Classic and New Markers in Diagnostics and Classification of Breast Cancer

IL21R hypomethylation as a biomarker for distinguishing benign and malignant breast tumours

Some benign and malignant breast tumours are similar in pathological morphology, which are difficult to be distinguished in clinical diagnosis. In this study, we intended to explore novel biomarkers for differential diagnosis of benign and malignant breast tumours. Methylation EPIC 850K beadchip and RNA-sequencing were used to analyse 29 tissue samples from patients with early-stage breast cancer (BC) and benign breast tumours for differently methylated and expressed genes. The altered methylation of IL21R was semi-quantitatively validated in an independent study with 566 tissue samples (279 BC vs. 287 benign breast tumours) using mass spectrometry. Binary logistic regression analysis was performed to evaluate the association between IL21R methylation and BC. BC-associated IL21R hypomethylation and overexpression were identified in the discovery round. In the validation round, BC patients presented significant IL21R hypomethylation compared to women with benign breast tumours (ORs ≥1.29 per-10% methylation, p-values ≤ 5.69E-14), and this hypomethylation was even enhanced in BC patients with ER-negative and PR-negative tumours as well as with triple-negative tumours. The methylation of IL21R showed efficient discriminatory power to distinguish benign breast tumours from BC (area under curve (AUC) = 0.88), and especially from ER-negative BC (AUC = 0.95), PR-negative BC (AUC = 0.93) and triple-negative BC (AUC = 0.96). We disclosed significant IL21R hypomethylation in patients with BC compared to women with benign breast tumours, and revealed the somatic change of DNA methylation could be a potential biomarker for molecular pathology of BC.

Integrated nomogram to predict HER2 expression in breast tumor: Clinical, Ultrasound, and Photoacoustic imaging approaches

BACKGROUND

HER2 is a key biomarker for breast cancer treatment and prognosis. Traditional assessment methods like immunohistochemistry (IHC) and fluorescence in situ hybridization (FISH) are effective but costly and time-consuming. Our model incorporates these methods alongside photoacoustic imaging to enhance diagnostic accuracy and provide more comprehensive clinical insights.

MATERIALS AND METHODS

A total of 301 breast tumors were included in this study, divided into HER2-positive (3+ or 2+ with gene amplification) and HER2-negative (below 3+ and 2+ without gene amplification) groups. Samples were split into training and validation sets in a 7:3 ratio. Statistical analyses involved t-tests, chi-square tests, and rank-sum tests. Predictive factors were identified using univariate and multivariate logistic regression, leading to the creation of three models: ModA (clinical factors only), ModB (clinical plus ultrasound factors), and ModC (clinical, ultrasound, and photoacoustic imaging-derived oxygen saturation (SO2)).

RESULTS

The area under the curve (AUC) for ModA was 0.756 (95 % CI: 0.69-0.82), ModB increased to 0.866 (95 % CI: 0.82-0.91), and ModC showed the highest performance with an AUC of 0.877 (95 % CI: 0.83-0.92). These results indicate that the comprehensive model combining clinical, ultrasound, and photoacoustic imaging data (ModC) performed best in predicting HER2 expression.

CONCLUSION

The findings suggest that integrating clinical, ultrasound, and photoacoustic imaging data significantly enhances the accuracy of predicting HER2 expression. For personalised breast cancer treatment, the integrated model could provide a comprehensive and reproducible decision support tool.

Overexpression of cytoplasmic poly(A)-binding protein 1 as a biomarker for the prognosis and selection of postoperative regimen in breast cancer

PURPOSE

The dysregulation of the cytoplasmic poly(A)-binding protein 1 (PABPC1) is involved in a variety of tumors but little is known about its role in human breast cancer. Therefore, the effect of PABPC1 in the prognosis and regimen selection in breast cancer patients was evaluated.

METHODS

A total of 791 cases of invasive breast cancer were included in this study, although only 416 were involved in subsequent analyses after the propensity score matching (PSM) test. PABPC1 expression was detected by immunohistochemistry. The relationship between PABPC1 expression and clinicopathological factors, postoperative regimens, and outcomes was determined.

RESULTS

In the total 791 cases, 583 cases were positive for PABPC1, but only 212 (26.8%) showed high PABPC1 expression (PABPC1-HE). The overall survival (OS) and disease-free survival (DFS) of PABPC1-HE patients after PSM were significantly worse than those in patients with PABPC1 low expression (PABPC1-LE), regardless of age, molecular type, tumor size, nodal status, or pStage. Postoperative chemotherapy (CT) increased the OS of PABPC1-HE patients but not that of PABPC1-LE patients. Among patients receiving endocrine therapy, those in the PABPC-LE group had an extended OS, while CT or chemoradiotherapy (CT/CRT) only significantly extended the OS time of PABPC-HE patients. CT/CRT did not significantly extend the survival of PABPC1-LE HER2-positive patients but extended the OS of PABPC1-HE HER2-positive patients. However, the OS of patients treated with CT/CRT + trastuzumab therapy was significantly longer than that of other patients under other therapies in the PABPC1-HE group, suggesting that PABPC1-HE might be sensitive to trastuzumab-based therapy. The multivariate analysis revealed that PABPC1-HE was an independent prognostic factor for both poor OS and DFS in breast cancer except luminal A type.

CONCLUSIONS

Our results revealed that PABPC1 might be considered as a biomarker to help in subtyping, as well as in the prognosis and regimen selection of breast cancer patients.

Biomarker profiling and integrating heterogeneous models for enhanced multi-grade breast cancer prognostication

BACKGROUND

Breast cancer remains a leading cause of female mortality worldwide, exacerbated by limited awareness, inadequate screening resources, and treatment options. Accurate and early diagnosis is crucial for improving survival rates and effective treatment.

OBJECTIVES

This study aims to develop an innovative artificial intelligence (AI) based model for predicting breast cancer and its various histopathological grades by integrating multiple biomarkers and subject age, thereby enhancing diagnostic accuracy and prognostication.

METHODS

A novel ensemble-based machine learning (ML) framework has been introduced that integrates three distinct biomarkers-beta-human chorionic gonadotropin (β-hCG), Programmed Cell Death Ligand 1 (PD-L1), and alpha-fetoprotein (AFP)-alongside subject age. Hyperparameter optimization was performed using the Particle Swarm Optimization (PSO) algorithm, and minority oversampling techniques were employed to mitigate overfitting. The model's performance was validated through rigorous five-fold cross-validation.

RESULTS

The proposed model demonstrated superior performance, achieving a 97.93% accuracy and a 98.06% F1-score on meticulously labeled test data across diverse age groups. Comparative analysis showed that the model outperforms state-of-the-art approaches, highlighting its robustness and generalizability.

CONCLUSION

By providing a comprehensive analysis of multiple biomarkers and effectively predicting tumor grades, this study offers a significant advancement in breast cancer screening, particularly in regions with limited medical resources. The proposed framework has the potential to reduce breast cancer mortality rates and improve early intervention and personalized treatment strategies.

Potential function of microRNA miRNA-206 in breast cancer pathogenesis: Mechanistic aspects and clinical implications

Breast cancer (BC) is a major public health problem that affects women worldwide. Growing evidence has highlighted the role of miRNA-206 in BC pathogenesis. Changes in its expression have diagnostic and prognostic potential as they are associated with clinicopathological parameters, including lymph node metastasis, overall survival, tumor size, metastatic stage, resistance to chemotherapy, and recurrence. In the present study, we summarized, assessed, and discussed the most recent understanding of the functions of miRNA-206 in BC. Unexpectedly, miRNA-206 was found to control both oncogenic and tumor-suppressive pathways. We also considered corresponding downstream effects and upstream regulators. Finally, we addressed the diagnostic and prognostic value of miRNA-206 and its potential for the development of new therapeutic strategies.

Recent advances in living cell nucleic acid probes based on nanomaterials for early cancer diagnosis

The early diagnosis of cancer is vital for effective treatment and improved prognosis. Tumor biomarkers, which can be used for the early diagnosis, treatment, and prognostic evaluation of cancer, have emerged as a topic of intense research interest in recent years. Nucleic acid, as a type of tumor biomarker, contains vital genetic information, which is of great significance for the occurrence and development of cancer. Currently, living cell nucleic acid probes, which enable the in situ imaging and dynamic monitoring of nucleic acids, have become a rapidly developing field. This review focuses on living cell nucleic acid probes that can be used for the early diagnosis of tumors. We describe the fundamental design of the probe in terms of three units and focus on the roles of different nanomaterials in probe delivery.

APOC1 is a prognostic biomarker associated with M2 macrophages in ovarian cancer

BACKGROUND

Recent studies have demonstrated that APOC1 is associated with cancer progression, exerting cancer-promoting and immune infiltration-promoting effects. Nevertheless, there is currently no report on the presence of APOC1 in ovarian cancer (OV).

METHOD

In this study, we conducted data analysis using the GEO and TCGA databases. We conducted a thorough bioinformatics analysis to investigate the function of APOC1 in OV, utilizing various platforms including cBioPortal, STRING, GeneMANIA, LinkedOmics, GSCALite, TIMER, and CellMarker. Additionally, we performed immunohistochemical staining on tissue microarrays and conducted in vitro cellular assays to validate our findings.

RESULT

Our findings reveal that APOC1 expression is significantly upregulated in OV compared to normal tissues. Importantly, patients with high APOC1 levels show a significantly poorer prognosis. Furthermore, our study demonstrated that APOC1 exerted a crucial function in promoting the capacity of ovarian cancer cells to proliferate, migrate, and invade. Additionally, we have identified that genes co-expressed with APOC1 are primarily associated with adaptive immune responses. Notably, the levels of APOC1 in OV exhibit a correlation with the presence of M2 Tumor-associated Macrophages (TAMs).

CONCLUSION

APOC1 emerges as a promising prognostic biomarker for OV and exhibits a significant association with M2 TAMs in OV.

Oncotype DX in Breast Cancer Management: Insights and Outcomes From the United Arab Emirates

Introduction Breast cancer remains the most significant cancer affecting women worldwide, with an increasing incidence, especially in developing regions. The introduction of genomic tests like Oncotype DX has revolutionized personalized treatment, allowing for more tailored approaches to therapy. This study focuses on the United Arab Emirates (UAE), where breast cancer is the leading cause of cancer-related deaths among women, aiming to assess the predictive accuracy of the Oncotype DX test in categorizing patients based on recurrence risk. Materials and methods A retrospective cohort study was conducted on 95 breast cancer patients diagnosed at Tawam Hospital between 2013 and 2017 who underwent Oncotype DX testing. Data on patient demographics, tumor characteristics, treatment details, and Oncotype DX scores were collected. Survival analysis was performed using the Kaplan-Meier method, with the chi-square goodness of fit test assessing the model's adequacy. Results The cohort's age range was 27-71 years, with a mean age of 50, indicating a significant concentration of cases in the early post-menopausal period. The Oncotype DX analysis classified 55 patients (57.9%) as low risk, 29 (30.5%) as medium risk, and 11 (11.6%) as high risk of recurrence. The majority, 73 patients (76.8%), did not receive chemotherapy, highlighting the test's impact on treatment decisions. The survival analysis revealed no statistically significant difference in recurrence rates across the Oncotype DX risk categories (p = 0.268231). Conclusion The Oncotype DX test provides a valuable genomic approach to categorizing breast cancer patients by recurrence risk in the UAE. While the test influences treatment decisions, particularly the use of chemotherapy, this study did not find a significant correlation between Oncotype DX risk categories and actual recurrence events. These findings underscore the need for further research to optimize the use of genomic testing in the UAE's diverse patient population and enhance personalized treatment strategies in breast cancer management.

MicroRNA signatures differentiate types, grades, and stages of breast invasive ductal carcinoma (IDC): miRNA-target interacting signaling pathways

BACKGROUND

Invasive ductal carcinoma (IDC) is the most common form of breast cancer which accounts for 85% of all breast cancer diagnoses. Non-invasive and early stages have a better prognosis than late-stage invasive cancer that has spread to lymph nodes. The involvement of microRNAs (miRNAs) in the initiation and progression of breast cancer holds great promise for the development of molecular tools for early diagnosis and prognosis. Therefore, developing a cost effective, quick and robust early detection protocol using miRNAs for breast cancer diagnosis is an imminent need that could strengthen the health care system to tackle this disease around the world.

METHODS

We have analyzed putative miRNAs signatures in 100 breast cancer samples using two independent high fidelity array systems. Unique and common miRNA signatures from both array systems were validated using stringent double-blind individual TaqMan assays and their expression pattern was confirmed with tissue microarrays and northern analysis. In silico analysis were carried out to find miRNA targets and were validated with q-PCR and immunoblotting. In addition, functional validation using antibody arrays was also carried out to confirm the oncotargets and their networking in different pathways. Similar profiling was carried out in Brca2/p53 double knock out mice models using rodent miRNA microarrays that revealed common signatures with human arrays which could be used for future in vivo functional validation.

RESULTS

Expression profile revealed 85% downregulated and 15% upregulated microRNAs in the patient samples of IDC. Among them, 439 miRNAs were associated with breast cancer, out of which 107 miRNAs qualified to be potential biomarkers for the stratification of different types, grades and stages of IDC after stringent validation. Functional validation of their putative targets revealed extensive miRNA network in different oncogenic pathways thus contributing to epithelial-mesenchymal transition (EMT) and cellular plasticity.

CONCLUSION

This study revealed potential biomarkers for the robust classification as well as rapid, cost effective and early detection of IDC of breast cancer. It not only confirmed the role of these miRNAs in cancer development but also revealed the oncogenic pathways involved in different progressive grades and stages thus suggesting a role in EMT and cellular plasticity during breast tumorigenesis per se and IDC in particular. Thus, our findings have provided newer insights into the miRNA signatures for the classification and early detection of IDC.

Lectins as a promising therapeutic agent for breast cancer: A review

Efficient treatment of cancer has been a subject of research by scientists for many years. Current treatments for cancer, such as radiotherapy, chemotherapy and surgery have been used in traditional combination therapy, but they have major setbacks like non-specificity, non-responsiveness in certain cancer types towards treatment, tumor recurrence, etc. Epidemiological data has shown that breast cancer accounts for 14% of cancer cases occurring in Indian women. In recent years, scientists have started to focus on the use of natural compounds like lectins obtained from various sources to counter the side effects of traditional therapy. Lectins like Sambucus nigra Agglutinin, Maackia amurensis lectin, Okra lectins, Haliclona caerulea lectin, Sclerotium rolfsii lectin, etc., have been discovered to have both diagnostic and therapeutic potential for breast cancer patients. Lectins have been found to have inhibitory effects on various cancer cell activities such as neo-angiogenesis, causing cell cycle arrest at the G1 phase, and inducing apoptosis. The major idea behind the use of lectins in cancer diagnostics and therapeutics is their capability to bind to glycosylated proteins that are expressed on the cell surface. This review focuses on an exploration of the roles of post-translational modification in cancer cells, especially glycosylation, and the potential of lectins in cancer diagnosis and therapeutics.

Nonlinear dynamics of estrogen receptor-positive breast cancer integrating experimental data: A novel spatial modeling approach

Oncology research has focused extensively on estrogen hormones and their function in breast cancer proliferation. Mathematical modeling is essential for the analysis and simulation of breast cancers. This research presents a novel approach to examine the therapeutic and inhibitory effects of hormone and estrogen therapies on the onset of breast cancer. Our proposed mathematical model comprises a nonlinear coupled system of partial differential equations, capturing intricate interactions among estrogen, cytotoxic T lymphocytes, dormant cancer cells, and active cancer cells. The model's parameters are meticulously estimated through experimental studies, and we conduct a comprehensive global sensitivity analysis to assess the uncertainty of these parameter values. Remarkably, our findings underscore the pivotal role of hormone therapy in curtailing breast tumor growth by blocking estrogen's influence on cancer cells. Beyond this crucial insight, our proposed model offers an integrated framework to delve into the complexity of tumor progression and immune response under hormone therapy. We employ diverse experimental datasets encompassing gene expression profiles, spatial tumor morphology, and cellular interactions. Integrating multidimensional experimental data with mathematical models enhances our understanding of breast cancer dynamics and paves the way for personalized treatment strategies. Our study advances our comprehension of estrogen receptor-positive breast cancer and exemplifies a transformative approach that merges experimental data with cutting-edge mathematical modeling. This framework promises to illuminate the complexities of cancer progression and therapy, with broad implications for oncology.

Atypical architectural distortion detection in digital breast tomosynthesis: a multi-view computer-aided detection model with ipsilateral learning

Objective.Breast architectural distortion (AD), a common imaging symptom of breast cancer, is associated with a particularly high rate of missed clinical detection. In clinical practice, atypical ADs that lack an obvious radiating appearance constitute most cases, and detection models based on single-view images often exhibit poor performance in detecting such ADs. Existing multi-view deep learning methods have overlooked the correspondence between anatomical structures across different views.Approach.To develop a computer-aided detection (CADe) model for AD detection that effectively utilizes the craniocaudal (CC) and mediolateral oblique (MLO) views of digital breast tomosynthesis (DBT) images, we proposed an anatomic-structure-based multi-view information fusion approach by leveraging the related anatomical structure information between these ipsilateral views. To obtain a representation that can effectively capture the similarity between ADs in images from ipsilateral views, our approach utilizes a Siamese network architecture to extract and compare information from both views. Additionally, we employed a triplet module that utilizes the anatomical structural relationship between the ipsilateral views as supervision information.Main results.Our method achieved a mean true positive fraction (MTPF) of 0.05-2.0, false positives (FPs) per volume of 64.40%, and a number of FPs at 80% sensitivity (FPs@0.8) of 3.5754; this indicates a 6% improvement in MPTF and 16% reduction in FPs@0.8 compared to the state-of-the-art baseline model.Significance.From our experimental results, it can be observed that the anatomic-structure-based fusion of ipsilateral view information contributes significantly to the improvement of CADe model performance for atypical AD detection based on DBT. The proposed approach has the potential to lead to earlier diagnosis and better patient outcomes.

Advances in Early Breast Cancer Risk Profiling: From Histopathology to Molecular Technologies

Early breast cancer (BC) is the definition applied to breast-confined tumors with or without limited involvement of locoregional lymph nodes. While risk stratification is essential for guiding clinical decisions, it can be a complex endeavor in these patients due to the absence of comprehensive guidelines. Histopathological analysis and biomarker assessment play a pivotal role in defining patient outcomes. Traditional histological criteria such as tumor size, lymph node involvement, histological type and grade, lymphovascular invasion, and immune cell infiltration are significant prognostic indicators. In addition to the hormone receptor, HER2, and-in specific scenarios-BRCA1/2 testing, molecular subtyping through gene expression profiling provides valuable insights to tailor clinical decision-making. The emergence of "omics" technologies, applicable to both tissue and liquid biopsy samples, has broadened our arsenal for evaluating the risk of early BC. However, a pressing need remains for standardized methodologies and integrated pathological models that encompass multiple analytical dimensions. In this study, we provide a detailed examination of the existing strategies for early BC risk stratification, intending to serve as a practical guide for histopathologists and molecular pathologists.

ETS transcription factor ELK3 in human cancers: An emerging therapeutic target

Cancer is a genetic and complex disorder, resulting from several events associated with onset, development, and metastasis. Tumor suppressors and oncogenes are among the main regulators of tumor progression, contributing to various cancer-related behaviors like cell proliferation, invasion, migration, epithelial-mesenchymal transition (EMT), cell cycle, and apoptosis. Transcription factors (TFs) could act as tumor suppressors or oncogenes in cancer progression. E-twenty-six/E26 (ETS) family of TFs have a winged helix-turn-helix (HLH) motif, which interacted with specific DNA regions with high levels of purines and GGA core. ETS proteins act as transcriptional repressors or activators to modulate the expression of target genes. ETS transcription factor ELK3 (ELK3), as a type of ETS protein, was shown to enhance in various cancers, suggesting that it may have an oncogenic role. These studies indicated that ELK3 promoted invasion, migration, cell cycle, proliferation, and EMT, and suppressed cell apoptosis. In addition, these studies demonstrated that ELK3 could be a promising diagnostic and prognostic biomarker in human cancer. Moreover, accumulating data proved that ELK3 could be a novel chemoresistance mediator in human cancer. Here, we aimed to explore the overall change of ELK3 and its underlying molecular mechanism in human cancers. Moreover, we aimed to investigate the potential role of ELK3 as a prognostic and diagnostic biomarker as well as its capability as a chemoresistance mediator in cancer.

Integrating Proteomics and Lipidomics for Evaluating the Risk of Breast Cancer Progression: A Pilot Study

Metastasis is a serious and often life-threatening condition, representing the leading cause of death among women with breast cancer (BC). Although the current clinical classification of BC is well-established, the addition of minimally invasive laboratory tests based on peripheral blood biomarkers that reflect pathological changes in the body is of utmost importance. In the current study, the serum proteome and lipidome profiles for 50 BC patients with (25) and without (25) metastasis were studied. Targeted proteomic analysis for concertation measurements of 125 proteins in the serum was performed via liquid chromatography-multiple reaction monitoring mass spectrometry (LC-MRM MS) using the BAK 125 kit (MRM Proteomics Inc., Victoria, BC, Canada). Untargeted label-free lipidomic analysis was performed using liquid chromatography coupled to tandem mass-spectrometry (LC-MS/MS), in both positive and negative ion modes. Finally, 87 serum proteins and 295 lipids were quantified and showed a moderate correlation with tumor grade, histological and biological subtypes, and the number of lymph node metastases. Two highly accurate classifiers that enabled distinguishing between metastatic and non-metastatic BC were developed based on proteomic (accuracy 90%) and lipidomic (accuracy 80%) features. The best classifier (91% sensitivity, 89% specificity, AUC = 0.92) for BC metastasis diagnostics was based on logistic regression and the serum levels of 11 proteins: alpha-2-macroglobulin, coagulation factor XII, adiponectin, leucine-rich alpha-2-glycoprotein, alpha-2-HS-glycoprotein, Ig mu chain C region, apolipoprotein C-IV, carbonic anhydrase 1, apolipoprotein A-II, apolipoprotein C-II and alpha-1-acid glycoprotein 1.

A new prognostic model including immune biomarkers, genomic proliferation tumor markers (AURKA and MYBL2) and clinical-pathological features optimizes prognosis in neoadjuvant breast cancer patients

Background

Up to 30% of breast cancer (BC) patients treated with neoadjuvant chemotherapy (NCT) will relapse. Our objective was to analyze the predictive capacity of several markers associated with immune response and cell proliferation combined with clinical parameters.

Methods

This was a single-center, retrospective cohort study of BC patients treated with NCT (2001-2010), in whom pretreatment biomarkers were analyzed: neutrophil-to-lymphocyte ratio (NLR) in peripheral blood, CD3+ tumor-infiltrating lymphocytes (TILs), and gene expression of AURKA, MYBL2 and MKI67 using qRT-PCR.

Results

A total of 121 patients were included. Median followup was 12 years. In a univariate analysis, NLR, TILs, AURKA, and MYBL2 showed prognostic value for overall survival. In multivariate analyses, including hormone receptor, HER2 status, and response to NCT, NLR (HR 1.23, 95% CI 1.01-1.75), TILs (HR 0.84, 95% CI 0.73-0.93), AURKA (HR 1.05, 95% CI 1.00-1.11) and MYBL2 (HR 1.19, 95% CI 1.05-1.35) remained as independent predictor variables.

Conclusion

Consecutive addition of these biomarkers to a regression model progressively increased its discriminatory capacity for survival. Should independent cohort studies validate these findings, management of early BC patients may well be changed.

NFR2/ABC transporter axis in drug resistance of breast cancer cells

Breast cancer is one of the most serious malignancies among women, accounting for about 12% of all cancers. The inherent complexity and heterogeneity of breast cancer results in failure to respond to treatment in the advanced stages of the disease. Breast cancer is caused by several genetic and environmental factors. One of the significant factors involved in the development of breast cancer is oxidative stress, which is generally regulated by nuclear factor erythroid 2-related factor 2 (NRF2). The level of NRF2 expression is low in healthy cells, which maintains the balance of the antioxidant system; however, its expression is higher in cancer cells, which have correlation characteristics such as angiogenesis, stem cell formation, drug resistance, and metastasis. Drug resistance increases with the upregulation of NRF2 expression, which contributes to cell protection. NRF2 controls this mechanism by increasing the expression of ATP-binding cassettes (ABCs). Considering the growing number of studies in this field, we aimed to investigate the relationship between NRF2 and ABCs, as well as their role in the development of drug resistance in breast cancer.

G0S2 promotes antiestrogenic and pro-migratory responses in ER+ and ER- breast cancer cells

G0/G1 switch gene 2 (G0S2) is known to inhibit lipolysis by inhibiting adipose triglyceride lipase (ATGL). In this report, we dissect the role of G0S2 in ER+ versus ER- breast cancer. Overexpression of G0S2 in ER- cells increased cell proliferation, while G0S2 overexpression in ER+ cells decreased cell proliferation. Transcriptome analysis revealed that G0S2 mediated distinct but overlapping transcriptional responses in ER- and ER+ cells. G0S2 reduced genes associated with an epithelial phenotype, especially in ER- cells, including CDH1, ELF3, STEAP4 and TACSTD2, suggesting promotion of the epithelial-mesenchymal transition (EMT). G0S2 also repressed estrogen signaling and estrogen receptor target gene signatures, especially in ER+ cells, including TFF1 and TFF3. In addition, G0S2 overexpression increased cell migration in ER- cells and increased estrogen deprivation sensitivity in ER+ cells. Interestingly, two genes downstream of ATGL in fat utilization and very important in steroid hormone biosynthesis, HMGCS1 and HMGCS2, were downregulated in G0S2 overexpressing ER+ cells. In addition, HSD17B11, a gene that converts estradiol to its less estrogenic derivative, estrone, was highly upregulated in G0S2 overexpressing ER+ cells, suggesting G0S2 overexpression has a negative effect on estradiol production and maintenance. High expression of G0S2 and HSD17B11 was associated with improved relapse-free survival in breast cancer patients while high expression of HMGSC1 was associated with poor survival. Finally, we deleted G0S2 in breast cancer-prone MMTV-PyMT mice. Our data indicates a complex role for G0S2 in breast cancer, dependent on ER status, that may be partially mediated by suppression of the estrogen signaling pathway.

A Comprehensive Review on Electrochemical Nano Biosensors for Precise Detection of Blood-Based Oncomarkers in Breast Cancer

Breast cancer (BC), one of the most common and life-threatening cancers, has the highest incidence rate among women. Early diagnosis of BC oncomarkers is considered the most effective strategy for detecting and treating BC. Finding the type and stage of BC in women as soon as possible is one of the greatest ways to stop its incidence and negative effects on medical treatment. The development of biosensors for early, sensitive, and selective detection of oncomarkers has recently attracted much attention. An electrochemical nano biosensor (EN) is a very suitable option for a powerful tool for cancer diagnosis. This comprehensive review provides information about the prevalence and pathobiology of BC, recent advances in clinically available BC oncomarkers, and the most common electrochemical nano biosensors for point-of-care (POC) detection of various BC oncomarkers using nanomaterial-based signal amplification techniques.

Proteomics-Based Identification of Dysregulated Proteins and Biomarker Discovery in Invasive Ductal Carcinoma, the Most Common Breast Cancer Subtype

Invasive ductal carcinoma (IDC) is the most common histological subtype of malignant breast cancer (BC), and accounts for 70–80% of all invasive BCs. IDC demonstrates great heterogeneity in clinical and histopathological characteristics, prognoses, treatment strategies, gene expressions, and proteomic profiles. Significant proteomic determinants of the progression from intraductal pre-invasive malignant lesions of the breast, which characterize a ductal carcinoma in situ (DCIS), to IDC, are still poorly identified, validated, and clinically applied. In the era of “6P” medicine, it remains a great challenge to determine which patients should be over-treated versus which need to be actively monitored without aggressive treatment. The major difficulties for designating DCIS to IDC progression may be solved by understanding the integrated genomic, transcriptomic, and proteomic bases of invasion. In this review, we showed that multiple proteomics-based techniques, such as LC–MS/MS, MALDI-ToF MS, SELDI-ToF-MS, MALDI-ToF/ToF MS, MALDI-MSI or MasSpec Pen, applied to in-tissue, off-tissue, BC cell lines and liquid biopsies, improve the diagnosis of IDC, as well as its prognosis and treatment monitoring. Classic proteomics strategies that allow the identification of dysregulated protein expressions, biological processes, and interrelated pathway analyses based on aberrant protein–protein interaction (PPI) networks have been improved to perform non-invasive/minimally invasive biomarker detection of early-stage IDC. Thus, in modern surgical oncology, highly sensitive, rapid, and accurate MS-based detection has been coupled with “proteome point sampling” methods that allow for proteomic profiling by in vivo “proteome point characterization”, or by minimal tissue removal, for ex vivo accurate differentiation and delimitation of IDC. For the detection of low-molecular-weight proteins and protein fragments in bodily fluids, LC–MS/MS and MALDI-MS techniques may be coupled to enrich and capture methods which allow for the identification of early-stage IDC protein biomarkers that were previously invisible for MS-based techniques. Moreover, the detection and characterization of protein isoforms, including posttranslational modifications of proteins (PTMs), is also essential to emphasize specific molecular mechanisms, and to assure the early-stage detection of IDC of the breast.

The Critical Impact of Sphingolipid Metabolism in Breast Cancer Progression and Drug Response

Breast cancer is the second leading cause of cancer-related death in women in the world, and its management includes a combination of surgery, radiation therapy, chemotherapy, and immunotherapy, whose effectiveness depends largely, but not exclusively, on the molecular subtype (Luminal A, Luminal B, HER2+ and Triple Negative). All breast cancer subtypes are accompanied by peculiar and substantial changes in sphingolipid metabolism. Alterations in sphingolipid metabolite levels, such as ceramides, dihydroceramide, sphingosine, sphingosine-1-phosphate, and sphingomyelin, as well as in their biosynthetic and catabolic enzymatic pathways, have emerged as molecular mechanisms by which breast cancer cells grow, respond to or escape therapeutic interventions and could take on diagnostic and prognostic value. In this review, we summarize the current landscape around two main themes: 1. sphingolipid metabolites, enzymes and transport proteins that have been found dysregulated in human breast cancer cells and/or tissues; 2. sphingolipid-driven mechanisms that allow breast cancer cells to respond to or evade therapies. Having a complete picture of the impact of the sphingolipid metabolism in the development and progression of breast cancer may provide an effective means to improve and personalize treatments and reduce associated drug resistance.