Mendelian randomization analysis of immune cell characteristics and genetic variants in cervical cancer risk: a genome-wide association study

Abstract

Background: Cervical cancer remains a significant global health concern, with both genetic and immunological factors potentially influencing susceptibility. This study aimed to investigate the causal relationships between immune cell characteristics, genetic variants, and cervical cancer risk using Mendelian randomization (MR) analysis.

Methods: We utilized genome-wide association study (GWAS) data from the UK Biobank, comprising 475,638 participants of European descent with approximately 24.2 million genetic variants. Instrumental variables were selected at a significance threshold of 1 × 10^-5 for immune characteristics.

Results: Forest plot analysis of genetic variants revealed minimal associations with cervical cancer risk, with most odds ratios close to 1.000 despite some statistically significant findings (p < 0.05). MR analyses demonstrated consistent negative correlations between SNP effects on certain immune cell populations and cervical cancer risk, though effect sizes remained small. Multiple MR methods produced similar summary estimates, suggesting no substantial causal relationship between the studied immune cell characteristics and cervical cancer susceptibility.

Conclusions: This comprehensive MR analysis found limited evidence for causal associations between immune cell-related genetic variants and cervical cancer risk. While some statistically significant relationships were observed, the effect sizes were notably small, indicating that these genetic determinants of immune.

Keywords: Cervical cancer; GWAS; Genetic variants; Immune cells; Mendelian randomization; Single-cell RNA sequencing; T cells.

Fig. 1

Differential gene expression analysis in cervical cancer immune microenvironment. This volcano plot shows differential gene expression analysis related to cervical cancer. The horizontal axis (BETA) represents the direction and magnitude of gene expression changes, while the vertical axis (-log(pvalue)) indicates statistical significance. Blue dots represent downregulated genes in cervical cancer (negative BETA values), red dots show upregulated genes (positive BETA values), and white or gray dots indicate non-significant changes. Bubble size (neglogp) reflects significance intensity. The plot highlights several key immune cell populations, including CD69+LGALS3A+ CD4 regulatory and non-regulatory T cells, CD8+ T cell absolute counts, and activated CD4 regulatory T cells. These results reveal T cell subset expression patterns in cervical cancer, providing important insights into the tumor immune microenvironment

Fig. 2

Genetic variant associations with cervical cancer risk. This forest plot presents an analysis of multiple genetic variants and their effects on cervical cancer risk. The table lists 27 genetic exposure identifiers (starting with GCST), the number of single nucleotide polymorphisms for each (nsnp), odds ratios with 95% confidence intervals (OR (95% CI)), and statistical significance (pval). Results show that most genetic variants have odds ratios very close to 1.000, indicating minimal impact on cervical cancer risk. While some variants reach statistical significance (p < 0.05), effect sizes remain limited, with the highest odds ratio at only 1.001 and the lowest at 0.998. These findings suggest that the studied genetic variants may have little or very weak association with cervical cancer development risk

Fig. 3

Mendelian randomization analysis of genetic variants and cervical cancer. A-D Overall, this figure appears to be presenting genetic association or causal inference analyses, possibly examining how specific genetic variants influence different immune cell types or related traits

Fig. 4

Genetic influences on cervical cancer: a multi-method analysis. A-D Overall, these results suggest a systematic investigation of how genetic variants influence immune cell populations, possibly exploring causal relationships between genetic architecture and immune regulation

Fig. 5

Exploring T-cell genetics in cervical cancer prevention. A-D Together, these results suggest a comprehensive analysis of how genetic variants affect T-cell populations, possibly investigating causal pathways between genetic architecture and immune cell differentiation or function

Fig. 6

T-cell subtypes and cervical cancer: a genetic perspective. A displays a scatter plot comparing two MR methods (inverse variance weighted vs. MR Egger) with different beta values. B shows a forest plot of genetic variants (labeled with rs numbers) and their effect sizes with confidence intervals. Most confidence intervals cross zero. C presents a regression analysis showing negative correlations between SNP effects on CD45RA+ CD4+ CD69+ monocytes and another immune parameter, with three different statistical models showing similar downward trends. D contains another forest plot similar to panel B, showing genetic variant effects with a red summary estimate line at the bottom

Fig. 7

Immune genetics in cervical cancer risk. A: A scatter plot showing negative correlation between SNP effects on CD45RA+ on monocytes (x-axis) and another immune parameter (y-axis). Multiple statistical models (inverse variance weighted, MR Egger, weighted median, and simple mode) all show negative trends. B: A forest plot of genetic variants (rs numbers) with their effect estimates and confidence intervals. Most variants show small positive effects, but confidence intervals generally cross zero. C: Another forest plot similar to panel B but with fewer variants, showing individual genetic effects and two summary estimates at the bottom (red lines). D: A scatter plot comparing two MR methods (inverse variance weighted vs. MR Egger) showing the distribution of beta values for different genetic variants