Expert Review of Proteomics最新文献

Introduction: Cardiovascular diseases (CVDs) are the leading causes of mortality and morbidity worldwide, making early diagnosis and effective treatment essential. As a promising and noninvasive research method, urine proteomics shows excellent potential to identify reliable urinary biomarkers that could enhance prediction, prevention, and prognosis in patients with CVD.

Areas covered: This review summarizes recent advancements in urinary protein biomarker profiling using urine proteomic techniques to identify potential CVD biomarkers. Additionally, it highlights potential disease biomarkers for the early detection, risk stratification, and monitoring of CVD, including hypertension, atherosclerosis, coronary artery disease, angina, myocardial infarction, heart failure, preeclampsia, and vasculitis. A literature search was conducted through Pubmed, Scopus, Google Scholar, and Web of Science. The period is January 2009 to February 2024.

Expert opinion: Over the past decade, urinary proteomics has been employed in CVD research, with the potential to facilitate the discovery of novel disease biomarkers and the exploration of prospective therapeutic targets. Proteomics-based multicenter cohort studies should be conducted in the future to gain deeper insights into the pathophysiological mechanisms of CVD, accelerate the identification of potential biomarkers for disease prediction, diagnosis, and treatment, and facilitate their clinical translation.

Introduction: Given the poor prognosis of patients with TNBC, it is urgent to identify new biomarkers and therapeutic targets to enable personalized treatment strategies and improve patient survival. Comprehensive insights beyond genomic and transcriptomic analysis are crucial to improved outcomes for patients. As proteins are the workhorses of cellular function with their activity primarily regulated by phosphorylation, advanced phosphoproteomics techniques, such as mass spectrometry and antibody arrays, are essential for elucidating kinase signaling pathways that drive TNBC progression and contribute to therapy resistance.

Area covered: This review discusses the critical need to integrate phosphoproteomics into TNBC research, evaluates commonly used technologies and their applications, and explores their advantages and limitations. We highlight significant findings from phosphoproteomic analyses in TNBC and address the challenges of implementing these technologies into clinical practice.

Expert opinion: Rapid advances in phosphoproteomics analysis facilitate subtype stratification, adaptive response monitoring, and identification of biomarkers and therapeutic targets in TNBC. However, challenges in analyzing protein phosphorylation, especially in deep spatially resolved analysis of malignant cells and the tumor ecosystem, hinder the translation of phosphoproteomics to the CLIA setting. Nonetheless, phosphoproteomics offers a powerful tool that, when integrated into routine clinical practice, has the potential to revolutionize patient care.

Introduction: Acute myeloid leukemia (AML) is an aggressive and poor-prognosis blood cancer. Despite a low mutation burden compared to other cancers, AML is heterogenous and identifying robust therapeutic targets has been difficult. Genomic profiling has greatly advanced our understanding of AML, and has revealed targets for AML therapy. However, only 50% of AML patients have gene mutations that are currently druggable, and relapse rates remain high. The addition of proteomic profiling is emerging to address these challenges.

Areas covered: Using references collected through Pubmed, we review recent studies that have combined genomic and proteomic profiling (i.e. proteogenomic profiling), as well as studies that have additionally integrated other omics approaches, such as phosphoproteomics. We highlight how proteogenomic profiling promises to deconvolve the cellular pathways driving leukemogenesis, uncover novel therapeutic targets, and identify biomarkers of response to novel and existing therapies.

Expert opinion: Proteogenomic profiling is providing unparalleled insight into AML, and is beginning to identify robust biomarkers. Standardization of workflows will be required before mass spectrometry-based proteomic assays can be integrated into routine clinical use. However, the demonstrated ability to adapt signatures into biomarker panels that can be assayed by existing clinical workflows is enabling current clinical translation.

Introduction: The extracellular matrix (ECM) is a highly organized and dynamic network of proteins and glycosaminoglycans that provides critical structural, mechanical, and biochemical support to cells. The functions of the ECM are directly influenced by the conformation of the proteins that compose it. ECM proteoforms, which can result from genetic, transcriptional, and/or post-translational modifications, adopt different conformations and, consequently, confer different structural properties and functionalities to the ECM in both physiological and pathological contexts.

Areas covered: In this review, we discuss how bottom-up proteomics has been applied to identify, map, and quantify post-translational modifications (e.g. additions of chemical groups, proteolytic cleavage, or cross-links) and ECM proteoforms arising from alternative splicing or genetic variants. We further illustrate how proteoform-level information can be leveraged to gain novel insights into ECM protein structure and ECM functions in health and disease.

Expert opinion: In the Expert opinion section, we discuss remaining challenges and opportunities with an emphasis on the importance of devising experimental and computational methods tailored to account for the unique biochemical properties of ECM proteins with the goal of increasing sequence coverage and, hence, accurate ECM proteoform identification.

Introduction: The identification and characterization of glycopeptides through LC-MS/MS and advanced enrichment techniques are crucial for advancing clinical glycoproteomics, significantly impacting the discovery of disease biomarkers and therapeutic targets. Despite progress in enrichment methods like Lectin Affinity Chromatography (LAC), Hydrophilic Interaction Liquid Chromatography (HILIC), and Electrostatic Repulsion Hydrophilic Interaction Chromatography (ERLIC), issues with specificity, efficiency, and scalability remain, impeding thorough analysis of complex glycosylation patterns crucial for disease understanding.

Areas covered: This review explores the current challenges and innovative solutions in glycopeptide enrichment and mass spectrometry analysis, highlighting the importance of novel materials and computational advances for improving sensitivity and specificity. It outlines the potential future directions of these technologies in clinical glycoproteomics, emphasizing their transformative impact on medical diagnostics and therapeutic strategies.

Expert opinion: The application of innovative materials such as Metal-Organic Frameworks (MOFs), Covalent Organic Frameworks (COFs), functional nanomaterials, and online enrichment shows promise in addressing challenges associated with glycoproteomics analysis by providing more selective and robust enrichment platforms. Moreover, the integration of artificial intelligence and machine learning is revolutionizing glycoproteomics by enhancing the processing and interpretation of extensive data from LC-MS/MS, boosting biomarker discovery, and improving predictive accuracy, thus supporting personalized medicine.

Objectives: Oral squamous cell carcinoma poses a substantial global health challenge marked by rising mortality rate. Recently, immunotherapy has shown promising results in cancer management by enhancing immune response. Thus, identifying additional immune-related markers is critical for advancing immunotherapy treatments.

Methods: Data-independent acquisition (DIA) mass spectrometry approach was used to explore differentially expressed immune-related proteins in oral cancer tissues compared to adjacent non-cancerous tissues. Functional significance was identified through Gene Ontology, pathway, and network analysis. Gene expression of identified proteins was validated using transcriptomic data.

Results: DIA analysis identified 29,459 precursors corresponding to 3429 proteins. Among these, 1060 proteins were differentially expressed, with 166 being immune-related. Differentially regulated proteins were involved in innate immune response, mitochondrial ATP synthesis, and neutrophil degranulation. Pathway analysis of immune-related proteins showed perturbation in anti-tumor immunity-related pathways such as interferon signaling, TCR signaling, PD-1 signaling, and antigen processing and presentation. Significance of these pathways was further reinforced by the strong interactions identified in the protein-protein interaction network analysis. Additionally, gene expression analysis showed similar mRNA expression patterns for key proteins involved in altered pathways, including ISG15, IFIT1/3, HLA-A/C and OAS2/3.

Conclusions: Further validation of these proteins could establish them as potential targets for personalized therapy.

Introduction: Periodontitis is a chronic inflammatory disease characterized by progressive soft tissue and alveolar bone loss due to interactions between microbial dental plaque and the host response. Despite extensive research on biomarkers from saliva or gingival crevicular fluid (GCF) for diagnosing periodontitis, clinical and radiological parameters remain the primary diagnostic tools.

Areas covered: This review discusses the ongoing research into salivary biomarkers for periodontitis diagnosis, emphasizing the need for reliable biomarkers to differentiate between periodontal health and disease. Salivary biomarker research has gained momentum with advancements in proteomic technologies, enabling noninvasive sample collection and revealing potential candidate biomarkers.

Expert opinion: Proteomic research since the early 2000s has identified promising biomarkers and provided insights into the pathogenesis of periodontitis. Bioinformatic analysis of proteomic data elucidates the underlying biological mechanisms. This review summarizes key findings and highlights common potential biomarkers identified through proteomic research in periodontology.

Introduction: Understanding the metabolic regulatory mechanisms leading to antibacterial resistance is important to develop effective control measures.

Areas covered: In this review, we summarize the progress on metabolic mechanisms of antibiotic resistance in clinically isolated bacteria, as revealed using proteomic approaches.

Expert opinion: Proteomic approaches are effective tools for uncovering clinically significant bacterial metabolic responses to antibiotics. Proteomics can disclose the associations between metabolic proteins, pathways, and networks with antibiotic resistance, and help identify their functional impact. The mechanisms by which metabolic proteins control the four generally recognized resistance mechanisms (decreased influx and targets, and increased efflux and enzymatic degradation) are particularly important. The proposed mechanism of reprogramming proteomics via key metabolites to enhance the killing efficiency of existing antibiotics needs attention.

Introduction: Breast cancer subtyping is used clinically for diagnosis, prognosis, and treatment decisions. Subtypes are categorized by cell of origin, histomorphology, gene expression signatures, hormone receptor status, and/or protein levels. Categorizing breast cancer based on gene expression signatures aids in assessing a patient's recurrence risk. Protein biomarkers, on the other hand, provide functional data for selecting therapies for primary and recurrent tumors. We provide an update on protein biomarkers in breast cancer subtypes and their application in prognosis and therapy selection.

Areas covered: Protein pathways in breast cancer subtypes are reviewed in the context of current protein-targeted treatment options. PubMed, Science Direct, Scopus, and Cochrane Library were searched for relevant studies between 2017 and 17 August 2024.

Expert opinion: Post-translationally modified proteins and their unmodified counterparts have become clinically useful biomarkers for defining breast cancer subtypes from a therapy perspective. Tissue heterogeneity influences treatment outcomes and disease recurrence. Spatial profiling has revealed complex cellular subpopulations within the breast tumor microenvironment. Deciphering the functional relationships between and within tumor clonal cell populations will further aid in defining breast cancer subtypes and create new treatment paradigms for recurrent, drug resistant, and metastatic disease.