Integration of bioinformatics and machine learning strategies identifies APM-related gene signatures to predict clinical outcomes and therapeutic responses for breast cancer patients

Keywords

Antigen processing and presentation machinery

Gene signatures

Breast cancer

Risk assessment

Gene mutation

Immunotherapy

AbbreviationsICB

immune checkpoint blockade

PD-L1

programmed cell death ligand 1

TNBC

triple negative breast cancer

TILs

tumor infiltrating lymphocytes

TAA

tumor-associated antigen

TSA

tumor-specific antigen

HLA

human leukocyte antigen

APM

antigen processing and presentation machinery

pAPCs

professional antigen presenting cells

ssGSEA

single-sample gene set enrichment analysis

RFS

recurrence-free survival

TCGA

The Cancer Genome Atlas

GEO

gene expression Omnibus

MAF

mutation annotation format

WGCNA

weighted gene co-expression network analysis

NMF

non-negative matrix factorization

MSigDB

molecular signatures database

RSF

random survival forest

Lasso

the least absolute shrinkage and selection operator

APMrs

APM-related risk score

TIDE

tumor immune dysfunction and exclusion

DEGs

differentially expressed genes

APMis

APM-related immunotherapeutic response score

ROC

receiver operating characteristic

PFS

progression-free survival

CSS

cancer-specific survival

TAP

antigen-processing-associated transporter protein

ERAP

ER-associated aminopeptidase

NSCLC

non-small-cell lung cancer

DCA

decision curve analysis

Data availability

All presented data and codes in this study are available from the corresponding author upon reasonable request.

© 2023 The Authors. Published by Elsevier Inc.

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