{"doi":"10.3389/fonc.2020.630994","title":"Genetically Predicted C-Reactive Protein Associated With Postmenopausal Breast Cancer Risk: Interrelation With Estrogen and Cancer Molecular Subtypes Using Mendelian Randomization","abstract":"BACKGROUND: Immune-related etiologic pathways that influence breast cancer risk are incompletely understood and may be confounded by lifestyles or reverse causality. Using a Mendelian randomization (MR) approach, we investigated the potential causal relationship between genetically elevated C-reactive protein (CRP) concentrations and primary invasive breast cancer risk in postmenopausal women. METHODS: We used individual-level data obtained from 10,179 women, including 537 who developed breast cancer, from the Women's Health Initiative Database for Genotypes and Phenotypes Study, which consists of five genome-wide association (GWA) studies. We examined 61 GWA single-nucleotide polymorphisms (SNPs) previously associated with CRP. We employed weighted/penalized weighted-medians and MR gene-environment interactions that allow instruments' invalidity to some extent and attenuate the heterogeneous estimates of outlying SNPs. RESULTS: In lifestyle-stratification analyses, genetically elevated CRP decreased risk for breast cancer in exogenous estrogen-only, estrogen + progestin, and past oral contraceptive (OC) users, but only among relatively short-term users (<5 years). Estrogen-only users for ≥5 years had more profound CRP-decreased breast cancer risk in dose-response fashion, whereas past OC users for ≥5 years had CRP-increased cancer risk. Also, genetically predicted CRP was strongly associated with increased risk for hormone-receptor positive or human epidermal growth factor receptor-2 negative breast cancer. CONCLUSIONS: Our findings may provide novel evidence on the immune-related molecular pathways linking to breast cancer risk and suggest potential clinical use of CRP to predict the specific cancer subtypes. Our findings suggest potential interventions targeting CRP-inflammatory markers to reduce breast cancer risk.","journal":"Frontiers in Oncology","year":2021,"id":175110,"datarank":0.47032413238937254,"base_score":3.1354942159291497,"endowment":3.1354942159291497,"self_citation_contribution":0.47032413238937254,"citation_network_contribution":0.0,"self_endowment_contribution":0.47032413238937254,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":22,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9601,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2021-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":282707,"name":"Jeanette C. Papp","orcid":"0000-0001-9561-7860","position":1,"is_corresponding":false},{"id":410101,"name":"Eric M. Sobel","orcid":"0000-0002-1718-0031","position":2,"is_corresponding":false},{"id":40483,"name":"Matteo Pellegrini","orcid":"0000-0001-9355-9564","position":3,"is_corresponding":false},{"id":291917,"name":"Herbert Yu","orcid":"0000-0003-3950-4815","position":4,"is_corresponding":false},{"id":265958,"name":"Zuo‐Feng Zhang","orcid":"0000-0002-4669-3995","position":5,"is_corresponding":false},{"id":410099,"name":"Su Yon Jung","orcid":"0000-0002-0513-1830","position":0,"is_corresponding":true}],"reference_count":65,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-07-18T23:47:11.249697Z","pmid":"33614510","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}