Biomarker For Breast Cancer Research Articles

Expression and Prognostic Potential of ESR1 in Breast Cancer

This study aims to explore the potential of ESR1 as a biomarker in breast cancer (BRCA) using bioinformatics analysis tools. The up-regulation of ESR1 expression in BRCA was investigated using UALCAN and GEPIA2, illustrating its role in BRCA progression. Furthermore, analyses based on various variables such as gender, age, race, and pathological stages of BRCA patients revealed a consistent up-regulation of ESR1, emphasizing its role in the development and progression of BRCA. Additionally, an analysis of ESR1 promoter methylation levels across various parameters revealed hypomethylation, affirming the inverse correlation between methylation and ESR1 expression. Prognostic analysis further indicated that overexpression of ESR1 is associated with poor overall survival, highlighting its potential as a prognostic biomarker in BRCA. Moreover, genetic mutation analysis using cBioPortal disclosed a minor role of ESR1 genetic mutations in BRCA, with only 2.5% of genetic alterations observed. The STRING and DAVID tools were utilized to conduct pathway enrichment analysis, revealing diverse biological functions of ESR1 and its 10 interconnected genes. Altogether, these results underscore the significance of understanding ESR1 up-regulation in BRCA and demonstrate its potential as a therapeutic, diagnostic, and prognostic biomarker.

Study on the expression and prognostic relationship of MYL6B in liver cancer based on bioinformatics.

Primary liver cancer is a prevalent and deadly cancer type. Despite treatment advances, prognosis remains poor, with high recurrence rates. Early detection is crucial but challenging due to the disease's insidious nature. Myosin proteins play significant roles in cancer development, influencing cell migration, invasion, and tumor suppression. MYL6B, a myosin light chain, is involved in various cellular processes and has been associated with poor prognosis in colorectal adenocarcinoma and potential as a biomarker in breast cancer. To investigate the expression of MYL6B in liver hepatocellular carcinoma (LIHC) and its impact on prognosis and potential mechanisms of action using bioinformatics methods. The expression of MYL6B in pan-cancer and normal tissues was analyzed using the gene expression profiling interactive analysis 2 and tumor immune estimation resource databases. The expression level of MYL6B in LIHC tissues and its relationship with prognosis were analyzed, immunohistochemical analysis of MYL6B and its effect on immune cell infiltration, and the protein network were further studied. MYL6B was highly expressed in diffuse large b-cell lymphoma, LIHC, pancreatic adenocarcinoma, skin cutaneous melanoma, thymoma, uterine corpus endometrial carcinoma, uterine carcinosarcoma, and lowly expressed in kidney chromophobe, acute myeloid leukemia, testicular germ cell tumors. The expression level of MYL6B was significantly different between cancer and normal tissues. It had a significant impact on both overall survival and disease-free survival. MYL6B is highly expressed in hepatocellular carcinoma and its expression level increases with cancer progression. High MYL6B expression is associated with poor prognosis in terms of overall survival and recurrence-free survival. The immunohistochemical level of MYL6B is high in hepatocellular carcinoma tissues, and MYL6B has a high level of immune infiltration inflammation. In protein network analysis, MYL6B is correlated with MYL2, MYL6, MYL9, MYLK4, MYLK2, MYL12A, MYL12B, MYH11, MYH9 and MYH10. The expression level of MYL6B in LIHC was significantly higher than in normal liver tissues, and it was correlated with the degree of differentiation survival rate, and immune infiltration. MYL6B is a potential target for LIHC treatment.

Rapid point-of-care breath test predicts breast cancer and abnormal mammograms in symptomatic women

Previous studies have reported volatile organic compounds (VOCs) in the breath as biomarkers of breast cancer. These biomarkers may be derived from cancer-associated fibroblasts, in which oxidative stress degrades polyunsaturated fatty acids to volatile alkanes and methylated alkane derivatives that are excreted in the breath. We evaluated a rapid point-of-care test for breath VOC biomarkers as predictors of breast cancer and abnormal mammograms.&#xD;Methods: We studied 593 women aged ≥ 18 yr referred to three sites for mammography for a symptomatic breast-related concern (e.g. breast mass, nipple discharge). A rapid point-of-care breath testing system collected and concentrated alveolar breath VOCs on a sorbent trap and analyzed them with gas chromatography and surface acoustic wave detection in < 6 min. Breath VOC chromatograms were randomly assigned to a training set or to a validation set. Monte Carlo analysis identified significant breath VOC biomarkers of breast cancer and abnormal mammograms in the training set, and these biomarkers were incorporated into a multivariate algorithm to predict disease in the validation set. &#xD;Results: Prediction of breast cancer: 50 women had biopsy-proven breast cancer (invasive cancer 41, ductal non-invasive cancer 9)&#xD;Unsplit data set: Breath VOCs identified breast cancer with 83% accuracy (area under curve of receiver operating characteristic), 82% sensitivity and 77.1% specificity.&#xD;Split data sets: Training set breath VOCs identified breast cancer with 80.3% accuracy, 84% sensitivity and 74.3% specificity. Corresponding values in the validation set were 68%% accuracy, 72.4% sensitivity and 61.5% specificity.&#xD;Prediction of BIRADS 4 and 5 mammograms (versus BIRADS 1, 2 and 3): &#xD;Unsplit data set: Breath VOCs identified abnormal mammograms with 76.2% accuracy.&#xD;Split data sets: Breath VOCs identified abnormal mammograms with 74.2% accuracy, 73.3% sensitivity and 60% specificity. Corresponding values in the validation set were 60.5% accuracy, 64.2% sensitivity and 51% specificity.&#xD;Conclusions: A rapid point-of-care test for breath VOC biomarkers predicted risk of breast cancer and abnormal mammograms in women with breast-related symptoms.&#xD;&#xD.

Comparison of Risk Stratification by CanAssist Breast Test Performed on Core Needle Biopsies Versus Surgical Specimens in Hormone Receptor-Positive, Her2-Negative Early Breast Cancer.

Introduction Core needle biopsies (CNB) are being increasingly utilized for biomarker, prognostic, and predictive testing in breast cancer (BC).CanAssist Breast (CAB) is a prognostic test performed to assess the 'risk of breast cancer recurrence' in early-stage hormone receptor-positive, Her2-negative BC patients. CAB segregates tumors as 'low risk' or 'high risk' for distant recurrence.Risk assessment done by CAB aids in planning and making adjuvant chemotherapy or hormone therapy decisions.CAB is typically performed on surgical specimens (SS).However, performing it on CNB does offer additional insights into tumor biology leading to different strategies for treatment planning; hence, we aimed to compare the risk stratification performance of CAB using CNB versus SS. Method We analyzed 103 paired formalin-fixed paraffin-embedded CNB and SS samples from hormone receptor-positive, Her2-negative early BC tissue samples submitted for performing CAB at OncoStem Diagnostics between November 2021 and September 2023. Concordance on 'risk categories' of CAB performed on CNB versus SS was reported using overall percentage agreement and Pearson correlation coefficient. Results We found excellent overall concordance of 92.2% for CAB risk stratification between paired CNB and SS tumor samples with a strong Pearson correlation coefficient of r= 0.8351 (p< 0.0001) when either SS or CNB was used as the gold standard.In prognostic testing patients with a 'low risk' of recurrence may avoid chemotherapy and hence it is crucial to assess the accuracy of CAB in the low-risk category. Additionally, in a real-world scenario, it is more likely that CAB will be performed on CNB first. Conclusion CAB when performed on CNB samples showed high concordance with SS thus demonstrating that CNB was a suitable sample for the CanAssist Breast test.The accuracy in the low-risk category is 97.5%, which ensures that physicians can reliably use prognostic information by testing CNB to guide adjuvant therapy decisions.

Explainable breast cancer molecular expression prediction using multi-task deep-learning based on 3D whole breast ultrasound

ObjectivesTo noninvasively estimate three breast cancer biomarkers, estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) and enhance performance and interpretability via multi-task deep learning.MethodsThe study included 388 breast cancer patients who received the 3D whole breast ultrasound system (3DWBUS) examinations at Xijing Hospital between October 2020 and September 2021. Two predictive models, a single-task and a multi-task, were developed; the former predicts biomarker expression, while the latter combines tumor segmentation with biomarker prediction to enhance interpretability. Performance evaluation included individual and overall prediction metrics, and Delong’s test was used for performance comparison. The models’ attention regions were visualized using Grad-CAM + + technology.ResultsAll patients were randomly split into a training set (n = 240, 62%), a validation set (n = 60, 15%), and a test set (n = 88, 23%). In the individual evaluation of ER, PR, and HER2 expression prediction, the single-task and multi-task models achieved respective AUCs of 0.809 and 0.735 for ER, 0.688 and 0.767 for PR, and 0.626 and 0.697 for HER2, as observed in the test set. In the overall evaluation, the multi-task model demonstrated superior performance in the test set, achieving a higher macro AUC of 0.733, in contrast to 0.708 for the single-task model. The Grad-CAM + + method revealed that the multi-task model exhibited a stronger focus on diseased tissue areas, improving the interpretability of how the model worked.ConclusionBoth models demonstrated impressive performance, with the multi-task model excelling in accuracy and offering improved interpretability on noninvasive 3DWBUS images using Grad-CAM + + technology.Critical relevance statementThe multi-task deep learning model exhibits effective prediction for breast cancer biomarkers, offering direct biomarker identification and improved clinical interpretability, potentially boosting the efficiency of targeted drug screening.Key PointsTumoral biomarkers are paramount for determining breast cancer treatment.The multi-task model can improve prediction performance, and improve interpretability in clinical practice.The 3D whole breast ultrasound system-based deep learning models excelled in predicting breast cancer biomarkers.Graphical

Serum exosomal miR-200c is a potential diagnostic biomarker for breast cancer

Background Breast cancer (BC) is one of the most common malignancies in women. Exosomes are widely found in body fluids and carry microRNAs (miRNAs) that reflect the biological properties of the parental cells. Our study aimed to investigate the differential expression of miR-200c in BC serum exosomes and its diagnostic value. Methodology miRNA profiles in culture supernatant exosomes of normal mammary epithelial cells MCF-10A and BC cells (MCF-7, MDA-MB-231, MCF-7 Taxol) were examined by miRNA deep sequencing to screen for significantly differentially expressed miRNAs; Transmission electron microscopy (TEM), Nanoparticle tracking analysis (NTA), and Western blot were used to identify exosomes; qPCR was used to detect the expression level of miR-200c in cellular exosomes and serum exosomes; The efficacy of individual and combined tests of each indicator to diagnose BC was evaluated using receiver operating characteristic (ROC) curves. Results We identified typical exosome features by TEM, NTA and Western blot, indicating successful exosome extraction. Then our miRNA sequencing results and qRT-PCR experiments showed that miR-200c was significantly down-regulated in BC cell exosomes. In addition, we divided the clinical serum samples into two cohorts according to region, and in independent cohort I, the serum exosomal miR-200c levels of BC patients were significantly lower than those of healthy controls. In cohort II, serum exosomal miR-200c expression was significantly lower in the BC group than in the control and benign breast disease (BBD) groups, whereas miR-200c expression in the BBD group was not statistically different from that in the control group. ROC analyses in both independent cohorts confirmed that serum exosomal miR-200c could differentiate between patients with and without BC disease and could be used as an early diagnostic marker for BC disease. Conclusion Serum exosome miR-200c can be used as a potential biomarker for the diagnosis of BC, and combined with conventional serum diagnostic markers AFP, CA125 and CA153 can help to improve diagnostic efficiency.

Identifying new biomarkers and potential therapeutic targets for breast cancer through the integration of human plasma proteomics: a Mendelian randomization study and colocalization analysis.

The proteome is a crucial reservoir of targets for cancer treatment. While some targeted therapies have been developed, there are still significant challenges in early diagnosis and treatment, highlighting the need to identify new biomarkers and therapeutic targets for breast cancer. Therefore, we conducted a comprehensive proteome-wide Mendelian randomization (MR) study to identify novel biomarkers and potential therapeutic targets for breast cancer. Protein quantitative trait locus (pQTL) data were extracted from two published plasma proteome-wide association studies. Genetic variants associated with breast cancer were obtained from the Breast Cancer Association Consortium, which included 133,384 cases and 113,789 controls, and the Finnish cohort study, comprising 18,786 cases and 182,927 controls. We employed summary-based MR and colocalization methods to identify potential drug targets for breast cancer, which were subsequently validated using a two-sample MR approach. Finally, a protein-protein interaction (PPI) network was constructed to detect interactions between the identified proteins and existing cancer drug targets. Gene-predicted levels of ten proteins were associated with breast cancer risk. Decreased levels of CASP8, DDX58, CPNE1, ULK3, PARK7, and BTN2A1, as well as increased levels of TNFRSF9, TNXB, DNPH1, and TLR1, were linked to an elevated risk of breast cancer. Among these, CASP8 and DDX58 were supported by tier-one evidence, while CPNE1, ULK3, PARK7, and TNFRSF9 received tier-two evidence support. The remaining proteins, TNXB, BTN2A1, DNPH1, and TLR1, were supported by tier-three evidence. CASP8, DDX58, CPNE1, ULK3, PARK7, and TNFRSF9 have already been identified as targets in drug development and potential therapeutic targets for breast cancer treatment. Additionally, ULK3 showed promise as a prognostic biomarker for breast cancer. The present study identified several novel potential drug targets and biomarkers for breast cancer, providing new insights into its diagnosis and treatment. The integration of PPI and druggability evaluations enhances the prioritization of these therapeutic targets, paving the way for future drug development efforts.

Tumorspheres as In Vitro Model for Identifying Predictive Chemoresistance and Tumor Aggressiveness Biomarkers in Breast and Colorectal Cancer.

Chemoresistance remains a major challenge in the treatment of breast and colorectal cancer. For this reason, finding reliable predictive biomarkers of response to chemotherapy has become a significant research focus in recent years. However, validating in vitro results may be problematic due to the outcome heterogeneity. In this study, we evaluate the use of tumorspheres as an in vitro model for validating biomarkers of chemoresistance in breast and colorectal cancer. Our investigation highlights the crucial role of inflammation-related pathways in modulating the response to chemotherapy. Using in silico approaches, we identified specific markers elevated in responders versus non-responders patients. These markers were consistently higher in three-dimensional (3D) tumorsphere models compared to traditional adherent cell culture models. Furthermore, the number of tumorspheres from breast and colorectal cancer cells increased in response to cisplatin and oxaliplatin treatment, respectively, whereas cell viability decreased in adherent cell culture. This differential response underscores the importance of the 3D tumorsphere model in mimicking the tumor microenvironment more accurately than adherent cell culture. The enhanced chemoresistance observed in the 3D tumorspheres model and their correlation of data with the in silico study suggest that 3D culture models are a better option to approach the in vivo model and also to validate in silico data. Our findings indicate that tumorspheres are an ideal model for validating chemoresistance biomarkers and exploring the interplay between inflammation and chemoresistance in breast and colon cancer.

Trimodal Multiplexed Lateral Flow Test Strips Assisted with a Portable Microfluidic Centrifugation Device.

During the COVID-19 pandemic, the use of lateral flow assays (LFAs) expanded significantly, offering testing beyond traditional health care. Their appeal lies in the ease of use, affordability, and quick results. However, LFAs often have lower sensitivity and specificity compared with ELISA and PCR tests. Efforts to improve LFAs have increased detection times and complexity, limiting their use in large-scale point-of-care settings. To address this, we propose a novel approach using probes that generate multiple signals to enhance the sensitivity and selectivity. This concept also allows multiplexed LFAs to detect multiple analytes concurrently. We developed a trimodal probe that integrates fluorescence, color, and magnetism into a single nanohybrid. The strong plasmonic absorption and high fluorescence of Au nanoparticles and polymer dots enable qualitative and semiquantitative diagnosis, while the magnetic signal facilitates accurate quantitative measurements. As proof-of-concept targets, we selected CYFRA 21-1 and CA15-3, biomarkers for lung and breast cancer, respectively. This trimodal LFA demonstrated a remarkable detection limit of 0.26 ng/mL for CYFRA 21-1 and 2.8 U/mL for CA15-3. To the best of our knowledge, this is the first platform of a trimodal LFA with multiplexing ability. The platform's accuracy and reliability were validated using clinical serum samples, showing excellent consistency with electrochemiluminescence immunoassay results. This universal concept can be applied to other targets, paving the way for the next-generation LFAs.

Major considerations on the relationship of triple negative breast cancer with microRNAs and nutrological triggers: a systematic review

Introduction: Triple-negative breast cancer (TNBC) accounts for 15% of all cases. Its incidence is usually higher in young women (under 40 years of age). The classification as triple-negative occurs because, in this case, there are no estrogen, progesterone, and HER2 receptors, which are responsible for controlling tumor growth. Higher levels of physical activity and better diet quality are associated with lower mortality from breast cancer in observational studies. Furthermore, physical activity and optimal nutrition can improve the relative dose intensity of chemotherapy, as they can activate miRNAs that regulate several biological processes. Objective: This was to conduct a systematic review to establish the main considerations of the relationship between triple-negative breast cancer and microRNAs as biomarkers and regulators of gene expression in cancer cells, as well as to show some nutritional triggers of microRNA activation desirable for breast cancer control. Methods: The PRISMA Platform systematic review rules were followed. The search was conducted from April to June 2024 in the Web of Science, Scopus, PubMed, Science Direct, Scielo, and Google Scholar databases. The quality of the studies was based on the GRADE and AMSTAR-2 instrument and the risk of bias was analyzed appropriately. According to the Cochrane instrument. Results and Conclusion: 112 articles were found, and 40 were evaluated in full and included in this article, 22 of which were included in the systematic review. Considering the Cochrane tool for risk of bias, the global assessment resulted in 15 studies with a high risk of bias and 25 studies that did not reach GRADE and AMSTAR-2. Most studies showed homogeneity in their results, with X2=81.5%&gt;50%. It was concluded that an increasing number of studies have shown that non-coding RNA (ncRNA), including microRNA (miRNA) and long non-coding RNA (lncRNA), play a significant role in tumorigenesis. Although a diet and exercise intervention did not affect relative dose intensity, the intervention was associated with a higher pCR in patients with hormone receptor-positive/human epidermal growth factor receptor 2-negative and triple-negative breast cancer undergoing neoadjuvant chemotherapy. High expression levels of miR-27a/b, miR-210, and miR-454 were associated with shorter overall survival, whereas high expression levels of miR-454 and miR-374a/b were associated with disease-free survival. The miRNAs associated with triple-negative breast cancer may provide new avenues for early diagnosis and treatment of breast cancer. Furthermore, specific miRNAs may serve as potential prognostic biomarkers in triple-negative breast cancer.

Androgen receptor expression and clinical characteristics in breast cancer

ObjectiveTo investigate the relationship between the expression of androgen receptor (AR) and clinical characteristics in breast cancer.Patients and methodsThe clinical records of all 432 patients tested for AR in our institution between January 2020 and May 2023 were reviewed. Clinical characteristics, age, menopausal status, tumor node metastasis (TNM) stage, distant metastasis, pathological complete response (pCR), histopathological features histological grade, estrogen receptor (ER), progesterone receptor, Her-2, Ki-67, and molecular subtype were registered for all patients.ResultsAbout 377 (87.27%) of the 432 patients had AR expression.No significant difference in AR expression was found with age, menopausal status, TNM stage of primary tumor, or pCR. AR was positively and significantly associated with the histological grade, and recurrence. The AR expression was significantly related with molecular subtypes, including ER, PR Her-2, Ki67 and molecular subtype. ER (OR = 10.489, 95%CI: 5.470–21.569), PR (OR = 7.690, 95%CI: 3.974–16.129, Her-2 (OR = 10.489, 95%CI: 2.779–23.490 and tumor recurrence (OR = 0.110, 95%CI: 0.031–0.377 were significant independent risk factors affecting AR expression.ConclusionsAR expression can serve as a reliable basis for judging the clinical molecular types and poor prognosis for breast cancer. AR may be a novel biomarker and target in AR-positive breast cancer depending on significant difference in AR expression among different molecular types of breast cancer.

Design and optimization of refractive index biosensor for MDA-MB-231 and MCF-7 breast cancer biomarker detection

Breast cancer is the leading malignancy in women and the 2nd widespread cancer globally. Earlier identification of breast cancer can improve treatment outcomes and prevent metastasis beyond the breast. Traditional screening tests are not sensitive enough for early diagnosis and have extended detection periods. Recent studies have explored diversified Breast cancer biosensor techniques, including optical, electrical, electrochemical, and mechanical biosensors. This paper nominates a circlet with a large plus sign refraction indices biosensor for the evaluation of two distinct breast cancer cells namely, MDA-MB-231 and MCF-7. The supreme sensitivity has been viewed as 1714.28 nm/RIU for the MDA-MB-231 and 1714.28 nm/RIU for the MCF-7. The supreme quality factor (QF) value for UC1(usual cell of MDA-MB-231) is 10.52, for CC1(cancer cell of MDA-MB-231) is 11.94, for UC2 (usual cell of MCF-7) is 10.64, and for CC2 (cancer cell of MCF-7) is 12.09. The minimal detection value (DL) for UC1 is 0.1573, CC1 is 0.1360, UC2 is 0.1554, and CC2 is 0.1341. This nominated sensor has the potential to sense breast tumor biomarkers.

LINC01614 activated by SP1 promoted malignant behavior of triple-negative breast cancer cells via the WNT/b-Catenin signaling pathway.

Triple-negative breast cancer (TNBC) represents the most aggressive subtype of breast cancer (BC), characterized by a dismal prognosis. Dysregulated long non-coding RNA LINC01614 might be a potential biomarker for BC as previously reported. Nevertheless, its functions and mechanism in TNBC cells are unclear. The study aimed to study the effects of LINC01614 on TNBC cell migration, invasion, and epithelial-mesenchymal transition (EMT) process as well as the related mechanism. Reverse transcription quantitative polymerase chain reaction was performed to detect the expression of LINC01614 and SP1 in TNBC cells and tissues. The cellular localization of LINC01614 was determined by subcellular fraction assays. Cell counting kit-8 and Transwell invasion assays were conducted for measurement of TNBC cell viability and invasive ability. Cell migration was performed using wound healing assays and Transwell migration assays. Chromatin immunoprecipitation assays and luciferase reporter assays were used to explore the interaction between SP1 and LINC01614. Western blotting was used to assess protein levels of factors involved in EMT process and Wnt/ß-catenin signaling in TNBC cells. LINC01614 expression was elevated in TNBC tissues and cells. LINC01614 knockdown inhibited cell viability as well as migratory and invasive abilities of TNBC cells. LINC01614 knockdown also obstructed EMT process, as shown by E-cadherin upregulation and vimentin downregulation in TNBC cells. SP1 directly bound to the promoter of LINC01614 and activated LINC01614 expression. SP1 overexpression reversed the suppressive effect of LINC01614 knockdown on TNBC cell migration, invasion, and EMT process. Protein levels of Wnt and ß-catenin were diminished by LINC01614 knockdown, and the trend was partially rescued by SP1 overexpression. SP1-induced LINC01614 promoted malignant behavior of TNBC cells by activating the Wnt/ß-catenin signaling pathway.

The breast cancer biomarkers associated with the development of the disease; an in-silico-based study

Introduction: Breast cancer (BC) is among the top causes of mortality among women worldwide. Identifying genes by differential expression associated with the development of the disease helps us to better understanding the molecular mechanisms of BC. Objectives: Our study used in-silico analysis to identify hub genes could trigger the development of BC. Materials and Methods: We identified GSE38959 and GSE45827 for differentially expressed genes (DEGs) in the Gene Expression Omnibus (GEO) database, with an adjusted P&lt;0.05. In both sets, logFC ≥ 2 and logFC ≤ -2 were observed in the DEGs that express themselves within cases and normal BC samples. A comparison was then performed, detecting two common datasets of DEGs using the GEO2R tool. Pathways were elucidated using the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology databases. Thereafter, protein-protein interactions (PPIs) were analyzed using Cytoscape and Gephi. Finally, a GEPIA analysis was conducted to validate the target genes. Results: Using the GEO, 322 common DEGs were identified and 65 hub genes as PPIs. The DEGs were enriched in functions associated with cell division, chromosomes, centromeric regions, microtubule binding, and the cell cycle based on the gene ontology (GO) and KEGG pathways analysis. The expression of 6 genes, CDK1, CCNB1, TOP2A, CXCL12, IGF1, and KIT, represented statistically significant values when the normal and tumor samples were compared via GEPIA analysis. Conclusion: This study introduced six genes (CDK1, CCNB1, TOP2A, CXCL12, IGF1, and KIT) with high expression significantly, which could act as a biomarker for BC development (P&lt;0.05 for all genes). Further comprehensive experimental in vivo studies are needed to describe their role in BC.

An Electrochemical Nucleic Acid Biosensor for Triple-Negative Breast Cancer Biomarker Detection.

Triple-negative breast cancer (TNBC) is the most aggressive subtype of breast cancer, affecting younger women and women of minorities. The nomenclature "triple negative" is derived from the absence of the three most common breast cancer biomarkers: progesterone receptor (PR), estrogen receptor (ER), and human epidermal growth factor receptor 2 (HER2). It derives its name from testing negative for these three most common breast cancer biomarkers. Currently, TNBC is diagnosed at advanced stages, necessitating the need for a diagnostic tool or method to identify this malignancy at an early stage prior to metastasis. In this study, a novel electrochemical biosensor was developed, optimized, and evaluated for the detection of microRNA-10b (miRNA-10b), marking the first use of this biomarker for the early diagnosis of TNBC. The biosensor demonstrated the ability to detect concentrations as low as 10 pM. Furthermore, the biosensor was specific toward the target biomarker, distinguishing non-target miRNAs of similar size. The efficacy of the biosensor for TNBC early diagnosis was further validated using human serum samples.

Bio-Pathological Functions of Posttranslational Modifications of Histological Biomarkers in Breast Cancer.

Proteins are the most common types of biomarkers used in breast cancer (BC) theranostics and management. By definition, a biomarker must be a relevant, objective, stable, and quantifiable biomolecule or other parameter, but proteins are known to exhibit the most variate and profound structural and functional variation. Thus, the proteome is highly dynamic and permanently reshaped and readapted, according to changing microenvironments, to maintain the local cell and tissue homeostasis. It is known that protein posttranslational modifications (PTMs) can affect all aspects of protein function. In this review, we focused our analysis on the different types of PTMs of histological biomarkers in BC. Thus, we analyzed the most common PTMs, including phosphorylation, acetylation, methylation, ubiquitination, SUMOylation, neddylation, palmitoylation, myristoylation, and glycosylation/sialylation/fucosylation of transcription factors, proliferation marker Ki-67, plasma membrane proteins, and histone modifications. Most of these PTMs occur in the presence of cellular stress. We emphasized that these PTMs interfere with these biomarkers maintenance, turnover and lifespan, nuclear or subcellular localization, structure and function, stabilization or inactivation, initiation or silencing of genomic and non-genomic pathways, including transcriptional activities or signaling pathways, mitosis, proteostasis, cell-cell and cell-extracellular matrix (ECM) interactions, membrane trafficking, and PPIs. Moreover, PTMs of these biomarkers orchestrate all hallmark pathways that are dysregulated in BC, playing both pro- and/or antitumoral and context-specific roles in DNA damage, repair and genomic stability, inactivation/activation of tumor-suppressor genes and oncogenes, phenotypic plasticity, epigenetic regulation of gene expression and non-mutational reprogramming, proliferative signaling, endocytosis, cell death, dysregulated TME, invasion and metastasis, including epithelial-mesenchymal/mesenchymal-epithelial transition (EMT/MET), and resistance to therapy or reversal of multidrug therapy resistance. PTMs occur in the nucleus but also at the plasma membrane and cytoplasmic level and induce biomarker translocation with opposite effects. Analysis of protein PTMs allows for the discovery and validation of new biomarkers in BC, mainly for early diagnosis, like extracellular vesicle glycosylation, which may be considered as a potential source of circulating cancer biomarkers.

Aptamer-guided Selective Delivery of Therapeutics to Breast Cancer Cells Expressing Specific Biomarkers

Abstract: Cancer biomarkers or tumor-associated antigens (TAA) are the focus area of current research in cancer biology for diagnosis, prognosis, screening, and targeted treatments. Breast cancer is the second most common type of cancer, affecting women more than men. Conventional methods and antibody-targeted therapies are less effective and suffer systemic cytotoxicity, poor tissue sensitivity, low penetration capacity, and reduced accumulation of the drug in tumor cells that limit its application and sometimes result in treatment failure. Opting for aptamer-mediated targeted delivery of various anti-cancer agents (drugs, siRNA, miRNA, shRNA and peptides) could possibly overcome these limitations by utilizing aptamer as a targeting ligand. The purpose of this article is to review the novel indicative biomarkers of breast cancer and also describe current applications of aptamer-guided active targeting systems in breast cancer therapy in vivo and in vitro.

SEMA7A as a Novel Prognostic Biomarker and Its Correlation with Immune Infiltrates in Breast Cancer.

The role of Semaphorin 7a (SEMA7A) in the initiation and progression of different types of cancerous lesions has been extensively studied. However, the prognostic significance of SEMA7A, specifically in breast cancer (BC), lacks clarity. We conducted an evaluation on the relationship between SEMA7A and the prognosis, immune invasion and tumor mutation burden in different types of cancer by analyzing data from The Cancer Genome Atlas database. The present study focused on investigating the expression level, mutation, immune correlation and coexpression of SEMA7A in BC, utilizing various databases such as the University of Alabama at Birmingham Cancer data analysis portal, cBioPortal and tumor immune estimation resource. Survival analysis was carried out using the Kaplan-Meier Plotter. Furthermore, we employed the R software package to generate receiver operating characteristic (ROC) curves and nomograms. Notably, P<0.05 was considered to indicate statistical significance. Using pancancer analysis, it has been observed that the expression of SEMA7A is elevated in various types of cancer and is strongly correlated with the prognosis of different cancer types. SEMA7A also exhibits a significant association with the tumor mutation burden of diverse types of cancer. Moreover, SEMA7A displays a notable increase in BC cases, and was indicated to have a substantial association with the abundance of immune infiltration. In-depth survival analysis demonstrated that elevated levels of SEMA7A expression are notably linked to shorter overall survival and distant metastasis-free survival among patients with BC. The efficiency of SEMA7A as a reliable prognostic biomarker for BC has been substantiated by the validation of ROC curves and nomograms. SEMA7A has the potential to function as a prognostic indicator for BC, and its correlation with immune infiltration in BC is significant.

Screen-Printed Electrodes as Low-Cost Sensors for Breast Cancer Biomarker Detection.

This review explores the emerging role of screen-printed electrodes (SPEs) in the detection of breast cancer biomarkers. We discuss the fundamental principles and fabrication techniques of SPEs, highlighting their adaptability and cost-effectiveness. The review examines various modification strategies, including nanomaterial incorporation, polymer coatings, and biomolecule immobilization, which enhance sensor performance. We analyze the application of SPEs in detecting protein, genetic, and metabolite biomarkers associated with breast cancer, presenting recent advancements and innovative approaches. The integration of SPEs with microfluidic systems and their potential in wearable devices for continuous monitoring are explored. While emphasizing the promising aspects of SPE-based biosensors, we also address current challenges in sensitivity, specificity, and real-world applicability. The review concludes by discussing future perspectives, including the potential for early screening and therapy monitoring, and the steps required for clinical implementation. This comprehensive overview aims to stimulate further research and development in SPE-based biosensors for improved breast cancer management.

Circulating Long-Non Coding RNA RP11-445H22.4 as a Diagnostic and Prognostic Biomarker of Breast Cancer

Circulating Long-Non Coding RNA RP11-445H22.4 as a Diagnostic and Prognostic Biomarker of Breast Cancer