Abstract

Background: Epigenetic dysregulation via aberrant DNA methylation has gradually become recognized as an efficacious signature for predicting tumor prognosis and response to therapeutic targets. However, reliable DNA methylation biomarkers describing tumorigenesis remain to be comprehensively explored regarding their prognostic and therapeutic potential in breast cancer (BC). Methods: Whole-genome methylation datasets integrated from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database were profiled (n = 1,268). A three-stage selection procedure (discovery, training, and external validation) was utilized to screen out the prominent biomarkers and establish a robust risk score from more than 300,000 CpG sites after quality control, rigorous filtering, and reducing dimension. Moreover, gene set enrichment analyses guided us to systematically correlate this epigenetic risk score with immunological characteristics, including immunomodulators, anti-cancer immunity cycle, immune checkpoints, tumor-infiltrating immune cells and a series of signatures upon modulating components within BC tumor microenvironment (TME). Multi-omics data analyses were performed to decipher specific genomic alterations in low- and high-risk patients. Additionally, we also analyzed the role of risk score in predicting response to several treatment options. Results: A 10-CpG-based prognostic signature which could significantly and independently categorize BC patients into distinct prognoses was established and sufficiently validated. And we hypothesize that this signature designs a non-inflamed TME in BC based on the evidence that the derived risk score is negatively correlated with tumor-associated infiltrating immune cells, anti-cancer immunity cycle, immune checkpoints, immune cytolytic activity, T cell inflamed score, immunophenoscore, and the vast majority of immunomodulators. The identified high-risk patients were characterized by upregulation of immune inhibited oncogenic pathways, higher TP53 mutation and copy number burden, but lower response to cancer immunotherapy and chemotherapy. Conclusion: Our work highlights the complementary roles of 10-CpG-based signature in estimating overall survival in BC patients, shedding new light on investigating failed events concerning immunotherapy at present.

Highlights

  • Breast cancer (BC) ranks third among the most common malignancies and is the leading cause of cancer-related death in females (Lin et al, 2019; Li et al, 2019b)

  • We developed the model for the prognosis prediction of BC patients in three stages: discovery, training, and validation stage

  • It was found that the majority of these CpG sites were significantly enriched within the island and opensea, whereas only a few of them were located in the N shelf and S shelf

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Summary

Introduction

Breast cancer (BC) ranks third among the most common malignancies and is the leading cause of cancer-related death in females (Lin et al, 2019; Li et al, 2019b). Postoperative local or distant recurrence rate remains high, even for patients who have received conventional therapies in the early stage, causing a pessimistic mortality rate within BC patients at present (Spronk et al, 2018; Chen et al, 2019). This could be attributed to the restricted and incomprehensive understanding of BC heterogeneity concerning carcinogenesis, invasiveness, progression, and metastasis (Tazaki et al, 2013). Reliable DNA methylation biomarkers describing tumorigenesis remain to be comprehensively explored regarding their prognostic and therapeutic potential in breast cancer (BC)

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