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. 2021 Feb 23;11(2):158.
doi: 10.3390/jpm11020158.

Differential Interactome Proposes Subtype-Specific Biomarkers and Potential Therapeutics in Renal Cell Carcinomas

Aysegul Caliskan  1   2 , Gizem Gulfidan  1 , Raghu Sinha  3 , Kazim Yalcin Arga  1
Affiliations
Free PMC article

Differential Interactome Proposes Subtype-Specific Biomarkers and Potential Therapeutics in Renal Cell Carcinomas

Aysegul Caliskan et al. J Pers Med. .
Free PMC article

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.

Keywords: diagnostic biomarker; docking; prognostic biomarker; protein interactome; renal cancers; virtual screening.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Differential interactome networks reconstructed with differential protein–protein interactions (dPPIs) around differentially interacting proteins (DIPs) in three Renal Cell Carcinoma RCC subtypes. Red nodes represent DIPs specific to the subtype of interest. ccRCC: Clear Cell Renal Carcinoma; pRCC: Papillary Renal Cell Carcinoma; chRCC: Chromophobe Renal Cell Carcinoma.
Figure 2
Figure 2
Bubble plots indicating protein expression levels of DIPs specific to three subtypes in different body fluids including serum, plasma, saliva, and urine. The x-axis indicates subtypes while the y-axis indicates protein symbols.
Figure 3
Figure 3
Diagnostic and prognostic performance analysis results for Renal Cell Carcinoma (RCC) subtypes. (A) Principal component analyses (PCA) plots, visualized by considering s-DIPs, indicating the individual differences in the gene expression profiles in tumor samples among the subtypes. (B) Kaplan–Meier curves estimating patients’ survival for three subtypes based on categorization of patients into high- and low-risk groups via prognostic index. ccRCC: Clear Cell Renal Carcinoma; pRCC: Papillary Renal Cell Carcinoma; chRCC: Chromophobe Renal Cell Carcinoma; HR: Hazard Ratio; PC: Principal component.
Figure 4
Figure 4
Virtual screening to identify potential hit drug candidates for pRCC. (A) Superposition of X-ray crystal structures of MET retrieved from RCSB for the validation of docking protocol. (B) 2D structures of ZINC molecules that showed high binding affinities to MET protein in virtual screening.
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