Integrated multi-omics analysis reveals the functional and prognostic significance of lactylation-related gene PRDX1 in breast cancer

Abstract

Background: Breast cancer (BRCA) is a significant threat to women's health worldwide, and its progression is closely associated with the tumor microenvironment and gene regulation. Lactylation modification, as a key epigenetic mechanism in cancer biology, has not yet been fully elucidated in the context of BRCA. This study examines the regulatory mechanisms of lactylation-related genes (LRGs), specifically PRDX1, and their prognostic significance in BRCA.

Methods: We integrated data from multiple databases, including Genome-Wide Association Study (GWAS) summary statistics, single-cell RNA sequencing, spatial transcriptomics, and bulk RNA sequencing data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Using Summary-based Mendelian Randomization (SMR) analysis, we identified LRGs associated with BRCA and comprehensively analysed the expression patterns of PRDX1, cell-cell communication networks, and spatial heterogeneity. Furthermore, we constructed and validated a prognostic model based on the gene expression profile of PRDX1-positive monocytes, evaluating it through Cox regression and LASSO regression analyses.

Results: PRDX1 was identified as a key LRG significantly associated with BRCA risk (p_SMR = 0.0026). Single-cell RNA sequencing analysis revealed a significant upregulation of PRDX1 expression in monocytes, with enhanced cell-cell communication between PRDX1-positive monocytes and fibroblasts. Spatial transcriptomics analysis uncovered heterogeneous expression of PRDX1 in the tumor nest regions, highlighting the spatial interaction between PRDX1-positive monocytes and fibroblasts. The prognostic model constructed based on the gene expression profile of PRDX1-positive monocytes demonstrated high accuracy in predicting patient survival in both the training and validation cohorts. High-risk patients exhibited immune-suppressive microenvironment characteristics, including reduced immune cell infiltration and upregulation of immune checkpoint gene expression.

Conclusion: This study reveals the key role of PRDX1 in BRCA progression, mainly through the regulation of the tumor microenvironment and immune escape mechanisms. The survival prediction model based on PRDX1 shows robust prognostic potential, and future research should focus on integrating PRDX1 with other biomarkers to enhance the precision of personalised medicine.

Keywords: PRDX1; breast cancer; lactylation; prognostic model; spatial transcriptomics.

FIGURE 1

The causal relationship between LRGs and breast cancer, as determined by SMR analysis. (A) Displays three genes (PRDX1, UROD, and RP11-767N6.2) on chromosome 1 and their genetic association with eQTL. (B) Shows the positive correlation between the effect size of the PRDX1 gene’s eQTL and GWAS effect size.

FIGURE 2

Single-cell analysis of BRCA patients. (A) PCA reveals differences in gene expression data distribution between BRCA and TNBC samples. (B) t-SNE clustering analysis identifies 27 distinct cell clusters. (C) Further analysis of the cell populations reveals seven cell types. (D) Bubble plot showing PRDX1 gene expression in different cell types. (E, F) Feature expression and density plots visualising the expression of the PRDX1 gene. (G) Cell-to-cell communication heatmap showing the communication activity between different cell types. (H) Cell communication network analysis indicates a potential specific communication pathway between PRDX1-positive monocytes and fibroblasts. (I) Cell communication network analysis indicates potential specific communication pathways between PRDX1-positive monocytes, fibroblasts, and B cells. (J) PCA plot of BRCA and TNBC samples after filtering for Monocyte cell subpopulations. (K) UMAP plot showing the distribution of cell clusters. (L, M) Visualisation of PRDX1 gene expression patterns in Monocyte cell subpopulations. (N) Pseudo-time analysis reveals the dynamic trajectory of Monocytes in the BRCA process.

FIGURE 3

Spatial transcriptomics analysis of BRCA patients. (A) Expression pattern of PRDX1 gene in BRCA tissue sections. (B) Data quality filtering process for PRDX1 gene expression. (C) Spatial distribution of PRDX1 gene expression after normalisation and dimensionality reduction. (D) UMAP-based clustering results of cell populations. (E–H) Deconvolution analysis showing the distribution of PRDX1-positive monocytes, PRDX1-negative monocytes, fibroblasts, and B cells in tissue sections. (I–K) Communication relationships between different cell types under various communication modes (intra, juxta, para). (L) Heatmap of intercellular communication pairs, revealing significant communication pairs and network structure between cell populations. (M) Network diagram showing the homotypic cell network of PRDX1-positive monocytes in tissue sections. (N, O) Heterotypic cell network diagram between PRDX1-positive/negative monocytes and fibroblasts. (P, Q) Enrichment plot showing the interaction between PRDX1-positive/negative monocytes and fibroblasts.

FIGURE 4

Prognostic model construction for BRCA patients. (A) Batch effect correction plot for BRCA samples from TCGA training set and GEO testing set. (B) Univariate Cox regression analysis identifies significant genes associated with BRCA survival. (C, D) LASSO regression model plot and cross-validation are used to select the optimal regularisation parameter (lambda). (E, F) Kaplan-Meier survival analysis curves for different risk groups of BRCA patients in the training and testing sets. (G, H) ROC curves for training and testing sets for 1-year, 3-year, and 5-year survival rates.

FIGURE 5

Risk score is highly correlated with clinical variables. (A) Forest plot of univariate Cox regression analysis. (B) Multivariate Cox regression analysis results. (C–K) Kaplan-Meier survival curves for different risk groups of BRCA patients stratified by age, gender, stage, T stage, M stage, and N stage.

FIGURE 6

Significant differences in immune characteristics between different risk groups. (A) Box plot showing the difference in immune cell infiltration levels between high-risk and low-risk groups. (B) Box plot and peak plot showing significant differences in the stromal score, immune score, ESTIMATE score, and tumor purity between high-risk and low-risk groups. (C, E) Differences in immune-related pathway scores between high-risk and low-risk groups. (D, F) Differences in the expression of immune checkpoint genes between high-risk and low-risk groups.

FIGURE 7

Function of PRDX1 in BRCA cells. (A–C) Transwell assays demonstrated the role of PRDX1 in cell migration and invasion. (D, E) Wound healing assays were performed at 0 and 24 h on HCC1806 and MDA-MB-231 cells to assess the role of PRDX1 in cell motility.