Meta-Analysis of MS-Based Proteomics Studies Indicates Interferon Regulatory Factor 4 and Nucleobindin1 as Potential Prognostic and Drug Resistance Biomarkers in Diffuse Large B Cell Lymphoma.
Mostafa EjtehadifarSara ZahediPaula GameiroJosé CabeçadasMaria Gomes da SilvaHans Christian BeckAna Sofia CarvalhoRune MatthiesenPublished in: Cells (2023)
The prognosis of diffuse large B cell lymphoma (DLBCL) is inaccurately predicted using clinical features and immunohistochemistry (IHC) algorithms. Nomination of a panel of molecules as the target for therapy and predicting prognosis in DLBCL is challenging because of the divergences in the results of molecular studies. Mass spectrometry (MS)-based proteomics in the clinic represents an analytical tool with the potential to improve DLBCL diagnosis and prognosis. Previous proteomics studies using MS-based proteomics identified a wide range of proteins. To achieve a consensus, we reviewed MS-based proteomics studies and extracted the most consistently significantly dysregulated proteins. These proteins were then further explored by analyzing data from other omics fields. Among all significantly regulated proteins, interferon regulatory factor 4 (IRF4) was identified as a potential target by proteomics, genomics, and IHC. Moreover, annexinA5 (ANXA5) and nucleobindin1 (NUCB1) were two of the most up-regulated proteins identified in MS studies. Functional enrichment analysis identified the light zone reactions of the germinal center (LZ-GC) together with cytoskeleton locomotion functions as enriched based on consistent, significantly dysregulated proteins. In this study, we suggest IRF4 and NUCB1 proteins as potential biomarkers that deserve further investigation in the field of DLBCL sub-classification and prognosis.
Keyphrases
- mass spectrometry
- diffuse large b cell lymphoma
- liquid chromatography
- epstein barr virus
- gas chromatography
- case control
- high performance liquid chromatography
- capillary electrophoresis
- high resolution
- systematic review
- multiple sclerosis
- dendritic cells
- ms ms
- transcription factor
- machine learning
- randomized controlled trial
- single cell
- tandem mass spectrometry
- deep learning
- immune response
- mesenchymal stem cells
- simultaneous determination
- human health
- electronic health record
- meta analyses
- climate change
- big data
- data analysis
- cell therapy