In silico meta-analyses
for putative biomarkers associated with Renal Cancer
Bhardwaj Anuradha and Ashra Himani
Res. J. Biotech.; Vol. 21(2); 286-292;
doi: https://doi.org/10.25303/212rjbt2860292; (2026)
Abstract
Renal cell carcinoma (RCC) is the most prevalent form of renal malignancy accounting
for around 3% of all adult malignancies. Although new targeted medications are continually
being developed, they are not able to treat all patients. Thus, a comprehensive
investigation of the mode of progression and biomolecular mechanism of renal cancer
is a need to identify its novel targets for better diagnostics and treatment strategies.
We aim to identify the potential biomarkers of renal cancer, infer the cellular
processes and pathways influenced by renal cancer, using in silico methods. We analysed
three profiles of gene expression (GSE168845, GSE66270 and GSE781) from Gene Expression
Omnibus (GEO) database to investigate possible treatment targets for RCC. Differentially
expressed genes (DEGs) between RC and normal renal tissues were found using the
GEO2R web program. Gene Ontology (GO) and KEGG pathway enrichment analysis were
carried out using the Enrichr web-based tool. The DEGs were then organized into
a protein-protein network using the STRING database tool. From an interaction network
of multiple genes, we filtered the critical hub genes using the CytoHubba application
of Cytoscape. To verify the predictive-value of the hub genes, we performed survival-analysis
using a renal cancer database by plotting the Kaplan-Meier plots.
We identified a set of 30 DEGs (24 upregulated genes and 6 downregulated genes).
Most of the DEGs were active in signaling and transportation mechanisms. The PPI
network and Cytohubba results revealed ten critical hub genes including UMOD, SLC34A1,
SLC22A6, SLC12A1, RHCG, NPHS2, KCNJ1, G6PC, FABP1 and ALB. The Kaplan-Meier plotter
database confirmed that few genes enhanced the chances of survival, while others
decreased them and some genes had no effect on RC patient survival. The identification
of DEGs and the enrichment of their biological functions/key pathways offers more
precise information about RC and allows identification of crucial biomarkers which
will aid future research and help in efficient therapeutic strategies.
