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Influence of Quercetin-Rich Food Intake on microRNA Expression in Lung Cancer Tissues

Tram K. Lam, Stephanie Shao, Yingdong Zhao, Francesco Marincola, Angela Pesatori, Pier Alberto Bertazzi, Neil E. Caporaso, Ena Wang and Maria Teresa Landi
Tram K. Lam
Divisions of 1Cancer Epidemiology and Genetics, and 2Cancer Treatment and Diagnosis, NCI/NIH; 3Department of Transfusion Medicine, Clinical Center and Center for Human Immunology, NIH, Bethesda, Maryland; 4School of Public Health, Yale University, New Haven, Connecticut; and 5Unit of Epidemiology, Fondazione IRCCS Ospedale Maggiore Policlinico and Department of Occupational and Environmental Health, Università degli Studi di Milano, Milan, Italy
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Stephanie Shao
Divisions of 1Cancer Epidemiology and Genetics, and 2Cancer Treatment and Diagnosis, NCI/NIH; 3Department of Transfusion Medicine, Clinical Center and Center for Human Immunology, NIH, Bethesda, Maryland; 4School of Public Health, Yale University, New Haven, Connecticut; and 5Unit of Epidemiology, Fondazione IRCCS Ospedale Maggiore Policlinico and Department of Occupational and Environmental Health, Università degli Studi di Milano, Milan, Italy
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Yingdong Zhao
Divisions of 1Cancer Epidemiology and Genetics, and 2Cancer Treatment and Diagnosis, NCI/NIH; 3Department of Transfusion Medicine, Clinical Center and Center for Human Immunology, NIH, Bethesda, Maryland; 4School of Public Health, Yale University, New Haven, Connecticut; and 5Unit of Epidemiology, Fondazione IRCCS Ospedale Maggiore Policlinico and Department of Occupational and Environmental Health, Università degli Studi di Milano, Milan, Italy
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Francesco Marincola
Divisions of 1Cancer Epidemiology and Genetics, and 2Cancer Treatment and Diagnosis, NCI/NIH; 3Department of Transfusion Medicine, Clinical Center and Center for Human Immunology, NIH, Bethesda, Maryland; 4School of Public Health, Yale University, New Haven, Connecticut; and 5Unit of Epidemiology, Fondazione IRCCS Ospedale Maggiore Policlinico and Department of Occupational and Environmental Health, Università degli Studi di Milano, Milan, Italy
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Angela Pesatori
Divisions of 1Cancer Epidemiology and Genetics, and 2Cancer Treatment and Diagnosis, NCI/NIH; 3Department of Transfusion Medicine, Clinical Center and Center for Human Immunology, NIH, Bethesda, Maryland; 4School of Public Health, Yale University, New Haven, Connecticut; and 5Unit of Epidemiology, Fondazione IRCCS Ospedale Maggiore Policlinico and Department of Occupational and Environmental Health, Università degli Studi di Milano, Milan, Italy
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Pier Alberto Bertazzi
Divisions of 1Cancer Epidemiology and Genetics, and 2Cancer Treatment and Diagnosis, NCI/NIH; 3Department of Transfusion Medicine, Clinical Center and Center for Human Immunology, NIH, Bethesda, Maryland; 4School of Public Health, Yale University, New Haven, Connecticut; and 5Unit of Epidemiology, Fondazione IRCCS Ospedale Maggiore Policlinico and Department of Occupational and Environmental Health, Università degli Studi di Milano, Milan, Italy
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Neil E. Caporaso
Divisions of 1Cancer Epidemiology and Genetics, and 2Cancer Treatment and Diagnosis, NCI/NIH; 3Department of Transfusion Medicine, Clinical Center and Center for Human Immunology, NIH, Bethesda, Maryland; 4School of Public Health, Yale University, New Haven, Connecticut; and 5Unit of Epidemiology, Fondazione IRCCS Ospedale Maggiore Policlinico and Department of Occupational and Environmental Health, Università degli Studi di Milano, Milan, Italy
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Ena Wang
Divisions of 1Cancer Epidemiology and Genetics, and 2Cancer Treatment and Diagnosis, NCI/NIH; 3Department of Transfusion Medicine, Clinical Center and Center for Human Immunology, NIH, Bethesda, Maryland; 4School of Public Health, Yale University, New Haven, Connecticut; and 5Unit of Epidemiology, Fondazione IRCCS Ospedale Maggiore Policlinico and Department of Occupational and Environmental Health, Università degli Studi di Milano, Milan, Italy
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Maria Teresa Landi
Divisions of 1Cancer Epidemiology and Genetics, and 2Cancer Treatment and Diagnosis, NCI/NIH; 3Department of Transfusion Medicine, Clinical Center and Center for Human Immunology, NIH, Bethesda, Maryland; 4School of Public Health, Yale University, New Haven, Connecticut; and 5Unit of Epidemiology, Fondazione IRCCS Ospedale Maggiore Policlinico and Department of Occupational and Environmental Health, Università degli Studi di Milano, Milan, Italy
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DOI: 10.1158/1055-9965.EPI-12-0745 Published December 2012
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    Figure 1.

    Mean expression levels for significant miR groups comparing the highest versus lowest tertile of quercetin-rich food intake in former smokers with adenocarcinoma.

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  • Table 1.

    Selected characteristics by sex-specific tertile (T1-T3) of quercetin-rich fooda intake in EAGLE, separately for histologic subtypes

    ADSQ
    Subject characteristicsT1 (n = 57)T2 (n = 47)T3 (n = 40)PT1 (n = 48)T2 (n = 42)T3 (n = 30)P
    Quercetin-rich food intakeb, median (IQR)0.79 (0.42)1.56 (0.46)2.45 (0.53)0.71 (0.37)1.47 (0.44)2.53 (0.67)
    Age, mean (SD)62.58 + 9.0364.58 + 8.2165.57 + 8.550.22c68.48 + 7.0969.02 + 6.3066.37+ 8.620.29c
    Male, n, %38 (66.67)25 (53.19)20 (50.0)0.20d46 (95.83)42 (100)30 (100)0.22d
    BMI, mean (SD)24.42 + 3.7625.59 + 3.9824.42 + 3.170.21c25.42 + 3.1026.73 + 3.7127.74 + 3.920.02c
    Smoking status, %0.01d0.08d
     Never4 (7.02)16 (34.04)7 (17.50)01 (2.38)0
     Former22 (38.60)12 (25.53)22 (55.0)18 (37.50)20 (47.62)20 (66.67)
     Current31 (54.39)19 (40.43)11 (27.50)30 (62.50)21 (50.0)10 (33.33)
    Pack years, median (IQR) intake40.0 (24.0)34.5 (32.0)33.0 (28.0)0.63e54.0 (28.25)46.25 (21.70)42.50 (33.0)0.22e
     Vegetablesb,fmedian (IQR)1.01 (0.43)2.03 (0.79)2.46 (1.93)<0.01e0.95 (0.51)1.68 (0.94)2.61 (1.83)<0.01e
     Fruitsb,g, median (IQR)0.96 (0.82)1.78 (1.28)2.80 (1.40)<0.01e1.03 (0.89)1.55 (0.69)3.00 (1.37)<0.01e
     Meatsb,h, median (IQR)0.70 (0.68)1.07 (0.79)0.94 (0.98)0.01e1.07 (0.94)1.23 (1.27)1.33 (0.62)0.06e
     Lifetime alcoholb, median (IQR)23.13 (25.17)14.79 (34.01)7.41 (19.58)0.01e30.69 (24.69)36.23 (20.30)31.18 (31.97)0.66e

    NOTE: Column percent totals may not sum to 100% because of rounding; bolded P values indicated statistical significance.

    Abbreviations: T1–T3 = first tertile through third tertile; IQR, interquartile range; SD, standard deviation.

    • ↵aQuercetin-rich foods: summary measure of apples, grapes, onions, artichoke/fennel/celery, beans, apricots, plums, turnips, peppers, strawberries, tomatoes, and broccoli.

    • ↵bFrequency (food groups, servingsper day; alcohol, grams per day).

    • ↵cANOVA test.

    • ↵dχ2 test.

    • ↵eNonparametric Kruskal–Wallis test.

    • ↵fTotal vegetables intake: summary measure of tomatoes, peppers, carrots, salad, peas, beans/chickpeas, mushrooms, broccoli, turnips, savoy, black cabbage, onions, cooked spinach/Swiss.

    • ↵gTotal fruits intake: summary measure of apples, pears, bananas, kiwis, oranges/grapefruits, mandarins/clementines, grapes, peaches/clingstones, apricots, plums, strawberries, melons, and fruit cocktails.

    • ↵hTotal meat intake: summary measure of cooked ham (prosciutto cotto), smoked ham (prosciutto crudo), cured ham (speck), salami, baloney (mortadella), wurstel, salted sliced beef, coppa, pancetta, and other types of processed meats.

  • Table 2.

    MiRs that significantly (at P < 0.05) differentiate highest (T3) versus lowest (T1) consumers of quercetin-rich food intake, separately by histology

    T1 mean ± SDT3 mean ± SDFold changeaPb
    AD (n = 97)
    hsa-miR-5020.085 ± 0.3530.202 ± 0.3501.1240.017
    hsa-mir-5640.565 ± 0.2580.449 ± 0.2730.8900.030
    hsa-miR-124a0.232 ± 0.4470.072 ± 0.4230.8520.044
    hsa-miR-125a0.625 ± 0.7231.034 ± 0.7151.5050.045
    SQ (n = 78)
    hsa-miR-5100.283 ± 0.3610.147 ± 0.2950.8720.003
    hsa-mir-6052.118 ± 0.8151.279 ± 0.7650.4320.004
    hsa-miR-155−5.113 ± 1.071−4.777 ± 0.7841.3990.012
    hsa-miR-373−0.005 ± 0.420−0.091 ± 0.3300.9170.014
    hsa-miR-4530.597 ± 0.3240.491 ± 0.2110.8990.017
    hsa-miR-5020.318 ± 0.3090.095 ± 0.2230.8010.017
    hsa-miR-18b−2.621 ± 0.735−2.227 ± 0.7241.4830.020
    hsa-miR-1831.160 ± 0.4950.779 ± 0.4700.6830.022
    hsa-mir-5730.267 ± 0.3550.126 ± 0.4060.8690.024
    hsa-miR-524a0.074 ± 0.259−0.082 ± 0.3410.8550.036
    hsa-mir-612−0.171 ± 0.851−0.104 ± 0.7191.0690.042
    hsa-miR-363a−0.076 ± 0.7780.124 ± 0.7031.2220.046
    • ↵aFold change is the ratio (T3/T1) of geometric means (>1.0 indicates upregulation and < 1.0 downregulation).

    • ↵bCoefficient P value from ANOVA model adjusted for age, sex, BMI, smoking status, non–quercetin-rich fruits and vegetables, red/processed meat, alcohol, and cigarette pack years.

  • Table 3.

    Influence of quercetin-rich food intake (T3 vs. T1) on individual miR, stratified by histology and smoking status

    AD
    Former smokers (n = 44)Current smokers (n = 42)
    miR nameT1 mean ± SDT3 mean ± SDFold changeaPbmiR nameT1 mean ± SDT3 mean ± SDFold changeaPb
    hsa-mir-641−0.048 ± 0.548−0.237 ± 0.8790.8280.003hsa-mir-5800.492 ± 0.3400.226 ± 0.2860.7670.003
    hsa-miR-29b−1.110 ± 1.390−1.021 ± 1.1581.0920.003hsa-miR-215−0.665 ± 0.403−0.932 ± 0.4560.7660.004
    hsa-miR-146a−4.714 ± 0.805−4.830 ± 1.3340.8900.006hsa-miR-194−0.726 ± 0.662−1.159 ± 0.8830.6480.011
    hsa-miR-500a0.646 ± 0.3600.471 ± 0.5390.8390.008hsa-mir-598−0.119 ± 0.498−0.674 ± 0.5380.5740.016
    hsa-let-7e−1.121 ± 0.808−0.882 ± 0.8041.2700.018hsa-miR-518a-2a0.077 ± 0.3430.004 ± 0.2280.9290.020
    hsa-miR-1340.404 ± 0.3040.354 ± 0.3220.9520.020hsa-miR-5030.147 ± 0.682−0.244 ± 0.4510.6770.037
    hsa-miR-26b−1.624 ± 1.514−0.930 ± 1.1572.0030.021hsa-miR-146b−4.682 ± 1.234−4.278 ± 1.3881.4970.043
    hsa-miR-302ca0.107 ± 0.3330.244 ± 0.3051.1470.023hsa-miR-3810.044 ± 0.432−0.127 ± 0.2690.8430.047
    hsa-miR-98−1.804 ± 1.199−1.798 ± 1.5271.0060.024
    hsa-let-7c−1.634 ± 1.559−1.265 ± 1.3261.4460.024
    hsa-miR-27a−1.351 ± 1.218−1.097 ± 1.0021.2900.025
    hsa-let-7a−2.044 ± 1.400−1.663 ± 1.2881.4640.026
    hsa-let-7g−2.283 ± 1.344−2.396 ± 1.4690.8930.026
    hsa-let-7i−2.153 ± 1.492−1.810 ± 1.3001.4090.028
    hsa-let-7f−2.512 ± 1.587−2.191 ± 1.4331.3770.030
    hsa-miR-195−2.106 ± 1.343−2.141 ± 1.1110.9660.031
    hsa-miR-16−2.852 ± 1.482−2.495 ± 1.0811.4290.032
    hsa-miR-146b−4.292 ± 1.012−4.254 ± 1.3361.0390.034
    hsa-miR-26a−0.943 ± 1.723−0.364 ± 1.5521.7830.034
    hsa-miR-19b−3.679 ± 1.048−3.947 ± 1.1080.7640.036
    hsa-mir-5640.556 ± 0.2630.495 ± 0.2200.9410.037
    hsa-miR-20a−4.321 ± 1.456−4.262 ± 1.0751.0610.041
    hsa-miR-106a−3.747 ± 1.533−3.702 ± 0.9881.0470.044
    hsa-miR-34a−0.896 ± 0.743−0.779 ± 0.5301.1240.046
    hsa-miR-92a−3.729 ± 1.292−3.614 ± 1.0511.1210.048
    SQ
    Former smokers (n = 38)Current smokers (n = 40)
    miR nameT1 mean ± SDT3 mean ± SDFold changeaPbmiR nameT1 mean ± SDT3 mean ± SDFold changeaPb
    hsa-miR-492−1.383 ± 0.540−1.262 ± 0.3941.1290.012hsa-miR-5020.354 ± 0.2220.108 ± 0.2200.7820.010
    hsa-miR-5100.408 ± 0.3740.166 ± 0.3070.7850.021hsa-mir-6052.198 ± 0.8611.105 ± 0.7010.3350.013
    hsa-miR-491−0.274 ± 0.798−0.121 ± 0.6381.1650.023hsa-miR-5060.245 ± 0.222−0.195 ± 0.6150.6440.017
    hsa-mir-612−0.264 ± 1.072−0.109 ± 0.7891.1680.025hsa-miR-1831.282 ± 0.5180.357 ± 0.3900.3970.028
    hsa-miR-500a0.387 ± 0.2780.291 ± 0.3850.9080.028hsa-miR-524a0.094 ± 0.294−0.170 ± 0.1510.7680.029
    hsa-mir-663−0.169 ± 0.551−0.117 ± 0.4951.0540.034
    hsa-miR-503−0.069 ± 0.710−0.149 ± 0.4170.9230.034
    hsa-miR-4530.584 ± 0.3240.482 ± 0.2260.9030.035
    hsa-mir-654−1.239 ± 0.422−1.111 ± 0.3661.1370.041
    hsa-mir-6580.211 ± 0.7930.123 ± 0.3720.9160.047

    NOTE: miRs are ordered by P value within strata.

    • ↵aFold change is the ratio (T3/T1) of geometric means (>1.0 indicates upregulation and < 1.0 downregulation).

    • ↵bCoefficient P value from ANOVA model adjusted for age, sex, BMI, non–quercetin-rich fruits and vegetables, red/processed meat, alcohol, and cigarette packyears.

  • Table 4.

    Influence of quercetin-rich food intake (T3 vs. T1) on family of functionala miR, stratified by histology and smoking status

    ADSQs
    FormerCurrentFormerCurrent
    Family FunctionmiRNA membersPaPaPaPa
    Let-7 familyhsa-miR-let-7aP < 0.001P = 0.426P = 0.366P = 0.988
    Tumor suppressorhsa-miR-let-7b
    hsa-miR-let-7c
    hsa-miR-let-7d
    hsa-miR-let-7e
    hsa-miR-let-7f
    hsa-miR-let-7g
    hsa-miR-let-7i
    hsa-miR-98
    hsa-miR-202
    miR-146 familyhsa-miR-146aP = 0.002P = 0.092P = 0.753P = 0.222
    Tumor growth and invasionhsa-miR-146b
    miR-26 familyhsa-miR-26aP = 0.010P = 0.623P = 0.588P = 0.664
    Apoptosishsa-miR-26b
    miR-17 familyhsa-miR-20aP = 0.031P = 0.943P = 0.766P = 0.283
    Tumor progressionhsa-miR-20b
    hsa-miR-106a
    hsa-miR-106b
    hsa-miR-17-5p
    hsa-miR-93
    miR-29 familyhsa-miR-29aP = 0.064P = 0.373P = 0.886P = 0.137
    DNA methylationhsa-miR-29b
    hsa-miR-29c
    miR-18 familyhsa-miR-18aP = 0.705P = 0.220P = 0.156P = 0.392
    Tumor progressionhsa-miR-18b
    miR-34 familyhsa-miR-34aP = 0.142P = 0.649P = 0.275P = 0.568
    Tumor suppressorhsa-miR-34c
    miR-19 familyhsa-miR-19aP = 0.072P = 0.991P = 0.608P = 0.103
    Tumor progressionhsa-miR-19b
    miR-15/16 familyhsa-miR-503P = 0.286P = 0.073P = 0.307P = 0.763
    Apoptosishsa-miR-15a
    hsa-miR-16
    hsa-miR-195
    hsa-miR-424

    NOTE: Only results of miR families that had at least 1 miR that were significant at P < 0.05 from individual-based miR analyses (Table 2); bolded P values indicated results that remained significant after Bonferroni correction for multiple comparisons. Models adjusted for age, sex, BMI, non–quercetin-rich fruits and vegetables, red/processed meat, alcohol, and cigarette pack years.

    *P value based on FCS as described in the Materials and Methods section.

    • ↵aRefer to Supplementary Table S5 for more detailed functions.

Additional Files

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    Files in this Data Supplement:

    • Supplementary Figure 1 - PDF file - 5418K, Hierarchical clustering heat map of miR expression levels for 25 miRs that displayed significant differential expression among former smokers with adenocarcinoma in the individual miR-based analysis. Average linkage was used to cluster miRs and samples with a distance metric of 1 minus centered correlation using BRB-ArrayTools version 4.2. Tertiles define subjects with low (Tertile 1) and high (Tertile 3) consumption of quercetin-rich food.
    • Supplementary Figure 2 - PDF file - 3580K, Hierarchical clustering heat map of miR expression levels for 10 miRs that displayed significant differential expression among former smokers with squamous cell carcinoma in the individual miR-based analysis. Average linkage was used to cluster miRs and samples with a distance metric of 1 minus centered correlation using BRB-ArrayTools version 4.2. Tertiles define subjects with low (Tertile 1) and high (Tertile 3) consumption of quercetin-rich food.
    • Supplementary Table 1 - PDF file - 51K, Selected characteristics of included and excluded EAGLE cases
    • Supplementary Table 2 - PDF file - 80K, List of miRs (N=198) retained from chip array analysis
    • Supplementary Table 3 - PDF file - 47K, Food groups and individual food items
    • Supplementary Table 4 - PDF file - 54K, All identified miR groups based on "seed" sequence (2-7nt)
    • Supplementary Table 5 - PDF file - 69K, Influence of quercetin-rich food intake (T3-vs-T1) on miR expression, by histology and smoking status
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Cancer Epidemiology Biomarkers & Prevention: 21 (12)
December 2012
Volume 21, Issue 12
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Influence of Quercetin-Rich Food Intake on microRNA Expression in Lung Cancer Tissues
Tram K. Lam, Stephanie Shao, Yingdong Zhao, Francesco Marincola, Angela Pesatori, Pier Alberto Bertazzi, Neil E. Caporaso, Ena Wang and Maria Teresa Landi
Cancer Epidemiol Biomarkers Prev December 1 2012 (21) (12) 2176-2184; DOI: 10.1158/1055-9965.EPI-12-0745

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Influence of Quercetin-Rich Food Intake on microRNA Expression in Lung Cancer Tissues
Tram K. Lam, Stephanie Shao, Yingdong Zhao, Francesco Marincola, Angela Pesatori, Pier Alberto Bertazzi, Neil E. Caporaso, Ena Wang and Maria Teresa Landi
Cancer Epidemiol Biomarkers Prev December 1 2012 (21) (12) 2176-2184; DOI: 10.1158/1055-9965.EPI-12-0745
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