Dithiothreitol-based protein equalization technology to unravel biomarkers for bladder cancer

Dithiothreitol-based protein equalisation in the context of multiple myeloma: Enhancing proteomic analysis and therapeutic insights

In this study, we employed the dithiothreitol-based protein equalisation technique and analytical proteomics to better understand myeloma diseases by comparing the proteomes of pellets and supernatants formed upon application of DTT on serum samples. The number of unique proteins found in pellets was 252 for healthy individuals and 223 for multiple myeloma patients. The comparison of these proteomes showed 97 dysregulated proteins. The unique proteins found for supernatants were 264 for healthy individuals and 235 for multiple myeloma patients. The comparison of these proteomes showed 87 dysregulated proteins. The analytical proteomic comparison of both groups of dysregulated proteins is translated into parallel dysregulated pathways, including chaperone-mediated autophagy and protein folding, addressing potential therapeutic interventions. Future research endeavours in personalised medicine should prioritize refining analytical proteomic methodologies using serum dithiothreitol-based protein equalisation to explore innovative therapeutic strategies. We highlight the advanced insights gained from this analytical strategy in studying multiple myeloma, emphasising its complex nature and the critical role of personalised, targeted analytical techniques in enhancing therapeutic efficacy in personalised medicine.

Effect of Tryptic Digestion on Sensitivity and Specificity in MALDI-TOF-Based Molecular Diagnostics through Machine Learning

The digestion of protein into peptide fragments reduces the size and complexity of protein molecules. Peptide fragments can be analyzed with higher sensitivity (often > 102 fold) and resolution using MALDI-TOF mass spectrometers, leading to improved pattern recognition by common machine learning algorithms. In turn, enhanced sensitivity and specificity for bacterial sorting and/or disease diagnosis may be obtained. To test this hypothesis, four exemplar case studies have been pursued in which samples are sorted into dichotomous groups by machine learning (ML) software based on MALDI-TOF spectra. Samples were analyzed in 'intact' mode in which the proteins present in the sample were not digested with protease prior to MALDI-TOF analysis and separately after the standard overnight tryptic digestion of the same samples. For each case, sensitivity (sens), specificity (spc), and the Youdin index (J) were used to assess the ML model performance. The proteolytic digestion of samples prior to MALDI-TOF analysis substantially enhanced the sensitivity and specificity of dichotomous sorting. Two exceptions were when substantial differences in chemical composition between the samples were present and, in such cases, both 'intact' and 'digested' protocols performed similarly. The results suggest proteolytic digestion prior to analysis can improve sorting in MALDI/ML-based workflows and may enable improved biomarker discovery. However, when samples are easily distinguishable protein digestion is not necessary to obtain useful diagnostic results.

Identification of early biomarkers in saliva in genetically engineered mouse model C(3)1-TAg of breast cancer

Breast cancer is one of leading causes of death worldwide in the female population. Deaths from breast cancer could be reduced significantly through earlier and more efficient detection of the disease. Saliva, an oral fluid that contains an abundance of protein biomarkers, has been recognized as a promising diagnostic biofluid that is easy to isolate through non-invasive techniques. Assays on saliva can be performed rapidly and are cost-effective. Therefore, our work aimed to identify salivary biomarkers present in the initial stages of breast cancer, where cell alterations are not yet detectable by histopathological analysis. Using state-of-the-art techniques, we employed a transgenic mouse model of mammary cancer to identify molecular changes in precancerous stage breast cancer through protein analysis in saliva. Through corroborative molecular approaches, we established that proteins related to metabolic changes, inflammatory process and cell matrix degradation are detected in saliva at the onset of tumor development. Our work demonstrated that salivary protein profiles can be used to identify cellular changes associated with precancerous stage breast cancer through non-invasive means even prior to biopsy-evident disease.

Luminescent Sensors Based on the Assembly of Coinage Metal Nanoclusters

Coinage metals, such as Cu, Ag and Au, can form nanoclusters, which, when functionalized with ligands, have unique electronic and optical properties and are widely used in biomedical imaging, remote sensing, labeling, catalytic, etc. The mechanisms, structures and properties of nanocluster assemblies have been well reviewed. However, the collections and analyses of nanocluster assemblies for sensor application are few. This review examines different nanocluster sensor platforms with a focus on the assembly and analysis of the assembly processes and examples of applications.

Developing non-invasive bladder cancer screening methodology through potentiometric multisensor urine analysis

We report on the feasibility study exploring the potential of a simple electrochemical multisensor system as a tool for distinguishing between urine samples from patients with confirmed bladder cancer (36 samples) and healthy volunteers (51 samples). The potentiometric sensor responses obtained in urine samples were employed as the input data for various machine learning classification algorithms (logistic regression, random forest, extreme gradient boosting classifier, support vector machine, and voting classifier). The performance metrics of the classifiers were evaluated via Monte-Carlo cross-validation. The best model combining all the acquired data from the people aged 19-88 with different tumor grades and malignancies, including patients with recurrent bladder cancer, yielded 72% accuracy, 71% sensitivity, and 58% specificity. It was found that these metrics can be improved to 76% accuracy, 80% sensitivity, and 75% specificity when only a limited age group (50-88 years of age) is considered. Taking into account the simplicity of the proposed screening method, this technique appears to be a promising tool for further research.

Detection of Novel Urine Markers Using Immune Complexome Analysis in Bladder Cancer Patients: A Preliminary Study

BACKGROUND/AIM

Little is known on urine biomarkers that are associated with malignant behavior in patients with bladder cancer (BC). Our aim was to identify BC-related factors in urine samples using our original method "immune complexome analysis", based on detecting the immune complex (IC).

PATIENTS AND METHODS

Immune complexome analysis was performed using urine samples from 97 BC patients, including 67 with non-muscle invasive BC (NMIBC).

RESULTS

Eight IC-antigens were recognized as candidates for BC-related factors from 20,165 proteins. IC-serum albumin, -fibrinogen γ chain, -hemoglobin subunit α, -hemoglobin subunit β, -ceruloplasmin, and fibrinogen β chain were significantly associated with either pathological features and/or outcome. IC-ceruloplasmin was most widely associated with pathological features in all BC patients and lamina propria invasion and urinary tract recurrence in NMIBC.

CONCLUSION

Based on detection of IC-antigens it was demonstrated that six IC-antigens, especially IC-ceruloplasmin, are potential urine biomarkers in BC.

Proteomic Exploration of Plasma Exosomes and Other Small Extracellular Vesicles in Pediatric Hodgkin Lymphoma: A Potential Source of Biomarkers for Relapse Occurrence

Exosomes and other small extracellular vesicles (EVs) are potential sources of cancer biomarkers. Plasma-derived EVs have not yet been studied in pediatric Hodgkin lymphoma (HL), for which predictive biomarkers of relapse are greatly needed. In this two-part proteomic study, we used two-dimensional difference gel electrophoresis (2D-DIGE) followed by liquid chromatography–tandem mass spectrometry (LC–MS/MS) to analyze EV proteins of plasma collected at diagnosis from children with nodular sclerosis HL, relapsed or not. EVs isolated using membrane affinity had radii ranging from 20 to 130 nm and contained the programmed cell death 6-interacting (ALIX) and the tumor susceptibility gene 101 (TSG101) proteins, whereas calnexin (CANX) was not detected. 2D-DIGE identified 16 spots as differentially abundant between non-relapsed and relapsed HL (|fold change| ≥ 1.5, p < 0.05). LC–MS/MS identified these spots as 11 unique proteins, including five more abundant in non-relapsed HL (e.g., complement C4b, C4B; fibrinogen γ chain, FGG) and six more abundant in relapsed HL (e.g., transthyretin, TTR). Shotgun LC–MS/MS on pooled EV proteins from non-relapsed HL identified 161 proteins, including 127 already identified in human exosomes (ExoCarta data). This EV cargo included 89 proteins not yet identified in exosomes from healthy plasma. Functional interrogation by the Database for Annotation, Visualization and Integrated Discovery (DAVID) revealed that the EV proteins participate in platelet degranulation and serine-type endopeptidase activity as the most significant Gene Ontology (GO) biological process and molecular function (p < 0.01).