The gut microbiome can reflect the health condition of the entire body. Firmicutes and Bacteroidetes, the major phyla of the colon, can influence diseases related to obesity which are also risk factors for breast cancer. Therefore, the Firmicutes/Bacteroidetes (F/B) ratio was analyzed in patients with breast cancer. Bacterial extracellular vesicles were extracted from the serum of patients with breast cancer and healthy controls. Phyla Firmicutes and Bacteroidetes were analyzed using microbiome sequencing. Prognostic factors for breast cancer and serological test results were analyzed for correlations with the F/B ratio. The F/B ratio was three times lower in patients with breast cancer than in healthy controls. In addition, the risk factor for breast cancer, such as fasting serum glucose, was found to be related to the F/B ratio. The F/B ratio can be used as a risk factor of breast cancer and as a clue to explain underlying mechanisms affecting the development of breast cancer.

The microbiome involved in the human estrogen metabolism is known as the estrobolome. This study aimed to show that the estrobolome can be used in breast cancer treatment. We first analyzed the blood microbiome composition of healthy controls and patients with breast cancer. In particular, we investigated the bacteria producing β−glucuronidase and/or β−galactosidase, which are involved in estrogen metabolism in the human body. Staphylococcus species were more abundant in healthy controls than in breast cancer patients and therefore were selected for further analyses. The effect of Staphylococcus aureus on endocrine therapy was analyzed by a combination treatment with tamoxifen. Analysis of the microbiome of blood samples showed that species producing β−glucuronidase were more abundant in breast cancer patients than in healthy controls. Further experiments confirmed that the efficacy of tamoxifen increased when administered in conjugation with the extracellular vesicles (EVs) of S. aureus. Based on our results, we deduced that S. aureus EVs could potentially be used as adjuvants for breast cancer treatment in the future.

Background: : The microbiome is important in the development and progression of breast cancer. This study investigated the effects of microbiome derived from Klebsiella on endocrine therapy of breast cancer using MCF7 cells. The bacterial extracellular vesicles (EVs) that affect endocrine therapy were established through experiments focused on tamoxifen efficacy. Methods: : The microbiomes of breast cancer patients and healthy controls were analyzed using next-generation sequencing. Among microbiome, Klebsiella was selected as the experimental material for the effect on endocrine therapy in MCF7 cells. MCF7 cells were incubated with tamoxifen in the absence/presence of bacterial EVs derived from Klebsiella pneumoniae and analyzed by quantitative real-time polymerase chain reaction and Western blot. Results: : Microbiome derived from Klebsiella is abundant in breast cancer patients especially luminal A subtype compared to healthy controls. The addition of EVs derived from K pneumoniae enhances the anti-hormonal effects of tamoxifen in MCF7 cells. The increased efficacy of tamoxifen is mediated via Cyclin E2 and p-ERK. Conclusion: : Based on experiments, the EVs derived from K pneumoniae are important in hormone therapy on MCF7 cells. This result provides new insight into breast cancer mechanisms and hormone therapy using Klebsiella found in the microbiome.

After publication of this supplement [1, 2], it was brought to our attention that due to an error authors were missing in the following abstracts. This has now been included in this correction.

Breast reconstruction during breast-conserving surgery (BCS) can improve the breast shape. This study introduces breast reconstruction in BCS with two types of acellular dermal matrix (ADM). The study included 134 patients who underwent BCS due to breast cancer from February 2018 to May 2021. This study was conducted by one surgeon, and is the result of a three-year study. The patient group who underwent BCS using ADM was mainly targeted at patients with minor to severe defects after the operation. The average age of the patients was 51.8 years, and the body mass index (BMI) was 23.8 kg/m. The specimen weight was 30–120 g. The average surgical time, including reconstruction, was 100.4 min, combined with reconstruction. There were minor complications in six patients. The advantage of using ADM is that it can quickly correct the shape of the breast after conventional BCS surgery. Pellet-type ADM, rather than sheet-type, can create a breast shape similar to that before surgery. Breast reconstruction using ADM can be an easy and convenient method for making a better shape from BCS.

Recurrence and metastasis of breast cancer (RMBC) have a decisive impact on patient survival, necessitating reliable biomarkers for its early prediction. This study used machine learning to evaluate blood microbiome profiles as predictive biomarkers of RMBC. A retrospective predictive analysis was conducted on 288 participants, including 96 patients with breast cancer and 192 healthy controls. After 7 years of follow-up, patients were classified into disease-free survival (DFS, n = 88) and RMBC (n = 8) groups. Blood microbiome composition was analysed using 16S rRNA sequencing, followed by quality control. The Synthetic Minority Oversampling Technique (SMOTE) was employed to address class imbalance. Eleven machine learning models were trained using leave-one-out cross-validation (LOOCV) and k-fold cross-validation, and evaluated based on the area under the receiver operating characteristic curve (AUROC), recall, precision, F1-score, and Matthews correlation coefficient (MCC). Alpha diversity was significantly lower in DFS and RMBC groups than in the healthy control group (p < 0.05), while beta diversity analysis revealed distinct clustering. The random forest achieved an AUROC of 0.94, a recall of 0.81, an F1-score of 0.83, and an MCC of 0.88. Enterobacter, Bacteroides, Klebsiella, and Bifidobacterium were among the key microbial genera predicting RMBC in the top five models. Blood microbiome profiling shows potential as a non-invasive RMBC biomarker. Machine learning effectively distinguished RMBC, warranting further validation.