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The prevalence of intracranial aneurysm (IA) is increasing, and the consequences of its rupture are severe. This study aimed to reveal specific, sensitive, and non‐invasive biomarkers for diagnosis and classification of ruptured and unruptured IA, to benefit the development of novel treatment strategies and therapeutics altering the course of the disease. We first assembled an extensive candidate biomarker bank of IA, comprising up to 717 proteins, based on altered proteins discovered in the current tissue and serum proteomic analysis, as well as from previous studies. Mass spectrometry assays for hundreds of biomarkers were efficiently designed using our proposed deep learning‐based method, termed DeepPRM. A total of 113 potential markers were further quantitated in serum cohort I (n = 212) & II (n = 32). Combined with a machine‐learning‐based pipeline, we built two sets of biomarker combinations (P6 & P8) to accurately distinguish IA from healthy controls (accuracy: 87.50%) or classify IA rupture patients (accuracy: 91.67%) upon evaluation in the external validation set (n = 32). This extensive circulating biomarker development study provides valuable knowledge about IA biomarkers. Synopsis: This study constructed a comprehensive mass spectrometry‐based proteomics strategy for serum protein biomarker discovery for intracranial aneurysm (IA). The presented workflow integrates the results of current proteome research and previously reported studies, yielding a comprehensive serum protein biomarker bank of IA.A highly efficient and timesaving PRM assay approach (DeepPRM) is proposed to facilitate targeted quantification of large‐scale candidate proteins.Machine learning on the serum proteome distinguishes IA from healthy controls with an accuracy of 87.50%, and ruptured from unruptured IA with an accuracy of 91.67%. [ABSTRACT FROM AUTHOR]

Abstract The prevalence of intracranial aneurysm (IA) is increasing, and the consequences of its rupture are severe. This study aimed to reveal specific, sensitive, and non‐invasive biomarkers for diagnosis and classification of ruptured and unruptured IA, to benefit the development of novel treatment strategies and therapeutics altering the course of the disease. We first assembled an extensive candidate biomarker bank of IA, comprising up to 717 proteins, based on altered proteins discovered in the current tissue and serum proteomic analysis, as well as from previous studies. Mass spectrometry assays for hundreds of biomarkers were efficiently designed using our proposed deep learning‐based method, termed DeepPRM. A total of 113 potential markers were further quantitated in serum cohort I (n = 212) & II (n = 32). Combined with a machine‐learning‐based pipeline, we built two sets of biomarker combinations (P6 & P8) to accurately distinguish IA from healthy controls (accuracy: 87.50%) or classify IA rupture patients (accuracy: 91.67%) upon evaluation in the external validation set (n = 32). This extensive circulating biomarker development study provides valuable knowledge about IA biomarkers.

Abstract: Complementary 2D-PAGE and ‘shotgun’ LC–MS/MS approaches were combined to identify medium and low-abundant proteins in sera of Cystic Fibrosis (CF) patients (mild or severe pulmonary disease) in comparison with healthy CF-carrier and non-CF carrier individuals aiming to gain deeper insights into the pathogenesis of this multifactorial genetic disease. 78 differentially expressed spots were identified from 2D-PAGE proteome profiling yielding 28 identifications and postulating the existence of post-translation modifications (PTM). The ‘shotgun’ approach highlighted altered levels of proteins actively involved in CF: abnormal tissue/airway remodeling, protease/antiprotease imbalance, innate immune dysfunction, chronic inflammation, nutritional imbalance and Pseudomonas aeruginosa colonization. Members of the apolipoproteins family (VDBP, ApoA-I, and ApoB) presented gradually lower expression from non-CF to CF-carrier individuals and from those to CF patients, results validated by an independent assay. The multifunctional enzyme NDKB was identified only in the CF group and independently validated by WB. Its functions account for ion sensor in epithelial cells, pancreatic secretion, neutrophil-mediated inflammation and energy production, highlighting its physiological significance in the context of CF. Complementary proteomics-based approaches are reliable tools to reveal pathways and circulating proteins actively involved in a heterogeneous disease such as CF. [ABSTRACT FROM AUTHOR]