Systematic analyses of the relationship between five CXCL8 gene polymorphisms and overall cancer risk

doi:10.21203/rs.3.rs-3348747/v1

Background

Interleukin-8 (IL-8/CXCL8) is a prototypical CXC family chemokine bearing an ELR motif that plays key roles in the onset and progression of a range of cancers in humans. Many prior studies have focused on exploring the relationship between CXCL8 gene polymorphisms and the risk of cancer. However, the statistical power of many of these reports was limited, yielding ambiguous or conflicting results in many cases.

Methods

Accordingly, the present meta-analysis of 28 related publications was conducted in an effort to better understand the association between these mutations and disease risk.

Results

The CXCL8 -353A/T polymorphism was associated with an increased overall cancer risk [A vs. T, odds ratio (OR) = 1.255, 95% confidence interval (CI) (1.079–1.459), P_{heterogeneity} = 0.449, P = 0.003]. The CXCL8 + 781T/C allele was similarly associated with a higher risk of cancer among Caucasians [TT vs. TC + CC, OR = 1.472, 95%CI (1.078–2.009), P_{heterogeneity} = 0.366, P = 0.015]. To better clarify the relationship between these mutations and clinical outcomes, tissue samples from oral cancer patients and healthy controls were analyzed. In these analyses, oral cancer patients carrying the CXCL + 781 TT + TC genotypes exhibited pronounced increases in serum levels of CXCL8 as compared to patients carrying the CC genotype (P < 0.01), and CXCL8 levels were also significantly higher in oral cancer patients with these two genotypes as compared to genotype-matched normal controls (P < 0.01).

Conclusion

These results thus suggest that the CXCL8-353 and + 781 polymorphisms may be associated with a greater risk of cancer development. The + 781 polymorphism may further offer value as a biomarker that can aid in the early identification and prognostic evaluation of oral cancer.

CXCL8

polymorphism

cancer

oral cancer

meta-analysis

risk

Over the past five decades, there have been profound achievements in the field of cancer research that have spurred the design of new analytical technologies, enabling details genetic studies that have provided nuanced insights into how best to detect, monitor, and treat affected patients. Although overall survival rates for many cancers have improved, the global burden of cancer continues to grow with a predicted 57% increase in incidence by 2040 that will translate to an approximately 64% increase in mortality rates [1–3]. Given the growing prevalence of many cancers, by the year 2030 cancer is predicted to surpass cardiovascular disease as the most common cause of death in the USA by the year 2030, killing an estimated 640,000 Americans per year [4].

Cancers are etiologically complex and shaped by a range of internal and external factors including genetics, endocrine activity, external environmental factors, body mass index (BMI), alcohol intake, and smoking history. Oncogenesis, tumor progression, and metastatic dissemination are highly dependent on the ability of tumors to establish an environment that is conducive to angiogenesis [5]. Localized angiogenic activity can be driven by the overproduction of pro-angiogenic factors relative to angiogenic inhibitors. Members of the CXC family of chemokines are cytokines that play particularly important roles as regulators of angiogenesis, serving as potent inhibitors or drivers of this process [5].

These differences in the angiogenic potential of CXC family chemokines are primarily related to the presence of the N-terminal Glu-Leu-Arg (ELR) motif, which is found in angiogenic member of this family (including CXCL1, CXCL2, CXCL3, CXCL5, CXCL6, CXCL7, and CXCL8)[6–8], whereas it is absent from not angiostatic family members (including CXCL4, CXCL9, CXCL10, and CXCL11) [9].

ELR + CXC chemokines serve as important regulators of the growth and progression of a range of cancers [10]. Of these, CXCL8 (IL-8) was first identified as a chemoattractant for leukocytes [11], and it has since also been shown to promote angiogenesis and proliferation [12, 13]. There is growing evidence supporting a role for IL-8 in the development of cancer [14]. Indeed, higher levels of IL-8 have been linked to the progression and recurrence of breast, prostate, gastric, and lung cancers[15–17].

Cytokine gene promoters harbor polymorphisms that can impact the production of these cytokines [18]. CXCL8 is encoded by the CXCL8 gene, which consists of a proximal promoter, four exons, and three introns present on chromosome 4q13-21 [15–17, 19]. Multiple CXCL8 polymorphisms have been documented to date, with strong evidence related to polymorphisms at the − 251 site [20]. Studies of polymorphisms at other sites (+ 781, -353, + 678, +1633, + 2767), however, have not been as in-depth. The present study was thus developed with the goal of conducting pooled analyses of all case-control studies focused on these polymorphisms in order to generate stronger evidence. In addition, in an effort to define novel biomarkers of oral cancer, differences in CXCL8 levels were compared between patients with oral cancer and healthy controls as a function of CXCL8 + 781 genotype.

Study selection and data extraction

Initially, the PubMed and Wanfang databases were searched for articles published as of July 20, 2023 using the keywords ‘IL-8’ or ‘interleukin-8’ or ‘CXCL8’, ‘polymorphism’ and ‘cancer’ or ‘tumor’. No language or publication year restrictions were imposed on the search. The references of retrieved articles and reviews were additionally manually searched for relevant studies. Eligible studies were those that: (a) evaluated correlations between cancer risk and one or more of the selected polymorphisms, (b) were case-control studies, (c) included age- and gender-matched control groups, and (d) had an available full-text manuscript. Studies were excluded if they: (a) lacked a control population, (b) did not provide genotype frequencies, (c)were duplicate studies, or (d) exhibited clear evidence of bias. Literature search results were reviewed by two investigators. Collected data from identified studies included first author, publication year, country, ethnicity, cancer type, genotypes in the case and control groups, source of controls, HWE analyses of controls, and genotyping methods.

Statistical analyses

Odds ratios (ORs) and 95% confidence intervals (CIs) were utilized to examine the association between CXCL8 polymorphisms and the risk of cancer based on genotypic frequency levels in cases and control subjects. Subgroup analyses were initially conducted stratified according to cancer type. Any cancers for which only one study was available were pooled under the category of “other cancers”. Ethnicity was classified as Caucasian, African, or Asian. Subgroup analyses were also conducted based on the source of control subjects, separately assessing population-based (PB) and hospital-based (HB) studies.

Pooled OR significance was assessed using the Z-test [21]. Chi-square-based Q tests were used to assess heterogeneity, with P < 0.05 being indicative of significant heterogeneity, in which case pooled ORs were analyzed with a random-effects model (DerSimonian and Laird method), whereas a fixed-effects model (Mantel–Haenszel method) was otherwise employed [22, 23]. For the + 781, -353, + 678, +1633, + 2767 polymorphisms in the CXCL8 gene, associations between genotype and cancer risk were assessed using dominant (MM + MW vs. WW), heterozygote comparison (MW vs. WW), allelic contrast (M-allele vs. W-allele), homozygote comparison (MM vs. WW), and recessive (MM vs. MW + WW) genetic models. Egger’s test was used to evaluate funnel plot asymmetry to detect publication bias [24], with P < 0.05 as the cut-off to define significance. The Pearson chi-square test for goodness of fit was used to detect departures from Hardy-Weinberg equilibrium (HWE) with respect to the frequencies of CXCL8 polymorphisms, using P < 0.05 as the cut-off to define significance. Stata v11.0 (StataCorp LP, TX, USA) was used to conduct statistical analyses.

Bioinformatics analyses

CXCL8 expression in most tumor types and paracancerous tissues were assessed with the GEPIA (http://gepia.cancer-pku.cn/) and UALCAN (https://ualcan.path.uab.edu/analysis.html) databases.

Genotyping

CXCL8 + 781 polymorphism genotyping has been performed with a range of techniques across studies, including qPCR, Taqman, amplification refractory mutation system-PCR, and restriction fragment length polymorphism PCR approaches. For the present study, CXCL8 + 781 polymorphism genotypes were assessed with a TaqMan assay using the approach documented by Castro et al. [25].

ELISA assay

Blood samples were collected in angicoagulant-free tubes, after which serum separator tubes (SSTs) were utilized and samples were allowed to clot overnight at 4°C or at room temperature for 2 h. Samples were then centrifuged (1000 ×g, 15 min), after which serum was collected and immediately assessed or stored at -20°C or -80°C for future analyses, minimizing repeated freezing and thawing. Serum CXCL8 levels were detected with an ELISA kit (Abcam Co. Ltd.). Absorbance at 450 nm was assessed, with correction at 540 or 570 nm. For further details, see the manufacturer’s website (https://www.abcam.cn/products/elisa/human-il-8-elisa-kit-ab214030.html).

Study population

In total, this study enrolled 85 patients from the Affiliated Hospital of Jiangnan University who were newly diagnosed with oral cancer from April 1, 2020 – September 1, 2022. All patients had pathologically confirmed oral cancer diagnoses as determined by pathologists from the Department of Pathology of the Affiliated Hospital of Jiangnan University. An age-matched healthy control group was additionally recruited during this same time period from among individuals undergoing routine physical examinations. All participants provided 3 mL samples of peripheral blood. The Institutional Review Board of the Affiliated Hospital of Jiangnan University approved this study, and all patients provided written informed consent prior to sample collection.

Study selection

An initial literature search identified 445 potentially relevant articles, for which 170 were duplicates, 126 were excluded because they were unrelated to the association between CXCL8 polymorphisms and the risk of cancer (n = 32), clinical trials (n = 16), meta-analyses (n = 28), randomized controlled trials (n = 6), reviews (n = 31), systematic reviews (n = 13), or lacked sufficient data as case-control studies (n = 45). An additional 101 case-control studies focused on the CXCL8 -251 site polymorphism. The remaining 28 studies were incorporated into the present meta-analysis (Fig. 1). The characteristics of these case-control studies are summarized in Table 1, and they included 3 studies focused on the − 353 site, 3 related to the + 678 site, 4 related to the + 1633 site, 5 related to the + 2767 site, and 23 related to the + 781 site (Fig. 1).

Table 1

Characteristics about polymorphisms in IL-8 gene polymorphisms and cancer risk for included studies.
Author	Year	Country	Ethnicity	Cancer type	Case	Control	SOC	Case			Control			HWE	Method
353								AA	AT	TT	AA	AT	TT
Wei	2007	China	Asian	nasopharyngeal carcinoma	280	290	PB	55	139	86	47	131	112	0.406	PCR-RFLP
Wang	2014	China	Asian	hepatocellular carcinoma	205	208	HB	37	95	73	34	94	80	0.474	PCR-RFLP/PCR-SSP
Zhang	2017	China	Asian	breast cancer	442	447	HB	358	67	17	337	82	28	< 0.001	PCR-RFLP
678								TT	TC	CC	TT	TC	CC
Wei	2007	China	Asian	nasopharyngeal carcinoma	280	290	PB	38	104	138	35	111	144	0.064	PCR-RFLP
Ahirwar	2010	India	Asian	bladder cancer	205	270	PB	24	32	149	22	61	187	< 0.001	AS-PCR
Wang	2014	China	Asian	hepatocellular carcinoma	205	208	HB	27	78	100	26	82	100	0.161	AS-PCR
1633								TT	TC	CC	TT	TC	CC
Chien	2011	China	Asian	hepatocellular carcinoma	131	340	HB	16	58	57	59	159	122	0.562	PCR-RFLP
Liu	2012	China	Asian	oral cancer	270	350	HB	44	126	100	61	164	125	0.569	PCR-RFLP
Koensgen	2014	Germany	Caucasian	ovarian cancer	246	62	HB	61	123	62	11	31	20	0.865	PCR-RFLP
Huang	2018	China	Asian	nasopharyngeal carcinoma	176	352	HB	35	83	58	73	158	121	0.109	PCR-RFLP
2767								TT	TA	AA	TT	TA	AA
Chien	2011	China	Asian	hepatocellular carcinoma	131	340	HB	16	55	60	59	156	125	0.392	PCR-RFLP
Liu	2012	China	Asian	oral cancer	270	350	HB	43	123	104	61	161	128	0.029	PCR-RFLP
Koensgen	2014	Germany	Caucasian	ovarian cancer	268	426	HB	57	124	87	61	228	137	0.029	PCR-RFLP
Hsieh	2007	China	Asian	leiomyoma	162	156	HB	23	71	68	32	65	59	0.078	PCR-RFLP
Huang	2018	China	Asian	nasopharyngeal carcinoma	176	352	HB	32	84	60	75	149	128	0.012	PCR-RFLP
781								TT	TC	CC	TT	TC	CC
Liu	2015	China	Asian	glioma	300	300	HB	37	97	166	31	108	161	0.049	PCR-RFLP
Kamangar	2006	USA	Caucasian	gastric cancer	111	208	PB	12	52	47	22	105	81	0.158	TaqMan
Bo	2010	China	Asian	gastric cancer	208	190	HB	39	99	70	32	82	76	0.225	PCR-RFLP
Chien	2011	China	Asian	hepatocellular carcinoma	131	340	HB	9	57	65	50	164	126	0.776	PCR-RFLP
Liu	2012	China	Asian	oral cancer	270	350	HB	35	118	117	52	169	129	0.781	PCR-RFLP
Qin	2012	China	Asian	oral cancer	150	150	HB	18	68	64	13	80	57	0.041	PCR-RFLP
Rafrafi	2013	Tunisia	African	lung cancer	170	225	PB	25	67	78	30	95	100	0.329	PCR-RFLP
Wang	2014	China	Asian	hepatocellular carcinoma	205	208	HB	20	105	80	36	96	76	0.549	PCR-RFLP/PCR-SSP
Koensgen	2014	Germany	Caucasian	ovarian cancer	267	426	HB	69	150	48	72	226	128	0.1	PCR-RFLP
Chen	2016	China	Asian	osteosarcoma	190	190	HB	14	90	86	12	88	90	0.116	PCR-RFLP
Taheri	2019	Iran	Asian	prostate cancer	355	200	HB	61	170	124	32	92	76	0.639	ARMS-PCR
Kaanane	2022	Morocco	African	lung cancer	150	150	PB	20	40	90	8	44	98	0.307	TaqMan
Alkanli	2023	Turkey	Caucasian	bladder cancer	88	89	HB	14	46	28	13	45	31	0.608	PCR-RFLP
Moreno-Guerrero	2021	Mexico	Mixed	neuroblastoma	27	38	HB	3	16	8	16	15	7	0.313	PCR-RFLP
Song	2009	China	Asian	gastric cancer	125	140	HB	27	58	40	24	63	53	0.48	PCR-RFLP
Fu	2016	China	Asian	glioma	127	284	HB	18	55	54	27	118	139	0.788	PCR-RFLP
Zhang	2017	China	Asian	breast cancer	442	447	HB	18	225	199	40	202	205	0.327	PCR-RFLP
Huang	2018	China	Asian	nasopharyngeal carcinoma	176	352	HB	23	74	79	56	164	132	0.671	PCR-RFLP
Ghazy	2021	Saudi Arabia	Asian	prostate cancer	40	40	HB	11	17	12	22	16	2	0.673	real-time PCR
Liao	2011	China	Asian	hepatocellular carcinoma	150	150	HB	18	68	64	13	80	57	0.041	PCR-RFLP
Elsamanoudy	2015	Egypt	African	hepatocellular carcinoma	112	105	HB	8	36	68	24	45	36	0.178	PCR-RFLP
Qin	2012	China	Asian	hepatocellular carcinoma	150	150	HB	18	68	64	13	80	57	0.041	PCR-RFLP
Lu	2015	China	Asian	hepatocellular carcinoma	454	446	HB	43	236	175	61	183	202	0.062	PCR-RFLP
HB: hospital-based; PB: population-based; SOC; source of control; PCR-RFLP: polymerase chain reaction followed by restriction fragment length polymorphism; SSP: sequence specific primer; AS: allele specific primer; ARMS: amplification refractory mutation system; MALDI-TOF MS: matrix-assisted laser desorption/Ionization time of flight mass spectrometry; HRM: high resolution melt; DHPLC: denaturing high performance liquid chromatography;CE-SSCP: capillary electrophoresis-single strand conformation polymorphism; HWE: Hardy-Weinberg equilibrium of control group

Pooled analyses

The results of pooled analyses pertaining to the CXCL8 -353 polymorphism are presented in Table 2. A significant increase in the association between this polymorphism and cancer risk was detected under four genetic models: OR = 1.255, 95%CI (1.079–1.459), P_{heterogeneity} = 0.449, P = 0.003 for A-allele vs. T-allele, Fig. 2; OR = 1.463, 95%CI (1.068–2.004), P_{heterogeneity} = 0.653, P = 0.018 for AA vs. TT; OR = 1.339, 95%CI (1.052–1.705), P_{heterogeneity} = 0.524, P = 0.018 for AA + AT vs. TT; OR = 1.297, 95%CI (1.031–1.632), P_{heterogeneity} = 0.784, P = 0.026 for AA vs. AT + TT.

Table 2

Stratified subgroups analyses for IL-8 genes polymorphisms and cancer susceptibility.
Variables	No	Case/Controls	M-allele vs. W-allele	MM vs. WW	MW vs. WW	MM + MW vs. WW	MM vs. MW + WW
			OR(95%CI) P_h P	OR(95%CI) P_h P	OR(95%CI) P_h P	OR(95%CI) P_h P	OR(95%CI) P_h P
IL-8 -353	3	927/945	1.255(1.079–1.459)0.449 0.003	1.463(1.068–2.004)0.653 0.018	1.269(0.980–1.643)0.732 0.070	1.339(1.052–1.705)0.524 0.018	1.297(1.031–1.632)0.784 0.026
IL-8 + 678	3	690/768	1.020(0.866–1.201)0.970 0.816	1.166(0.837–1.623)0.814 0.364	0.881(0.696–1.115)0.390 0.291	0.952(0.770–1.178)0.785 0.652	1.203(0.875–1.655)0.702 0.256
IL-8 + 1633	4	823/1104	0.968(0.804–1.166)0.166 0.733	0.937(0.651–1.349)0.197 0.727	0.978(0.789–1.211)0.569 0.837	0.963(0.769–1.206)0.304 0.740	0.935(0.719–1.215)0.372 0.614
IL-8 + 2767	5	1007/1624	0.930(0.799–1.082)0.149 0.347	0.875(0.626–1.224)0.094 0.435	0.924(0.774–1.102)0.577 0.380	0.913(0.774–1.078)0.532 0.283	0.905(0.638–1.283)0.034 0.575
IL-8 + 781
Total	23	4398/5178	0.942(0.836–1.062)0.000 0.329	0.904(0.694–1.178)0.000 0.454	0.966(0.846–1.104)0.003 0.617	0.953(0.824–1.102)0.000 0.518	0.917(0.733–1.146)0.000 0.446
Ethnicity
Asian	16	3473/3937	0.948(0.854–1.054)0.005 0.323	0.884(0.692–1.131)0.003 0.327	0.969(0.841–1.117)0.021 0.667	0.956(0.833–1.098)0.013 0.523	0.893(0.715–1.116)0.004 0.321
Caucasian	3	466/723	1.189(0.856–1.653)0.043 0.302	1.528(0.773–3.020)0.063 0.223	1.225(0.761–1.973)0.067 0.404	1.276(0.746–2.181)0.024 0.374	1.472(1.078–2.009)0.366 0.015
African	3	432/480	0.826(0.406–1.681)0.000 0.598	0.809(0.199–3.297)0.000 0.767	0.746(0.461–1.205)0.070 0.231	0.750(0.370–1.521)0.001 0.426	0.932(0.282–3.077)0.001 0.907
Cancer type
Gastric cancer	3	444/538	1.119(0.931–1.346)0.456 0.232	1.267(0.864–1.859)0.675 0.226	1.118(0.845–1.479)0.411 0.434	1.148(0.883–1.494)0.368 0.303	1.171(0.826–1.659)0.859 0.375
Hepatocellular carcinoma	6	1202/1399	0.778(0.595–1.019)0.000 0.068	0.602(0.357–1.017)0.004 0.058	0.826(0.575–1.187)0.001 0.301	0.763(0.529-1.100)0.000 0.147	0.659(0.415–1.045)0.011 0.076
Prostate cancer	2	395/240	0.613(0.183–2.056)0.001 0.428	0.352(0.026–4.717)0.003 0.431	0.535(0.089–3.207)0.031 0.494	0.429(0.048–3.804)0.006 0.447	0.622(0.183–2.114)0.018 0.447
Oral cancer	2	420/500	0.877(0.724–1.063)0.470 0.181	0.866(0.548–1.368)0.289 0.536	0.765(0.579–1.012)0.956 0.061	0.783(0.600-1.021)0.792 0.070	1.014(0.627–1.639)0.247 0.955
Lung cancer	2	320/375	1.181(0.819–1.704)0.127 0.373	1.617(0.650–4.022)0.083 0.302	0.939(0.674–1.306)0.791 0.707	1.065(0.785–1.443)0.363 0.687	1.650(0.694–3.924)0.089 0.257
Glioma	2	427/584	1.117(0.883–1.414)0.235 0.355	1.343(0.888–2.031)0.366 0.163	0.990(0.728–1.346)0.270 0.949	1.069(0.780–1.463)0.228 0.680	1.346(0.906–2.002)0.543 0.142
Other cancer	6	1190/1542	0.976(0.747–1.274)0.000 0.856	0.858(0.426–1.727)0.000 0.667	0.980(0.868–1.107)0.096 0.358	1.080(0.790–1.477)0.009 0.629	0.801(0.451–1.422)0.000 0.448
Source of control
HB	20	3967/4595	0.920(0.806–1.050)0.000 0.215	0.849(0.635–1.135)0.000 0.268	0.971(0.834–1.130)0.001 0.701	0.942(0.798–1.110)0.000 0.474	0.863(0.678–1.099)0.000 0.232
PB	3	431/583	1.087(0.846–1.397)0.191 0.514	1.336(0.735–2.428)0.147 0.343	0.911(0.693–1.198)0.919 0.505	1.002(0.776–1.294)0.512 0.987	1.383(0.799–2.393)0.170 0.247
P_h: value of Q-test for heterogeneity test; P: Z-test for the statistical significance of the OR; HB: hospital-based; PB: population-based

Pooled analyses focused on the CXCL8 + 781 polymorphism failed to detect any significant association with overall cancer risk, and the same was true when conducting subgroup analyses based on cancer type or the source of control subjects (Table 2). However, ethnicity-based subgroup analyses revealed an increase in risk associated with the + 781 polymorphism among Caucasians [TT vs. TC + CC, OR = 1.472, 95%CI (1.078–2.009), P_{heterogeneity} = 0.366, P = 0.015, Fig. 3]( Table 2).

For the three other analyzed CXCL8 polymorphisms (+ 678, + 1633, +2767), no significant associations with overall cancer risk were detected for different variant genotypes under the analyzed genetic models (Table 2).

Publication bias analyses

The potential for publication bias was next evaluated with Begg’s funnel plots and Egger’s test. Funnel plots appeared to exhibit some asymmetry, suggesting some potential bias with respect to allele comparisons for the selected CXCL8 polymorphisms (Not Shown). Egger’s test confirmed this evidence of publication bias (Not Shown).

To better explore CXCL8 expression in tumor tissue samples, the UALCAN database was next utilized, revealing that relative to corresponding normal tissue controls, CXCL8 levels in tumors were elevated in colon adenocarcinoma (P < 0.01), rectum adenocarcinoma (P < 0.01), stomach adenocarcinoma (P < 0.01), thyroid carcinoma (P < 0.01), and head and neck squamous cell carcinoma (P < 0.01), whereas it was downregulated in bladder urothelial carcinoma as compared to tumor tissues (P < 0.01) (Fig. 4). These trends toward altered CXCL8 expression were further confirmed with the GEPIA database. To better understand the functions of CXCL8, the STRING database was leveraged to identify the 10 proteins that most closely interact with CXCL8 (Fig. 5).

Given the upregulation of CXCL8 in most tumor tissues and the close association between cancer risk and two CXCL8 gene polymorphisms, the impact of different polymorphic loci on the production of CXCL8 was next assessed in oral cancer patients and healthy controls. Ultimately this approach revealed that serum CXCL8 concentrations were significantly higher in oral cancer patients harboring the TT + TC genotypes as compared to the CC genotype (P < 0.01). Serum CXCL8 levels in oral cancer patients with the TT + TC genotypes were also significantly elevated as compared to levels in normal control subjects (P < 0.01) (Fig. 6).

Single nucleotide polymorphisms (SNPs) are the most common type of genetic variant present within the human genome. Point mutations with minor alleles of at least 1% in at least one population are regarded as SNPs [26]. SNPs that present within regulatory regions have the potential to impact transcriptional activity, while SNPs located in 3’-untranslated regions may impact the stability of the encoded RNA, SNPs in splice sites can affect splicing activity, and SNPs within coding regions can impact the sequence of the final protein. Different SNPs are believed to be key contributors to variations among individuals with respect to susceptibility to cancer and other diseases [27].

This meta-analysis is the first publication to have conducted a systematic evaluation of the relationship between five CXCL8 polymorphisms and overall cancer risk. This pooled analysis incorporated a total of 7845 cases and 9619 controls, including 927 cases and 945 controls pertaining to the − 353 SNP, 690 cases and 768 controls related to the + 678 SNP, 823 cases and 1104 controls related to the + 1633 SNP, 1007 cases and 1624 controls related to the + 2767 SNP, and 4398 cases and 5178 controls related to the + 781 SNP. In pooled analyses, a significant link between the − 353 polymorphism and an elevated risk of cancer was noted, while the + 781 polymorphism was specifically associated with greater cancer risk among Caucasians although this same association was not detected in African or Asian populations.

These findings may be influenced by a range of variables. For one, differences in ethnicity distributions in the case and control groups may have confounded the pooled analyses. Cancers are also complex multifactorial diseases such that both genetic and environmental factors ultimately shape disease onset and progression, with no single factor having a major effect on disease susceptibility in many cases [28]. Exposure to carcinogenic risk factors including radiation, infectious agents, dietary factors, and tobacco smoke can all raise the risk of oncogenesis, but precisely quantifying the magnitude of the risk associated with these exposures can be challenging. Lastly, the specific polymorphic sites within the CXCL8 gene can have varied functional effects, leading to distinct changes in CXCL8 expression that may ultimately translate to shifts in the risk of developing cancer.

Intensive research efforts in recent years have focused on developing approaches to detecting tumors during their earlier stages of development, providing a means of improving survival outcomes for affected patients. Early detection strategies can improve the efficacy of surgery and other interventional approaches while mitigating the economic and psychological burden associated with an advanced disease diagnosis. It is thus essential that easy-to-use, minimally invasive, cost-effective technologies be developed capable of detecting tumors when they are precancerous lesions or remain in the early stages of disease [29]. The present results revealed that CXCL8 expression was elevated in most surveyed tumor types as compared to corresponding normal tissues. Higher CXCL8 levels were also observed in the serum of oral cancer patients with the T-allele or TT genotype, suggesting this may offer value as a biomarker suitable for use when detecting oral cancer. Future studies may be able to apply these results to guide diagnostic and therapeutic approaches aimed at abrogating cancer-related risk.

Cancer develops in progresses in a manner that is complex and driven by a wide range of interacting factors. As such, efforts focused solely on a single polymorphism are inherently limited. As such, the STRING database was leveraged to identify other related genes that may be related to oncogenic risk. Among the 10 most closely associated proteins identified in this analysis, the CCL2 -2518A/G polymorphism is reportedly closely related to the risk of gynecological cancer [30]. Moreover, the CCR2 -V64I polymorphism is potentially associated with the incidence of cancers including oral, cervical, and bladder cancers [31], while IL4 rs2243250 and rs79071878 have been linked to oncogenesis in certain cancers and ethnic groups [32]. In light of these analyses, further in-depth studies focused on these CXCL8-related genes and gene-gene interactions are warranted in order to better guide efforts to treat oral cancer and other malignancies.

This study is subject to multiple limitations. For one, although all relevant articles were incorporated into the present meta-analysis, the overall sample size remained relatively small, and these numbers were further reduced when stratifying studies according to ethnicity, cancer type, or source of controls. There were also relatively few case-control studies focused on the + 678, +1633, + 2767, or -353 polymorphisms were also limited. Secondly, Egger’s test detected evidence of significant publication bias that is likely to have impacted these results. Third, the risk of cancer in patients harboring these polymorphisms may be influenced by gene-gene, gene-environment, and other polymorphic interactions. Moreover, some of the included studies have relatively limited numbers of case and control participants. This meta-analysis was also performed based upon estimates that were not adjusted, and future efforts to obtain details pertaining to patient age, sex, and tumor staging may permit more granular and precise analyses. Lastly, the overall results of this study are not representative of all cancer types, as only certain cancers were available for analysis and the number of patients varied markedly among cancer types.

In conclusion, the results of the present meta-analysis support a potential link between the CXCL8 -353 and + 781 polymorphisms and an overall increase in cancer risk in the general population or in individuals of particular ethnicities. The + 781 polymorphism was additionally established as a potential diagnostic biomarker for oral cancer. Even so, further large-scale studies with more substantial sample sizes and simultaneous analyses of multiple SNPs in one or more CXCL8 polymorphisms will be essential to reliably clarify the CXCL8-specific genetic antecedent of solid tumor development.

Authors’ contributions

JP, DL, and YNW contributed to the conception of the study. DK, and YW performed the data collection and data analysis. GW, HJL, and HC wrote the manuscript. The authors have read and approved the final manuscript.

Funding

Not applicable

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Ethics approval and consent to participate

This study was carried out in strict accordance with relevant regulations. Our study was approved by the Clinical Research Ethics Committee of the Affiliated Hospital of Jiangnan University (Wuxi, China). All patients agreed to participate in our research and signed relevant informed consent.

Consent for publication

Not applicable

Competing interests

The authors declare that they have no competing interests.

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No competing interests reported.

Systematic analyses of the relationship between five CXCL8 gene polymorphisms and overall cancer risk

Status:

Version 1

Abstract

Background

Methods

Results

Conclusion

Figures

Introduction

Materials and methods

Study selection and data extraction

Statistical analyses

Bioinformatics analyses

Genotyping

ELISA assay

Study population

Results

Study selection

Pooled analyses

Publication bias analyses

Discussion

Declarations

References

Additional Declarations

Status:

Version 1