Expert Review of Proteomics最新文献

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.

Introduction: Biomarker discovery is increasingly moving from single omics to multiomics, as well as from multi-cell omics to single-cell omics. These transitions have increasingly adopted digital transformation technologies to accelerate the progression from data to insight. Here, we will discuss the concept of 'digitalomics' and how digital transformation directly impacts biomarker discovery. This will ultimately assist clinicians in personalized therapy and precision-medicine treatment decisions.

Areas covered: Genotype-to-phenotype-based insight generation involves integrating large amounts of complex multiomic data. This data integration and analysis is aided through digital transformation, leading to better clinical outcomes. We also highlight the challenges and opportunities of Digitalomics, and provide examples of the application of Artificial Intelligence, cloud- and high-performance computing, and use of tensors for multiomic analysis workflows.

Expert opinion: Biomarker discovery, aided by digital transformation, is having a significant impact on cancer, cardiovascular, infectious, immunological, and neurological diseases, among others. Data insights garnered from multiomic analyses, combined with patient meta data, aids patient stratification and targeted treatment across a broad spectrum of diseases. Digital transformation offers time and cost savings while leading to improved patent healthcare. Here, we highlight the impact of digital transformation on multiomics- based biomarker discovery with specific applications related to oncology.

Introduction: A comprehensive and global knowledge of protein target engagement is of vital importance for mechanistic studies and in drug development. Since its initial introduction, the cellular thermal shift assay (CETSA) has proven to be a reliable and flexible technique that can be widely applied to multiple contexts and has profound applications in facilitating the identification and assessment of protein target engagement.

Areas covered: This review introduces the principle of CETSA, elaborates on western blot-based CETSA and MS-based thermal proteome profiling (TPP) as well as the major applications and prospects of these approaches.

Expert opinion: CETSA primarily evaluates a given ligand binding to a particular target protein in cells and tissues with the protein thermal stabilities analyzed by western blot. When coupling mass spectrometry with CETSA, thermal proteome profiling allows simultaneous proteome-wide experiment that greatly increased the efficiency of target engagement evaluation, and serves as a promising strategy to identify protein targets and off-targets as well as protein-protein interactions to uncover the biological effects. The CETSA approaches have broad applications and potentials in drug development and clinical research.

Introduction: Glycosylation, the process of glycan synthesis and attachment to target molecules, is a crucial and common post-translational modification (PTM) in mammalian cells. It affects the protein's hydrophilicity, charge, solubility, structure, localization, function, and protection from proteolysis. Aberrant glycosylation in proteins can reveal new detection and therapeutic Glyco-biomarkers, which help to improve accurate early diagnosis and personalized treatment. This review underscores the pivotal role of glycans and glycoproteins as a source of biomarkers in human diseases, particularly cancer.

Areas covered: This review delves into the implications of glycosylation, shedding light on its intricate roles in cancer-related cellular processes influencing biomarkers. It is underpinned by a thorough examination of literature up to June 2024 in PubMed, Scopus, and Google Scholar; concentrating on the terms: (Glycosylation[Title/Abstract]) OR (Glycan[Title/Abstract]) OR (glycoproteomics[Title/Abstract]) OR (Proteoglycans[Title/Abstract]) OR (Glycomarkers[Title/Abstract]) AND (Cancer[Title/Abstract]) AND ((Diagno*[Title/Abstract]) OR (Progno*[Title/Abstract])).

Expert opinion: Glyco-biomarkers enhance early cancer detection, allow early intervention, and improve patient prognoses. However, the abundance and complex dynamic glycan structure may make their scientific and clinical application difficult. This exploration of glycosylation signatures in cancer biomarkers can provide a detailed view of cancer etiology and instill hope in the potential of glycosylation to revolutionize cancer research.

Introduction: The introduction of trapped ion mobility spectrometry (TIMS) in combination with fast high-resolution time-of-flight (TOF) mass spectrometry to the proteomics field led to a jump in protein identifications and quantifications, as well as a lowering of the limit of detection for proteins from biological samples. Parallel Accumulation-Serial Fragmentation (PASEF) is a driving force for this development and has been adapted to discovery as well as targeted proteomics.

Areas covered: Over the last decade, the PASEF concept has been optimized and led to the implementation of eleven new measurement techniques. In this review, we describe all currently described PASEF measurement techniques and their application to clinical proteomics. Literature was searched using PubMed and Google Scholar search engines.

Expert opinion: The use of a dual TIMS tunnel has revolutionized the depth and the speed of proteomics measurements. Currently, we witness how this technique is pushing clinical proteomics forward.

Introduction: Metaproteomics offers insights into the function of complex microbial communities, while it is also capable of revealing microbe-microbe and host-microbe interactions. Data-independent acquisition (DIA) mass spectrometry is an emerging technology, which holds great potential to achieve deep and accurate metaproteomics with higher reproducibility yet still facing a series of challenges due to the inherent complexity of metaproteomics and DIA data.

Areas covered: This review offers an overview of the DIA metaproteomics approaches, covering aspects such as database construction, search strategy, and data analysis tools. Several cases of current DIA metaproteomics studies are presented to illustrate the procedures. Important ongoing challenges are also highlighted. Future perspectives of DIA methods for metaproteomics analysis are further discussed. Cited references are searched through and collected from Google Scholar and PubMed.

Expert opinion: Considering the inherent complexity of DIA metaproteomics data, data analysis strategies specifically designed for interpretation are imperative. From this point of view, we anticipate that deep learning methods and de novo sequencing methods will become more prevalent in the future, potentially improving protein coverage in metaproteomics. Moreover, the advancement of metaproteomics also depends on the development of sample preparation methods, data analysis strategies, etc. These factors are key to unlocking the full potential of metaproteomics.

Introduction: Kidney transplantation significantly improves the lives of those with end-stage kidney disease, offering best alternative to dialysis. However, transplant success is threatened by the acute and chronic rejection mechanisms due to complex immune responses against the new organ.

Areas covered: The ongoing research into biomarkers holds promise for revolutionizing the early detection and monitoring of the graft health. Liquid biopsy techniques offer a new avenue, with several diagnostic, predictive, and prognostic biomarkers showing promise in detecting and monitoring kidney diseases and an early and chronic allograft rejection.

Expert opinion: Evaluating the protein composition related to kidney transplant results could lead to identifying biomarkers that provide insights into the graft functionality. Non-invasive proteomic biomarkers can drastically enhance clinical outcomes and change the way how kidney transplants are evaluated for patients and physicians if they succeed in this transition. Hence, the advancement in proteomic technologies, leads toward a significant improvement in understanding of the protein markers and molecular mechanisms linked to the outcomes of kidney transplants. However, the road from discovery to the use of such proteins in clinical practice is long, with a need for continuous validation and beyond the singular research team with comprehensive infrastructure and across research groups collaboration.

Introduction: Dengue virus (DENV) infection remains one of the most significant infectious diseases in humans. Several efforts have been made to address its molecular mechanisms. Over the last 10 years, proteomics has been widely applied to investigate various aspects of DENV infection.

Areas covered: In this review, we briefly introduce common proteomics approaches using various mass spectrometric modalities followed by summarizing all the discoveries obtained from proteomic investigations of DENV infection over the last 10 years. These include the data on DENV-vector interactions and host responses to address the DENV biology and disease mechanisms. Moreover, applications of proteomics to disease prevention, diagnosis, vaccine design, development of anti-DENV agents and other new treatment strategies are discussed.

Expert opinion: Despite efforts on disease prevention, DENV infection is still a significant global healthcare burden that affects the general population. As summarized herein, proteomic technologies with high-throughput capabilities have provided more in-depth details of protein dynamics during DENV infection. More extensive applications of proteomics and other powerful research tools would provide a promise to better cope and prevent this mosquito-borne infectious disease.

Introduction: Oral squamous cell carcinoma (OSCC) represents the most prevalent form of oral cancer. Potentially malignant disorders of oral mucosa exhibit an elevated propensity for malignant progression. A substantial proportion of cases are discerned during advanced stages, significantly impacting overall survival. This investigation aims to ascertain salivary metabolites with potential utility in the early detection of OSCC.

Methods: A search encompassing PubMed, EMBASE, Scopus, Ovid, Science Direct, and Web of Science databases was conducted to identify eligible articles. The search strategy employed precise terms. The quality assessment of the included studies was executed using the QUADAS 2 ROB tool. This was registered with PROSPERO CRD42021278217.

Results: Upon removing duplicate articles and publications that didn't satisfy the inclusion criteria, seven articles were included in the current study. The Random Effects Maximum Likelihood (REML) model adopted for quantitative synthesis identified Nacetyl glucosamine as the sole metabolite in two studies included in this metaanalysis. The pathways significantly influenced by these identified metabolites were delineated.

Conclusion: This study highlights Nacetyl glucosamine as a distinctive metabolite with the potential to serve as an early diagnostic marker for OSCC. Nevertheless, further research is warranted to validate its clinical utility.