# Integrative analysis of efferocytosis- and invasion-related genes as potential biomarkers and therapeutic targets in breast cancer

## TL;DR

This study identifies genes related to efferocytosis and invasion that could serve as biomarkers and therapeutic targets for breast cancer.

## Contribution

A novel prognostic risk model using efferocytosis- and invasion-related genes is developed for breast cancer.

## Key findings

- 32 efferocytosis- and invasion-related genes, including ANO6 and PLGRKT, showed differential expression in breast cancer.
- A risk score formula using ANO6 and PLGRKT predicted survival outcomes with significant differences between high- and low-risk groups.
- ANO6 and PLGRKT were linked to biological processes like bleb assembly and phagocytosis regulation.

## Abstract

Breast cancer remains a primary source of cancer-related mortality among females worldwide. This investigation sought to evaluate the distinctive expression patterns of genes linked to efferocytosis and invasion in breast cancer and their prognostic implications. Through bioinformatics analyses, a robust prognostic risk modelwas developed. Breast cancer datasets were procured and processed from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), and GeneCards databases. Differential expression analysis was executed utilizing DESeq2, identifying genes with|logFC| >1 and p-value < 0.05. Efferocytosis-related genes and invasion-related genes were curated from GeneCards and PubMed, resulting in 127 overlapping genes. A prognostic risk model was developed utilizing univariate Cox regression, Least Absolute Shrinkage and Selection Operator regression, and multivariate Cox regression analyses. A sum of 7860 differentially expressed genes was ascertained in the TCGA-BRCA dataset, comprising 4130 elevated and 11,990 reduced expressions. Among them, 32 efferocytosis- and invasion-related genes exhibited differential expression, including ANO6 and PLGRKT. Univariate Cox regression pinpointed ANO6 and PLGRKT as significant prognostic markers. The Least Absolute Shrinkage and Selection Operator regression yielded the risk score formula: Risk score = ANO6 × (0.328) + PLGRKT × (-0.277). Kaplan-Meier survival analysis suggested a notable difference in survival outcomes between high-risk and low-risk cohorts (p-value < 0.01). Multivariate Cox analysis confirmed risk score, age, NStage subgroups N1-N3, and TStage subgroup T4 as statistically significant prognostic predictors. Functional enrichment analysis suggested that ANO6 and PLGRKT were implicated in biological processes like bleb assembly and the positive regulation of phagocytosis. Gene Set Enrichment Analysis identified notable pathway associations, encompassing the KEAP1/NFE2L2 Pathway and TP53 Regulation of Metabolic Genes. In conclusion, the developed prognostic risk model effectively predicts survival outcomes in patients with breast cancer, with ANO6 and PLGRKT being pivotal in tumor progression. These observations provide essential knowledge for therapeutic intervention strategies and enhanced clinical care in breast cancer.

The online version contains supplementary material available at 10.1007/s12672-025-03346-w.