Prognosis Of Breast Cancer Patients Research Articles

Genetic overlap between breast cancer and sarcopenia: exploring the prognostic implications of SLC38A1 gene expression

BackgroundSarcopenia, an age-related syndrome characterized by a decline in muscle mass, not only affects patients’ quality of life but may also increase the risk of breast cancer recurrence and reduce survival rates. Therefore, investigating the genetic mechanisms shared between breast cancer and sarcopenia is significant for the prevention, diagnosis, and treatment of breast cancer.MethodsThis study downloaded gene expression datasets and clinical data related to breast cancer and skeletal muscle aging from the GEO database. Data preprocessing, integration, differential gene identification, functional enrichment analysis, and construction of protein-protein interaction networks were performed using R language. Subsequently, COX proportional hazards model analysis and survival analysis were conducted, and survival curves and nomograms were generated. The expression levels of genes in tissues were detected using qRT-PCR, and the Radiant DICOM viewer software was used to delineate the pectoralis major muscle area in CT images.ResultsWe identified 152 differentially expressed genes (P < .05) and 226 sarcopenia-related genes (r > .4) associated with skeletal muscle aging. The TCGA-BRCA dataset revealed 106 genes associated with breast cancer (P < .05, logFC = 1). Functional enrichment analysis indicated significant enrichment in cell proliferation and growth pathways. The PPI network identified critical molecules involved in muscle aging and tumor progression. After dimensionality reduction, a strong correlation was observed between the expression of the muscle aging-related gene set and the prognosis of breast cancer patients (P < .01). The expression of SLC38A1 identified through multivariate COX analysis was significantly associated with poor prognosis in breast cancer patients (P = .03). Incorporating SLC38A1 expression, the prognostic model precisely forecasted breast cancer survival (P < .01). External validation confirmed the higher expression of the SLC38A1 gene in breast cancer tissues compared to adjacent non-cancerous tissues (P < .01). The SLC38A1 index, calculated in combination with the patient’s age and BMI, can optimize the prognostic prediction model, providing a powerful tool for personalized treatment of breast cancer.ConclusionHigh SLC38A1 gene expression was significantly associated with poor prognosis in breast cancer patients. The combination of SLC38A1 expression and the pectoralis major muscle area provided an optimized prognostic prediction model, offering a potential tool for personalized breast cancer treatment.

Characteristics of positive and negative effects on the quality of life of breast cancer patients

BackgroundPostoperative and ongoing treatment of breast cancer is traumatic to women with breast cancer and may lead to positive and negative psychological diseases, which can affect the prognosis of patients with breast cancer. Positive affect (PA) and negative affect (NA) significantly impact the prognosis of postoperative breast cancer patients. However, the effects of specific emotions on patient prognosis and the relationships between them are still unclear.MethodsA case–control study was conducted to investigate the characteristics of PA and NA and their effects on Quality of Life (QoL) in breast cancer patients. 442 postoperative breast cancer patients and 444 healthy women were recruited from November to December 2021. For the observation group, participants were eligible if they (1) were aged between 16 and 80 years and (2) had a confirmed pathological diagnosis of breast cancer and (3) possessed the ability to read text, voluntarily agreed to participate, provided informed consent, and cooperated with the study procedures. The control group consisted of individuals without breast cancer who met all other criteria, as stated above. Patients were excluded from the study if they (1) had severe comorbid conditions causing functional impairment or life-threatening risks or (2) suffered from mental disorders or were unconscious at the time of the study. For the control group, individuals with a pathological diagnosis of breast cancer were also excluded. All other exclusion criteria were consistent with those for the observation group. The demographic information and clinicopathological information of the participants were collected. The Positive Affect and Negative Affect Scale (PANAS) is a scale containing 20 emotional scores related to emotion and is used to assess the level of positive and negative affect. The Quality of Life Questionnaire Core 30 (QLQ-C30) was used to assess the quality of life of cancer patients in 15 domains. SPSS 24.0 was used to analyze and process the data. Independent-sample t-tests, one-way ANOVA and Pearson correlation analysis, were used to analyze PA and NA in different treatment phases. Polynomial regression and response surface analysis were conducted to assess the relationships among PA, NA, and QoL. The results were considered statistically significant at P < 0.05.ResultsCompared with healthy women, breast cancer patients had lower scores on "alert" and "distressed" but higher scores on "inspired," "scared," and "afraid." There were no significant differences in the relationships between PA and NA between patients and healthy women (P > 0.05). During the initial treatment phase, the highest proportion of patients had an NA greater than the PA (PA < NA). In the rehabilitation phase, the highest proportion of patients had PA greater than or equal to the NA (PA ≥ NA). The relationship between PA and QoL was an inverted U-shaped curve (P < 0.05), whereas the relationship between NA and QoL was a positive U-shaped curve (P < 0.05). There was an interaction effect between PA and NA (P < 0.001). The relationships among PA, NA, and QoL varied across treatment phases. In the initial treatment phase, the presence of both positive and negative affect improved QoL. In the endocrine therapy phase, excessive positive effects decrease QoL. During the rehabilitation phase, QoL improved as positive affect increased relative to negative affect.ConclusionThere are significant differences in the distributions of positive and negative effects in breast cancer patients at different treatment phases. The impact of PA and NA on QoL varies by treatment phase. Our findings have important implications for tailoring emotional interventions for breast cancer patients at different stages of treatment.

Detection of Circulating Tumor Cells in the Prognostic Significance of Patients With Breast Cancer: A Retrospective Study.

Breast cancer (BC) is an aggressive tumor. Circulating tumor cells (CTCs) are a potential biomarker for the prognosis of cancer patients. This study aimed to explore the prognostic significance of CTCs in patients with BC. Retrospectively, 108 BC patients were collected between January 2011 and December 2019, while 10 patients with benign nodules were included as controls. CTCs with different phenotypes of patients were isolated using CanPatrol and tricolor RNA insitu hybridization (RNA-ISH) methods. Estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor-2 (HER-2) levels were measured by immunohistochemistry (IHC). The progression-free survival (PFS) was calculated using the Kaplan-Meier method. Independent risk factors for BC recurrence were determined by Cox proportional risk regression analysis. The higher the cancer stage (p = 0.00), the higher the ki-67 expression level (p < 0.01), and the lower the histologic grade (p < 0.01), the higher the number of CTCs. The PFS of patients with high CTCs was shorter than that of patients with low CTCs (p < 0.05). Total CTCs (≥ 6) and positive mesenchymal CTCs (MCTCs) were also associated with recurrence and metastasis. Total CTCs in BC patients have an independent influence on PFS reduction. Higher total CTCs and MCTCs in peripheral blood are biomarkers for predicting the prognosis of BC patients. HER-2 high expression is also associated with the prognosis of the disease.

A Novel Effective Models for Identifying BRCA Patients and Optimizing Clinical Treatments.

This study aimed to develop an effective model that identifies high-risk breast cancer (BRCA) patients and optimizes clinical treatments. This study includes five public datasets, TCGA-BRCA as the training dataset and other cohorts as the validation datasets. Machine learning algorithms for finding key tumor-associated immune gene pairs (TAIGPs). These TAIGPs were used to construct tumor-associated immune gene pair index (TAIGPI) by multivariate analysis and further validated on the validation datasets. In addition, the differences in clinical prognosis, biological characteristics, and treatment benefits between high and low TAIGPI groups were further analyzed. The TAIGPI was established by 36 TAIGPs. Better clinical outcomes in the low TAIGPI patients, with consistent results, were also obtained in the validation datasets. The study showed that patients in the low TAIGPI group had a high infiltration of immune cells and low proliferative activity of tumor cells. In contrast, patients in the high TAIGPI group exhibited low infiltration of immune cells and high proliferative activity of tumor cells. In addition, patients in the low TAIGPI group are more likely to benefit from chemotherapy, adjuvant chemotherapy, or immunotherapy. The TAIGPI can be an effective predictive strategy for the clinical prognosis of breast cancer patients, providing new insights into personalized treatment options for breast cancer patients.

WWP1 targeting PTEN for polyubiquitination to promote bone metastasis of luminal breast cancer

Luminal breast cancer exhibits a high incidence of bone recurrence when metastasizing to distant organs. The mechanisms underlying the organotropism of luminal breast cancer cells remain unclear. In this study, we aimed to determine the role of WWP1 (WW domain-containing E3 ubiquitin protein ligase 1)-PTEN (phosphatase and tensin homolog deleted on chromosome ten) interaction in bone tropism in luminal breast cancer. We observed that WWP1 was overexpressed in luminal breast cancer tissues and associated with poor prognosis in breast cancer patients. In luminal breast cancer cells, WWP1 was found to mediate PTEN ubiquitination, resulting in the functional loss of PTEN. As a result, we demonstrate that WWP1 contributes to bone tropism in luminal breast cancer cells via the polyubiquitination of PTEN. Consequently, WWP1-mediated PTEN polyubiquitination contributed to the early metastasis of luminal breast cancer cells to the bone. Thus, our study provides a mechanistic insight into the bone tropism of luminal breast cancer cells and proposes a potential therapeutic strategy for mitigating cancer metastasis to the bone.

Muscle quality may improve with regular exercise, improving the prognosis for breast cancer patients

Muscle quality may improve with regular exercise, improving the prognosis for breast cancer patients

Determining new disulfidptosis-associated lncRNA signatures pertinent to breast cancer prognosis and immunological microenvironment.

Disulfidptosis is a novel form of cell death triggered by disulfide stress that may have important implications for breast cancer (BC) pathogenesis. Nevertheless, studies identifying disulfidptosis-associated long non-coding RNAs (lncRNAs) in BC have not been reported. This study aimed to investigate the prognostic potential of disulfidptosis-related lncRNAs in BC. RNA-sequencing data and clinical information of BC patients were obtained from The Cancer Genome Atlas (TCGA) database. The lncRNAs associated with disulfidptosis were identified through co-expression analysis. Subsequently, a risk signature consisting of 10 lncRNAs was constructed using univariate Cox and least absolute shrinkage and selection operator (LASSO) analyses, and its predictive power was validated. The risk signature was found to be an independent prognostic factor for BC patients. Notably, the two subgroups defined by the risk signature exhibited different mutant gene profiles, and risk scores were significantly correlated with tumor mutation burden (TMB). Additionally, single-sample gene set enrichment analysis (ssGSEA) and immune checkpoint analyses indicated that the predicted trait was significantly associated with the immune status of BC patients. Furthermore, 55 potential anticancer drugs were identified that were associated with the signature. In this study, we successfully developed a prognostic model based on disulfidptosis-related lncRNAs, which enhances the accuracy of predicting the prognosis of BC patients. This model also offers a potential target and theoretical foundation for BC treatment, laying a robust groundwork for future research on the functional roles of disulfidptosis-associated lncRNAs in BC.

Prognostic prediction for HER2-low breast cancer patients using a novel machine learning model

BackgroundsTo develop a machine learning (ML) model for predicting the prognosis of breast cancer (BC) patients with low human epidermal growth factor receptor 2 (HER2) expression, and to investigate the association between clinicopathological characteristics and outcomes in HER2-low BC (HLBC) patients.MethodsA retrospective analysis was conducted on data from 998 female HLBC patients treated at the Breast Center of the Fourth Hospital of Hebei Medical University (Hebei, China) between January 1, 2017, and December 31, 2020. To address class imbalance, the synthetic minority over-sampling technique was applied. Feature selection was performed using the least absolute shrinkage and selection operator, followed by construction of the prediction model using the random forest algorithm. Model performance, including specificity and accuracy, was assessed using the receiver operating characteristic (ROC) curve and confusion matrix, comparing it against other ML models. Additionally, the log-rank test was employed to examine the relationship between selected features and patient outcomes in HLBC.ResultsThe random survival forest model demonstrated superior accuracy and specificity in predicting survival outcomes for HLBC patients. Compared with other ML models, it achieved more precise predictions of Disease-Free Survival (DFS) at 1, 2, and 3 years, with the area under the ROC curve (AUC) in the test and training cohorts measured at 0.726 and 0.819, 0.712 and 0.776, and 0.685 and 0.774, respectively. The analysis further identified a strong correlation between poor prognosis in HLBC patients and factors such as axillary lymph node dissection, family history, elevated topoisomerase (TOPO)-2 expression, advanced clinical stage, negative progesterone receptor status, P53 mutation, and increased Ki67 expression, observed across both cohorts.ConclusionsA novel ML model was developed for accurate prognosis prediction in HLBC patients, offering valuable insights into prognostic risk factors. This model equips clinicians with enhanced data to guide treatment decisions, ultimately contributing to improved patient outcomes.

Medical history of thyroid cancer does not impair prognosis in non-metastatic breast cancer patients: an analysis study based on SEER database and external cohort

Medical history of thyroid cancer does not impair prognosis in non-metastatic breast cancer patients: an analysis study based on SEER database and external cohort

The role of C1orf50 in breast cancer progression and prognosis.

Although the prognosis of breast cancer has significantly improved compared to other types of cancer, there are still some patients who expire due to recurrence or metastasis. Therefore, it is necessary to develop a method to identify patients with poor prognosis at the early stages of cancer. In the process of discovering new prognostic markers from genes of unknown function, we found that the expression of C1orf50 determines the prognosis of breast cancer patients, especially for those with Luminal A breast cancer. This study aims to elucidate the molecular role of C1orf50 in breast cancer progression. Bioinformatic analyses of the breast cancer dataset of TCGA, and in vitro analyses, reveal the molecular pathways influenced by C1orf50 expression. C1orf50 knockdown suppressed the cell cycle of breast cancer cells and weakened their ability to maintain the undifferentiated state and self-renewal capacity. Interestingly, upregulation of C1orf50 increased sensitivity to CDK4/6 inhibition. In addition, C1orf50 was found to be more abundant in breast cancer cells than in normal breast epithelium, suggesting C1orf50's involvement in breast cancer pathogenesis. Furthermore, the mRNA expression level of C1orf50 was positively correlated with the expression of PD-L1 and its related factors. These results suggest that C1orf50 promotes breast cancer progression through cell cycle upregulation, maintenance of cancer stemness, and immune evasion mechanisms. Our study uncovers the biological functions of C1orf50 in Luminal breast cancer progression, a finding not previously reported in any type of cancer.

Obesity Is an Independent Prognostic Factor That Reduced Pathological Complete Response in Operable Breast Cancer Patients

Background and Objectives: Obesity is a significant risk factor for the development of breast cancer (BC) and associated poorer outcomes. A pathological complete response (pCR) with neoadjuvant chemotherapy (NACT) correlates with improved long-term prognosis in BC patients. In this study, we aimed to investigate the predictive effect of obesity on achieving pCR following NACT. Methods: This single-center retrospective study included patients with operable BC who were treated with NACT. Patients were categorized based on their pre-chemotherapy body mass index (BMI), including non-obese (&lt;30 kg/m2) and obese (≥30 kg/m2) groups, and pathological responses to NACT were compared. Results: A total of 191 female patients were included in this study; of these, 83 (43.4%) were obese and 108 (56.6%) were in the non-obese group. Obesity was more common in postmenopausal patients, and the median age of obese patients was significantly higher compared to non-obese patients. Patients in the obese group demonstrated significantly lower pCR rates compared to the non-obese group (30% vs. 45%, p = 0.03). The histological subtype assessment indicated that only in the HR-positive/HER2-negative patients was the pCR rate significantly lower in the obese group compared to the non-obese group (11% vs. 27%, p = 0.05). According to menopausal assessment, a significant difference in pCR rates was observed only among postmenopausal patients, with rates of 29% in the obese group compared to 52% in the non-obese group (p = 0.03). In logistic regression analysis, obesity (OR: 0.52, 95% CI: 0.28–0.97; p = 0.04) and a low Ki-67 score (HR: 2.7, 95% CI: 1.37–5.53; p = 0.003) were independently associated with a decreased rate of pCR. Conclusions: The impact of obesity on achieving pCR in BC patients undergoing NACT remains controversial. Our study revealed that obesity was an independently significant negative predictive factor for achieving pCR.

Evaluation of PD-1 Gene Susceptibility and Prognosis in Breast Cancer Patients in Iranian Population

Programmed cell death-1 (PD1) protein has an inhibitory effect that reduces the activity of T cells. In this study, gene expression PD1 in patients with breast cancer compared to healthy subjects discussed. This study was performed on 25 patients with breast cancer as a case group and 25 healthy individuals as a control group. Real-time PCR method was used to evaluate gene expression. For the case group, the mean age was 2.75 ± 12.18 and for the control group 30.6 ± 2.6. The results of our study showed that the expression of PD1 gene in the case group was higher than the control (p&lt;0.05). As a result of our study show that expression of gene in the development of breast cancer, So PD1 can be used as prognostic gene in breast cancer study be considered. Keywords: Breast cancer, Gene expresion, Programmed cell death-1

DNMT1 rs2228611, rs2228612 and DNMT3A rs2276598, rs752208 Polymorphisms and Their Association with Breast Cancer Phenotype and Prognosis.

Background and Objectives: Breast cancer is a leading cause of cancer-related deaths globally. This study investigates the impact of genetic polymorphisms in DNA methyltransferases (DNMT1 and DNMT3A) on breast cancer pathomorphology and patient prognosis. Specifically, we focused on DNMT1 polymorphisms rs2228611 and rs2228612 and DNMT3A polymorphisms rs2276598 and rs752208. Materials and Methods: Conducted at the Institute of Oncology of the Lithuanian University of Health Sciences, this study included 201 Lithuanian women with early-stage breast cancer. DNA was extracted from peripheral blood samples, and genotyping for the specified polymorphisms was performed using the PCR-RFLP assay. Statistical analyses were applied to evaluate associations between polymorphisms and clinicopathological characteristics. Results: The non-carriers of the DNMT1 rs2228611 G allele were less likely to be diagnosed at an older age, while the DNMT3A rs752208 T allele was linked to lower-grade tumors. Survival analysis indicated a potential relationship between DNMT3A rs752208 and overall survival, although no significant findings were observed in progression-free or metastasis-free survival. Conclusions: This study suggests that the DNMT1 and DNMT3A polymorphisms may influence breast cancer pathomorphology and prognosis. The DNMT1 rs2228611 G allele may be associated with earlier onset, and the DNMT3A rs752208 T allele might correlate with less aggressive tumors. These findings underscore the potential of DNMT gene polymorphisms as prognostic biomarkers in breast cancer, warranting further investigation with larger sample sizes.

Down-regulation of ESRP2 inhibits breast cancer cell proliferation via inhibiting cyclinD1

Epithelial splicing regulatory protein 2 (ESRP2),an important alternative splicing protein of mRNA, is reported to have a dual role in tumors, which can promote or inhibit the occurrence and development of tumors. However, the function and mechanism of ESRP2 in breast cancer (BC) remain unclear. The distribution of ESRP2 expression in breast cancer and the correlation between ESRP2 expression and the overall survival rate were detected by The Cancer Genome Atlas (TCGA) database. Gene Ontology(GO)analysis, containing biological process, cellular components, and molecular function, was utilized to evaluate the potential mechanism of ESRP2 in breast cancer. The ESRP2 expression in breast cancer cell lines was detected by real-time quantitative PCR analysis (RT-qPCR) and western blotting. Cell clone was performed to examine the proliferation of ESRP2 knockdown in MCF-7 cells. The cell cycle was measured by flow cytometry assays. The role of ESRP2 knockdown in synergistic effect with chemotherapeutic agents was also determined by MTT assay. Bioinformatics analysis demonstrated that the ESRP2 gene was elevated in breast cancer cells and its overexpression was strongly correlated with shorter overall survival. GO analysis revealed that ESRP2 expression was related to cell proliferation. ESRP2 mRNA and protein expression were elevated in breast cancer cell lines, compared to the normal human breast cell line MCF-10 A. Dwon-regulation of ESRP2 inhibited cell proliferation and promoted the sensitivity of chemotherapy drug, Cisplatin(DDP) and Paclitaxel (TAXOL), in MCF-7 cells.Additionally, ESRP2 knockdown obstructed the cell cycle at the G1 phase and caused a decrease in cyclinD1 protein expression. These findings reveal that ESRP2 is highly expressed in breast cancer and is correlated with poor prognosis in breast cancer patients. ESRP2 knockdown can inhibit MCF-7 cell proliferation by arresting the cell cycle at the G1 phase and promoting the sensitivity of chemotherapy drugs (DDP and TAXOL)in MCF-7 cells. ESRP2 may be required for the regulation of breast cancer progression, as well as a critical target for the clinical treatment of breast cancer.

Pretreatment plasma vitamin D and response to neoadjuvant chemotherapy in breast cancer: evidence from pooled analysis of cohort studies

Background: Biological evidence has revealed anti-tumor effect of vitamin D, but whether it could predict the response to neoadjuvant chemotherapy (NAC) in breast cancer (BC) patients remains inconclusive. The aim was to investigate the association between pretreatment vitamin D level and response to NAC and subsequent survival outcomes in BC patients. Materials and Methods: We systematically searched the Medline, Embase, Cochrane Library and Web of Science databases and clinical trial registries to identify relevant articles from inception to October 8, 2024. Eligible studies investigating the associations between pretreatment plasma vitamin D and response to NAC in BC patients were selected according to the predefined criteria, with the study characteristics extracted by two reviewers. The primary outcome was pathological complete response (pCR), while overall pathological response and event-free survival (EFS) were adopted as secondary outcomes. Summary effect estimates of odds ratios (ORs) or hazard ratios (HRs) with 95% confidence intervals (CIs) were pooled using a random-effects model. Subgroup and sensitivity analyses were performed based on study characteristics and methodological quality. Results: Six retrospective cohort studies involving 1,291 BC patients were included. We observed a significant association between pretreatment vitamin D deficiency and 50% increased odds of non-pCR after NAC (OR=1.50, 95%CI 1.11-2.03, P=0.008) with no heterogeneity (I2=0%). We also identified a significant association of vitamin D with overall pathological response (OR=1.33, 95%CI 1.01-1.75, P=0.046). A similar association with EFS (HR=1.27, 95%CI 0.92-1.75, P=0.139) was also noted although the effect estimate was not statistically significant. Sensitivity analyses based on methodological quality showed consistent findings. Conclusion: Pretreatment vitamin D deficiency is associated with an inferior response to NAC in BC patients. Our meta-analysis advocates further prospective studies with large sample sizes before vitamin D supplementation could be administered to improve NAC response and subsequent prognosis of BC patients.

Integration of Machine Learning and Experimental Validation to Identify Anoikis-Related Prognostic Signature for Predicting the Breast Cancer Tumor Microenvironment and Treatment Response.

Background/Objectives: Anoikis-related genes (ANRGs) are crucial in the invasion and metastasis of breast cancer (BC). The underlying role of ANRGs in the prognosis of breast cancer patients warrants further study. Methods: The anoikis-related prognostic signature (ANRS) was generated using a variety of machine learning methods, and the correlation between the ANRS and the tumor microenvironment (TME), drug sensitivity, and immunotherapy was investigated. Moreover, single-cell analysis and spatial transcriptome studies were conducted to investigate the expression of prognostic ANRGs across various cell types. Finally, the expression of ANRGs was verified by RT-PCR and Western blot analysis (WB), and the expression level of PLK1 in the blood was measured by the enzyme-linked immunosorbent assay (ELISA). Results: The ANRS, consisting of five ANRGs, was established. BC patients within the high-ANRS group exhibited poorer prognoses, characterized by elevated levels of immune suppression and stromal scores. The low-ANRS group had a better response to chemotherapy and immunotherapy. Single-cell analysis and spatial transcriptomics revealed variations in ANRGs across cells. The results of RT-PCR and WB were consistent with the differential expression analyses from databases. NU.1025 and imatinib were identified as potential inhibitors for SPIB and PLK1, respectively. Additionally, findings from ELISA demonstrated increased expression levels of PLK1 in the blood of BC patients. Conclusions: The ANRS can act as an independent prognostic indicator for BC patients, providing significant guidance for the implementation of chemotherapy and immunotherapy in these patients. Additionally, PLK1 has emerged as a potential blood-based diagnostic marker for breast cancer patients.

The influence of treatment intervals on prognosis in young breast cancer patients: Insights from the French National cohort

BackgroundSuboptimal treatment delays is known to impact prognosis of patients with cancer but optimal timing in specific subgroups remains poorly studied. This study aimed to analyze treatment delays in young women treated for a breast cancer (BC) on and its impact on their prognosis using French Nationwide Data. MethodsUsing the CAREPAT-YBC Cohort based on the French National Healthcare System Database, we analyzed disease-free survival (DFS) in 22,093 young women (18–45 years) who underwent either surgery-chemotherapy-radiotherapy pathway (adjuvant setting, 15,433 patients) or chemotherapy-surgery-radiotherapy pathway (neoadjuvant setting, 6660 patients), according to delays between the different pathways. ResultsFor the adjuvant chemotherapy-radiotherapy interval, the best timing was 17–31 days with increased risk above this delay. For the neoadjuvant setting, the optimal neoadjuvant chemotherapy-surgery interval was 17–31 days, while ≤15 days (HR 1.44, 95%CI 1.21–1.71) or ≥62 days (HR 2.07, 95%CI 1.36–3.15) showed poorer prognosis. Combining best timing into an "optimal pathway" was associated with respectively a 1.2-fold decreased risk for recurrence or mortality. ConclusionOptimizing treatment intervals enhance BC survival in younger age.

FABP4-mediated lipid accumulation and lipolysis in tumor-associated macrophages promote breast cancer metastasis.

A high density of tumor-associated macrophages (TAMs) is associated with poorer prognosis and survival in breast cancer patients. Recent studies have shown that lipid accumulation in TAMs can promote tumor growth and metastasis in various models. However, the specific molecular mechanisms that drive lipid accumulation and tumor progression in TAMs remain largely unknown. Herein, we demonstrated that unsaturated fatty acids (FAs), unlike saturated ones, are more likely to form lipid droplets in murine macrophages. Specifically, unsaturated FAs, including linoleic acids (LA), activate the FABP4/CEBPα pathway, leading to triglyceride synthesis and lipid droplet formation. Furthermore, FABP4 enhances lipolysis and FA utilization by breast cancer cell lines, which promotes cancer cell migration in vitro and metastasis in vivo. Notably, a deficiency of FABP4 in murine macrophages significantly reduces LA-induced lipid metabolism. Therefore, our findings suggest FABP4 as a crucial lipid messenger that facilitates unsaturated FA-mediated lipid accumulation and lipolysis in TAMs, thus contributing to the metastasis of breast cancer.

FABP4-mediated lipid accumulation and lipolysis in tumor-associated macrophages promote breast cancer metastasis

A high density of tumor-associated macrophages (TAMs) is associated with poorer prognosis and survival in breast cancer patients. Recent studies have shown that lipid accumulation in TAMs can promote tumor growth and metastasis in various models. However, the specific molecular mechanisms that drive lipid accumulation and tumor progression in TAMs remain largely unknown. Herein, we demonstrated that unsaturated fatty acids (FAs), unlike saturated ones, are more likely to form lipid droplets in murine macrophages. Specifically, unsaturated FAs, including linoleic acids (LA), activate the FABP4/CEBPα pathway, leading to triglyceride synthesis and lipid droplet formation. Furthermore, FABP4 enhances lipolysis and FA utilization by breast cancer cell lines, which promotes cancer cell migration in vitro and metastasis in vivo. Notably, a deficiency of FABP4 in murine macrophages significantly reduces LA-induced lipid metabolism. Therefore, our findings suggest FABP4 as a crucial lipid messenger that facilitates unsaturated FA-mediated lipid accumulation and lipolysis in TAMs, thus contributing to the metastasis of breast cancer.

The correlation between multi-mode ultrasonographic features of breast cancer and axillary lymph node metastasis.

This study aimed to explore the correlation between multi-mode ultrasonographic features of breast cancer and axillary lymph node metastasis. A total of 196 patients with surgically confirmed breast cancer between September 2019 and December 2023 were included. Data on preoperative B-mode ultrasound (US), color Doppler, and shear wave elastography (SWE) features of breast cancer masses were collected and analyzed to determine their correlation with axillary lymph node metastasis. The area under the receiver operating characteristic curve (AUC) of B-mode US, color Doppler, SWE, and the multi-mode predictive model for evaluating axillary lymph node metastasis were compared. Among the 196 patients, 70 had positive axillary lymph nodes, while 126 had negative axillary lymph nodes. There was no significant difference in the color features between the negative and positive axillary lymph node groups. Multifocality/multicentricity, architectural distortion, microcalcifications, and the "stiff rim" sign in SWE were identified as independent risk factors to predict axillary lymph node metastasis according to binary logistic regression analysis. The AUC of the predictive model based on these independent risk factors was 0.803 (95% CI: 0.739-0.867), which was significantly higher than that of B-mode US or SWE alone. Multifocality/multicentricity, architectural distortion, microcalcifications, and the "stiff rim" sign in SWE were found to be valuable for predicting axillary lymph node metastasis in patients with breast cancer. The predictive model developed in this study, combining the multi-mode ultrasonographic features of breast cancer masses, could serve as a noninvasive and convenient method to predict axillary lymph node status. This approach could aid in clinical decision-making and individualized treatment to improve the prognosis of breast cancer patients.