Biomarker For Diagnosis Of Breast Cancer Research Articles

Hsa_circRNA_000166 accelerates breast cancer progression via the regulation of the miR-326/ELK1 and miR-330-5p/ELK1 axes

Purposes To probe the expression, clinical significance, roles, and molecular mechanisms of circRNA_000166 in breast cancer (BC). Methods Clinical tissue samples were gathered from 84 BC patients who underwent surgery at the Affiliated Hospital of Chengde Medical College. Clinical data were obtained from medical records and postoperative follow-up. Expression levels of circRNA_000166, miR-326, miR-330-5p, and ELK1 mRNA in BC tissues and cells were measured by qRT-PCR, and ELK1 protein levels were assessed by WB. Pearson’s correlation analysis evaluated the interrelationships between these RNAs in clinical samples. Luciferase reporter assays verified the interactions between miR-326/miR-330-5p and circRNA_000166, as well as between miR-326/miR-330-5p and ELK1. Cell proliferation, migration, and apoptosis were examined using CCK-8, colony formation, transwell, and flow cytometry assays, respectively. Results CircRNA_000166 was highly expressed in BC tissues and inversely correlated with miR-326/miR-330-5p levels but positively with ELK1 mRNA levels. ELK1 mRNA also inversely associated with miR-326/miR-330-5p levels in BC tissues. Importantly, our findings demonstrated that circRNA_000166 targets miR-326 and miR-330-5p, while ELK1 is the target of miR-326 and miR-330-5p in BC cells. CircRNA_000166 levels positively correlated with tumour size, TNM stage, histological grade, and lymph node metastasis, and negatively associated with postoperative progression-free survival (PFS) and overall survival (OS) in BC patients. CircRNA_000166 was also highly expressed in BC cells, and knockdown of circRNA_000166 reduced proliferation and migration, and increased apoptosis via miR-326/ELK1 and miR-330-5p/ELK1 pathways in vitro. Conclusion CircRNA_000166 enhances BC progression through miR-326/ELK1 and miR-330-5p/ELK1 pathways and shows potential as a biomarker for BC diagnosis and treatment.

Identification of a Novel Biomarker Panel for Breast Cancer Screening.

Breast cancer remains a major public health concern, and early detection is crucial for improving survival rates. Metabolomics offers the potential to develop non-invasive screening and diagnostic tools based on metabolic biomarkers. However, the inherent complexity of metabolomic datasets and the high dimensionality of biomarkers complicates the identification of diagnostically relevant features, with multiple studies demonstrating limited consensus on the specific metabolites involved. Unlike previous studies that rely on singular feature selection techniques such as Partial Least Square (PLS) or LASSO regression, this research combines supervised and unsupervised machine learning methods with random sampling strategies, offering a more robust and interpretable approach to feature selection. This study aimed to identify a parsimonious and robust set of biomarkers for breast cancer diagnosis using metabolomics data. Plasma samples from 185 breast cancer patients and 53 controls (from the Cooperative Human Tissue Network, USA) were analyzed. This study also overcomes the common issue of dataset imbalance by using propensity score matching (PSM), which ensures reliable comparisons between cancer and control groups. We employed Univariate Naïve Bayes, L2-regularized Support Vector Classifier (SVC), Principal Component Analysis (PCA), and feature engineering techniques to refine and select the most informative features. Our best-performing feature set comprised 11 biomarkers, including 9 metabolites (SM(OH) C22:2, SM C18:0, C0, C3OH, C14:2OH, C16:2OH, LysoPC a C18:1, PC aa C36:0 and Asparagine), a metabolite ratio (Kynurenine-to-Tryptophan), and 1 demographic variable (Age), achieving an area under the ROC curve (AUC) of 98%. These results demonstrate the potential for a robust, cost-effective, and non-invasive breast cancer screening and diagnostic tool, offering significant clinical value for early detection and personalized patient management.

Evaluation the Levels of Yap1 Protein and NF-Κb in Serum of Women with Breast Cancer

Breast cancer (BC) is the type of cancer that is most frequently diagnosed and is the primary cause of cancer-related deaths in women globally. BC is intimately linked to both apoptosis and inflammation. This study aimed to evaluate NF-kappaB (NF-κB) and Yes-associated protein (Yap1) biomarkers in the serum of women patients with (BC). This case-control research recruited a total of 90 women, consisting of 60 women diagnosed with breast cancer and 30 healthy controls of the same age. This research detected a statistically significant rise (P<0.05) in the levels of NF-κB and Yap1 in the serum of the BC group compared to the control group. from the results of Receiver operating characteristic (ROC) analysis, the present results indicate an optimal NF-κB cut‑off value of more than 3.06 ng/ml resulted in an AUC value of 0.934. And is considered an excellent diagnostic marker, also. An optimal Yap1cutt-off value of more than 13.72 ng/ml resulted in an AUC value of 0.997 Yap1, while There was no significant difference P>0.05 in NF-κB and Yap1 levels among BC women according to clinical-pathological, molecular type and phenotype clinical and pathological characteristics. We conclude that These results indicate the roles of NFκB and Yap1 as biomarkers for breast cancer diagnosis.

Revealing the Molecular Signatures of miR-185-5p on Breast Cancer Cells Using Proteomic Analysis.

Breast cancer is a heterogeneous type of disease in which genetic and environmental factors play a crucial role. There are several types of treatment for breast cancer (BC) patients. However, the biggest problem in the treatment of breast cancer is the resistance that occurs during the treatment with chemotherapeutic agents. Usnic acid, a secondary metabolite of lichen, has been identified as a drug candidate molecule in cancer treatment. The determination of miRNA target proteins is essential for the understanding of molecular mechanisms of miRNA-related tumorigenesis. We determined that mir-185-5p has therapeutic potential at the miRNA level by applying usnic acid to BT-474 breast cancer cells in a previous study. Herein, we aimed to investigate the molecular mechanisms of miR-185-5p on BT-474 breast cancer cells using a proteomics approach. We explored the changes in the protein expression level of BT-474 breast cancer cells in response to the up-regulation of miR-185-5p after applying usnic acid as a novel candidate anti-- cancer drug molecule. We performed quantitative proteome analysis based on an LC-MS/MS assay, which was validated by western blotting. The differentially expressed proteins were analyzed using the latest data available in bioinformatics tools. The up-regulated expression of YWHAE, Cathepsin D, and the down-regulated levels of PAK-1 were demonstrated by western blot assay. According to the results, 86 proteins showing >2-fold change were identified as differentially expressed between breast cancer and normal breast epithelial cells. The apoptosis pathway was the main clade containing most of the proteins regulated by miR-185-5p. The results indicate that miR-185-5p modulates apoptosis signaling pathways in BT-474 breast cancer cells. Breast cancer inhibition due to increased expression of YWHAE, Cathepsin D, and decreased expression of PAK-1 is likely to be mediated by inducing miR-185-5p mediated apoptosis. In this study, the identification of miR-185-5p protein targets demonstrated the potential for the development of targeted therapy and the development of miRNA-based therapeutics and presented it as a biomarker for breast cancer diagnosis, prognosis, and treatment response. In this regard, proteome analyses provided an understanding of the molecular mechanism underlying the effect of miR-185-5p on breast cancer.

Correlation Analysis of Genetic Mutations and Galectin Levels in Breast Cancer Patients.

Galectins are innate immune system regulators associated with disease progression in cancer. This paper aims to investigate the correlation between mutated cancer-critical genes and galectin levels in breast cancer patients to determine whether galectins and genetic profiles can be used as biomarkers for disease and potential therapy targets. Prisma Health Cancer Institute's Biorepository provided seventy-one breast cancer samples, including all four stages spanning the major molecular subtypes and histologies. Hotspot mutation statuses of cancer-critical genes were determined using multiplex PCR in tumor samples from the same patients by Precision Genetics and the University of South Carolina Functional Genomics Core Facility. The galectin-1, -3, and -9 levels in patients' sera were analyzed using Enzyme-linked Immunosorbent Assay (ELISA). An analysis was performed using JMP software to compare mean and median serum galectin levels between samples with and without specific cancer-critical genes, including pooled t-test, Wilcoxon Rank Sum Test, ANOVA, and Steel Dwass Test (α=0.05). Our analysis indicates that KIT mutations correlate with elevated serum levels of galectin-9 in patients with breast cancer. In patients with Luminal A subtype, FLT3 mutation correlates with lower serum galectin-1 and -9 levels and TP53 mutations correlate with higher serum galectin-3 levels. Patients with invasive ductal carcinoma had significantly higher serum galectin-3 levels than patients with ductal carcinoma in situ. Patients with both TP53 and PIK3CA mutations exhibit elevated serum galectin-3 levels, while patients with one or neither mutation show no significant difference in serum galectin-3 levels. In addition, metastatic breast cancer samples were more likely to have a KIT or PIK3CA mutation compared to primary breast cancer samples. The relationship between genetic mutations and galectin levels has the potential to identify appropriate candidates for combined therapy, targeting genetic mutations and galectins. Further understanding of the effect of genetic mutations and galectin levels on cancer progression and metastasis could aid in the search for biomarkers for breast cancer diagnosis, disease progression, and prognosis.

Abstract 1843: Integrating machine learning with microfluidic technologies for proteomic profiling of extracellular vesicles in triple-negative breast cancer

Abstract Accurate diagnosis of breast cancer using circulating biomarkers present in plasma remains an important challenge. In particular, protein changes in tumor-derived extracellular vesicles (tdEVs) have emerged as potential biomarkers for breast cancer diagnosis because they accurately reflect dynamic changes in tumors. In this study, we compared the proteomes of extracellular vesicle (EV) isolated from the plasma of 100 breast cancer patients and 30 healthy individuals who visited Severance Hospital between Mar 2010 and Dec 2015. Microfluidic chip-based protocol that facilitates the removal of contaminants such as albumin and immunoglobulins was used for the extraction of enriched tdEVs with small amounts of plasma. Comparative analysis of the proteomes identified 26 significant biomarkers that could indicate differences between breast cancer patients and healthy individuals. Using the LsBoost-CNN-SVM hybrid machine learning algorithm, we especially identified key EV protein biomarkers for detecting triple-negative breast cancer (TNBC), a breast cancer subtype with a poor prognosis and a high recurrence rate, as well as lacking therapeutic and diagnostic targets. Remarkably, a signature consisting of three EV proteins, specifically extracellular matrix protein 1 (ECM1), mannose-binding lectin 2 (MBL2), and biotinidase (BTD), effectively distinguished TNBC patients from healthy individuals. In our proteomic analysis set (n=73), this signature exhibited impressive performance, achieving a sensitivity of 93.3% and specificity of 93% in the accurate discrimination of TNBC from the control group. The validation set (n=40) confirmed these findings, with 100% sensitivity and 80% specificity. This signature not only served as a diagnostic tool but also provided valuable insights into the risk of recurrence and patient prognosis. We found a novel diagnostic approach that holds the potential to revolutionize breast cancer diagnostics by enhancing the reliability of tumor-related information obtained from blood samples. Citation Format: Min Woo Kim, Jee Ye Kim, Young Kim, Suji Lee, Sol Moon, Ju-yong Hyon, Kyung-A Hyun, Yeji Yang, Seongmin Ha, Sunyoung Park, Hogyeong Gawk, Haeji Lee, Eun Hee Han, Jin Young Kim, Hyo-Il Jung, Young-Ho Chung, Seung Il Kim. Integrating machine learning with microfluidic technologies for proteomic profiling of extracellular vesicles in triple-negative breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1843.

Plasma Thioredoxin Reductase as a Potential Diagnostic Biomarker for Breast Cancer

BackgroundEarly diagnosis of breast cancer is critical to the treatment and prognosis of breast cancer patients. Our aim is to explore more practical and effective diagnostic methods to facilitate early treatment and improve prognosis for breast cancer patients. Materials and MethodsThe Mann–Whitney U test, receiver operating characteristic curve, Youden index, Chi-square test, and Fisher's exact test were used to determine whether plasma thioredoxin reductase (TrxR) could be used for the clinical diagnosis of breast cancer. The Wilcoxon signed-rank test was used to validate the prognostic potential of plasma TrxR activity assessment. ResultsA total of 761 patients were included, including 537 cases of breast cancer and 224 cases of benign breast diseases. Plasma TrxR activity in the breast cancer group [8.0 (6.0, 9.45) U/mL] was significantly higher than that in the benign group [3.05 (1.20, 6.275) U/mL]. The diagnostic efficiency of TrxR for breast cancer was higher than that of other conventional breast cancer biomarkers, with an area under the curve of 0.821 (95% CI = 0.791-0.852). In addition, TrxR can be used in combination with conventional tumor markers to further improve the diagnostic efficiency. The optimal TrxR threshold for identifying benign and malignant diseases is 7.45 U/mL. We detected plasma TrxR activity and serum tumor markers before and after antitumor therapies in 333 breast cancer patients and found that their trends were basically the same, with a significant decrease in plasma TrxR activity after treatment. ConclusionPlasma TrxR activity can be used as a suitable biomarker for breast cancer diagnosis and efficacy assessment.

Tumor-derived extracellular vesicles as a biomarker for breast cancer diagnosis and metastasis monitoring

It is imperative to explore biomarkers that are both precise and readily accessible in the comprehensive management of breast cancer.A multicenter cohort, including 512 breast cancer patients and 198 nonneoplastic individuals, was recruited to detect the level of tumor-derived extracellular vesicles using our method based on dual DNA tetrahedral nanostructures. The level of tumor-derived extracellular vesicles was significantly higher in newly diagnosed breast cancer patients than in nonneoplastic individuals at a cutoff value of 3.58 U/μL. For postoperative metastasis monitoring, the level of tumor-derived extracellular vesicles was significantly higher in breast cancer patients with metastasis than in those without metastasis at a cutoff value of 3.91 U/μL. Its efficacy of diagnosis and metastasis monitoring was superior to traditional tumor markers. Elevated level of tumor-derived extracellular vesicles served as a predictive biomarker for diagnosis and metastasis monitoring in breast cancer patients.

CIRCULATING PROTEINS AS DIAGNOSTIC BIOMARKERS IN BREAST CANCER

Purpose: Summary of tumor-produced circulating proteins as biomarkers in breast cancer (BC) detection. Subjects and method: A meta-analysis of 50 researches on protein biomarkers in BC diagnosis. Results: Many studies have shown that tumors continuously release components such as protein, RNA, and DNA into the bloodstream during apotosis. There are different protein markers, as tissue markers and serum markers involved assisting diagnosis, but none of them can independently establish the final diagnotic BC at an early stage. Therefore, scientific communities have been putting lots of effort to discover biomarkers for better identifying this malignant disease at earlier stages.

A Drug Molecule-Modified Graphene Field-Effect Transistor Nanosensor for Rapid, Label-Free, and Ultrasensitive Detection of Estrogen Receptor α Protein.

Estrogen receptor α (ERα) is an important biomarker in breast cancer diagnosis and treatment. Sensitive and accurate detection of ERα protein expression is crucial in guiding selection of an appropriate therapeutic strategy to improve the effectiveness and prognosis of breast cancer treatment. Herein, we report a liquid-gated graphene field-effect transistor (FET) biosensor that enables rapid, sensitive, and label-free detection of the ERα protein by employing a novel drug molecule as a capture probe. The drug molecule was synthesized and subsequently immobilized onto the sensing surface of the fabricated graphene FET, which was able to distinguish the ERα-positive from the ERα-negative protein. The developed sensor not only demonstrated a low detection limit (LOD: 2.62 fM) but also achieved a fast response to ERα protein samples within 30 min. Moreover, depending on the relationship between the change of dirac point and the ERα protein concentrations, the dissociation constant (Kd) was estimated to be 7.35 ± 0.06 pM, indicating that the drug probe-modified graphene FET had a good affinity with ERα protein. The nanosensor was able to analyze ERα proteins from 36 cell samples lysates. These results show that the graphene FET sensor was able to differentiate between ERα-positive and ERα-negative cells, indicating a promising biosensor for the ultrasensitive and rapid detection of ERα protein without antibody labeling.

Unveiling promising breast cancer biomarkers: an integrative approach combining bioinformatics analysis and experimental verification

BackgroundBreast cancer remains a significant health challenge worldwide, necessitating the identification of reliable biomarkers for early detection, accurate prognosis, and targeted therapy.Materials and methodsBreast cancer RNA expression data from the TCGA database were analyzed to identify differentially expressed genes (DEGs). The top 500 up-regulated DEGs were selected for further investigation using random forest analysis to identify important genes. These genes were evaluated based on their potential as diagnostic biomarkers, their overexpression in breast cancer tissues, and their low median expression in normal female tissues. Various validation methods, including online tools and quantitative Real-Time PCR (qRT-PCR), were used to confirm the potential of the identified genes as breast cancer biomarkers.ResultsThe study identified four overexpressed genes (CACNG4, PKMYT1, EPYC, and CHRNA6) among 100 genes with higher importance scores. qRT-PCR analysis confirmed the significant upregulation of these genes in breast cancer patients compared to normal samples.ConclusionsThese findings suggest that CACNG4, PKMYT1, EPYC, and CHRNA6 may serve as valuable biomarkers for breast cancer diagnosis, and PKMYT1 may also have prognostic significance. Furthermore, CACNG4, CHRNA6, and PKMYT1 show promise as potential therapeutic targets. These findings have the potential to advance diagnostic methods and therapeutic approaches for breast cancer.

Relevance Analysis of TPM2 and Clinicopathological Characteristics in Breast Cancer.

The function of tropomyosin 2 (TPM2) in breast cancer is still far understudied. In this study, we aim to explore the roles of TPM2 in breast cancer progression. This research included 155 breast cancer tissues. The expression of TPM2 was analyzed by immunohistochemical staining and grading. The mRNA expression of TPM2 in pan-cancer was analyzed with The Cancer Genome Atlas (TCGA) data plate form. The differential expression of TPM2 protein and the differential promoter methylation level of TPM2 between breast cancer tissues and normal breast tissues were analyzed by the UALCAN online database. The relationship between TPM2 and signaling pathways was interpreted by Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA) pathway enrichment analyses. The survival curve of TPM2 was analyzed across the Kaplan-Meier plotter online database. Furthermore, the relationship between TPM2 expression and infiltrating macrophages was validated through in vitro co-culture experiments. TPM2 expression was significantly down-regulated in breast cancer samples. In addition, TPM2 expression was correlated with lymph node metastasis and high-grade histopathological morphology. The receiver operating characteristic (ROC) curve indicated that TPM2 expression could well distinguish between normal breast tissue and breast cancer tissue. TPM2 may have potential value in breast cancer diagnosis. Bioinformatics analysis illustrated that TPM2 was mainly involved in extracellular matrix organization, collagen fibril organization, cell junction assembly, focal adhesion, cAMP signaling pathway, estrogen signaling pathway, Wnt signaling pathway, and adaptive immune system. TPM2 expression was correlated with immune infiltrating cells and immune checkpoint molecules. Our in vitro co-culture experiments showed that the M2 macrophages could upregulate the expression of TPM2. TPM2 may play key roles in breast cancer occurrence and development, especially in cancer metastasis. TPM2 may be a potential biomarker for breast cancer diagnosis.

Multi-omic profiling of breast tumor microenvironment uncovers a role of mitochondrial calcium gatekeepers.

In this study, we aimed to elucidate the role of mitochondrial calcium uptake 1/2 (MiCU1/2) in breast cancer (BRCA) by employing a comprehensive multi-omics approach. Unlike previous research, we utilized a novel web application tailored for whole tumor tissue, single-cell, and spatial transcriptomics analysis to investigate the association between MiCU1/2 and the tumor immune microenvironment (TIME). Our gene set enrichment analysis (GSEA) provided insights into the primary biological effects of MiCU1/2, while our CRISPR-based drug screening repository identified potential effective drugs. Our study revealed that high MiCU1/2 expression serves as an independent diagnostic biomarker, correlating with advanced clinical status and indicating poorer recurrence-free survival (RFS) rates in BRCA patients. Additionally, spatial transcriptome analysis highlighted the heightened expression of MiCU1/2 in tumors and its relevance in surrounding immune cells. Furthermore, using the CIBERSORT algorithm, we discovered a positive correlation between MiCU1/2 levels and macrophage infiltration, underscoring their potential impact on immune infiltration. We also identified expression patterns of immune-related genes associated with responses against various immune cell types, including CXCL, MIF, GDF, SPP1, and IL16. Finally, our pharmacogenomic screening identified potential small molecule drugs capable of effectively targeting breast cancer cells with elevated MiCU1/2 expression. Overall, our study establishes MiCU1/2 as a promising novel biomarker for BRCA diagnosis and prognostic prediction, as well as a potential therapeutic target, highlighting the importance of exploring these pathways to advance patient care and outcomes in BRCA treatment.

Evaluation of some biomarkers of osteoporosis in women with breast cancer in Al-Najaf Governorate

Introduction: Osteoporosis is a systemic disease that affects the mineral density of bones and their structure, which leads to an increase in the softness of the bones. One in three women after the age of fifty worldwide is affected. Both breast cancer and osteoporosis are common diseases in women. Breast cancer is the most common type of cancer in women, with one in eight women developing breast cancer in their lifetime.Aim of the study Evaluation of some biomarkers of osteoporosis in women with breast cancer represented by cathepsin K, parathyroid hormone, calcium and vitamin D.
 methods: The current study was conducted at the National Cancer Center in Al-Najaf province for the period from 8/15/2022 to 1/20/2023. The study included 60 women with breast cancer and 30 healthy women free of diseases, and it was considered a control group. The ages of patients and healthy people ranged between (30-68) years old, vitamin D, calcium, parathyroid hormone, and cathepsin K were evaluated.
 Results: For the indicators of vitamin D and parathyroid hormone (PTH), the results of the statistical analysis did not record any significant difference (p>0.05) between the two groups under study. The results of the statistical analysis showed a significant increase (p<0.0001) in the levels of calcium (Ca) and cathepsin K in infected women compared with healthy women. Likelihood (p < 0.005) and calcium with a probability level (p < 0.0001).
 Conclusion
 Depending on the results reached through the current study, we conclude that there is a high rate of osteoporosis in women with breast cancer. prevalent. Also, an increase in the level of calcium in women with breast cancer, with no difference in parathyroid hormone, which is the most important biomarker for breast cancer diagnosis. An increase in the level of cathepsin K and its negative effect on the level of calcium in women with breast cancer.

Identification of Potential Biomarkers and Metabolic Changes in the Serum of Breast Lump Patients Among Kelantanese Based on 1H NMR Metabolomics

This case-control study, conducted at the Hospital USM BestARi unit, aimed to identify the serum metabolic fingerprint among individuals with breast lumps and healthy controls, and to discover potential biomarkers. Serum samples from healthy controls, benign breast lump patients, and malignant breast lump patients were analyzed using proton nuclear magnetic resonance spectroscopy (1H NMR). A multivariate data analysis approach was employed, with the OPLS-DA and clustered heat map techniques effectively differentiating between the three groups. The study revealed significant metabolite variations across the groups and proposed D-glucose, glycerol, and glycine as potential biomarkers for breast cancer diagnosis. Metabolic pathways such as alanine, aspartate, glutamate metabolism, and glycine, serine, and threonine metabolism were implicated. The metabolomics approach coupled with multivariate analysis successfully identified key metabolites leading to group separation and suggested altered metabolic pathways. However, further research and integration with other ‘omics’ technologies are necessary for clinical translation.

Identifying tumor cell-released extracellular vesicles as biomarkers for breast cancer diagnosis by a three-dimensional hydrogel-based electrochemical immunosensor

Tumor cell-released LC3+ extracellular vesicles (LC3+ EVs) participate in immunosuppression during autophagy and contribute to the occurrence and development of breast cancer. In view of the strong association between the LC3+ EVs and breast cancer, developing an effective strategy for the quantitative detection of LC3+ EVs levels with high sensitivity to identify LC3+ EVs as new biomarkers for accurate diagnosis of breast cancer is crucial, but yet not been reported. Herein, an ultrasensitive electrochemical immunosensor is presented for the quantitative determination of LC3+ EVs using a three-dimensional graphene oxide hydrogel-methylene blue composite as a redox probe, showing a low detection limit and a wide linear range. With this immunosensor, the expression levels of LC3+ EVs in various practical sample groups including different cancer cell lines, the peripheral blood of tumor-bearing mice before and after immunotherapy, and the peripheral blood from breast cancer patients with different subtypes and stages were clearly distinguished. This study demonstrated that LC3+ EVs were superior as biomarkers for the accurate diagnosis of breast cancer compared to traditional biomarkers, particularly for cancer subtype discrimination. This work would provide a new noninvasive detection tool for the early diagnosis and prognosis assessment of breast cancer in clinics.

Discovery of lipid profiles in plasma-derived extracellular vesicles as biomarkers for breast cancer diagnosis.

Lipids are a major component of extracellular vesicles; however, their significance in tumorigenesis and progression has not been well elucidated. As we previously found that lipid profiles drastically changed in breast tumors upon progression, we hypothesized that lipid profiles of plasma-derived extracellular vesicles could be utilized as breast cancer biomarkers. Here, we adopted modified sucrose cushion ultracentrifugation to isolate plasma-derived extracellular vesicles from breast cancer (n = 105), benign (n = 11), and healthy individuals (n = 43) in two independent cohorts (n = 126 and n = 33) and conducted targeted lipidomic analysis. We established a breast cancer diagnostic model comprising three lipids that showed favorable performance with the area under the receiver operating characteristic curve of 0.759, 0.743, and 0.804 in the training, internal validation, and external test sets, respectively. Moreover, we identified several lipids that could effectively discriminate breast cancer progression and subtypes: phosphatidylethanolamines and phosphatidylserines were relatively higher in Stage III, whereas phosphatidylcholines and sphingomyelins were higher in Stage IV; phosphatidylcholines and ceramides were correspondingly concentrated in HER2-positive patients, while lysophosphatidylcholines and polyunsaturated triglycerides were concentrated in the triple-negative breast cancer subtype. Lipid profiling of plasma-derived extracellular vesicles is a non-invasive and promising approach for diagnosing, staging, and subtyping breast cancer.

Human milk EV-miRNAs: a novel biomarker for air pollution exposure during pregnancy

Exposure to ambient and near-roadway air pollution during pregnancy has been linked with several adverse health outcomes for pregnant women and their babies. Emerging research indicates that microRNA (miRNA) expression can be altered by exposure to air pollutants in a variety of tissues. Additionally, miRNAs from breast tissue and circulating miRNAs have previously been proposed as a biomarker for breast cancer diagnosis and prognosis. Therefore, this study sought to evaluate the associations between pregnancy exposures to ambient (PM10, PM2.5, NO2, O3) and near-roadway air pollution (total NO x , freeway NO x , non-freeway NO x ) with breast milk extracellular vesicle miRNA (EV-miRNA), measured at 1-month postpartum, in a cohort of 108 Latina women living in Southern California. We found that PM10 exposure during pregnancy was positively associated with hsa-miR-200c-3p, hsa-miR-200b-3p, and hsa-let-7c-5p, and was negatively associated with hsa-miR-378d. We also found that pregnancy PM2.5 exposure was positively associated with hsa-miR-200c-3p and hsa-miR-200b-3p. First and second trimester exposure to PM10 and PM2.5 was associated with several EV-miRNAs with putative messenger RNA targets related to cancer. This study provides preliminary evidence that air pollution exposure during pregnancy is associated with human milk EV-miRNA expression.

Biomarkers in Breast Cancer: An Old Story with a New End.

Breast cancer is the second most frequent cancer in the world. It is a heterogeneous disease and the leading cause of cancer mortality in women. Advances in molecular technologies allowed for the identification of new and more specifics biomarkers for breast cancer diagnosis, prognosis, and risk prediction, enabling personalized treatments, improving therapy, and preventing overtreatment, undertreatment, and incorrect treatment. Several breast cancer biomarkers have been identified and, along with traditional biomarkers, they can assist physicians throughout treatment plan and increase therapy success. Despite the need of more data to improve specificity and determine the real clinical utility of some biomarkers, others are already established and can be used as a guide to make treatment decisions. In this review, we summarize the available traditional, novel, and potential biomarkers while also including gene expression profiles, breast cancer single-cell and polyploid giant cancer cells. We hope to help physicians understand tumor specific characteristics and support decision-making in patient-personalized clinical management, consequently improving treatment outcome.

CLDN6 inhibits breast cancer metastasis through WIP-dependent actin cytoskeleton-mediated autophagy

BackgroundAs a breast cancer suppressor gene, CLDN6 overexpression was found to inhibit breast cancer metastasis in our previous studies, but the specific mechanism remains unclear. This study aimed to clarify the role and mechanism of CLDN6 in inhibiting breast cancer metastasis.MethodsWestern blot, immunofluorescence and transmission electron microscopy were performed to detect autophagy. Wound healing, transwell assays and lung metastasis mouse models were used to examine breast cancer metastasis. Phalloidin staining and immunofluorescent staining were used to observe actin cytoskeleton. mRNA seq, RT-PCR, western blot, chromatin immunoprecipitation, dual luciferase reporter assay, co-immunoprecipitation and immunofluorescence were performed to define the molecular mechanism. The expression levels and clinical implication of CLDN6, WIP and LC3 in breast cancer tissues were evaluated using immunohistochemistry.ResultsWe demonstrated that CLDN6 inhibited breast cancer metastasis through autophagy in vitro and vivo. We unraveled a novel mechanism that CLDN6 regulated autophagy via WIP-dependent actin cytoskeleton assembly. Through its PDZ-binding motif, overexpressed CLDN6 interacted with JNK and upregulated JNK/c-Jun pathway. C-Jun promoted WIP expression at the transcriptional level. Notably, we observed c-Jun transcriptionally upregulated CLDN6 expression, and there was a positive feedback loop between CLDN6 and JNK/c-Jun. Finally, we found that CLDN6, WIP and LC3 expression correlated with each other, and WIP expression was significantly associated with lymph node metastasis of breast cancer patients.ConclusionsThe data provide a new insight into the inhibitory effects of CLDN6-mediated autophagy on breast cancer metastasis, and revealed the new mechanism of CLDN6 regulating autophagy through WIP-dependent actin cytoskeleton. Our findings enrich the theoretical basis for CLDN6 as a potential biomarker for breast cancer diagnosis and therapy.