Exploring the Role of Inflammatory Cytokines in Cervical cancer Pathogenesis: Evidence from Mendelian Randomization Analysis

doi:10.21203/rs.3.rs-5043422/v1

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Exploring the Role of Inflammatory Cytokines in Cervical cancer Pathogenesis: Evidence from Mendelian Randomization Analysis

https://doi.org/10.21203/rs.3.rs-5043422/v1

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Background:

Cervical cancer (CC) is a prevalent malignancy worldwide, which seriously threatens women's quality of life and health. Although CC etiology remains uncertain, mounting evidence suggests that inflammatory cytokines (CKs) contribute to CC pathogenesis. Nonetheless, more research is required to determine if there is a causal connection between them. Therefore, our study performed a Mendelian randomized (MR) study to investigate the causal link between inflammatory CKs and CC.

Methods:

The inflammatory CK data are derived from two European population databases: one containing 41 inflammatory CKs and the other containing 91 inflammatory CKs. The CC data came from the UK Biobank (n ≤ 408961), including 1659 cases of CC and 381902 controls of European ancestry. Our study employed the inverse variance weighted, MR-Egger, weighted median, and weighted mode to analyze the causal relation between inflammatory CKs and CC. Additionally, multiple sensitivity analyses, including MRE intercept test, MR-PRESSO and Leave One Out, were deployed to further validate the robustness of the results. Eventually, a reverse MR analysis was carried out.

RESULTS:

The MR results showed that the increase of the Monokine triggered by gamma interferon )INF-γ( level was negatively correlated with CC (odds ratio (OR) = 0.84, 95% confidence interval (95% CI): 0.72–0.99, P = 0.044). Elevated cystatin D (CysD), Interleukin-8 (IL-8), Leukemia Inhibitory Factor (LIF), and Monocyte chemoattractant protein 2 (MCP-2) levels were positively correlated with CC occurrence (OR = 1.18, 95% CI:1.02–1.36, P = 0.025; OR = 1.41, 95% CI:1.02–1.95, P = 0.035; OR = 1.39, 95% CI:1.00–1.94, P = 0.044; OR = 1.76, 95% CI:1.25–2.47, P = 9×10^–4), which aligned with sensitivity analyses results. Reverse MR Results showed that CC had no effect on 132 inflammatory CKs.

Conclusion:

Herein, the MR analysis demonstrated a potential causal connection between INF-γ, CysD, IL-8, LIF, and MCP-2 levels and CC risk. The role of inflammatory CKs in CC occurrence and development needs further investigation.

Inflammatory cytokines

Mendelian randomization

Cervical cancer

Bidirectional

Phenotypic-MR analysis

Cervical cancer (CC) represents a major malignancy that endangers women's lives and health globally. In 2022, the International Agency for Research on Cancer (IARC) stated that CC incidence and mortality ranked eighth and ninth in the world, with 661,021 new cases and 348,189 deaths, respectively[1]. Moreover, in recent years, studies have found that CC has gradually become younger, which has brought a serious psychological burden to women around the world and even seriously threatened women's quality of life and life health[2]. The cause of CC has been elucidated to have a probable association with human papillomavirus (HPV) infection, smoking, sexually transmitted diseases, immune suppression, and other factors, among which immune system abnormalities are indispensable in the occurrence and development of CC[3].

Inflammatory cytokines (CKs), a protein or peptide group with multiple biological activities produced by immune cells, can lead to immune deficiencies, allergic reactions, and autoimmune diseases[4]. Inflammatory CKs have been revealed to have a close link to CC occurrence and development[5]. On the one hand, Interleukin (IL)-1/2/4/8, Tumour necrosis factor-α, and other inflammatory CKs secreted by helper T cells contribute to CC occurrence, invasion, and metastasis[6–8]. On the other hand, CC can also stimulate the production of inflammatory CKs. For example, patients having uterine cancer experienced significantly higher serum levels of TNF-α, interferon-β, soluble triggering receptors expressed on myeloid cells-1, and IL-1/8 than healthy women[9]. However, patients with CC have been reported to have significantly lower serum levels of inflammatory CKs, including IL-8, C-C chemokine receptor 6 (CCR6), and C-C motif chemokine ligand 20 (CCL20) than healthy women[10, 11]. Despite the increasing number of studies elucidating a causal relation between inflammatory CKs and CC, assessing the probable causal connection between these CKs and CC is crucial due to ongoing debates and limitations in traditional observational study designs.

Mendelian randomization (MR) represents an excellent statistical analysis approach that draws etiological conclusions through genetic variation as instrumental variables (IVs)[12]. Based on the random distribution of germline genetic variation in the process of meiosis, MR overcomes the impact of potential confounding factors and is not affected by reverse causality, so MR produces more reliable results[13, 14]. Current genome-wide association study (GWAS) data include IV of 132 inflammatory CKs, but no MR study has focused on CC. Accordingly, conducting a more comprehensive study is necessary to clarify the causal connection between inflammatory CK levels and CC to increase our understanding of CC pathogenesis.

Therefore, by employing the published GWAS data and the most comprehensive database of 132 inflammatory CKs and CC, we carried out bidirectional MR analysis to systematically determine the causal link between inflammatory CKs and CC.

Study Design

The design of this study used the STROBE-MR guidelines, which were developed specifically for two-sample MR[15]. Figure 1 depicts the entire MR design schematic, serving as a visual reference for the methodology we utilized. In this study, we used bidirectional MR analysis. In the positive analysis, we employed 132 inflammatory CKs obtained through two databases as exposures while employing CC as the outcome, aiming at exploring the potential of distinct inflammatory CKs in CC occurrence and development. Our study assessed the causal link between CC and 132 inflammatory CKs in a reverse analysis. When using IVs for MR analysis to determine a causal exposure-outcome association, the chosen IVs must satisfy three crucial assumptions: (1) Exposure is strongly linked to IVs. (2) IVs are unaffected by any confounding factors. (3) IVs do not have a direct impact on the outcomes but only influence them through exposure[16]. This study relies on a publicly available GWAS database and does not necessitate any further informed consent or ethical approval.

Participants and data sources

The GWAS database related to 132 inflammatory CKs was acquired by accessing the GWAS database of Bristo University (https://data.bris.ac.uk/data/dataset) and the GWAS catalog database (ID: GCST90274758-GCST90274848), which comprised 41 and 91 inflammatory CKs, respectively(S1 Table, S2 Table)[17]. The GWAs database related to CC comes from the UK Biobank, which contains 408961 whites of British European origin. Disease GWASs from the UK Biobank were conducted through the Scalable and Accurate Implementation of Generalised Mixed Model (SAIGE V .0.29) method for accounting for unbalanced case-control ratios[18]. We selected 783 traits (diseases), 1659 cases, and 381,902 controls of European ancestry for phenotypic-MR analysis[19]. Summary statistics of single nucleotide polymorphisms (SNPs) related to CC can be downloaded from the PheWeb GWAs (https://pheweb.sph.umich.edu/)[20].

In this study, we screened the significantly related SNPs (p < 5 × 10^–6), setting the linkage disequilibrium standard as R² < 0.001 and the genetic distance as 10000kb[21]. To guarantee the independence of the included SNPs, we eliminated the highly relevant SNPs. Finally, in order to eliminate potential weak IVs, we use F-statistic > 10 as the filtering criterion to exclude non-conforming snps[22].

Statistical Analysis

After extracting the SNP data of inflammatory CKs from the GWAS database, the F statistics of inflammatory CKs were first calculated, seeking the evaluation of IV intensity. If F > 10, the correlation is strong enough[23]. Then, we use inverse variance weighted (IVW), MR-Egger (MRE), weighted median (WME), and weighted models (WMO) to analyze the causal association between inflammatory factors and CC, in which IVW is the main analysis method because IVW can adjust the weight to improve the estimation accuracy without requiring adherence to the weak IVS hypothesis[24], so we can get more robust estimation results. The MRE, while resembling IVW, exhibits less accuracy and efficiency in its estimation. The WME method utilizes the median of the IVW ratio estimates[25, 26]. Compared with IVW and MRE, this approach is more robust to outliers. The WMO method employs the mode of the IVW ratio estimates[26]. This method is more powerful than the MRE approach and weaker than the IVW and WME approach[27]. Although MRE, WME, and WMO are inefficient, they can be used to eliminate potential confounding factors.

To ensure the results' stability and reliability, we carried out a heterogeneity test, Horizontal multiplicity test, and sensitivity analysis. First, Cochran's Q test and MRE were deployed with the aim of evaluating the selected SNP heterogeneity. When Q pval > 0.05, the fixed effect IVW method was utilized; conversely, when Q pval is less than or equal to 0.05, the random effect IVW method is used[28]; secondly, we use MRE regression analysis horizontal multiple effects, and the effect estimation value is expressed by intercept; moreover, we use MR pleiotropy residual sum and outlier (MRPRESSO) test for the identification of probable multiple-effect outliers. Finally, we use Leave One Out (LOO) for sensitivity analysis, one by one, to test the impact of each SNP on the results[29].

Since we conducted a bidirectional MR analysis between each inflammatory CK and CC, We use the Bonferroni to correct the multiple comparisons, and when P < 3.8 × 10^–4(0.05/132), the analysis results are considered to be statistically significant. However, due to the fact that the corrected P value is quite small due to multiple analyses, some otherwise relevant positive results will be ignored, which we call suggestive correlation results; that is, the P value is between 3.8 × 10^–4 and 0.05. These positive results are also in our analysis[30, 31]. The MR analyses were carried out in the R software (version 4.3.3) employing "TwoSampleMR," "Mendelian-Randomization," and "MRPRESSO" packages[32, 33].

1. The causal connection between inflammatory CKs and CC

In the prospective MR analysis, with P < 5 × 10^–6 and R² < 0.001 as significance criteria, we identified 1705 SNPs associated with 132 inflammatory CKs, with F-statistics of 12–1477(S3 Table), indicating that the IVs in this study are robust. we found that five inflammatory CKs were correlated with CC occurrence and development by IVW analysis, and a forest map (Fig. 2) was drawn to represent the findings more intuitively(S4 Table). Among them, INF-γ level exhibited a negative correlation with CC risk (OR = 0.84, 95% CI:0.72–0.99, P = 0.044), and CC risk was positively correlated with CysD, IL-8, LIF and MCP-2 levels (OR = 1.18, 95% CI:1.02–1.36, P = 0.025; OR = 1.41, 95% CI:1.02–1.95, P = 0.035; OR = 1.39, 95% CI:1.00–1.94, P = 0.044; OR = 1.76, 95% CI:1.25–2.47, P = 9×10^–4). In MR analysis, the consistency of MRE, WME, and WMO methods with IVW trends emphasizes the reliability of these results. Nonetheless, reverse MR analysis revealed no causal relation between CC and the above-mentioned CKs.

2. Sensitivity analysis

A set of sensitivity analyses were carried out to verify the stability and reliability of the causal link between inflammatory CKs and CC (Table 1). Integrated Cochran's Q test and MRE methods were deployed to analyze the heterogeneity and found that the P values of IFN-γ, CysD, IL-8, LIF, and MCP-2 were all above 0.05, indicating that no heterogeneity was observed. Then, MRE regression intercept was deployed, aiming at analyzing horizontal multiplicity, revealing that there was no horizontal multiplicity between IFN-γ, CysD, IL-8, LIF, MCP-2, and CC ( intercept P = 0.3189 for IFN-γ, intercept P = 0.8333 for CysD, intercept P = 0.1489 for IL-8, intercept P = 0.9719 for LIF, intercept P = 0.8881 for MCP-2), visualizing the data as a scatter plot (Fig. 3). Finally, we used LOO for the robustness validation of the positive results, demonstrating SNP absence with a strong effect on the results. The forest plot (Fig. 4) depicts the results of the analysis. To sum up, our results are robust and suggest that a variety of inflammatory CKs may have a causal relationship with CC.

Table 1

Sensitivity analysis results (inflammatory cytokines on cervical cancer).
Genes	Heterogeneity analysis				Horizontal pleiotropy
	MR Egger		IVW		Egger intercept	p-value
	Cochran’s Q	Q_pval	Cochran’s Q	Q_pval	Egger intercept	p-value
IFN-γ	8.937	0.443	10.050	0.436	0.044	0.318
CysD	8.963	0.833	9.092	0.087	0.005	0.724
IL-8	19.435	0.148	20.051	0.169	0.019	0.516
LIF	2.261	0.971	2.874	0.969	-0.029	0.456
MCP-2	1.138	0.888	1.262	0.938	0.021	0.742

Our study deployed several MR methods to determine the causal link between inflammatory CKs and CC. To our knowledge, we are the first to demonstrate a causal relation between 132 inflammatory CKs and CC, revealing that low IFN-γ levels and high CysD, IL-8, LIF, and MCP-2 levels are associated with a higher CC risk.

IFN-γ, a CK of the type II interferon family, has the functions of activating macrophages, anti-tumor, anti-virus, and immune regulation[5, 34]. Nonetheless, epidemiological evidence is limited for the relation between IFN-γ and CC. For example, in an observational study that included 30 patients with CC and 30 controls, patients with CC had higher IFN-γ levels, unlike the control group (P<0.05)[35]. In addition, patients with CC experienced significantly decreased plasma IFN-γ levels after radical hysterectomy, which may be related to the 5-year recurrence rate and/or CC metastasis[36]. However, our MR analysis found that IFN-γ possesses a probable protective effect on CC risk, suggesting that the results of observational studies may necessitate additional investigation. Some studies have found that IFN-γ can overexpress indoleamine-2,3-dioxygenase-1 (IDO1) and induce its activity in CC cells, resulting in tryptophan conversion to canine uric acid, thus promoting autophagy induction in CC cells, further inducing macrophage activation and phagocytosis, and finally limiting tumor growth[37]. Although our MR analysis showed a causal correlation between heightened IFN-γ levels and decreased CC risk, reverse MR analysis demonstrated no causal connection between CC and IFN-γ. In view of these contradictory studies, further research is required, especially in the direction of potential mechanisms, which is essential to clarify the potential role of IFN-γ in CC development.

CysD, a member of the cathepsin inhibitor cysteine aminotransferase superfamily, is a small protein composed of 112 amino acids encoded by CST5[38]. CysD can inhibit cathepsin H, S, and L, thus playing the role of immunosuppression and anti-tumor[39]. So far, there are no studies on CysD in CC, but some studies have found that CYSD is an anti-tumor gene for hepatitis B virus-associated hepatocellular carcinoma (HCC) as well as colon, gastric, and prostate cancers[40–42]. The CST5 expression level in HCC tissues of patients with hepatitis B virus-associated HCC has been indicated to be much higher in comparison to normal liver tissues. Moreover, CST5 has been elucidated to be the downstream target of p53[40], and p53 is an important tumor suppressor gene in CC[43], so we can boldly speculate that CST5 may possess a certain role in CC occurrence and development. Our MR analysis investigated the causal connection between CysD and CC, and the results were consistent with our guess that elevated CysD levels were positively correlated with CC risk. Therefore, we can conclude that CysD may contribute to CC occurrence and development, but we still need to do a lot of clinical and basic research to verify this. Finally, our reverse MR analysis revealed the absence of a direct causal relation between CC and CysD.

IL-8, known as CXC chemokine ligand 8 (CXCL8), is a CK of the CXC chemokine family[44, 45]. One case-control report included 227CC patients and 419 healthy female controls. The serum IL-18 level in CC patients was significantly higher than that in healthy controls (110.5 ± 252.3 vs. 90.3 ± 215.4%, P < 0.05)[46]. However, the results of another case-control report manifested that the serum LI-8 levels in CC patients were lower than those in control groups (292.79 ± 6.24 vs. 340.52 ± 11.91%, P < 0.05)[11]. Although our MR analysis showcased a direct causal connection between heightened IL-8 and increased CC risk, the link between IL-8 and CC pathogenesis remains unclearly studied, but it has been reported that IL-8 can down-regulate protein Numb and up-regulate IL-8 receptor (IL-8R) A/B and signal-regulated protein kinase (ERKs)[47]. Thus, participate in CC occurrence and development. In view of the inconsistent results, additional studies should determine the precise mechanism of IL-8 in CC occurrence and development.

LIF is a multifunctional CK of the IL-6 superfamily, which consists of 180 amino acids[48, 49]. The LIF level in CC has been reported to be significantly overexpressed than that in normal cervical tissue, and high expression is related to the low survival rate of patients with CC[50]. However, up to now, no study has been found between serum LIF and CC, although serum LIF has been revealed to be significantly increased in patients with pancreatic, rectal, and prostate cancers, among others[51]. Our MR analysis found a causal correlation between LIF and CC, but there is a lack of research to further verify it. Some studies have found that LIF treatment of HPV-16 transformed CC cells can inhibit the virus length control region (LCR), resulting in a significant decrease in E6/E7mRNA abundance[52]. It has also been reported that LIF is the direct transcriptional target of tumor suppressor gene p53, and the combination of p53 and LIF can improve the expression and function of LIF in mouse uterus[53]. To sum up, LIF is involved in CC occurrence and development, but we need further clinical research and basic research to clarify the probable mechanism behind LIF in CC.

MCP-2, also known as C-C motif chemokine 8 (CCL8), is a CK of the CC chemokine family, which has the function of recruiting macrophages into the hypoxic tumor microenvironment[54]. The expression of MCP-2 is significantly increased in breast cancer, colorectal cancer, glioblastoma, lung cancer, and other patients[55]. However, there is no research on serum MCP-2 and CC. In a clinical control trial including 9 cases of p53 wild-type serous ovarian cancer and 62 cases of p53 mutant serous ovarian cancer, the expression level of MCP-2 in p53 mutant ovarian cancer was significantly increased[56], while the p53 mutant was closely related to CC[57]. In addition, our MR analysis elucidated a positive relation between the increase of MCP-2 and the increased risk of CC. We can speculate that MCP-2 is crucial in CC occurrence and development. Hypoxia can increase ZEB1 expression in CC cells, directly promote MCP-2 production, and attract macrophages via the CCR2-NF-κB pathway, leading to CC progression[58]. Therefore, we need to further verify the causal link between MCP-2 and CC.

Until now, although many studies have manifested the vital role of inflammatory CKs in cancer occurrence and development, no MR analysis has verified the causal link between inflammatory CKs and CC. To our knowledge, this systematic and comprehensive MR study is the first on the causal connection between inflammatory CKs and CC. We have found a potential causal association between IFN-γ, CysD, IL-8, LIF, MCP-2, and CC. Finally, a reverse MR analysis was also performed to further verify the causal relation between CC and them.

Although MR analysis is a data analysis method based on large-scale experimental samples, it also has some constraints. (1) While we conducted Mendelian Randomization (MR) analysis using GWAS data from European populations, the applicability of these findings to other populations requires further validation. We adopted a relaxed significance threshold of p < 5 × 10^–6 for selecting SNPs, which might incorporate false positive variations, potentially leading to errors in our analysis. (3) The data of CC are unique to women, while inflammatory CKs come from men and women; due to the lack of specific information, we are unable to carry out subgroup analysis, which may lead to biased results. (4) After Bonferron correction, we revealed that inflammatory CKs and CC risk had no statistically significant correlation, so we need more comprehensive data for further research.

This study utilized various MR analysis methods to investigate the causal relationship between inflammatory CKs and CC. Our results indicate a potential causal connection between IFN-γ, CysD, IL-8, LIF, MCP-2, and changes in CC risk. Further research is needed to validate these findings and determine their utility as biomarkers for early diagnosis and targets for immunotherapy in CC patients.

CC:Cervical cancer

CKs:Cytokines

GWAS:genome-wide association study

HPV:Human papillomavirus

IVW:inverse variance weighting

MRE:MR-Egger

WME:weighted median

WMO:weighted mode

LOO:Leave One Out

MCP-2:Monocyte chemoattractant protein 2

IL-8:Interleukin8

LIF:Leukemia Inhibitory Factor

INF-γ:Interferon gamma

CysD:Cystatin D

MR:Mendelian randomization

Ethics approval and consent to participate

Not applicable. All data were downloaded from the internet.

Consent for publication

Not applicable.

Availability of data

The data used in the present study are all publicly available at https://gwas.mrcieu.ac.uk/. The original contributions presented in the study are included in the article/Supplementary Material.

Competing interests

The author declares that there is no financial support for this study.

Funding

Not applicable.

Acknowledgements

We are grateful to everyone who participated in the Ovarian cancer and Inflammatory cytokine Cohort Study.

Clinical trial number

Not applicable.

Authors' information

Authors and Affiliations

Department of Gynecology, Shiyan People's Hospital, Shiyan Street, Bao'an District, 518000, Shenzhen, Guangdong, China

Yuqiang Zhang, Juan Chen, Chaoqun Zheng, Xiaohai Sun, Zhaomei Zhong

Department of Anesthesiology, The Fifth People's Hospital of Longgang District, 518115, Shenzhen, China

Danfen Luo

Contributions

All authors contributed to the article, YZ: data analysis and writing of the manuscript. DL and JC: data collection. CZ and XS: literature collection and summarization. ZZ: supervision and revision of the manuscript. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Zhaomei Zhong.

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No competing interests reported.

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Exploring the Role of Inflammatory Cytokines in Cervical cancer Pathogenesis: Evidence from Mendelian Randomization Analysis

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Abstract

Figures

Introduction

METHODS

Study Design

Participants and data sources

Statistical Analysis

Results

1. The causal connection between inflammatory CKs and CC

2. Sensitivity analysis

Discussion

Conclusions

Abbreviations

Declarations

References

Additional Declarations

Supplementary Files

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