Abstract

Background: Cervical cancer is a significant health burden, especially in less developed countries with limited access to HPV vaccines and screening. Dysregulation of immune cells, interleukin-6 (IL-6), and proinflammatory mediators have been implicated in cancer progression. SNPs in the IL-6 gene are thought to influence cervical cancer. A meta-analysis investigated the relationship between the IL-6 rs1800797 polymorphism and cervical cancer risk.

Methods: We conducted data mining on the PubMed database to identify relevant studies meeting specific criteria, including genotype data for IL-6 rs1800797, publication between 2015 and 2023, and reporting covariate risk factors. The meta-analysis comprised six publications focusing on the polymorphism at rs1800797 in IL-6 associated with cervical cancer.

Results: None of the five genetic models studied proved a significant link between the IL-6 rs1800797 polymorphism and cervical cancer risk. Because it included data from many ethnic groups, some racial groups may not experience the same consequences as others, based on this meta-analysis. The research revealed substantial heterogeneity. Egger's test and sensitivity analysis showed no evidence of publication bias.

Conclusion: Based on this comprehensive meta-analysis, we find no evidence that the IL-6 rs1800797 polymorphism contributes substantially to cervical cancer risk. However, further study is needed to investigate possible connections with additional IL-6 polymorphisms and the interplay between genetic and environmental variables in the development of cervical cancer. Identifying reliable tumor markers for cancer therapy remains an important area of investigation.

Introduction

Women worldwide have cervical cancer at a rate three times greater than that of breast and colorectal cancers, with 569,000 new cases annually. It occurs in the cells of the cervix and is predominantly caused by an infection with the Human Papillomavirus (HPV), with a higher incidence rate in less developed countries, likely due to the high cost of HPV vaccines and less accessible screening1. It has been reported that low- and middle-income countries have an 18-fold increase in deaths compared to high-income countries2. The adherence of rural women to cervical cancer screening highly depends on their knowledge of the condition and the screening process. Limited awareness about the disease or misconceptions about preventative measures may hinder screening uptake3. The study examined the factors affecting rural women's commitment to cervical cancer screening in South Chennai4. All women need to be informed about cervical cancer and its prevention. A recent study recommends that awareness among women regarding cervical cancer and ways to prevent it should be heightened5. Cancer progression is often associated with the dysregulation of various immune cells; moreover, cytokines and chemokines can induce inflammation, including Interleukin-6, an essential cytokine whose dysfunction can lead to chronic inflammation, autoimmunity, and cancers6.

Interleukin-6 (IL-6) is a bioactive peptide located on chromosome 7 (7p21-14), with a length of 5 kb, consisting of four introns and five exons7. It modulates several cellular mechanisms such as cell proliferation, differentiation, immune response, invasion, metastasis, and tumorigenesis8. Factors such as Single Nucleotide Polymorphism (SNP) may also influence gene expressions; hence, polymorphisms in specific genotypes of IL-6 have a higher chance of developing cervical cancer9. The SNP is possibly due to differences in the production of cytokines in the regulatory regions, and genetic polymorphisms result from modifications in the function of proteins10. The polymorphism at the 5' flanking regions affects expressions based on the IL-6 promoter (rs1800795, rs1800796, and rs1800797)11. Although several hereditary predispositions to cervical cancer have been reported, the results have been inconsistent due to varying sample sizes, genders, and ethnicities. Hence, a meta-analysis is conducted to determine the connection between the rs1800797 variant and cervical cancer.

Methods Literature Search

After data mining the PubMed database, we identified several relevant studies on cervical cancer/cervical carcinoma and genetic variations (polymorphisms/single nucleotide polymorphisms, SNP/SNV/Mutation). To be included in the meta-analysis, studies had to meet certain conditions. These included reporting covariate risk factors such as HPV status, smoking status, oral contraceptive use, age at menarche, age at menopause, age at first pregnancy, and parity. They also had to provide case-control genotype data for the polymorphic variant, complete research articles, and be published between 2015 and 2023. Only polymorphisms referred to in two or more papers were considered.

Data Extraction and Quality Assessment

Six papers were chosen for further meta-analysis, including data on various factors. When citing a study, it is essential to include critical details such as the first author's name, year of publication, median age (including standard deviation), gender distribution, smoking and HPV statuses, ages at menarche and menopause, ages at marriage and first pregnancies, number of children, use of oral contraceptives, genetic variations, and specific data on case-control groups. The selected papers specifically focused on one gene and one genetic polymorphism. The Newcastle-Ottawa Scale was created to evaluate the quality of case-control studies that are deemed acceptable12. This scale comprises three domains: selection, exposure, and comparison, each assessed based on five parameters, including the Hardy-Weinberg equilibrium (HWE) index, the number of cases and controls, the method of association assessment, and the genotyping method. A maximum score of 8 indicates high quality in the evaluation process.

Statistical Analysis

Individual analyses were conducted for gene polymorphisms with at least two available studies, estimating Odds Ratios (OR) and their corresponding confidence intervals (CI). These were derived from the primary data obtained during the review. We estimated odds ratios (ORs) for each genetic polymorphism and conducted heterogeneity tests using Cochran's Q to assess variations within and between studies. The null hypothesis for these tests suggests that any observed variability is due to chance alone, indicating no significant differences in associations among studies. The alternative hypothesis proposes the presence of heterogeneity in the associations between studies. To quantify the level of heterogeneity, we calculated I2 based on Cochran's Q. The I2 value indicates the degree of heterogeneity, with low heterogeneity (I2: 25-50%), moderate heterogeneity (I2: 50-75%), and high heterogeneity (I2 > 75%)13, 14. If I2 is less than 50%, we used a fixed-effects model; if I2 is more than 50%, random effects were chosen to interpret the results.

Heterogeneity indicates notable variations among the studies included in the meta-analysis. Delving deeper into the origins of this heterogeneity could offer valuable insights. Potential sources of heterogeneity may stem from differences in study design, characteristics of the study populations, or methods of measurement. Exploring these factors could elucidate why certain studies diverge in their findings and enhance the understanding of the overall results.

The Cochran's Q test was employed to determine whether to use the random or fixed-effects models for pooled odds ratio estimation for each polymorphism. The random-effects model was utilized if the two measures showed significance with a significance level (α) of 0.05 (corresponding to at least moderate I2). If both tests were not statistically significant (equivalent to low I2), fixed-effects models were employed. We performed sensitivity analyses on polymorphisms with more than four studies. To ensure consistency of results, this was done by removing studies iteratively and recalculating the pooled odds ratio (OR) each time. Additionally, the Egger Test was used to assess publication bias for polymorphisms with more than five studies, regardless of whether a significant association was observed15, 16.

The significance level for all analyses was set at 0.05, a commonly used value in the field's original studies. This choice was made to address the limitation of statistical power in the meta-analysis and minimize the risk of Type II errors. Despite this, all reported p-values are provided for transparency, allowing readers to interpret the results critically, regardless of whether the associations were significant17, 18.

× Figure 1 . Flow chart of meta-analysis (PRISMA flow diagram). Figure 1 . Flow chart of meta-analysis (PRISMA flow diagram). × Figure 2 . The forest plot shows the heterogeneity in the random effects model. Figure 2 . The forest plot shows the heterogeneity in the random effects model. × Figure 3 . Funnel plot for the publication bias. Figure 3 . Funnel plot for the publication bias.

Table 1.

Characteristics of the study included in a meta-analysis

Study Name Year Country Source of DNA No of Cases No of Control NOS Score Genotyping Method Agne et al. 2021 Lithuania Blood 89 84 6 Real-time PCR Kushwah et al. 2020 India Venous Blood 246 246 6 PCR-RFLP Maneesh et al. 2015 India Peripheral Blood 100 100 7 PCR-RFLP Monishita et al. 2023 Bangladesh Venous Blood 126 120 8 PCR-RFLP Sabrina et al. 2016 Tunisia Blood 112 164 8 Real-time PCR Sayma et al. 2020 Bangladesh Blood 252 228 7 ARMS-PCR

Table 2.

Genotyping and allele frequency of IL-6 (rs1800797) gene polymorphism