BRIEF RESEARCH REPORT article
Front. Med.
Sec. Pathology
Volume 12 - 2025 | doi: 10.3389/fmed.2025.1676630
Correlations between biological markers of the perirenal adipose tissue and clinical features of patients with localized kidney cancer
Provisionally accepted
Leonardo Rafael RomeoMatías Nuñez
Matías FerrandoConstanza Matilde López-Fontana
Rubén Walter Carón
Flavia Bruna
Virginia Pistone-Creydt*- CONICET Mendoza, Mendoza, Argentina
Select one of your emails
You have multiple emails registered with Frontiers:
Notify me on publication
Please enter your email address:
If you already have an account, please login
You don't have a Frontiers account ? You can register here
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 fat striation degree in imaging studies. Moreover, PAT (perirenal adipose tissue)-related adipokine signatures reflect systemic metabolic 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 how the disease will develop. This could lead to more accurate prognoses and more effective treatments.
Keywords: Human adipose tissue, renal cancer, Epithelial-stromal interactions, Machinelearning, Leptin, Adiponectin
Received: 30 Jul 2025; Accepted: 09 Oct 2025.
Copyright: © 2025 Romeo, Nuñez, Ferrando, López-Fontana, Carón, Bruna and Pistone-Creydt. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Virginia Pistone-Creydt, vpistonec@gmail.com
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.