The risk of early-onset (EO) breast cancer is known to be increased in relatives of EO breast cancer patients, but less is known about the familial risk of other EO cancers. We assessed familial risks of EO cancers (aged ≤40 years) other than breast cancer in 54 753 relatives of 5562 women with EO breast cancer (probands) by using a population-based cohort from Finland. Standardized incidence ratios (SIRs) and 95% confidence intervals (CI) were estimated by using gender-, age- and period-specific cancer incidences of the general population as reference. The risk of any cancer excluding breast cancer in first-degree relatives was comparable to population cancer risk (SIR 0.99, 95% CI: 0.84-1.16). Siblings' children of women with EO breast cancer were at an elevated risk of EO testicular and ovarian cancer (SIR = 1.74, 95% CI: 1.07-2.69 and 2.69, 95% CI: 1.08-5.53, respectively). The risk of EO pancreatic cancer was elevated in siblings of the probands (7.61, 95% CI: 1.57-22.23) and an increased risk of any other cancer than breast cancer was observed in children of the probands (1.27, 95% CI: 1.03-1.55). In conclusion, relatives of women with EO breast cancer are at higher familial risk of certain discordant EO cancers, with the risk extending beyond first-degree relatives.
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Sci Rep
December 2024
Department of Clinical Pharmacy, Baoshan Hospital Affiliated to, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
This study investigates the potential treatment of breast cancer utilizing Gentiana robusta King ex Hook. f. (QJ) through an integrated approach involving network pharmacology, molecular docking, and molecular dynamics simulation.
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December 2024
Department of Breast Surgery, Second Affiliated Hospital of Dalian Medical University, No. 467 Zhongshan Road, Shahekou District, Dalian, China.
Early prediction of patient responses to neoadjuvant chemotherapy (NACT) is essential for the precision treatment of early breast cancer (EBC). Therefore, this study aims to noninvasively and early predict pathological complete response (pCR). We used dynamic ultrasound (US) imaging changes acquired during NACT, along with clinicopathological features, to create a nomogram and construct a machine learning model.
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