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

## TL;DR

This study explores how proteins in kidney fat tissue correlate with kidney cancer features, potentially improving diagnosis and treatment.

## Contribution

The study introduces a new approach to kidney cancer by linking clinical features with PAT biomarkers for better prognosis and treatment.

## Key findings

- Unsupervised machine learning separated healthy and kidney tumor patients based on protein expression in hAT.
- Lower adiponectin expression was linked to undifferentiated tumors and smoking history.
- Leptin expression correlated with tumor size and dissection difficulty, influenced by factors like male sex and smoking.

## Abstract

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 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.