Within-person reproducibility of proteoforms related to inflammation and renal dysfunction

Gao, Jie; McCann, Adrian; Laupsa‐Borge, Johnny; Nygård, Ottar; Ueland, Per Magne; Meyer, Klaus

doi:10.1038/s41598-022-11520-1

Cited by 4 publications

(4 citation statements)

References 36 publications

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“…For example, protein isoforms may differ in domain composition, where consequently each isoform may have substantially different functions and influence disease predisposition or progression. Advances in characterizing the proteomic landscape of lung cancers such as non-small cell lung cancer (NSCLC) and squamous cell lung cancer have enabled identification of important protein biomarkers [4][5][6], however, few proteoforms relevant to lung cancer have been identified [7], as these studies are limited to only single or few protein [8][9][10] or proteoforms arising from different genes [11]. Unbiased readout technologies, such as highresolution quantitative mass spectrometry (MS), can be employed to infer and quantify peptides and proteins with high confidence (e.g., < 1% false discovery rate (FDR)).…”

Section: Introductionmentioning

confidence: 99%

Functionally distinct BMP1 isoforms show an opposite pattern of abundance in plasma from non-small cell lung cancer subjects and controls

Donovan

Huang²,

Blume³

et al. 2023

PLoS ONE

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Advancements in deep plasma proteomics are enabling high-resolution measurement of plasma proteoforms, which may reveal a rich source of novel biomarkers previously concealed by aggregated protein methods. Here, we analyze 188 plasma proteomes from non-small cell lung cancer subjects (NSCLC) and controls to identify NSCLC-associated protein isoforms by examining differentially abundant peptides as a proxy for isoform-specific exon usage. We find four proteins comprised of peptides with opposite patterns of abundance between cancer and control subjects. One of these proteins, BMP1, has known isoforms that can explain this differential pattern, for which the abundance of the NSCLC-associated isoform increases with stage of NSCLC progression. The presence of cancer and control-associated isoforms suggests differential regulation of BMP1 isoforms. The identified BMP1 isoforms have known functional differences, which may reveal insights into mechanisms impacting NSCLC disease progression.

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Section: Introductionmentioning

confidence: 99%

Functionally distinct BMP1 isoforms show an opposite pattern of abundance in plasma from non-small cell lung cancer subjects and controls

Donovan

Huang²,

Blume³

et al. 2023

PLoS ONE

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“…First, a single measurement of blood biomarkers at baseline may not reflect long-term exposures. However, previous evidence suggests that one-time measurement of the included biomarkers (e.g., CysC, testosterone, and 25[OH]D) could reliably categorize average levels over at least a 4-year period [ 8 , 42 , 43 ]. Second, we were unable to evaluate the potential effect of estrogens on mortality because the assay used in the UK Biobank to assess estradiol levels was not sufficiently sensitive to measure the typically low concentrations in postmenopausal women and men.…”

Section: Discussionmentioning

confidence: 99%

Development and validation of a blood biomarker score for predicting mortality risk in the general population

Yang

Miao

et al. 2023

J Transl Med

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Background Blood biomarkers for multiple pathways, such as inflammatory response, lipid metabolism, and hormonal regulation, have been suggested to influence the risk of mortality. However, few studies have systematically evaluated the combined predictive ability of blood biomarkers for mortality risk. Methods We included 267,239 participants from the UK Biobank who had measurements of 28 blood biomarkers and were free of cardiovascular disease (CVD) and cancer at baseline (2006–2010). We developed sex-specific blood biomarker scores for predicting all-cause mortality risk in a training set of 247,503 participants from England and Wales, and validated the results in 19,736 participants from Scotland. Cox and LASSO regression analyses were performed to identify independent predictors for men and women separately. Discrimination and calibration were evaluated by C-index and calibration plots, respectively. We also assessed mediating effects of the biomarkers on the association between traditional risk factors (current smoking, obesity, physical inactivity, hypertension, diabetes) and mortality. Results A total of 13 independent predictive biomarkers for men and 17 for women were identified and included in the score development. Compared to the lowest tertile of the score, the highest tertile showed a hazard ratio of 5.36 (95% confidence interval [CI] 5.04–5.71) in men and 4.23 (95% CI 3.87–4.62) in women for all-cause mortality. In the validation set, the score yielded a C-index of 0.73 (95% CI 0.72–0.75) in men and 0.70 (95% CI 0.68–0.73) in women for all-cause mortality; it was also predictive of CVD (C-index of 0.76 in men and 0.79 in women) and cancer (C-index of 0.70 in men and 0.67 in women) mortality. Moreover, the association between traditional risk factors and all-cause mortality was largely mediated by cystatin C, C-reactive protein, 25-hydroxyvitamin D, and hemoglobin A1c. Conclusions We established sex-specific blood biomarker scores for predicting all-cause and cause-specific mortality in the general population, which hold the potential to identify high-risk individuals and improve targeted prevention of premature death.

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“…The training set consisted of data from 284 retrospective participants, while the test set was comprised of 116 prospectively enrolled participants [ 16 ]. Patients were eligible for the test set only if they were judged to have stable angina.…”

Section: Methodsmentioning

confidence: 99%

A Machine Learning Framework for Diagnosing and Predicting the Severity of Coronary Artery Disease

Ainiwaer¹,

Hou²,

Kadier³

et al. 2023

Rev. Cardiovasc. Med.

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Background: Although machine learning (ML)-based prediction of coronary artery disease (CAD) has gained increasing attention, assessment of the severity of suspected CAD in symptomatic patients remains challenging. Methods: The training set for this study consisted of 284 retrospective participants, while the test set included 116 prospectively enrolled participants from whom we collected 53 baseline variables and coronary angiography results. The data was pre-processed with outlier processing and One-Hot coding. In the first stage, we constructed a ML model that used baseline information to predict the presence of CAD with a dichotomous model. In the second stage, baseline information was used to construct ML regression models for predicting the severity of CAD. The non-CAD population was included, and two different scores were used as output variables. Finally, statistical analysis and SHAP plot visualization methods were employed to explore the relationship between baseline information and CAD. Results: The study included 269 CAD patients and 131 healthy controls. The eXtreme Gradient Boosting (XGBoost) model exhibited the best performance amongst the different models for predicting CAD, with an area under the receiver operating characteristic curve of 0.728 (95% CI 0.623–0.824). The main correlates were left ventricular ejection fraction, homocysteine, and hemoglobin ( p 0.001). The XGBoost model performed best for predicting the SYNTAX score, with the main correlates being brain natriuretic peptide (BNP), left ventricular ejection fraction, and glycated hemoglobin ( p 0.001). The main relevant features in the model predictive for the GENSINI score were BNP, high density lipoprotein, and homocysteine ( p 0.001). Conclusions: This data-driven approach provides a foundation for the risk stratification and severity assessment of CAD. Clinical Trial Registration: The study was registered in www.clinicaltrials.gov protocol registration system (number NCT05018715).

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Within-person reproducibility of proteoforms related to inflammation and renal dysfunction

Cited by 4 publications

References 36 publications

Functionally distinct BMP1 isoforms show an opposite pattern of abundance in plasma from non-small cell lung cancer subjects and controls

Functionally distinct BMP1 isoforms show an opposite pattern of abundance in plasma from non-small cell lung cancer subjects and controls

Development and validation of a blood biomarker score for predicting mortality risk in the general population

A Machine Learning Framework for Diagnosing and Predicting the Severity of Coronary Artery Disease

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