Identification of candidate serum biomarkers of childhood-onset growth hormone deficiency using SWATH-MS and feature selection

Ortea, Ignacio; Ruiz-Sánchez, Isabel; Cañete, Ramón; Caballero-Villarraso, Javier; Cañete, María Dolores

doi:10.1016/j.jprot.2018.01.003

Cited by 16 publications

(17 citation statements)

References 46 publications

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“…Three studies used the same image database such that only the latest published study was included. At last, this systematic review included 16 studies (6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21) (Table 2), with the diagnosis of general pituitary neoplasms, acromegaly, Cushing’s disease, craniopharyngioma and growth hormone deficiency. More than half of the studies were published in the recent 2 years.…”

Section: Resultsmentioning

confidence: 99%

“…The majority of the studies (6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 20) (76.5%) used the diagnosis of a specific disease as the outcome, only four studies (17, 18, 19, 21) tested on the treatment outcome. In the diagnostic studies, three studies (7, 12, 14) used image features to categorize magnetic resonance images (MRIs), two (6, 13) used face photos to predict acromegaly, two (15, 20) predicted growth home deficiency using serum proteins, two (10, 11) used histological spectrum to predict histology diagnosis, one (9) used serum proteins to predict pituitary adenoma and one (8) predicted surgical phase using videos. In studies on treatment outcomes, one study (17) predicted poor early postoperative outcome, one (18) predicted gross-total resection, one (19) predicted response to somatostatin analogs and one (21) predicted growth after growth hormone treatment.…”

Section: Resultsmentioning

confidence: 99%

“…One study (17) compared several models and chose the one with the best performance. With regard to validation methods, five studies (8, 9, 12, 15, 19) used k-fold cross-validation, two studies (14, 20) used bootstrap and three studies (6, 7, 10) used leave-one-out cross-validation. One study (18) used cross-validation but without holdout and five studies (11, 13, 16, 17, 21) did not report the validation method.…”

Section: Resultsmentioning

confidence: 99%

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A systematic review on machine learning in sellar region diseases: quality and reporting items

Qiao

2019

Endocrine Connections

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Introduction Machine learning methods in sellar region diseases present a particular challenge because of the complexity and the necessity for reproducibility. This systematic review aims to compile the current literature on sellar region diseases that utilized machine learning methods and to propose a quality assessment tool and reporting checklist for future studies. Methods PubMed and Web of Science were searched to identify relevant studies. The quality assessment included five categories: unmet needs, reproducibility, robustness, generalizability and clinical significance. Results Seventeen studies were included with the diagnosis of general pituitary neoplasms, acromegaly, Cushing’s disease, craniopharyngioma and growth hormone deficiency. 87.5% of the studies arbitrarily chose one or two machine learning models. One study chose ensemble models, and one study compared several models. 43.8% of studies did not provide the platform for model training, and roughly half did not offer parameters or hyperparameters. 62.5% of the studies provided a valid method to avoid over-fitting, but only five reported variations in the validation statistics. Only one study validated the algorithm in a different external database. Four studies reported how to interpret the predictors, and most studies (68.8%) suggested possible clinical applications of the developed algorithm. The workflow of a machine-learning study and the recommended reporting items were also provided based on the results. Conclusions Machine learning methods were used to predict diagnosis and posttreatment outcomes in sellar region diseases. Though most studies had substantial unmet need and proposed possible clinical application, replicability, robustness and generalizability were major limits in current studies.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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A systematic review on machine learning in sellar region diseases: quality and reporting items

Qiao

2019

Endocrine Connections

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“…Protein samples from mitochondrial extracts for SWATH-MS measurements were alkylated and trypsin-digested as described (Vowinckel et al, 2013;Ortea et al, 2018) and performed at the Proteomic Facility of the Insituto Maimónides de Investigación Biomédica de Córdoba, Spain.…”

Section: Protein Relative Quantitation By Swath-ms Acquisition and Anmentioning

confidence: 99%

“…A data dependent acquisition (DDA) approach using nano-LC-MS/MS was first performed to generate the SWATH-MS spectral library as described (Ortea et al, 2018).…”

Section: Protein Relative Quantitation By Swath-ms Acquisition and Anmentioning

confidence: 99%

HCN Regulates Cellular Processes through Posttranslational Modification of Proteins by S-cyanylation

et al. 2018

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Hydrogen cyanide (HCN) is coproduced with ethylene in plant cells and is primarily enzymatically detoxified by the mitochondrial b-CYANOALANINE SYNTHASE (CAS-C1). Permanent or transient depletion of CAS-C1 activity in Arabidopsis (Arabidopsis thaliana) results in physiological alterations in the plant that suggest that HCN acts as a gasotransmitter molecule. Label-free quantitative proteomic analysis of mitochondrially enriched samples isolated from the wild type and cas-c1 mutant revealed significant changes in protein content, identifying 451 proteins that are absent or less abundant in cas-c1 and 353 proteins that are only present or more abundant in cas-c1. Gene ontology classification of these proteins identified proteomic changes that explain the root hairless phenotype and the altered immune response observed in the cas-c1 mutant. The mechanism of action of cyanide as a signaling molecule was addressed using two proteomic approaches aimed at identifying the S-cyanylation of Cys as a posttranslational modification of proteins. Both the 2-imino-thiazolidine chemical method and the direct untargeted analysis of proteins using liquid chromatography-tandem mass spectrometry identified a set of 163 proteins susceptible to S-cyanylation that included SEDOHEPTULOSE 1,7-BISPHOSPHATASE (SBPase), the PEPTIDYL-PROLYL CIS-TRANS ISOMERASE 20-3 (CYP20-3), and ENOLASE2 (ENO2). In vitro analysis of these enzymes showed that S-cyanylation of SBPase Cys 74 , CYP20-3 Cys 259 , and ENO2 Cys 346 residues affected their enzymatic activity. Gene Ontology classification and protein-protein interaction cluster analysis showed that S-cyanylation is involved in the regulation of primary metabolic pathways, such as glycolysis, and the Calvin and S-adenosyl-Met cycles.

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Proteomic investigation on bio-corona of Au, Ag and Fe nanoparticles for the discovery of triple negative breast cancer serum protein biomarkers

Chantada-Vázquez

López

Vence

et al. 2020

Journal of Proteomics

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Identification of candidate serum biomarkers of childhood-onset growth hormone deficiency using SWATH-MS and feature selection

Cited by 16 publications

References 46 publications

A systematic review on machine learning in sellar region diseases: quality and reporting items

A systematic review on machine learning in sellar region diseases: quality and reporting items

HCN Regulates Cellular Processes through Posttranslational Modification of Proteins by S-cyanylation

Proteomic investigation on bio-corona of Au, Ag and Fe nanoparticles for the discovery of triple negative breast cancer serum protein biomarkers

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