Skip to main content
PMC home page
BioMed Research International logoLink to BioMed Research International
. 2014 Oct 27;2014:453012. doi: 10.1155/2014/453012

Breast Cancer Risk in Rheumatoid Arthritis: An Update Meta-Analysis

Guo Tian 1, Jia-Ning Liang 1,2, Zhuo-Yun Wang 3, Dian Zhou 2,*
PMCID: PMC4227363  PMID: 25405203

Abstract

Background. The incidence of breast cancer in RA patients remains controversial. Thus we performed a meta-analysis to investigate the impact of RA on breast cancer. Methods. Published literature was available from PubMed, Embase, and Cochrane Library. Pooled standardized incidence rate (SIR) was computed by random-effect model analysis. Results. We identified 16 separate studies in the present study, in which the number of patients ranged from 458 to 84,475. We did not find the increased cancer risk in RA patients (SIR = 0.86, 95% CI = 0.72–1.02). However, subgroup analysis showed that breast cancer risk in RA patients was positively different in Caucasians (SIR = 0.82, 95% CI = 0.73–0.93) and non-Caucasians (SIR = 1.21, 95% CI = 1.19–1.23), respectively. In subgroup analysis by style, a reduced incidence was found in hospital-based case subjects (SIR = 0.82, 95% CI = 0.69–0.97). Similarly, subgroup analysis for adjusted factors indicated that in A3 (age and sex) and A4 (age, sex, and race/ethnicity) the risk was decreased (SIR = 0.87, 95% CI = 0.76–0.99; SIR = 0.63, 95% CI = 0.59–0.67). Conclusions. The meta-analysis revealed no increased breast cancer risk in RA patients. However, in the subgroup analysis, the risk of breast cancer is increased in non-Caucasians patients with RA while it decreased in Caucasian population, hospital-based case subjects, and A3 group. Such relationship may provide preference for risk of breast cancer in different population.

1. Introduction

Rheumatoid arthritis is a common type of autoimmune disease characterized by synovitis and joint destruction, which could lower the quality of life and life expectancy. With the improvement of treatment, the survival of RA patients has been increased. Recently, it has been reported that many comorbidities may arise during the RA disease course. Among these comorbidities, cancer is an attractive issue. Recently, many studies have analyzed the association between RA and breast cancer. Abnormal regulations of the host's immune surveillance are considered as cause of development of tumors [1]. Immune suppression might be responsible for the increased risk of many cancers, obvious after organ transplantation and immunodeficiency viral infections [2, 3]. The pathogenetic mechanisms of interaction between RA and cancer are still unknown. A previous meta-analysis by Smitten et al. observed a decrease in risk for breast cancer in RA patients many years ago without focusing on specific subgroup [4]. Recently, emerging studies have shown the possible risk of breast cancer in RA, but the incidence of breast cancer in this population remains controversial [5–7]. This update review aims to compare the incidence of breast cancer in patients with RA versus the general population through observational studies. We hope that this could offer an actual view of clinically estimating the risk.

2. Material and Methods

2.1. Search Strategy

This meta-analysis was conducted with reference to the following standard guidelines [8]. Relevant publications were identified through a systematic literature search using the keywords “RA,” “Rheumatoid Arthritis,” “breast,” “tumor,” “tumour∗,” “carcinoma∗,” “oncology∗,” “neoplas∗,” “cancer,” “malignanc∗,” “standardized incidence rate,” and “SIR” in PubMed, Embase, and Cochrane Library. Other literatures were available from cross-references within both original and review articles. We only collected data from the full-published paper, excluding any meeting or conference abstract. No language or race restrictions were applied.

2.2. Inclusion Criteria

A recruited study had to meet the following criteria: (1) it was a cohort study; (2) sample size was not less than 100 patients; (3) the study offered necessary information like standardized incidence rate (SIR) and its 95% confidence interval (CI) of breast cancer in RA patients.

2.3. Exclusion Criteria

The exclusion criteria were as follows: (1) conference abstracts, case reports, and review articles; (2) not cohort studies; (3) not control subjects; (4) duplicate reports or unclear data description. If the same cohort study appeared in other publications, only the latest article was selected into our study.

2.4. Data Extraction

Information was carefully extracted from these recruited publications independently by two of the authors (by Guo Tian and Jia-Ning Liang), including author, year of publication, country, calendar period, total number of RA patients, person-year of follow-up, sources of patients, observed/expected, and standardized incidence rate (SIR) with its 95% CI. If original necessary data were unavailable in relevant articles, a request was sent to the author for additional data.

2.5. Literature Quality Assessment

An independent literature search was performed (by Guo Tian and Jia-Ning Liang) with the same method. The contents of abstracts were checked independently by two investigators (Guo Tian and Zhuo-Yun Wang) to decide whether they were available for inclusion. References in the studies were reviewed (by Guo Tian and Zhuo-Yun Wang) to identify additional studies. When discrepancies occurred, a third investigator (Dian Zhou) made further assessment and a final decision was made by the majority of the votes. The Newcastle-Ottawa quality assessment scale was implemented to assess the quality of the methodology in the included studies. This scale comprising eight questions with nine possible points was reviewed by a star system according to the selection populations, comparability of groups, and outcome. The qualities of the recruited studies were evaluated and examined by two reviewers (Guo Tian and Jia-Ning Liang), respectively.

2.6. Statistical Analysis

In the meta-analysis, we used SIR with its 95% CI to combine these pieces of data. Heterogeneity assumption in studies was checked with the Q statistic [9]. Meanwhile, we assessed the effect of heterogeneity in these studies by the following method: I 2 = 100% × (Q − df)/Q [10]. A significant Q statistic (P < 0.10) suggested heterogeneity across studies, and then the result of the random-effect model was selected. If not, the result of the fixed effect model was selected. Additionally, publication bias was investigated with the funnel plot and Egger's linear regression test [11]. All the analyses were performed using the software Stata 12.0 version (StataCorp LP, College Station, TX, USA).

3. Results

3.1. Characteristics of Eligible Studies

Characteristics of studies eligible for the current meta-analysis appeared in Table 1. The study selection process is shown in Figure 1, and four hundred fifty-three of 469 papers were excluded (135 not in human; 113 not cohort studies; 5 duplicate publications; 1 meta-analysis; 26 case reports; 5 reviews; 4 not available data). A total of final 16 articles met our inclusion criteria [5–7, 12–24]. These cohort studies ranged from 458 to 84,475 patients and had mean follow-up times from 3.7 to 10 years.

Table 1.

Summary of studies of RA and breast cancer incidence.

Study Calendar period N total Sources of RA patients Style Mean/median follow-up (year) Breast cancer
(observed/expected)		 Adjusted factors for SIR SIR (95% CI)
Mercer et al., (2013) [5] 2002–2009 3,771 The BSRBR (British Society for Rheumatology Biologics Register) Control Centre Consortium Population-based 3.7 30/28.16 Age, sex, and calendar year 1.07 (0.72–1.52)
Infante-Rivard et al., (2012) [12] 1976–2001 1,437 Quebec Tumuor Registry, Quebec Institute of Statistics and Health, Canada Population-based 5 22/25.01 Age, sex, and calendar year 0.88 (0.58–1.34)
Yamada et al., (2011) [6] 2001–2005 7,566 IORRA (Institute of Rheumatology, Rheumatoid Arthritis formally known as J-ARAMIS) Population-based NA 20 Age and sex 1.05 (0.64–1.62)
Chen et al., (2011) [7] 1996–2007 23,644 The National Health Research Institute of Taiwan Population-based 5.90 ± 2.87 123/101.44 Age, sex, and calendar year 1.21 (1.19–1.23)
Parikh-Patel et al., (2009) [13] 1991–2002 84,475 California's Office of Statewide Planning and Health Development Hospital-based 4.8 842/1343.5 Age, sex, and race/ethnicity 0.63 (0.59–0.67)
Abásolo et al., (2008) [14] 1999–2005 789 The EMECAR cohort (Estudio de la Morbilidad y Expresion Clinica de la Artritis Reumatoide) Population-based 3.95 2 Age and sex 0.90 (0.10–3.20)
Hemminki et al., (2008) [15] 1980–2004 42,262 The Swedish Hospital Discharge Register Hospital-based NA 642 Age, sex, period, region, and socioeconomic status 0.97 (0.90–1.05)
Buchbinder et al., (2008) [16] 1986–1998 458 Victorian State Cancer Registry Population-based 9.3 4/6.1 Age, sex, and calendar year 0.70 (0.20–1.70)
Wolfe and Michaud (2007) [17] 1998–2005 13,869 The US National Data Bank for Rheumatic Diseases Population-based 4.1 102 Age and sex 0.80 (0.60–0.90)
Setoguchi et al., (2006) [18] NA 29,422 Health care utilization databases from US and Canada Population-based NA 112/126.6 Age and sex 0.90 (0.73–1.06)
Askling et al., (2005) [19] 1990–2003 53,067 The Swedish Inpatient Register Hospital-based 5.6 471 Age, sex, and calendar year 0.83 (0.76–0.91)
Askling et al., (2005) [19] (Early RA) 1995–2003 3,703 The Early Arthritis Register Population-based 3.6 13 Age, sex, and calendar year 0.60 (0.30–1.00)
Askling et al., (2005) [19] (TNF antagonist RA) 1999–2003 4,160 Swedish structured postmarketing surveillance program Population-based 2.3 8 Age, sex, and calendar year 0.40 (0.20–0.90)
Thomas et al., (2000) [20] 1981–1996 26,623 Scottish Cancer Registry Hospital-based 5.3 249 Age, sex, site, and calendar year 0.95 (0.83–1.07)
Cibere et al., (1997) [21] 1966–1974 862 Rheumatic Disease Unit Population-based 17.4 18/22.8 Age, sex, and calendar year 0.90 (0.46–1.24)
Mellemkjær et al., (1996) [22] 1977–1987 20,699 Danish Hospital Discharge Register Hospital-based 7 186/229.8 Age, sex, and calendar year 0.80 (0.70–0.90)
Moritomo et al., (1995) [23] 1980–1989 655 Center for Adult Diseases, Osaka, Japan Population-based 10 3/1.79 Age, sex, site, and calendar year 1.68 (0.34–4.91)
Gridley et al., (1993) [24] 1965–1983 11,683 Swedish Hospital Inpatient Register Hospital-based 8.6 106 Age, sex, site, and calendar year 0.79 (0.60–1.00)
Open in a new tab

N: the number of RA patients.

NA: not available.

Figure 1.

Figure 1

Open in a new tab

3.2. Study Quality

With regard to cohort studies, 83% were of high quality (NOS score > 6), with an average NOS score of 6.2. The quality ratings of each study according to NOS criteria are listed in Table 2. Given the comparability, the quality was relatively low because many studies did not report on the control for RA, an essential potential confounder. Additionally, four studies definitely fulfilled the American College of Rheumatology classification criteria for diagnosis of RA, while in the other studies the diagnostic criteria for RA were unknown.

Table 2.

Quality assessment of included studies.

Study Whether the American College of Rheumatology
(ACR) or not		 Selection Comparability Outcome Total awarded
 stars
1 Representativeness of
the exposed cohort		 2 Selection of the
nonexposed cohort		 3 Ascertainment
of exposure		 4 Demonstration
that outcome of
interest was not
present at start of study		 1 Comparability of cohorts
on the basis of
the design or analysis		 1 Assessment of outcome 2 Was follow-up
long enough for
outcomes to occur?		 3 Adequacy
of follow-up
of cohorts (<20%)
Mercer et al., (2013) [5] NA ★ ★ ★ NA ★ ★ ★ NA 6
Infante-Rivard et al., (2012) [12] NA NA ★ ★ NA ★ ★ ★ NA 6
Yamada et al., (2011) [6] Y ★ NA ★ NA ★ ★ ★ NA 7
Chen et al., (2011) [7] Y ★ ★ ★ ★ ★ ★ ★ NA 7
Parikh-Patel et al., (2009) [13] NA NA ★ ★ ★ ★ ★ ★ NA 6
Abásolo et al., (2008) [14] Y ★ ★ ★ NA ★ ★ ★ ★ 7
Hemminki et al., (2008) [15] NA ★ ★ ★ ★ ★ ★ ★ NA 6
Buchbinder et al., (2008) [16] Y ★ ★ ★ NA ★ ★ ★ NA 6
Wolfe and Michaud (2007) [17] NA ★ ★ ★ NA ★ ★ NA NA 5
Setoguchi et al., (2006) [18] NA ★ ★ ★ NA ★ ★ ★ NA 6
Askling et al., (2005) [19] NA ★ ★ ★ NA ★ ★ ★ NA 7
Thomas et al., (2000) [20] NA ★ ★ ★ NA ★ ★ ★ ★ 7
Cibere et al., (1997) [21] NA ★ ★ ★ NA ★ ★ ★ NA 5
Mellemkjær et al., (1996) [22] NA ★ ★ ★ NA ★ ★ ★ ★ 7
Moritomo et al., (1995) [23] NA ★ ★ ★ NA ★ ★ ★ NA 6
Gridley et al., (1993) [24] NA ★ ★ ★ ★ ★ ★ ★ NA 6
Open in a new tab

NA: not available.

Y: yes.

★: available.

3.3. Meta-Analysis Results

3.3.1. Analysis in Patients with RA

The Q-test of heterogeneity was not significant and then the original SIRs were pooled by means of the random-effect models (P < 0.001, I 2 = 96.8%). We did not detect the increased cancer risk in RA patients (SIR = 0.86, 95% CI = 0.72–1.02). Subgroup analysis was performed by ethnics. It showed that breast cancer risk in RA patients was positively different in Caucasians and non-Caucasians (Figure 2). Likewise, in subgroup analysis by style, a reduced incidence was found in hospital-based case subjects but not in population-based subjects (Figure 3). And in subgroup analysis for adjusted factors, the risk was decreased in A3 and A4 groups (Figure 4).

Figure 2.

Figure 2

Open in a new tab
Figure 3.

Figure 3

Open in a new tab
Figure 4.

Figure 4

Open in a new tab

3.3.2. Sensitivity Analysis and Publication Bias

We conducted a sensitivity analysis in each group to assess the stability of this meta-analysis. When any single study was removed, the relevant pooled SIRs were not radically changed. It well suggested the stability of the meta-analysis. Funnel plot asymmetry was evaluated by the method of Egger's linear regression test. The result showed that there was a significant publication bias in the total population while not existing in Caucasians and non-Caucasians, respectively (t = −2.47, P = 0.025; t = 0.49, P = 0.633; and t = −0.1, P = 0.939). And publication bias was observed in population-based group but not in hospital-based group (t = −3.59, P = 0.005 and t = 0.76, P = 0.49). Also, A1 (age, sex, site, and calendar year) and A3 revealed no publication bias except A2 group (t = 0.14, P = 0.911; t = 0.56, P = 0.629; and t = −2.65, P = 0.033).

4. Discussion

Breast cancer is one of the worst death-related cancers for women in the world [25]. Reduced risk of breast cancer was observed in Caucasians patients with RA. These results are consistent with previous reports from Western countries in RA patients [6, 7, 12–22, 24]. It has been reported that the epidemiology of malignancy in Japan differed from that in Western countries. In Japanese population, breast cancer became the worst type of tumor followed by uterus and stomach cancers in women [6].

Currently, the RA patients in Taiwan are mainly treated by simultaneously using kinds of immunomodulatory drugs, such as prednisolone, NSAIDs, methotrexate, and biologic agents. In addition, antirheumatic drugs like azathioprine, hydroxychloroquine, cyclophosphamide, and mycophenolate mofetil are applied at times. Immunosuppressive therapy may increase the cancer risk in the population [7]. In particular, long-term continuous administration of them could add a great risk of developing opportunistic infections and cancer [26]. However, in these studies, the detailed doses were unclear. Thus, the discrepancy of doses of medication in Caucasians and non-Caucasians RA patients could result in different breast cancer incidence. It needs further studies to explore.

Breast density and BMI are needed to understand the possible implications of the increased breast cancer risk among RA patients from four case-control studies [27]. It is reported that diverse patterns of breast density by ethnicity are consistent with ethnic differences in breast cancer risk. With regard to the percentage of breast density, Asian women have higher breast density but Pacific women have lower breast density. Breast density may be a potentially important factor to New Zealand's well-known inequalities in breast cancer incidence. Asian women in American studies have been detected to have both lower [28] and higher breast densities in contrast with white women. Nevertheless, these results are possibly affected by the method of measuring breast density and confounding by various mean age at menopause [29]. It indicated that percent density could be not a marker of ethnic differences due to different breast size and significant variation by ethnicity [28].

It has been attractive for several decades about resemblance between the pathologies of autoimmune diseases and cancer. Though cancer study traditionally focuses on the tumor cells, host/tumor cell interactions in the tumor microenvironment are gradually regarded as important elements of tumor progression [30]. Their interactions both in the tumor and in the adjacent stromal cells could lead to the expression of stromal factors, including growth factors, chemokines, cytokines, proteases, and vascular-stimulating factors [31]. Carcinoma-associated fibroblasts (CAFs) appear in inflammatory environments, like RA, where they may be main components of the proliferating pannus and lead to angiogenesis and matrix remodeling [31]. In breast cancer, CAFs also become important cellular components of the tumor microenvironment [30]. CAFs can be regarded as a heterogeneous population [32]. In spite of their essential role in tumor progression, the features of CAFs need to be further clarified. In addition, elevated levels of IL-6 have been linked to poor prognosis in breast cancer patients and IL-6 plays vital roles not only in tumorigenesis, but also in inflammatory diseases including RA [33, 34]. They may partly explain the disease pathology of breast cancer in RA.

Recently, emerging studies have suggested that genetic polymorphism may play an important role both in the RA and in breast cancer. Li et al. [35] conducted a meta-analysis in Asian and non-Asian patients and found that miRNA-210 may be a better tumor predictor in Asian breast cancer patients. Cyclooxygenase two (COX-2) is a vital enzyme metabolizing arachidonic acid. Polymorphism −765 G/C in COX-2-encoding gene promoter is concerned with development of breast cancer and RA [36]. Glinskii et al. [37] found rs2670660 allele-specific gene expression signatures which seem to be suitable for investigating the activated states of innate immunity pathways from human disorders like RA and breast cancer. Nuclear factor-κB (NF-κB) regulates many genes for immune response, cell adhesion, differentiation, and proliferation. NF-κB dysregulation may be associated with both inflammatory diseases and immune deficiencies like RA and several cancers including breast cancer [38]. In addition, the correlation of some estrogen receptor (ER)α genotypes with breast cancer [39] and some autoimmune diseases including RA [40] has been previously reported. In the different populations, various life styles and environmental factors result in different gene-environment interactions and thus might explain different cancer susceptibilities [41].

In subgroup analysis by style, although a reduced incidence was found in hospital-based case subjects in 6 cohort studies, the use of them might have led to selection bias because those suffering from mild RA would have made them more inclined to be away from hospitals. Nevertheless, this selection bias was minimized in the remaining population-based cohort studies.

Within stratified analysis for adjusted factors, it seems that the decreased age- and sex-specific incidence may be potential factors to decrease the risk. For breast cancer with the increased SIR, an early age (especially less than 50 years) of RA diagnosis was a risk factor [15]. Even another study showed that this SIR was 2.19 in females with age < 40. However, the risk tended to be reduced with aging [13]. Those elder RA patients might have better overall health than the general population. But younger RA patients with higher risks could be weaker in the general population. They may face more risk factors for breast cancer.

The present meta-analysis has several limitations. First, the sample size of non-Caucasians was relatively small, result of which may be partly biased, and thus the analysis may have insufficient statistical power to obtain a more real SIR and its 95% CI. Second, publication bias among included studies was identified while not detected when stratified by ethnicities. It is possible that some studies were not included in this analysis or some unpublished studies with null results were not found. Third, a more precise analysis could be conducted, if individual data including cultural factors, age, menopausal status, smoking, and other environmental factors may account for the ethnic difference between them. Fourth, a relatively high level of heterogeneity showed the instability of the result, which could come from the data sources.

In conclusion, the present study indicates that the breast cancer risk is not increased in overall RA patients. Nevertheless, increased risk of breast cancer is observed in non-Caucasians patients with RA while decreased risk is detected in Caucasian population, hospital-based case subjects, and A3 group. Although breast cancer risk prediction remains imperfect, this might provide preference for risk of breast cancer in different population. However, potential mechanism between RA and breast cancer risk is still unclear. Therefore, the conclusion should be interpreted with caution and more large-scale studies are warranted to confirm the results in the future.

Conflict of Interests

The authors declare that there is no conflict of interests regarding the publication of this paper.

References

  • 1.Mechanisms of Carcinogenesis in Risk Identification. Iarc Working Group Meeting. Lyon, 11-18 June 1991. IARC Scientific Publication. 1992;116:1–608. [PubMed] [Google Scholar]
  • 2.Vajdic C. M., McDonald S. P., McCredie M. R. E., Van Leeuwen M. T., Stewart J. H., Law M., Chapman J. R., Webster A. C., Kaldor J. M., Grulich A. E. Cancer incidence before and after kidney transplantation. Journal of the American Medical Association. 2006;296(23):2823–2831. doi: 10.1001/jama.296.23.2823. [DOI] [PubMed] [Google Scholar]
  • 3.Adami J., Gäbel H., Lindelöf B., Ekström K., Rydh B., Glimelius B., Ekbom A., Adami H.-O., Granath F. Cancer risk following organ transplantation: a nationwide cohort study in Sweden. The British Journal of Cancer. 2003;89(7):1221–1227. doi: 10.1038/sj.bjc.6601219. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.Smitten A. L., Simon T. A., Hochberg M. C., Suissa S. A meta-analysis of the incidence of malignancy in adult patients with rheumatoid arthritis. Arthritis Research and Therapy. 2008;10(2, article R45) doi: 10.1186/ar2404. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5.Mercer L. K., Davies R., Galloway J. B., Low A., Lunt M., Dixon W. G., Watson K. D., Symmons D. P. M., Hyrich K. L. Risk of cancer in patients receiving non-biologic disease-modifying therapy for rheumatoid arthritis compared with the UK general population. Rheumatology. 2013;52(1):91–98. doi: 10.1093/rheumatology/kes350.kes350 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6.Yamada T., Nakajima A., Inoue E., Tanaka E., Taniguchi A., Momohara S., Yamanaka H. Incidence of malignancy in Japanese patients with rheumatoid arthritis. Rheumatology International. 2011;31(11):1487–1492. doi: 10.1007/s00296-010-1524-0. [DOI] [PubMed] [Google Scholar]
  • 7.Chen Y. J., Chang Y. T., Wang C. B., Wu C. Y. The risk of cancer in patients with rheumatoid arthritis: a nationwide cohort study in Taiwan. Arthritis and Rheumatism. 2011;63(2):352–358. doi: 10.1002/art.30134. [DOI] [PubMed] [Google Scholar]
  • 8.Stroup D. F., Berlin J. A., Morton S. C., Olkin I., Williamson G. D., Rennie D., Moher D., Becker B. J., Sipe T. A., Thacker S. B. Meta-analysis of observational studies in epidemiology: a proposal for reporting. Journal of the American Medical Association. 2000;283(15):2008–2012. doi: 10.1001/jama.283.15.2008. [DOI] [PubMed] [Google Scholar]
  • 9.Cochran W. G. The combination of estimates from different experiments. Biometrics. 1954;10(1):101–129. doi: 10.2307/3001666. [DOI] [Google Scholar]
  • 10.Higgins J. P. T., Thompson S. G. Quantifying heterogeneity in a meta-analysis. Statistics in Medicine. 2002;21(11):1539–1558. doi: 10.1002/sim.1186. [DOI] [PubMed] [Google Scholar]
  • 11.Egger M., Smith G. D., Schneider M., Minder C. Bias in meta-analysis detected by a simple, graphical test. British Medical Journal. 1997;315(7109):629–634. doi: 10.1136/bmj.315.7109.629. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Infante-Rivard C., Rivard G.-E., Derome F., Cusson A., Winikoff R., Chartrand R., Guay J.-P. A retrospective cohort study of cancer incidence among patients treated with radiosynoviorthesis. Haemophilia. 2012;18(5):805–809. doi: 10.1111/j.1365-2516.2012.02802.x. [DOI] [PubMed] [Google Scholar]
  • 13.Parikh-Patel A., White R. H., Allen M., Cress R. Risk of cancer among rheumatoid arthritis patients in California. Cancer Causes and Control. 2009;20(6):1001–1010. doi: 10.1007/s10552-009-9298-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Abásolo L., Júdez E., Descalzo M. Á., González-Álvaro I., Jover J. A., Carmona L. Cancer in rheumatoid arthritis: occurrence, mortality, and associated factors in a south european population. Seminars in Arthritis and Rheumatism. 2008;37(6):388–397. doi: 10.1016/j.semarthrit.2007.08.006. [DOI] [PubMed] [Google Scholar]
  • 15.Hemminki K., Li X., Sundquist K., Sundquist J. Cancer risk in hospitalized rheumatoid arthritis patients. Rheumatology. 2008;47(5):698–701. doi: 10.1093/rheumatology/ken130. [DOI] [PubMed] [Google Scholar]
  • 16.Buchbinder R., Barber M., Heuzenroeder L., Wluka A. E., Giles G., Hall S., Harkness A., Lewis D., Littlejohn G., Miller M. H., Ryan P. F. J., Jolley D. Incidence of melanoma and other malignancies among rheumatoid arthritis patients treated with methotrexate. Arthritis Care and Research. 2008;59(6):794–799. doi: 10.1002/art.23716. [DOI] [PubMed] [Google Scholar]
  • 17.Wolfe F., Michaud K. Biologic treatment of rheumatoid arthritis and the risk of malignancy: analyses from a large US observational study. Arthritis and Rheumatism. 2007;56(9):2886–2895. doi: 10.1002/art.22864. [DOI] [PubMed] [Google Scholar]
  • 18.Setoguchi S., Solomon D. H., Weinblatt M. E., Katz J. N., Avorn J., Glynn R. J., Cook E. F., Carney G., Schneeweiss S. Tumor necrosis factor α antagonist use and cancer in patients with rheumatoid arthritis. Arthritis and Rheumatism. 2006;54(9):2757–2764. doi: 10.1002/art.22056. [DOI] [PubMed] [Google Scholar]
  • 19.Askling J., Fored C. M., Brandt L., Baecklund E., Bertilsson L., Feltelius N., Cöster L., Geborek P., Jacobsson L. T., Lindblad S., Lysholm J., Rantapää-Dahlqvist S., Saxne T., Klareskog L. Risks of solid cancers in patients with rheumatoid arthritis and after treatment with tumour necrosis factor antagonists. Annals of the Rheumatic Diseases. 2005;64(10):1421–1426. doi: 10.1136/ard.2004.033993. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.Thomas E., Brewster D. H., Black R. J., Macfarlane G. J. Risk of malignancy among patients with rheumatic conditions. International Journal of Cancer. 2000;88(3):497–502. [PubMed] [Google Scholar]
  • 21.Cibere J., Sibley J., Haga M. Rheumatoid arthritis and the risk of malignancy. Arthritis and Rheumatism. 1997;40(9):1580–1586. doi: 10.1002/art.1780400906. [DOI] [PubMed] [Google Scholar]
  • 22.Mellemkjær L., Linet M. S., Gridley G., Frisch M., Møller H., Olsen J. H. Rheumatoid arthritis and cancer risk. European Journal of Cancer A. 1996;32(10):1753–1757. doi: 10.1016/0959-8049(96)00210-9. [DOI] [PubMed] [Google Scholar]
  • 23.Moritomo H., Ueda T., Hiyama T., Hosono N., Mori S., Komatsubara Y. The risk of cancer in rheumatoid patients in Japan. Scandinavian Journal of Rheumatology. 1995;24(3):157–159. doi: 10.3109/03009749509099305. [DOI] [PubMed] [Google Scholar]
  • 24.Gridley G., McLaughlin J. K., Ekbom A., Klareskog L., Adami H.-O., Hacker D. G., Hoover R., Fraumeni J. F., Jr. Incidence of cancer among patients with rheumatoid arthritis. Journal of the National Cancer Institute. 1993;85(4):307–311. doi: 10.1093/jnci/85.4.307. [DOI] [PubMed] [Google Scholar]
  • 25.Jemal A., Bray F., Center M. M., Ferlay J., Ward E., Forman D. Global cancer statistics. CA Cancer Journal for Clinicians. 2011;61(2):69–90. doi: 10.3322/caac.20107. [DOI] [PubMed] [Google Scholar]
  • 26.Tomasoni S., Remuzzi G., Benigni A. Allograft rejection: acute and chronic studies. Contributions to Nephrology. 2008;159:122–134. doi: 10.1159/000125672. [DOI] [PubMed] [Google Scholar]
  • 27.Conroy S. M., Woolcott C. G., Koga K. R., Byrne C., Nagata C., Ursin G., Vachon C. M., Yaffe M. J., Pagano I., Maskarinec G. Mammographic density and risk of breast cancer by adiposity: an analysis of four case-control studies. International Journal of Cancer. 2012;130(8):1915–1924. doi: 10.1002/ijc.26205. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.Tseng M., Byrne C., Evers K. A., London W. T., Daly M. B. Acculturation and breast density in foreign-born, U.S. Chinese women. Cancer Epidemiology Biomarkers and Prevention. 2006;15(7):1301–1305. doi: 10.1158/1055-9965.EPI-06-0159. [DOI] [PubMed] [Google Scholar]
  • 29.Ursin G., Ma H., Wu A. H., Bernstein L., Salane M., Parisky Y. R., Astrahan M., Siozon C. C., Pike M. C. Mammographic density and breast cancer in three ethnic groups. Cancer Epidemiology Biomarkers and Prevention. 2003;12(4):332–338. [PubMed] [Google Scholar]
  • 30.Bhowmick N. A., Neilson E. G., Moses H. L. Stromal fibroblasts in cancer initiation and progression. Nature. 2004;432(7015):332–337. doi: 10.1038/nature03096. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31.Mueller M. M., Fusenig N. E. Friends or foes—bipolar effects of the tumour stroma in cancer. Nature Reviews Cancer. 2004;4(11):839–849. doi: 10.1038/nrc1477. [DOI] [PubMed] [Google Scholar]
  • 32.Sugimoto H., Mundel T. M., Kieran M. W., Kalluri R. Identification of fibroblast heterogeneity in the tumor microenvironment. Cancer Biology and Therapy. 2006;5(12):1640–1646. doi: 10.4161/cbt.5.12.3354. [DOI] [PubMed] [Google Scholar]
  • 33.Drygin D., Ho C. B., Omori M., Bliesath J., Proffitt C., Rice R., Siddiqui-Jain A., O'Brien S., Padgett C., Lim J. K. C., Anderes K., Rice W. G., Ryckman D. Protein kinase CK2 modulates IL-6 expression in inflammatory breast cancer. Biochemical and Biophysical Research Communications. 2011;415(1):163–167. doi: 10.1016/j.bbrc.2011.10.046. [DOI] [PubMed] [Google Scholar]
  • 34.Zhang G.-J., Adachi I. Serum interleukin-6 levels correlate to tumor progression and prognosis in metastatic breast carcinoma. Anticancer Research. 1999;19(2):1427–1432. [PubMed] [Google Scholar]
  • 35.Li Y., Ma X., Zhao J., et al. Microrna-210 as a prognostic factor in patients with breast cancer: meta-analysis. Cancer Biomark. 2013;13(6):471–481. doi: 10.3233/CBM-130385. [DOI] [PubMed] [Google Scholar]
  • 36.Pietras T., Szemraj J., Panek M., Witusik A., Banasiak M., Antczak A., Górski P. Functional polymorphism of cyclooxygenase-2 gene (G-765C) in chronic obstructive pulmonary disease patients. Molecular Biology Reports. 2012;39(3):2163–2167. doi: 10.1007/s11033-011-0964-2. [DOI] [PubMed] [Google Scholar]
  • 37.Glinskii A. B., Ma J., Ma S., Grant D., Lim C.-U., Sell S., Glinsky G. V. Identification of intergenic trans-regulatory RNAs containing a disease-linked SNP sequence and targeting cell cycle progression/differentiation pathways in multiple common human disorders. Cell Cycle. 2009;8(23):3925–3942. doi: 10.4161/cc.8.23.10113. [DOI] [PubMed] [Google Scholar]
  • 38.Sun X. F., Zhang H. NFKB and NFKBI polymorphisms in relation to susceptibility of tumour and other diseases. Histology and histopathology. 2007;22(12):1387–1398. doi: 10.14670/HH-22.1387. [DOI] [PubMed] [Google Scholar]
  • 39.Hill S. M., Fuqua S. A. W., Chamness G. C., Greene G. L., McGuire W. L. Estrogen receptor expression in human breast cancer associated with an estrogen receptor gene restriction fragment length polymorphism. Cancer Research. 1989;49(1):145–148. [PubMed] [Google Scholar]
  • 40.Ushiyama T., Mori K., Inoue K., Huang J., Nishioka J., Hukuda S. Association of oestrogen receptor gene polymorphisms with age at onset of rheumatoid arthritis. Annals of the Rheumatic Diseases. 1999;58(1):7–10. doi: 10.1136/ard.58.1.7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 41.Dick D. M. Gene-environment interaction in psychological traits and disorders. Annual Review of Clinical Psychology. 2011;7:383–409. doi: 10.1146/annurev-clinpsy-032210-104518. [DOI] [PMC free article] [PubMed] [Google Scholar]

Articles from BioMed Research International are provided here courtesy of Wiley

RESOURCES