10965064 (information gain: − 0.78%) and a stronger synergy between rs72703442 and rs2025327 (information gain: 0.26%).
Table 7
Analysis of SNP-SNP interaction models using MDR method
Bal. Acc Balanced accuracy, CVC Cross-validation consistently, CI Confidence interval, OR Odds ratio
p values were calculated by χ2 test
p < 0.01 indicates statistical strongly significant
Fig. 1
The tree diagram analysis among SNP interaction
×
The correlation of MIR31HG haplotypes with SONFH susceptibility
We also examined the impacts of MIR31HG haplotypes on SONFH susceptibility. As shown in Fig. 2, a linkage disequilibrium (LD) block was comprised of three SNPs including rs72703442, rs2025327 and rs55683539. The frequency distribution of haplotypes in case and control group is presented in Table 8. To examine the effect of haplotypes on SONFH risk, a haplotype-based logistic regression method was carried out in the case–control cohort, however, no significant association was found.
Fig. 2
The linkage disequilibrium structure of seven SNPs in the MIR31HG gene. The numbers in squares are D′ values
Table 8
Relationships of MIR31HG haplotypes with SONFH risk
Blocks
SNPs
Haplotype
Frequency
Crude analysis
Adjusted by age and gender
Case
Control
OR (95% CI)
p
OR (95% CI)
p
Block 1
rs72703442|rs2025327|rs55683539
ATT
0.138
0.159
0.84 (0.60–1.18)
0.305
0.82 (0.58–1.17)
0.271
rs72703442|rs2025327|rs55683539
CTT
0.093
0.084
1.11 (0.75–1.65)
0.607
1.16 (0.76–1.75)
0.497
rs72703442|rs2025327|rs55683539
CCC
0.108
0.122
0.86 (0.60–1.25)
0.435
0.82 (0.56–1.21)
0.319
rs72703442|rs2025327|rs55683539
CTC
0.343
0.369
0.89 (0.70–1.14)
0.360
0.88 (0.68–1.12)
0.300
SNP Single nucleotide polymorphism, CI Confidence interval, OR Odds ratio
p values were calculated by logistic regression adjusted by age and gender
×
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Discussion
SONFH is multi-layered and intricate disease with femoral neck fracture or bone tissue disorder, whose symptoms and signs are diverse, and the time and degree of pain attack are different However, SONFH still has the basis of pathological evolution. There are no specific clinical manifestations of ONFH, so it is difficult to make a diagnosis of ONFH from the patient’s symptoms and clinical examination [16]. With the development of modern precision medicine in recent years, an in-depth research on stem cells, molecular biology, and the exact pathogenesis of SONFH has been analyzed. A large number of experiments have shown that the increase in reactive oxygen species (ROS) caused by hormone use is related to the occurrence and development of SONFH [17, 18]. The the frequent collapse of the femoral head and hip joint dysfunction makes the treatment of SONFH difficult [4, 19].
LncRNA consists of a non-protein coding transcripts with approximately 200 nucleotides [6], which are drawn into various cellular processes such as chromatin remodeling, post-transcriptional processing and transcription process [20], involved in the occurrence, progression, and metastasis of human cancers, and played corresponding roles. Among those cancers, lncRNAs are more widely researched in osteosarcoma, including lncRNA-21A, UCA1, MEG3, HULC, and MIR31HG [21]. MIR31HG acts as an oncogene in osteosarcoma to promote tumor progression via regulation of tumor suppressor miR-361 and its target genes [9, 21]. Taken together, studying SONFH in the field of exploring lncRNAs is highly needed and promising. Moreover, according to our current research results, there is a significant correlation between MIR31HG polymorphism and SONFH susceptibility in the Chinese Han population.
MIR31HG is a kind of lncRNA that can be expressed in human bone cells, and it involves autoimmune in the recent research reports. The most noteworthy thing is that there is no research on the correlation between MIR31HG polymorphism and SONFH susceptibility. Our research is the first to find a significant risk connection between MIR31HG genetic variations and SONFH susceptibility in the Han population in China. The locus in MIR31HG has only been reported in IgAN currently [8]. In future studies, if SNP assessment is used as a type of risk marker, patients with high risk of ONFH can be identified through screening, and the dosage of steroids then can be differentiated based on individual differences, which can prevent the development of SONFH [1].
Therefore, we are committed to investigating the association between the MIR31HG gene polymorphism and the risk of SONFH disease. Our study results of genotyping showed that rs10965059-T allele frequency in the case group (0.103) was lower than that in the control group (0.169), and the reduced risk of SONFH was found. The stratified analysis results showed that rs10965059 was associated with the reduced risk of SONFH in subjects aged > 40 years (p < 0.001), and males (p < 0 .001). Consequently, we speculated that age and gender may interact with MIR31HG genetic polymorphisms on SONFH occurrence. Moreover, rs10965059 was associated with the reduced risk of bilateral SONFH (p = 0.002).
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However, there are other candidate genes in the research on SONFH, and the research on MIR31HG is relatively rare. Nonetheless, our current work has some limitations. First of all, the relationship between SNPs and SONFH risk was investigated in the early stage, and the relationship among gene-environment interactions needs to be studied in the later work. Second, we have successfully demonstrated the relationship between MIR31HG polymorphisms and SONFH, and the molecular mechanism of SONFH will be studied in the future work. Patients were all from Shandong, Inner Mongolia and adjacent areas, which are in low population mobility. It is easy to carry out population-based research. As is known to all, this is the first study to probe into the effect of MIR31HG mutation on SONFH, which may provide a scientific basis for future research of MIR31HG on the molecular mechanism of SONFH.
Conclusion
Our study indicates that rs10965059 in MIR31HG is a protective SNP for SONFH, which offers a new insight for the molecular mechanism and provides a new major candidate gene in the study progression of SONFH. In the future, we will continue to collect samples to expand the sample size for confirming our results in a larger cohort of subjects.
Acknowledgments
The authors thank all participants and volunteers in this study.
Declarations
Ethics approval and consent to participate
The protocol for this study was approved by the Ethics Committee of the Affiliated Hospital of Weifang Medical University and Second Affiliated Hospital of Inner Mongolia Medical University, and was in line with the Helsinki declaration. And the participant’s signature informed consent was received.
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Consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
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