Clinicopathological Characteristics of Breast Cancer Patients with HER-2 Low Expression Receiving Neoadjuvant Therapy.

INTRODUCTION: Human epidermal growth factor receptor-2 (HER-2) low expression breast malignant tumors have become a research hotspot in recent years, but it is still unclear whether HER-2 low expression represents a special subtype of breast cancer. However, this molecular type requires more effective treatment regimens in the neoadjuvant therapy stage. METHODS: This study enrolled breast cancer patients who were treated at Harbin Medical University Cancer Hospital with neoadjuvant treatment between October 2011 and May 2019 and was a single-center retrospective study. RESULTS: A total of 1,053 breast cancer patients who received preoperative therapy, including 279 (26%) HER-2 low expression patients, were included in this retrospective study. The HER-2 low expression group had a higher proportion of patients under 50 years old than the other two molecular subtype groups (p = 0.047, 62.0% vs. 57.2% and 52.5%), and the percentage of patients with Ki67 index above 15% was lower than that in HER-2-negative and HER-2-positive patients (p < 0.001, 50.2% vs. 63.6% and 71.5%). Most of the patients with HER-2 low expression were hormone receptor (HR) positive (p < 0.001, 85.7% vs. 60.4% and 36.0%), and their pathologic complete response (pCR) rate after neoadjuvant therapy was significantly lower than that of HER-2-negative and HER-2-positive patients (p < 0.001, 5.7% vs. 11.8% and 20.5%). The results of the subgroup analysis showed HR-positive patients with HER-2 low expression had a lower pCR rate (p < 0.001, 4.6% vs. 14.6%) and objective response rate (p = 0.001, 77.8% vs. 91.0%) than HER-2-positive patients and had no significant difference in these rates compared to HER-2-negative patients. There were no significant differences in overall survival (OS) and disease-free survival (DFS) up to 67 months (the median follow-up time) among HER-2 low, HER-2-negative, and HER-2-positive patients. The results of Cox hazard proportional showed that the Ki67 index and T stage (T3) were independent influencing factors for DFS. In terms of OS, Ki67 index, P53, T stage, and objective response were independent influencing factors for OS in HER-2 low expression patients. CONCLUSIONS: In general, further studies are needed to confirm that HER-2 low expression is a special breast cancer molecular subtype. The efficacy of neoadjuvant therapy in patients with HER-2 low expression is relatively poor, and the efficacy of neoadjuvant therapy can predict the prognosis of patients with HER-2 low expression.

Predicting Odor Sensory Attributes of Unidentified Chemicals in Water Using Fragmentation Mass Spectra with Machine Learning Models.

Knowing odor sensory attributes of odorants lies at the core of odor tracking when addressing waterborne odor issues. However, experimental determination covering tens of thousands of odorants in authentic water is not pragmatic due to the complexity of odorant identification and odor evaluation. In this study, we propose the first machine learning (ML) model to predict odor perception/threshold aiming at odorants in water, which can use either molecular structure or MS2 spectra as input features. We demonstrate that model performance using MS2 spectra is nearly as good as that using unequivocal structures, both with outstanding accuracy. We particularly show the model's robustness in predicting odor sensory attributes of unidentified chemicals by using the experimentally obtained MS2 spectra from nontarget analysis on authentic water samples. Interpreting the developed models, we identify the intricate interaction of functional groups as the predominant influence factor on odor sensory attributes. We also highlight the important roles of carbon chain length, molecular weight, etc., in the inherent olfactory mechanisms. These findings streamline the odor sensory attribute prediction and are crucial advancements toward credible tracking and efficient control of off-odors in water.

Prediction of nonsentinel lymph node metastasis in breast cancer patients based on machine learning.

BACKGROUND: Develop the best machine learning (ML) model to predict nonsentinel lymph node metastases (NSLNM) in breast cancer patients. METHODS: From June 2016 to August 2022, 1005 breast cancer patients were included in this retrospective study. Univariate and multivariate analyses were performed using logistic regression. Six ML models were introduced, and their performance was compared. RESULTS: NSLNM occurred in 338 (33.6%) of 1005 patients. The best ML model was XGBoost, whose average area under the curve (AUC) based on 10-fold cross-verification was 0.722. It performed better than the nomogram, which was based on logistic regression (AUC: 0.764 vs. 0.706). CONCLUSIONS: The ML model XGBoost can well predict NSLNM in breast cancer patients.

Prognostic value of a modified systemic inflammation score in breast cancer patients who underwent neoadjuvant chemotherapy.

BACKGROUND AND PURPOSE: The modified systemic inflammation score (mSIS) system, which is constructed based on the neutrophil to lymphocyte ratio (NLR) and albumin (Alb), has not been applied to evaluate the prognosis of malignant breast cancer patients who underwent neoadjuvant chemotherapy (NAC). The present study aimed to explore the relationship between the mSIS and overall survival (OS), disease-free survival (DFS) and pathological complete response (pCR). METHODS: A total of 305 malignant breast tumor patients who underwent NAC were incorporated into this retrospective analysis. We determined OS and DFS using K-M survival curves and the log-rank test. The relationship between the mSIS and OS and DFS was evaluated by a Cox regression model. A nomogram was constructed based on Cox regression analysis. RESULTS: Patients in the mSIS low-risk group had better 5- and 8-year OS rates than those in the mSIS high-risk group (59.8% vs. 77.0%; 50.1% vs. 67.7%; X2 = 8.5, P = 0.0035, respectively). Patients in the mSIS (1 + 2 score) + pCR subgroup had the highest 5- and 8-year OS and disease-free survival (DFS) rates (OS: 55.0% vs. 75.7% vs. 84.8, 42.8% vs. 65.7% vs. 79.8%, X2 = 16.6, P = 0.00025; DFS: 38.8% vs. 54.7% vs. 76.3%, 33.3% vs. 42.3 vs. 72.1%, X2 = 12.4, P = 0.002, respectively). Based on the mSIS, clinical T stage and pCR results, the nomogram had better predictive ability than the clinical TNM stage, NLR and Alb. CONCLUSIONS: mSIS is a promising prognostic tool for malignant breast tumor patients who underwent NAC, and the combination of mSIS and pCR is helpful in enhancing the ability to predict a pCR.

Formation of Nitrite and Hydrogen Peroxide in Water during the Vacuum Ultraviolet Irradiation Process: Impacts of pH, Dissolved Oxygen, and Nitrate Concentration.

Photolysis via vacuum ultraviolet (VUV) irradiation is a robust technology capable of inactivating pathogens and degrading micropollutants, and therefore, its application has recently attracted great interest. However, VUV irradiation of water may yield nitrite (NO2-, a regulated carcinogenic contaminant) and hydrogen peroxide (H2O2, a compound linked to aging, inflammation, and cancer), thus motivating us to better understand its risks. By applying a novel H2O2 detection method insensitive to coexisting compounds, this study clearly observed concurrent and substantial formations of NO2- and H2O2 during VUV irradiation of various synthetic and real waters. Increasing pH and/or decreasing oxygen promoted the conversion of nitrate (NO3-) into NO2- but suppressed the H2O2 formation, suggesting that there was a transition of radicals from oxidizing species like hydroxyl radicals to reducing species like hydrogen atoms and hydrated electrons. Under low light dose conditions, both NO2- and H2O2 were formed concurrently; however, under high radiation dosage conditions, the patterns conducive to NO2- formation were opposite to those conducive to H2O2 formation. Real water irradiation proved the formation of NO2- and H2O2 at levels near to or greater than current drinking water regulatory limits. Hence, the study reminds of a holistic view of benefits and disbenefits of a VUV process.

Breast cancer-derived exosomes regulate cell invasion and metastasis in breast cancer via miR-146a to activate cancer associated fibroblasts in tumor microenvironment.

OBJECTIVE: To explore the effects of breast cancer (BC)-derived exosomes on invasion and migration of BC cells. METHODS: Exosomes (Exo-MA, Exo-M7, Exo-M1) were extracted from normal breast epithelial cells (MCF-10A), BC cells (MCF-7/MDA-MB-231) and BC cells with miR-146a overexpression or knockdown using multi-step differential centrifugation. Morphologies and sizes of exosomes were observed by transmission electron microscope (TEM) and particle size analysis respectively. BC mouse models were injected with DIR labeled Exo-MA, Exo-M7 or Exo-M1. The epithelial-mesenchymal transition (EMT) in BC cells was determined by PCR and Western blot. PKH67 labeled Exo-MA, Exo-M7 and Exo-M1 were incubated with NFs or MCF-7 to measure the activation of CAFs. Cell invasion and migration abilities were determined by scratch test and Transwell assay. RESULTS: Exo-MA, Exo-M7, Exo-M1 were successfully extracted with positive expressions of Alix, CD63 and TSG101. Contents of Ki67, N-cadherin, Vimentin and Snail-1 were increased but E-cadherin was decreased, compared to Exo-MA group. Exo-M7 or Exo-M1 could increase BC cell proliferation and enhance EMT in nude mouse. Exo-M7 and Exo-M1 could accelerate the transformation of NFs into CAFs and promote the recruitment of CAFs in MCF-7. Transfection of miR-146a could promote the transformation of NFs into CAFs and promote cell invasion and migration of MCF-7 cells. As a target gene of miR-146a, TXNIP could inhibit the activation of CAFs. miR-146a overexpression or TXNIP silence enhance the activation of Wnt signal pathway. CONCLUSION: BC-derived exosomes promote the activation of CAFs through miR-146a/TXNIP axis to activate Wnt pathway, which in turn enhances invasion and metastasis of BC cells.

Long non-coding RNA SATB2-AS1 inhibits microRNA-155-3p to suppress breast cancer cell growth by promoting breast cancer metastasis suppressor 1-like.

BACKGROUND: Long noncoding RNAs (lncRNAs) and microRNAs (miRNAs) play vital roles in human cancers. Nevertheless, the effects of lncRNAs and miRNAs on breast cancer (BC) remain to be further investigated. This study was designed to testify the roles of lncRNA antisense transcript of SATB2 protein (SATB2-AS1) and microRNA-155-3p (miR-155-3p) in BC progression. METHODS: Levels of SATB2-AS1, miR-155-3p and breast cancer metastasis suppressor 1-like (BRMS1L) in BC were determined. The prognostic role of SATB2-AS1 in BC patients was assessed. The screened cells were respectively introduced with altered SATB2-AS1 or miR-155-3p to figure out their roles in malignant phenotypes of BC cells. The effect of varied SATB2-AS1 and miR-155-3p on BC cells in vivo was observed. Dual luciferase reporter gene assay and RNA-pull down assay were implemented to detect the targeting relationship of SATB2-AS1, miR-155-3p, and BRMS1L. RESULTS: SATB2-AS1 and BRMS1L were decreased while miR-155-3p was increased in BC cells and tissues. Patients with lower SATB2-AS1 expression had poor prognosis. Elevated SATB2-AS1 and inhibited miR-155-3p were able to restrain malignant behaviors of BC cells in vitro, as well as decelerate tumor growth in vivo. Oppositely, inhibited SATB2-AS1 and amplified miR-155-3p had converse effects on BC cell growth. MiR-155-3p mimic abrogated the impact of overexpressed SATB2-AS1. SATB2-AS1 could sponge miR-155-3p, and BRMS1L was the target gene of miR-155-3p. CONCLUSION: Elevated SATB2-AS1 and inhibited miR-155-3p could suppress the malignant phenotypes of BC cells, thereby restricting the development of BC.

HOXD3 Plays a Critical Role in Breast Cancer Stemness and Drug Resistance.

BACKGROUND/AIMS: Homeobox D3 (HOXD3) is a member of the homeobox family of genes that is known primarily for its transcriptional regulation of morphogenesis in all multicellular organisms. In this study, we sought to explore the role that HOXD3 plays in the stem-like capacity, or stemness, and drug resistance of breast cancer cells. METHODS: Expression of HOXD3 in clinical breast samples were examined by RT-PCR and immunohistochemistry. HOXD3 expression in breast cancer cell lines were analyzed by RT-PCR and western blot. Ability of drug resistance in breast cancer cells were elevated by MTT cell viability and colony formation assays. We examined stemness using cell fluorescent staining, RT-PCR and western blot for stem cell marker expression. Finally, activity of wnt signaling was analyzed by FOPflash luciferase assays. RT-PCR and western blot were performed for downstream genes of wnt signaling. RESULTS: We demonstrated that HOXD3 is overexpressed in breast cancer tissue as compared to normal breast tissue. HOXD3 overexpression enhances breast cancer cell drug resistance. Furthermore, HOXD3 upregulation in the same cell lines increased sphere formation as well as the expression levels of stem cell biomarkers, suggesting that HOXD3 does indeed increase breast cancer cell stemness. Because we had previously shown that HOXD3 expression is closely associated with integrin ß3 expression in breast cancer patients, we hypothesized that HOXD3 may regulate breast cancer cell stemness and drug resistance through integrin ß 3. Cell viability assays showed that integrin ß 3 knockdown increased cell viability and that HOXD3 could not restore cancer cell stemness or drug resistance. Given integrin ß 3's relationship with Wnt/ß-catenin signaling, we determine whether HOXD3 regulates integrin ß 3 activity through Wnt/ß-catenin signaling. We found that, even though HOXD3 increased the expression of Wnt/ß-catenin downstream genes, it did not restore Wnt/ß-catenin signaling activity, which was inhibited in integrin ß3 knockdown breast cancer cells. CONCLUSION: We demonstrate that HOXD3 plays a critical role in breast cancer stemness and drug resistance via integrin ß3-mediated Wnt/ß-catenin signaling. Our findings open the possibility for improving the current standard of care for breast cancer patients by designing targeted molecular therapies that overcome the barriers of cancer cell stemness and drug resistance.

LAPTM4B Predicts Axillary Lymph Node Metastasis in Breast Cancer and Promotes Breast Cancer Cell Aggressiveness in Vitro.

PURPOSE: Lysosome-associated protein transmembrane-4 beta (LAPTM4B) is associated with the prognosis of several human malignancies. In this study, the role of LAPTM4B in the metastatic potential of breast cancer (BC) and its underlying molecular mechanisms were investigated. METHODS: The relationship between LAPTM4B expression and axillary lymph node metastasis was determined in 291 BC specimens by immunohistochemistry. The expression of LAPTM4B in paired BC cells was overexpressed and inhibited to analyse the role of LAPTM4B in the aggressiveness of BC. Cell proliferation, migration and invasion were assessed in vitro. Metastasis-related protein levels were detected through Western blot. RESULTS: Immunohistochemical staining demonstrated that high expression level of LAPTM4B was independently associated with axillary lymph node metastasis (odds ratio=2.428; 95%CI=1.333- 4.425; P=0.004). The LAPTM4B inhibition in MCF-7 cells inhibited cell proliferation, migration, invasion, and resulted in simultaneous downregulation of phosphorylated N-cadherin, vimentin, and upregulation of E-cadherin. By contrast, the LAPTM4B overexpression promoted cell proliferation, migration, invasion, and led to simultaneous upregulation of N-cadherin, vimentin, and downregulation of E-cadherin in T47D cells. CONCLUSIONS: High expression level of LAPTM4B predicts tumor metastatic potential in patients with BC. Our results provide the first evidence of the role of LAPTM4B as an Epithelial-mesenchymal transition (EMT) inducer that promotes aggressiveness in BC cells.

Integration of nontarget analysis with machine learning modeling for prioritization of odorous volatile organic compounds in surface water.

Assessing the odor risk caused by volatile organic compounds (VOCs) in water has been a big challenge for water quality evaluation due to the abundance of odorants in water and the inherent difficulty in obtaining the corresponding odor sensory attributes. Here, a novel odor risk assessment approach has been established, incorporating nontarget screening for odorous VOC identification and machine learning (ML) modeling for odor threshold prediction. Twenty-nine odorous VOCs were identified using two-dimensional gas chromatography-time of flight mass spectrometry from four surface water sampling sites. These identified odorants primarily fell into the categories of ketones and ethers, and originated mainly from biological production. To obtain the odor threshold of these odorants, we trained an ML model for odor threshold prediction, which displayed good performance with accuracy of 79%. Further, an odor threshold-based prioritization approach was developed to rank the identified odorants. 2-Methylisoborneol and nonanal were identified as the main odorants contributing to water odor issues at the four sampling sites. This study provides an accessible method for accurate and quick determination of key odorants in source water, aiding in odor control and improved water quality management. ENVIRONMENTAL IMPLICATION: Water odor episodes have been persistent and significant issues worldwide, posing severe challenges to water treatment plants. Unpleasant odors in aquatic environments are predominantly caused by the occurrence of a wide range of volatile organic chemicals (VOCs). Given the vast number of newly-detected VOCs, experimental identification of the key odorants becomes difficult, making water odor issues complex to control. Herein, we propose a novel approach integrating nontarget analysis with machine learning models to accurate and quick determine the key odorants in waterbodies. We use the approach to analyze four samples with odor issues in Changsha, and prioritized the potential odorants.

Predicting reactivity dynamics of halogen species and trace organic contaminants using machine learning models.

Reactions of reactive halogen species (Cl•, Br•, and Cl2•-) with trace organic contaminants (TrOCs) have received much attention in recent years, and their k values are fundamental parameters for understanding their reaction mechanisms. However, k values are usually unknown. In this study, we developed machine learning (ML)-based quantitative structure-activity relationship (QSAR) models to predict k values. We tested five algorithms, namely, random forest, neural network, XGBoost, support vector machine (SVM), and multilinear regression, using molecular descriptors (MDs) and molecular fingerprints (MFs) as inputs. The optimal algorithms were MD-XGBoost for Cl• and Br•, and MF-SVM for Cl2•-, respectively, with R2test values of 0.876, 0.743, and 0.853. We found that electron-withdrawing/donating groups tended to interfere with the reactivity of Cl2•- more than Cl• and Br•. This explains why MFs are better inputs for predictive models of Cl2•-, whereas MDs are more suitable for Cl• and Br•. Furthermore, we interpreted the models using SHAP analysis, and the results indicated that our models accurately predicted k values both statistically and mechanistically. Our models provide useful tools for obtaining unknown k values and help researchers understand the inherent relationships between the models.

The prediction of pCR and chemosensitivity for breast cancer patients using DLG3, RADL and Pathomics signatures based on machine learning and deep learning.

BACKGROUND: Limited studies have investigated the predictive value of multiomics signatures (radiomics, deep learning features, pathological features and DLG3) in breast cancer patients who underwent neoadjuvant chemotherapy (NAC). However, no study has explored the relationships among radiomic, pathomic signatures and chemosensitivity. This study aimed to predict pathological complete response (pCR) using multiomics signatures, and to evaluate the predictive utility of radiomic and pathomic signatures for guiding chemotherapy selection. METHODS: The oncogenic function of DLG3 was explored in breast cancer cells via DLG3 knockdown. Immunohistochemistry (IHC) was used to evaluate the relationship between DLG3 expression and docetaxel/epirubin sensitivity. Machine learning (ML) and deep learning (DL) algorithms were used to develop multiomics signatures. Survival analysis was conducted by K-M curves and log-rank. Multivariate logistic regression analysis was used to develop nomograms. RESULTS: A total of 311 patients with malignant breast tumours who underwent NAC were retrospectively included in this multicentre study. Multiomics (DLG3, RADL and PATHO) signatures could accurately predict pCR (AUC: training: 0.900; testing: 0.814; external validation: 0.792). Its performance is also superior to that of clinical TNM staging and the single RADL signature in different cohorts. Patients in the low DLG3 group more easily achieved pCR, and those in the high RADL Signature_pCR and PATHO_Signature_pCR (OR = 7.93, 95 % CI: 3.49-18, P < 0.001) groups more easily achieved pCR. In the TEC regimen NAC group, patients who achieved pCR had a lower DLG3 score (4.00 ± 2.33 vs. 6.43 ± 3.01, P < 0.05). Patients in the low RADL_Signature_DLG3 and PATHO_Signature_DLG3 groups had lower DLG3 IHC scores (P < 0.05). Patients in the high RADL signature, PATHO signature and DLG3 signature groups had worse DFS and OS. CONCLUSIONS: Multiomics signatures (RADL, PATHO and DLG3) demonstrated great potential in predicting the pCR of breast cancer patients who underwent NAC. The RADL and PATHO signatures are associated with DLG3 status and could help doctors or patients choose proper neoadjuvant chemotherapy regimens (TEC regimens). This simple, structured, convenient and inexpensive multiomics model could help clinicians and patients make treatment decisions.

Expression of MAC30 protein is related to survival and clinicopathological variables in breast cancer.

BACKGROUND AND OBJECTIVES: MAC30 is a protein with unknown function that is differentially expressed in certain malignancies. The aims of this study were to evaluate the relationship between MAC30 expression and clinicopathologic features while investigate the prognostic value of MAC30 expression in breast cancer. METHODS: Immunohistochemistry was used to examine MAC30 expression in 243 breast cancer tissues, meanwhile in 59 matched adjacent noncancerous tissues and 46 benign breast tumor tissues as controls. The correlation of MAC30 expression with clinicopathological parameters was assessed using χ(2) analysis. The Kaplan-Meier method and Cox proportional hazards model were used to predict factors with a significant independent prognostic value. RESULTS: MAC30 was overexpressed in breast cancer compared with matched adjacent noncancerous tissues and benign breast tumor (both P < 0.001). Moreover, MAC30 expression was correlated with tumor size, TNM stage, lymph node metastasis, and recurrence. Furthermore, it was shown that patients with high MAC30 expression had significantly poorer overall survival (OS) and disease-free survival (DFS; P = 0.002 and P = 0.007, respectively). Multivariate Cox regression analysis revealed that high MAC30 expression was an independent prognostic factor for both OS and DFS (P = 0.018 and P = 0.044, respectively). CONCLUSIONS: Overexpression of MAC30 was associated with tumor progression, recurrence, and poor survival in breast cancer. Testing expression of MAC30 will be helpful for predicting prognosis in breast cancer.

Predictive value of controlling nutritional status score in postoperative recurrence and metastasis of breast cancer patients with HER2-low expression.

Background: To investigate the predictive value of controlling nutritional status (CONUT) score in Postoperative Recurrence and Metastasis of Breast Cancer Patients with HER2-Low Expression. Methods: The clinicopathological data of 697 female breast cancer patients who pathology confirmed invasive ductal carcinoma and surgery in Harbin Medical University Tumor Hospital from January 2014 to January 2017 were retrospectively analyzed. The relationship between CONUT score and various clinicopathological factors as well as prognosis was evaluated. Results: Based on the cut-off point of ROC curve, compared with the low CONUT score group, the high CONUT score group had worse 5-year RFS. In subgroup analysis, compared with the low CONUT group, the high CONUT group had worse prognosis at different TNM stages. Univariate and multivariate results showed that the low CONUT score group had better overall survival and recurrence-free survival than the high CONUT group. Conclusion: CONUT score is an independent predictor of postoperative recurrence and metastasis in HER2-low breast cancer patients. It is may be used as an effective tool to predict the recurrence and metastasis of HER2-low breast cancer.

Lower subcutaneous fat index predicts bone metastasis in breast cancer.

BACKGROUND: Bone metastases affect 50% to 70% of breast cancer (BC) patients and have a high mortality rate. Adipose tissue loss plays a pivotal role in the progression of cancer. OBJECTIVE: This study aims to evaluate the prognostic value of adipose tissue for bone metastasis in BC patients. METHODS: 517 BC patients were studied retrospectively. Patients' characteristics before the surgery were collected. Quantitative measurements of the subcutaneous fat index (SFI) were performed at the level of the eleventh thoracic vertebra. In order to adjust for the heterogeneity between the low SFI and high SFI groups, propensity score matching (PSM) was used. The Kaplan-Meier method was used to estimate the 5-year bone metastatic incidence. The prognostic analysis was performed with the Cox regression models. RESULTS: Compared with the patients without bone metastasis, the patients with bone metastasis had reduced SFI levels. In addition, Kaplan-Meier analysis revealed that patients with low SFI were more likely to develop bone metastases. The independent predictive value of SFI for bone metastases was confirmed by Cox regression analysis. The survival analysis was repeated after PSM with a 1:1 ratio, yielding similar results (P< 0.05). CONCLUSIONS: SFI is an independent predictor of bone metastasis in BC patients.

The tremendous clinical potential of the microbiota in the treatment of breast cancer: the next frontier.

Although significant advances have been made in the diagnosis and treatment of breast cancer (BC) in recent years, BC remains the most common cancer in women and one of the main causes of death among women worldwide. Currently, more than half of BC patients have no known risk factors, emphasizing the significance of identifying more tumor-related factors. Therefore, we urgently need to find new therapeutic strategies to improve prognosis. Increasing evidence demonstrates that the microbiota is present in a wider range of cancers beyond colorectal cancer. BC and breast tissues also have different types of microbiotas that play a key role in carcinogenesis and in modulating the efficacy of anticancer treatment, for instance, chemotherapy, radiotherapy, and immunotherapy. In recent years, studies have confirmed that the microbiota can be an important factor directly and/or indirectly affecting the occurrence, metastasis and treatment of BC by regulating different biological processes, such as estrogen metabolism, DNA damage, and bacterial metabolite production. Here, we review the different microbiota-focused studies associated with BC and explore the mechanisms of action of the microbiota in BC initiation and metastasis and its application in various therapeutic strategies. We found that the microbiota has vital clinical value in the diagnosis and treatment of BC and could be used as a biomarker for prognosis prediction. Therefore, modulation of the gut microbiota and its metabolites might be a potential target for prevention or therapy in BC.

Naples score: a novel prognostic biomarker for breast cancer patients undergoing neoadjuvant chemotherapy.

BACKGROUND AND PURPOSE: The Naples Score (NPS) is a novel prognostic indicator that has been used in various cancers, but its potential in breast malignant tumor patients receiving neoadjuvant chemotherapy (NAC) has not been discovered. This study aimed to investigate the relationship between NPS and overall survival (OS) and disease-free survival (DFS) in breast cancer patients. METHODS: A total of 217 breast cancer patients undergoing NAC were incorporated into this retrospectively research. K-M survival curves and log-rank tests are used to determine OS and DFS. Cox regression model was used to evaluate the relationship between NPS and OS and DFS. Nomogram was developed based on the results of multivariate Cox regression analysis. Prognostic models were internally validated using bootstrapping and the consistency index (C-index). RESULTS: Age group was correlated with NPS (p < 0.05). Low and moderate Naples risk patients had higher 5-year OS and DFS rates than high risk Naples patients (93.8% vs. 75.4% vs. 60.0%; X2 = 9.2, P = 0.01; 82.4% vs 64.5% vs 43.7%; X2 = 7.4, P = 0.024; respectively). The nomogram based on demonstrated good performance in predicting OS and DFS (AUC = 0.728, 0.630; respectively). CONCLUSIONS: In breast cancer patients who have undergone NAC, NPS is a novel prognostic indicator. NPS combined with clinicopathological features showed good predictive ability, and its performance was better than that of traditional pathological TNM staging.

Gold nanoparticle doped Cuhemin nanosheets with a remodeling tumor microenvironment for multiple radiotherapy sensitization.

Effective radiosensitizers are urgently needed due to the serious negative effects that high radiation doses might have. We created an integrated nano-system (Cuhemin-Au) made of Cuhemin nanosheets and Au nanoparticles (Au NPs) for sensitizing radiotherapy to solve this issue. This system can manifest enzyme-like activities to universally suppress the resistance pathways in breast cancer cells for amplifying radiation damage. Cuhemin-Au NPs increase the energy deposition of radiation owing to the high X-ray attenuation coefficient of Au. In addition, Cuhemin-Au has peroxidase (POD)-like and glucose oxidase (Gox)-like activity, and can also consume intracellular GSH, which can reduce intracellular GSH levels to reduce tumor cells' capacity to repair DNA and deplete intracellular glucose via their characteristic Gox-like catalytic activities, which can cause an increase in the oxidative stress and further produce H2O2. Cuhemin-Au then produced ˙OH, which upsets redox equilibrium and destroys mitochondria, leading to radiation sensitivity, after reacting with enough hydrogen peroxide in tumor cells. Cuhemin-Au combined with low dose RT (4 Gy) could significantly limit tumor development with fewer adverse effects, according to in vivo and in vitro experiments. This platform generated a fresh concept for the construction of a radiotherapy sensitization system and accomplished synergistic radiotherapy sensitization.

Ultrasound Triggered Tumor Metabolism Suppressor Induces Tumor Starvation for Enhanced Sonodynamic Immunotherapy of Breast Cancer.

Introduction: Sonodynamic therapy (SDT) as an emerging tumor treatment gained wide attention. However, tumor vascular destruction and oxygen depletion in SDT process may lead to further hypoxia. This may lead to enhanced glycolysis, lactate accumulation, and immunosuppression. Methods: A glycolysis inhibitor (3PO) loaded and PEG modified black phosphorus nanosheets (BO) is constructed for potent starvation therapy and efficient immune activation. Results: Under ultrasound irradiation, the BO can produce ROS to destroy tumors and tumor blood vessels and lead to further hypoxia and nutrients block. Then, the released 3PO inhibits tumor glycolysis and prevents the hypoxia-induced glycolysis and lactate accumulation. Both SDT and 3PO can cut off the source of lactic acid, as well as achieve antitumor starvation therapy through the blockade of the adenosine triphosphate (ATP) supply. In addition, the combination of starvation treatment and SDT further facilitates dendritic cells (DC) maturation, promotes antigen presentation by DCs, and eventually propagates the antitumor immunity and inhibition of abscopal tumor growth. Conclusion: This is the first time that combines SDT with inhibition of glycolysis, achieving admirable tumor treatment and decreasing adverse events caused by SDT process and that has caused good immune activation. Our system provides a new idea for the future design of anti-tumor nanomedicines.