Analysis of Interleukin-6 Gene Variants ( rs1800795,rs1800796,rs1554606,rs1800797,rs2069840,rs12700386,and rs2069861 ) as Prognostic Markers in Breast Cancer: A Systematic Review,Meta-Analysis,and Network Analysis

Abstract

Interleukin-6 (IL-6) has obviously tumor-promoting and tumor-inhibitory effects and can induce an epithelial–mesenchymal transition phenotype in human breast cancer (BC) cells and implicate its potential to promote BC metastasis. Herein, we aimed to evaluate the association of IL-6 variants (rs1800795, rs1800796, rs1554606, rs1800797, rs2069840, rs12700386, and rs2069861) with the susceptibility to BC. The databases of PubMed/Medline, Web of Science, Scopus, and Cochrane Library were searched until December 19, 2022, without any restrictions. The quality assessment of each study was performed based on the Newcastle-Ottawa Scale tool. The Review Manager 5.3 software presented the effect sizes including odds ratio (OR) along with a 95% confidence interval (CI). Both publication bias and sensitivity analyses were carried out by the Comprehensive Meta-Analysis version 2.0 software. A total of 2,508 records were identified among databases and at last, 27 articles were entered into the meta-analysis. Seven polymorphisms of IL-6 were entered into the analyses. Just rs1800797 polymorphism in the dominant model (OR = 1.51; 95% CI = 1.15–2.00; P = 0.003) and rs2069840 polymorphism in heterozygous (OR = 0.89; 95% CI = 0.81–0.97; P = 0.008) and dominant (OR = 0.91; 95% CI = 0.84–0.99; P = 0.02) models had a significant association with the BC risk. In conclusion, among 7 polymorphisms and despite a few included cases, the present meta-analysis recommended that the AA+GA genotype of rs1800797 polymorphism had a significantly elevated risk and the GC and the CC+GC genotypes of rs2069840 polymorphism had a protective role in the BC patients.

Introduction

Breast cancer (BC) is currently one of the most commonly diagnosed cancers and the fifth leading cause of cancer-related death, with an estimated 2.3 million new cases globally, based on GLOBOCAN 2020 data (Sung et al., 2021). In the world, BC was responsible for 684,996 deaths at an age-adjusted rate of 13.6 per 100,000 (Ferlay et al., 2018). BC incidence and death rates have elevated for the past 3 decades (Sharma, 2019). About 80% of BC patients are >50 years old, whereas >40% are >65 years old (Benz, 2008; McGuire et al., 2015; Siegel et al., 2014).

Around 13% to 19% of BC patients reported a first-degree relative with the disease (Cancer, 2001). A connection of common pathological markers of estrogen receptor (ER), progesterone receptor, and human epidermal growth factor receptor 2 is used to classify breast tumors into intrinsic subtypes (Carey et al., 2006). Biological therapy or targeted therapy can be given at any stage of breast treatment—before surgery (neoadjuvant therapy) or after surgery (adjuvant therapy) (Hortobagyi, 2005; Najafi et al., 2018; Payandeh et al., 2015).

Apart from environmental and demographic factors (Amirifard et al., 2017; Couch et al., 2001; Hopper et al., 2018; Jones et al., 2017; Najafi et al., 2019), histological or pathological markers can be risk factors for the survival, progression, and recurrence of BC (Najafi et al., 2020; Payandeh et al., 2016; Shahriari-Ahmadi et al., 2017). In multicellular organisms, cytokines are intercellular mediators that control cell survival, growth, differentiation, and impressible functions (Heinrich et al., 1998; Nicolini et al., 2006), and they are likely included in the mechanism of tumor cell evasion in the immunosurveillance system (Nicolini et al., 2006). Different cytokines can create important roles in controlling one's immune system.

One of important cytokines—interleukin-6 (IL-6)—has obviously tumor-promoting and tumor-inhibitory effects (Knüpfer and Preiss, 2007). This cytokine can induce an epithelial–mesenchymal transition phenotype in human BC cells and implicate its potential to promote BC metastasis (Sullivan et al., 2009). IL-6 plays significant roles in regulating hematopoiesis, immune cell activation, inflammation, and carcinogenesis (Hirano, 1998). In terms of treatment, IL-6 is also significant in BC because it can affect the response to therapy. For example, elevated IL-6 levels have been associated with resistance to chemotherapy and endocrine therapy (Beyranvand Nejad et al., 2021).

Several genetic mutations have been reported to be related to an increased risk of BC (Amirifard et al., 2016; Bell et al., 2017; Shiovitz and Korde, 2015; Wirapati et al., 2008). The relationship between polymorphism in interleukin and BC risk was previously studied by many studies but with controversial results (Xu and Wang, 2020). There are several polymorphisms in the IL-6 promoter area, which are thought to have a complex interactive effect on IL-6 expression (Terry et al., 2000). Two meta-analyses checked the association between polymorphisms of IL-6 and BC susceptibility (Xu and Wang, 2020; Yu et al., 2010).

This is while other meta-analyses checked IL-6 polymorphisms in several cancers including BC (Barek et al., 2021; Du et al., 2015; Liu et al., 2012; Peng et al., 2018; Xu et al., 2011; Zhai et al., 2017; Zhou et al., 2018). Among these meta-analyses, 2 (Peng et al., 2018; Zhou et al., 2018) reported rs1800795, rs1800796, and rs1800797 polymorphisms, 1 (Xu and Wang, 2020) rs1800795 and rs1800796 polymorphisms, 4 (Liu et al., 2012; Xu et al., 2011; Yu et al., 2010; Zhai et al., 2017) rs1800795 polymorphism, 1 (Du et al., 2015) rs1800796 polymorphism, and 1 (Barek et al., 2021) rs2069840 polymorphism.

By reporting an updated meta-analysis on IL-6 levels in BC, researchers and clinicians can better understand its role in BC progression and treatment response. This knowledge can help guide the development of targeted therapies and strategies to improve patient outcomes. Herein, we aimed to evaluate the association of 7 polymorphisms of IL-6 (rs1800795, rs1800796, rs1554606, rs1800797, rs2069840, rs12700386, and rs2069861) and the risk of BC with more studies and sufficient analyses in a systematic review and meta-analysis.

Materials and Methods

Study design and registration

This meta-analysis was done according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses protocols (Moher et al., 2009). The PI/ECO (population, intervention/exposure, comparison, and outcome) question was: Are IL-6 polymorphisms associated with the risk of BC in the case–control studies?

Identification of articles

A comprehensive search was done in the databases of PubMed/Medline, Web of Science, Scopus, and Cochrane Library until December 19, 2022, without any restrictions to retrieve the relevant articles by 1 author (M.S.). Then, the titles/abstracts of each article were evaluated by the same author (M.S.). After that, full texts of the retrieved articles were downloaded according to the eligibility criteria.

The search strategy included (“IL-6” or “IL6” or “interleukin-6” or “interleukin 6”) and (“mammary gland tumor*” or “breast malignanc*” or “breast tumor*” or “breast cancer” or “breast carcinoma*” or “breast neoplasm*” or “breast adenocarcinoma”) and (“allele*” or “genotype*” or “variant*” or “variation*” or “polymorphism*” or “genetic*” or “mutation*” or “genome”). The references of the retrieved articles were reviewed to ensure that no related study was missed. Another author (A.A.) re-evaluated the processes. A disagreement between the authors was resolved by another author (N.M.).

Inclusion and exclusion criteria

The inclusion criteria: (1) Case–control or cohort studies reporting polymorphisms of IL-6 with minimum of 2 studies in BC patients and controls in humans. (2) BC patients were pathologically and/or clinically diagnosed. (3) Studies including sufficient data for reporting distributions of alleles and genotypes. (4) Studies with any value of the Hardy–Weinberg equilibrium (HWE) in controls. On the contrary, review articles, articles with incomplete data, meta-analyses, articles with a lack of a control group, animal articles, conference papers, comment papers, book chapters, duplicate articles, and articles including controls with a systematic disease were excluded.

Data summary

The following variables of each study included in the meta-analysis were separately extracted by 2 authors (M.S. and M.T.). They were first author's surname, publication year, country, ethnicity, numbers of cases and controls, source of controls, distributions of genotypes and alleles of IL-6 polymorphisms, HWE, and quality score. The disagreement between the 2 authors was resolved by a short discussion.

Quality evaluation

The quality assessment of each study was performed by 1 author (M.S.) based on the Newcastle-Ottawa Scale (NOS) tool (Stang, 2010) on 3 broad perspectives: the selection (4 scores); the comparability (2 scores); and the ascertainment (3 scores). The maximum possible score was 9 and a score of ≥7 was considered as a high-quality score.

Search tool for retrieval of interacting genes

Search tool for retrieval of interacting genes (STRING), the protein–protein interaction (PPI) network software (Szklarczyk et al., 2015), was used to check the functional interactions among the examined genes [https://string-db.org/ (accessed on April 11, 2023)]. Interaction sources and species limited to “Homo sapiens” and an interaction score of >0.900 were applied to construct the PPI networks. In the networks, the nodes show the proteins and edges show the interactions. STRING was performed to seek potential interactions among differentially expressed genes related to different tissues.

Statistical analyses

The Review Manager 5.3 software presented the effect sizes including odds ratio (OR) along with a 95% confidence interval (CI) of the prevalence of genotypes and alleles of IL-6 in the BC patients compared with controls. The Z-test was used to estimate the pooled OR significance and a P value (2-sided) <0.05 was considered significant. A P _{heterogeneity} < 0.1 (I ² > 50%) recommended significant heterogeneity and hence a random-effects model (DerSimonian and Laird, 2015) and if the heterogeneity was insignificant, a fixed-effect model (Mantel and Haenszel, 1959) was used.

The subgroup analyses and the random-effects meta-regression analyses were performed according to a number of variables.

With regard to publication bias, the degree of asymmetry was tested by Egger's test (Egger et al., 1997) and the potential publication bias was considered in a Begg's funnel plot (Begg and Mazumdar, 1994). The P values of Egger's and Begg's tests were extracted and a P value (2-sided) <0.10 showed the being of the publication bias.

With regard to sensitivity analysis, both “one-study-removed” and “cumulative” analyses were used to evaluate the stability/consistency of pooled ORs.

Both publication bias and sensitivity analyses were carried out by the Comprehensive Meta-Analysis version 2.0 software.

To address false-positive or negative conclusions from meta-analyses (Imberger et al., 2016), trial sequential analysis (TSA) was carried out using TSA software (version 0.9.5.10 beta) (Wetterslev et al., 2017). The required information size (RIS) with an alpha risk of 5%, a beta risk of 20%, and a 2-sided boundary type was calculated. We assessed heterogeneity (D ²), the incidence in intervention arm, and the incidence in control arm of each genetic model for each polymorphism of IL-6. If the Z-curve reached the RIS line, sufficient cases were involved in the studies, and the conclusions were trustworthy. If not, the content of the information was not large enough and there was a necessity for more documents.

Compliance with ethical standards

All analyses in this meta-analysis were based on previous published studies, thus no ethical approval and patient consent are required.

Results

Study selection

A total of 2,508 records were identified among databases (Supplementary Fig. S1). After removing duplicates and irrelevant records based on titles and abstracts, 57 full-text articles were assessed. Among the assessed articles, 30 articles were excluded and at last, 27 articles were entered into the meta-analysis.

Articles' characteristics

The features of 27 included articles in the meta-analysis are (Alshatwi et al., 2012; AlSuhaibani et al., 2016; Balasubramanian et al., 2006; Chérel et al., 2009; Dossus et al., 2010; Gonullu and Basturk, 2007; Gonzalez-Zuloeta Ladd et al., 2006; Gordeeva et al., 2021; He et al., 2011; Hefler et al., 2005; Jabłonowska et al., 2010; Javalera et al., 2020; Joshi et al., 2014; Litovkin et al., 2007; Madeleine et al., 2011; Padala et al., 2022; Pooja et al., 2012; Sa-Nguanraksa et al., 2016; Schonfeld et al., 2010; Skerrett et al., 2005; Slattery et al., 2014; Slattery et al., 2008; Smith et al., 2004; Snoussi et al., 2005; Vogel et al., 2007; Zhang et al., 2016; Zhu et al., 2017) shown in Table 1.

Table 1.

Characteristics of the Articles

First author, publication year	Country	Ethnicity	Sample size of cases/controls	Source of control	Polymorphism (P value for HWE)	NOS score
Smith et al. (2004)	UK	Caucasian	144/224	HB	rs1800795 (0.258)	7
Hefler et al. (2005)	Austria	Caucasian	269/227	HB	rs1800795 (0.935)	8
Skerrett et al. (2005)	USA	Mixed	88/102	HB	rs1800795 (0.720)	6
Snoussi et al. (2005)	Tunisia	Mixed	305/200	PB	rs1800795 (0.830) and rs1800797 (0.896)	6
Balasubramanian et al. (2006)	UK	Caucasian	497/490	HB	rs1800795 (0.759)	7
González-Zuloeta Ladd et al. (2006)	Netherlands	Caucasian	171/3,651	PB	rs1800795 (0.246)	6
Gonullu and Basturk (2007)	Turkey	Caucasian	38/24	HB	rs1800795 (<0.001)	7
Litovkin et al. (2007)	Ukraine	Caucasian	73/78	HB	rs1800795 (0.490)	6
Vogel et al. (2007)	Denmark	Caucasian	361/361	HB	rs1800795 (0.728)	8
Slattery et al. (2008)	USA	Mixed	1,746/2,055	PB	rs1800795 (NA)	8
Chérel et al. (2009)	France	Caucasian	293/112	PB	rs1800795 (0.695)	6
Dossus et al. (2010)	Germany	Caucasian	6,190/8,066	HB	rs1800795 (<0.001), rs2069840 (0.417), rs12700386 (0.647), and rs2069861 (0.424)	8
Jabłonowska et al. (2010)	Poland	Caucasian	41/30	HB	rs1800795 (0.219)	7
Schonfeld et al. (2010)	USA	Mixed	859/1,083	HB	rs1800795 (0.016) and rs1800797 (0.013)	6
He et al. (2011)	China	Asian	176/200	HB	rs1800795 (0.943), rs1800796 (0.027), and rs1800797 (0.886)	7
Madeleine et al. (2011)	USA	Mixed	877/900	PB	rs1800795 (0.968), rs1554606 (0.646), rs2069840 (0.533), rs12700386 (<0.001), and rs2069861 (0.818)	8
Alshatwi et al. (2012)	Saudi Arabia	Caucasian	100/100	PB	rs1554606 (<0.001)	7
Pooja et al. (2012)	India	Asian	200/200	HB	rs1800795 (<0.001)	7
Joshi et al. (2014)	India	Asian	178/232	PB	rs1800795 (0.269)	9
Slattery et al. (2014)	USA	Caucasian	3,567/4,157	PB	rs1800795 (NA), rs1800797 (NA), and rs2069840 (0.149)	7
Sa-Nguanraksa et al. (2016)	Thailand	Asian	389/78	HB	rs1800795 (<0.001) and rs1800797 (0.863)	7
Zhang et al. (2016)	China	Asian	136/150	HB	rs1800795 (NA), rs1800796 (0.016), and rs1800797 (NA)	7
AlSuhaibani et al. (2016)	Egypt	Caucasian	80/80	PB	rs1800795 (0.096)	7
Zhu et al. (2017)	China	Asian	1,514/1,540	PB	rs1800796 (0.204)	9
Javalera et al. (2020)	Mexico	Mixed	208/200	PB	rs1800795 (0.632) and rs1800797 (0.827)	7
Gordeeva et al. (2021)	Russia	Caucasian	521/299	HB	rs1800795 (0.696), rs1800796 (0.020), and rs1554606 (0.725)	7
Padala et al. (2022)	India	Asian	300/300	PB	rs1800795 (<0.001), rs1800796 (<0.001), and rs1800797 (<0.001)	7

HB, hospital-based; HWE, Hardy–Weinberg equilibrium; NA, not available; NOS, Newcastle-Ottawa Scale; PB, population-based.

The articles were published from 2004 to 2023. Country of performed study, ethnicity of individuals, sample size of cases/controls, source of control, reported polymorphism (s), deviation from the HWE, and the NOS score of each article were other variables in Table 1.

Reported polymorphisms

Based on all case–control studies included in this systematic review and meta-analysis, 22 polymorphisms of IL-6 were reported so that we could perform the pooled analysis for 7 polymorphisms (with green color) that had minimum of 2 published studies (Supplementary Fig. S2).

Pooled analysis

The results of pooled analyses based on 5 genetic models for 7 polymorphisms are shown in Table 2. The results reported that just rs1800797 polymorphism in the dominant model (OR = 1.51; 95% CI = 1.15–2.00; P = 0.003) and rs2069840 polymorphism in the heterozygous (OR = 0.89; 95% CI = 0.81–0.97; P = 0.008) and dominant (OR = 0.91; 95% CI = 0.84–0.99; P = 0.02) models had a significantly elevated and decreased risks in the BC patients compared with the controls, respectively.

Table 2.

Results of Pooled Analysis of the Association Between Alleles and Genotypes of Interleukin-6 Polymorphisms and the Risk of Breast Cancer

Polymorphism	Number of studies	Genetic model	Model	OR	95% CI		Test for overall effect		Heterogeneity
Polymorphism	Number of studies	Genetic model	Model	OR	Min	Max	Z	P	χ ²	df	P	I ²
rs1800795	23	C versus G	R	1.03	0.92	1.16	0.48	0.63	110.53	21	<0.00001	81%
	23	CC versus GG	R	1.11	0.93	1.33	1.19	0.23	50.99	19	<0.00001	63%
	23	GC versus GG	R	1.03	0.90	1.17	0.43	0.67	54.24	21	<0.0001	61%
	25	CC+GC versus GG	R	1.10	0.95	1.27	1.29	0.20	161.17	23	<0.00001	86%
	23	CC versus GC+GG	R	1.06	0.93	1.22	0.85	0.40	37.99	19	0.006	50%
rs1800796	5	C versus G	R	0.79	0.56	1.10	1.38	0.17	35.15	4	<0.00001	89%
	5	CC versus GG	R	0.70	0.34	1.44	0.98	0.33	17.53	4	0.002	77%
	5	GC versus GG	R	0.82	0.53	1.27	0.88	0.38	33.42	4	<0.00001	88%
	5	CC+GC versus GG	R	0.78	0.49	1.23	1.08	0.28	39.90	4	<0.00001	90%
	5	CC versus GC+GG	R	0.74	0.42	1.32	1.02	0.31	13.22	4	0.01	70%
rs1554606	3	G versus T	F	0.96	0.86	1.06	0.81	0.42	1.28	2	0.53	0%
	3	GG versus TT	F	0.92	0.74	1.15	0.75	0.45	3.52	2	0.17	43%
	3	GT versus TT	F	0.91	0.76	1.09	1.02	0.31	2.24	2	0.33	11%
	3	TT+GT versus TT	F	0.92	0.78	1.09	0.99	0.32	2.62	2	0.27	24%
	3	TT versus GT+TT	F	0.97	0.80	1.18	0.30	0.76	1.31	2	0.52	0%
rs1800797	7	A versus G	R	1.32	0.90	1.95	1.43	0.15	36.33	5	<0.00001	86%
	7	AA versus GG	R	1.47	0.73	2.96	1.07	0.28	16.63	4	0.002	76%
	7	GA versus GG	R	1.41	1.01	1.97	1.99	0.05	14.65	5	0.01	66%
	8	AA+GA versus GG	R	1.51	1.15	2.00	2.94	0.003	31.14	6	<0.0001	81%
	7	AA versus GA+GG	R	1.30	0.70	2.39	0.84	0.40	13.43	4	0.009	70%
rs2069840	3	C versus G	R	0.75	0.31	1.82	0.64	0.52	916.43	2	<0.00001	100%
	3	CC versus GG	R	0.86	0.71	1.04	1.53	0.13	7.16	2	0.03	72%
	3	GC versus GG	F	0.89	0.81	0.97	2.67	0.008	0.29	2	0.86	0%
	3	CC+GC versus GG	F	0.91	0.84	0.99	2.27	0.02	1.47	2	0.48	0%
	3	CC versus GC+GG	R	0.99	0.91	1.09	0.11	0.91	4.95	2	0.08	60%
rs12700386	2	G versus C	R	1.27	0.70	2.31	0.78	0.44	33.36	1	<0.00001	97%
	2	GG versus CC	F	1.00	0.84	1.19	0.01	0.99	0.83	1	0.36	0%
	2	CG versus CC	R	1.62	0.58	4.57	0.92	0.36	61.88	1	<0.00001	98%
	2	CC+CG versus CC	R	1.48	0.64	3.43	0.91	0.36	48.06	1	<0.00001	98%
	2	GG versus CG+CC	R	0.86	0.53	1.39	0.62	0.53	3.31	1	0.07	70%
rs2069861	2	T versus C	F	1.01	0.93	1.09	0.16	0.87	0.16	1	0.69	0%
	2	TT versus CC	F	0.98	0.66	1.44	0.13	0.90	0.40	1	0.53	0%
	2	CT versus CC	F	1.01	0.93	1.10	0.23	0.82	0.54	1	0.46	0%
	2	TT+CT versus CC	F	1.01	0.93	1.10	0.20	0.84	0.35	1	0.55	0%
	2	TT versus CT+CC	F	0.97	0.66	1.43	0.14	0.89	0.44	1	0.51	0%

Bold numbers denote statistically significant data (P < 0.05).

CI, confidence interval; F, fixed-effects; I ², heterogeneity; OR, odds ratio; R, random-effects.

Network analysis

Based on the STRING database, a PPI network of 35 examined genes in the studies included in this meta-analysis is shown in Supplementary Fig. S3. IL-6 protein has an experimentally determined interaction with IL-R6 and curated database interaction with VEGF, IL-10, IL-17A, IL-1B, IL-18, IL-4, TNF-α, NFKB1, IL-12B, and CXCL8 (IL-8) proteins.

Subgroup analysis

For 3 polymorphisms (rs1800795, rs1800796, and rs1800797) including sufficient studies, the subgroup analyses based on sample size, the HWE deviation, source of controls, and ethnicity were performed (Table 3). The results recommended ethnicity and the HWE for rs1800796 polymorphism and sample size, the HWE, ethnicity, and source of controls for rs1800797 polymorphism were effective factors.

Table 3.

Subgroup Analysis of Association Between Three Interleukin-6 Polymorphisms with Sufficient Studies in Breast Cancer Patients Compared with Controls

Polymorphism	Subgroup	Variable (N)	Variable	Allelic	Homozygous	Heterozygous	Dominant	Recessive
rs1800795	Sample size	≥500 (9^a)	OR	1.08	1.13	1.10	1.17	0.96
			95% CI	0.98–1.20	0.95–1.34	0.96–1.27	0.99–1.39	0.89–1.03
			P value	0.13	0.18	0.16	0.07	0.27
			I ²	75%	60%	65%	91%	39%
		<500 (14)	OR	0.96	1.08	0.92	1.04	1.15
			95% CI	0.71–1.30	0.70–1.69	0.69–1.23	0.76–1.42	0.81–1.63
			P value	0.78	0.72	0.59	0.80	0.43
			I ²	84%	68%	58%	71%	57%
	HWE	No deviation from HWE (16)	OR	0.99	1.06	0.99	1.01	1.05
			95% CI	0.84–1.16	0.92–1.22	0.90–1.10	0.86–1.18	0.93–1.19
			P value	0.87	0.40	0.89	0.92	0.46
			I ²	79%	48%	40%	51%	34%
		Deviation from HWE (6)	OR	1.13	1.14	1.13	1.30	1.07
			95% CI	0.91–1.40	0.82–1.59	0.82–1.56	0.96–1.76	0.84–1.38
			P value	0.28	0.43	0.46	0.09	0.57
			I ²	87%	81%	83%	86%	70%
	Source of controls	PB (8)	OR	1.02	1.43	1.04	1.16	1.32
			95% CI	0.73–1.43	0.91–2.25	0.77–1.40	0.87–1.54	0.92–1.88
			P value	0.91	0.12	0.82	0.31	0.13
			I ²	92%	74%	78%	88%	66%
		HB (15^a)	OR	1.01	1.01	1.00	1.05	0.93
			95% CI	0.97–1.05	0.93–1.10	0.94–1.06	0.89–1.24	0.86–1.01
			P value	0.73	0.76	0.97	0.59	0.08
			I ²	46%	38%	41%	78%	0%
	Ethnicity	Caucasian (12^b)	OR	0.97	1.09	0.99	1.06	0.95
			95% CI	0.83–1.14	0.88–1.34	0.93–1.05	0.88–1.28	0.87–1.03
			P value	0.73	0.44	0.67	0.56	0.21
			I ²	81%	52%	49%	72%	45%
		Asian (6)	OR	0.80	1.22	0.77	0.93	1.27
			95% CI	0.37–1.77	0.51–2.89	0.34–1.72	0.44–1.98	0.73–2.22
			P value	0.59	0.66	0.52	0.86	0.40
			I ²	95%	87%	87%	90%	73%
		Mixed (5^b)	OR	1.03	1.02	1.09	1.13	0.98
			95% CI	0.95–1.12	0.85–1.23	0.96–1.24	0.84–1.52	0.83–1.15
			P value	0.49	0.82	0.19	0.42	0.81
			I ²	32%	0%	43%	93%	0%
rs1800796	Sample size	≥500 (3)	OR	0.81	0.81	0.79	0.77	0.86
			95% CI	0.54–1.21	0.29–2.24	0.50–1.26	0.47–1.27	0.39–1.89
			P value	0.30	0.69	0.33	0.30	0.71
			I ²	90%	85%	86%	88%	81%
		<500 (2)	OR	0.76	0.52	0.89	0.81	0.59
			95% CI	0.33–1.79	0.13–2.14	0.22–3.65	0.20–3.19	0.29–1.19
			P value	0.54	0.37	0.87	0.76	0.4
			I ²	91%	68%	94%	95%	15%
	HWE	No deviation from HWE (1)	OR	1.13	1.29	1.12	1.14	1.24
			95% CI	1.01–1.28	0.94–1.77	0.96–1.31	0.99–1.32	0.91–1.69
			P value	0.04	0.11	0.14	0.07	0.17
			I ²	–	–	–	–	–
		Deviation from HWE (4)	OR	0.71	0.49	0.75	0.70	0.55
			95% CI	0.52–0.97	0.32–0.75	0.43–1.30	0.41–1.18	0.38–0.79
			P value	0.03	0.0009	0.30	0.18	0.001
			I ²	75%	36%	85%	85%	0%
	Source of controls	PB (2)	OR	0.91	0.75	0.95	0.89	0.81
			95% CI	0.58–1.43	0.25–2.27	0.64–1.42	0.51–1.55	0.35–1.91
			P value	0.68	0.61	0.82	0.68	0.63
			I ²	92%	92%	71%	85%	91%
		HB (3)	OR	0.71	0.66	0.76	0.72	0.63
			95% CI	0.43–1.16	0.33–1.32	0.35–1.66	0.34–1.53	0.32–1.25
			P value	0.17	0.24	0.49	0.39	0.18
			I ²	83%	44%	90%	90%	0%
	Ethnicity	Caucasian (1)	OR	0.61	1.94	0.57	0.57	2.14
			95% CI	0.44–0.86	0.08–47.81	0.40–0.81	0.40–0.82	0.09–52.71
			P value	0.004	0.68	0.002	0.002	0.64
			I ²	–	–	–	–	–
		Asian (4)	OR	0.84	0.66	0.90	0.84	0.71
			95% CI	0.58–1.22	0.31–1.42	0.55–1.48	0.49–1.43	0.39–1.31
			P value	0.35	0.29	0.69	0.52	0.27
			I ²	89%	83%	88%	90%	77%
rs1800797	Sample size	≥500 (3)	OR	1.57	1.82	1.65	1.71	1.54
			95% CI	0.94–2.65	0.77–4.29	1.05–2.59	1.26–2.32	0.73–3.25
			P value	0.09	0.17	0.03	0.0006	0.26
			I ²	94%	87%	82%	87%	84%
		<500 (4^b)	OR	1.00	0.68	1.07	1.29	0.65
			95% CI	0.72–1.39	0.23–2.05	0.72–1.57	0.81–2.03	0.22–1.94
			P value	0.98	0.49	0.75	0.28	0.44
			I ²	0%	0%	0%	64%	0%
	HWE	No deviation from HWE (4)	OR	1.26	1.05	1.29	1.28	0.96
			95% CI	1.02–1.55	0.48–2.32	0.98–1.71	0.97–1.68	0.44–2.11
			P value	0.04	0.90	0.07	0.08	0.92
			I ²	31%	0%	20%	24%	0%
		Deviation from HWE (2)	OR	1.64	1.87	1.70	1.44	1.56
			95% CI	0.70–3.81	0.63–5.54	0.82–3.56	1.22–1.70	0.61–3.98
			P value	0.25	0.26	0.16	<0.0001	0.35
			I ²	97%	93%	91%	95%	92%
	Source of controls	PB (3^b)	OR	1.59	1.76	1.69	1.76	1.55
			95% CI	0.97–2.62	0.67–4.60	1.09–2.61	1.31–2.35	0.68–3.55
			P value	0.07	0.25	0.02	0.0001	0.30
			I ²	88%	68%	71%	75%	59%
		HB (4)	OR	1.06	1.09	1.16	1.14	0.99
			95% CI	0.93–1.21	0.84–1.42	0.95–1.42	0.94–1.38	0.78–1.25
			P value	0.36	0.51	0.14	0.17	0.92
			I ²	0%	0%	6%	0%	0%
	Ethnicity	Mixed (3)	OR	1.17	1.10	1.25	1.22	0.99
			95% CI	0.95–1.44	0.85–1.41	1.06–1.48	1.04–1.43	0.79–1.24
			P value	0.14	0.47	0.008	0.01	0.93
			I ²	54%	0%	0%	9%	0%
		Asian (4)	OR	1.21	3.20	1.10	1.17	2.51
			95% CI	0.38–3.90	1.95–5.24	0.31–3.85	0.32–4.28	1.55–4.05
			P value	0.75	<0.00001	0.88	0.81	0.0002
			I ²	68%	4%	69%	72%	0%

Bold numbers denote statistically significant data (P < 0.05).

Two studies more in dominant model.

One study more in dominant model.

Sensitivity analysis

Both sensitivity analyses (one-study-removed and cumulative analyses) confirmed the stability of the pooled results for 7 polymorphisms (data were not reported).

Meta-regression

For 3 polymorphisms (rs1800795, rs1800796, and rs1800797) as important variants in the promoter region of IL-6 gene including sufficient studies, the meta-regression analysis based on 3 factors (the publication year, the sample size, and the HWE) was performed (Table 4). The findings reported 3 factors for rs1800795, the sample size and the HWE for rs1800796, and the publication year and the sample size for rs1800797 were confounding factors on the pooled results.

Table 4.

Meta-Regression Analysis of Association Between Three Interleukin-6 Polymorphisms with Sufficient Studies in Breast Cancer Patients Compared with Controls

Polymorphism	Subgroup	Variable	Allelic	Homozygous	Heterozygous	Dominant	Recessive
rs1800795	Publication year	Point estimate	0.00909	0.02538	−0.00269	0.03102	0.02708
		95% CI	−0.00154 to 0.01972	0.00351–0.04725	−0.01870 to 0.01332	0.01846–0.04358	0.00719–0.04698
		P value	0.09383	0.02294	0.74210	<0.00001	0.00763
	Sample size	Point estimate	− <0.00001	− <0.00001	− <0.00001	0.00002	−0.00001
		95% CI	0.00001 to <0.00001	−0.00002 to 0.00001	−0.000012 to <0.00001	0.00001 to 0.00002	−0.00002 to − <0.00001
		P value	0.54970	0.51737	0.35039	0.00003	0.02816
	HWE	Point estimate	−0.01276	−0.03942	0.00466	−0.24012	0.09796
		95% CI	−0.11745 to 0.09193	−0.25719 to 0.17836	−0.15415 to 0.16346	−0.39046 to −0.08979	−0.09808 to 0.29400
		P value	0.81123	0.72277	0.95418	0.00174	0.32740
rs1800796	Publication year	Point estimate	−0.01464	−0.09571	−0.00292	−0.00289	−0.08165
		95% CI	−0.04852 to 0.01924	−0.19545 to 0.00403	−0.04931 to 0.04347	−0.04838 to 0.04260	−0.16890 to 0.00560
		P value	0.39710	0.06001	0.90198	0.90093	0.06663
	Sample size	Point estimate	0.00018	0.00037	0.00018	0.00021	0.00032
		95% CI	0.00011–0.00026	0.00016–0.00058	0.00008–0.00028	0.00011–0.00031	0.00013–0.00051
		P value	<0.00001	0.00046	0.00069	0.00004	0.00089
	HWE	Point estimate	2.43436	4.91949	2.44509	2.84818	4.10919
		95% CI	1.42747–3.44124	2.24202–7.59695	1.07719–3.81299	1.52530–4.17105	1.70767–6.51070
		P value	<0.00001	0.00032	0.00046	0.00002	0.00080
rs1800797	Publication year	Point estimate	0.03198	0.06881	0.02200	0.03650	0.06009
		95% CI	0.01302–0.05095	0.02562–0.11200	−0.00495 to 0.04896	0.01224–0.06075	0.01852–0.10166
		P value	0.00095	0.00179	0.10953	0.00318	0.00461
	Sample size	Point estimate	−0.00028	−0.00054	−0.00019	0.00003	−0.00048
		95% CI	−0.00043 to −0.00013	−0.00090 to −0.00018	−0.00040 to 0.00003	0.00001–0.00006	−0.00082 to −0.00013
		P value	0.00021	0.00326	0.08384	0.01746	0.00683
	HWE	Point estimate	−0.00194	−0.36970	−0.07916	−0.12905	−0.28144
		95% CI	−0.28558 to 0.28171	−1.35953 to 0.62014	−0.47278 to 0.31446	−0.50530 to 0.24720	−1.26046 to 0.69759
		P value	0.98933	0.46415	0.69346	0.50143	0.57315

Bold numbers donate statistically significant (p < 0.05).

These findings are important because they help researchers understand how these factors might influence the results of genetic association studies for rs1800795, rs1800796, and rs1800797 polymorphisms. This can guide future research and help improve the accuracy and reliability of these studies. It is also crucial for interpreting the results correctly and understanding their implications for disease risk and treatment.

Trial sequential analysis

A TSA was performed for each genetic model in 2 polymorphisms (rs1800795 and rs1800797), shown in Supplementary Figs. S4–S13. The Z-curve line did not reach RIS line and, therefore, more information (studies and cases) is needed to confirm the results for these polymorphisms.

Publication bias

The funnel plots of 5 polymorphisms with minimum of 3 studies (rs1800795, rs1800796, rs1554606, rs1800797, and rs2069840) are shown in Supplementary Figs. S14–S38 for finding the publication bias. Table 5 shows the P values of Egger's and Begg's tests. The data showed that there was publication bias for rs1800795 polymorphism in the recessive model, rs1554606 polymorphism in the heterozygous model, and rs2069840 polymorphism in the allelic and the homozygous models (P < 0.10).

Table 5.

The Results of Publication Bias

Polymorphism	Model	P value (Egger's test)	P value (Begg's test)
rs1800795	Allelic	0.865	0.713
	Homozygous	0.489	0.604
	Heterozygous	0.688	0.932
	Dominant	0.271	0.620
	Recessive	0.081	0.330
rs1800796	Allelic	0.170	0.327
	Homozygous	0.485	1.000
	Heterozygous	0.417	0.624
	Dominant	0.291	0.624
	Recessive	0.591	1.000
rs1554606	Allelic	0.216	0.117
	Homozygous	0.206	0.117
	Heterozygous	0.083	0.117
	Dominant	0.113	0.117
	Recessive	0.323	0.117
rs1800797	Allelic	0.864	0.573
	Homozygous	0.864	0.624
	Heterozygous	0.836	0.347
	Dominant	0.415	0.453
	Recessive	0.804	0.624
rs2069840	Allelic	0.070	0.117
	Homozygous	0.042	0.117
	Heterozygous	0.936	0.601
	Dominant	0.857	0.601
	Recessive	0.359	0.601

Bold numbers denote statistically significant data (P < 0.10).

Discussion

IL-6, one of the main cytokines in the tumor microenvironment, is an important factor found in high concentrations and its overexpression has been reported in almost all types of tumors (Coussens and Werb, 2002; Deng et al., 2016; Kumari et al., 2016; Maeda and Omata, 2008). The high levels of IL-6 is reflected in the tumor microenvironment (Chonov et al., 2019), where it can advance tumorigenesis by regulating all cancer signs and several signaling pathways, including survival, apoptosis, angiogenesis, proliferation, invasion, and metastasis (Chang et al., 2013; Chen and Zhou, 2015; Choi et al., 2012; Demirci et al., 2012; Grivennikov and Karin, 2008; Rojas et al., 2011).

Among meta-analyses, Xu and Wang (2020) concluded that rs1800796 polymorphism was significantly associated with susceptibility to BC in East Asians from China. Yu et al. (2010), Liu et al. (2012), Xu et al. (2011), Zhai et al. (2017), Xu and Wang (2020), Zhou et al. (2018), and Peng et al. (2018) concluded that rs1800795 polymorphism was not associated with susceptibility to BC. In addition, Zhou et al. (2018) and Peng et al. (2018) suggested that rs1800796 and rs1800797 polymorphisms within the IL-6 gene promoter are not associated with BC risk. Barek et al. (2021) concluded that rs2069840 polymorphism was significantly associated with susceptibility to BC.

However, it is important to note that these polymorphisms may have associations with other types of cancer. For example, rs1800797 was reported to have an association with hepatocellular carcinoma risk (Aleagha et al., 2020), and rs1800795, rs1800796, and rs1800797 may be tumor markers for cancer therapy (Peng et al., 2018). In addition, rs2069840 may be associated with multiple myeloma, gastric cancer, and prostate cancer (Barek et al., 2021). The presence of publication bias could lead to an overestimation of the effect sizes observed for rs1800795, rs1554606, and rs2069840 polymorphisms in our meta-analysis. This means that the reported effects may appear stronger than they actually are, as studies with nonsignificant or smaller effects are less likely to be published.

As the present meta-analysis shows, a strange interaction of IL-6 protein with several other biomarkers IL-R6, VEGF, IL-10, IL-17A, IL-1B, IL-18, IL-4, TNF-α, NFKB1, IL-12B, and IL-8 proteins has been identified. In several studies, the interaction of IL-6 with VEGF, NFKB1 (Liang et al., 2017), IL-R6 (Uciechowski and Dempke, 2020), IL-10 (Herbeuval et al., 2004), IL-17A (Wang et al., 2009), and IL-8 (Hu et al., 1993; Padala et al., 2022) in tumor cells or tissues has been investigated.

These interactions can vary depending on the context, such as the type of cells involved or the presence of other signaling molecules. These interactions can influence the behavior of cancer cells, affecting their growth, invasiveness, and response to treatment. Understanding these interactions can provide valuable insights for developing new therapeutic strategies for BC. However, more research is needed to fully understand these complex interactions and their implications for cancer treatment.

The difference between the results of studies can be due to the difference between the clinicopathological, demographical, and environmental factors. The present meta-analysis reported that ethnicity was effective factor for the association between rs1800796 and rs1800797 polymorphisms and BC risk. Studies reported that lymph node involvement (AlSuhaibani et al., 2016; Carey et al., 2006; Gonullu and Basturk, 2007), age and race (Abana et al., 2017), Elston–Ellis grade (Chérel et al., 2009), tumor size (Chérel et al., 2009; Iacopetta et al., 2004), histological grade (Iacopetta et al., 2004), and ER status (DeMichele et al., 2003; Iacopetta et al., 2004) were effective factors on the association of rs1800795 polymorphism and the risk of BC.

There are several limitations: (1) A lack of sufficient studies for most polymorphisms and it needs more designed studies. (2) Several studies had no high quality and one of the effective factors on this issue is that in the studies, demographical variables had been not matched between BC patients and controls. Therefore, the researchers should pay to criteria for a well-designed study. (3) There was high heterogeneity across the studies in most analyses. (4) There was publication bias for several analyses. (5) There was no sufficient study on Asian and mixed ethnicities. In contrast, the stability of the results was the strength of the present meta-analysis.

Perspective section

Despite the need for further studies, the findings of this meta-analysis recommended that among 7 polymorphisms, rs1800797 polymorphism had a significantly elevated risk based on the dominant model and rs2069840 polymorphism had a protective role based on the heterozygous and dominant models in the BC patients. Publication year, sample size, the HWE, ethnicity, and source of controls could be effective factors in the pooled results for some polymorphisms.

Conclusions

The present meta-analysis recommended that the AA+GA genotype of rs1800797 polymorphism had a significantly elevated risk and the GC and the CC+GC genotypes of rs2069840 polymorphism had a protective role in the BC patients. Several variables such as publication year, sample size, the HWE, ethnicity, and source of controls could be confounding factors on the pooled results for rs1800796 and rs1800797 polymorphisms. To find out the more accurate relationship between IL-6 and inflammatory processes in BC patients, it is necessary that the researchers perform well-designed studies with more cases in different areas of the world.

Footnotes

Authors' Contributions

Conceptualization by A.A. and M.S. Methodology, validation, writing—original draft preparation, visualization, and project administration by N.M. and M.S. Software and formal analysis by M.S. Investigation by A.A. and M.T. Writing—review and editing by A.A., M.T., and M.S. Supervision by A.A.

Author Disclosure Statement

No competing financial interests exist.

Funding Information

No funding was received for this article.

All authors are primarily involved in education or medical research and are not directly funded by the government.

Supplementary Material

Supplementary Figure S1

Supplementary Figure S2

Supplementary Figure S3

Supplementary Figure S4

Supplementary Figure S5

Supplementary Figure S6

Supplementary Figure S7

Supplementary Figure S8

Supplementary Figure S9

Supplementary Figure S10

Supplementary Figure S11

Supplementary Figure S12

Supplementary Figure S13

Supplementary Figure S14

Supplementary Figure S15

Supplementary Figure S16

Supplementary Figure S17

Supplementary Figure S18

Supplementary Figure S19

Supplementary Figure S20

Supplementary Figure S21

Supplementary Figure S22

Supplementary Figure S23

Supplementary Figure S24

Supplementary Figure S25

Supplementary Figure S26

Supplementary Figure S27

Supplementary Figure S28

Supplementary Figure S29

Supplementary Figure S30

Supplementary Figure S31

Supplementary Figure S32

Supplementary Figure S33

Supplementary Figure S34

Supplementary Figure S35

Supplementary Figure S36

Supplementary Figure S37

Supplementary Figure S38

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