Elevated levels of serum CDCP1 in individuals recovering from severe COVID-19 disease

Blanco, José-Ramón; Cobos-Ceballos, María-Jesús; Navarro, Francisco; Sanjoaquín, Isabel; Armiñanzas, Carlos; Bernal, Enrique; Buzón-Martín, Luis; Viribay, Miguel; Pérez‐Martínez, Laura; Espejo-Pérez, Simona; Valencia, Borja; Guzman-Aguilar, Jesus; Ruiz-Cubillan, Juan-Jose; Alcalde, Consuelo; Gutierrez-Herrero, Fernando Gustavo; Olalla, Julián; Andrés-Esteban, Eva-Maria; Jurado-Gámez, Bernabé; Ugedo, Javier

doi:10.18632/aging.203898

Cited by 3 publications

(3 citation statements)

References 35 publications

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“…CCL19 plays a role in attracting immune cells to the site of inflammation or infection in general, and this has been shown in the presence of A. fumigatus as well [ 45 , 46 ]. Less information specifically directed towards (fungal) infections is available for CDCP1 but it has for example been described in association with severe COVID-19 infection [ 47 ]. As concentrations of these proteins were not significantly different between uninfected and infected patients, and they are described in literature in many other types of infections (and even in autoimmune disease), one could suggest that these proteins are just markers of general immune activation.…”

Section: Discussionmentioning

confidence: 99%

Circulatory Inflammatory Proteins as Early Diagnostic Biomarkers for Invasive Aspergillosis in Patients with Hematologic Malignancies—an Exploratory Study

Aerts,

Ricaño-Ponce,

Bruno

et al. 2024

Mycopathologia

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Objectives Invasive aspergillosis (IA) is a major cause of mortality in immunocompromised patients and it is difficult to diagnose because of the lack of reliable highly sensitive diagnostics. We aimed to identify circulating immunological markers that could be useful for an early diagnosis of IA. Methods We collected longitudinally serum samples from 33 cases with probable/proven IA and two matched control cohorts without IA (one with microbiological and clinical evidence of bacterial or viral non-fungal pneumonia and one without evidence of infection, all matched for neutropenia, primary underlying disease, and receipt of corticosteroids/other immunosuppressants) at a tertiary university hospital. In addition, samples from an independent cohort (n = 20 cases of proven/probable IA and 20 matched controls without infection) were obtained. A panel of 92 circulating proteins involved in inflammation was measured by proximity extension assay. A random forest model was used to predict the development of IA using biomarkers measured before diagnosis. Results While no significant differences were observed between IA cases and infected controls, concentrations of 30 inflammatory biomarkers were different between cases and non-infected controls, of which nine were independently replicated: PD-L1, MMP-10, Interleukin(IL)-10, IL-15RA, IL-18, IL-18R1, CDCP1, CCL19 and IL-17C. From the differential abundance analysis of serum samples collected more than 10 days before diagnosis and at diagnosis, increased IL-17C concentrations in IA patients were replicated in the independent cohort. Conclusions An increased circulating concentration of IL-17C was detected both in the discovery and independent cohort, both at the time of diagnosis and in samples 10 days before the diagnosis of IA, suggesting it should be evaluated further as potential (early) biomarker of infection.

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Section: Discussionmentioning

confidence: 99%

Circulatory Inflammatory Proteins as Early Diagnostic Biomarkers for Invasive Aspergillosis in Patients with Hematologic Malignancies—an Exploratory Study

Aerts,

Ricaño-Ponce,

Bruno

et al. 2024

Mycopathologia

View full text Add to dashboard Cite

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“…Importantly, the features that our model used to drive clustering of the datasets are consistent with data from other published 'omics data analysis. Examples include elevated protein expression with severity of COVID-19 for proteins including: 1) Syndecan-1 (SYND1) [32][33][34], 2) S100 calcium binding protein A12 (EN-RAGE or S100A12) [35][36][37], 3) Hepatocyte Growth Factor (HGF) [38][39][40] and 4) CUB domain containing protein 1 (CDCP1) [41]. Examples for elevated RNA transcript expression with severity of COVID-19 for genes included 1) IFNA17 [42,43], 2) ARG1 [44,45].…”

Section: Discussionmentioning

confidence: 99%

MixOmics Integration of Biological Datasets Identifies Highly Correlated Key Variables of COVID-19 severity

Harriott,

Chimenti,

Ryan

2023

Preprint

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Background: Despite several years since the COVID-19 pandemic was declared, challenges remain in understanding the factors that can predict the severity of COVID-19 disease and complications of SARS-CoV-2 infection. While many large-scale Multiomic datasets have been published, integration of these datasets has the potential to substantially increase the biological insight gained allowing a more complex comprehension of the disease pathogenesis. Such insight may improve our ability to predict disease progression, detect severe cases more rapidly and develop effective therapeutics. Methods: In this study we have applied an innovative machine learning algorithm to delineate COVID-severity based on integration of paired samples of proteomic and transcriptomic data from a small cohort of patients testing positive for SARS-CoV-2 infection with differential disease severity. Targeted plasma proteomics and an onco-immune targeted transcriptomic panel was performed on sequential samples from a cohort of 23 severe, 21 moderate and 10 mild COVID-19 patients. We applied DIABLO, a new integrative method, to identify multi-omics biomarker panels that can discriminate between multiple phenotypic groups, such as the varied severity of disease in COVID-19 patients. Results: As COVID-19 severity is known among our sample group, we can train models using this as the outcome variable and calculate features that are important predictors of severe disease. In this study, we detect highly correlated key variables of severe COVID-19 using transcriptomic discriminant analysis and multi-omics integration methods. Conclusions: This approach highlights the power of data integration from a small cohort of patients offering a better biological understanding of the molecular mechanisms driving COVID-19 severity and an opportunity to improve prediction of disease trajectories and targeted therapeutics.

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“…CCL19 plays a role in attracting immune cells to the site of inflammation or infection in general, and this has been shown in the presence of A. fumigatus as well [44,45]. Less information specifically directed towards (fungal) infections is available for CDCP1 but it has for example been described in association with severe COVID-19 infection [46]. As concentrations of these proteins were not significantly different between uninfected and infected patients, and they are described in literature in many other types of infections (and even in autoimmune disease), one could suggest that these proteins are just markers of general immune activation.…”

Section: Discussionmentioning

confidence: 99%

Circulatory inflammatory proteins as early diagnostic biomarkers for invasive aspergillosis in patients with hematologic malignancies

Aerts,

Ponce,

Bruno

et al. 2023

Preprint

View full text Add to dashboard Cite

Objectives Invasive aspergillosis (IA) is a major cause of mortality in immunocompromised patients and it is difficult to diagnose because of the lack of reliable highly sensitive diagnostics. We aimed to identify circulating immunological markers that could be useful for an early diagnosis of IA. Methods We collected longitudinally serum samples from 33 cases with probable/proven IA and two matched control cohorts without IA (one with microbiological and clinical evidence of bacterial or viral non-fungal pneumonia and one without evidence of infection, all matched for neutropenia, primary underlying disease, and receipt of corticosteroids/other immunosuppressants) at a tertiary university hospital. In addition, samples from an independent cohort (n = 20 cases of proven/probable IA and 20 matched controls without infection) were obtained. A panel of 92 circulating proteins involved in inflammation was measured by proximity extension assay. A random forest model was used to predict the development of IA using biomarkers measured before diagnosis. Results Concentrations of 30 inflammatory biomarkers were different between cases and non-infected controls, of which nine were independently replicated: PD-L1, MMP-10, Interleukin(IL)-10, IL-15RA, IL-18, IL-18R1, CDCP1, CCL19 and IL-17C. From the differential abundance analysis of serum samples collected more than 10 days before diagnosis and at diagnosis, increased IL-17C concentrations in IA patients were replicated in the independent cohort. Conclusions An increased circulating concentration of IL-17C was detected both in the discovery and independent cohort, both at the time of diagnosis and in samples 10 days before the diagnosis of IA, suggesting it could be an (early) biomarker of infection.

show abstract

Elevated levels of serum CDCP1 in individuals recovering from severe COVID-19 disease

Cited by 3 publications

References 35 publications

Circulatory Inflammatory Proteins as Early Diagnostic Biomarkers for Invasive Aspergillosis in Patients with Hematologic Malignancies—an Exploratory Study

Circulatory Inflammatory Proteins as Early Diagnostic Biomarkers for Invasive Aspergillosis in Patients with Hematologic Malignancies—an Exploratory Study

MixOmics Integration of Biological Datasets Identifies Highly Correlated Key Variables of COVID-19 severity

Circulatory inflammatory proteins as early diagnostic biomarkers for invasive aspergillosis in patients with hematologic malignancies

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