Elevated CD14 in B cells associates with reduced ovarian cancer risk via CD80 + dendritic cell interaction: a multi-omics study

Abstract

Introduction: Ovarian cancer (OC) is a highly aggressive malignancy characterized by a complex immune microenvironment. B cells, essential components of immunological regulation, have been implicated in the progression of ovarian cancer. However, the precise mechanisms by which B cells and immune molecules influence ovarian cancer risk remain poorly understood.

Methods: This study employed single-cell RNA sequencing (scRNA-seq) to analyze peripheral blood mononuclear cells (PBMCs) from ovarian cancer patients and healthy donors. Differential gene expression analysis identified CD14 as a critical gene in B cells. Mendelian randomization (MR) analysis, using exposure data from eQTL and pQTL databases, was performed to evaluate the association between CD14 and ovarian cancer risk. Mediation analysis was conducted to assess the role of CD80 on myeloid dendritic cells in mediating the relationship between CD14 and ovarian cancer.

Results: The analysis demonstrated that CD14 expression was significantly downregulated in B cells from ovarian cancer patients compared to healthy donors. MR analysis revealed a significant association between elevated CD14 expression and reduced ovarian cancer risk. Mediation analysis indicated that CD80 mediated 26.2% of this effect.

Conclusion: These findings highlight CD14 as a key regulator of ovarian cancer risk, with CD80 serving as a mediator of the immune response in this context. This study provides insights into potential immune modulation strategies for ovarian cancer therapy.

Keywords: CD14; CD80; Immune Cells; Mendelian Randomization; Ovarian Cancer.

Fig. 1

Study flow chart

Fig. 2

A graph depicting the methodologies employed in this study. The overall impacts of CD14 and OC were analyzed as follows: (1) indirect effects, utilizing a two-step approach (where a represents the influence of CD14 on immune cells, b denotes the effect of immune cells on OC, and c signifies the total effect of CD14 as the exposure and OC as the outcome; the indirect effect is calculated as (c’ = c - a × b))

Fig. 3

t-SNE analysis revealing distinct clustering between cells from ovarian cancer patients and healthy donors. Nine major cell populations were identified, including Monocytes, T cells, NK cells, B cells, HSC-G-CSF, Pre-B cells (CD34−), Platelets, MEPs (megakaryocyte-erythroid progenitors), and an undefined category (NA)

Fig. 4

A bar chart showing that CD14 expression in the B cells of ovarian cancer patients was significantly decreased (p < 0.05)

Fig. 5

A bar chart of GO and KEGG analysis results. P<0.05

Fig. 6

MR Analysis of CD14 (eQTL) and OC. A Leave-one-out forest map; B funnel diagram; C Scatter plot: Lines in black, red, green, and blue represent IVW, MR-Egger, weighted median, and weighted mode methods; D Forest map for the sensitivity analysis

Fig. 7

MR Analysis of CD14(pQTL) and OC. A Leave-one-out forest map; B funnel diagram; C Scatter plot: Lines in black, red, green, and blue represent IVW, MR-Egger, weighted median, and weighted mode methods; D Forest map for the sensitivity analysis

Fig. 8

MR Analysis of CD14 and CD80. A Leave-one-out forest map; B funnel diagram; C Scatter plot: Lines in black, red, green, and blue represent IVW, MR-Egger, weighted median, and weighted mode methods; D Forest map for the sensitivity analysis

Fig. 9

Results of MR Analysis of CD80 and OC. A: Leave-one-out forest map; B funnel diagram; C Scatter plot: Lines in black, red, green, and blue represent IVW, MR-Egger, weighted median, and weighted mode methods; D Forest map for the sensitivity analysis

Fig. 10

Schematic overview of the mediating effect of CD80