An Investigation of the Effects of B7-H4 Gene rs10754339 and miR-125a Gene rs12976445 on Cancer Susceptibility

JIN Yu Chen DONG Li Juan YANG Qin Yue XIONG Wei Ning WANG Wei Yi FENG Xian Hong YU Wei HUANG Wei CHEN Bi Feng

JIN Yu Chen, DONG Li Juan, YANG Qin Yue, XIONG Wei Ning, WANG Wei Yi, FENG Xian Hong, YU Wei, HUANG Wei, CHEN Bi Feng. An Investigation of the Effects of B7-H4 Gene rs10754339 and miR-125a Gene rs12976445 on Cancer Susceptibility[J]. Biomedical and Environmental Sciences, 2023, 36(9): 814-825. doi: 10.3967/bes2023.074
Citation: JIN Yu Chen, DONG Li Juan, YANG Qin Yue, XIONG Wei Ning, WANG Wei Yi, FENG Xian Hong, YU Wei, HUANG Wei, CHEN Bi Feng. An Investigation of the Effects of B7-H4 Gene rs10754339 and miR-125a Gene rs12976445 on Cancer Susceptibility[J]. Biomedical and Environmental Sciences, 2023, 36(9): 814-825. doi: 10.3967/bes2023.074

doi: 10.3967/bes2023.074

An Investigation of the Effects of B7-H4 Gene rs10754339 and miR-125a Gene rs12976445 on Cancer Susceptibility

More Information
    Author Bio:

    JIN Yu Chen, male, born in 1998, M.Sc., majoring in tumor biology

    DONG Li Juan, female, born in 1998, M.Sc., majoring in tumor biology

    Corresponding author: CHEN Bi Feng, E-mail: cbifeng@whut.edu.cnHUANG Wei, E-mail: huangwei2020@whut.edu.cn
  • CHEN Bi Feng and HUANG Wei designed and supervised the study. FENG Xian Hong collected the blood samples. JIN Yu Chen, DONG Li Juan, and YANG Qin Yue performed the research study and collected the data. XIONG Wei Ning, WANG Wei Yi, and Yu Wei analyzed the data. JIN Yu Chen and DONG Li Juan wrote the manuscript. CHEN Bi Feng and HUANG Wei revised the manuscript critically and made final approval of the manuscript.
  • The authors declare that they have no conflict of interest in this work.
  • &These authors contributed equally to this work.
    • 关键词:
    •  / 
    •  / 
    •  / 
    •  / 
    •  
    CHEN Bi Feng and HUANG Wei designed and supervised the study. FENG Xian Hong collected the blood samples. JIN Yu Chen, DONG Li Juan, and YANG Qin Yue performed the research study and collected the data. XIONG Wei Ning, WANG Wei Yi, and Yu Wei analyzed the data. JIN Yu Chen and DONG Li Juan wrote the manuscript. CHEN Bi Feng and HUANG Wei revised the manuscript critically and made final approval of the manuscript.
    The authors declare that they have no conflict of interest in this work.
    &These authors contributed equally to this work.
    注释:
    1) AUTHOR CONTRIBUTIONS: 2) CONFLICT OF INTEREST:
  • S2.  The flow diagram of the literature review process for the meta-analysis of rs10754339 and cancer risk (A) and the meta-analysis of rs12976445 and cancer risk (B).

    Figure  1.  Sensitivity analysis of rs10754339 and overall cancer risk under A vs. G in the total population (A), Chinese population (B) and Caucasian population (C). Sensitivity analysis of rs10754339 and breast cancer risk under A vs. G (D).

    Figure  2.  Sensitivity analysis of rs12976445 and overall cancer risk under T vs. C in the total population (A), in the Asian population (B), based on PCR-RFLP (C), in the Chinese population (D) and in the Iranian population (E). Sensitivity analysis of rs12976445 and specific cancer risk (F) lung cancer; (G) prostate cancer.

    Figure  3.  Trial sequential analysis of rs10754339 and overall cancer risk in the total population [(A) A vs. G, (B) AA vs. AG, and (C) AA vs. AG + GG] and the Chinese population [(D) A vs. G, (E) AA vs. AG, and (F) AA vs. AG + GG], trial sequential analysis of rs10754339 and breast cancer risk [(G) A vs. G, (H) AA vs. AG, and (I) AA vs. AG + GG].

    Figure  4.  Trial sequential analysis of rs12976445 and overall cancer risk in the Chinese population under T vs. C (A), TT vs. CC (B), TT + TC vs. CC (C), and TT vs. TC + CC (D).

    S5.  In silico expression analysis of B7-H4 mRNA expression in relation to different genotypes of rs10754339 (A) and miR-125a mRNA expression in relation to different genotypes of rs12976445 (B).

    S1.  The genotyping diagrams of rs10754339 (A, B) and rs12976445 (C, D) by Sanger sequencing and PCR-RFLP assay.

    S3.  Forest plots for the meta-analysis of 10754339 and overall cancer risk under A vs. G in the total population (A) and the Chinese population (B). Forest plots for the meta-analysis of rs10754339 and breast cancer risk (C).

    S4.  Forest plots for the meta-analysis of rs12976445 and overall cancer risk under T vs. C in the total population (A), in the Asian population (B), based on PCR-RFLP (C), and in the Chinese population (D).

    Table  1.   Oligonucleotides and restriction enzymes of rs10754339 and rs12976445 for genotyping

    LocusPrimer Sequence (5’–3’)Annealing
    temperature (°C)
    Restriction enzymeDigested fragments
    rs10754339F: TGCTGACGGCTATCCGACTC
    R: GCTTCTCCTGTATGCCCCTAAA
    51MlsI, 37 °CAA: 117 bp + 174 bp
    AG: 291 bp + 117 bp + 174 bp
    GG: 291 bp
    rs12976445F: TCCCTCTTATTCTGGCATTC
    R: GTCAGGTTTCAGTTGGTGGT
    59SduI, 37 °CCC: 238 bp + 110 bp
    CT: 348 bp + 238 bp + 110 bp
    TT: 348 bp
    下载: 导出CSV

    Table  2.   Characteristics of participants in this study

    GroupAge, n (%)Gender, n (%)Smoking status, n (%)Drinking status, n (%)P-value1
    ≤ 60 years> 60 yearsMaleFemaleEverNeverEverNever
    Normal controls
    (n = 800)
    434 (54.3)366 (45.7)558 (69.7)242 (30.3)209 (26.1)591 (73.9)237 (29.6)563 (70.4)
    Lung cancer
    patients (n = 550)
    306 (55.6)244 (44.4)373 (67.9)177 (32.1)150 (27.3)400 (72.7)170 (31.0)380 (69.0)0.6150.4510.6390.613
    Gastric cancer
    patients (n = 460)
    252 (54.8)208 (45.2)323 (70.3)137 (29.7)132 (28.8)328 (71.2)148 (32.1)312 (67.9)0.8550.8620.3230.344
    Liver cancer
    patients (n = 480)
    280 (58.3)200 (41.7)343 (71.5)137 (28.5)140 (29.2)340 (70.8)158 (32.9)322 (67.1)0.1540.5170.2370.217
      Note. 1Two-sided χ2 test for the distributions of age (1st column), gender (2nd column), smoking status (3rd column) and drinking status (4th column) between lung/gastric/liver cancer patients and normal controls.
    下载: 导出CSV

    Table  3.   Genotype and allele distributions of rs10754339 and rs12976445 polymorphism and their association with cancer risk

    GroupAllele, n (%)Genotype, n (%)HWE1Genetic comparison [P-value, OR (95% CI)]2
    rs10754339AGAAAGGGA vs. GAA vs. AGAA vs. GGAG vs. GGAA vs. AG + GGAA + AG
    vs. GG
    Normal controls
    (n = 800)
    1,443
    (0.902)
    157
    (0.098)
    650
    (0.415)
    143
    (0.179)
    7
    (0.009)
    0.961
    Lung cancer
    patients (n = 550)
    949
    (0.863)
    151
    (0.137)
    409
    (0.744)
    131
    (0.238)
    10
    (0.018)
    0.002, 0.68
    (0.54–0.87)
    0.006, 0.69
    (0.53–0.90)
    0.099, 0.44
    (0.17–1.17)
    0.381, 0.64
    (0.24–1.73)
    0.003, 0.67
    (0.52–0.87)
    0.135, 0.48
    (0.18–1.26)
    Gastric cancer
    patients (n = 460)
    797
    (0.866)
    123
    (0.134)
    344
    (0.748)
    109
    (0.237)
    7
    (0.015)
    0.006, 0.71
    (0.55–0.91)
    0.011, 0.69
    (0.52–0.92)
    0.237, 0.53
    (0.18–1.52)
    0.621, 0.76
    (0.26–2.24)
    0.007, 0.68
    (0.52–0.90)
    0.298, 0.57
    (0.20–1.64)
    Liver cancer
    patients (n = 480)
    858
    (0.894)
    102
    (0.106)
    384
    (0.800)
    90
    (0.188)
    6
    (0.013)
    0.509, 0.92
    (0.70–1.19)
    0.671, 0.94
    (0.70–1.26)
    0.506, 0.69
    (0.23–2.07)
    0.589, 0.73
    (0.24–2.26)
    0.583, 0.92
    (0.69–1.23)
    0.519, 0.70
    (0.23–2.09)
    rs12976445CTCCCTTTT vs. CTT vs. TCTT vs. CCTC vs. CCTT vs. TC + CCTT + TC
    vs. CC
    Normal controls
    (n = 800)
    1,435
    (0.897)
    165
    (0.103)
    642
    (0.803)
    151
    (0.189)
    7
    (0.009)
    0.847
    Lung cancer
    patients (n = 550)
    940
    (0.855)
    160
    (0.145)
    405
    (0.736)
    130
    (0.236)
    15
    (0.027)
    0.001, 1.48
    (1.17–1.87)
    0.054, 2.49
    (0.99–6.29)
    0.009, 3.40
    (1.37–8.40)
    0.022, 1.37
    (1.05–1.78)
    0.012, 3.18
    (1.29–7.84)
    0.004, 1.46
    (1.13–1.88)
    Gastric cancer
    patients (n = 460)
    811
    (0.882)
    109
    (0.118)
    358
    (0.778)
    95
    (0.207)
    7
    (0.015)
    0.234, 1.17
    (0.90–1.51)
    0.400, 1.59
    (0.54–4.67)
    0.278, 1.79
    (0.62–5.15)
    0.411, 1.13
    (0.85–1.50)
    0.298, 1.75
    (0.61–5.02)
    0.306, 1.16
    (0.88–1.53)
    Liver cancer
    patients (n = 480)
    829
    (0.864)
    131
    (0.136)
    359
    (0.748)
    111
    (0.231)
    10
    (0.021)
    0.011, 1.37
    (1.08–1.76)
    0.191, 1.94
    (0.72–5.26)
    0.059, 2.56
    (0.96–6.77)
    0.053, 1.32
    (1.00–1.73)
    0.076, 2.41
    (0.91–6.38)
    0.022, 1.37
    (1.05–1.79)
      Note. 1Genotypic frequencies of rs10754339 and rs12976445 in controls were tested for departure from Hardy-Weinberg equilibrium (HWE) using two-sided χ2 test. 2The P value and OR (95% CI) were calculated for each genetic comparison between lung/gastric/liver cancer patients and normal controls, and adjusted for age, sex, smoking, and drinking status. OR, odd ratio; CI, confidence interval.
    下载: 导出CSV

    Table  4.   Meta-analysis of the association between rs10754339 and cancer risk

    Genetic modelHeterogeneity testPooled OR
    (95% CI)
    Hypothesis testBegg's testEgger's testStudies
    (n)
    QPI2ZPZPZP
    rs10754339 and total cancer risk (2,936 case/3,669 control)
    A vs. G16.520.02157.60.78 (0.66–0.93)2.850.0040.620.5361.590.1628
    AA vs. AG15.700.02855.40.76 (0.62–0.91)2.880.0040.120.9021.250.2598
    AA vs. GG3.440.841< 0.0010.66 (0.46–0.94)2.270.0230.870.3861.430.2038
    AG vs. GG1.380.986< 0.0010.91 (0.63–1.31)0.530.5960.120.9020.620.5568
    AA vs. AG + GG17.080.01759.00.75 (0.62–0.91)2.860.0040.370.7111.440.2008
    AA + AG vs. GG2.900.894< 0.0010.73 (0.51–1.04)1.740.0820.870.3861.040.3398
    rs10754339 and total cancer risk in Chinese population (2,843 case/3,609 control)
    A vs. G3.190.670< 0.0010.75 (0.67–0.83)5.64< 0.0010.001.0000.140.8946
    AA vs. AG3.770.583< 0.0010.72 (0.64–0.81)5.42< 0.0010.380.707−0.040.9716
    AA vs. GG0.820.976< 0.0010.63 (0.44–0.91)2.490.0130.380.707−1.090.3396
    AG vs. GG0.910.969< 0.0010.89 (0.61–1.29)0.640.5220.750.452−1.670.1716
    AA vs. AG+GG3.760.584< 0.0010.71 (0.64–0.80)5.74< 0.0010.380.7070.040.9706
    AA + AG vs. GG1.060.958< 0.0010.70 (0.49–1.01)1.930.0540.380.707−1.540.1996
    rs10754339 and total cancer risk in Caucasian population (93 case/60 control)
    A vs. G6.680.01085.01.73 (0.24–12.54)0.540.5872
    AA vs. AG6.220.01383.91.58 (0.16–15.73)0.390.6982
    AA vs. GG1.080.3007.12.50 (0.34–18.25)0.910.3652
    AG vs. GG< 0.0010.992< 0.0011.98 (0.20–19.25)0.590.5572
    AA vs. AG + GG6.840.00985.41.73 (0.18–16.42)0.470.6352
    AA+AG vs. GG0.690.406< 0.0012.36 (0.32–17.50)0.840.4032
    rs10754339 and breast cancer risk (1,384 case/1,239 control)
    A vs. G0.230.973< 0.0010.73 (0.64–0.85)4.21< 0.0011.020.308−1.930.1934
    AA vs. AG0.270.966< 0.0010.68 (0.57–0.80)4.43< 0.0011.700.089−2.120.0784
    AA vs. GG0.070.996< 0.0010.70 (0.44–1.10)1.560.118−0.341.0000.500.6664
    AG vs. GG0.200.978< 0.0011.03 (0.65–1.64)0.120.906−0.341.0001.310.3214
    AA vs. AG + GG0.160.984< 0.0010.68 (0.57–0.80)4.54< 0.0011.700.089−2.120.0784
    AA + AG vs. GG0.080.994< 0.0010.80 (0.51–1.25)1.000.318−0.341.0000.170.8784
    下载: 导出CSV

    S1.   Characteristics of the included studies for the meta-analysis of rs10754339 and rs12976445

    References
    (author, year)
    Country/ethnicityCancer typeGenotyping
    assay1
    Case, control (n)HWE2Quality
    control3
    (Y/N)
    rs10754339TotalA/GAA/AG/GG
    Asuman et al., 2013USA/CaucasianBreast cancerPCR-RFLP31, 3054/8, 55/524/6/1,
    26/3/1
    0.167Y
    Asuman et al., 2017Turkey/CaucasianBladder cancerPCR-RFLP62, 30117/7,
    47/13
    55/7/0,
    18/11/1
    0.660Y
    Jin et al., 2022China/AsianLiver cancerPCR-RFLP480, 800858/102,
    1,443/157
    384/90/6,
    650/143/7
    0.779Y
    Jin et al., 2022China/AsianLung cancerPCR-RFLP550, 800949/151,
    1,443/157
    409/131/10,
    650/143/7
    0.779Y
    Jin et al., 2022China/AsianGastric cancerPCR-RFLP460, 800797/123,
    1,443/157
    344/109/7,
    650/143/7
    0.779Y
    Li et al., 2009China/AsianBreast cancerPCR-RFLP287, 305434/140,
    493/117
    159/116/12,
    198/97/10
    0.652Y
    Tsai et al., 2015China/AsianBreast cancerPCR-RFLP566/400978/154,
    720/80
    420/138/8,
    324/72/4
    1.000Y
    Zhang et al., 2009China/AsianBreast cancerPCR-RFLP500, 504753/247,
    808/200
    277/199/24,
    324/160/20
    0.965Y
    rs12976445TotalT/CTT/CT/CC
    Hossein et al., 2018Iranian/AsianColorectal cancerTP-ARMS-PCR373, 372438/308,
    431/313
    118/202/53,
    116/199/57
    0.060Y
    Jin et al., 2022China/AsianLiver cancerPCR-RFLP480, 800131/829,
    165/1,435
    10/111/359,
    7/151/642
    0.779Y
    Jin et al., 2022China/AsianLung cancerPCR-RFLP550, 800160/940,
    165/1,435
    15/130/405,
    7/151/642
    0.779Y
    Jin et al., 2022China/AsianGastric cancerPCR-RFLP460, 800109/811,
    165/1,435
    7/95/358,
    7/151/642
    0.779Y
    Mohan et al., 2018Indian/AsianProstate cancerPCR-RFLP100, 10099/101,
    122/78
    28/43/29,
    37/48/15
    0.930Y
    Morteza et al., 2020Iranian/AsianProstate cancerPCR-RFLP150, 150137/163,
    143/157
    28/81/41,
    33/77/40
    0.723Y
    Sun et al., 2021China/AsianLung cancerTaqman503, 548109/897,
    114/982
    8/93/402,
    2/110/436
    0.198Y
    Tomasz et al., 2020Polish/CaucasianBreast cancerPCR-RFLP175, 129241/109,
    173/85
    80/81/14,
    54/65/10
    0.111Y
      Note. 1PCR-RFLP, polymerase chain reaction-restriction fragment length polymorphism; TP-ARMS-PCR, tetra‐primer amplification refractory mutation systems polymerase chain reaction; 2Genotypic frequencies of rs10754339 and rs12974339 in normal controls was tested for departure from Hardy-Weinberg equilibrium (HWE) using the χ2 test. 3Quality control was conducted when sample of cases and controls was genotyped.
    下载: 导出CSV

    Table  5.   Meta-analysis of the association between rs12976445 and cancer risk

    Genetic modelHeterogeneity testPooled OR
    (95% CI)
    Hypothesis testBegg's testEgger's testStudies
    (n)
    QPI2ZPZPZP
    rs12976445 and cancer risk (2,791 case/3,699 control)
    T vs. C18.780.00962.71.09 (0.93−1.28)1.100.2731.610.108−1.910.1058
    TT vs. TC10.100.18330.71.13 (0.92−1.39)1.160.2471.610.1082.450.0578
    TT vs. CC19.640.00664.41.33 (0.81−2.18)1.140.2541.610.1081.320.2368
    TC vs. CC10.770.14935.01.13 (0.99−1.28)1.850.0652.350.019−2.280.0638
    TT vs. TC + CC15.780.02755.61.26 (0.89−1.79)1.300.1941.110.2662.150.0758
    TT + TC vs. CC14.090.05050.31.11 (0.92−1.34)1.060.2881.910.051−2.410.0618
    rs12976445 and cancer risk in Asian population (2,616 case/3,570 control)
    T vs. C18.740.00568.01.09 (0.91−1.30)0.950.3401.800.072−1.980.1047
    TT vs. TC10.020.12440.11.12 (0.89−1.41)0.940.3481.500.1332.480.0567
    TT vs. CC19.570.00369.31.40 (0.79−2.45)1.160.2480.900.3681.280.2587
    TC vs. CC10.480.10642.71.13 (1.00−1.29)1.910.0571.850.058−2.520.0547
    TT vs. TC + CC15.720.01561.81.33 (0.86−2.06)1.290.1980.900.3682.020.0997
    TT + TC vs. CC13.890.03156.81.11 (0.91−1.36)1.020.3091.870.055−1.910.1127
    rs12976445 and cancer risk in Chinese population (1,993 case/2,948 control)
    T vs. C4.320.22930.61.28 (1.13−1.45)3.81< 0.0011.700.089−2.450.0574
    TT vs. TC1.390.708< 0.0012.29 (1.34−3.89)3.050.002−0.341.0001.230.3454
    TT vs. CC1.200.754< 0.0012.79 (1.66−4.71)3.86< 0.001−0.341.0000.440.7004
    TC vs. CC4.410.22132.01.19 (1.03−1.37)2.350.0191.700.089−2.250.0604
    TT vs. TC + CC1.200.752< 0.0012.68 (1.59−4.51)3.71< 0.001−0.341.0000.590.6144
    TT + TC vs. CC4.610.20334.91.25 (1.09−1.43)3.120.0021.700.089−2.250.0604
    rs12976445 and cancer risk in Iranian population (523 case/522 control)
    T vs. C0.330.563< 0.0011.00 (0.84−1.19)0.010.9942
    TT vs. TC0.400.528< 0.0010.95 (0.72−1.27)0.330.7422
    TT vs. CC0.460.497< 0.0011.00 (0.69−1.46)0.010.9942
    TC vs. CC0.030.859< 0.0011.07 (0.77−1.49)0.380.7052
    TT vs. TC + CC0.480.489< 0.0010.97 (0.74−1.27)0.230.8192
    TT + TC vs. CC0.140.713< 0.0011.04 (0.76−1.43)0.250.7992
    rs12976445 and prostate cancer risk (250 case/250 control)
    T vs. C2.210.13754.70.79 (0.62−1.02)1.840.0662
    TT vs. TC0.010.917< 0.0010.82 (0.53−1.27)0.870.3832
    TT vs. CC2.010.15650.20.61 (0.37−1.01)1.940.0522
    TC vs. CC2.880.09065.20.72 (0.33−1.56)0.830.4072
    TT vs. TC + CC0.240.623< 0.0010.74 (0.49−1.11)1.450.1462
    TT + TC vs. CC3.330.06870.00.67 (0.31−1.47)1.000.3192
    rs12976445 and lung cancer risk (1,053 case/1,348 control)
    T vs. C3.520.06171.61.26 (0.89−1.76)1.320.1882
    TT vs. TC0.480.490< 0.0012.99 (1.36−6.61)2.710.0072
    TT vs. CC0.070.789< 0.0013.65 (1.67−7.97)3.240.0012
    TC vs. CC3.680.05572.81.13 (0.76−1.66)0.600.5462
    TT vs. TC + CC0.130.719< 0.0013.49 (1.60−7.61)3.140.0022
    TT + TC vs. CC3.860.05074.11.20 (0.82−1.77)0.930.3532
    rs12976445 and total cancer risk based on PCR-RFLP (1,915 case/2,779 control)
    T vs. C17.380.00471.21.10 (0.88−1.38)0.870.3851.860.057−2.670.0526
    TT vs. TC6.360.27321.41.16 (0.88−1.54)1.080.2820.750.4521.670.1716
    TT vs. CC16.810.00570.31.27 (0.67−2.43)0.720.4691.130.2601.610.1846
    TC vs. CC8.500.13141.11.19 (1.02−1.38)2.280.0231.860.057−2.770.0506
    TT vs. TC + CC12.430.02959.81.28 (0.81−2.01)1.060.2910.380.7071.800.1466
    TT + TC vs. CC12.210.03259.11.12 (0.87−1.44)0.880.3811.880.060−2.610.0536
    下载: 导出CSV
  • [1] Siegel RL, Miller KD, Fuchs HE, et al. Cancer statistics, 2022. CA Cancer J Clin, 2022; 72, 7−33. doi:  10.3322/caac.21708
    [2] Cao W, Chen HD, Yu YW, et al. Changing profiles of cancer burden worldwide and in China: a secondary analysis of the global cancer statistics 2020. Chin Med J (Engl), 2021; 134, 783−91. doi:  10.1097/CM9.0000000000001474
    [3] Rebbeck TR. Biomarkers of inherited susceptibility and cancer. IARC Sci Publ, 2004; 91−103.
    [4] NIHR Global Health Research Unit on Global Surgery. Quality and outcomes in global cancer surgery: protocol for a multicentre, international, prospective cohort study (GlobalSurg 3). BMJ Open, 2019; 9, e026646. doi:  10.1136/bmjopen-2018-026646
    [5] Multhoff G, Molls M, Radons J. Chronic inflammation in cancer development. Front Immunol, 2012; 2, 98.
    [6] Prasad DVR, Richards S, Mai XM, et al. B7S1, a novel B7 family member that negatively regulates T cell activation. Immunity, 2003; 18, 863−73. doi:  10.1016/S1074-7613(03)00147-X
    [7] Sica GL, Choi IH, Zhu GF, et al. B7-H4, a molecule of the B7 family, negatively regulates T cell immunity. Immunity, 2003; 18, 849−61. doi:  10.1016/S1074-7613(03)00152-3
    [8] Zang XX, Loke P, Kim J, et al. B7x: a widely expressed B7 family member that inhibits T cell activation. Proc Natl Acad Sci USA, 2003; 100, 10388−92. doi:  10.1073/pnas.1434299100
    [9] Li MX, Che N, Feng Y, et al. B7-H4 expression promotes non-small cell lung cancer progression via AMPK/mTOR signaling. Exp Mol Pathol, 2022; 125, 104755. doi:  10.1016/j.yexmp.2022.104755
    [10] Wang JK, Wang Z, Li GD. MicroRNA-125 in immunity and cancer. Cancer Lett, 2019; 454, 134−45. doi:  10.1016/j.canlet.2019.04.015
    [11] Hudson TJ. Wanted: regulatory SNPs. Nat Genet, 2003; 33, 439−40. doi:  10.1038/ng0403-439
    [12] El Din GS, Youness RA, Assal RA, et al. MiRNA-506-3p directly regulates rs10754339 (A/G) in the immune checkpoint protein B7-H4 in breast cancer. Microrna, 2020; 9, 346−53.
    [13] Lehmann TP, Korski K, Ibbs M, et al. Rs12976445 variant in the pri-miR-125a correlates with a lower level of hsa-miR-125a and ERBB2 overexpression in breast cancer patients. Oncol Lett, 2013; 5, 569−73. doi:  10.3892/ol.2012.1040
    [14] Zhang J, Zhang MY, Jiang W, et al. B7-H4 gene polymorphisms are associated with sporadic breast cancer in a Chinese Han population. BMC Cancer, 2009; 9, 394. doi:  10.1186/1471-2407-9-394
    [15] Li DL, Zhang J, Fu ZK, et al. Association between polymorphisms of B7-H4 gene and the risk of sporadic breast cancer in Heilongjiang Province. Cancer Res Clin, 2009; 21, 232−5. (In Chinese
    [16] Özgöz A, Samli H, Öztürk KH, et al. An investigation of the effects of FGFR2 and B7-H4 polymorphisms in breast cancer. J Cancer Res Ther, 2013; 9, 370−5. doi:  10.4103/0973-1482.114434
    [17] Tsai SM, Wu SH, Hou MF, et al. The immune regulator VTCN1 gene polymorphisms and its impact on susceptibility to breast cancer. J Clin Lab Anal, 2015; 29, 412−8. doi:  10.1002/jcla.21788
    [18] Özgöz A, Şamli M, Dinçel D, et al. Association of B7-H4 gene polymorphisms in urothelial bladder cancer. Turk J Med Sci, 2017; 47, 443−6. doi:  10.3906/sag-1603-54
    [19] Sadeghi H, Nazemalhosseini-Mojarad E, Yaghoob-Taleghani M, et al. MiR-30a promoter variation contributes to the increased risk of colorectal cancer in an Iranian population. J Cell Biochem, 2019; 120, 7734−40. doi:  10.1002/jcb.28047
    [20] Damodaran M, Paul SFD, Venkatesan V. Genetic polymorphisms in miR-146a, miR-196a2 and miR-125a genes and its association in prostate cancer. Pathol Oncol Res, 2020; 26, 193−200. doi:  10.1007/s12253-018-0412-x
    [21] Hakimian M, Ghorbian S. Negative associations between the has-miR-27a and hsa-miR-125a gene variations and prostate cancer susceptibility. Mol Biol Rep, 2020; 47, 4209−14. doi:  10.1007/s11033-020-05548-7
    [22] Lehmann TP, Miskiewicz J, Szostak N, et al. In vitro and in silico analysis of miR-125a with rs12976445 polymorphism in breast cancer patients. Appl Sci (Basel), 2020; 10, 7275. doi:  10.3390/app10207275
    [23] Sun JS. Association between MicroRNA-125a rs12976445 polymorphism and lung cancer susceptibility in Northeast Chinese population. China Medical University. 2021. (In Chinese)
    [24] Thorlund K, Wetterslev J, Brok J, et al. User manual for trial sequential analysis (TSA). Cent Clin Interv Res, 2011; 1−115.
    [25] Lu XL, Liu MT, Liao YX, et al. Meta-analysis of the association between mTORC1-related genes polymorphisms and cancer risk. Pathol Res Pract, 2022; 229, 153696. doi:  10.1016/j.prp.2021.153696
    [26] Yu YH, Mao LP, Cheng ZN, et al. A novel regQTL-SNP and the risk of lung cancer: a multi-dimensional study. Arch Toxicol, 2021; 95, 3815−27. doi:  10.1007/s00204-021-03170-5
  • [1] Xiaoyun Shan, Yuting Li, Xiayu Zhao, Yichun Hu, Siran Li, Huidi Zhang, Yang Cao, Rui Wang, Lichen Yang.  Threshold-Effect Associations of Serum 25-hydroxyvitamin D on Bone Turnover Markers and GC rs2282679 Variants in Chinese Women of Childbearing Age . Biomedical and Environmental Sciences, 2024, 37(): 1-14. doi: 10.3967/bes2024.151
    [2] HU Wei Qiong, ZHOU Wei Guang, ZHOU Guang Wei, LIAO Jia Xi, SHI Jia Xing, XIE FengYang, LI Shou Heng, WANG Yong, FENG Xian Hong, GU Xiu Li, CHEN Bi Feng.  A Comprehensive Study of the Association between LEPR Gene rs1137101 Variant and Risk of Digestive System Cancers . Biomedical and Environmental Sciences, 2024, 37(5): 445-456. doi: 10.3967/bes2024.051
    [3] Krishnamoorthy Kokilavani, Ancy Terryna Chanayiltharayil John, Bindu Salim, Madhulika Vijayakumar, Sankar Ganesh Jeyaraj.  Improved Specificity for Breast Cancer Screening Using an Oncogenic (miRNA-21) and a Gene Suppressor (miRNA-195) miRNA in the Serum for a Point of Care (POC) Screening Solution . Biomedical and Environmental Sciences, 2023, 36(6): 549-552. doi: 10.3967/bes2023.067
    [4] LI Xian Yang, HUANG Zhi Jun, LI Jian Ping, GAO Xue Ren.  rs3735664 Polymorphism Affecting ELFN1-AS1 Adsorption on miR-1231 is Associated with Colorectal Cancer Susceptibility and Tumor Stage . Biomedical and Environmental Sciences, 2023, 36(4): 381-383. doi: 10.3967/bes2023.045
    [5] GAO Xue Ren, WANG Xin Yu, LI Xian Yang, SUN Yu Qi, ZHANG Shu Long.  Clinical Significance and Function of MALAT1 Gene Expression and the rs619586 Polymorphism in Colorectal Cancer . Biomedical and Environmental Sciences, 2022, 35(8): 768-771. doi: 10.3967/bes2022.101
    [6] CHAI Long Long, XIAO Ying Xuan, MENG Ling Han, WANG Huan, FENG Xian Hong, GU Xiu Li, LI Qi Chang, XIONG Qian Tao, CHEN Bi Feng.  The rs2227481 C>T Polymorphism in the IL22 Gene Promoter Significantly Reduces the Risk of Liver, Lung, and Gastric Cancer in a Han Chinese Population . Biomedical and Environmental Sciences, 2021, 34(7): 572-576. doi: 10.3967/bes2021.079
    [7] SU Liang, TIAN Yan, FU Ming, ZHANG Rui Zhong, OU Xiu Fei, XIA Hui Min, LI Rui Zhuo.  Association of miRNA-492 rs2289030 G>C and miRNA-938 rs2505901 T>C Gene Polymorphisms with Biliary Atresia Susceptibility . Biomedical and Environmental Sciences, 2021, 34(7): 577-580. doi: 10.3967/bes2021.080
    [8] HAN Han, ZHAO Ming Xu, WANG Yan, WANG Jun, REN Bing Nan, GE Hui Na, WANG Teng, SUN Bei Bei, BA Yue, LI Wen Jie.  Significant Polymorphisms of Vitamin D Receptor Gene (rs2189480 and rs3847987) Related to the Risk of Type 2 Diabetes in Henan Rural Area . Biomedical and Environmental Sciences, 2019, 32(1): 58-62. doi: 10.3967/bes2019.009
    [9] SHI Xiao Wei, ZHANG Feng, LI Zhi Yan, LYU Ai Li, GUO Xiong.  Polymorphism in rs2229783 of the Alpha 1(Ⅺ) Collagen Gene Is Associated with Susceptibility to but not Severity of Kashin-Beck Disease in a Northwest Chinese Han Population . Biomedical and Environmental Sciences, 2018, 31(4): 322-326. doi: 10.3967/bes2018.042
    [10] GAO Li Yun, ZHANG Feng Quan, ZHAO WEI Hui, HAN Guang Liang, WANG Xiao, LI Qiang, GAO Shan Shan, WU Wei Dong.  LncRNA H19 and Target Gene-mediated Cleft Palate Induced by TCDD . Biomedical and Environmental Sciences, 2017, 30(9): 676-680. doi: 10.3967/bes2017.090
    [11] LIN Nan, MU Xin Lin, WANG Gui Lian, REN Yu Ang, TANG De Liang, WANG Bin, LI Zhi Wen, SU Shu, KAN Hai Dong, TAO Shu.  A Genetic Susceptibility Study of Lung Cancer Risk Potentially Associated with Polycyclic Aromatic Hydrocarbon Inhalation Exposure . Biomedical and Environmental Sciences, 2017, 30(10): 772-776. doi: 10.3967/bes2017.104
    [12] YIN Wen Jiao, SHEN LiPing, WANG FuZhen, ZHANG GuoMin, ZHENG Hui, WANG Feng, LIU Tie Zhu, MENG Qing Ling, YI Yao, CUI FuQiang, BI ShengLi.  Hepatitis B Immunoprophylactic Failure and Characteristics of the Hepatitis B Virus Gene in Mother-Infant Pairs in Parts of China . Biomedical and Environmental Sciences, 2016, 29(11): 790-801. doi: 10.3967/bes2016.106
    [13] Mohammad Naderi, Mohammad Hashemi, Gholamreza Bahari.  Lack of Association between rs4331426 Polymorphism in the Chr18q11.2 Locus and Pulmonary Tuberculosis in an Iranian Population . Biomedical and Environmental Sciences, 2016, 29(7): 516-520. doi: 10.3967/bes2016.067
    [14] CHEN Yun Xin, SHEN Dan Hua, LI Jin Tao.  Viral Etiology Relationship between Human Papillomavirus and Human Breast Cancer and Target of Gene Therapy . Biomedical and Environmental Sciences, 2016, 29(5): 331-339. doi: 10.3967/bes2016.043
    [15] ZHOU Jun Mei, ZHANG Ming, WANG Shu, WANG Bing Yuan, HAN Cheng Yi, REN Yong Cheng, ZHANG Lu, ZHANG Hong Yan, YANG Xiang Yu, ZHAO Yang, HU Dong Sheng.  Association of the ADIPOQ Rs2241766 and Rs266729 Polymorphisms with Metabolic Syndrome in the Chinese Population:A Meta-analysis . Biomedical and Environmental Sciences, 2016, 29(7): 505-515. doi: 10.3967/bes2016.066
    [16] MA Yan, LIU Yu Hong, ZHANG Zhi Guo, WANG Li, YANG Xiao Wei, LI Chuan You, CHE Nan Ying.  Interleukin 8 Gene Polymorphisms Are Not Associated with Tuberculosis Susceptibility in the Chinese Population . Biomedical and Environmental Sciences, 2016, 29(2): 158-161. doi: 10.3967/bes2016.019
    [17] FANG Guo Feng, FAN Xue Yun, SHEN Fu Hai.  The Relationship between Polymorphisms of Interleukin-4 Gene and Silicosis . Biomedical and Environmental Sciences, 2011, 24(6): 678-682. doi: 10.3967/0895-3988.2011.06.013
    [18] FENG CAO, TAI-YI JIN, YUAN-FEN ZHOU.  Inhibitory Effect of Isoflavones on Prostate Cancer Cells and PTEN Gene . Biomedical and Environmental Sciences, 2006, 19(1): 35-41.
    [19] LIN XIE, Gui-Zhi JU, SHU-ZHENG LIU, JIE-PING SHI, YA-QIN YU, Jun Wei.  Searching for a Schizophrenia Susceptibility Gene in the 22q11 Region . Biomedical and Environmental Sciences, 2005, 18(1): 31-35.
    [20] QING-WEN MA, GUO-FANG LIN, JI-GANG CHEN, CUI-QING XIANG, WEI-CHAO GUO, KLAUS GOLKA, Jian-Hua SHEN.  Polymorphism of N-acetyltransferase 2 (NAT2) Gene Polymorphism in Shanghai population:Occupational and Non-occupational Bladder Cancer Patient Groups . Biomedical and Environmental Sciences, 2004, 17(3): 291-298.
  • 22281+Supplementary Materials.pdf
  • 加载中
图(10) / 表ll (6)
计量
  • 文章访问数:  819
  • HTML全文浏览量:  360
  • PDF下载量:  47
  • 被引次数: 0
出版历程
  • 收稿日期:  2022-08-23
  • 录用日期:  2023-01-04
  • 网络出版日期:  2023-06-05
  • 刊出日期:  2023-09-20

An Investigation of the Effects of B7-H4 Gene rs10754339 and miR-125a Gene rs12976445 on Cancer Susceptibility

doi: 10.3967/bes2023.074
    作者简介:

    JIN Yu Chen, male, born in 1998, M.Sc., majoring in tumor biology

    DONG Li Juan, female, born in 1998, M.Sc., majoring in tumor biology

    通讯作者: CHEN Bi Feng, E-mail: cbifeng@whut.edu.cnHUANG Wei, E-mail: huangwei2020@whut.edu.cn
注释:
1) AUTHOR CONTRIBUTIONS: 2) CONFLICT OF INTEREST:

English Abstract

JIN Yu Chen, DONG Li Juan, YANG Qin Yue, XIONG Wei Ning, WANG Wei Yi, FENG Xian Hong, YU Wei, HUANG Wei, CHEN Bi Feng. An Investigation of the Effects of B7-H4 Gene rs10754339 and miR-125a Gene rs12976445 on Cancer Susceptibility[J]. Biomedical and Environmental Sciences, 2023, 36(9): 814-825. doi: 10.3967/bes2023.074
Citation: JIN Yu Chen, DONG Li Juan, YANG Qin Yue, XIONG Wei Ning, WANG Wei Yi, FENG Xian Hong, YU Wei, HUANG Wei, CHEN Bi Feng. An Investigation of the Effects of B7-H4 Gene rs10754339 and miR-125a Gene rs12976445 on Cancer Susceptibility[J]. Biomedical and Environmental Sciences, 2023, 36(9): 814-825. doi: 10.3967/bes2023.074
    • Cancer has become a serious threat to public health and the economy worldwide[1]. Lung cancer has been the highest cause of cancer-related morbidity and mortality, closely followed by gastric cancer and liver cancer[2]. Cancer is a complex disease involving multiple factors, including environmental factors (e.g., alcohol intake, tobacco use) and genetic factors, as well as their interactions[3]. Although clinical treatment of cancer has improved greatly, the overall outcome is still unsatisfactory, and the five-year survival rate is still low[4]. Thus, the identification of reliable genetic biomarkers is of great importance for risk prediction and early detection of cancer.

      Immunity plays an important role in carcinogenesis, and chronic inflammation is recognized as a condition favoring the development of cancer[5]. The B7-H4 is an immune checkpoint that plays an essential role in carcinogenesis, and it is able to affect T-cell functions (cytotoxicity, cytokine secretion, and cell proliferation), epithelial-to-mesenchymal transition, and cancer stemness[6-9]. The miR-125a is an inflamma-miRNA involved in the regulation of innate and adaptive immune responses and regulates immune and inflammation processes in human cancers[10]. Aberrant expressed B7-H4 gene and miR-125a gene occur in many malignant tumors, whereas the underlying mechanism remains largely unknown.

      Single nucleotide polymorphism (SNP), a well-defined genetic variation, has been extensively applied in the medical field. Regulatory SNPs (rSNPs) are a group of SNPs that participate in the regulation of gene expression and protein structural/functional behavior[11]. Among the enormous SNPs in the B7-H4 gene and miR-125a gene, two rSNPs, namely, rs10754339[12] and rs12976445[13], have been highlighted in the literature[14-23]. However, the results were contradictory rather than conclusive. To further explore their contribution to carcinogenesis, we conducted a case-control study to investigate the effects of rs12976445 and rs10754339 on susceptibility to lung, liver, and gastric cancer in the Chinese population of Hubei Province. Additionally, we estimated the association of rs12976445/rs10754339 and cancer susceptibility via a meta-analysis by combining the data of previous related studies and those of the present study.

    • A total of 1,490 cancer patients (lung/gastric/liver/: 550/460/480) and 800 controls were enrolled in this study. All cancer patients were confirmed histopathologically and recruited from the Hubei Cancer Hospital and Wuhan Xinzhou District People’s Hospital. Individuals to be included in the control were selected from cancer-free individuals who visited the Wuhan Xinzhou District People’s Hospital for regular physical examinations. All participants were biologically unrelated Han Chinese living in the Hubei Province of China. Each participant signed an informed consent form. This study was approved by the ethical committee of the Wuhan University of Technology.

    • The peripheral blood samples of all studied subjects were collected into blood vacuum tubes containing ethylenediaminetetraacetic acid (EDTA) and stored at 4 °C. Genomic DNA was extracted from the blood samples using the TIANamp Blood DNA Kit (DP348; TianGen Biotech, Beijing, China) within one week, according to the manufacturer’s instructions. Finally, the DNA samples were stored at −20 °C before use.

    • The genotyping of rs10754339 and rs12976445 was performed by the polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) method. Table 1 shows the PCR primer sequences and the restriction enzymes used. The accuracy of the PCR-RFLP was confirmed by sequencing the PCR products from 10% randomly selected samples, and there were no differences in results between the two methods. Supplementary Figure S1 (available in www.besjournal.com) displays the genotyping diagrams of rs10754339 and rs12976445 by PCR-RFLP and Sanger sequencing.

      Table 1.  Oligonucleotides and restriction enzymes of rs10754339 and rs12976445 for genotyping

      LocusPrimer Sequence (5’–3’)Annealing
      temperature (°C)
      Restriction enzymeDigested fragments
      rs10754339F: TGCTGACGGCTATCCGACTC
      R: GCTTCTCCTGTATGCCCCTAAA
      51MlsI, 37 °CAA: 117 bp + 174 bp
      AG: 291 bp + 117 bp + 174 bp
      GG: 291 bp
      rs12976445F: TCCCTCTTATTCTGGCATTC
      R: GTCAGGTTTCAGTTGGTGGT
      59SduI, 37 °CCC: 238 bp + 110 bp
      CT: 348 bp + 238 bp + 110 bp
      TT: 348 bp
    • SPSS 15.0 was used to perform all the statistical analyses. χ2 test was applied to determine the statistical differences in age, gender, smoking status, and drinking status between cancer patients and the control group. The genotypic frequencies of rs10754339 and rs12976445 were tested for deviation from the Hardy-Weinberg equilibrium (HWE) in the control group. Furthermore, the logistic regression analysis under six genetic comparisons was used to estimate the effects of rs10754339 and rs12976445 on cancer susceptibility. The level of significance was set at P < 0.05, and the Bonferroni correction was applied for multiple comparisons (P < 0.0084, 0.05/6).

    • Related literature up to March of 2022 was searched in PubMed, ISI Web of Science, CNKI, and Wanfang databases with the terms “rs10754339 and cancer/tumor” or “rs12976445 and cancer/tumor.” Supplementary Figure S2 (available in www.besjournal.com) shows the flow diagram of the literature retrieving process. References listed in retrieved articles were checked for missing information. For a study to be included in the meta-analysis, it had to meet the following criteria: (1) it is a human study, (2) it is an investigation of rs10754339/rs12976445 and cancer risk, (3) it is a case-control study, (4) frequency data are extractable, and (5) the control group conforms to the HWE. The STATA 14.0 was used to conduct all the statistical analyses, including the heterogeneity test, pooled ORs, publication bias, and sensitivity analysis. P < 0.05 was set as the significance level, and Bonferroni correction was applied for multiple comparisons (P < 0.0084, 0.05/6). Trial sequential analysis (TSA) was used to assess whether the evidence of this meta-analysis was enough and conclusive[24], and the interpretation of TSA results has been described in our previous article[25].

      Figure S2.  The flow diagram of the literature review process for the meta-analysis of rs10754339 and cancer risk (A) and the meta-analysis of rs12976445 and cancer risk (B).

    • The principal characteristics of the age, gender, smoking status, and alcohol status of studied subjects are presented in Table 2. The distributions of these four variables between cancer patients and the control group did not differ significantly, suggesting the cases and controls were well-matched in this study.

      Table 2.  Characteristics of participants in this study

      GroupAge, n (%)Gender, n (%)Smoking status, n (%)Drinking status, n (%)P-value1
      ≤ 60 years> 60 yearsMaleFemaleEverNeverEverNever
      Normal controls
      (n = 800)
      434 (54.3)366 (45.7)558 (69.7)242 (30.3)209 (26.1)591 (73.9)237 (29.6)563 (70.4)
      Lung cancer
      patients (n = 550)
      306 (55.6)244 (44.4)373 (67.9)177 (32.1)150 (27.3)400 (72.7)170 (31.0)380 (69.0)0.6150.4510.6390.613
      Gastric cancer
      patients (n = 460)
      252 (54.8)208 (45.2)323 (70.3)137 (29.7)132 (28.8)328 (71.2)148 (32.1)312 (67.9)0.8550.8620.3230.344
      Liver cancer
      patients (n = 480)
      280 (58.3)200 (41.7)343 (71.5)137 (28.5)140 (29.2)340 (70.8)158 (32.9)322 (67.1)0.1540.5170.2370.217
        Note. 1Two-sided χ2 test for the distributions of age (1st column), gender (2nd column), smoking status (3rd column) and drinking status (4th column) between lung/gastric/liver cancer patients and normal controls.
    • Table 3 shows the allelic and genotypic distributions of rs10754339 and rs12976445, as well as their contribution to cancer susceptibility. No deviation from HWE was observed for rs10754339 or rs12976445 in the control group (P > 0.05). In this study, rs10754339 was significantly associated with the risk of lung and gastric cancer but not with liver cancer. The A allele of rs10754339 was associated with a lower risk of lung cancer (A vs. G, P = 0.002, OR = 0.68, 95% CI: 0.54–0.87) and gastric cancer (A vs. G, P = 0.006, OR = 0.71, 95% CI: 0.55–0.91) than the G allele. Concordantly, carriers of the AA genotype were less inclined to suffer from lung cancer than carriers of the AG/AG + GG genotype (AA vs. AG, P = 0.006, OR = 0.69, 95% CI: 0.53–0.90; AA vs. AG + GG, P = 0.003, OR = 0.67, 95% CI: 0.52–0.87), and were less inclined to suffer from gastric cancer than AG + GG genotype carriers (AA vs. AG + GG, P = 0.007, OR = 0.68, 95% CI: 0.52–0.90). Additionally, the rs12976445 was significantly associated with the risk of lung cancer but not liver or gastric cancer. Specifically, the T allele of rs12976445 conferred a higher risk for lung cancer than the C allele (T vs. C, P = 0.001, OR = 1.48, 95% CI: 1.17–1.87), and individuals carrying the genotype with at least one T allele had a higher lung cancer risk than that CC carriers (TT + TC vs. CC, P = 0.004, OR = 1.46, 95% CI: 1.13–1.88).

      Table 3.  Genotype and allele distributions of rs10754339 and rs12976445 polymorphism and their association with cancer risk

      GroupAllele, n (%)Genotype, n (%)HWE1Genetic comparison [P-value, OR (95% CI)]2
      rs10754339AGAAAGGGA vs. GAA vs. AGAA vs. GGAG vs. GGAA vs. AG + GGAA + AG
      vs. GG
      Normal controls
      (n = 800)
      1,443
      (0.902)
      157
      (0.098)
      650
      (0.415)
      143
      (0.179)
      7
      (0.009)
      0.961
      Lung cancer
      patients (n = 550)
      949
      (0.863)
      151
      (0.137)
      409
      (0.744)
      131
      (0.238)
      10
      (0.018)
      0.002, 0.68
      (0.54–0.87)
      0.006, 0.69
      (0.53–0.90)
      0.099, 0.44
      (0.17–1.17)
      0.381, 0.64
      (0.24–1.73)
      0.003, 0.67
      (0.52–0.87)
      0.135, 0.48
      (0.18–1.26)
      Gastric cancer
      patients (n = 460)
      797
      (0.866)
      123
      (0.134)
      344
      (0.748)
      109
      (0.237)
      7
      (0.015)
      0.006, 0.71
      (0.55–0.91)
      0.011, 0.69
      (0.52–0.92)
      0.237, 0.53
      (0.18–1.52)
      0.621, 0.76
      (0.26–2.24)
      0.007, 0.68
      (0.52–0.90)
      0.298, 0.57
      (0.20–1.64)
      Liver cancer
      patients (n = 480)
      858
      (0.894)
      102
      (0.106)
      384
      (0.800)
      90
      (0.188)
      6
      (0.013)
      0.509, 0.92
      (0.70–1.19)
      0.671, 0.94
      (0.70–1.26)
      0.506, 0.69
      (0.23–2.07)
      0.589, 0.73
      (0.24–2.26)
      0.583, 0.92
      (0.69–1.23)
      0.519, 0.70
      (0.23–2.09)
      rs12976445CTCCCTTTT vs. CTT vs. TCTT vs. CCTC vs. CCTT vs. TC + CCTT + TC
      vs. CC
      Normal controls
      (n = 800)
      1,435
      (0.897)
      165
      (0.103)
      642
      (0.803)
      151
      (0.189)
      7
      (0.009)
      0.847
      Lung cancer
      patients (n = 550)
      940
      (0.855)
      160
      (0.145)
      405
      (0.736)
      130
      (0.236)
      15
      (0.027)
      0.001, 1.48
      (1.17–1.87)
      0.054, 2.49
      (0.99–6.29)
      0.009, 3.40
      (1.37–8.40)
      0.022, 1.37
      (1.05–1.78)
      0.012, 3.18
      (1.29–7.84)
      0.004, 1.46
      (1.13–1.88)
      Gastric cancer
      patients (n = 460)
      811
      (0.882)
      109
      (0.118)
      358
      (0.778)
      95
      (0.207)
      7
      (0.015)
      0.234, 1.17
      (0.90–1.51)
      0.400, 1.59
      (0.54–4.67)
      0.278, 1.79
      (0.62–5.15)
      0.411, 1.13
      (0.85–1.50)
      0.298, 1.75
      (0.61–5.02)
      0.306, 1.16
      (0.88–1.53)
      Liver cancer
      patients (n = 480)
      829
      (0.864)
      131
      (0.136)
      359
      (0.748)
      111
      (0.231)
      10
      (0.021)
      0.011, 1.37
      (1.08–1.76)
      0.191, 1.94
      (0.72–5.26)
      0.059, 2.56
      (0.96–6.77)
      0.053, 1.32
      (1.00–1.73)
      0.076, 2.41
      (0.91–6.38)
      0.022, 1.37
      (1.05–1.79)
        Note. 1Genotypic frequencies of rs10754339 and rs12976445 in controls were tested for departure from Hardy-Weinberg equilibrium (HWE) using two-sided χ2 test. 2The P value and OR (95% CI) were calculated for each genetic comparison between lung/gastric/liver cancer patients and normal controls, and adjusted for age, sex, smoking, and drinking status. OR, odd ratio; CI, confidence interval.
    • Supplementary Table S1 (available in www.besjournal.com) shows the main features of the included studies in the meta-analysis of rs10754339 and cancer susceptibility. The statistical results are presented in Table 4 and Supplementary Figure S3 (available in www.besjournal.com). It was observed that rs10754339 was significantly associated with total cancer risk under A vs. G (P = 0.004, OR = 0.78, 95% CI: 0.66–0.93), AA vs. AG (P = 0.004, OR = 0.76, 95% CI: 0.62–0.91), and AA vs. AG + GG (P = 0.004, OR = 0.75, 95% CI: 0.62–0.91). Interestingly, the ethnicity-stratified analysis showed that the association between rs10754339 and total cancer risk especially existed in the Chinese population. Furthermore, there was a significant association between rs10754339 and breast cancer risk.

      Table 4.  Meta-analysis of the association between rs10754339 and cancer risk

      Genetic modelHeterogeneity testPooled OR
      (95% CI)
      Hypothesis testBegg's testEgger's testStudies
      (n)
      QPI2ZPZPZP
      rs10754339 and total cancer risk (2,936 case/3,669 control)
      A vs. G16.520.02157.60.78 (0.66–0.93)2.850.0040.620.5361.590.1628
      AA vs. AG15.700.02855.40.76 (0.62–0.91)2.880.0040.120.9021.250.2598
      AA vs. GG3.440.841< 0.0010.66 (0.46–0.94)2.270.0230.870.3861.430.2038
      AG vs. GG1.380.986< 0.0010.91 (0.63–1.31)0.530.5960.120.9020.620.5568
      AA vs. AG + GG17.080.01759.00.75 (0.62–0.91)2.860.0040.370.7111.440.2008
      AA + AG vs. GG2.900.894< 0.0010.73 (0.51–1.04)1.740.0820.870.3861.040.3398
      rs10754339 and total cancer risk in Chinese population (2,843 case/3,609 control)
      A vs. G3.190.670< 0.0010.75 (0.67–0.83)5.64< 0.0010.001.0000.140.8946
      AA vs. AG3.770.583< 0.0010.72 (0.64–0.81)5.42< 0.0010.380.707−0.040.9716
      AA vs. GG0.820.976< 0.0010.63 (0.44–0.91)2.490.0130.380.707−1.090.3396
      AG vs. GG0.910.969< 0.0010.89 (0.61–1.29)0.640.5220.750.452−1.670.1716
      AA vs. AG+GG3.760.584< 0.0010.71 (0.64–0.80)5.74< 0.0010.380.7070.040.9706
      AA + AG vs. GG1.060.958< 0.0010.70 (0.49–1.01)1.930.0540.380.707−1.540.1996
      rs10754339 and total cancer risk in Caucasian population (93 case/60 control)
      A vs. G6.680.01085.01.73 (0.24–12.54)0.540.5872
      AA vs. AG6.220.01383.91.58 (0.16–15.73)0.390.6982
      AA vs. GG1.080.3007.12.50 (0.34–18.25)0.910.3652
      AG vs. GG< 0.0010.992< 0.0011.98 (0.20–19.25)0.590.5572
      AA vs. AG + GG6.840.00985.41.73 (0.18–16.42)0.470.6352
      AA+AG vs. GG0.690.406< 0.0012.36 (0.32–17.50)0.840.4032
      rs10754339 and breast cancer risk (1,384 case/1,239 control)
      A vs. G0.230.973< 0.0010.73 (0.64–0.85)4.21< 0.0011.020.308−1.930.1934
      AA vs. AG0.270.966< 0.0010.68 (0.57–0.80)4.43< 0.0011.700.089−2.120.0784
      AA vs. GG0.070.996< 0.0010.70 (0.44–1.10)1.560.118−0.341.0000.500.6664
      AG vs. GG0.200.978< 0.0011.03 (0.65–1.64)0.120.906−0.341.0001.310.3214
      AA vs. AG + GG0.160.984< 0.0010.68 (0.57–0.80)4.54< 0.0011.700.089−2.120.0784
      AA + AG vs. GG0.080.994< 0.0010.80 (0.51–1.25)1.000.318−0.341.0000.170.8784

      Table S1.  Characteristics of the included studies for the meta-analysis of rs10754339 and rs12976445

      References
      (author, year)
      Country/ethnicityCancer typeGenotyping
      assay1
      Case, control (n)HWE2Quality
      control3
      (Y/N)
      rs10754339TotalA/GAA/AG/GG
      Asuman et al., 2013USA/CaucasianBreast cancerPCR-RFLP31, 3054/8, 55/524/6/1,
      26/3/1
      0.167Y
      Asuman et al., 2017Turkey/CaucasianBladder cancerPCR-RFLP62, 30117/7,
      47/13
      55/7/0,
      18/11/1
      0.660Y
      Jin et al., 2022China/AsianLiver cancerPCR-RFLP480, 800858/102,
      1,443/157
      384/90/6,
      650/143/7
      0.779Y
      Jin et al., 2022China/AsianLung cancerPCR-RFLP550, 800949/151,
      1,443/157
      409/131/10,
      650/143/7
      0.779Y
      Jin et al., 2022China/AsianGastric cancerPCR-RFLP460, 800797/123,
      1,443/157
      344/109/7,
      650/143/7
      0.779Y
      Li et al., 2009China/AsianBreast cancerPCR-RFLP287, 305434/140,
      493/117
      159/116/12,
      198/97/10
      0.652Y
      Tsai et al., 2015China/AsianBreast cancerPCR-RFLP566/400978/154,
      720/80
      420/138/8,
      324/72/4
      1.000Y
      Zhang et al., 2009China/AsianBreast cancerPCR-RFLP500, 504753/247,
      808/200
      277/199/24,
      324/160/20
      0.965Y
      rs12976445TotalT/CTT/CT/CC
      Hossein et al., 2018Iranian/AsianColorectal cancerTP-ARMS-PCR373, 372438/308,
      431/313
      118/202/53,
      116/199/57
      0.060Y
      Jin et al., 2022China/AsianLiver cancerPCR-RFLP480, 800131/829,
      165/1,435
      10/111/359,
      7/151/642
      0.779Y
      Jin et al., 2022China/AsianLung cancerPCR-RFLP550, 800160/940,
      165/1,435
      15/130/405,
      7/151/642
      0.779Y
      Jin et al., 2022China/AsianGastric cancerPCR-RFLP460, 800109/811,
      165/1,435
      7/95/358,
      7/151/642
      0.779Y
      Mohan et al., 2018Indian/AsianProstate cancerPCR-RFLP100, 10099/101,
      122/78
      28/43/29,
      37/48/15
      0.930Y
      Morteza et al., 2020Iranian/AsianProstate cancerPCR-RFLP150, 150137/163,
      143/157
      28/81/41,
      33/77/40
      0.723Y
      Sun et al., 2021China/AsianLung cancerTaqman503, 548109/897,
      114/982
      8/93/402,
      2/110/436
      0.198Y
      Tomasz et al., 2020Polish/CaucasianBreast cancerPCR-RFLP175, 129241/109,
      173/85
      80/81/14,
      54/65/10
      0.111Y
        Note. 1PCR-RFLP, polymerase chain reaction-restriction fragment length polymorphism; TP-ARMS-PCR, tetra‐primer amplification refractory mutation systems polymerase chain reaction; 2Genotypic frequencies of rs10754339 and rs12974339 in normal controls was tested for departure from Hardy-Weinberg equilibrium (HWE) using the χ2 test. 3Quality control was conducted when sample of cases and controls was genotyped.
    • The main features of the included studies in the meta-analysis of rs12976445 and cancer susceptibility are presented in Supplementary Table S1. The statistical results are shown in Table 5 and Supplementary Figure S4 (available in www.besjournal.com). No significant association between rs12976445 and total cancer risk was observed. However, the stratified analysis by ethnicity revealed that rs12976445 was significantly associated with total cancer risk in the Chinese population under T vs. C, TT vs. TC, TT vs. CC, TT vs. TC + CC and TT + TC vs. CC. Moreover, the stratified analysis by cancer type identified a significant association between rs12976445 and lung cancer risk under TT vs. TC, TT vs. CC and TT vs. TC + CC.

      Table 5.  Meta-analysis of the association between rs12976445 and cancer risk

      Genetic modelHeterogeneity testPooled OR
      (95% CI)
      Hypothesis testBegg's testEgger's testStudies
      (n)
      QPI2ZPZPZP
      rs12976445 and cancer risk (2,791 case/3,699 control)
      T vs. C18.780.00962.71.09 (0.93−1.28)1.100.2731.610.108−1.910.1058
      TT vs. TC10.100.18330.71.13 (0.92−1.39)1.160.2471.610.1082.450.0578
      TT vs. CC19.640.00664.41.33 (0.81−2.18)1.140.2541.610.1081.320.2368
      TC vs. CC10.770.14935.01.13 (0.99−1.28)1.850.0652.350.019−2.280.0638
      TT vs. TC + CC15.780.02755.61.26 (0.89−1.79)1.300.1941.110.2662.150.0758
      TT + TC vs. CC14.090.05050.31.11 (0.92−1.34)1.060.2881.910.051−2.410.0618
      rs12976445 and cancer risk in Asian population (2,616 case/3,570 control)
      T vs. C18.740.00568.01.09 (0.91−1.30)0.950.3401.800.072−1.980.1047
      TT vs. TC10.020.12440.11.12 (0.89−1.41)0.940.3481.500.1332.480.0567
      TT vs. CC19.570.00369.31.40 (0.79−2.45)1.160.2480.900.3681.280.2587
      TC vs. CC10.480.10642.71.13 (1.00−1.29)1.910.0571.850.058−2.520.0547
      TT vs. TC + CC15.720.01561.81.33 (0.86−2.06)1.290.1980.900.3682.020.0997
      TT + TC vs. CC13.890.03156.81.11 (0.91−1.36)1.020.3091.870.055−1.910.1127
      rs12976445 and cancer risk in Chinese population (1,993 case/2,948 control)
      T vs. C4.320.22930.61.28 (1.13−1.45)3.81< 0.0011.700.089−2.450.0574
      TT vs. TC1.390.708< 0.0012.29 (1.34−3.89)3.050.002−0.341.0001.230.3454
      TT vs. CC1.200.754< 0.0012.79 (1.66−4.71)3.86< 0.001−0.341.0000.440.7004
      TC vs. CC4.410.22132.01.19 (1.03−1.37)2.350.0191.700.089−2.250.0604
      TT vs. TC + CC1.200.752< 0.0012.68 (1.59−4.51)3.71< 0.001−0.341.0000.590.6144
      TT + TC vs. CC4.610.20334.91.25 (1.09−1.43)3.120.0021.700.089−2.250.0604
      rs12976445 and cancer risk in Iranian population (523 case/522 control)
      T vs. C0.330.563< 0.0011.00 (0.84−1.19)0.010.9942
      TT vs. TC0.400.528< 0.0010.95 (0.72−1.27)0.330.7422
      TT vs. CC0.460.497< 0.0011.00 (0.69−1.46)0.010.9942
      TC vs. CC0.030.859< 0.0011.07 (0.77−1.49)0.380.7052
      TT vs. TC + CC0.480.489< 0.0010.97 (0.74−1.27)0.230.8192
      TT + TC vs. CC0.140.713< 0.0011.04 (0.76−1.43)0.250.7992
      rs12976445 and prostate cancer risk (250 case/250 control)
      T vs. C2.210.13754.70.79 (0.62−1.02)1.840.0662
      TT vs. TC0.010.917< 0.0010.82 (0.53−1.27)0.870.3832
      TT vs. CC2.010.15650.20.61 (0.37−1.01)1.940.0522
      TC vs. CC2.880.09065.20.72 (0.33−1.56)0.830.4072
      TT vs. TC + CC0.240.623< 0.0010.74 (0.49−1.11)1.450.1462
      TT + TC vs. CC3.330.06870.00.67 (0.31−1.47)1.000.3192
      rs12976445 and lung cancer risk (1,053 case/1,348 control)
      T vs. C3.520.06171.61.26 (0.89−1.76)1.320.1882
      TT vs. TC0.480.490< 0.0012.99 (1.36−6.61)2.710.0072
      TT vs. CC0.070.789< 0.0013.65 (1.67−7.97)3.240.0012
      TC vs. CC3.680.05572.81.13 (0.76−1.66)0.600.5462
      TT vs. TC + CC0.130.719< 0.0013.49 (1.60−7.61)3.140.0022
      TT + TC vs. CC3.860.05074.11.20 (0.82−1.77)0.930.3532
      rs12976445 and total cancer risk based on PCR-RFLP (1,915 case/2,779 control)
      T vs. C17.380.00471.21.10 (0.88−1.38)0.870.3851.860.057−2.670.0526
      TT vs. TC6.360.27321.41.16 (0.88−1.54)1.080.2820.750.4521.670.1716
      TT vs. CC16.810.00570.31.27 (0.67−2.43)0.720.4691.130.2601.610.1846
      TC vs. CC8.500.13141.11.19 (1.02−1.38)2.280.0231.860.057−2.770.0506
      TT vs. TC + CC12.430.02959.81.28 (0.81−2.01)1.060.2910.380.7071.800.1466
      TT + TC vs. CC12.210.03259.11.12 (0.87−1.44)0.880.3811.880.060−2.610.0536
    • Removal of any single case-control study from the analysis of rs10754339 and rs12976445 in the allele model did not significantly affect the consistency of pooled ORs (Figures 12). Moreover, potential publication bias was assessed for rs10754339 and rs12976445, and the results did not show any evidence of publication bias in any of the genetic comparisons (Tables 45), suggesting the statistical results were credible. As shown in Figures 34, the cumulative Z-curves crossed both the traditional boundary (Z = 1.96) and trial sequential monitoring boundary in the TSA of rs10754339 and cancer risk, as well as in the TSA of rs12976445 and cancer risk, indicating that true positive results could be obtained.

      Figure 1.  Sensitivity analysis of rs10754339 and overall cancer risk under A vs. G in the total population (A), Chinese population (B) and Caucasian population (C). Sensitivity analysis of rs10754339 and breast cancer risk under A vs. G (D).

      Figure 2.  Sensitivity analysis of rs12976445 and overall cancer risk under T vs. C in the total population (A), in the Asian population (B), based on PCR-RFLP (C), in the Chinese population (D) and in the Iranian population (E). Sensitivity analysis of rs12976445 and specific cancer risk (F) lung cancer; (G) prostate cancer.

      Figure 3.  Trial sequential analysis of rs10754339 and overall cancer risk in the total population [(A) A vs. G, (B) AA vs. AG, and (C) AA vs. AG + GG] and the Chinese population [(D) A vs. G, (E) AA vs. AG, and (F) AA vs. AG + GG], trial sequential analysis of rs10754339 and breast cancer risk [(G) A vs. G, (H) AA vs. AG, and (I) AA vs. AG + GG].

      Figure 4.  Trial sequential analysis of rs12976445 and overall cancer risk in the Chinese population under T vs. C (A), TT vs. CC (B), TT + TC vs. CC (C), and TT vs. TC + CC (D).

    • B7-H4, an immune checkpoint, and miR-125a, an immunoregulator, have attracted much attention in cancer immunology research. The rs10754339, located in the 3'-UTR of the B7-H4 gene, has been reported to impair the regulation of miR-506-3p on B7-H4[12]. The rs12976445 within the promoter region of miR‐125a affects miR‐125a expression, and bioinformatic analysis predicted a possible GATA-1 binding site by the presence of rs12976445[13]. Therefore, we evaluated the effects of the rs10754339 and rs12976445 genotypes on the expression levels of the B7-H4 gene and the miR‐125a gene, respectively. Expression quantitative trait loci (eQTL) analysis on the GTEx portal website (http://www.gtexportal.org/) revealed that B7-H4 mRNA differentially expressed in pancreas tissues depending on the three genotypes (P < 0.001) with AA tissues showing the highest expression level. The miR‐125a mRNA is differentially expressed in whole blood depending on the three genotypes (P < 0.001), with TT samples showing the highest expression level (Supplementary Figure S5, available in www.besjournal.com). These findings indicated that the B7-H4 gene rs10754339 and the miR‐125a gene rs12976445 are two functional variants that may affect individual susceptibility to cancer.

      Figure S5.  In silico expression analysis of B7-H4 mRNA expression in relation to different genotypes of rs10754339 (A) and miR-125a mRNA expression in relation to different genotypes of rs12976445 (B).

      The association of the B7-H4 gene rs10754339 polymorphism with breast cancer has been repeatedly investigated[14-17], and studies have recorded a significant positive association except for Özgöz et al.[16]. This discrepancy might be mainly attributed to the inadequate power in some studies caused by insufficient sample size. Indeed, in the study of Özgöz et al. (31 breast cancer patients/30 healthy women)[16], rs10754339 showed an association in the same direction (ORs > 1 for G allele and AG genotype) as reported in the other three studies[14,15,17]. Additionally, Özgöz et al. revealed that rs10754339 was a genetic marker for bladder cancer[18]. This study has added to the list of cancers associated with rs10754339 by showing that rs10754339 was significantly associated with the risk of lung and gastric cancer. The link between rs10754339 and lung cancer susceptibility might be attributed to the G > A transition inhibiting miR-506-3p during B7-H4 expression[11], leading to the aberrant expression of B7-H4, which finally leads to the occurrence of lung cancer via AMPK/mTOR signaling[9,26]. Although statistically not significant, we observed a similar tendency that the A allele and AA genotype were more frequent in liver cancer patients than in normal individuals, suggesting that rs10754339 has the potential to be a predictive biomarker for liver cancer, but a confirmatory study with a greater number of participants is required.

      Previous studies have explored the contribution of miR‐125a gene rs12976445 polymorphism to colorectal cancer (CRC)[19], prostate cancer (PCa)[20,21], breast cancer[22], and lung cancer[23]. Consistent with the findings of Sun et al.[23], we also found a connection between rs12976445 and lung cancer risk. However, we found no significant association of rs12976445 with susceptibility to CRC, breast cancer, liver cancer, or gastric cancer. Interestingly, Hakimian et al. did not find an association between rs12976445 and PCa susceptibility[21], whereas Damodaran et al. came to the opposite conclusion[20]. Two possible reasons may explain the discrepancy. First, the sample sizes of these two studies were too small (300 subjects and 200 subjects, respectively), which might have reduced the statistical power and increased the margin of error. Therefore, their findings may not be completely convincing. Secondly, the different living environments, lifestyles, and genetic backgrounds of different ethnic descents (Iranians and Indians) might also have led to the discrepancy.

      To improve the statistical power and resolve the discordant results of previous studies, a meta-analysis was conducted to further evaluate the effects of rs10754339 and rs12976445 on cancer susceptibility. We found that rs10754339 was significantly associated with total cancer risk, especially in the Chinese population, and a strong connection between rs10754339 and breast cancer risk was also observed. Additionally, rs12976445 was significantly associated with total cancer risk only in the Chinese population. The cancer type-stratified analysis showed that rs12976445 was closely related to lung cancer risk, which confirmed the findings of our case-control study. However, no association was observed between rs12976445 and prostate cancer risk and between rs12976445 and total cancer risk based on the PCR-RFLP method.

      There are several limitations of this study. First, the studied subjects were all enrolled in the hospital, which could not fully rule out selection bias. Secondly, our findings on the association between rs10754339/rs12976445 and the risk of liver, lung, and gastric cancer apply only to the Hubei Han Chinese; thus, further confirmatory studies in other ethnic populations are required. Thirdly, although rs12976445 was predicted to regulate the expression of miR‐125a by affecting the binding affinity of GATA-1, the direct relationship between rs12976445 and miR‐125a expression still needs to be validated by a functional study. Finally, owing to the limited number of studies for specific cancer types, the cancer type‐stratified analysis was only conducted for rs10754339 and breast cancer, rs12976445 and prostate cancer, and rs12976445 and lung cancer. Therefore, replication studies and updated meta-analysis are needed to gain a better understanding of the correlation between rs10754339/rs12976445 and cancer susceptibility.

      In conclusion, our case-control study indicated that the B7-H4 gene rs10754339 is associated with the susceptibility to lung and gastric cancer in the Chinese population and that the miR‐125a gene rs12976445 may be a genetic factor of lung cancer in the Chinese population. Meta-analysis indicated that both rs10754339 and rs12976445 may be contributing to cancer susceptibility in the Chinese population. However, additional studies with larger sample sizes in different ethnic populations are required to confirm our present findings.

    • Figure S1.  The genotyping diagrams of rs10754339 (A, B) and rs12976445 (C, D) by Sanger sequencing and PCR-RFLP assay.

      Figure S3.  Forest plots for the meta-analysis of 10754339 and overall cancer risk under A vs. G in the total population (A) and the Chinese population (B). Forest plots for the meta-analysis of rs10754339 and breast cancer risk (C).

      Figure S4.  Forest plots for the meta-analysis of rs12976445 and overall cancer risk under T vs. C in the total population (A), in the Asian population (B), based on PCR-RFLP (C), and in the Chinese population (D).

参考文献 (26)
补充材料:
22281+Supplementary Materials.pdf

目录

    /

    返回文章
    返回