Common molecular links and therapeutic insights between type 2 diabetes and kidney cancer

Abstract

Introduction: Type 2 diabetes (T2D) is considered as a risk factor for kidney cancer (KC). However, so far, there is no study in the literature that has explored genetic factors through which T2D drive the development and progression of KC. Therefore, this study attempted to explore T2D- and KC-causing shared key genes (sKGs) for revealing shared pathogenesis and therapeutic drugs as their common treatments.

Methods: The integrated bioinformatics and system biology approaches were utilized in this study. The statistical LIMMA approach was used based web-tool GEO2R to detect differentially expressed genes (DEGs) through transcriptomics analysis. Then upregulated and downregulated DEGs for T2D and KC were combined to obtained shared DEGs (sDEGs) between T2D and KC. The STRING database was used to construct the protein-protein interaction (PPI) network of sDEGs. Then Cytohubba plugin-in Cytoscape were used in the PPI network to disclose the sKGs based on different topological measures. The RegNetwork database was used in NetworkAnalyst to analyze co-regulatory networks of sKGs with transcription factors (TFs) and micro-RNAs to identify key TFs and miRNAs as the transcriptional and post-transcriptional regulators of sKGs, respectively. AutoDock Vina is a tool used for molecular docking. ADME/T properties were 24 assessed using pkCSM and SwissADME.

Results: At first, 74 shared DEGs (sDEGs) were identified that can distinguish both KC and T2D patients from control samples. Through protein-protein interaction (PPI) network analysis, top-ranked 6 sDEGs (CD74, TFRC, CREB1, MCL1, SCARB1 and JUN) were detected as the sKGs that drive both KC and T2D development and progression. The most common sKG 'CD74' is associated with key pathways, such as NF-κB signaling transduction, apoptotic processes, B cell proliferation. Differential expression patterns of sKGs validated by independent datasets of NCBI database for T2D and TCGA and GTEx databases for KC. Furthermore, sKGs were found to be significant at several CpG sites in DNA methylation studies. Regulatory network analysis identified three TFs proteins (SMAD5, ATF1 and NR2F1) and two miRNAs (hsa-mir-1-3p and hsa-mir-34a-5p) as the regulators of sKGs. The enrichment analysis of sKGs with KEGG-pathways and Gene Ontology (GO) terms revealed some crucial shared pathogenetic mechanisms (sPM) between two diseases. Finally, sKGs-guided four potential therapeutic drug molecules (Imatinib, Pazopanib hydrochloride, Sorafenib and Glibenclamide) were recommended as the common therapies for KC with T2D.

Conclusion: The results of this study may be useful resources for the diagnosis and therapy of KC with the co-existence of T2D.

Fig 1. The Schematic diagram about the relationship between T2D and KC.

It highlights how hyperglycemia and IR in T2D contribute to oxidative stress, inflammation, and altered immune responses, which may promote KC development.

Fig 2. The Protein–protein interaction (PPI) network of shared DEGs (sDEGs) for T2D on KC to identify sKGs.

Orange colour nodes indicate the sKGs. This network highlights the complex interactions among sDEGs and identifies sKGs. Orange colour nodes indicate the sKGs. These sKGs may play critical roles in the molecular crosstalk linking T2D and KC pathogenesis.

Fig 3. sKGs regulatory network (A) The JASPAR database-based on sKGs-TFs interaction network.

(B) The TarBase database-based on miRNA-sKGs interaction network. sKGs are shown as green color octagons in both A and B, while TFs and miRNAs are displayed as pink color hexagons in A and B, respectively.

Fig 4. The Ramachandran plot illustrates the phi-psi angles for each residue of beta-tubulin.

The red areas show the most favorable phi-psi angle combinations. The white area shows an unfavorable phi-psi combination.

Fig 5. The molecular docking score matrix displays strong binding affinities between target proteins and drug agents represented in red, while weak bindings are shown in green.

The X-axis represents the top 30 ranked drug agents (selected from 156), while the Y-axis shows the proposed receptors in order.

Fig 6. To confirm the accuracy of the docking procedure, the co-crystallized ligand structure from the TFRC, and MCL1 (PDB ID: 60KD, and 3WIX) was re-docked.

As demonstrated, the docked ligand (red) closely resembles the crystallized ligand (purple) (RMSD = 1.398 Å, and 0.539 respectively).

Fig 7. Some important docking results with the protein-ligand complexes.

Top-ranked four drug-target complexes highlighting their 3-dimension (3D) view and interacting residues. Complexes: (a) indicated MCL1-Imatinib, (b) NR2F1- Pazopanib hydrochloride, (c) SCRB1-Sorafenib, and (d) TFRC- Glibenclamide.

Fig 8. Verification of the suggested shared key genes (sKGs) and potential therapeutic agents for T2D and KC through the literature review.

(A) Verification of the suggested T2D and KC-causing sKGs (B) Verification of the suggested drug-agents.