Breast Cancer Survival Research Articles

Towards key genes identification for breast cancer survival risk with neural network models

Breast cancer, one common malignant tumor all over the world, has a considerably high rate of recurrence, which endangers the health and life of patients. While more and more data have been available, how to leverage the gene expression data to predict the survival risk of cancer patients and identify key genes has become a hot topic for cancer research. Therefore, in this work, we investigate the gene expression and clinical data of breast cancer patients, specifically a novel framework is proposed focusing on the survival risk classification and key gene identification task. We firstly combine the differential expression and univariate Cox regression analysis to achieve dimensional reduction of gene expression data. The median survival time is subsequently proposed as the risk classification threshold and a learning model based on neural network is trained to classify the survival risk of patients. Innovatively, in this work, the activation region visualization technology is selected as the identification tool, which identify 20 key genes related to the survival risk of breast cancer patients. We further analyze the gene function of these 20 key genes based on STRING database. It is critical to learn that, the genetic biomarkers identified in this paper may possess value for the following clinical treatment of breast cancer according to the literature findings. Importantly, the genetic biomarkers identified in this paper may possess value for the following clinical treatment of breast cancer according to the literature findings. Our work accomplishes the objective of proposing a targeted approach to enhancing the survival analysis and therapeutic strategies in breast cancer through advanced computational techniques and gene analysis.

Trends in Presentation, Management, and Survival of Women With Breast Cancer in a Multiethnic, Middle-Income Asian Setting.

Granular data on breast cancer (BC) are pertinent for surveillance, planning, and monitoring of cancer care delivery. We determined the trends in clinical presentation, management, and survival of women with BC in a multiethnic middle-income Asian setting over 15 years. Data of 7,478 Malaysian women newly diagnosed with invasive BC between 2005 and 2019 from three hospital-based cancer registries were included. Trends in demographic, tumor, and treatment characteristics were compared across period 1 (P1): 2005-2009, period 2 (P2): 2010-2014, and period 3 (P3): 2015-2019. Overall survival and net survival were determined. More women in P3 than P1 were older than 60 years at diagnosis. Only a marginal increase in proportion of women with stage I disease was observed (23.7% v 27.2% in P1 and P3, respectively, P = .004). Nonetheless, patients were increasingly presenting with smaller tumors, fewer axillary node involvement, well-differentiated tumors, and hormone receptor expression in recent times. Proportion of women with human epidermal growth factor receptor 2 (HER2)-overexpressed tumors significantly decreased. Among indicated patients, receipt of anticancer therapies was somewhat similar over the calendar periods, except for neoadjuvant chemotherapy and anti-HER2 therapy, where increases in administration were noted. Significant improvements in survival were observed over the 15 years, particularly for HER2-overexpressed BCs. Although the improvements in BC survival that we have observed validate ongoing cancer control efforts and treatment advances, study findings suggest that more could be done for earlier detection and improved access to effective therapies in our settings.

Analysis of the Relationship Between Primary Tumor Site and Clinicopathological Characteristics and Survival Prognosis of Breast Cancer Patients Based on SEER Database

PurposeThis study aimed to analyze the association between the primary tumor site and clinicopathological characteristics and survival prognosis of breast cancer (BC) patients using a large population database. MethodsBC patients screened in the Surveillance, Epidemiology, and End Results (SEER) database were categorized into 6 groups based on primary sites. Descriptive statistics, Kaplan-Meier curves, Cox regression models, forest plots were used to assess the effect of primary sites on overall survival (OS) and breast cancer-specific survival (BCSS). Multivariate Cox proportional analyses were conducted to calculate hazard ratios (HRs) and adjusted subgroups’ hazard ratios (AHRs). Nomograms were utilized to predict OS and BCSS. ResultsAmong 193,043 BC patients, the highest incidence was found in the upper outer quadrant (52.60%). Central portion patients are associated with more clinical features indicating a poor prognosis, and had worse OS and BCSS than other sites. Univariate and multifactorial Cox analyses showed associations between OS/BCSS and various factors. Subgroup analyses revealed differences in OS and BCSS between central portion and upper outer quadrant varied among age, T and N stage. The nomogram was established to predict the survival of central portion BC patients. ConclusionsPrimary tumor site is associated with clinicopathological features and prognosis of BC, may be influenced by age at diagnosis and T and N stage. Central portion BC patients have worse prognosis due to older age at diagnosis, higher T stage and higher likelihood of lymph node metastasis. Early diagnosis and treatment may help to improve survival of central portion BC.

Age-Stratified Assessment of the Impact of Breast Cancer Knowledge on the Lifestyle and Dietary Patterns Among Nigerian Females.

Breast cancer (BC) is a major cause of cancer-related mortality in Nigeria, which is exacerbated by a lack of understanding of how knowledge of BC risk factors influences the lifestyle and dietary patterns of Nigerians across age groups. This study evaluated the influence of knowledge of BC risks on lifestyle and dietary patterns across age groups, aiming to inform early management, prevention, and survival rates. A cross-sectional survey was conducted in Ota, Ado-odo, Ogun State, Nigeria, involving participants from six educational institutions. The study assessed knowledge of BC risks factors and lifestyle/dietary habits using questionnaires. Results were analyzed using Epi Info software and SPSS version 20. The participants' age ranged from 13 to 60 years, with a mean of 21 years. The age categories of the participants were 75.24% adolescents (13-19), 18.68% young adults (20-40), and 6.08% middle-aged (41-60). Middle-aged individuals had the least knowledge about smoking (7.58%), alcohol (26.09%), antibiotics (7.25%), and red meat/smoked foods (20.09%), which translated into their poor dietary/lifestyle patterns. Young adults showed profound physical inactivity (71.00%), while adolescents consumed more carbonated drinks (71.53%), which corresponded to lack of knowledge about the effects of physical inactivity (10.85%) and consumption of carbonated drinks (13.70%) on BC risk. The study found that understanding BC risk factors directly affects lifestyle and dietary patterns. Age groups with higher knowledge had better lifestyle/dietary patterns. This highlights the need for a tailored intervention on lifestyle and dietary education across all age groups, especially the middle-aged, so as to inform a deliberate adjustment in lifestyle and dietary habits, which play crucial roles in BC incidence, survival, and prevention.

Quality of Life Gain Following Treatment Among Breast Cancer Survivors With and Without HIV.

Women living with HIV (WLWH) experience decreased breast cancer survival. We sought to determine whether WLWH surviving breast cancer also experienced different quality of life (QOL) gain. Women who enrolled in the Thabatse Cancer Cohort across oncology centers in Botswana for the initial treatment of stage I-III breast cancer from October 2010 to February 2022 were included. Exclusion criteria were no documented definitive therapy and incomplete data at treatment end or 24 ± 3 months after treatment. QOL was measured quarterly using the SF-8 questionnaire. G methods using weighted exposure and outcome modules were used to mitigate potential bias from imbalances in demographic and cancer characteristics by HIV status. Primary analysis was change in physical component summary (PCS) and mental component summary (MCS) from treatment end to 24 months after treatment for WLWH compared with women without HIV. Of 603 women enrolled, the final analysis included 298, comprising 85 WLWH and 213 women without HIV. Most common reasons for exclusion were no documented definitive treatment (n = 114) and death before 21 months after treatment (n = 137). WLWH were younger, were less wealthy, and had more estrogen receptor/progesterone receptor positive tumors. Overall, PCS and MCS significantly increased from treatment end to 24 months after treatment, from 50.8 to 52.8 and 51.8 to 53.7, respectively. There was no difference in the change of the PCS or MCS with HIV infection, 2.2 (95% CI, -0.4 to 4.9) and 0.6 (95% CI, -1.7 to 2.9), respectively. HIV infection did not impede QOL gain at 24 months after treatment in women surviving breast cancer. Further work is needed to clarify the role of HIV on specific treatment-related morbidities and in other malignancies.

Enhancing Breast Cancer Survival Prognosis Through Omic and Non-Omic Data Integration

Background and ObjectiveCancer, the second leading cause of death globally, claimed 685,000 lives among 2.3 million women affected by breast cancer in 2020. Cancer prognosis plays a pivotal role in tailoring treatments and assessing efficacy, emphasizing the need for a comprehensive understanding. The goal is to develop predictive model capable of accurately predicting patient outcomes and guiding personalized treatment strategies, thereby advancing precision medicine in breast cancer care. MethodsThis project addresses limitations in current cancer prognosis models by integrating omics and non-omics data. While existing models often neglect crucial omics data like DNA methylation and miRNA, the method utilizes the TCGA dataset to incorporate these data types along with others. Employing mRMR feature selection and CNN models for each type of data for feature extraction, features are stacked and a Random Forest classifier is employed for final prognosis. ResultThe proposed method is applied to the dataset to predict whether the patient is a long-time or a short-time survivor. This strategy showcases excellent performance, with an AUC value of 0.873, precision at 0.881, and sensitivity reaching 0.943. With an accuracy rate of 0.861, signaling an improvement of 11.96% compared to prior studies. ConclusionIn conclusion, integrating diverse data with advanced machine learning holds promise for improving breast cancer prognosis. Addressing model limitations and leveraging comprehensive datasets can enhance accuracy, paving the way for better patient care. Further refinement offers potential for significant advancements in cancer prognosis and treatment strategies.

Impact of contralateral prophylactic mastectomy on survival outcomes in patients with unilateral breast cancer: A systematic review and meta-analysis.

To synthesize contemporary evidence of the impact of contralateral prophylactic mastectomy (CPM) on survival outcomes in patients with unilateral breast cancer (UBC). PubMed, EMBASE and Scopus databases were searched for observational studies published up to November 15, 2023. Random-effects model was used to obtain pooled effect estimates that were reported as hazards ratio (HR) with 95% confidence intervals (CI). The outcomes of interest were overall survival (OS), breast cancer-specific survival (BCSS), recurrence free survival (RFS) and risk of contralateral breast cancer (CBC). Twenty-one studies were included. Most studies had a retrospective design. CPM was associated with significant improvement of OS (HR 0.80, 95% CI: 0.75, 0.85), BCCS (HR 0.82, 95% CI: 0.74, 0.90), and RFS (HR 0.72, 95% CI: 0.60, 0.86) and significantly reduced risk of CBC (HR 0.05, 95% CI: 0.03, 0.09) in patients with UBC. No evidence of publication bias was detected. Our results provide strong evidence supporting the positive impact of CPM on survival outcomes in patients with UBC. Further research and long-term follow-up studies are warranted to validate these findings.

A Regularized Cox Hierarchical Model for Incorporating Annotation Information in Predictive Omic Studies.

Associated with high-dimensional omics data there are often "meta-features" such as biological pathways and functional annotations, summary statistics from similar studies that can be informative for predicting an outcome of interest. We introduce a regularized hierarchical framework for integrating meta-features, with the goal of improving prediction and feature selection performance with time-to-event outcomes. A hierarchical framework is deployed to incorporate meta-features. Regularization is applied to the omic features as well as the meta-features so that high-dimensional data can be handled at both levels. The proposed hierarchical Cox model can be efficiently fitted by a combination of iterative reweighted least squares and cyclic coordinate descent. In a simulation study we show that when the external meta-features are informative, the regularized hierarchical model can substantially improve prediction performance over standard regularized Cox regression. We illustrate the proposed model with applications to breast cancer and melanoma survival based on gene expression profiles, which show the improvement in prediction performance by applying meta-features, as well as the discovery of important omic feature sets with sparse regularization at meta-feature level. The proposed hierarchical regularized regression model enables integration of external meta-feature information directly into the modeling process for time-to-event outcomes, improves prediction performance when the external meta-feature data is informative. Importantly, when the external meta-features are uninformative, the prediction performance based on the regularized hierarchical model is on par with standard regularized Cox regression, indicating robustness of the framework. In addition to developing predictive signatures, the model can also be deployed in discovery applications where the main goal is to identify important features associated with the outcome rather than developing a predictive model.

Leveraging Naive Bayes for Enhanced Survival Analysis in Breast Cancer

The study aims to predict breast cancer survival using Naïve Bayes techniques by comparing different machine learning models on a comprehensive dataset of patient records. The main classification groups were survival and non-survival. The objective was to assess the performance of the Naïve Bayes classifier in the field of data mining and to achieve significant results in survival classification, aligning with current academic research. The Naive Bayes classifier attained an average accuracy of 91.08%, indicating consistent performance, though with some variability across different folds. Conversely, Logistic Regression achieved a higher accuracy of 94.84%, demonstrating proficiency in recognizing instances of class 1, yet encountering challenges with class 0.The Decision Tree model, with an accuracy of 93.42%, exhibited similar performance patterns. With an accuracy of 95.68%, Random Forest surpassed the Decision Tree. Nonetheless, all models encountered challenges in accurately classifying instances of class 0. The Naive Bayes algorithm was juxtaposed with K-Nearest Neighbors (KNN) and Support Vector Machines (SVM). Future research aims to enhance prediction models with novel methods and tackle the challenge of accurately identifying instances of class 0.

Prognostic value of structural variants in early breast cancer patients

Genomic analysis of structural variants(SVs) in breast cancer (BC) patients has been conducted, but the relationship between genomic alterations and BC prognosis remains unclear. We performed RNA sequencing of 297 early BC fresh-frozen tissues. We identified SVs using three tools (STAR.Arriba, STAR.fusion, and STAR.SEQR) with the COSMIC and Mitelman databases as guide references. We found a median of five to eight fusions per sample. In BC intrinsic subtypes, normal subtype had the fewest fusions (median: 1, interquartile range [IQR]: 0, 3) followed by luminal A (median: 5.5, IQR: 2.75, 10.25), luminal B (median: 9, IQR: 6, 16.5), HER2-enriched (median: 9, IQR: 6, 16.5) and basal (median 10, IQR: 6, 15.5) subtypes (p < 0.05). Intrachromosomal fusion was more frequent observed rather than interchromosomal fusion. In location, chromosome 17 had the most fusions followed by chromosome 1 and 11. When samples were divided into high and low fusion groups based on a cut-off value of 11 fusions, five-year event-free survival (5Y-EFS) was 68.1% in the high fusion group (n = 72) and 80.1% in the low fusion group (n = 125) (p = 0.024) while 75.6% among all patients (95% confidence interval: 0.699, 0.819). Among BC subtype, TNBCs with more fusions had shorter EFS compared to those with fewer fusions (5Y-EFS rate: 65.1% vs. 85.7%; p = 0.013) but no EFS differences were observed in other BC subtypes. ESTIMATE ImmuneScore was also associated with the number of fusions in TNBC (p < 0.005) and TNBCs with high ImmuneScore had better 5Y-EFS compared to those with low ImmuneScore (p = 0.041). In conclusion, diverse fusions were observed by BC subtype, and the number of fusions was associated with BC survival outcome and immune status in TNBC.

Associations Between Perceived Discrimination, Screening Mammography, and Breast Cancer Stage at Diagnosis: A Prospective Cohort Analysis

BackgroundDespite higher breast cancer screening rates, black women still are more likely to have late-stage disease diagnosed. This disparity is influenced in part by structural and interpersonal racism. This prospective study sought to determine how interpersonal factors, including perceived discrimination, influence screening and stage of disease at diagnosis.MethodsA prospective cohort study analyzed adult women with stages I to IV breast cancer from the Miami Breast Cancer Disparities Study. Perceived discrimination and mistrust of providers were assessed using previously validated questionnaires. Multivariable logistic regression was used to evaluate the odds of screening mammography utilization and late-stage breast cancer at diagnosis.ResultsThe study enrolled 342 patients (54.4 % Hispanic, 15.8 % white, and 17.3 % black). Multivariate regression, after control for both individual- and neighborhood-level factors, showed that a higher level of perceived discrimination was associated with greater odds of late-stage disease (adjusted odds ratio [aOR], 1.06; range, 1.01–1.12); p = 0.022) and lower odds of screening mammography (aOR, 0.96; range, 0.92–0.99; p = 0.046). A higher level of perceived discrimination also was negatively correlated with multiple measures of provider trust.DiscussionThis study identified that high perceived level of discrimination is associated with decreased odds of ever having a screening mammogram and increased odds of late-stage disease. Efforts are needed to reach women who experience perceived discrimination and to improve the patient–provider trust relationship because these may be modifiable risk factors for barriers to screening and late-stage disease presentation, which ultimately have an impact on breast cancer survival.

Bibliometric analysis of phosphoglycerate kinase 1 expression in breast cancer and its distinct upregulation in triple-negative breast cancer.

Phosphoglycerate kinase 1 (PGK1) has been identified as a possible biomarker for breast cancer (BC) and may play a role in the development and advancement of triple-negative BC (TNBC). To explore the PGK1 and BC research status and PGK1 expression and mechanism differences among TNBC, non-TNBC, and normal breast tissue. PGK1 and BC related literature was downloaded from Web of Science Core Collection Core Collection. Publication counts, key-word frequency, cooperation networks, and theme trends were analyzed. Normal breast, TNBC, and non-TNBC mRNA data were gathered, and differentially expressed genes obtained. Area under the summary receiver operating characteristic curves, sensitivity and specificity of PGK1 expression were determined. Kaplan Meier revealed PGK1's prognostic implication. PGK1 co-expressed genes were explored, and Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and Disease Ontology applied. Protein-protein interaction networks were constructed. Hub genes identified. PGK1 and BC related publications have surged since 2020, with China leading the way. The most frequent keyword was "Expression". Collaborative networks were found among co-citations, countries, institutions, and authors. PGK1 expression and BC progression were research hotspots, and PGK1 expression and BC survival were research frontiers. In 16 TNBC vs non-cancerous breast and 15 TNBC vs non-TNBC datasets, PGK1 mRNA levels were higher in 1159 TNBC than 1205 non-cancerous breast cases [standardized mean differences (SMD): 0.85, 95% confidence interval (95%CI): 0.54-1.16, I² = 86%, P < 0.001]. PGK1 expression was higher in 1520 TNBC than 7072 non-TNBC cases (SMD: 0.25, 95%CI: 0.03-0.47, I² = 91%, P = 0.02). Recurrence free survival was lower in PGK1-high-expression than PGK1-low-expression group (hazard ratio: 1.282, P = 0.023). PGK1 co-expressed genes were concentrated in ATP metabolic process, HIF-1 signaling, and glycolysis/gluconeogenesis pathways. PGK1 expression is a research hotspot and frontier direction in the BC field. PGK1 may play a strong role in promoting cancer in TNBC by mediating metabolism and HIF-1 signaling pathways.

Clinical and Genomic Risk for Late Breast Cancer Recurrence and Survival

BackgroundThe 21-gene recurrence score (RS) assay (Oncotype DX) is used to guide adjuvant chemotherapy use for patients with hormone receptor–positive, HER2 (human epidermal growth factor receptor 2)-negative, axillary node-negative breast cancer. Its role, however, in providing prognostic information for late distant recurrence when added to clinicopathologic prognostic factors is unknown.MethodsA patient-specific meta-analysis including 10,004 women enrolled in three trials was updated using extended follow-up data from TAILORx, integrating the RS with histologic grade, tumor size, and age at surgery for the RSClin tool. Cox models integrating clinicopathologic factors and the RS were compared by using likelihood ratio (LR) tests. External validation of prognosis for distant recurrence in years 0 to 10 and 5 to 10 was performed in an independent cohort of 1098 women in a real-world registry.ResultsRSClin provided significantly more prognostic information than either the clinicopathologic factors (ΔLR chi-square, 86.2; P<0.001) or RS alone (ΔLR chi-square, 131.0; P<0.001). The model was prognostic in an independent cohort for distant recurrence by 10 years after diagnosis (standardized hazard ratio, 1.56; 95% confidence interval, 1.25 to 1.94), was associated with late distant recurrence risk between 5 and 10 years after diagnosis (standardized hazard ratio, 1.78; 95% confidence interval, 1.25 to 2.55), and approximated the observed 10-year distant recurrence risk (Lin concordance, 0.87) and 5- to 10-year distant recurrence risk (Lin concordance, 0.92).ConclusionsThe 21-gene RS is prognostic for distant recurrence and overall survival in early breast cancer. A model integrating the 21-gene RS and clinicopathologic factors improved estimates of distant recurrence risk compared with either used individually and stratified late distant recurrence risk. (Funded by the National Cancer Institute, National Institutes of Health [U10CA180820, U10CA180794, UG1CA189859, U10CA180868, and U10CA180822] and others.)

Identifying subtypes and developing prognostic models based on N6-methyladenosine and immune microenvironment related genes in breast cancer

Breast cancer (BC) is the most prevalent cancer in women globally. The tumor microenvironment (TME), comprising epithelial tumor cells and stromal elements, is vital for breast tumor development. N6-methyladenosine (m6A) modification plays a key role in RNA metabolism, influencing its various aspects such as stability and translation. There is a notable link between m6A methylation and immune cells in the TME, although this relationship is complex and not fully deciphered. In this research, BC expression and clinicopathological data from TCGA were scrutinized to assess expression profiles, mutations, and CNVs of 31 m6A genes and immune microenvironment-related genes, examining their correlations, functions, and prognostic impacts. Lasso and Cox regression identified prognostic genes for constructing a nomogram. Single-cell analyses mapped the distribution and patterns of these genes in BC cell development. We investigated associations between gene-derived risk scores and factors like immune infiltration, TME, checkpoints, TMB, CSC indices, and drug response. As a complement to computational analyses, in vitro experiments were conducted to confirm these expression patterns. We included 31 m6A regulatory genes and discovered a correlation between these genes and the extent of immune cell infiltration. Subsequently, a 7-gene risk score was generated, encompassing HSPA2, TAP1, ULBP2, CXCL1, RBP1, STC2, and FLT3. It was observed that the low-risk group exhibited better overall survival (OS) in BC, with higher immune scores but lower tumor mutational burden (TMB) and cancer stem cell (CSC) indices, as well as lower IC50 values for commonly used drugs. To enhance clinical applicability, age and stage were incorporated into the risk score, and a more comprehensive nomogram was constructed to predict OS. This nomogram was validated and demonstrated good predictive performance, with area under the curve (AUC) values for 1-year, 3-year, and 5-year OS being 0.848, 0.807, and 0.759, respectively. Our findings highlight the profound impact of prognostic-related genes on BC immune response and prognostic outcomes, suggesting that modulation of the m6A-immune pathway could offer new avenues for personalized BC treatment and potentially improve clinical outcomes.

Complement protein expression changes in various conditions of breast cancer: in-silico analyses-experimental research.

Breast cancer is the most prevalent cancer diagnosed in females worldwide. The known biomarkers are insufficient to understand the actual prognosis of breast cancer, and identifying new biomarkers is desirable and valuable data to improve the patient's survival. Many inflammatory biomarkers, such as the complement system, can be regarded as prognostic values and as potent inflammatory mediators; complement proteins have a critical role in tumorigenesis. In the current study, the authors aim to investigate complement protein expression changes, particularly complement 3 (C3), complement 7 (C7), complement factor B (CFB), and complement factor D (CFD), in various conditions of breast cancer using in-silico tools. The intent data were extracted using webtools, including; Kaplan-Meier plotter, BcGenExMiner, UALCAN, cbioportal, GeneMania, and Enrichr. To select valid data, a P greater than 0.05 was considered. The current study clarified that 21 complement genes correlated to survival conditions. Also, down or upregulation of extracted genes and breast cancer statuses were identified. Additionally, expression level difference of complement genes in various breast cancer four stages was detected. Ultimately, co-expression genes with complement genes were extracted and networked. Changes in the expression of complement proteins can strongly correlate to breast cancer's prognosis, status, and survival. Furthermore, considering the vital role of CFD and CFB complement proteins in the alternative pathway in different stages of breast cancer, CFD and CFB can be regarded as reliable prognostic values for diagnosis.

CD133 expression is associated with less DNA repair, better response to chemotherapy and survival in ER-positive/HER2-negative breast cancer.

CD133, a cancer stem cells (CSC) marker, has been reported to be associated with treatment resistance and worse survival in triple-negative breast cancer (BC). However, the clinical relevance of CD133 expression in ER-positive/HER2-negative (ER + /HER2-) BC, the most abundant subtype, remains unknown. The BC cohorts from the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC, n = 1904) and The Cancer Genome Atlas (TCGA, n = 1065) were used to obtain biological variables and gene expression data. Epithelial cells were the exclusive source of CD133 gene expression in a bulk BC. CD133-high ER + /HER2- BC was associated with CD24, NOTCH1, DLL1, and ALDH1A1 gene expressions, as well as with WNT/β-Catenin, Hedgehog, and Notch signaling pathways, all characteristic for CSC. Consistent with a CSC phenotype, CD133-low BC was enriched with gene sets related to cell proliferation, such as G2M Checkpoint, MYC Targets V1, E2F Targets, and Ki67 gene expression. CD133-low BC was also linked with enrichment of genes related to DNA repair, such as BRCA1, E2F1, E2F4, CDK1/2. On the other hand, CD133-high tumors had proinflammatory microenvironment, higher activity of immune cells, and higher expression of genes related to inflammation and immune response. Finally, CD133-high tumors had better pathological complete response after neoadjuvant chemotherapy in GSE25066 cohort and better disease-free survival and overall survival in both TCGA and METABRIC cohorts. CD133-high ER + /HER2- BC was associated with CSC phenotype such as less cell proliferation and DNA repair, but also with enhanced inflammation, better response to neoadjuvant chemotherapy and better prognosis.

The association of breast cancer patients survival and prior menopausal hormone therapy in women with type 2 diabetes

We investigated the association of prediagnostic use of menopausal hormone therapy (MHT) with breast cancer survival among women with type 2 diabetes (T2D). The study cohort was identified from a Finnish nationwide diabetes database, and consisted of women with T2D, who were diagnosed with breast cancer between 2000 and 2011 (n = 3189). The patients were classified according to their previous MHT use: systemic MHT, local MHT, and no history of any MHT. The cumulative mortality from breast cancer, cardiovascular diseases, and other causes in three MHT groups was described by the Aalen-Johansen estimator. The cause-specific mortality rates were analyzed by Cox models, and adjusted hazard ratios (HRs) were estimated for the use of MHT. The breast cancer mortality appeared to be lower among systemic MHT users (HR 0.49, 95% Cl 0.36–0.67) compared with non-users of MHT. The mortality from cardiovascular diseases and from other causes of death was found to be lower among systemic MHT users, (HR 0.49, 95% Cl 0.32–0.74), and (HR 0.51, 95% Cl 0.35–0.76), respectively. In conclusion, prediagnostic systemic MHT use is associated with reduced breast cancer, cardiovascular, and other causes of mortality in women with T2D.

Survival benefits of propofol-based versus inhalational anesthesia in non-metastatic breast cancer patients: a comprehensive meta-analysis

Whether the anesthesia technique, inhalational general anesthesia (IGA) or propofol-based anesthesia (PBA), influences the long-term survival of non-metastatic breast cancer (eBC) remain unclear and controversial. We carried out a literature search on 16thJuly, 2022 for studies comparing IGA and PBA in eBC undergoing standard surgery, according to PRISMA 2020. The major endpoint in our study was overall survival (OS). Seventeen studies including four randomized clinical trials and thirteen retrospective cohort studies were included in the meta-analysis. Ten studies provided data for crude OS in unweighted eBC patients (imbalance in baseline characteristics). The summarized estimate HRs of the PBA group versus the IGA group (ten studies, N = 127,774, IGA group: 92,592, PBA group: 35,182.) was 0.83 (95%CI: 0.78–0.89). Compared with IGA, PBA was associated with both better 1-year OS (two studies, N = 104,083, IGA group: 84,074, PBA group: 20,009. Pooled HR = 0.80, 0.73–0.89) and 5-year OS (six studies, N = 121,580, IGA group: 89,472, PBA group: 32,108. HR = 0.80, 0.74–0.87). Ten studies applied PSM method to balance the baseline characteristics. In these weighted patients, PBA still showed a better OS (ten studies, N = 105,459, IGA group: 79,095, PBA group: 26,364. HR = 0.93, 0.87–1.00), a better 1-year OS (two studies, N = 83,007, IGA group: 67,609, PBA group: 15,398. HR = 0.88, 0.78–0.98) and a trend towards a better 5-year OS (nine studies, N = 121,580, IGA group: 76,797, PBA group: 24,066. HR = 0.95, 0.88–1.03). Loco-regional recurrence-free survival (LRRFS) was also better in PBA group (HR = 0.73, 0.61–0.86). The present study is the first comprehensive meta-analysis to demonstrate that propofol-based anesthesia could significantly improve OS and LRRFS in non-metastatic breast cancer patients, compared with inhalational anesthesia.

Eribulin versus S-1 as first or second-line chemotherapy to assess health-related quality of life and overall survival in HER2-negative metastatic breast cancer (RESQ study): a non-inferiority, randomised, controlled, open-label, phase 3 trial

Eribulin prolongs overall survival (OS) of patients with human epidermal growth factor receptor 2 (HER2)-negative metastatic breast cancer (MBC), particularly in later chemotherapy (ChT) treatment. However, the health-related quality of life (HRQoL) and efficacy of first or second-line therapy in eribulin-treated patients remain unknown. Using eribulin in the first- or second-line may demonstrate the non-inferiority of HRQoL compared to S-1, an oral 5-fluorouracil derivative, while maintaining OS. This randomised, controlled, open-label, phase III trial was conducted at 50 hospitals in Japan. Patients were enrolled from June 2016 and October 2019. Patients with HER2-negative MBC once under or no previous ChT were randomly assigned (1:1) to receive eribulin or S-1. HRQoL was assessed using the European Organization for Research and Treatment of Cancer (EORTC) Quality of Life Questionnaire-Core 30 (QLQ-C30) every six weeks until week 24 and every nine weeks until week 42. The primary endpoint was the deterioration defined as more than 10 points worsening of the general health score of QLQ-C30 or death within one year after randomisation. The secondary endpoints included OS. (Trial ID: UMIN000021398). Three hundred and two patients were enrolled, with 152 and 148 assigned to the eribulin and S-1 groups, respectively. The questionnaire compliance rate was 85.6%. Risk difference of global health status deterioration through one year was-0.66% (95% CI:-12.47-11.16; non-inferiority P=0.077) for eribulin compared to S-1 groups. Median time to first deterioration for global health status score was 5.64 (95% CI: 3.51-8.00) and 5.28 months (95% CI: 3.28-7.80) in the eribulin and S-1 groups, respectively. The median OS was 34.7 and 27.8 months, (HR: 0.72, 95% CI: 0.54-0.96; P=0.026); the median progression-free survival was 7.57 and 6.75 months in the eribulin and S-1 groups, (HR: 0.88, 95% CI: 0.67-1.16; P=0.35), respectively. No new adverse events occurred. The time of the first clinical deterioration was similar between the two groups and OS significantly increased in eribulin-treated patients. This study was funded by CSPOR-BC and Eisai CO., Ltd.

Establishing nomograms for predicting disease-free survival and overall survival in patients with breast cancer

Background Prognostic factors-based nomograms have been utilised to detect the likelihood of the specific cancer events. We have focused on the roles of aldehyde dehydrogenase 1 (ALDH1) and p-AKT in predicting the prognosis of BC patients. This study was designed to establish nomograms based on the integration of aldehyde dehydrogenase 1 (ALDH1) and p-AKT in predicting the disease-free survival (DFS) and overall survival (OS) of breast cancer (BC) patients. Methods Demographic and clinical data were obtained from BC patients admitted to our hospital between September 2015 and August 2016. Univariate and multivariate Cox regression analyses were utilised to analyse the risk factors of recurrence and mortality. The nomograms for predicting the DFS and OS were established using the screened risk factors. Stratified analysis was performed with the cut-off value of exp (pi) of 4.0-fold in DFS and OS, respectively. Results Multivariate Cox regression analysis indicated that ALDH, p-AKT and pathological stage III were independent risk factors for the recurrence among BC patients. ALDH1, p-AKT, pathological stage III and ER-/PR-/HER2- were independent risk factors for the mortality among BC patients. The established nomograms based on these factors were effective for predicting the DFS and OS with good agreement to the calibration curve and acceptable area under the receiver operating characteristic (ROC) curve. Finally, stratified analyses showed patients with a low pi showed significant decrease in the DFS and OS compared with those of high risk. Conclusion We established nomograms for predicting the DFS and OS of BC patients based on ALDH1, p-AKT and pathological stages. The ER-/PR-/HER2- may be utilised to predict the OS rather than DFS in the BC patients.