Analysis of the tumor characteristics in young age breast cancer patients using collaborative stage data of the Korea Central Cancer Registry

Kim, Junyup; Hong, Seri; Lee, Jae Jun; Won, Young‐Joo; Lee, Eun Sook; Kang, Hyejin; Lee, Seeyoun; Han, Jai Hong; Lee, Eun‐Gyeong; Jo, Heein; Kim, Hyun Hee; Jung, So‐Youn

doi:10.1007/s10549-021-06107-9

Cited by 9 publications

(7 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…First, study participants in the cohort were enrolled exclusively in Korea. In addition, the incidence rate of BC in Korea is different from that in the West, and its incidence in young women is higher than that in Western countries 26 . Therefore, caution should be exercised when generalizing these results.…”

Section: Discussionmentioning

confidence: 94%

Prediction of breast cancer using blood microbiome and identification of foods for breast cancer prevention

Yang

Kwon

et al. 2023

Sci Rep

View full text Add to dashboard Cite

The incidence of breast cancer (BC) is increasing in South Korea, and diet is closely related to the high prevalence of BC. The microbiome directly reflects eating habits. In this study, a diagnostic algorithm was developed by analyzing the microbiome patterns of BC. Blood samples were collected from 96 patients with BC and 192 healthy controls. Bacterial extracellular vesicles (EVs) were collected from each blood sample, and next-generation sequencing (NGS) of bacterial EVs was performed. Microbiome analysis of patients with BC and healthy controls identified significantly higher bacterial abundances using EVs in each group and confirmed the receiver operating characteristic (ROC) curves. Using this algorithm, animal experiments were performed to determine which foods affect EV composition. Compared to BC and healthy controls, statistically significant bacterial EVs were selected from both groups, and a receiver operating characteristic (ROC) curve was drawn with a sensitivity of 96.4%, specificity of 100%, and accuracy of 99.6% based on the machine learning method. This algorithm is expected to be applicable to medical practice, such as in health checkup centers. In addition, the results obtained from animal experiments are expected to select and apply foods that have a positive effect on patients with BC.

show abstract

Section: Discussionmentioning

confidence: 94%

Prediction of breast cancer using blood microbiome and identification of foods for breast cancer prevention

Yang

Kwon

et al. 2023

Sci Rep

View full text Add to dashboard Cite

show abstract

“…It is worth noting that younger patients with breast cancer who are premenopausal have a relatively poorer treatment prognosis than do postmenopausal patients [ 33 , 34 ]. Additionally, young patients with breast cancer in Korea often present with more aggressive tumor characteristics and a worse survival rate [ 35 ]. High-dose adjuvant chemotherapy is considered a more favorable treatment option for premenopausal patients with breast cancer, a high-risk group, than for postmenopausal patients [ 36 ].…”

Section: Discussionmentioning

confidence: 99%

Osteoporosis Associated with Breast Cancer Treatments Based on Types of Hormonal Therapy: A Cross-Sectional Study Using Korean National Sample Data

Wang,

Lim,

Hwang

et al. 2023

Medicina

View full text Add to dashboard Cite

Background and Objectives: This study aimed to investigate osteoporosis-related treatments and the overall anticancer drug treatment tendencies, with a focus on selective estrogen receptor modulators (SERMs) and aromatase inhibitors (AIs), in Korean patients with breast cancer from 2010 to 2019. Materials and Methods: Data were obtained from the Health Insurance Review and Assessment Service. Patients with breast cancer (International Classification of Diseases, 10th Revision code: C50) as a principal diagnosis at least once from 2010 to 2019 were included. Those with osteoporosis (M80, M81, or M82) as a principal or sub-diagnosis or those who received osteoporosis treatment at least once were categorized as the osteoporosis-related treatment group, and others as the non-osteoporosis-related treatment group. The trends of drug prescriptions and treatment costs in patient groups were evaluated using descriptive statistics. Results: Among all included patients, those aged 45–54 years (40.20%) without osteoporosis treatment and those aged 55–64 years (34.11%) with osteoporosis treatment were the most common. SERM was the most commonly prescribed anticancer drug (29.20%) in the entire patient group, followed by AIs (20.83%). Patients without osteoporosis treatment had the highest prescription rate of SERM (31.48%), and those with osteoporosis treatment had a higher prescription rate of AIs (34.28%). Additionally, SERM and AIs were prescribed most frequently before and after the age of 55 years, respectively, regardless of the presence of treatment. Conclusions: This study found that osteoporosis-related treatment and patient age were associated with anticancer drug prescriptions. The present findings would help clinicians and researchers in the clinical diagnosis and treatment of breast cancer.

show abstract

“…Age is a critical determinant for the long-term survival of BC patients. Young patients, when compared to patients in an older age group, typically have poorer prognoses [4][5][6]. An extensive number of research has demonstrated that YBC patients often exhibit more aggressive biological behaviors and less favorable phenotypes.…”

Section: Introductionmentioning

confidence: 99%

“…An extensive number of research has demonstrated that YBC patients often exhibit more aggressive biological behaviors and less favorable phenotypes. These include a higher incidence of triple-negative breast cancer (TNBC), symptom manifestation in a later stage, larger tumors, increased lymph node involvement, higher histological grades, a positive family history, and a heightened risk of BRCA1/2 mutations [6][7][8][9][10]. Consequently, YBC patients, in contrast to their older counterparts, often have lower 5-year sur-Am J Cancer Res 2024;14(4):1609-1621 vival rates, marked by a significantly elevated risk of overall tumor recurrence and distant metastasis [11][12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Development and validation of a random survival forest model for predicting long-term survival of early-stage young breast cancer patients based on the SEER database and an external validation cohort

2024

Am J Cancer Res

View full text Add to dashboard Cite

Young breast cancer (YBC) patients often face a poor prognosis, hence it's necessary to construct a model that can accurately predict their long-term survival in early stage. To realize this goal, we utilized data from the Surveillance, Epidemiology, and End Results (SEER) databases between January 2010 and December 2020, and meanwhile, enrolled an independent external cohort from Tianjin Medical University Cancer Institute and Hospital. The study aimed to develop and validate a prediction model constructed using the Random Survival Forest (RSF) machine learning algorithm. By applying the Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis, we pinpointed key prognostic factors for YBC patients, which were used to create a prediction model capable of forecasting the 3-year, 5-year, 7-year, and 10-year survival rates of YBC patients. The RSF model constructed in the study demonstrated exceptional performance, achieving C-index values of 0.920 in the training set, 0.789 in the internal validation set, and 0.701 in the external validation set, outperforming the Cox regression model. The model's calibration was confirmed by Brier scores at various time points, showcasing its excellent accuracy in prediction. Decision curve analysis (DCA) underscored the model's importance in clinical application, and the Shapley Additive Explanations (SHAP) plots highlighted the importance of key variables. The RSF model also proved valuable in risk stratification, which has effectively categorized patients based on their survival risks. In summary, this study has constructed a well-performed prediction model for the evaluation of prognostic factors influencing the long-term survival of early-stage YBC patients, which is significant in risk stratification when physicians handle YBC patients in clinical settings.

show abstract

Analysis of the tumor characteristics in young age breast cancer patients using collaborative stage data of the Korea Central Cancer Registry

Cited by 9 publications

References 28 publications

Prediction of breast cancer using blood microbiome and identification of foods for breast cancer prevention

Prediction of breast cancer using blood microbiome and identification of foods for breast cancer prevention

Osteoporosis Associated with Breast Cancer Treatments Based on Types of Hormonal Therapy: A Cross-Sectional Study Using Korean National Sample Data

Development and validation of a random survival forest model for predicting long-term survival of early-stage young breast cancer patients based on the SEER database and an external validation cohort

Contact Info

Product

Resources

About