Interleukin-6 as a prognostic marker for breast cancer: a meta-analysis


Aims and background

Interleukin-6 (IL-6) has been shown to promote tumor survival, metastasis, and angiogenesis, in addition to possessing antitumor activities. In light of the conflicting data, we sought to determine whether IL-6 could be used as a prognostic factor for patients with breast cancer.


Eligible studies describing the use of IL-6 as a prognostic factor for breast cancer were identified. Data describing overall survival (OS), disease-free survival (DFS), and clinicopathologic features were collected and analyzed.


Thirteen articles containing 3,224 breast cancer patients were identified. The results showed that IL-6 expression was not associated with lymph node metastasis, tumor size, or histologic grade. Moreover, there was no correlation between IL-6 expression and DFS. However, the combined hazard ratio (95% confidence interval) for OS was 2.15 (1.46, 3.17). Sensitivity analysis further demonstrated that, for OS, the results of this meta-analysis were stable. A subgroup analysis showed that the source used to detect IL-6 levels may have altered the pooled results for OS.


Taken together, these results indicate that IL-6 expression is associated with poor prognosis for breast cancer and the prognostic role is affected by the source used to detect IL-6 levels.

Tumori 2015; 101(5): 535 - 541




ShuChen Lin, ZhiHua Gan, Kun Han, Yang Yao, DaLiu Min

Article History


Financial support: None.
Conflict of interest: None.

This article is available as full text PDF.

Download any of the following attachments:


Breast cancer (BC) is the most frequent type of cancer diagnosed and is the leading cause of cancer death in women. Although new treatments for BC have been developed, this type of cancer still accounts for 23% (1.38 million) of newly diagnosed cancer cases and 14% of total cancer deaths (1). Therefore, it is important that high-risk patients be identified as early as possible using novel diagnostic and treatment approaches in order to reduce the mortality associated with this disease. For these reasons, significant efforts have been made to identify prognostic biomarkers.

Interleukin-6 (IL-6) is a proinflammatory cytokine composed of 184 amino acids. Following the binding of IL-6 to its receptor, IL-6R, JAK kinase activation is triggered, which in turn activates signal transducer and activator of transcription 3 (STAT3). Activated STAT3 then translocates to the nucleus to regulate inflammation, immune responses, hematopoiesis, and oncogenesis (2). Interleukin-6 is produced by several cell types, including tumor-associated macrophages, myeloid-derived suppressor cells, CD4+ T cells, and fibroblasts, and then directly or indirectly influences tumor initiation and progression. Interleukin-6 also induces factors such as bcl2, bcl-XL, matrix metalloproteinases, basic fibroblast growth factor, and vascular endothelial growth factor to promote tumor survival, invasion, metastasis, and angiogenesis (3). In addition, IL-6 possesses antitumor activities by promoting the infiltration of CD8+ effector T cells into the tumor stroma and lymphocyte trafficking to the lymph nodes (3). Therefore, IL-6 levels represent an important prognostic factor in several cancers, including digestive cancers, lung cancer, ovarian cancer, prostate cancer, and kidney cancer (4). Furthermore, in several studies of BC, clinical evidence suggests that IL-6 provides prognostic value.

In this meta-analysis, we aimed to determine the prognostic importance of elevated IL-6 levels for clinicopathologic data, disease-free survival (DFS), and overall survival (OS) among patients with BC and identify factors that could affect the prognostic role of IL-6.


Literature search

A systematic literature search was performed for studies published up to February 12, 2015, using PubMed, EMBASE, and the Cochrane Library. The search strategies employed subject headings, keywords, and freedom words, and the list of publications was restricted to those published in English. The search terms included the following words, variously combined: “breast cancer,” “breast carcinoma,” “interleukin-6,” “IL-6,” “outcome,” “prognosis.” The search also included articles presented at the annual meetings of the European Society of Medical Oncology and the American Society of Medical Oncology. The detailed search strategies are available in the supplementary Appendix, available online at

Inclusion and exclusion criteria

Studies were included if they met the following criteria: 1) they contained a pathologic diagnosis of BC, 2) they described an association between IL-6 and OS, DFS, or clinicopathologic features, and 3) they were original articles. Reviews, comments, and book chapters were excluded. Duplicate studies were also excluded by checking the names of the authors and study details. Since all of the eligible studies were observational, a quality score was not assessed (5).

Data extraction

All the following information was independently extracted by 2 reviewers (S.L. and Z.G.): the first author’s last name, publication year, country, BC stage, sample size, method by which IL-6 expression levels were measured, cutoff values to assess IL-6 positivity, clinicopathologic features, and survival data (including OS, DFS, and follow-up data). For cases that were missing follow-up data, accrual period, median follow-up period, date of analysis, and date of submission were used to estimate the minimum and maximum follow-up values, as described by Tierney et al (6). Any discrepancies were resolved by consensus.

Statistical analysis

Correlations between IL-6 expression levels and clinical parameters associated with BC, including tumor size, lymph node metastasis, estrogen receptor positivity, and histologic grade, were analyzed. Based on histologic grade, good differentiation (G1) and moderate differentiation (G2) were combined. Pooled estimates of odds ratios (ORs) with 95% confidence intervals (CIs) were used to estimate the association. Hazard ratios (HRs) were also extracted, along with corresponding 95% CIs to estimate the association between IL-6 and OS and DFS for patients with BC. When data were only available as Kaplan-Meier curves, survival rates were extracted using Engauge Digitizer version 4.1 software. The number of patients at risk, follow-up data, and other essential information were used to estimate HRs according to previously described methods (6). A pooled HR >1 indicated a worse outcome for the IL-6 high/positive group compared to the low/negative group, and vice versa. If heterogeneity existed (p<0.05 or I2>50%), a random-effects model was applied. Otherwise, a fixed-effects model was used. A sensitivity analysis was used to investigate the influence of individual studies on the pooled HR by omitting one study at a time and recalculating the pooled HR. Subgroup stratification analysis and meta-regression were also performed to identify sources of heterogeneity according to follow-up, region, stage, and source used to detect IL-6 levels. Publication bias was assessed using Egger et al (7) and Begg and Mazumdar (8) tests. All data analyses were performed using Stata version 12.0 software (Stata Corporation, College Station, TX, USA).


Identification of eligible studies

The search strategies identified 1028 articles. A review of the title and abstract for each of these studies resulted in the exclusion of 1002 articles, which included duplicated articles, nonoriginal articles, non-BC-related studies, or laboratory-based studies. Of the remaining 26 studies selected for systematic review, 13 studies were excluded due to a lack of information to analysis, and not relevant to the study. Therefore, 13 publications met the inclusion criteria for analysis of the prognostic value of IL-6 for BC (9-10-11-12-13-14-15-16-17-18-19-20-21) (Fig. 1). The latter included a total of 3224 patients presenting with different stages of BC, and the sample sizes ranged from 41 to 1699 participants. In addition, DFS data were extracted from 3 articles and OS data was extracted from 9 articles (Tab. I).

Meta-analysis flow chart. ASCO = American Society of Medical Oncology; ESMO = European Society of Medical Oncology.

Main characteristics of the included studies

First author (ref. no.) Year Region BC stage Sample size, n Sample and detection method for IL-6 Cutoff for IL-6 positivity, pg/mL Positive numbers Duration of follow-up, mo
BC = breast cancer; ELISA = enzyme-linked immunosorbent assay; HER2 = human epidermal receptor 2; IHC = immunohistochemistry; IL = interleukin; NP = not provided; PCR = polymerase chain reaction.
Tripsianis et al (9) 2013 Greece I-IV 130 Serum, ELISA 7.12 65 68
Cho et al (10) 2013 Korea I-III for HER2+ 41 Serum, ELISA Median levels 22 60.9
Cho et al (10) 2013 Korea I-III for HER2- 186 Serum, ELISA Median levels 90 60.9
Rajski et al (11) 2012 Switzerland I-II 295 Tissue, real-time PCR NP 117 214
Trédan et al (12) 2011 France III-IV 103 Serum, ELISA 8 NP 42
Bozcuk et al (13) 2004 Turkey IV 43 Serum, ELISA 5 NP 28
Bachelot et al (14) 2003 France IV 87 Serum, ELISA 8 NP 64
Salgado et al (15) 2003 Belgium IV 96 Serum, ELISA 6.6 47 25.7
Karczewska et al (16) 2000 Poland I-III 75 Tissue, real-time PCR NP 44 71
Zhang et al (17) 1999 Japan IV 41 Serum, ELISA 4 21 110
Hartman et al (18) 2013 UK and Canada NP 1699 Tissue, microarray Median levels 849 300
Fontanini et al (19) 1999 Italy I-III 149 IHC Scores, 2-7 123 -
Lv et al (20) 2011 China NP 200 Serum, luminex technology 14 NP -
Robinson et al (21) 1998 USA NP 66 Tissue, IHC NP 40 -

Correlation of IL-6 expression with clinicopathologic data

An analysis of the pooled results found that IL-6 expression correlated with estrogen receptor (ER) expression without heterogeneity (pooled OR 1.65, 95% CI 1.01-2.72, p = 0.047). However, there was no correlation between IL-6 expression and other clinicopathologic data including lymph node metastasis, tumor size, or histologic grade (Tab. II). A meta-analysis could only be performed if more than 2 studies were available. Therefore, IL-6 expression could not be analyzed in relation to other clinicopathologic parameters including human epidermal receptor 2 (HER2) expression, progesterone receptor expression, Ki-67 index, lymphovascular invasion, local recurrence, distant metastasis, and advanced stage of disease.

Main results for meta-analysis between IL-6 and clinicopathologic parameters

Clinical parameters Reference numbers of studies Overall OR (95% CI) Heterogeneity test, Q, I2, p value Model
CI = confidence interval; ER = estrogen receptor; IL = interleukin; OR = odds ratio; Q = heterogeneity chi-square.
Lymph nodes (N1 vs N0) 9, 10, 16, 19, 20 0.77 (0.29-2.06) 26.3, 85.1%, <0.001 Random-effect
ER (ER+ vs ER-) 16, 19, 20, 21 1.65 (1.01-2.72) 2.76, 0.0%, 0.430 Fixed-effect
Tumor size, cm (≥2 vs <2) 9, 10, 16, 19 0.99 (0.61-1.62) 1.95, 0.0%, 0.583 Fixed-effect
Histologic grade (G3 vs G1, G2) 9, 10, 16 1.36 (0.91-2.03) 3.70, 45.9%, 0.158 Fixed-effect

IL-6 expression and DFS

These 3 studies did not include patients with stage IV BC. In particular, the study by Cho et al (10) showed that the HR (95% CI) for DFS in the HER2+ subgroup and HER2- subgroup was 0.23 (0.03, 1.66) and 2.54 (0.94, 6.89), respectively. The pooled HR showed that high IL-6 levels were not significantly associated with DFS in relation to BC (HR 0.57, 95% CI 0.18-1.75, p = 0.324) (Fig. 2). In addition, significant heterogeneity (p = 0.002, I2 = 79.8%) was observed when the pooled HR for DFS was analyzed using a random-effects model.

Interleukin-6 expression and disease-free survival. CI = confidence interval; HR = hazard ratio.

IL-6 expression and OS

The pooled HR showed that high IL-6 levels positively correlated with OS in BC (HR 2.15, 95% CI 1.46-3.17, p<0.001) (Fig. 3). Significant heterogeneity (p<0.001, I2 = 80.3%) was also observed when the pooled HR for OS was analyzed using a random-effects model.

Interleukin-6 expression and overall survival. CI = confidence interval; HR = hazard ratio.

Sensitivity analysis

Sensitivity analysis showed that the pooled estimate of the effect of IL-6 expression on the OS of patients with BC did not vary substantially with the exclusion of any one study. In particular, exclusion of the Hartman et al (18) study, which accounted for approximately 66.1% of all patients included in the meta-analysis, resulted in a pooled HR of 2.41 (range 1.61-3.60, p<0.001), thereby demonstrating that the results of this meta-analysis are stable (supplementary Fig. 1, available online at

Subgroup analysis

To identify the source of the heterogeneity observed (I2 = 80.3%), subgroup analysis of duration of follow-up, geographic area, stage, and source used to detect IL-6 levels was performed. The source used to detect IL-6 levels was identified as the only source of heterogeneity. When the method used to determine IL-6 levels was examined, the summary estimate was stronger for studies using serum to detect IL-6 (HR 2.69, 95% CI 2.07-3.49) compared with studies employing tumor tissues (HR 1.23, 95% CI 1.12-1.36) (p heterogeneity = 0.043). A stronger association between IL-6 expression and OS was observed in studies with shorter follow-up times (summary HR 2.88, 95% CI 1.92-4.33) versus longer follow-up times (summary HR 1.42, 95% CI 0.94-2.14). However, no heterogeneity was observed between them (p heterogeneity = 0.207). In the subgroup analysis by stage, a stronger association was observed (summary HR 3.14, 95% CI 2.22-4.45) in stage IV subgroup, while the stage I-III subgroup exhibited no association. However, no heterogeneity was observed between them (p heterogeneity = 0.950). Also, region was not the source of heterogeneity (p heterogeneity = 0.530) (Tab. III).

Association between IL-6 and OS stratified by follow-up period, study region, source used to detect IL-6 levels, and stage

Stratified analysis No. studies Pooled HR (95% CI) p Value Heterogeneity Between-subgroups p value
I2, % p Value
CI = confidence interval; HR = hazard ratio; IL = interleukin; OS = overall survival.
Follow-up, mo 0.207
 <70 5 2.88 (1.92-4.33) <0.001 47.7 0.105
 >70 4 1.42 (0.94-2.14) 0.095 62.90 0.044
Region 0.530
 Asia 2 3.91 (2.04-7.52) <0.001 0.00 0.380
 Other 7 1.88 (1.27-2.79) 0.002 79.7 <0.001
Source 0.043
 Serum 6 2.69 (2.07-3.49) <0.001 36.4 0.164
 Tumor tissues 3 1.23 (1.12-1.36) <0.001 24.0 0.268
Stage 0.950
 I-III 2 0.82 (0.18-3.68) 0.796 61.1 0.109
 IV 4 3.14 (2.22-4.45) <0.001 1.7 0.384
 Others 3 1.86 (1.05-3.27) 0.032 81.3 0.005

Publication bias

For DFS, p values for the Begg’s and Egger’s tests were 0.734 and 0.661, respectively, indicating there was no publication bias (supplementary Fig. 2, A and B, available online at For OS, the Begg’s test indicated there was no publication bias among the studies examined with regard to risk ratio (p = 0.251). However, evaluation of OS using the Egger’s test indicated that publication bias was present (p = 0.029) (supplementary Fig. 2, C and D, available online at


The meta-analysis described in this report was designed to determine if IL-6 could be used as a predictive biomarker for BC survival. It differs from previous meta-analyses by examining the relationship between the IL-6 promoter and BC risk (22, 23). To our knowledge, this meta-analysis is the first to systematically report on the prognostic role of IL-6 in the context of BC survival. Overall, IL-6 expression was found to correlate with survival.

Interleukin-6 has been shown to promote or inhibit the growth of BC cells depending on the degree of Jak/Stat3 pathway activation and hormone receptor status (24). Moreover, IL-6 regulates the self-renewal, survival, and invasion of BC stem cells, which directly contributes to cancer recurrence and metastasis (25). For example, in a clinical study of BC, high levels of IL-6 expression were found to represent a marker of good prognosis (16). However, this relationship was not observed in other studies (11, 26), and many other studies reported that high levels of IL-6 expression contributed to a poor clinical outcome (9, 12-13-14-15, 17, 18, 27). In the present meta-analysis, the pooled risks for IL-6 expression and OS were high, with a global HR of 2.15 (displaying clear heterogeneity). Sensitivity analysis also demonstrated that exclusion of a single study did not significantly affect the results, thereby suggesting a stable relationship between IL-6 expression and OS.

It is possible that the heterogeneity observed in the present meta-analysis was due to differences in the baseline characteristics of the patients (i.e., age, cancer stage, geographic location of the population studied), the cutoff values used to define levels of IL-6, the duration of follow-up, and/or the source used to detect IL-6. To explore the source of interstudy heterogeneity, a meta-regression and subgroup analysis of follow-up period, geographic location, stage, and source used to detect IL-6 levels were performed. The latter was identified as the only source of heterogeneity. The summary estimate was stronger for studies using serum to detect IL-6, when compared with studies employing tumor tissues. Significant heterogeneity was observed between subgroups (p heterogeneity = 0.043). In the review by Knüpfer and Preiss (28), IL-6 serum level was a negative prognosticator in breast tumor patients, while the role of IL-6 within breast tumor tissues was complex. In different studies concerning BC tissues, IL-6 may serve as a positive prognosticator or a negative prognosticator. As mentioned above, IL-6 can be released by either epithelial tumor cells or stromal cells, and this makes it more difficult to assess the role of IL-6 in cancer progression. Therefore, we suggest that additional studies using standardized technical protocols such as laser-capture microdissection to enrich tumor cells or stromal cells are warranted. Regarding the duration of follow-up, this factor may have altered the pooled HR value from 2.88 (1.92-4.33) to 1.42 (0.94-2.14), with no heterogeneity observed between subgroups (p heterogeneity = 0.207). In a study by Hartman et al (18), which included the largest number of patients with BC analyzed to date, the follow-up period extended for up to 300 months. For this cohort, a positive role for IL-6 was observed, with an HR of 1.23 (1.11-1.36). In the present meta-analysis, the association between IL-6 expression and OS with longer follow-up times may not have been significant due to the limited number of studies that were included in the analysis. When stratified by the stage, we observed significantly different pooled results, which suggested that IL-6 may play a different prognostic role on different stages. However, no interstudy heterogeneity was ­observed between them. We could not rule out the possibility that when the number of the studies was large enough, significant interstudy heterogeneity could be achieved. Therefore, additional studies are needed to confirm these results. Furthermore, due to the low power of the meta-regression analysis, it is also possible that other covariates may contribute to the observed heterogeneity.

Interleukin-6 expression was previously reported to be positively associated with lymph node involvement (27, 29, 30), histologic grade (29), and cancer stage (27, 29, 31, 32). It was also reported to be an indicator of treatment response (33, 34). However, in a study by Nariţa et al (35), IL-6 positively correlated with ER expression, yet not with lymph node metastasis, tumor size, or histologic grade. Although these results are consistent with those of the present meta-analysis, additional studies are needed to explore the potential reasons for these discrepancies.

In the present meta-analysis, IL-6 expression was linked to OS, and not DFS, for patients with BC. Several reasons may contribute to these results. First, as mentioned above, IL-6 may play different prognostic roles on different stages. The DFS data included patients with stage I-III BC, while OS data included patients with mixed stages. This selection bias may potentially affect the pooled results. Second, other factors such as follow-up period, geographic location, HER2 status, and the source used to detect IL-6 levels may potentially affect the pooled results of DFS. However, due to the limited information, we could not conduct subgroup analysis. Third, besides affecting the tumor growth, IL-6 may negatively affect the quality of life, and this may lead to the different pooled results of DFS and OS. Recent studies have shown that high levels of IL-6 expression contribute to depression (33), treatment-related fatigue (36), physical and cognitive impairments (37), and cancer cachexia (38). Therefore, it is supposed that neutralization of IL-6 may ameliorate the severity of these symptoms. In a study by Ando et al (39), a patient presenting with stage IIIA lung cancer and cancer cachexia was treated with tocilizumab (an anti-IL-6 receptor antibody). This treatment reduced the severity of symptoms experienced by the patient, improved the patient’s appetite, and C-reactive protein levels normalized. As a result, physical recovery, weight gain, and normalization of albumin levels were observed. Tumor progression was also observed, yet the overall condition of the patient remained stable over time. Thus, additional studies are needed to investigate the effects of anti-IL-6 receptor antibody on quality of life and survival.

The present study had several limitations. First, the sample size and the number of studies included in the meta-analysis were relatively small. Second, selection bias existed based on the exclusion of studies due to a lack of pertinent information and the language in which the studies were published. Third, although a systematic search was conducted, publication bias was detected using the Egger test for OS. It is also possible that studies with positive results are more likely to be published than those without positive findings, and this type of publication bias would lead to overestimates in the present meta-analysis.

In conclusion, the present meta-analysis indicated that high IL-6 expression is associated with poor OS in patients with BC. However, there was no correlation between IL-6 expression and lymph node metastasis, tumor size, histologic grade, or DFS. Subgroup analysis showed that the prognostic role of IL-6 is affected by the source used to detect IL-6 levels. Large-scale, multicenter studies are needed to confirm these results.


The authors thank the authors whose publications were included in the meta-analysis.


Financial support: None.
Conflict of interest: None.
  • 1. Jemal A.,Bray F.,Center MM.,Ferlay J.,Ward E.,Forman D. Global cancer statistics. CA Cancer J Clin 2011; 61: 69-90 Google Scholar
  • 2. Guo Y.,Xu F.,Lu T.,Duan Z.,Zhang Z. Interleukin-6 signaling pathway in targeted therapy for cancer. Cancer Treat Rev 2012; 38: 904-910 Google Scholar
  • 3. Fisher DT.,Appenheimer MM.,Evans SS. The two faces of IL-6 in the tumor microenvironment. Semin Immunol 2014; 26: 38-47 Google Scholar
  • 4. Taniguchi K.,Karin M. IL-6 and related cytokines as the critical lynchpins between inflammation and cancer. Semin Immunol 2014; 26: 54-74 Google Scholar
  • 5. Altman DG. Systematic reviews of evaluations of prognostic variables. BMJ 2001; 323: 224-228 Google Scholar
  • 6. Tierney JF.,Stewart LA.,Ghersi D.,Burdett S.,Sydes MR. Practical methods for incorporating summary time-to-event data into meta-analysis. Trials 2007; 8: 16- Google Scholar
  • 7. Yusuf S.,Peto R.,Lewis J.,Collins R.,Sleight P. Beta blockade during and after myocardial infarction: an overview of the randomized trials. Prog Cardiovasc Dis 1985; 27: 335-371 Google Scholar
  • 8. Begg CB.,Mazumdar M. Operating characteristics of a rank correlation test for publication bias. Biometrics 1994; 50: 1088-1101 Google Scholar
  • 9. Tripsianis G.,Papadopoulou E.,Romanidis K. Overall survival and clinicopathological characteristics of patients with breast cancer in relation to the expression pattern of HER-2, IL-6, TNF-α and TGF-β1. Asian Pac J Cancer Prev 2013; 14: 6813-6820 Google Scholar
  • 10. Cho YA.,Sung MK.,Yeon JY.,Ro J.,Kim J. Prognostic role of interleukin-6, interleukin-8, and leptin levels according to breast cancer subtype. Cancer Res Treat 2013; 45: 210-219 Google Scholar
  • 11. Rajski M.,Vogel B.,Baty F.,Rochlitz C.,Buess M. Global gene expression analysis of the interaction between cancer cells and osteoblasts to predict bone metastasis in breast cancer. PLoS ONE 2012; 7: - Google Scholar
  • 12. Trédan O.,Ray-Coquard I.,Chvetzoff G. Validation of prognostic scores for survival in cancer patients beyond first-line therapy. BMC Cancer 2011; 11: 95- Google Scholar
  • 13. Bozcuk H.,Uslu G.,Samur M. Tumour necrosis factor-alpha, interleukin-6, and fasting serum insulin correlate with clinical outcome in metastatic breast cancer patients treated with chemotherapy. Cytokine 2004; 27: 58-65 Google Scholar
  • 14. Bachelot T.,Ray-Coquard I.,Menetrier-Caux C.,Rastkha M.,Duc A.,Blay JY. Prognostic value of serum levels of interleukin 6 and of serum and plasma levels of vascular endothelial growth factor in hormone-refractory metastatic breast cancer patients. Br J Cancer 2003; 88: 1721-1726 Google Scholar
  • 15. Salgado R.,Junius S.,Benoy I. Circulating interleukin-6 predicts survival in patients with metastatic breast cancer. Int J Cancer 2003; 103: 642-646 Google Scholar
  • 16. Karczewska A.,Nawrocki S.,Breborowicz D.,Filas V.,Mackiewicz A. Expression of interleukin-6, interleukin-6 receptor, and glycoprotein 130 correlates with good prognoses for patients with breast carcinoma. Cancer 2000; 88: 2061-2071 Google Scholar
  • 17. Zhang GJ.,Adachi I. Serum interleukin-6 levels correlate to tumor progression and prognosis in metastatic breast carcinoma. Anticancer Res 1999; 19: 1427-1432 Google Scholar
  • 18. Hartman ZC.,Poage GM.,den Hollander P. Growth of triple-negative breast cancer cells relies upon coordinate autocrine expression of the proinflammatory cytokines IL-6 and IL-8. Cancer Res 2013; 73: 3470-3480 Google Scholar
  • 19. Fontanini G.,Campani D.,Roncella M. Expression of interleukin 6 (IL-6) correlates with oestrogen receptor in human breast carcinoma. Br J Cancer 1999; 80: 579-584 Google Scholar
  • 20. Lv M.,Xiaoping X.,Cai H. Cytokines as prognostic tool in breast carcinoma. Front Biosci 2011; 16: 2515-2526 Google Scholar
  • 21. Robinson EK.,Sneige N.,Grimm EA. Correlation of interleukin 6 with interleukin 1alpha in human mammary tumours, but not with oestrogen receptor expression. Cytokine 1998; 10: 970-976 Google Scholar
  • 22. Chérel M.,Campion L.,Bézieau S. Molecular screening of interleukin-6 gene promoter and influence of -174G/C polymorphism on breast cancer. Cytokine 2009; 47: 214-223 Google Scholar
  • 23. Yu KD.,Di GH.,Fan L.,Chen AX.,Yang C.,Shao ZM. Lack of an association between a functional polymorphism in the interleukin-6 gene promoter and breast cancer risk: a meta-analysis involving 25,703 subjects. Breast Cancer Res Treat 2010; 122: 483-488 Google Scholar
  • 24. Dethlefsen C.,Højfeldt G.,Hojman P. The role of intratumoral and systemic IL-6 in breast cancer. Breast Cancer Res Treat 2013; 138: 657-664 Google Scholar
  • 25. Sansone P.,Storci G.,Tavolari S. IL-6 triggers malignant features in mammospheres from human ductal breast ­carcinoma and normal mammary gland. J Clin Invest 2007; 117: 3988-4002 Google Scholar
  • 26. Dean-Colomb W.,Hess KR.,Young E. Elevated serum P1NP predicts development of bone metastasis and survival in early-stage breast cancer. Breast Cancer Res Treat 2013; 137: 631-636 Google Scholar
  • 27. Ravishankaran P.,Karunanithi R. Clinical significance of preoperative serum interleukin-6 and C-reactive protein level in breast cancer patients. World J Surg Oncol 2011; 9: 18- Google Scholar
  • 28. Knüpfer H.,Preiss R. Significance of interleukin-6 (IL-6) in breast cancer [review]. Breast Cancer Res Treat 2007; 102: 129-135 Google Scholar
  • 29. Goswami B.,Mittal P.,Gupta N. Correlation of levels of IL-6 with tumor burden and receptor status in patients of locally advanced carcinoma breast. Indian J Clin Biochem 2013; 28: 90-94 Google Scholar
  • 30. Won HS.,Kim YA.,Lee JS. Soluble interleukin-6 receptor is a prognostic marker for relapse-free survival in estrogen receptor-positive breast cancer. Cancer Invest 2013; 31: 516-521 Google Scholar
  • 31. Gupta N.,Goswami B.,Mittal P. Effect of standard anthracycline based neoadjuvant chemotherapy on circulating levels of serum IL-6 in patients of locally advanced carcinoma breast: a prospective study. Int J Surg 2012; 10: 638-640 Google Scholar
  • 32. Benoy I.,Salgado R.,Colpaert C.,Weytjens R.,Vermeulen PB.,Dirix LY. Serum interleukin 6, plasma VEGF, serum VEGF, and VEGF platelet load in breast cancer patients. Clin Breast Cancer 2002; 2: 311-315 Google Scholar
  • 33. Jehn CF.,Flath B.,Strux A. Influence of age, performance status, cancer activity, and IL-6 on anxiety and depression in patients with metastatic breast cancer. Breast Cancer Res Treat 2012; 136: 789-794 Google Scholar
  • 34. Semesiuk NI.,Zhylchuk A.,Bezdenezhnykh N. Disseminated tumor cells and enhanced level of some cytokines in bone marrow and peripheral blood of breast cancer patients as predictive factors of tumor progression. Exp Oncol 2013; 35: 295-302 Google Scholar
  • 35. Nariţa D.,Seclaman E.,Ursoniu S.,Ilina R.,Cireap N.,Anghel A. Expression of CCL18 and interleukin-6 in the plasma of breast cancer patients as compared with benign tumor patients and healthy controls. Rom J Morphol Embryol 2011; 52: 1261-1267 Google Scholar
  • 36. Saligan LN.,Kim HS. A systematic review of the association between immunogenomic markers and cancer-related fatigue. Brain Behav Immun 2012; 26: 830-848 Google Scholar
  • 37. Ishikawa T.,Kokura S.,Sakamoto N. Relationship between circulating cytokine levels and physical or psychological functioning in patients with advanced cancer. Clin Biochem 2012; 45: 207-211 Google Scholar
  • 38. Argilés JM.,Busquets S.,Toledo M.,López-Soriano FJ. The role of cytokines in cancer cachexia. Curr Opin Support Palliat Care 2009; 3: 263-268 Google Scholar
  • 39. Ando K.,Takahashi F.,Motojima S. Possible role for tocilizumab, an anti-interleukin-6 receptor antibody, in treating cancer cachexia. J Clin Oncol 2013; 31: e69-e72 Google Scholar



  • Department of Oncology, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai - People’s Republic of China
  • ShuChen Lin and ZhiHua Gan contributed equally to this work

Article usage statistics

The blue line displays unique views in the time frame indicated.
The yellow line displays unique downloads.
Views and downloads are counted only once per session.

This article has supplementary materials available to download.