Biomarker For Diagnosis Of Breast Cancer Research Articles

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.

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.

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.

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.

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.

Potential Early Markers for Breast Cancer: A Proteomic Approach Comparing Saliva and Serum Samples in a Pilot Study.

Breast cancer is the second leading cause of death for women in the United States, and early detection could offer patients the opportunity to receive early intervention. The current methods of diagnosis rely on mammograms and have relatively high rates of false positivity, causing anxiety in patients. We sought to identify protein markers in saliva and serum for early detection of breast cancer. A rigorous analysis was performed for individual saliva and serum samples from women without breast disease, and women diagnosed with benign or malignant breast disease, using isobaric tags for relative and absolute quantitation (iTRAQ) technique, and employing a random effects model. A total of 591 and 371 proteins were identified in saliva and serum samples from the same individuals, respectively. The differentially expressed proteins were mainly involved in exocytosis, secretion, immune response, neutrophil-mediated immunity and cytokine-mediated signaling pathway. Using a network biology approach, significantly expressed proteins in both biological fluids were evaluated for protein-protein interaction networks and further analyzed for these being potential biomarkers in breast cancer diagnosis and prognosis. Our systems approach illustrates a feasible platform for investigating the responsive proteomic profile in benign and malignant breast disease using saliva and serum from the same women.

Comprehensive bioinformatics analysis of CYB561 expression in breast cancer: Link between prognosis and immune infiltration

<b>Background:</b> Cytochrome b561 (CYB561) plays a critical role in neuroendocrine function, cardiovascular regulation, and tumor growth; however, the prognostic value of CYB561 in patients with breast cancer and the relationship between CYB561 expression and immune infiltration in breast cancer remain unclear. <b>Methods:</b> The mRNA expression and clinical data of patients with breast cancer were obtained from The Cancer Genome Atlas database. Functional enrichment analysis was used to explore underlying biological functions associated with CYB561. The methylation status of CYB561 was analyzed using the MethSurv database. The enrichment score of immune cell infiltration for CYB561 in breast cancer was calculated using single-sample gene set enrichment analysis. The prognostic value of CYB561 was evaluated using Kaplan-Meier method and Cox regression analysis. Based on the results of the multivariate Cox analysis, a nomogram was constructed to predict the effect of CYB561 expression on overall survival (OS). <b>Results:</b> The results showed that CYB561 was highly expressed in breast cancer tissues. Hypomethylation of CYB561 is associated with an unfavorable prognosis. In multivariate Cox regression analysis, CYB561 was an independent prognostic factor for OS. Functional enrichment analysis indicated that estrogen signaling pathway, inflammatory response, KRAS signaling pathway, epithelial-mesenchymal transition, leukocyte migration, and regulation of lymphocyte activation were strongly enriched in the low CYB561 expression group. Additionally, CYB561 expression was negatively correlated with immune infiltration of B cells, plasmacytoid dendritic cells, dendritic cells, and neutrophils. <b>Conclusion:</b> CYB561 may serve as a potential biomarker for breast cancer diagnosis and prognosis.

Semaphorin 4C accelerates disease progression and enables disease detection in breast cancer

Semaphorins constitute a diverse family of widely expressed transmembrane, diffusible, and GPI-linked proteins with versatile physiologic functions in orchestrating nerve system development, immune homeostasis, angiogenesis, and cell metabolism. Accumulating evidence highlights semaphorins as essential regulators of tumorigenesis by coordinating the cell-cell communications in the tumor microenvironment. Semaphorin 4C (SEMA4C) is a member of the fourth class of semaphorins with high affinity to Plexin-B2 and its interplay with cancer has long been a significant knowledge gap. Here, this perspective summarizes the recent progress in the understanding of SEMA4C in cancer and comprehensively delineates the discovery of SEMA4C in lymphatic vessels of breast cancer, the mechanisms by which SEMA4C promotes the invasiveness, proliferation, metastasis, and drug resistance of breast cancer, and the explorations of leveraging serum SEMA4C in breast cancer detection, highlighting SEMA4C as a critical driver of breast cancer progression, an effective biomarker for breast cancer diagnosis, and potential therapeutic target for breast cancer treatment.

MiR-144 as a novel biomarker in breast cancer diagnosis and treatment

Exosomes naturally carry the biomolecules in the body; they perform this task efficiently without compromising the immune system and by breaking through all the biological barriers, so they can be the best choice for designing and introducing a drug and gene transfer system. Extraction of the exosomes from the cell culture medium was performed by precipitation with an Exoquick kit solution. Nanoparticle specificity analysis was performed using scanning electron microscopy and dynamic light scattering. Trizol reagent (Invitrogen) was used for RNA extraction. Single-strand cDNA synthesis was performed from the miRNA and RT-PCR. Data were analyzed using a threshold cycle comparative method and cell cycle analysis using flow cytometry. Exosomes containing miR-144 can dramatically decrease the expression level of crucial TGF-β pathway genes, SMAD4 and TGF-βR2, in breast cancer cells. Botulinum toxin A inhibits cancer cell growth by inhibiting the TGF-β pathway. The simultaneous combination of engineered exosomes containing miR-144 and bacterial botulinum toxin A has increased effects on inhibiting the TGF-β signaling pathway. It causes cell cycle arrest in breast cancer cells. The present study's findings showed that overexpression of miR-144 in breast tumor cells results in the packaging of miRNA in exosomes derived from these cells. As a result, the exosomal platform for nucleic acid transfer to the cell appears to be an effective transducer for gene transfer to the cell. It could be used as a suitable adjunct to cancer therapeutic studies. Keywords: Breast cancer, botulinum toxin A, exosome, miRNA, biomarker.

Expression Analysis of TREM2 and TC2N Genes in Human Breast Cancer Tissues

Background: Since breast cancer is the most common type of cancer in women around the world, finding new biomarkers for early diagnosis of breast cancer is invaluable. Objectives: This research assessed the mRNA expression of triggering receptors expressed on myeloid cell 2 (TREM2) and tandem C2 domains nuclear protein (TC2N) genes among Iranian patients with breast cancer. Methods: We acquired 50 samples of cancerous breast tumors and corresponding adjacent non-cancerous tissues from Iranian women. The gene expression of TREM2 and TC2N was measured by quantitative real-time polymerase chain reaction (q-RT-PCR). In addition, the association between TREM2 and TC2N levels with various clinicopathologic characteristics was also investigated. Results: The increased levels of TREM2 and TC2N mRNAs were shown in breast cancerous tissues in comparison with adjacent non-cancerous tissues (P &lt; 0.05). Among the clinicopathological characteristics evaluated, tumor size, necrosis, and lymphatic tissue invasion were significantly associated with high TREM2 expression. A significant relationship was also seen between increased TC2N expression and tumor grade. Sensitivity and specificity were shown at 84% and 94%, respectively, for TREM2 and 72% and 100% for TC2N. Conclusions: The data suggest that TREM2 expression, but not TC2N, could be a suitable biomarker for breast cancer diagnosis.

Autoantibodies as biomarkers for breast cancer diagnosis and prognosis.

Breast cancer is the most common cancer in women worldwide and is a substantial public health problem. Screening for breast cancer mainly relies on mammography, which leads to false positives and missed diagnoses and is especially non-sensitive for patients with small tumors and dense breasts. The prognosis of breast cancer is mainly classified by tumor, node, and metastasis (TNM) staging, but this method does not consider the molecular characteristics of the tumor. As the product of the immune response to tumor-associated antigens, autoantibodies can be detected in peripheral blood and can be used as noninvasive, presymptomatic, and low-cost biomarkers. Therefore, autoantibodies can provide a possible supplementary method for breast cancer screening and prognosis classification. This article introduces the methods used to detect peripheral blood autoantibodies and the research progress in the screening and prognosis of breast cancer made in recent years to provide a potential direction for the examination and treatment of breast cancer.