SYSTEMATIC REVIEW article

Front. Oncol., 19 May 2023

Sec. Cancer Genetics

Volume 13 - 2023 | https://doi.org/10.3389/fonc.2023.1125189

Genetic alterations in LEP and ADIPOQ genes and risk for breast cancer: a meta-analysis

  • Department of General Surgery, China-Japan Friendship Hospital, Beijing, China

Abstract

Introduction:

Breast cancer has a strong genetic predisposition, and its genetic architecture is not fully understood thus far. In this study, we aimed to perform a meta-analysis to evaluate the association of genetic alterations in LEP and ADIPOQ genes, as well as their receptor-encoded genes with risk for breast cancer.

Methods:

Only published studies conducted in humans and written in English were identified by searching PubMed, SCOPUS, CINAHIL and Embase from their inception to October 2022. Eligibility assessment and data collection were completed independently by two researchers. Statistical analyses were done using the STATA software.

Results:

After literature search, 33 publications were eligible for inclusion. Overall, LEP gene rs7799039-G allele (odds ratio [OR]: 0.78, 95% confidence interval [CI]: 0.62 to 0.98) and ADIPOQ gene rs1501299-T allele (OR: 1.41, 95% CI: 1.06 to 1.88) were associated with the significant risk of breast cancer. In subgroup analyses, differences in menopausal status, obesity, race, study design, diagnosis of breast cancer, genotyping method and sample size might account for the divergent observations of individual studies. Circulating leptin levels were comparable across genotypes of LEP gene rs7799039, as well as that of LEPR gene rs1137101 (P>0.05). Begg’s funnel plots seemed symmetrical, with the exception of LEPR gene rs1137100 and ADIPOQ gene rs1501299.

Discussion:

Taken together, we found, in this meta-analysis, that LEP gene rs7799039 and ADIPOQ gene rs1501299 were two promising candidate loci in predisposition to breast cancer risk.

Introduction

Breast cancer is a leading cause of death in women (1). Global statistics show that the incidence rate of breast cancer was annually increased by 0.5% during the period from 2010 to 2019 (2). Breast cancer is a multifactorial malignancy that has a genetic predisposition (3). Prior studies have demonstrated that the development of mammary carcinoma in the opposite breast of familial patients with unilateral disease was three times higher than that in sporadic patients (4). Recently, a growing number of genome-wide association studies have been conducted to decipher the genetic architecture of breast cancer worldwide (59). In spite of great endeavors, deciphering genetic codes of breast cancer is still in its infancy. Evaluating genes with definitive biological function and direct implications in breast carcinogenesis represents a good alternative. Echoing this claim, obesity-related cytokines such as leptin and adiponectin are increasingly recognized as promising candidates in the development of breast cancer (10).

It is widely recognized that obesity is linked to an enhanced risk of tumorigenesis (11). Leptin as an inducer of epithelial-mesenchymal transition was found to promote tumor progression and metastasis (11). Experimental data supported that leptin can influence mammary tumor growth and progression through regulation of autocrine/paracrine factors and by modulating the extracellular matrix composition (12). Clinical evidence showed that women with breast cancer had increased levels of circulating leptin and its receptor (13). Another important obesity-related cytokine, adiponectin, was found to be capable to induce autophagic cell death in breast cancer cells through STK11/LKB1-mediated activation of the AMPK-ULK1 axis (14). There is evidence that circulating adiponectin levels were lower in women with breast cancer than in healthy controls, especially in postmenopausal women (15). Grossmann and Cleary have written an excellent review and highlighted the balance between leptin and adiponectin in the control of mammary tumorigenesis (16). Specifically, imbalance in leptin-adiponectin levels and leptin receptor expression was found to precipitate the progression of triple negative breast cancer (17). Above data collectively support the contributory roles of leptin and adiponectin in the pathogenesis of breast cancer. We thereby hypothesize that genes coding leptin (LEP) and adiponectin (ADIPOQ) and their receptors are promising candidates in predisposition to breast cancer risk.

To test this hypothesis, we conducted a meta-analysis on genetic alterations in LEP and ADIPOQ genes as well as their receptor-encoded genes by pooling published summary data, aiming to evaluate their association with risk for breast cancer, as well as circulating leptin and adiponectin levels.

Methods

Meta-analysis guideline

The conduct of this meta-analysis complied with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guideline (18).

Search strategy

Only peer-reviewed published studies were retrieved in this meta-analysis by searching PubMed, SCOPUS, CINAHIL and Embase electronic datasets from their inception to October 2022. The key words used for indexing studies in above datasets were formulated from the MeSH (Medical Subject Headings) database, and they are expressed in logistic relations, that is, (“breast cancer” or “breast neoplasm” or “breast tumor” or “breast carcinoma” or “cancer or breast” or “mammary cancer”) and (“leptin” or “lep” or “leptin receptor” or “adiponectin” or “ADIPOQ” or “adiponectin receptor” or “ADIPOQR”) and (“polymorphism” or “variant” or “mutation” or “mutant” or “SNP” or “allele” or “genotype”). The search process was completed by two researchers (X.L. and C.L.) independently. Search results from different datasets were managed by the ENDNOTE software version X9.3.3, and duplicate records were deleted.

In addition, potential missing studies were complemented by checking the references of reviews, meta-analyses and major original articles in search results.

Eligibility criteria

Eligible studies were expected to meet all five inclusion criteria (1): breast cancer as clinical outcome (2); involvement of both breast cancer patients and control participants (3); complete genetic data (genotypes or alleles or effect sizes) of any genetic alteration in LEP or ADIPOQ genes or their receptor-encoded genes between patients with breast cancer and controls or mean or median values of circulating leptin or adiponectin levels for single genotypes or their combination (4); publication using English language (5); valid diagnosis of breast cancer.

Meanwhile, other forms of publications such as comment, editorial, perspective, letter to the editor and case report/series were not covered.

Data collection

Collection of necessary data from eligible studies was independently conducted by two researchers (X.L. and C.L.). Items of data covered surname of first author, year of publication, study design, race, country where study participants resided in, menopause status, source of control participants, factors matched between patients and controls, genotyping method, diagnostic criteria of breast cancer, sample size, chronological age, age at menarche, age of first delivery, nulliparous, percentage of ER, PR and Her-2 of patients with breast cancer, height, weight, body mass index, cigarette smoking, alcohol drinking, family history of breast cancer and genotypes of genetic alteration between patients and controls.

If there was disagreement between the two researchers, original article was assessed, and if necessary, a third researcher (W.P.) was involved.

Data analyses

Summary data from identified eligible studies were pooled by the Stata software version 15.0. To derive a sufficient power to detect significance, a minimum number of eligible studies was set at 3 for genetic alterations analyzed in this study. The association between genetic alterations and breast cancer was expressed as odds ratio (OR) and 95% confidence interval (95% CI). The association between genetic alterations and circulating leptin or adiponectin levels was expressed as standardized mean difference (SMD) and its 95% CI. Effect-size estimates were generated using the DerSimonian-Laird method and under the random-effects model. Heterogeneity between studies was justified by using a percent, inconsistency index (I2), and I2 over 50% or the probability of associated χ2 test less than 0.1 was indicative of statistical significance. Exploring sources of heterogeneity was implemented by using subgroup analyses according to categorical items of interest. Contribution of each study to overall OR was illustrated by sensitivity analyses.

Publication bias was judged by the Begg’s funnel plot and Egger’s linear regression tests from visual and statistical aspects, respectively. In the case of evident publication bias, the trim-and-fill method was used to take theoretically missing studies into consideration when estimating effect-size estimates.

Results

Eligible studies

Initial search of 4 public datasets identified a total of 596 publications after deleting duplicates. Only 33 of these publications were eligible for inclusion (10, 1950). The selection process of eligible articles was displayed in the form of flow diagram (Figure 1). In the case of publications containing more than one group, each group was treated separately. Finally, 55 studies were meta-analyzed for the association between 5 genetic alterations in 3 genes (LEP, leptin receptor [LEPR] and ADIPOQ) and breast cancer risk, 4 studies for the association between rs7799039 genotypes and circulating leptin levels, and 8 studies for the association between rs1137101 genotypes and circulating leptin levels.

Figure 1

The baseline characteristics of 55 studies in this meta-analysis are presented in Table 1.

Table 1

AuthorYearMenopauseCountryRaceStudy designSource of controlsMatched itemsGenotyping methodDiagnosis of breast cancer
Li et al2022pre and postChinaEast AsianRetrospectivePopulationageArrayhistologically-confirmed
Li et al. (ER+)2022pre and postChinaEast AsianRetrospectivePopulationageArrayhistologically-confirmed
Li et al. (ER-)2022pre and postChinaEast AsianRetrospectivePopulationageArrayhistologically-confirmed
Li et al. (Normal)2022pre and postChinaEast AsianRetrospectivePopulationageArrayhistologically-confirmed
Li et al. (Obese)2022pre and postChinaEast AsianRetrospectivePopulationageArrayhistologically-confirmed
Atoum et al2022pre and postJordanMiddle EasternRetrospectiveHospitalNARFLPpathology-based
Atoum et al. (Normal)2022pre and postJordanMiddle EasternRetrospectiveHospitalNARFLPpathology-based
Atoum et al. (Obese)2022pre and postJordanMiddle EasternRetrospectiveHospitalNARFLPpathology-based
Özgöz et al. (ER+)2021postTurkeyMiddle EasternRetrospectiveHospitalNAArrayhospital-diagnosed
Hołysz2021postPolishEuropeanRetrospectivePopulationNARFLPhospital-diagnosed
Hołysz (ER+)2021postPolishEuropeanRetrospectivePopulationNARFLPhospital-diagnosed
Hołysz (ER-)2021postPolishEuropeanRetrospectivePopulationNARFLPhospital-diagnosed
Mahmoud et al. (Obese)2020postEzypeMiddle EasternRetrospectivePopulationBMIRFLPhospital-diagnosed
Cerda-Flores et al2020pre and postMexicoHispanicRetrospectiveHospitalNATaqManhistologically-confirmed
Pasha et al. (Obese)2019pre and postEzypeMiddle EasternRetrospectiveHospitalageRFLPhistologically-confirmed
Macias-Gomez et al2019pre and postJaliscoHispanicRetrospectiveHospitalNARFLPhistologically-confirmed
Geriki et al2019pre and postIndiaEast AsianProspectiveHospitalageRFLPhospital-diagnosed
Liu et al. (premeno)2018preChinaEast AsianProspectiveHospitalresidenceArrayhistologically-confirmed
Liu et al. (postmeno)2018postChinaEast AsianProspectiveHospitalresidenceArrayhistologically-confirmed
Rodrigo et al2017pre and postSri LankaEast AsianRetrospectiveHospitalage, BMI, menopausal statusSNaPshothospital-diagnosed
Rodrigo et al. (premeno)2017pre and postSri LankaEast AsianRetrospectiveHospitalage, BMI, menopausal statusSNaPshothospital-diagnosed
Rodrigo et al. (postmeno)2017pre and postSri LankaEast AsianRetrospectiveHospitalage, BMI, menopausal statusSNaPshothospital-diagnosed
El-Hussiny et al2017pre and postEzypeMiddle EasternRetrospectiveHospitalNARFLPhospital-diagnosed
Khandouzi et al2016pre and postIndiaEast AsianRetrospectiveHospitalNARFLPhospital-diagnosed
Erbay et al2016pre and postTurkeyMiddle EasternRetrospectiveHospitalNARFLPhistologically-confirmed
Rostami et al2015pre and postIranMiddle EasternRetrospectiveHospitalage, sexRFLPhospital-diagnosed
Mohammadzadeh et al2015pre and postIranMiddle EasternRetrospectiveHospitalage, BMI, menopausal statusRFLPhospital-diagnosed
Mohammadzadeh et al. (premeno)2015pre and postIranMiddle EasternRetrospectiveHospitalage, BMI, menopausal statusRFLPhospital-diagnosed
Mohammadzadeh et al. (postmeno)2015pre and postIranMiddle EasternRetrospectiveHospitalage, BMI, menopausal statusRFLPhospital-diagnosed
Mahmoudi et al2015pre and postIranMiddle EasternRetrospectiveHospitalNARFLPpathology-based
Karakus et al2015pre and postTurkeyMiddle EasternRetrospectiveHospitalNARFLPhistologically-confirmed
Mohammadzadeh et al2014pre and postIranMiddle EasternRetrospectiveHospitalageRFLPhospital-diagnosed
Robles et al. (obese)2013pre and postMexicoHispanicRetrospectiveHospitalNARFLPhospital-diagnosed
Robles et al. (obese, premeno)2013pre and postMexicoHispanicRetrospectiveHospitalNARFLPhospital-diagnosed
Robles et al. (obese, postmeno)2013pre and postMexicoHispanicRetrospectiveHospitalNARFLPhospital-diagnosed
Kaklamani et al. (AA)2013postUSAAmericanProspectivePopulationNAArrayhospital-diagnosed
Kaklamani et al. (Hispanics)2013postUSAAmericanProspectivePopulationNAArrayhospital-diagnosed
Kim et al2012pre and postKoreaEast AsianRetrospectiveHospitalageArrayhospital-diagnosed
Gu et al2012preUSA (Caucasian)AmericanProspectivePopulationageArrayhospital-diagnosed
Nyante et al2011pre and postUSAAmericanProspectivePopulationage, raceArrayhistologically-confirmed
Cleveland et al2010pre and postUSAAmericanProspectivePopulationageRFLPhistologically-confirmed
Cleveland et al. (permeno, BMI<30)2010preUSAAmericanProspectivePopulationageRFLPhistologically-confirmed
Cleveland et al. (permeno, BMI>=30)2010preUSAAmericanProspectivePopulationageRFLPhistologically-confirmed
Cleveland et al. (postmeno, BMI<30)2010postUSAAmericanProspectivePopulationageRFLPhistologically-confirmed
Cleveland et al. (postmeno, BMI>=30)2010postUSAAmericanProspectivePopulationageRFLPhistologically-confirmed
Teras et al2009postUSAAmericanProspectivePopulationage, race and blood draw dateArrayhospital-diagnosed
Okobia et al2008pre and postNigeriaAfricanProspectiveHospitalageRFLPhospital-diagnosed
Okobia et al. (premeno)2008preNigeriaAfricanProspectiveHospitalageRFLPhospital-diagnosed
Okobia et al. (postmeno)2008postNigeriaAfricanProspectiveHospitalageRFLPhospital-diagnosed
Kaklamani et al2008pre and postUSAAmericanRetrospectiveHospitalgender, regionArrayhospital-diagnosed
Han et al2008pre and postChinaEast AsianRetrospectiveHospitalage, region, raceRFLPpathology-based
Liu et al2007pre and postChinaEast AsianRetrospectivePopulationageRFLPhospital-diagnosed
Gallicchio et al2007pre and postUSAAmericanProspectivePopulationNATaqManhospital-diagnosed
Woo et al2006pre and postKoreaEast AsianRetrospectivePopulationageSequencinghospital-diagnosed
Snoussi et al2006pre and postTunisiaMiddle EasternRetrospectivePopulationNARFLPhistologically-confirmed

Characteristics of 33 publications in this meta-analysis.

NA, Not available.

Overall association analyses

The association of 5 genetic alterations with breast cancer risk was displayed in the form of forest plots under allele mode of inheritance (Figure 2). Overall, LEP gene rs7799039-G allele and LEPR gene rs1137100-A allele were associated with reduced breast cancer risk relative to the corresponding reference alleles, and the risk was close to statistical significance. By contrast, ADIPOQ gene rs1501299-T allele increased breast cancer risk significantly by 26% (OR: 1.26, 95% CI: 1.00 to 1.59) relative to the corresponding G allele. The I2 ranged from 62% to 85.4%, denoting the moderate-strong evidence of heterogeneity between studies.

Figure 2

Besides allele mode, pooled estimates under dominant and genotype modes of inheritance are shown in Supplementary Figure 1 and Supplementary Figure 2, respectively. Under dominant mode, the protective effects of LEP gene rs7799039 GG plus GA genotypes and LEPR gene rs1137100 AA plus AG genotypes on breast cancer risk dwindled, and the risk conferred by ADIPOQ gene rs1501299 TT plus TG genotypes was enhanced, with OR of 1.41 (95% CI: 1.06 to 1.88). Under genotype mode, LEP gene rs7799039-GG was associated with a 22% reduced risk of breast cancer significantly (OR: 0.78, 95% CI: 0.62 to 0.98), and no significance was detected for the other comparisons.

Subgroup analyses by menopause and obesity

The association of 5 genetic alterations with breast cancer stratified by menopause and obesity is summarized in Table 2.

Table 2

Genetic alterationsSubgroupsNAllele mode (R vs. W)Dominant mode (RR plus RW vs. WW)Genotype mode (RR vs. WW)
OR95% CIPI2PhetOR95% CIPI2PhetOR95% CIPI2Phet
Menopausal
LEP rs7799039Both120.96.76 - 1.220.75573.39%<0.0011.02.69 - 1.510.92075.14%<0.0010.77.51 - 1.150.20051.23%0.020
Postmenopausal40.95.81 - 1.110.49740.24%0.1700.87.68 - 1.100.24432.39%0.2180.93.65 - 1.340.69445.53%0.138
Premenopausal30.87.75 - 1.020.0760%0.9590.81.65 - 1.010.0550%0.9130.75.53 - 1.070.1140%0.991
LEPR rs1137100Both50.53.27 - 1.020.05880.30%<0.0010.56.26 - 1.220.14268.24%0.0130.23.07 -.820.02463.02%0.044
Postmenopausal31.01.88 - 1.170.8480%0.6521.25.94 - 1.670.1250%0.9031.10.64 - 1.890.7250%0.770
Premenopausal11.04.81 - 1.320.782NANA1.02.77 - 1.350.889NANA1.22.55 - 2.670.626NANA
LEPR rs1137101Both131.01.71 - 1.440.97189.79%<0.0010.96.66 - 1.410.84481.01%<0.0011.09.49 - 2.430.82787.79%<0.001
Postmenopausal51.01.89 - 1.130.9210%0.4790.99.78 - 1.260.95938.07%0.1671.02.78 - 1.330.8840%0.444
Premenopausal40.97.77 - 1.230.82450.80%0.1070.97.77 - 1.230.81312.38%0.3311.01.55 - 1.860.96953.27%0.093
ADIPOQ rs1501299Both61.531.11 - 2.110.01085.71%<0.0011.651.17 - 2.340.00580.19%<0.0011.94.94 - 4.010.07181.38%<0.001
Postmenopausal40.92.72 - 1.180.52360.57%0.0551.09.70 - 1.690.70076.76%0.0050.63.39 - 1.020.06138.86%0.195
ADIPOQ rs2241766Both60.98.63 - 1.520.91988.67%<0.0011.02.63 - 1.640.95387.43%<0.0010.73.29 - 1.790.48671.19%0.004
Postmenopausal30.86.66 - 1.130.27825.77%0.2600.81.57 - 1.160.25545.58%0.1590.92.44 - 1.920.8220%0.795
Obesity
LEP rs7799039NA71.02.79 - 1.330.87777.40%<0.0010.99.67 - 1.460.96078.79%<0.0011.01.59 - 1.740.96464.78%0.009
Normal30.89.75 - 1.060.20235.74%0.2110.78.63 -.980.0330%0.9950.78.57 - 1.060.11323.70%0.270
Obese30.93.63 - 1.360.68967.74%0.0450.90.46 - 1.750.75564.75%0.0590.86.38 - 1.920.70868.13%0.043
Overweight30.75.50 - 1.120.15683.23%0.0030.70.36 - 1.340.27581.80%0.0040.63.31 - 1.280.20476.81%0.013
LEPR rs1137100NA50.79.53 - 1.170.24286.48%<0.0010.78.45 - 1.360.37474.68%0.0030.50.13 - 1.930.31379.53%0.002
Normal10.99.46 - 2.130.972NANA1.28.51 - 3.220.602NANA0.47.08 - 2.750.401NANA
Overweight20.92.73 - 1.140.4380%0.4131.02.61 - 1.710.93949.36%0.1600.97.51 - 1.840.92029.68%0.233
LEPR rs1137101NA90.98.78 - 1.210.82161.02%0.0090.96.72 - 1.290.80655.09%0.0230.99.56 - 1.760.96562.79%0.009
Normal40.92.75 - 1.130.41859.01%0.0621.03.68 - 1.550.90780.37%0.0020.92.70 - 1.210.5520%0.573
Obese50.68.33 - 1.400.29393.76%<0.0010.57.25 - 1.290.17791.02%<0.0010.76.21 - 2.680.66787.94%<0.001
Overweight41.10.55 - 2.200.78794.06%<0.0011.21.68 - 2.140.51379.95%0.0021.01.23 - 4.450.98592.64%<0.001
ADIPOQ rs1501299NA51.25.70 - 2.220.45797.26%<0.0012.70.30 - 24.180.37599.66%<0.0011.04.64 - 1.680.88172.39%0.006
Normal12.411.60 - 3.61<0.001NANA2.631.58 - 4.40<0.001NANA5.141.90 - 13.900.001NANA
Obese11.651.02 - 2.680.043NANA3.761.69 - 8.400.001NANANANANANANA
Overweight31.29.74 - 2.260.37083.28%0.0031.54.98 - 2.410.06156.85%0.0991.01.20 - 5.180.99185.60%0.001
ADIPOQ rs2241766NA50.81.67 -.990.04352.82%0.0760.81.66 - 1.000.05145.18%0.1210.57.29 - 1.140.11350.80%0.087
Obese13.722.08 - 6.64<0.001NANA4.052.10 - 7.81<0.001NANA6.221.31 - 29.600.022NANA
Overweight30.68.33 - 1.410.30082.98%0.0030.66.28 - 1.540.33084.30%0.0020.88.41 - 1.880.7350%0.438

Subgroup association analyses of 5 genetic alterations with breast cancer by menopause and obesity.

R, risk allele; W, wild allele; OR, odds ratio; 95% CI, 95% confidence interval; NA, not available.

By menopausal status, LEPR gene rs1137100-AA genotype carriers conferred a significantly reduced risk of breast cancer compared with GG genotype carriers (OR: 0.23, 95% CI: 0.07 to 0.82) in both premenopausal and postmenopausal women. For ADIPOQ gene rs1501299, the risk for breast cancer was significant under both allele (OR: 1.53, 95% CI: 1.11 to 2.11) and dominant (OR: 1.65, 95% CI: 1.17 to 2.34) modes of inheritance in both premenopausal and postmenopausal women.

By obesity, the association of LEP gene rs7799039 GG plus GA genotypes with breast cancer was substantiated in normal-weight women (OR: 0.78, 95% CI: 0.63 to 0.98).

Subgroup analyses by other features

Table 3 shows the subgroup association of 5 genetic alterations with breast cancer stratified by other features of interest. For LEP rs7799039, the association was significant in studies with prospective design, with histologically-confirmed breast cancer, and involving sample size exceeding 300 under three genetic modes of inheritance. For ADIPOQ rs1501299, significance was noticed in women from East Asia, in studies involving hospital-sourced controls, in studies adopting RFLP technique, and in studies with histologically-confirmed breast cancer under both allele and dominant modes.

Table 3

SubgroupsNAllele mode (R vs. W)Dominant mode (RR plus RW vs. WW)Genotype mode (RR vs. WW)
OR95% CIPI2PhetOR95% CIPI2PhetOR95% CIPI2Phet
LEP rs7799039
RaceEast Asian60.95.72 - 1.250.71373.18%0.0021.02.71 - 1.480.90970.63%0.0040.76.47 - 1.210.24745.95%0.099
Middle Eastern60.87.63 - 1.200.39574.22%0.0020.79.46 - 1.360.39077.12%0.0010.69.38 - 1.270.23757.85%0.037
Others21.18.83 - 1.660.3620%0.8701.51.81 - 2.810.1940%0.8711.46.71 - 3.020.3090%0.876
Western60.91.80 - 1.040.18421.13%0.2750.87.67 - 1.110.26326.57%0.2350.83.65 - 1.060.1287.64%0.368
Study designProspective70.89.81 -.980.0130.43%0.4200.81.70 -.930.0030%0.9610.78.62 -.970.0256.88%0.375
Retrospective130.99.80 - 1.240.96172.64%<0.0011.10.75 - 1.610.61775.05%<0.0010.86.58 - 1.270.43852.30%0.014
Control sourceHospital130.97.80 - 1.180.76168.23%<0.0011.02.77 - 1.350.89069.26%<0.0010.85.61 - 1.180.32939.71%0.069
Population70.86.735 - 1.010.06050.62%0.0590.80.60 - 1.070.13447.57%0.0760.72.52 - 1.010.05448.79%0.069
MatchedNA80.88.72 - 1.080.21856.86%0.0230.91.63 - 1.330.63558.98%0.0170.82.54 - 1.260.36055.84%0.027
Yes120.93.79 - 1.110.43967.01%<0.0010.92.72 - 1.190.55767.35%<0.0010.76.58 -.990.03930.25%0.150
Genotyping methodArray41.19.81 - 1.750.37074.71%0.0081.31.78 - 2.200.31579.21%0.0021.06.62 - 1.820.82119.42%0.293
RFLP160.87.76 -.990.03958.48%0.0020.85.68 - 1.070.16156.96%0.0030.74.57 -.940.01542.80%0.036
Diagnosis of BCHistologically90.87.78 -.970.01129.49%0.1830.79.68 -.910.0015.65%0.3880.77.59 -.990.04132.94%0.154
Non-histologically111.04.80 - 1.350.77574.10%<0.0011.27.84 - 1.910.25774.35%<0.0010.87.57 - 1.330.51248.94%0.033
Sample sizeTotal sample size <300111.11.89 - 1.400.34953.30%0.0181.35.97 - 1.880.07949.86%0.0301.02.72 - 1.440.9298.77%0.361
Total sample size >30090.81.70 -.930.00262.74%0.0060.72.60 -.87<0.00144.68%0.0710.67.51 -.890.00654.16%0.026
LEPR rs1137100
RaceEast Asian70.70.47 - 1.040.07780.53%<0.0010.86.60 - 1.250.43663.25%0.0120.45.19 - 1.040.06164.83%0.014
Western31.00.85 - 1.180.9940%0.6181.88.97 - 3.660.0620%0.5581.66.83 - 3.290.1500%0.539
Study designProspective31.03.90 - 1.170.7200%0.6581.10.89 - 1.360.3790%0.7221.04.57 - 1.900.8990%0.826
Retrospective70.68.44 - 1.050.08278.44%<0.0010.83.44 - 1.580.57268.72%0.0040.51.18 - 1.440.20474.20%0.002
Control sourceHospital50.63.39 - 1.020.05986.99%<0.0010.77.48 - 1.240.28475.03%0.0030.43.16 - 1.130.08671.71%0.007
Population51.00.85 - 1.160.9660%0.9121.42.89 - 2.260.1390%0.5961.40.74 - 2.660.3000%0.402
MatchedNA30.96.77 - 1.190.7090%0.3701.35.65 - 2.820.42563.77%0.0631.23.56 - 2.690.60649.24%0.139
Yes70.74.52 - 1.050.09680.38%<0.0010.85.54 - 1.330.47261.08%0.0170.42.15 - 1.140.08965.26%0.013
Genotyping methodArray60.71.49 - 1.040.08283.64%<0.0010.77.45 - 1.290.31667.13%0.0090.39.12 - 1.310.12772.09%0.006
RFLP40.96.78 - 1.180.7120%0.5741.27.75 - 2.170.37847.27%0.1281.08.54 - 2.180.82238.50%0.181
Diagnosis of BCHistologically21.08.90 - 1.300.4300%0.6251.10.89 - 1.360.3760%0.4221.05.56 - 1.940.8900%0.539
Non-histologically80.74.53 - 1.030.07376.21%<0.0010.84.46 - 1.550.58663.51%0.0080.55.21 - 1.410.21269.10%0.004
Sample sizeTotal sample size <30060.64.36 - 1.130.12282.04%<0.0010.76.31 - 1.870.55073.51%0.0020.45.11 - 1.790.25779.34%0.001
Total sample size >30041.00.88 - 1.130.9330%0.5271.03.86 - 1.250.7260%0.5380.89.54 - 1.470.6590%0.768
LEPR rs1137101
RaceEast Asian121.10.85 - 1.420.48282.69%<0.0011.03.81 - 1.320.79173.30%<0.0011.33.68 - 2.620.41171.79%<0.001
Middle Eastern60.63.32 - 1.230.17292.57%<0.0010.69.23 - 2.120.51891.44%<0.0010.61.20 - 1.850.38086.45%<0.001
Others20.83.64 - 1.100.1910%0.3760.77.49 - 1.210.2590%0.6430.69.39 - 1.210.1910%0.404
Western71.01.85 - 1.200.90953.33%0.0450.96.74 - 1.250.75749.69%0.0641.00.72 - 1.400.99748.78%0.069
Study designProspective90.94.84 - 1.050.27821.40%0.2530.92.78 - 1.080.30221.86%0.2490.85.65 - 1.100.21421.20%0.254
Retrospective180.95.71 - 1.270.71489.78%<0.0010.95.68 - 1.330.76685.33%<0.0011.04.55 - 1.950.90785.04%<0.001
Control sourceHospital140.986.67 - 1.460.94490.78%<0.0011.01.64 - 1.580.96986.48%<0.0011.21.53 - 2.750.65486.43%<0.001
Population130.88.76 - 1.010.06062.62%0.0010.82.70 -.960.01547.33%0.0300.83.62 - 1.100.19047.03%0.036
MatchedNA90.85.54 - 1.350.49691.06%<0.0010.92.46 - 1.830.81089.54%<0.0010.86.41 - 1.830.69984.39%<0.001
Yes180.97.81 - 1.160.73680.28%<0.0010.93.77 - 1.110.40265.99%<0.0011.01.67 - 1.540.95373.42%<0.001
Genotyping methodArray110.88.76 - 1.030.11745.24%0.0510.84.70 – 1.000.04842.97%0.0630.88.57 - 1.350.54622.61%0.235
RFLP160.92.70 - 1.220.56990.44%<0.0010.92.64 - 1.320.65585.79%<0.0010.93.57 - 1.530.78085.77%<0.001
Diagnosis of BCHistologically150.87.68 - 1.110.25690.10%<0.0010.86.65 - 1.140.28786.41%<0.0010.91.55 - 1.520.72383.81%<0.001
Non-histologically121.04.81 - 1.340.77568.36%<0.0011.00.73 - 1.370.98650.91%0.0211.00.58 - 1.730.99867.17%0.001
Sample sizeTotal sample size <300140.99.67 - 1.470.96487.87%<0.0011.08.61 - 1.920.79284.38%<0.0011.14.56 - 2.330.71982.24%<0.001
Total sample size >300130.92.76 - 1.100.34783.05%<0.0010.88.73 - 1.060.16972.82%<0.0010.87.57 - 1.330.52175.81%<0.001
ADIPOQ rs1501299
RaceEast Asian21.921.27 - 2.910.00259.17%0.1182.151.57 - 2.96<0.0010%0.3302.45.51 - 11.860.26672.26%0.058
Middle Eastern31.18.70 - 1.980.53474.93%0.0191.66.81 - 3.390.16774.21%0.0210.77.09 - 6.360.81086.01%0.008
Others21.631.33 - 1.99<0.0010%0.4201.791.38 - 2.32<0.0010%0.7702.481.55 - 3.96<0.0010%0.337
Western30.88.79 -.990.0290%0.8360.89.77 - 1.030.1220%0.5850.72.55 -.940.0160%0.931
Study designProspective31.17.70 - 1.960.54090.71%<0.0011.17.65 - 2.100.60288.14%<0.0011.29.50 - 3.340.59984.40%0.002
Retrospective71.31.99 - 1.720.05781.09%<0.0011.561.12 - 2.170.00977.36%<0.0011.24.60 - 2.550.56780.99%<0.001
Control sourceHospital71.381.02 - 1.880.03985.52%<0.0011.541.12 - 2.120.00877.68%<0.0011.50.73 - 3.0890.26982.90%<0.001
Population30.99.74 - 1.330.93968.86%0.0401.18.66 - 2.100.57584.49%0.0020.75.51 - 1.080.1210%0.767
MatchedNA71.18.90 - 1.560.22982.49%<0.0011.28.93 - 1.760.13578.58%<0.0011.16.64 - 2.090.63076.12%<0.001
Yes31.50.78 - 2.910.22891.09%<0.0012.01.82 - 4.920.12990.72%<0.0011.80.26 - 12.650.55792.60%<0.001
Genotyping methodArray50.96.75 - 1.220.73381.56%<0.0011.02.77 - 1.360.88177.78%0.0010.81.49 - 1.340.40775.91%0.002
RFLP51.761.46 - 2.12<0.0010%0.4432.091.62 - 2.70<0.00110.23%0.3482.881.57 - 5.300.00124.13%0.266
Diagnosis of BCHistologically31.591.33 - 1.91<0.0010%0.6281.731.37 - 2.20<0.0010%0.8182.451.59 - 3.79<0.0010%0.626
Non-histologically71.13.87 - 1.470.35583.45%<0.0011.31.93 - 1.840.12683.53%<0.0010.87.51 - 1.480.60572.74%0.003
Sample sizeTotal sample size <30051.50.99 - 2.280.05578.31%0.0011.871.20 - 2.910.00563.90%0.0261.85.51 - 6.680.34784.43%<0.001
Total sample size >30051.09.84 - 1.400.52584.67%<0.0011.15.83 - 1.600.39484.75%<0.0010.95.61 - 1.480.80666.82%0.017
ADIPOQ rs2241766
RaceEast Asian11.26.88 - 1.790.207100%NA1.37.90 - 2.080.139100%NA1.11.43 - 2.870.8360%NA
Middle Eastern31.36.50 - 3.740.54890.75%<0.0011.36.43 - 4.320.60290.66%<0.0011.56.34 - 7.210.57156.67%0.099
Others20.53.32 -.880.01449.64%0.1590.55.30 - 1.010.05356.37%0.1300.20.09 -.45<0.0010%0.871
Western30.83.67 - 1.030.09341.25%0.1820.80.62 - 1.050.10349.41%0.1390.79.49 - 1.270.3310%0.696
Study designProspective20.95.75 - 1.210.6710%0.6960.94.72 - 1.220.6360%0.7590.97.41 - 2.290.9490%0.529
Retrospective70.91.62 - 1.350.65386.82%<0.0010.92.60 - 1.430.71585.84%<0.0010.73.34 - 1.590.43065.55%0.008
Control sourceHospital70.91.62 - 1.350.65386.82%<0.0010.92.60 - 1.430.71585.84%<0.0010.73.34 - 1.590.43065.55%0.008
Population20.95.75 - 1.210.6710%0.6960.94.72 - 1.220.6360%0.7590.97.41 - 2.290.9490%0.529
MatchedNA70.82.63 - 1.080.16868.69%0.0040.84.63 - 1.120.22864.68%0.0090.61.31 - 1.200.15142.14%0.110
Yes21.60.32 - 8.020.57196.43%<0.0011.60.271 - 9.4110.60696.20%<0.0011.87.23 - 15.490.56484.78%0.010
Genotyping methodArray50.75.63 -.900.00139.40%0.1590.74.61 -.890.00132.69%0.2030.60.31 - 1.150.12251.87%0.081
RFLP41.21.58 - 2.560.61287.81%<0.0011.29.58 - 2.860.52986.27%<0.0011.22.34 - 4.460.76153.34%0.092
Diagnosis of BCHistologically41.00.43 - 2.340.99991.82%<0.0011.07.45 - 2.530.88389.92%<0.0010.62.09 - 4.290.63279.72%0.002
Non-histologically50.88.69 - 1.110.27861.51%0.0340.85.63 - 1.150.28669.24%0.0110.84.56 - 1.260.4020%0.891
Sample sizeTotal sample size <30041.00.40 - 2.510.99290.38%<0.0010.99.36 - 2.750.98589.95%<0.0011.06.24 - 4.630.93855.92%0.078
Total sample size >30050.85.68 - 1.080.18568.91%0.0120.86.67 - 1.110.23965.31%0.0210.66.34 - 1.260.20958.73%0.046

Subgroup association analyses of 5 genetic alterations with breast cancer by other features.

R, risk allele; W, wild allele; OR, odds ratio; 95% CI, 95% confidence interval; NA, not available.

Sensitivity analyses

Sensitivity analyses were performed for 5 genetic alterations associated with breast cancer under allele mode of inheritance, respectively (Supplementary Figure 3). There was no observably significant impact of any individual studies on overall effect-size estimates for 5 genetic alterations evaluated.

Publication bias

Publication bias was assessed in the form of funnel plots and regression tests. As shown in Figure 3, Begg’s funnel plots seemed symmetrical, with the exception of LEPR gene rs1137100 and ADIPOQ gene rs1501299, which was confirmed by the Egger’s regression tests (P: 0.075 and 0.077, respectively). Filled funnel plots revealed that two studies and one study were theoretically missing for LEPR gene rs1137100 and ADIPOQ gene rs1501299, respectively. After taking these missing studies into consideration, effect-size estimates were changed slightly.

Figure 3

Circulating leptin levels

Figure 4 presents the comparison of circulating leptin levels between genotypes of LEP gene rs7799039 and LEPR gene rs1137101. There was no noticeable difference for all comparisons (P>0.05).

Figure 4

Discussion

The aim of this meta-analysis was to examine the association of 5 genetic alterations in LEP and ADIPOQ genes, as well as their receptor-encoded genes, with breast cancer risk and circulating leptin levels. Importantly, we found that LEP gene rs7799039 and ADIPOQ gene rs1501299 were two promising candidate loci in predisposition to breast cancer risk. Additionally, we found that differences in menopausal status, obesity, race, study design, diagnosis of breast cancer, genotyping method and sample size might account for the divergent results of previous studies in the literature. To the best of our knowledge, this meta-analysis is thus far the most comprehensive on the susceptibility of LEP and ADIPOQ genes to breast cancer.

Breast cancer has a strong genetic predisposition, and the heritability among first degree relatives is estimated to be around 35% (51, 52). To unravel the genetic linings of breast cancer, a large panel of studies have been conducted, and many genes were identified to be susceptible to breast cancer, such as breast cancer susceptibility gene (BRCA) (53). However, for the majority of identified genes, uncertainty still exists over which gene is actually involved in the pathogenesis of breast cancer. One of the biggest hurdles is lack of reproducibility across single studies. The reasons for this irreproducibility are mainly attributed to insufficient power to detect significance, discrepant sampling criteria of participants and varying characteristics of participants. Taking these possible reasons into consideration, we in this meta-analysis tested the hypothesis that genes encoding leptin and adiponectin and their receptors are potential candidates to breast cancer. Our findings supported this hypothesis by showing that LEP gene rs7799039 and ADIPOQ gene rs1501299 were two promising breast cancer-susceptibility loci. By contrast, a recent meta-analysis by Sayad and coworkers did not support the association of LEP gene rs7799039 and LEPR gene rs1137100 with breast cancer (54). It is possibly because of the differing number of eligible studies involved between the meta-analysis by Sayad and coworkers (54) and the present meta-analysis. As far as we know, we, for the first time, meta-analyzed the association between ADIPOQ gene and breast cancer.

To seek possible reasons behind the irreproducible findings of previous studies, we further conducted subgroup analyses for the association of 5 genetic alterations with breast cancer under three genetic modes of inheritance. It is of importance to see that menopausal status, obesity and race were potential attributes responsible for this irreproducibility. The impact of menopausal status and obesity on breast carcinogenesis has been well established, with evidence from both clinical and experimental aspects (5557). The attribute race merited special discussion, as it is not uncommon to notice that a genetic alteration is associated with a disease in one racial group but not in another (58, 59). Given that linkage disequilibrium and genetic sequences may not be identical across races, it is a wise choice to establish candidate genes and genetic alterations within each race or ethnicity group.

Although the significant association between LEP, LEPR and ADIPOQ genes and breast cancer risk, we did not notice remarkable differences in circulating leptin levels across genotypes of their genetic alterations. The possibility for this phenomenon might be the limited number of studies measuring and comparing circulating leptin levels across genotypes. Another possibility is that studies for the association with breast cancer and circulating leptin levels are not identical, and differences in study designs, sample sizes and participant characteristics may matter. Practically, it is expected to validate the association with circulating leptin levels by large, well-designed cohorts in the future.

Limitations

Some possible limitations needed to be addressed for this meta-analysis. The first is the probability of selection bias. This meta-analysis merely retrieved published studies in English, and studies written the other languages known as “grey” literature were not covered. The second limitation is the cross-sectional nature of all retrieved studies, and the association derived in this meta-analysis cannot imply the cause-and-effect relationship, calling for further investigations to fill this gap in knowledge. The third limitation is the insufficient power in most subgroup association analyses. The fourth limitation is that this meta-analysis is based on summary estimates, instead of individual participant data, which made the statistical correction for some confounding factors such as menopausal status and body mass index impractical. The fifth limitation is the possibility of publication bias for two of five genetic alterations assessed in this meta-analysis; however, incorporation of adding theoretically missing studies did not materially change our effect-size estimates.

In conclusion, through a comprehensive analysis of 33 publications, we found that LEP gene rs7799039 and ADIPOQ gene rs1501299 were two promising candidate loci in predisposition to breast cancer risk. Additionally, we found that differences in menopausal status, obesity, race, study design, diagnosis of breast cancer, genotyping method and sample size might account for the divergent results of previous studies in the literature. We agree that further investigations from genetic and experimental points of view are necessary to ascertain the implication of these genes in the pathogenesis of breast cancer, which might shed more light on knowledge and preferences toward breast cancer screening for high-risk women.

Statements

Data availability statement

The original contributions presented in the study are included in the article/Supplementary Material. Further inquiries can be directed to the corresponding author.

Author contributions

W-zP conceived the study; XL and C-fL conducted the literature search. XL and C-fL extracted the required data. XL and C-fL performed data analysis and interpretation. W-zP and JZ did statistical analyses. W-zP and XL drafted the manuscript. All authors contributed to the writings and approved the final version of the manuscript.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fonc.2023.1125189/full#supplementary-material

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Summary

Keywords

breast cancer, genetic alternation, meta-analysis, risk, association

Citation

Peng W, Liu X, Li C and Zhao J (2023) Genetic alterations in LEP and ADIPOQ genes and risk for breast cancer: a meta-analysis. Front. Oncol. 13:1125189. doi: 10.3389/fonc.2023.1125189

Received

19 December 2022

Accepted

10 May 2023

Published

19 May 2023

Volume

13 - 2023

Edited by

Ricardo Ribeiro, Universidade do Porto, Portugal

Reviewed by

Manish Charan, The Ohio State University, United States; Hortensia Moreno-Macias, Universidad Autonoma Metropolitana, Mexico; Kyoung Sik Park, Konkuk University Medical Center, Republic of Korea

Updates

Copyright

*Correspondence: Wei-zhao Peng,

Disclaimer

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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