ArticleBritish journal of cancer2024
A proteome-wide association study identifies putative causal proteins for breast cancer risk.
Article in British journal of cancer, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Integrative Proteogenomics and Single-Cell Transcriptomics Prioritize Candidate Causal Proteins and Therapeutic Targets in Age-Related Macular Degeneration.International journal of molecular sciences · 2026Article
- The STAT3-CCND2 Axis Drives a Proliferative Metaplastic Precursor Population in Gastric Intestinal Metaplasia.Journal of cellular and molecular medicine · 2026Article
- EffectorFisher: association of disease phenotype with pangenomic protein-isoform profiles for improved prediction of fungal pathogenicity effectors.Scientific reports · 2026Article
- Identification of PD-1-Related Genes as Prognostic Biomarkers in Lung Adenocarcinoma.Human mutation · 2026Article
- Targeting Lactylation for Cancer: Mechanisms, Effects, and Therapeutic Prospects.International journal of molecular sciences · 2025Review
- Integrated multi-omics analysis reveals the functional and prognostic significance of lactylation-related gene PRDX1 in breast cancer.Frontiers in molecular biosciences · 2025Article
- Understanding genetic architecture of breast cancer: how can proteome-wide association studies contribute?British journal of cancer · 2024Article
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13 authors.
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Abstract
backgroundGenome-wide association studies (GWAS) have identified more than 200 breast cancer risk-associated genetic loci, yet the causal genes and biological mechanisms for most loci remain elusive. Proteins, as final gene products, are pivotal in cellular function. In this study, we conducted a proteome-wide association study (PWAS) to identify proteins in breast tissue related to breast cancer risk.
methodsWe profiled the proteome in fresh frozen breast tissue samples from 120 cancer-free European-ancestry women from the Susan G. Komen Tissue Bank (KTB). Protein expression levels were log2-transformed then normalized via quantile and inverse-rank transformations. GWAS data were also generated for these 120 samples. These data were used to build statistical models to predict protein expression levels via cis-genetic variants using the elastic net method. The prediction models were then applied to the GWAS summary statistics data of 133,384 breast cancer cases and 113,789 controls to assess the associations of genetically predicted protein expression levels with breast cancer risk overall and its subtypes using the S-PrediXcan method.
resultsA total of 6388 proteins were detected in the normal breast tissue samples from 120 women with a high detection false discovery rate (FDR) p value < 0.01. Among the 5820 proteins detected in more than 80% of participants, prediction models were successfully built for 2060 proteins with R > 0.1 and P < 0.05. Among these 2060 proteins, five proteins were significantly associated with overall breast cancer risk at an FDR p value < 0.1. Among these five proteins, the corresponding genes for proteins COPG1, DCTN3, and DDX6 were located at least 1 Megabase away from the GWAS-identified breast cancer risk variants. COPG1 was associated with an increased risk of breast cancer with a p value of 8.54 × 10
conclusionWe conducted the first breast-tissue-based PWAS and identified seven proteins associated with breast cancer, including five proteins not previously implicated. These findings help improve our understanding of the underlying genetic mechanism of breast cancer development.
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