Abstract
Although many studies have been conducted on single gene therapies in cancer patients, the reality is that tumor arises from different coordinating protein groups. Unveiling perturbations in protein interactome related to the tumor formation may contribute to the development of effective diagnosis, treatment strategies, and prognosis. In this study, considering the clinical and transcriptome data of three Renal Cell Carcinoma (RCC) subtypes (ccRCC, pRCC, and chRCC) retrieved from The Cancer Genome Atlas (TCGA) and the human protein interactome, the differential protein–protein interactions were identified in each RCC subtype. The approach enabled the identification of differentially interacting proteins (DIPs) indicating prominent changes in their interaction patterns during tumor formation. Further, diagnostic and prognostic performances were generated by taking into account DIP clusters which are specific to the relevant subtypes. Furthermore, considering the mesenchymal epithelial transition (MET) receptor tyrosine kinase (PDB ID: 3DKF) as a potential drug target specific to pRCC, twenty-one lead compounds were identified through virtual screening of ZINC molecules. In this study, we presented remarkable findings in terms of early diagnosis, prognosis, and effective treatment strategies, that deserve further experimental and clinical efforts.
Highlights
Kidney cancer is among the 10 most common cancers in adults and renal cell carcinoma (RCC) shows a steady increase in prevalence [1]
We offer drug candidates that showed an inhibitory effect on mesenchymal epithelial transition (MET) receptor tyrosine kinase which is one of the differentially interacting proteins (DIPs) that have activated interactions in the case of pRCC
We examined transcriptomic data for three common subtypes of RCC with an adequate number of samples (n > 24) in both normal and tumor groups
Summary
Kidney cancer is among the 10 most common cancers in adults and renal cell carcinoma (RCC) shows a steady increase in prevalence [1]. The most prevalent one among kidney cancers is ccRCC which represents 75–80% of RCC [3] and derives its name from its clear cytoplasm on the pathologic analysis [4]. More than sixty percent of patients are incidentally noticed at imaging investigations [5], and metastasis has already begun in nearly 20–30% of the patients when diagnosed [6]. In this context, biomarker identification from secretion fluids is extremely important for early diagnosis. All types of kidney cancers are different in many respects including tumor location within the kidney, the cell type from which they originate, and alterations on their genotype, making it even more crucial to characterize the pathology of each type and to identify specific proteins as druggable targets
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