Blood-based protein biomarkers in breast cancer

Proteomic and lipidomic analysis of low-density lipoprotein identifies potential biomarkers of early estrogen receptor-positive breast cancer

Estrogen receptor (ER)-positive breast cancer (BC) is a prevalent and fatal cancer among women, and there is a need to identify molecules involved in the disease pathophysiology which could also serve as biomarkers for early detection. Detection of cancer markers in whole plasma produces excessive information, and identifying important markers involved in cancer progression is challenging. We identified a BC-specific low-density lipoprotein (LDL) particle isolated by ultracentrifugation from the plasma of ER-positive BC patients. This LDL has an aberrant proteome and lipidome, significantly different from that of LDL from healthy women, including a high association with the pro-tumor chemokines CXCL4 and CXCL7, and an enrichment with the lipid subclasses phosphatidylethanolamine, ceramide, triglycerides, lysophosphatidylcholine, phosphatidylserine, phosphatidic acid, and sphingomyelin. In contrast, phosphatidylinositol species were significantly less abundant in LDL from tumor patients than in control. Moreover, BC-associated LDL has a distinct effect on macrophage phenotype, inducing an increased gene expression of IL1β, IL8 and CD206 and decreased gene expression of TNFα, a gene signature characteristic of tumor-associated macrophages (TAMs). This suggests that this formerly unrecognized form of LDL may represent LDL particles that are recruited by the tumor microenvironment to support tumor progression by inducing discrete subsets of TAMs. In conclusion, these data offer BC-associated LDL as an early biomarker detection platform for ER-positive BC. Furthermore, LDL-associated proteins and lipids that promote BC progression may also serve in the future as novel targets for BC therapies.

Towards precision oncology: a multi-level cancer classification system integrating liquid biopsy and machine learning

BACKGROUND

Millions of people die from cancer every year. Early cancer detection is crucial for ensuring higher survival rates, as it provides an opportunity for timely medical interventions. This paper proposes a multi-level cancer classification system that uses plasma cfDNA/ctDNA mutations and protein biomarkers to identify seven distinct cancer types: colorectal, breast, upper gastrointestinal, lung, pancreas, ovarian, and liver.

RESULTS

The proposed system employs a multi-stage binary classification framework where each stage is customized for a specific cancer type. A majority vote feature selection process is employed by combining six feature selectors: Information Value, Chi-Square, Random Forest Feature Importance, Extra Tree Feature Importance, Recursive Feature Elimination, and L1 Regularization. Following the feature selection process, classifiers-including eXtreme Gradient Boosting, Random Forest, Extra Tree, and Quadratic Discriminant Analysis-are customized for each cancer type individually or in an ensemble soft voting setup to optimize predictive accuracy. The proposed system outperformed previously published results, achieving an AUC of 98.2% and an accuracy of 96.21%. To ensure reproducibility of the results, the trained models and the dataset used in this study are made publicly available via the GitHub repository ( https://github.com/SaraEl-Metwally/Towards-Precision-Oncology ).

CONCLUSION

The identified biomarkers enhance the interpretability of the diagnosis, facilitating more informed decision-making. The system's performance underscores its effectiveness in tissue localization, contributing to improved patient outcomes through timely medical interventions.

Navigating the Collective: Nanoparticle-Assisted Identification of Leader Cancer Cells During Migration

Cancer-related deaths primarily occur due to metastasis, a process involving the migration and invasion of cancer cells. In most solid tumors, metastasis occurs through collective cell migration (CCM), guided by "cellular leaders". These leader cells generate forces through actomyosin-mediated protrusion and contractility. The cytoskeletal mechanisms employed by metastatic cells during the migration process closely resemble the use of the actin cytoskeleton in endocytosis. In our previous work, we revealed that tumor cells exhibiting high metastatic potential (MP) are more adept at encapsulating 100-200 nm nanoparticles than those with lower MP. The objective of this study was to investigate whether nanoparticle encapsulation could effectively differentiate leader tumor cells during their CCM. To achieve our objectives, we employed a two-dimensional CCM model grounded in the wound-healing ("scratch") assay, utilizing two breast cancer cell lines, MCF7 and MDA-MB-231, which display low and high migratory potential, respectively. We conducted calibration experiments to identify the "optimal time" at which cells exhibit peak speed during wound closure. Furthermore, we carried out experiments to assess nanoparticle uptake, calculating the colocalization coefficient, and employed phalloidin staining to analyze the anisotropy and orientation of actin filaments. The highest activity for low-MP cells was achieved at 2.6 h during the calibration experiments, whereas high-MP cells were maximally active at 3.9 h, resulting in 8% and 11% reductions in wound area, respectively. We observed a significant difference in encapsulation efficiency between leader and peripheral cells for both high-MP (p < 0.013) and low-MP (p < 0.02) cells. Moreover, leader cells demonstrated a considerably higher anisotropy coefficient (p < 0.029), indicating a more organized, directional structure of actin filaments compared to peripheral cells. Thus, nanoparticle encapsulation offers a groundbreaking approach to identifying the most aggressive and invasive leader cells during the CCM process in breast cancer. Detecting these cells is crucial for developing targeted therapies that can effectively curb metastasis and improve patient outcomes.

A Targeted Proteomics Approach Reveals a Serum Protein Signature as a Diagnostic Biomarker for Colorectal Cancer

Background

Circulating proteins secreted by tumors are an important source of cancer biomarkers. This study aims to investigate the changes in the content of tumor immune-related circulating proteins in peripheral blood from patients with colorectal cancer (CRC).

Methods

Olink's proximity extension assay was used to detect the levels of 92 tumor immune-related circulating proteins in peripheral blood from CRC patients. An enzyme-linked immunosorbent assay was performed to detect the levels of six proteins. Elastic network regression was used to establish the model, and the performance of the model was verified by multiple iterations of cross-validation.

Results

The best serum protein signature that was composed of six proteins (IL7, CXCL12, IL10, IL15, CXCL1, and MCP-3) was selected. The area under the curve value of this signature was 0.9924 in the training set and 0.8992 in the total set. IL7 and IL15 levels were significantly higher in the ≥4 cm tumor volume group than in the <4 cm tumor volume group (P = 0.0113 and P = 0.004, respectively). MCP-3 levels were significantly higher in the distant metastasis group than in the non-distant metastasis group (P =0.0465). There was a significant difference in MCP-3 levels among different tumor, node, metastasis stages (P = 0.0496). CXCL1 levels were positively correlated with the absolute count of basophils (R = 0.3220, P = 0.0273), and IL10 levels were positively correlated with the absolute count of neutrophils (R = 0.38737, P = 0.0078). CXCL1, IL7, and IL15 were independent prognostic factors of CRC (hazard ratio [HR] = 0.62, P = 0.006; HR = 0.57, P = 0.006; and HR = 0.64, P = 0.011, respectively).

Conclusion

The best serum protein signature model (IL7, CXCL12, IL10, IL15, CXCL1, and MCP-3) was able to distinguish CRC patients from healthy controls. These proteins were also involved in the occurrence and development of CRC.

Progress on the mechanism of action of emodin against breast cancer cells

At present, the role of active ingredients of traditional Chinese medicine in tumor therapy has gradually attracted people's attention, and anthraquinones, which are structurally similar to adriamycin and epirubicin, are one of the hotspots of research. Emodin (1,3,8-trihydroxy-6-methylanthraquinone) is a natural anthraquinone compound isolated from rhubarb, Polygonum cuspidatum, and aloe vera. In recent years, emodin has received widespread attention for its remarkable anti-tumor effects, and its anti-breast cancer effects are manifested as induction of apoptosis, inhibition of tumor cell proliferation, inhibition of invasion and metastasis of tumor cells, and anti-tumor drug resistance. Moreover, emodin can act against multiple types of breast cancer cells by acting on different targets. In this paper, we reviewed the latest research progress on the anti-breast cancer effects of emodin and its anti-tumor mechanism, to provide reference and information for the treatment of breast cancer and the development of anti-tumor drugs.

A review of intact glycopeptide enrichment and glycan separation through hydrophilic interaction liquid chromatography stationary phase materials

Protein glycosylation, one of the most important biologically relevant post-translational modifications for biomarker discovery, faces analytical challenges due to heterogeneous glycosite, diverse glycans, and mass spectrometry limitations. Glycopeptide enrichment by removing abundant hydrophobic peptides helps overcome some of these obstacles. Hydrophilic interaction liquid chromatography (HILIC), known for its selectivity, glycan separations, intact glycopeptide enrichment, and compatibility with mass spectrometry, has seen recent advancements in stationary phases like Amide-80, glycoHILIC, amino acids or peptides for improved HILIC-based glycopeptide analysis. Utilization of these materials can improve glycopeptide enrichment through solid-phase extraction and separation via high-performance liquid chromatography. Additionally, using glycopeptides themselves to modify HILIC stationary phases holds promise for improving selectivity and sensitivity in glycosylation analysis. Additionally, HILIC has capability to assess the information about glycosites and structural information of glycans. This review summarizes recent breakthroughs in HILIC stationary materials, highlighting their impact on glycopeptide analysis. Ongoing research on advanced materials continues to refine HILIC's performance, solidifying its value as a tool for exploring protein glycosylation.

Multi-omic analysis identifies metabolic biomarkers for the early detection of breast cancer and therapeutic response prediction

Reliable blood-based tests for identifying early-stage breast cancer remain elusive. Employing single-cell transcriptomic sequencing analysis, we illustrate a close correlation between nucleotide metabolism in the breast cancer and activation of regulatory T cells (Tregs) in the tumor microenvironment, which shows distinctions between subtypes of patients with triple-negative breast cancer (TNBC) and non-TNBC, and is likely to impact cancer prognosis through the A2AR-Treg pathway. Combining machine learning with absolute quantitative metabolomics, we have established an effective approach to the early detection of breast cancer, utilizing a four-metabolite panel including inosine and uridine. This metabolomics study, involving 1111 participants, demonstrates high accuracy across the training, test, and independent validation cohorts. Inosine and uridine prove predictive of the response to neoadjuvant chemotherapy (NAC) in patients with TNBC. This study deepens our understanding of nucleotide metabolism in breast cancer development and introduces a promising non-invasive method for early breast cancer detection and predicting NAC response in patients with TNBC.

Exploring novel protein-based biomarkers for advancing breast cancer diagnosis: A review

This review provides a contemporary examination of the evolving landscape of breast cancer (BC) diagnosis, focusing on the pivotal role of novel protein-based biomarkers. The overview begins by elucidating the multifaceted nature of BC, exploring its prevalence, subtypes, and clinical complexities. A critical emphasis is placed on the transformative impact of proteomics, dissecting the proteome to unravel the molecular intricacies of BC. Navigating through various sources of samples crucial for biomarker investigations, the review underscores the significance of robust sample processing methods and their validation in ensuring reliable outcomes. The central theme of the review revolves around the identification and evaluation of novel protein-based biomarkers. Cutting-edge discoveries are summarised, shedding light on emerging biomarkers poised for clinical application. Nevertheless, the review candidly addresses the challenges inherent in biomarker discovery, including issues of standardisation, reproducibility, and the complex heterogeneity of BC. The future direction section envisions innovative strategies and technologies to overcome existing challenges. In conclusion, the review summarises the current state of BC biomarker research, offering insights into the intricacies of proteomic investigations. As precision medicine gains momentum, the integration of novel protein-based biomarkers emerges as a promising avenue for enhancing the accuracy and efficacy of BC diagnosis. This review serves as a compass for researchers and clinicians navigating the evolving landscape of BC biomarker discovery, guiding them toward transformative advancements in diagnostic precision and personalised patient care.

Precision cancer classification using liquid biopsy and advanced machine learning techniques

Cancer presents a significant global health burden, resulting in millions of annual deaths. Timely detection is critical for improving survival rates, offering a crucial window for timely medical interventions. Liquid biopsy, analyzing genetic variations, and mutations in circulating cell-free, circulating tumor DNA (cfDNA/ctDNA) or molecular biomarkers, has emerged as a tool for early detection. This study focuses on cancer detection using mutations in plasma cfDNA/ctDNA and protein biomarker concentrations. The proposed system initially calculates the correlation coefficient to identify correlated features, while mutual information assesses each feature's relevance to the target variable, eliminating redundant features to improve efficiency. The eXtrem Gradient Boosting (XGBoost) feature importance method iteratively selects the top ten features, resulting in a 60% dataset dimensionality reduction. The Light Gradient Boosting Machine (LGBM) model is employed for classification, optimizing its performance through a random search for hyper-parameters. Final predictions are obtained by ensembling LGBM models from tenfold cross-validation, weighted by their respective balanced accuracy, and averaged to get final predictions. Applying this methodology, the proposed system achieves 99.45% accuracy and 99.95% AUC for detecting the presence of cancer while achieving 93.94% accuracy and 97.81% AUC for cancer-type classification. Our methodology leads to enhanced healthcare outcomes for cancer patients.

Multiply-mesoporous hydrophilic titanium dioxide nanohybrid for the highly-performed enrichment of N-glycopeptides from human serum

N-glycopeptide is considered as one of significant biomarkers which provide guidance for the diagnosis and drug design of diseases. However, the direct analysis of N-glycopeptides is nearly impracticable mainly owing to their extremely low abundance and grave signal suppression from other interfering substances in the bio-samples. In this research, a multiply-mesoporous hydrophilic TiO2 nanohybrid (mM-TiO2@Cys) was synthesized by immobilizing Cys on a TiO2 substrate with hierarchical mesopores to achieve the highly-performed enrichment of N-glycopeptides. With the advantages of superior hydrophilicity and multiply-mesoporous structure, the obtained material exhibited an excellent selectivity (IgG digests and BSA digests at the molar ratio of 1/500), a high sensitivity (1 fmol μL-1 for IgG digests) and a good size-exclusion ability (IgG digests, IgG and BSA at the molar ratio of 1/500/500) in the enrichment of N-glycopeptides from IgG digests. As a result, 281 N-glycopeptides corresponded with 109 glycoproteins were identified from 2 μL serum digests of the patients with nasopharyngeal carcinoma, and 181 N-glycopeptides corresponded with 78 glycoproteins were identified from 2 μL serum digests of the healthy volunteers, revealing the potential application value of mM-TiO2@Cys in glycoproteomics.

Circulating Proteins as Diagnostic Markers in Gastric Cancer

Gastric cancer (GC) is a highly malignant disease affecting humans worldwide and has a poor prognosis. Most GC cases are detected at advanced stages due to the cancer lacking early detectable symptoms. Therefore, there is great interest in improving early diagnosis by implementing targeted prevention strategies. Markers are necessary for early detection and to guide clinicians to the best personalized treatment. The current semi-invasive endoscopic methods to detect GC are invasive, costly, and time-consuming. Recent advances in proteomics technologies have enabled the screening of many samples and the detection of novel biomarkers and disease-related signature signaling networks. These biomarkers include circulating proteins from different fluids (e.g., plasma, serum, urine, and saliva) and extracellular vesicles. We review relevant published studies on circulating protein biomarkers in GC and detail their application as potential biomarkers for GC diagnosis. Identifying highly sensitive and highly specific diagnostic markers for GC may improve patient survival rates and contribute to advancing precision/personalized medicine.

Method Comparison and Clinical Performance of Breast Cancer Tumor Markers on Novel Multiplex Immunoassay and Automatized LOCI Technology Platforms

Tumor marker determinations are valuable tools for the guidance of breast cancer patients during the course of disease. They are assessed on diverse analytical platforms that may be associated with differences according to the methods applied and the clinical performance. To investigate the method dependency and clinical significance of breast cancer protein tumor markers, CEA, CA 15-3, CA 125, CA 19-9 and AFP were measured in a total of 154 biobanked samples from 77 patients with breast cancer, 10 with DCIS, 31 with benign breast diseases and 36 healthy controls using a Millipore multiplex biomarker panel (MP) and an automized version of the routinely used Vista LOCI technology. The markers were compared between methods and investigated for diagnostic performance. CEA, CA 15-3 and AFP showed good correlations between both platforms with correlation coefficients of R = 0.85, 0.85 and 0.92, respectively, in all samples, but similarly also in the various subgroups. CA 125 and CA 19-9 showed only moderate correlations (R = 0.71 and 0.56, respectively). Absolute values were significantly higher for CEA, CA 15-3, CA 125 and AFP in the Vista LOCI as compared with the MP method and vice versa for CA 19-9. The diagnostic performance for discrimination of breast cancer from healthy controls was similar for both methods with AUCs in ROC curves for CEA (MP 0.81, 95% CI 0.72-0.91; LOCI 0.81; 95% CI 0.72-0.91) and CA-15-3 (MP 0.75, 95% CI 0.65-0.86; LOCI 0.67, 95% CI 0.54-0.79). Similar results were obtained for the comparison of breast cancer with benign breast diseases regarding CEA (AUC MP 0.62, 95% CI 0.51-0.73; LOCI 0.64, 95% CI 0.53-0.74) and CA-15-3 (MP 0.70, 95% CI 0.6-0.81; LOCI 0.66, 95% CI 0.54-0.77). Both platforms show moderate to good method comparability for tumor markers with similar clinical performance. However, absolute levels in individual patients should be interpreted with care.

A novel hydrophilic polymer-coated magnetic nanomaterial based on the HILIC strategy for fast separation of glycopeptides and glycosylated exosomes

Novel hydrophilic poly(N, N-methylenebisacrylamide/1,2-epoxy-5-hexene) coated magnetic nanospheres functionalized with 2-aminopurine (denoted as Fe3O4@poly(MBA/EH)@2AP) for enriching glycopeptides and glycosylated exosomes were successfully obtained using a simple and green method on the basis of the HILIC (hydrophilic interaction liquid chromatography) enrichment strategy. The high density of polar groups endows the material with amazing hydrophilicity, enabling the nanomaterial to successfully capture glycopeptides and glycosylated exosomes within 1 min. Meanwhile, the materials demonstrated great sensitivity (0.01 fmol/μL), good loading capability (125 μg/mg), high selectivity (BSA:HRP = 1000:1), and repeatability (more than 10 times). Besides, the material was applied in the analysis of bio-samples, a total of 290 glycosylated peptides and 184 glycosylation sites mapping to 185 glycoproteins were identified in the serum of uremic patients. Besides, 42 glycopeptides were enriched from the saliva of healthy people. At the same time, it was verified by TEM and western blot that the complete glycosylated exosomes were successfully captured from the serum of the uremic patients. All experiments have demonstrated that Fe3O4@poly(MBA/EH)@2AP has a promising future in practical applications.

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

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

New hydrophilic material based on hydrogel polymer for the selective enrichment of intact glycopeptides from serum protein digests

The paper describes the preparation and characterization of a new HILIC material for the enrichment of N-linked glycopeptides. The material was prepared using 2-acrylamido-2-methyl-1-propanesulfonic acid as the monomer and ethylene glycol dimethacrylate as the cross-linker. The material was developed by a Box-Behnken experimental design, taking into consideration the amount of monomer-to-crosslinker ratio, the composition, and the amount of porogen mixture. By this approach, the property of the resulting polymer could be fine-tuned to modulate the hydrophilicity and porosity. As HILIC enrichment is mostly dependent on hydrophilic interactions, including H-bonding, the amount of swelling was expected to have an important function, therefore the optimization considered a monomer percent in the range of 20-80%, which implied very different water swelling capacities. After assessing the potential of this new polymer family on fetuin digests, the 17 materials resulting from the Box-Behnken experimental design were used for the enrichment of glycopeptides from serum protein digests. The materials displayed a superior performance over cotton HILIC enrichment, both in terms of the number of enriched N-linked glycopeptides and selectivity, providing up to 762 N-linked glycopeptides with 77% selectivity. The optimization indicated that a high amount of monomer significantly affected the number of enriched glycopeptides, which is also closely connected with the hydrogel nature of the resulting polymers. The results not only provide one additional HILIC material for the enrichment of glycopeptides but also pave the way for the use and development of hydrogel materials for the enrichment of N-linked glycopeptides.

Clinical application of serum-based proteomics technology in human tumor research

The rapid development of proteomics technology in the past decades has led to further human understanding of tumor research, and in some ways, the technology plays a very important supporting role in the early detection of tumors. Human serum has been shown to contain a variety of proteins closely related to life activities, and the dynamic change in proteins can often reflect the physiological and pathological conditions of the body. Serum has the advantage of easy extraction, so the application of proteomics technology in serum has become a hot spot and frontier area for the study of malignant tumors. However, there are still many difficulties in the standardized use of proteomic technologies, which inevitably limit the clinical application of proteomic technologies due to the heterogeneity of human proteins leading to incomplete whole proteome populations, in addition to most serum protein markers being now not highly specific in aiding the early detection of tumors. Nevertheless, further development of proteomics technologies will greatly increase our understanding of tumor biology and help discover more new tumor biomarkers with specificity that will enable medical technology.

The Role of Silver Nanoparticles in the Diagnosis and Treatment of Cancer: Are There Any Perspectives for the Future?

Cancer is a fatal disease with a complex pathophysiology. Lack of specificity and cytotoxicity, as well as the multidrug resistance of traditional cancer chemotherapy, are the most common limitations that often cause treatment failure. Thus, in recent years, significant efforts have concentrated on the development of a modernistic field called nano-oncology, which provides the possibility of using nanoparticles (NPs) with the aim to detect, target, and treat cancer diseases. In comparison with conventional anticancer strategies, NPs provide a targeted approach, preventing undesirable side effects. What is more, nanoparticle-based drug delivery systems have shown good pharmacokinetics and precise targeting, as well as reduced multidrug resistance. It has been documented that, in cancer cells, NPs promote reactive oxygen species (ROS) production, induce cell cycle arrest and apoptosis, activate ER (endoplasmic reticulum) stress, modulate various signaling pathways, etc. Furthermore, their ability to inhibit tumor growth in vivo has also been documented. In this paper, we have reviewed the role of silver NPs (AgNPs) in cancer nanomedicine, discussing numerous mechanisms by which they render anticancer properties under both in vitro and in vivo conditions, as well as their potential in the diagnosis of cancer.

Diagnostic accuracy of a three-protein signature in women with suspicious breast lesions: a multicenter prospective trial

BACKGROUND

Mammography screening has been proven to detect breast cancer at an early stage and reduce mortality; however, it has low accuracy in young women or women with dense breasts. Blood-based diagnostic tools may overcome the limitations of mammography. This study assessed the diagnostic performance of a three-protein signature in patients with suspicious breast lesions.

FINDINGS

This trial (MAST; KCT0004847) was a prospective multicenter observational trial. Three-protein signature values were obtained using serum and plasma from women with suspicious lesions for breast malignancy before tumor biopsy. Additionally, blood samples from women who underwent clear or benign mammography were collected for the assays. Among 642 participants, the sensitivity, specificity, and overall accuracy values of the three-protein signature were 74.4%, 66.9%, and 70.6%, respectively, and the concordance index was 0.698 (95% CI 0.656, 0.739). The diagnostic performance was not affected by the demographic features, clinicopathologic characteristics, and co-morbidities of the participants.

CONCLUSIONS

The present trial showed an accuracy of 70.6% for the three-protein signature. Considering the value of blood-based biomarkers for the early detection of breast malignancies, further evaluation of this proteomic assay is warranted in larger, population-level trials. This Multi-protein Assessment using Serum to deTermine breast lesion malignancy (MAST) was registered at the Clinical Research Information Service of Korea with the identification number of KCT0004847 ( https://cris.nih.go.kr ).

Human serum N-glycome profiling via the newly developed asparagine immobilized cellulose/polymer nanohybrid

Human serum N-linked glycans expression levels change during the disease progression. The low abundance, structural diversity, and coexisting matrices hinder their detection in mass spectrometry analysis. Considering the hydrophilic nature of N-glycans, cellulose/polymer (1,2-Epoxy-5-hexene) nanohybrid is fabricated with oxirane groups functionalized of asparagine to develop solid phase extraction based hydrophilic interaction liquid chromatography sorbent (cellulose/1,2-Epoxy-5-hexene/asparagine). The morphology, elemental analysis, and surface properties are studied through scanning electron microscopy, energy dispersive X-ray spectroscopy, and Fourier-transform infrared spectroscopy. The large surface area of cellulose/polymer nanohybrid (2.09 × 10²  m² /g) facilitates the high density of asparagine immobilization resulting in better hydrophilic interaction liquid chromatography enrichment under optimized conditions. The enrichment capability of nanohybrid/asparagine is assessed by the N-Linked glycans released from ovalbumin and immunoglobulin G where 23 and 13 N-glycans are detected respectively. The nanohybrid/asparagine shows selectivity of 1:1200 with spiked bovine serum albumin and sensitivity down to 100 attomole. Human serum profiling for N-glycans identifies 52 glycan structures. This new enrichment strategy enriches serum N-linked glycans in the presence of salts, proteins, endogenous serum peptides, and so forth.

Current and future applications of biomarkers in samples collected through minimally invasive methods for cancer medicine and population-based research

Despite advances in cancer medicine and research, invasive and potentially risky procedures such as biopsies, venous blood tests, imaging, colonoscopy, and pap smear tests are still primarily used for screening, staging, and assessing response to therapy. The development and interdisciplinary use of biomarkers from urine, feces, saliva, scent, and capillary blood collected with minimally invasive methods represents a potential opportunity for integration with biomarker analysis for cancers, both in clinical practice (e.g., in screening, treatment, and disease monitoring, and improved quality of life for patients) and population-based research (e.g., in epidemiology/public health, studies of social and environmental determinants, and evolutionary medicine). In this article, we review the scientific rationale, benefits, challenges, and potential opportunities for measuring cancer-related biomarkers in samples collected through minimally invasive methods.

Longitudinal Serum Protein Analysis of Women with a High Risk of Developing Breast Cancer Reveals Large Interpatient Versus Small Intrapatient Variations: First Results from the TESTBREAST Study

The prospective, multicenter TESTBREAST study was initiated with the aim of identifying a novel panel of blood-based protein biomarkers to enable early breast cancer detection for moderate-to-high-risk women. Serum samples were collected every (half) year up until diagnosis. Protein levels were longitudinally measured to determine intrapatient and interpatient variabilities. To this end, protein cluster patterns were evaluated to form a conceptual basis for further clinical analyses. Using a mass spectrometry-based bottom-up proteomics strategy, the protein abundance of 30 samples was analyzed: five sequential serum samples from six high-risk women; three who developed a breast malignancy (cases) and three who did not (controls). Serum samples were chromatographically fractionated and an in-depth serum proteome was acquired. Cluster analyses were applied to indicate differences between and within protein levels in serum samples of individuals. Statistical analyses were performed using ANOVA to select proteins with a high level of clustering. Cluster analyses on 30 serum samples revealed unique patterns of protein clustering for each patient, indicating a greater interpatient than intrapatient variability in protein levels of the longitudinally acquired samples. Moreover, the most distinctive proteins in the cluster analysis were identified. Strong clustering patterns within longitudinal intrapatient samples have demonstrated the importance of identifying small changes in protein levels for individuals over time. This underlines the significance of longitudinal serum measurements, that patients can serve as their own controls, and the relevance of the current study set-up for early detection. The TESTBREAST study will continue its pursuit toward establishing a protein panel for early breast cancer detection.

Circulating proteins as predictive and prognostic biomarkers in breast cancer

Breast cancer (BC) is the most common cancer and among the leading causes of cancer death in women. It is a heterogeneous group of tumours with numerous morphological and molecular subtypes, making predictions of disease evolution and patient outcomes difficult. Therefore, biomarkers are needed to help clinicians choose the best treatment for each patient. For the last years, studies have increasingly focused on biomarkers obtainable by liquid biopsy. Circulating proteins (from serum or plasma) can be used for inexpensive and minimally invasive determination of disease risk, early diagnosis, treatment adjusting, prognostication and disease progression monitoring. We provide here a review of the main published studies on serum proteins in breast cancer and elaborate on the potential of circulating proteins to be predictive and/or prognostic biomarkers in breast cancer.

Circulating proteins reveal prior use of menopausal hormonal therapy and increased risk of breast cancer

Accessible risk predictors are crucial for improving the early detection and prognosis of breast cancer. Blood samples are widely available and contain proteins that provide important information about human health and disease, however, little is still known about the contribution of circulating proteins to breast cancer risk prediction. We profiled EDTA plasma samples collected before diagnosis from the Swedish KARMA breast cancer cohort to evaluate circulating proteins as molecular predictors. A data-driven analysis strategy was applied to the molecular phenotypes built on 700 circulating proteins to identify and annotate clusters of women. The unsupervised analysis of 183 future breast cancer cases and 366 age-matched controls revealed five stable clusters with distinct proteomic plasma profiles. Among these women, those in the most stable cluster (N = 19; mean Jaccard index: 0.70 ± 0.29) were significantly more likely to have used menopausal hormonal therapy (MHT), get a breast cancer diagnosis, and were older compared to the remaining clusters. The circulating proteins associated with this cluster (FDR < 0.001) represented physiological processes related to cell junctions (F11R, CLDN15, ITGAL), DNA repair (RBBP8), cell replication (TJP3), and included proteins found in female reproductive tissue (PTCH1, ZP4). Using a data-driven approach on plasma proteomics data revealed the potential long-lasting molecular effects of menopausal hormonal therapy (MHT) on the circulating proteome, even after women had ended their treatment. This provides valuable insights concerning proteomics efforts to identify molecular markers for breast cancer risk prediction.

Metabolic Features of Saliva in Breast Cancer Patients

The aim of the work was to study the metabolic characteristics of saliva in breast cancer and the subsequent assessment of the potential information content of its individual biochemical indicators. The study included 487 patients of the Omsk Clinical Oncology Center with morphologically verified breast cancer and 298 volunteers without breast pathologies. Saliva samples were obtained from all patients before the start of treatment, and the values of 34 biochemical indicators were determined. It has been shown that concentration of total protein, urea, uric acid (UA), the total content of α-amino acids and lipid peroxidation products, and the activity of metabolic and antioxidant enzymes (in particular catalase—CAT) of saliva changed significantly in breast cancer. Biochemical indicators characterizing early breast cancer have been identified, which can be used for timely diagnosis in addition to existing methods. The coefficients UA/Urea and UA·CAT/Urea are proposed, for which the maximum deviation from the norm was observed in the early stages of the disease. It was shown that for ductal breast cancer, changes in the activity of metabolic enzymes of saliva were more pronounced, while, for lobular breast cancer, the indicators of enzymatic and non-enzymatic components of antioxidant protection changed. The results confirmed the potential importance of saliva in the diagnosis of breast cancer.

Cost-Sensitive KNN Algorithm for Cancer Prediction Based on Entropy Analysis

Early diagnosis of cancer is beneficial in the formulation of the best treatment plan; it can improve the survival rate and the quality of patient life. However, imaging detection and needle biopsy usually used not only find it difficult to effectively diagnose tumors at early stage, but also do great harm to the human body. Since the changes in a patient’s health status will cause changes in blood protein indexes, if cancer can be diagnosed by the changes in blood indexes in the early stage of cancer, it can not only conveniently track and detect the treatment process of cancer, but can also reduce the pain of patients and reduce the costs. In this paper, 39 serum protein markers were taken as research objects. The difference of the entropies of serum protein marker sequences in different types of patients was analyzed, and based on this, a cost-sensitive analysis model was established for the purpose of improving the accuracy of cancer recognition. The results showed that there were significant differences in entropy of different cancer patients, and the complexity of serum protein markers in normal people was higher than that in cancer patients. Although the dataset was rather imbalanced, containing 897 instances, including 799 normal instances, 44 liver cancer instances, and 54 ovarian cancer instances, the accuracy of our model still reached 95.21%. Other evaluation indicators were also stable and satisfactory; precision, recall, F1 and AUC reach 0.807, 0.833, 0.819 and 0.92, respectively. This study has certain theoretical and practical significance for cancer prediction and clinical application and can also provide a research basis for the intelligent medical treatment.

Nanostructure stable hydrophilic hierarchical porous metal-organic frameworks for highly efficient enrichment of glycopeptides

Protein glycosylation plays a vital role in many physiological activities in organisms. Due to the low abundance of glycopeptides and the interference of numerous non-glycopeptides in biological samples, selective enrichment of glycopeptides is of great significance for their successful identification. Metal organic frameworks (MOFs) materials are appropriate for glycopeptides enrichment by virtue of their large specific surface area and outstanding hydrophilic properties. However, the instability of hydrophilic MOFs in acidic solutions have severely limited their applications. In this work, a rational facile strategy was established to synthesize a stable hydrophilic hierarchical porous MOF (denoted as HP-MOF-Arg@mSiO₂). This new material improved the selectivity and sensitivity of enrichment for glycopeptides via modification of arginine groups. More importantly, the mesoporous silica layer was introduced to enhance the stability of MOFs in aqueous solution and achieve the size exclusion effect of large-size proteins in complex samples. Overall, owing to the unique hierarchical porous and the hydrophilic modification, the synthesized HP-MOF-Arg@mSiO₂ materials showed excellent hydrophilicity and hydrolytic stability, resulting in outstanding specific separation capacity in glycopeptides enrichment. A total of 521 and 342 glycopeptides were respectively captured from 2 μL human serum digests and mouse testis tissue digests, revealing the potential of the materials in the study of glycoproteomics in complex biological samples.

Multichannel dual protein sensing using amphiphilic supramolecular assemblies

Protein sensing strategies have implications in detection of many human pathologies. Here, a supramolecular strategy for sensing two different proteins using a multichannel readout approach is outlined. Protein-ligand binding or enzymatic cleavage can both be programmed to induce supramolecular disassembly, which leads to fluorescence enhancement via aggregation-induced emission (AIE), protein-induced fluorescence enhancement (PIFE), or disassembly-induced fluorescence enhancement (DIFE). The accompanying signal change from two different fluorophores and their patterns are then used for specific protein sensing.

Breast cancer glycan biomarkers: their link to tumour cell metabolism and their perspectives in clinical practice

INTRODUCTION

Breast cancer (BCa) is the most common cancer type diagnosed in women and 5^th most common cause of deaths among all cancer deaths despite the fact that screening program is at place. This is why novel diagnostics approaches are needed in order to decrease number of BCa cases and disease mortality.

AREAS COVERED

In this review paper, we aim to cover some basic aspects regarding cellular metabolism and signalling in BCa behind altered glycosylation. We also discuss novel exciting discoveries regarding glycan-based analysis, which can provide useful information for better understanding of the disease. The final part deals with clinical usefulness of glycan-based biomarkers and the clinical performance of such biomarkers is compared to already approved BCa biomarkers and diagnostic tools based on imaging.

EXPERT OPINION

Recent discoveries suggest that glycan-based biomarkers offer high accuracy for possible BCa diagnostics in blood, but also for better monitoring and management of BCa patients. The review article was written using Web of Science search engine to include articles published between 2019 and 2021.

Blood-based protein biomarkers in bladder urothelial tumors

Bladder cancer (BC) is the fifth most common cancer with a high prevalence rate. It is classically classified in two groups, namely non-muscle invasive (NMIBC) and muscle invasive (MIBC). NMIBC accounts for 75% of cases and has a better prognosis than MIBC. However, 30-50% of the NMIBC patients will show recurrences throughout their lives, and about 10-20% of them will progress to MIBC, with frequent metastasis and a reduced survival rate. The diagnosis of bladder cancer is confirmed by direct visualization of the tumour and other mucosal abnormalities with endoscopic excision using cystoscopy and transurethral resection of the bladder (TURBT). An adequate TURBT requires complete resection of all visible tumour with appropriate sampling of the bladder to assess the depth of invasion. However, for many years, researchers have attempted to identify and utilise urinary markers for bladder cancer detection. Voided urine cytology has been the mainstay of urine-based diagnosis of bladder cancer since originally described by Papanicolau and Marshall. Nonetheless, urine cytology has several drawbacks, including a poor sensitivity for low-grade/stage tumours, a lack of interobserver consistency and a variable range of readings (e.g., atypical, atypical-suspicious, non-diagnostic). These shortcomings have inspired the search for more sensitive bladder cancer biomarkers. To bring precision medicine to genitourinary oncology, the analysis of the plasma/serum wide genome and proteome offers promising possibilities.

Circulating immune cell populations related to primary breast cancer, surgical removal, and radiotherapy revealed by flow cytometry analysis

BACKGROUND

Advanced breast cancer (BC) impact immune cells in the blood but whether such effects may reflect the presence of early BC and its therapeutic management remains elusive.

METHODS

To address this question, we used multiparametric flow cytometry to analyze circulating leukocytes in patients with early BC (n = 13) at the time of diagnosis, after surgery, and after adjuvant radiotherapy, compared to healthy individuals. Data were analyzed using a minimally supervised approach based on FlowSOM algorithm and validated manually.

RESULTS

At the time of diagnosis, BC patients have an increased frequency of CD117⁺CD11b⁺ granulocytes, which was significantly reduced after tumor removal. Adjuvant radiotherapy increased the frequency of CD45RO⁺ memory CD4⁺ T cells and CD4⁺ regulatory T cells. FlowSOM algorithm analysis revealed several unanticipated populations, including cells negative for all markers tested, CD11b⁺CD15^low, CD3⁺CD4^-CD8^-, CD3⁺CD4⁺CD8⁺, and CD3⁺CD8⁺CD127⁺CD45RO⁺ cells, associated with BC or radiotherapy.

CONCLUSIONS

This study revealed changes in blood leukocytes associated with primary BC, surgical removal, and adjuvant radiotherapy. Specifically, it identified increased levels of CD117⁺ granulocytes, memory, and regulatory CD4⁺ T cells as potential biomarkers of BC and radiotherapy, respectively. Importantly, the study demonstrates the value of unsupervised analysis of complex flow cytometry data to unravel new cell populations of potential clinical relevance.

One-step hexaplex immunoassays by on-line paper substrate-based electrospray ionization mass spectrometry for combined cancer biomarker screening

Mass spectrometry (MS) is attractive as a multiplexed immunoassay readout benefiting from its high sensitivity, speed and mass resolution. Here, a simple paper-based hexaplex immunoassay with an on-line MS readout was proposed, using functionalized paper as the immune substrates, along with rhodamine-based mass tags assembled on gold nanoparticles prepared as the mass probes (MPs). Simultaneous immune capture and labeling were conducted in one step on paper substrates in 96-well plates with a high throughput within 30 minutes, and the on-line efficient dissociation of the mass tags highly facilitated the hexaplex readout of the immune signals by a newly established on-line paper substrate-based electrospray ionization-MS setup. Six MPs were synthesized for the simultaneous quantification of six important cancer protein markers (cancer antigen 15-3, cancer antigen 19-9, carcinoma embryonic antigen, cancer antigen 125, human epididymis protein 4, and alpha fetoprotein) using only 10 μL serum, presenting satisfactory sensitivity, accuracy and specificity. This platform was further tested in screening for the six biomarkers in serum samples of patients with breast, liver and gastric cancers, showing its high potential for sensitive and specific early cancer diagnosis.

All That Glitters Is Not Silver-A New Look at Microbiological and Medical Applications of Silver Nanoparticles

Silver and its nanoparticles (AgNPs) have different faces, providing different applications. In recent years, the number of positive nanosilver applications has increased substantially. It has been proven that AgNPs inhibit the growth and survival of bacteria, including human and animal pathogens, as well as fungi, protozoa and arthropods. Silver nanoparticles are known from their antiviral and anti-cancer properties; however, they are also very popular in medical and pharmaceutical nanoengineering as carriers for precise delivery of therapeutic compounds, in the diagnostics of different diseases and in optics and chemistry, where they act as sensors, conductors and substrates for various syntheses. The activity of AgNPs has not been fully discovered; therefore, we need interdisciplinary research to fulfil this knowledge. New forms of products with silver will certainly find application in the future treatment of many complicated and difficult to treat diseases. There is still a lack of appropriate and precise legal condition regarding the circulation of nanomaterials and the rules governing their safety use. The relatively low toxicity, relative biocompatibility and selectivity of nanoparticle interaction combined with the unusual biological properties allow their use in animal production as well as in bioengineering and medicine. Despite a quite big knowledge on this topic, there is still a need to organize the data on AgNPs in relation to specific microorganisms such as bacteria, viruses or fungi. We decided to put this knowledge together and try to show positive and negative effects on prokaryotic and eukaryotic cells.

Breast cancer-associated skeletal muscle mitochondrial dysfunction and lipid accumulation is reversed by PPARG

The peroxisome proliferator-activated receptors (PPARs) have been previously implicated in the pathophysiology of skeletal muscle dysfunction in women with breast cancer (BC) and animal models of BC. This study investigated alterations induced in skeletal muscle by BC-derived factors in an in vitro conditioned media (CM) system and tested the hypothesis that BC cells secrete a factor that represses PPAR-γ (PPARG) expression and its transcriptional activity, leading to downregulation of PPARG target genes involved in mitochondrial function and other metabolic pathways. We found that BC-derived factors repress PPAR-mediated transcriptional activity without altering protein expression of PPARG. Furthermore, we show that BC-derived factors induce significant alterations in skeletal muscle mitochondrial function and lipid accumulation, which are rescued with exogenous expression of PPARG. The PPARG agonist drug rosiglitazone was able to rescue BC-induced lipid accumulation but did not rescue effects of BC-derived factors on PPAR-mediated transcription or mitochondrial function. These data suggest that BC-derived factors alter lipid accumulation and mitochondrial function via different mechanisms that are both related to PPARG signaling, with mitochondrial dysfunction likely being altered via repression of PPAR-mediated transcription, and lipid accumulation being altered via transcription-independent functions of PPARG.

Oncological Ligand-Target Binding Systems and Developmental Approaches for Cancer Theranostics

Targeted treatment of cancer hinges on the identification of specific intracellular molecular receptors on cancer cells to stimulate apoptosis for eventually inhibiting growth; the development of novel ligands to target biomarkers expressed by the cancer cells; and the creation of novel multifunctional carrier systems for targeted delivery of anticancer drugs to specific malignant sites. There are numerous receptors, antigens, and biomarkers that have been discovered as oncological targets (oncotargets) for cancer diagnosis and treatment applications. Oncotargets are critically important to navigate active anticancer drug ingredients to specific disease sites with no/minimal effect on surrounding normal cells. In silico techniques relating to genomics, proteomics, and bioinformatics have catalyzed the discovery of oncotargets for various cancer types. Effective oncotargeting requires high-affinity probes engineered for specific binding of receptors associated with the malignancy. Computational methods such as structural modeling and molecular dynamic (MD) simulations offer opportunities to structurally design novel ligands and optimize binding affinity for specific oncotargets. This article proposes a streamlined approach for the development of ligand-oncotarget bioaffinity systems via integrated structural modeling and MD simulations, making use of proteomics, genomic, and X-ray crystallographic resources, to support targeted diagnosis and treatment of cancers and tumors.

Cyclic Peptide Mimotopes for the Detection of Serum Anti-ATIC Autoantibody Biomarker in Hepato-Cellular Carcinoma

Tumor-associated (TA) autoantibodies have been identified at the early tumor stage before developing clinical symptoms, which holds hope for early cancer diagnosis. We identified a TA autoantibody from HBx-transgenic (HBx-tg) hepatocellular carcinoma (HCC) model mouse, characterized its target antigen, and examined its relationship to human HCC. The mimotopes corresponding to the antigenic epitope of TA autoantibody were screened from a random cyclic peptide library and used for the detection of serum TA autoantibody. The target antigen of the TA autoantibody was identified as an oncogenic bi-functional purine biosynthesis protein, ATIC. It was upregulated in liver cancer tissues of HBx-tg mouse as well as human HCC tissues. Over-expressed ATIC was also secreted extracellularly via the cancer-derived exosomes, which might cause auto-immune responses. The cyclic peptide mimotope with a high affinity to anti-ATIC autoantibody, CLPSWFHRC, distinguishes between serum samples from HCC patients and healthy subjects with 70.83% sensitivity, 90.68% specificity (AUC = 0.87). However, the recombinant human ATIC protein showed a low affinity to anti-ATIC autoantibody, which may be incompatible as a capture antigen for serum TA autoantibody. This study indicates that anti-ATIC autoantibody can be a potential HCC-associated serum biomarker and suggests that autoantibody biomarker's efficiency can be improved by using antigenic mimicry to native antigens present in vivo.

Phage-mediated double-nanobody sandwich immunoassay for detecting alpha fetal protein in human serum

Alpha fetal protein (AFP) is a significant biomarker of liver cancer. Herein we developed a novel phage-mediated double-nanobody sandwich immunoassay (P-ELISA) for sensitive detection of AFP in serum, where the phage displayed the nanobody for antigen recognition and multiple copies of major coat protein pVIII for signal amplification. The expressed nanobody Nb-A1 and the phage-displayed nanobody phage-A2 served as the capture antibody and detection antibody, respectively. Based on the optimal experimental conditions, the P-ELISA has a half maximal saturation concentration of 24.85 ng mL^-1 and a limit of detection of 0.237 ng mL^-1 for AFP. The P-ELISA is highly selective for AFP and ignorable cross-reactions were observed with other tested cancer biomarkers. After elimination of the matrix effect by 30-fold dilution with 0.5 × PBS, clinical serum samples were analyzed by the P-ELISA. The results correlated well with those of the AFP commercial ELISA kit and the Roche E601 automatic chemiluminescence analyzer. Thus, it showed the potential of the recombinant phage for highly sensitive and selective detection of AFP and provides a novel detection model for the other disease-related biomarkers.

Electrochemical Nanobiosensors for Detection of Breast Cancer Biomarkers

This comprehensive review paper describes recent advances made in the field of electrochemical nanobiosensors for the detection of breast cancer (BC) biomarkers such as specific genes, microRNA, proteins, circulating tumor cells, BC cell lines, and exosomes or exosome-derived biomarkers. Besides the description of key functional characteristics of electrochemical nanobiosensors, the reader can find basic statistic information about BC incidence and mortality, breast pathology, and current clinically used BC biomarkers. The final part of the review is focused on challenges that need to be addressed in order to apply electrochemical nanobiosensors in a clinical practice.

Identification of a Profile of Neutrophil-Derived Granule Proteins in the Surface of Gold Nanoparticles after Their Interaction with Human Breast Cancer Sera

It is well known that the interaction of a nanomaterial with a biological fluid leads to the formation of a protein corona (PC) surrounding the nanomaterial. Using standard blood analyses, alterations in protein patterns are difficult to detect. PC acts as a “nano-concentrator” of serum proteins with affinity for nanoparticles’ surface. Consequently, characterization of PC could allow detection of otherwise undetectable changes in protein concentration at an early stage of a disease, such as breast cancer (BC). Here, we employed gold nanoparticles (AuNPs_diameter: 10.02 ± 0.91 nm) as an enrichment platform to analyze the human serum proteome of BC patients (n = 42) and healthy controls (n = 42). Importantly, the analysis of the PC formed around AuNPs after their interaction with serum samples of BC patients showed a profile of proteins that could differentiate breast cancer patients from healthy controls. These proteins developed a significant role in the immune and/or innate immune system, some of them being neutrophil-derived granule proteins. The analysis of the PC also revealed serum proteome alterations at the subtype level.

Use of Lectin-based Affinity Techniques in Breast Cancer Glycoproteomics: A Review

Changes in glycoprotein content, altered glycosylations, and aberrant glycan structures are increasingly recognized as cancer hallmarks. Because breast cancer is one of the most common causes of cancer deaths in the world, it is highly urgent to find other reliable biomarkers for its initial diagnosis and to learn as much as possible about this disease. In this Review, the applications of lectins to a screening of potential breast cancer biomarkers published during recent years are overviewed. These data provide a deeper insight into the use of modern strategies, technologies, and scientific knowledge in glycoproteomic breast cancer research. Particular attention is concentrated on the use of lectin-based affinity techniques, applied independently or most frequently in combination with mass spectrometry, as an effective tool for the targeting, separation, and reliable identification of glycoprotein molecules. Individual procedures and lectins used in published glycoproteomic studies of breast-cancer-related glycoproteins are discussed. The summarized approaches have the potential for use in diagnostic and predictive applications. Finally, the use of lectins is briefly discussed from the view of their future applications in the analysis of glycoproteins in cancer.

A Validation Study of a Multiple Reaction Monitoring-Based Proteomic Assay to Diagnose Breast Cancer

Purpose

Currently, the standard screening tool for breast cancer is screening mammography. There have been many efforts to develop a blood-based diagnostic assay for breast cancer diagnosis; however, none have been approved for clinical use at this time. The purpose of this study was to determine the accuracy of a novel blood-based proteomic test for aiding breast cancer diagnosis in a relatively large cohort of cancer patients.

Methods

A blood-based test using multiple reaction monitoring (MRM) measured by mass spectrometry to quantify 3 peptides (apolipoprotein C-1, carbonic anhydrase 1, and neural cell adhesion molecule L1-like protein) present in human plasma was investigated. A total of 1,129 blood samples from 575 breast cancer patients, 454 healthy controls, and 100 patients with other malignancies were used to verify and optimize the assay.

Results

The diagnostic sensitivity, specificity, and accuracy of the MRM-based proteomic assay were 71.6%, 85.3%, and 77%, respectively; the area under the receiver operating characteristic curve was 0.8323. The proteomic assay did not demonstrate diagnostic accuracy in patients with other types of malignancies including thyroid, pancreatic, lung, and colon cancers. The diagnostic performance of the proteomic assay was not associated with the timing of blood sampling before or after anesthesia.

Conclusion

The data demonstrated that an MRM-based proteomic assay that measures plasma levels of three specific peptides can be a useful tool for breast cancer screening and its accuracy is cancer-type specific.

Proteomic investigation on bio-corona of Au, Ag and Fe nanoparticles for the discovery of triple negative breast cancer serum protein biomarkers

Nowadays, there are no targeted therapeutic modalities for triple negative breast cancer (TNBC). This disease is associated with poor prognosis and worst clinical outcome because of the aggressive nature of the tumor, delayed diagnosis, and non-specific symptoms in the early stages. Therefore, identification of novel specific TNBC serum biomarkers for screening and therapeutic purposes remains an urgent clinical requirement. New user-friendly and cheap methods for biomarker identification are needed, and nanotechnology offers new opportunities. When dispersed in blood, nanoparticles (NPs) are covered by a protein shell termed "protein corona" (PC). While alterations in protein patterns are challeging to detect by conventional blood analyses, PC acts as a "nano-concentrator" of serum proteins with affinity for NPs' surface. So, the characterization of PC could allow the detection of otherwise undetectable changes in protein concentration at an early stage of the disease or after chemotherapy or surgery. To explore this research idea, serum samples from 8 triple negative breast cancer (TNBC) patients and 8 patients without malignancy were allowed to interact with gold nanoparticles (AuNPs: 10.02 ± 0.91 nm), silver nanoparticles (AgNPs: 9.73 ± 1.70 nm) and magnetic nanoparticles (MNPs: (9.30 ± 0.67 nm). Here, in order to identify biomarker candidates in serum of TNBC patients, these nanomaterials were combined with electrophoretic separation (SDS-PAGE) to performed qualitative and quantitative comparisons of the serum proteomes of TNBC patients (n = 8) and healthy controls (n = 8) by liquid chromatography tandem-mass spectrometry (LC-MS/MS) analysis. The results were validated through a sequential window acquisition of all theoretical mass spectra (SWATH) analysis, performed in total serum samples (patients and controls) using this approach as a multiple reaction monitoring (MRM) analysis. SIGNIFICANCE: It is well known that several proteins presented in human serum are important biomarkers for the diagnosis or prognosis of different diseases, as triple negative breast cancer (TNBC). Determining how nanomaterials as gold nanoparticles (AuNPs: 10.02 ± 0.91 nm), silver nanoparticles (AgNPs: 9.73 ± 1.70 nm) and magnetic nanoparticles (MNPs: (9.30 ± 0.67 nm) interact with human serum will assist not only in understanding their effects on the biological system (biocompability and toxicity), but also to obtain information for developing novel nanomaterials with high specificity and selectivity towards proteins with an important biological function (prognostic and diagnostic protein biomarkers).

Plasma membrane proteins: A new probe for the characterization of breast cancer

This work aimed to characterize normal, benign and malignant excised breast tissues through the analysis of the FTIR spectra of their plasma membrane proteins. Tissue characterization parameters such as peak position, peak intensity, area under the peak, relative peak intensity and relative area under peak were evaluated mainly for protein spectral peaks; 1150 cm^-1, Amide I, Amide II, Amide III, and Amide A. The sensitivity, specificity and diagnostic accuracy for each parameter were obtained and Receiver Operating Characteristic (ROC) Curves were plotted. Results showed significant spectral differences between normal and benign tissues compared to malignant tissues at 1536 and 1645 cm^-1. The three tissues could be distinguished at 2900 cm^-1, where the malignant peak uniquely split into two separate peaks. ROC curves showed that the Amide A peak position yielded a higher accuracy compared to all other investigated characterization parameters. The deconvolution of Amide I revealed the conformational changes in plasma proteins characterizing the transformation to malignancy (a decrease in the percentage of alpha helix accompanied by an increase in the percentage of beta sheets). The use of the present structure-based analysis in conjunction with histopathological examination of excised breast tissues would offer an enhanced characterization that might reduce possible personal diagnostic mistakes.

Phytic acid-based copper(ii) ion-selective electrode on graphene oxide for potentiometric immunoassay of breast cancer antigen 15-3

A potentiometric immunoassay based on a copper(ii) ion-selective electrode (Cu-ISE) was designed for the quantitative determination of carbohydrate antigen 15-3 (CA 15-3; a tumor marker associated with breast cancer).