Correlations between biological markers of the perirenal adipose tissue and clinical features of patients with localized kidney cancer

Autores
Romeo, Leonardo Rafael; Nuñez, Matias; Ferrando, Matías; López Fontana, Constanza Matilde; Caron, Ruben Walter; Bruna, Flavia Alejandra; Pistone Creydt, Virginia
Año de publicación
2025
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Among the different types of cells that surround renal epithelial cells, human renal adipose tissue (hAT) is one of the most abundant. We have previously characterized the expression of different proteins in hAT (adiponectin, adiponectin receptor 1, leptin, leptin receptor, perilipin 1, and metalloprotease (1). In this study, we evaluated if the differential proteins expression as a whole was sufficient to separate healthy patients from patients with kidney cancer, using unsupervised machine learning algorithms; and the correlation between adiponectin and leptin expression with clinical characteristics of kidney cancer patients. Considering the six biological variables evaluated in the different hAT fragments, we were able to separate healthy from kidney tumor patients by unsupervised machine learning algorithms projection. In addition, a decrease in adiponectin expression was found in patients with a more undifferentiated tumor as well as in patients with a history of smoking. Also, there was a positive correlation between leptin, tumor size and difficulty in tumor dissection. The parameters that increase the difficulty in dissection are male sex, smoking history, tumor size and the fat striation degree in imaging studies. Moreover, PAT (perirenal adipose tissue)-related adipokine signatures reflectsystemicmetabolic dysfunction, including features of metabolic syndrome, offering additional value for anticipating surgical complexity and refining prognostic stratification. This project represents a new way of looking at kidney cancer, by correlating clinical features with specific biomarkers, we may be able to identify patterns that might predict howthediseasewilldevelop.Thiscouldleadtomoreaccurateprognoses and more effective treatments.
Fil: Romeo, Leonardo Rafael. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto de Medicina y Biología Experimental de Cuyo; Argentina
Fil: Nuñez, Matias. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte. Instituto de Investigaciones en Biodiversidad y Medioambiente. Universidad Nacional del Comahue. Centro Regional Universidad Bariloche. Instituto de Investigaciones en Biodiversidad y Medioambiente; Argentina
Fil: Ferrando, Matías. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto de Medicina y Biología Experimental de Cuyo; Argentina
Fil: López Fontana, Constanza Matilde. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto de Medicina y Biología Experimental de Cuyo; Argentina
Fil: Caron, Ruben Walter. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto de Medicina y Biología Experimental de Cuyo; Argentina
Fil: Bruna, Flavia Alejandra. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto de Medicina y Biología Experimental de Cuyo; Argentina
Fil: Pistone Creydt, Virginia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto de Medicina y Biología Experimental de Cuyo; Argentina
Materia
Unsupervised Machine Learning
AI in Healthcare
Kidney Cancer
Perirenal Fat
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/289974
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/289974
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Correlations between biological markers of the perirenal adipose tissue and clinical features of patients with localized kidney cancerRomeo, Leonardo RafaelNuñez, MatiasFerrando, MatíasLópez Fontana, Constanza MatildeCaron, Ruben WalterBruna, Flavia AlejandraPistone Creydt, VirginiaUnsupervised Machine LearningAI in HealthcareKidney CancerPerirenal Fathttps://purl.org/becyt/ford/3.4https://purl.org/becyt/ford/3https://purl.org/becyt/ford/2.6https://purl.org/becyt/ford/2https://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1Among the different types of cells that surround renal epithelial cells, human renal adipose tissue (hAT) is one of the most abundant. We have previously characterized the expression of different proteins in hAT (adiponectin, adiponectin receptor 1, leptin, leptin receptor, perilipin 1, and metalloprotease (1). In this study, we evaluated if the differential proteins expression as a whole was sufficient to separate healthy patients from patients with kidney cancer, using unsupervised machine learning algorithms; and the correlation between adiponectin and leptin expression with clinical characteristics of kidney cancer patients. Considering the six biological variables evaluated in the different hAT fragments, we were able to separate healthy from kidney tumor patients by unsupervised machine learning algorithms projection. In addition, a decrease in adiponectin expression was found in patients with a more undifferentiated tumor as well as in patients with a history of smoking. Also, there was a positive correlation between leptin, tumor size and difficulty in tumor dissection. The parameters that increase the difficulty in dissection are male sex, smoking history, tumor size and the fat striation degree in imaging studies. Moreover, PAT (perirenal adipose tissue)-related adipokine signatures reflectsystemicmetabolic dysfunction, including features of metabolic syndrome, offering additional value for anticipating surgical complexity and refining prognostic stratification. This project represents a new way of looking at kidney cancer, by correlating clinical features with specific biomarkers, we may be able to identify patterns that might predict howthediseasewilldevelop.Thiscouldleadtomoreaccurateprognoses and more effective treatments.Fil: Romeo, Leonardo Rafael. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto de Medicina y Biología Experimental de Cuyo; ArgentinaFil: Nuñez, Matias. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte. Instituto de Investigaciones en Biodiversidad y Medioambiente. Universidad Nacional del Comahue. Centro Regional Universidad Bariloche. Instituto de Investigaciones en Biodiversidad y Medioambiente; ArgentinaFil: Ferrando, Matías. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto de Medicina y Biología Experimental de Cuyo; ArgentinaFil: López Fontana, Constanza Matilde. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto de Medicina y Biología Experimental de Cuyo; ArgentinaFil: Caron, Ruben Walter. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto de Medicina y Biología Experimental de Cuyo; ArgentinaFil: Bruna, Flavia Alejandra. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto de Medicina y Biología Experimental de Cuyo; ArgentinaFil: Pistone Creydt, Virginia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto de Medicina y Biología Experimental de Cuyo; ArgentinaFrontiers Media2025-11-02info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfapplication/pdfapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/289974Romeo, Leonardo Rafael; Nuñez, Matias; Ferrando, Matías; López Fontana, Constanza Matilde; Caron, Ruben Walter; et al.; Correlations between biological markers of the perirenal adipose tissue and clinical features of patients with localized kidney cancer; Frontiers Media; Frontiers in Medicine; 12; 2-11-2025; 1-82296-858XCONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.frontiersin.org/articles/10.3389/fmed.2025.1676630/fullinfo:eu-repo/semantics/altIdentifier/doi/10.3389/fmed.2025.1676630info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2026-08-25T15:36:42Zoai:ri.conicet.gov.ar:11336/289974instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982026-08-25 15:36:43.25CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Correlations between biological markers of the perirenal adipose tissue and clinical features of patients with localized kidney cancer
title Correlations between biological markers of the perirenal adipose tissue and clinical features of patients with localized kidney cancer
spellingShingle Correlations between biological markers of the perirenal adipose tissue and clinical features of patients with localized kidney cancer
Romeo, Leonardo Rafael
Unsupervised Machine Learning
AI in Healthcare
Kidney Cancer
Perirenal Fat
title_short Correlations between biological markers of the perirenal adipose tissue and clinical features of patients with localized kidney cancer
title_full Correlations between biological markers of the perirenal adipose tissue and clinical features of patients with localized kidney cancer
title_fullStr Correlations between biological markers of the perirenal adipose tissue and clinical features of patients with localized kidney cancer
title_full_unstemmed Correlations between biological markers of the perirenal adipose tissue and clinical features of patients with localized kidney cancer
title_sort Correlations between biological markers of the perirenal adipose tissue and clinical features of patients with localized kidney cancer
dc.creator.none.fl_str_mv Romeo, Leonardo Rafael
Nuñez, Matias
Ferrando, Matías
López Fontana, Constanza Matilde
Caron, Ruben Walter
Bruna, Flavia Alejandra
Pistone Creydt, Virginia
author Romeo, Leonardo Rafael
author_facet Romeo, Leonardo Rafael
Nuñez, Matias
Ferrando, Matías
López Fontana, Constanza Matilde
Caron, Ruben Walter
Bruna, Flavia Alejandra
Pistone Creydt, Virginia
author_role author
author2 Nuñez, Matias
Ferrando, Matías
López Fontana, Constanza Matilde
Caron, Ruben Walter
Bruna, Flavia Alejandra
Pistone Creydt, Virginia
author2_role author
author
author
author
author
author
dc.subject.none.fl_str_mv Unsupervised Machine Learning
AI in Healthcare
Kidney Cancer
Perirenal Fat
topic Unsupervised Machine Learning
AI in Healthcare
Kidney Cancer
Perirenal Fat
purl_subject.fl_str_mv https://purl.org/becyt/ford/3.4
https://purl.org/becyt/ford/3
https://purl.org/becyt/ford/2.6
https://purl.org/becyt/ford/2
https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Among the different types of cells that surround renal epithelial cells, human renal adipose tissue (hAT) is one of the most abundant. We have previously characterized the expression of different proteins in hAT (adiponectin, adiponectin receptor 1, leptin, leptin receptor, perilipin 1, and metalloprotease (1). In this study, we evaluated if the differential proteins expression as a whole was sufficient to separate healthy patients from patients with kidney cancer, using unsupervised machine learning algorithms; and the correlation between adiponectin and leptin expression with clinical characteristics of kidney cancer patients. Considering the six biological variables evaluated in the different hAT fragments, we were able to separate healthy from kidney tumor patients by unsupervised machine learning algorithms projection. In addition, a decrease in adiponectin expression was found in patients with a more undifferentiated tumor as well as in patients with a history of smoking. Also, there was a positive correlation between leptin, tumor size and difficulty in tumor dissection. The parameters that increase the difficulty in dissection are male sex, smoking history, tumor size and the fat striation degree in imaging studies. Moreover, PAT (perirenal adipose tissue)-related adipokine signatures reflectsystemicmetabolic dysfunction, including features of metabolic syndrome, offering additional value for anticipating surgical complexity and refining prognostic stratification. This project represents a new way of looking at kidney cancer, by correlating clinical features with specific biomarkers, we may be able to identify patterns that might predict howthediseasewilldevelop.Thiscouldleadtomoreaccurateprognoses and more effective treatments.
Fil: Romeo, Leonardo Rafael. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto de Medicina y Biología Experimental de Cuyo; Argentina
Fil: Nuñez, Matias. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte. Instituto de Investigaciones en Biodiversidad y Medioambiente. Universidad Nacional del Comahue. Centro Regional Universidad Bariloche. Instituto de Investigaciones en Biodiversidad y Medioambiente; Argentina
Fil: Ferrando, Matías. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto de Medicina y Biología Experimental de Cuyo; Argentina
Fil: López Fontana, Constanza Matilde. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto de Medicina y Biología Experimental de Cuyo; Argentina
Fil: Caron, Ruben Walter. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto de Medicina y Biología Experimental de Cuyo; Argentina
Fil: Bruna, Flavia Alejandra. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto de Medicina y Biología Experimental de Cuyo; Argentina
Fil: Pistone Creydt, Virginia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto de Medicina y Biología Experimental de Cuyo; Argentina
description Among the different types of cells that surround renal epithelial cells, human renal adipose tissue (hAT) is one of the most abundant. We have previously characterized the expression of different proteins in hAT (adiponectin, adiponectin receptor 1, leptin, leptin receptor, perilipin 1, and metalloprotease (1). In this study, we evaluated if the differential proteins expression as a whole was sufficient to separate healthy patients from patients with kidney cancer, using unsupervised machine learning algorithms; and the correlation between adiponectin and leptin expression with clinical characteristics of kidney cancer patients. Considering the six biological variables evaluated in the different hAT fragments, we were able to separate healthy from kidney tumor patients by unsupervised machine learning algorithms projection. In addition, a decrease in adiponectin expression was found in patients with a more undifferentiated tumor as well as in patients with a history of smoking. Also, there was a positive correlation between leptin, tumor size and difficulty in tumor dissection. The parameters that increase the difficulty in dissection are male sex, smoking history, tumor size and the fat striation degree in imaging studies. Moreover, PAT (perirenal adipose tissue)-related adipokine signatures reflectsystemicmetabolic dysfunction, including features of metabolic syndrome, offering additional value for anticipating surgical complexity and refining prognostic stratification. This project represents a new way of looking at kidney cancer, by correlating clinical features with specific biomarkers, we may be able to identify patterns that might predict howthediseasewilldevelop.Thiscouldleadtomoreaccurateprognoses and more effective treatments.
publishDate 2025
dc.date.none.fl_str_mv 2025-11-02
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/289974
Romeo, Leonardo Rafael; Nuñez, Matias; Ferrando, Matías; López Fontana, Constanza Matilde; Caron, Ruben Walter; et al.; Correlations between biological markers of the perirenal adipose tissue and clinical features of patients with localized kidney cancer; Frontiers Media; Frontiers in Medicine; 12; 2-11-2025; 1-8
2296-858X
CONICET Digital
CONICET
url http://hdl.handle.net/11336/289974
identifier_str_mv Romeo, Leonardo Rafael; Nuñez, Matias; Ferrando, Matías; López Fontana, Constanza Matilde; Caron, Ruben Walter; et al.; Correlations between biological markers of the perirenal adipose tissue and clinical features of patients with localized kidney cancer; Frontiers Media; Frontiers in Medicine; 12; 2-11-2025; 1-8
2296-858X
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
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info:eu-repo/semantics/altIdentifier/doi/10.3389/fmed.2025.1676630
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
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dc.publisher.none.fl_str_mv Frontiers Media
publisher.none.fl_str_mv Frontiers Media
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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