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Identification of diagnostic biomarkers in kidney cancer using RNAseq data analysis and their confirmation by qRT-PCR
Fateme Sadat Zendehdel, Seyyed Abdolhamid Angaji , Behnaz Beikzadeh, Behzad Narouie , Mahdi Mohammadi,
Volume 82, Issue 6 (9-2024)
Abstract
Background: Clear renal cell carcinoma (ccRCC) is the most common malignant kidney tumor and has a high mortality rate. The pathogenesis of this cancer is complex and efficient biomarkers for its diagnosis and prediction are limited. This study aimed to identify predictive genes in ccRCC through analysis and laboratory validation ccRCC is the most common malignant kidney tumor and has a high mortality rate.
Methods: The present study was a case-control study in which samples were taken from patients and healthy individuals from Labafi Nejad Hospital in Tehran between October 2012 and April 2014, and laboratory tests were performed on the samples.
First, genes with differential expression in ccRCC patients were identified by bioinformatics using gene expression profile data from the Gene Expression Omnibus (GEO) database with accession number GSE213324. Data analysis was performed using Galaxy web server, protein-protein interactions were checked using Cytoscape and STRING app software, and finally two genes were selected for real-time PCR testing.
Results: Analysis identified 4,065 genes with differential expression in RCC tissues compared to healthy tissues. These genes are involved in immune responses and renal disease pathways, suggesting their potential role in disease development. After constructing the protein-protein interaction network and identifying differentially expressed genes in kidney tissue and blood, two genes, MTTP and CALCA, were selected for further investigation. In the Mann-Whitney U test, the expression of the CALCA gene decreased significantly in the patient group (P<0.05). On the other hand, the MTTP gene showed a decrease in expression, but not significantly. The AUC calculated to evaluate the diagnostic accuracy for the CALCA gene was 0.64 and significant (P<0.05), demonstrating its potential as a useful biomarker for ccRCC diagnosis. However, the AUC for the MTTP gene was not significant.
Conclusion: The reduction in CALCA expression could serve as a useful biomarker for diagnosing ccRCC.

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