Abstract
Objective: Breast cancer seriously endangers women’s health. It is very important to analyze the lifestyle and genetic risk factors for people with malignant or suspected malignant nodules in breast cancer screening for the prevention of breast cancer. Methods: A total of 3,142 urban female residents in southern China completed a clinical screening for breast cancer. The participants completed questionnaires on living environmental factors and underwent clinical imaging examinations and genetic testing of 73 SNP loci. According to the BI-RADS classification results, the population was divided into positive and negative groups. Key factors were identified through intergroup differences and association analysis. Lifestyle models, SNP models, and lifestyle + PRS models were constructed. ROC curves and nomograms were used to evaluate the classification effect of the model. Results: There were 10 lifestyle factors that were significantly different between the groups, 4 of which were significantly associated with positive breast imaging results (p < 0.05), including age (OR = 0.972, 95% CI: 0.957–0.988), duration of breastfeeding (0.982, 0.970–0.994), history of benign breast disease (1.838, 1.299–2.599), and high-fat diet (1.507, 1.166–1.947). There were 5 significant SNPs, including BRCA2-rs1799955, TLR1-rs4833095, ZNF365-rs10822013, SLC4A7-rs4973768, and BRCA2-rs144848. The AUC values for the lifestyle, SNP, and lifestyle + PRS models were 0.625, 0.598, and 0.633, respectively. The C index of the lifestyle + PRS model was 0.633. Conclusion: Advocating breastfeeding, reducing the intake of high-fat diets, and protecting breast health may help lower the risk of positive results in breast screenings. The combination of lifestyle factors and genetic factors can enhance the predictive power of the model.
Journal Section:
Research Article
References
1.
Bray
F
, Laversanne
M
, Sung
H
, Ferlay
J
, Siegel
RL
, Soerjomataram
I
, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries
. CA Cancer J Clin
. 2024
;74
(3
):229
–63
. 2.
Sung
H
, Ferlay
J
, Siegel
RL
, Laversanne
M
, Soerjomataram
I
, Jemal
A
, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries
. CA Cancer J Clin
. 2021
;71
(3
):209
–249
. 3.
Łukasiewicz
S
, Czeczelewski
M
, Forma
A
, Baj
J
, Sitarz
R
, Stanisławek
A
. Breast cancer-epidemiology, risk factors, classification, prognostic markers, and current treatment strategies-an updated review
. Cancers
. 2021
;13
(17
):4287
. 4.
Gail
MH
, Brinton
LA
, Byar
DP
, Corle
DK
, Green
SB
, Schairer
C
, et al. Projecting individualized probabilities of developing breast cancer for white females who are being examined annually
. J Natl Cancer Inst
. 1989
;81
(24
):1879
–86
. 5.
Crispo
A
, D’Aiuto
G
, De Marco
M
, Rinaldo
M
, Grimaldi
M
, Capasso
I
, et al. Gail model risk factors: impact of adding an extended family history for breast cancer
. Breast J
. 2008
;14
(3
):221
–7
. 6.
Rockhill
B
, Spiegelman
D
, Byrne
C
, Hunter
DJ
, Colditz
GA
. Validation of the Gail et al. model of breast cancer risk prediction and implications for chemoprevention
. J Natl Cancer Inst
. 2001
;93
(5
):358
–66
. 7.
Bondy
ML
, Newman
LA
. Assessing breast cancer risk: evolution of the Gail Model
. J Natl Cancer Inst
. 2006
;98
(17
):1172
–3
. 8.
van Harssel
JJ
, van Roozendaal
CE
, Detisch
Y
, Brandão
RD
, Paulussen
ADC
, Zeegers
M
, et al. Efficiency of BRCAPRO and Myriad II mutation probability thresholds versus cancer history criteria alone for BRCA1/2 mutation detection
. Fam Cancer
. 2010
;9
(2
):193
–201
. 9.
McCarthy
AM
, Guan
Z
, Welch
M
, Griffin
ME
, Sippo
DA
, Deng
Z
, et al. Performance of breast cancer risk-assessment models in a large mammography cohort
. J Natl Cancer Inst
. 2020
;112
(5
):489
–97
. 10.
Han
Y
, Lv
J
, Yu
C
, Guo
Y
, Bian
Z
, Hu
Y
, et al. Development and external validation of a breast cancer absolute risk prediction model in Chinese population
. Breast Cancer Res
. 2021
;23
(1
):62
. 11.
Wang
Y
, Gao
Y
, Battsend
M
, Chen
K
, Lu
W
, Wang
Y
. Development of a risk assessment tool for projecting individualized probabilities of developing breast cancer for Chinese women
. Tumour Biol
. 2014
;35
(11
):10861
–9
. 12.
Sickles EA DOC
; Bassett
LW
. ACR BI-RADS® Atlas, Breast Imaging Reporting and Data System[M]. vol. 2013. ACR BI-RADS® Mammography
: Reston, VA
: American College of Radiology
; 2013
.13.
Colditz
GA
, Atwood
KA
, Emmons
K
, Monson
RR
, Willett
WC
, Trichopoulos
D
, et al. Harvard report on cancer prevention volume 4: harvard cancer risk index. Risk index working group, harvard center for cancer prevention
. Cancer Causes Control
. 2000
;11
(6
):477
–88
. 14.
Mendelson
EB
. ACR BIRADS® ultrasound. ACR BI-RADS® atlas, breast imaging reporting and data System[M]
. Reston
: American College of Radiology
; 2013
.Vol. 2013.15.
Gabriel
S
, Ziaugra
L
, Tabbaa
D
. SNP genotyping using the Sequenom MassARRAY iPLEX platform
. Current protocols in human genetics
; 2009
, [Chapter 2]:Unit 2.12.16.
Yang
Z
, Fang
X
, Pei
X
, Li
H
. Polymorphisms in the ERCC1 and XPF genes and risk of breast cancer in a Chinese population
. Genet Test Mol Biomark
. 2013
;17
(9
):700
–6
. 17.
Peng
L
, Huang
C
, Xing
S
, Li
D
, He
C
, He
Y
, et al. Impacts of LOC105371267 variants on breast cancer susceptibility in northern Chinese han females: a population-based case-control study
. J Oncol
. 2021
;2021
:4990695
. 18.
Checka
CM
, Chun
JE
, Schnabel
FR
, Lee
, J
, Toth
, H
. The relationship of mammographic density and age: implications for breast cancer screening
. AJR Am J Roentgenol
. 2012
;198
(3
):W292
–295
. 19.
Sung
H
, Ren
J
, Li
J
, Pfeiffer
RM
, Wang
Y
, Guida
JL
, et al. Breast cancer risk factors and mammographic density among high-risk women in urban China
. NPJ Breast Cancer
. 2018
;4
:3
. 20.
Pike
MC
, Krailo
MD
, Henderson
BE
, Casagrande
JT
, Hoel
DG
. “Hormonal” risk factors, “breast tissue age” and the age-incidence of breast cancer
. Nature
. 1983
;303
(5920
):767
–70
. 21.
Hack
CC
, Emons
J
, Jud
SM
, Heusinger
K
, Adler
W
, Gass
P
, et al. Association between mammographic density and pregnancies relative to age and BMI: a breast cancer case-only analysis
. Breast Cancer Res Treat
. 2017
;166
(3
):701
–8
. 22.
Azrad
M
, Blair
CK
, Rock
CL
, Sedjo
RL
, Wolin
KY
, Demark-Wahnefried
W
. Adult weight gain accelerates the onset of breast cancer
. Breast Cancer Res Treat
. 2019
;176
(3
):649
–56
. 23.
Yan
H
, Ren
W
, Jia
M
, Xue
P
, Li
Z
, Zhang
S
, et al. Breast cancer risk factors and mammographic density among 12518 average-risk women in rural China
. BMC cancer
. 2023
;23
(1
):952
. 24.
Lofterød
T
, Frydenberg
H
, Flote
V
, Eggen
AE
, McTiernan
A
, Mortensen
ES
, et al. Exploring the effects of lifestyle on breast cancer risk, age at diagnosis, and survival: the EBBA-Life study
. Breast Cancer Res Treat
. 2020
;182
(1
):215
–27
. 25.
Shang
MY
, Guo
S
, Cui
MK
, Zheng
YF
, Liao
ZX
, Zhang
Q
, et al. Influential factors and prediction model of mammographic density among Chinese women
. Medicine
. 2021
;100
(28
):e26586
. 26.
Dandamudi
A
, Tommie
J
, Nommsen-Rivers
L
, Couch
S
. Dietary patterns and breast cancer risk: a systematic review
. Anticancer Res
. 2018
;38
(6
):3209
–22
. 27.
Chlebowski
RT
. Nutrition and physical activity influence on breast cancer incidence and outcome
. Breast
. 2013
;22
(Suppl 2
):S30
–7
. 28.
Freedman
LS
, Potischman
N
, Kipnis
V
, Midthune
D
, Schatzkin
A
, Thompson
FE
, et al. A comparison of two dietary instruments for evaluating the fat-breast cancer relationship
. Int J Epidemiol
. 2006
;35
(4
):1011
–21
. 29.
Unar-Munguía
M
, Torres-Mejía
G
, Colchero
MA
, González de Cosío
T
. Breastfeeding mode and risk of breast cancer: a dose-response meta-analysis
. J Hum Lact
. 2017
;33
(2
):422
–34
. 30.
Russo
J
, Balogh
GA
, Heulings
R
, Mailo
DA
, Moral
R
, Russo
PA
, et al. Molecular basis of pregnancy-induced breast cancer protection
. Eur J Cancer Prev
. 2006
;15
(4
):306
–42
. 31.
Hartmann
LC
, Sellers
TA
, Frost
MH
, Lingle
WL
, Degnim
AC
, Ghosh
K
, et al. Benign breast disease and the risk of breast cancer
. N Engl J Med
. 2005
;353
(3
):229
–37
. 32.
Román
M
, Louro
J
, Posso
M
, Alcántara
R
, Peñalva
L
, Sala
M
, et al. Breast density, benign breast disease, and risk of breast cancer over time
. Eur Radiol
. 2021
;31
(7
):4839
–47
. 33.
Visscher
DW
, Frank
RD
, Carter
JM
, Vierkant
RA
, Winham
SJ
, Heinzen
EP
, et al. Breast cancer risk and progressive histology in serial benign biopsies
. J Natl Cancer Inst
. 2017
;109
(10
):djx035
. 34.
Gail
MH
. Discriminatory accuracy from single-nucleotide polymorphisms in models to predict breast cancer risk
. J Natl Cancer Inst
. 2008
;100
(14
):1037
–41
. 35.
McCarthy
AM
, Keller
B
, Kontos
D
, Boghossian
L
, McGuire
E
, Bristol
M
, et al. The use of the Gail model, body mass index and SNPs to predict breast cancer among women with abnormal (BI-RADS 4) mammograms
. Breast Cancer Res
. 2015
;17
(1
):1
. 36.
Wacholder
S
, Hartge
P
, Prentice
R
, Garcia-Closas
M
, Feigelson
HS
, Diver
WR
, et al. Performance of common genetic variants in breast-cancer risk models
. N Engl J Med
. 2010
;362
(11
):986
–93
. 37.
Zheng
W
, Wen
W
, Gao
YT
, Shyr
Y
, Zheng
Y
, Long
J
, et al. Genetic and clinical predictors for breast cancer risk assessment and stratification among Chinese women
. J Natl Cancer Inst
. 2010
;102
(13
):972
–81
. 38.
Barnes
DR
, Rookus
MA
, McGuffog
L
, Leslie
G
, Mooij
TM
, Dennis
J
, et al. Polygenic risk scores and breast and epithelial ovarian cancer risks for carriers of BRCA1 and BRCA2 pathogenic variants
. Genet Med
. 2020
;22
(10
):1653
–66
. © 2025 S. Karger AG, Basel
Copyright / Drug Dosage / Disclaimer
Copyright: All rights reserved. No part of this publication may be translated into other languages, reproduced or utilized in any form or by any means, electronic or mechanical, including photocopying, recording, microcopying, or by any information storage and retrieval system, without permission in writing from the publisher.
Drug Dosage: The authors and the publisher have exerted every effort to ensure that drug selection and dosage set forth in this text are in accord with current recommendations and practice at the time of publication. However, in view of ongoing research, changes in government regulations, and the constant flow of information relating to drug therapy and drug reactions, the reader is urged to check the package insert for each drug for any changes in indications and dosage and for added warnings and precautions. This is particularly important when the recommended agent is a new and/or infrequently employed drug.
Disclaimer: The statements, opinions and data contained in this publication are solely those of the individual authors and contributors and not of the publishers and the editor(s). The appearance of advertisements or/and product references in the publication is not a warranty, endorsement, or approval of the products or services advertised or of their effectiveness, quality or safety. The publisher and the editor(s) disclaim responsibility for any injury to persons or property resulting from any ideas, methods, instructions or products referred to in the content or advertisements.
2025
You do not currently have access to this content.
