Drug Repurposing Research Articles

8308 Up-regulation of Diacylglycerol kinase gamma (DGKG) in aggressive recurrent pituitary tumors could be targeted by Dasatinib reducing cell proliferation and induced apoptosis

Abstract Disclosure: K. Taniguchi-Ponciano: None. I. Remba: None. M. Loza-Mejia: None. J. Salazar: None. A. Mendez-Perez: None. C. Aguilar-Flores: None. A. Chavez-Gonzalez: None. E. Ortiz-Reyes: None. L. Bonifaz: None. D. Marrero-Rodríguez: None. M. Mercado: None. Pituitary tumors (PT) represent 15% of all intracranial tumors. Some PTs exhibit an aggressive behavior, growing rapidly and invading surrounding tissues, and are frequently resistant to multimodal treatment showing multiple recurrences. Pituitary tumors often recur after initial surgery, particularly when invasive, precluding complete resection of the lesion, which can only be achieved in ∼40-50% of all patients.We have previously characterized transcriptome and exome from 22 tumor lesions from 11 patients by RNA- and DNAseq and methylome profile by microarrays of aggressive, both primary and recurrent PT from the same patient to identify molecular pharmacological targets.Among the differentially expressed genes in most recurrent tumors we found those related to fatty acid biosynthesis and metabolism, phosphatidylinositol, glycerophospholipid and phospholipase D signaling. Importantly, one gene found in most of the lipid-related pathways was diacylglycerol kinase gamma (DGKG). DGKG gene expression was confirmed by immunofluorescence and did not seem to be regulated by DNA methylation. And DGKG showed allelic c.G947A:p.R316K variant in all primary and recurrent tumor tissues analyzed, more research is needed to understand genetic variant impact on protein function. We then performed DGKG molecular docking for FDA-approved drug repurposing. Human DGKG tridimensional model was downloaded from AlphaFold and Autodock Vina, Molegro Virtual Docker, PyMol v2.5.2 program and the Protein-Ligand Interaction Profiler web tool were used. The in silico ADMET profiles were generated in ADMETLab 2.0 using only FDA approved drugs. We identified Dasatinib as a strong candidate for DGKG targeting. We then exposed GH3 cell line to increasing concentrations of Dasatinib (1, 2.5 y 5 μM) using DMSO as vehicle. After cell viability was assessed, approximatively 31% of all live GH3 cells were at proliferative state without any treatment, that was taken as our basal quantity to compare the GH3 cells treated with the TKI’s. Dasatinib reduced approximatively 28% the cell proliferation at 1 μM (p=0.0048), at 2.5 μM reduced nearly 50% of cell proliferation (p=0.003) and at 5 μM inhibited nearly 98% of cell proliferation (p=0.0395) by means of Click-iT EdU Cell proliferation Kit. We then, evaluated Dasatinib apoptosis induction capacity. Interestingly, the 1 μM Dasatinib concentration induced apoptosis (p=0.0001) as concentrations increased, cell death also increased considerably (p=0.0001) measured by Anexin V-FITC and DAPI assay. The RNAseq data from GH3 cells exposed to Dasatinib show diminished gene expression of cell cycle related genes and glycolysis.In conclusion, we observed DGKG upregulated in recurrent aggressive pituitary tumors participating in lipid metabolism pathways and could be targeted by Dasatinib at very low doses. Presentation: 6/3/2024

Drug repositioning identifies potential autophagy inhibitors for the LIR motif p62/SQSTM1 protein

Autophagy is a critical cellular process for degrading damaged organelles and proteins under stressful conditions and has casually been shown to contribute to tumor survival and drug resistance. Sequestosome-1 (SQSTM1/p62) is an autophagy receptor that interacts with its binding partners via the LC3-interacting region (LIR). The p62 protein has been a highly researched target for its critical role in selective autophagy. In this study, we aimed to identify FDA-approved drugs that bind to the LIR motif of p62 and inhibit its LIR function, which could be useful targets for modulating autophagy. To this, the homology model of the p62 protein was predicted using biological data, and docking analysis was performed using Molegro Virtual Docker and PyRx softwares. We further assessed the toxicity profile of the drugs using the ProTox-II server and performed dynamics simulations on the effective candidate drugs identified. The results revealed that the kanamycin, velpatasvir, verteporfin, and temoporfin significantly decreased the binding of LIR to the p62 protein. Finally, we experimentally confirmed that Kanamycin can inhibit autophagy-associated acidic vesicular formation in breast cancer MCF-7 and MDA-MB 231 cells. These repositioned drugs may represent novel autophagy modulators in clinical management, warranting further investigation.

Sex differences in the orofacial antinociceptive effect of metformin and the role of transient receptor potential channels.

Metformin is classified as a biguanide and is used in the treatment of type 2 diabetes. It is used worldwide and has been investigated in drug repositioning. The present study aims to investigate whether there is sexual dimorphism in the orofacial antinociceptive effect of metformin and the participation of TRP channels. Acute nociceptive behavior was induced by administering cinnamaldehyde or capsaicin to the upper lip. Nociceptive behavior was assessed through orofacial rubbing, and the effects of pre-treatment with metformin (125 or 250mg/Kg) or vehicle (control) were tested on the behavior. Nociceptive behavior was also induced by formalin injected into the temporomandibular joint. The chronic pain model involved infraorbital nerve transection (IONX) was evaluated using Von Frey electronic filaments. Trpv1 gene expression was analyzed in the nerve ganglion. Docking experiments were performed. Metformin, but not the vehicle, produced antinociception (p < 0.0001) in all acute nociceptive behaviors in both sexes, and these effects were attenuated by the TRPV1 antagonist capsazepine and the TRPA1 antagonist HC-030031. In IONX with better (**p < 0.01, ****p < 0.0001 vs. control) results in females. TRPV1 gene expression was observed in the metformin treated group (*p < 0.05 vs. control). Docking experiments revealed that metformin may interact with TRPV1 and TRPA1 channels. Metformin promotes orofacial antinociception in both sexes in acute pain and is more effective in chronic pain in females than in males, through the modulation of TRPV1 and TRPA1 channels. These preclinical findings suggest a potential repositioning of metformin as an analgesic agent in acute and chronic orofacial pain states.

Drug Repurposing: Research Progress of Niclosamide and Its Derivatives on Antibacterial Activity.

The development of antibiotic resistance complicates the treatment of infectious diseases and is a global public health threat. However, drug repurposing can address this resistance issue and reduce research and development costs. Niclosamide is a salicylanilide compound approved by the Food and Drug Administration (FDA), and it has been used clinically for treating parasitic infections for many years. Recent studies have shown that niclosamide can inhibit bacterial and fungus activity by affecting the quorum sensing system, biofilm formation, cell membrane potential, and other mechanisms. Here, we discuss recent advances in the antimicrobial applications of niclosamide and its derivatives to provide new perspectives in treating infectious diseases.

A High-Throughput Drug Repurposing Strategy to Treat TBX2 and/or TBX3 Dependent Cancers.

The highly homologous T-box transcription factors TBX2 and TBX3 are critical for embryonic development, and their overexpression in postnatal tissues contributes to a wide range of malignancies, including melanoma and rhabdomyosarcoma. Importantly, when TBX2 and TBX3 are depleted in cancers where they are overexpressed, the malignant phenotype is inhibited, and they have therefore been regarded as druggable targets. However, the time and costs associated with de novo drug development are challenging and result in drugs that are costly, especially for patients in low- and middle-income countries. In the current study, we therefore combined a targeted and drug repurposing approach to identify drugs that are expected to be more efficacious and cost-effective with significantly reduced side effects. A high-throughput cell-based immunofluorescence screen was performed to identify drugs in the Pharmakon 1600 drug library that can negatively regulate TBX2 and/or TBX3 levels. "Hit" drugs were validated for their effect on TBX2/TBX3 levels and cytotoxicity in TBX2/TBX3-dependent melanoma and rhabdomyosarcoma cells. To this end, immunofluorescence, western blotting, quantitative real-time PCR, and MTT cell viability assays were performed. Niclosamide, piroctone olamine, and pyrvinium pamoate, were identified as TBX2 and/or TBX3-targeting drugs, and they exhibited cytotoxicity in a TBX2/TBX3-dependent manner. Furthermore, these "Hit" drugs were shown to induce senescence and/or apoptosis. Niclosamide, piroctone olamine, and pyrvinium pamoate are promising, cost-effective therapeutic agents for the treatment of TBX2/TBX3-dependent cancers.

In Silico Screening of FDA‐Approved Anthelmintic Drugs Against LuxR Protein as Quorum‐Sensing Inhibitors and Exploration of Their Binding Mechanism and Electronics Properties

AbstractTreating bacterial infections triggered by multidrug‐resistant bacteria is a global priority. Issue of multidrug resistance can be managed by inhibiting bacterial quorum sensing mechanism. Quorum‐sensing inhibitors (QSIs) are promising alternatives to antibiotics. Herein, a library of FDA‐approved anthelmintic medications was screened using in silico techniques for LuxR protein associated with the quorum‐sensing (QS) mechanism of Staphylococcus aureus. Drug repurposing can reduce the time and financial risk of drug development. Seven FDA‐approved drugs (Fenbendazole, Mebendazole, Flubendazole, Praziquantel, Oxyclozanide, Rafoxanide, and Albendazole) showed strong binding affinity to the LuxR protein with binding free energy less than −6.00 kcal/mol. Molecular dynamic simulation (100 ns) of the docked complexes revealed that Fenbendazole and Rafoxanide remain at the binding pocket of the LuxR protein most of the time. During the whole simulation period, backbone of all seven complexes displayed small fluctuation in the RMSD and Rg. The free energy landscape of LuxR–Rafoxanide and LuxR–Fenbendazole complexes showed promising stability compared to the rest of the complexes. Both the complexes showed excellent MMPBSA binding free energy: −92.882 ± 15.636 kJ/mol (LuxR–Rafoxanide) and −74.873 ± 9.233 kJ/mol (LuxR–Fenbendazole). The reactivity of the ligands was ascertained from their DFT‐based various descriptors.

DTA Atlas: A massive-scale drug repurposing database

The drug development process is costly and time-consuming. Repurposing existing approved drugs, an efficient and cost-effective strategy, involves assessing numerous drug-protein pairs to uncover new interactions. While modern in silico methods enhance scalability, an open database for projected drug-target interactions across the entire human proteome is still lacking. In this work, we introduce an open database of predicted drug-target interactions, termed DTA Atlas, covering the entire human proteome as well as a wide range of marketed drugs, resulting in over 220 million drug-target pairs. The database integrates 4 billion affinity predictions from advanced deep neural networks and offers a user-friendly web interface, enabling users to explore drug-target affinity predictions for the human proteome. To the best of our knowledge, DTA Atlas represents the first comprehensive collection of drug-target binding strength predictions. It is open-source and can serve as an important resource for drug development, drug repurposing, toxicity studies and more.

Implications of trinodal inhibitions and drug repurposing in MAPK pathway: A putative remedy for breast cancer

Breast cancer has been one of the supreme causes of cancer-related deaths among women worldwide. To make the case even more compounded, due to innate or acquired causes, cancer cells often develop resistance against the available chemotherapy or monotargeted treatments. This resistance is concomitant with increased activation of the MAPK (mitogen-activated protein kinase) signaling pathway. This study simultaneously targets three imperative intermediates in this pathway using molecular docking and real-time simulation. Docking was performed via the integrated AutoDock Vina 1.1.2 & 1.2.5 of the PyRx software, while the Discovery Studio (BIOVIA) v24.1.0.23298 was utilized to conduct the simulation. The aim is to investigate the therapeutic prospects of known potential inhibitors of the targeted intermediates and repurposable drugs to comprehend the effectiveness of targeting these trinodes simultaneously. The target points were deemed to be PDPK1 (3-phosphoinositide-dependent protein kinase 1), ERK1/2 (extracellular signal-related protein kinases 1/2), and mTOR (mammalian target of Rapamycin). Our study reveals that out of the candidate inhibitors chosen for each node, MP7 exhibited the most superior binding affinities for all three: −10.918 kcal/mol for PDPK1, −10.224 kcal/mol for ERK1, −10.134 kcal/mol for ERK2, and −9.2 kcal/mol for mTOR (via AutoDock Vina 1, .2.5). Some scores with MP7 were often higher than the available single-targeted drugs for different nodes in the MAPK pathway. Additionally, a total of 1867 repurposed analgesic, antibiotic, and antiparasitic drugs, including Zavegepant (−13.399 kcal/mol for PDPK1), Adozelesin (−11.74 kcal/mol for mTOR) and Modoflaner (−11.29 kcal/mol for PDPK1), showed promising binding energetics while targeting our triad points than other compounds used. This approach prompts for mitigating not only breast cancer but other elusive diseases as well, with state-of-the-art multitargeted therapies coupled with bioinformatic strategies.

Exploring the Artificial Intelligence and Its Impact in Pharmaceutical Sciences: Insights Toward the Horizons Where Technology Meets Tradition.

The technological revolutions in computers and the advancement of high-throughput screening technologies have driven the application of artificial intelligence (AI) for faster discovery of drug molecules with more efficiency, and cost-friendly finding of hit or lead molecules. The ability of software and network frameworks to interpret molecular structures' representations and establish relationships/correlations has enabled various research teams to develop numerous AI platforms for identifying new lead molecules or discovering new targets for already established drug molecules. The prediction of biological activity, ADME properties, and toxicity parameters in early stages have reduced the chances of failure and associated costs in later clinical stages, which was observed at a high rate in the tedious, expensive, and laborious drug discovery process. This review focuses on the different AI and machine learning (ML) techniques with their applications mainly focused on the pharmaceutical industry. The applications of AI frameworks in the identification of molecular target, hit identification/hit-to-lead optimization, analyzing drug-receptor interactions, drug repurposing, polypharmacology, synthetic accessibility, clinical trial design, and pharmaceutical developments are discussed in detail. We have also compiled the details of various startups in AI in this field. This review will provide a comprehensive analysis and outline various state-of-the-art AI/ML techniques to the readers with their framework applications. This review also highlights the challenges in this field, which need to be addressed for further success in pharmaceutical applications.

TarKG: a comprehensive biomedical knowledge graph for target discovery.

Target discovery is a crucial step in drug development, as it directly affects the success rate of clinical trials. Knowledge graphs (KGs) offer unique advantages in processing complex biological data and inferring new relationships. Existing biomedical KGs primarily focus on tasks such as drug repositioning and drug-target interactions, leaving a gap in the construction of KGs tailored for target discovery. We established a comprehensive biomedical KG focusing on target discovery, termed TarKG, by integrating seven existing biomedical KGs, nine public databases, and traditional Chinese medicine knowledge databases. TarKG consists of 1143313 entities and 32806467 relations across 15 entity categories and 171 relation types, all centered around 3 core entity types: Disease, Gene, and Compound. TarKG provides specialized knowledges for the core entities including chemical structures, protein sequences, or text descriptions. By using different KG embedding algorithms, we assessed the knowledge completion capabilities of TarKG, particularly for disease-target link prediction. In case studies, we further examined TarKG's ability to predict potential protein targets for Alzheimer's disease (AD) and to identify diseases potentially associated with the metallo-deubiquitinase CSN5, using literature analysis for validation. Furthermore, we provided a user-friendly web server (https://tarkg.ddtmlab.org) that enables users to perform knowledge retrieval and relation inference using TarKG. TarKG is accessible at https://tarkg.ddtmlab.org.

Repurposing doxycycline for Alzheimer's treatment: Challenges from a nano-based drug delivery perspective

Drug repurposing, also known as drug repositioning, involves identifying new applications for drugs whose effects in a disease are already established. Doxycycline, a broad-spectrum antibiotic belonging to the tetracycline class, has demonstrated potential activity against neurodegenerative diseases like Alzheimer's and Parkinson's. However, despite its promise, the repurposed use of doxycycline encounters challenges in reaching the brain in adequate concentrations to exert its effects. To address this issue, nanostructured systems offer an innovative approach that can enhance brain targeting and the desired therapeutic outcomes. This review discusses the advances in doxycycline repurposing for Alzheimer's disease, presenting physicochemical and biological aspects that permeate doxycycline's repositioning and its application in nano-based delivery systems.

PocketDTA: an advanced multimodal architecture for enhanced prediction of drug-target affinity from 3D structural data of target binding pockets.

Accurately predicting the drug-target binding affinity (DTA) is crucial to drug discovery and repurposing. Although deep learning has been widely used in this field, it still faces challenges with insufficient generalization performance, inadequate use of 3D information, and poor interpretability. To alleviate these problems, we developed the PocketDTA model. This model enhances the generalization performance by pre-trained models ESM-2 and GraphMVP. It ingeniously handles the first 3 (top-3) target binding pockets and drug 3D information through customized GVP-GNN Layers and GraphMVP-Decoder. In addition, it uses a bilinear attention network to enhance interpretability. Comparative analysis with state-of-the-art (SOTA) methods on the optimized Davis and KIBA datasets reveals that the PocketDTA model exhibits significant performance advantages. Further, ablation studies confirm the effectiveness of the model components, whereas cold-start experiments illustrate its robust generalization capabilities. In particular, the PocketDTA model has shown significant advantages in identifying key drug functional groups and amino acid residues via molecular docking and literature validation, highlighting its strong potential for interpretability. Code and data are available at: https://github.com/zhaolongNCU/PocketDTA.

Topical Anti-ulcerogenic Effect of the Beta-adrenergic Blockers on Diabetic Foot Ulcers: Recent Advances and Future Prospectives.

Patients with diabetes suffer from major complications like Diabetic Retinopathy, Diabetic Coronary Artery Disease, and Diabetic Foot ulcers (DFUs). Diabetes complications are a group of ailments whose recovery time is especially delayed, irrespective of the underlying reason. The longer duration of wound healing enhances the probability of problems like sepsis and amputation. The delayed healing makes it more critical for research focus. By understanding the molecular pathogenesis of diabetic wounds, it is quite easy to target the molecules involved in the healing of wounds. Recent research on beta-adrenergic blocking drugs has revealed that these classes of drugs possess therapeutic potential in the healing of DFUs. However, because the order of events in defective healing is adequately defined, it is possible to recognize moieties that are currently in the market that are recognized to aim at one or several identified molecular processes. The aim of this study was to explore some molecules with different therapeutic categories that have demonstrated favorable effects in improving diabetic wound healing, also called the repurposing of drugs. Various databases like PubMed/Medline, Google Scholar and Web of Science (WoS) of all English language articles were searched, and relevant information was collected regarding the role of beta-adrenergic blockers in diabetic wounds or diabetic foot ulcers (DFUs) using the relevant keywords for the literature review. The potential beta-blocking agents and their mechanism of action in diabetic foot ulcers were studied, and it was found that these drugs have a profound effect on diabetic foot ulcer healing as per reported literatures. There is a need to move forward from preclinical studies to clinical studies to analyze clinical findings to determine the effectiveness and safety of some beta-antagonists in diabetic foot ulcer treatment.

Benchmarking reverse docking through AlphaFold2 human proteome.

Predicting the binding of ligands to the human proteome via reverse-docking methods enables the understanding of ligand's interactions with potential protein targets in the human body, thereby facilitating drug repositioning and the evaluation of potential off-target effects or toxic side effects of drugs. In this study, we constructed 11 reverse docking pipelines by integrating site prediction tools (PointSite and SiteMap), docking programs (Glide and AutoDock Vina), and scoring functions (Glide, Autodock Vina, RTMScore, DeepRMSD, and OnionNet-SFCT), and then thoroughly benchmarked their predictive capabilities. The results show that the Glide_SFCT (PS) pipeline exhibited the best target prediction performance based on the atomic structure models in AlphaFold2 human proteome. It achieved a success rate of 27.8% when considering the top 100 ranked prediction. This pipeline effectively narrows the range of potential targets within the human proteome, laying a foundation for drug target prediction, off-target assessment, and toxicity prediction, ultimately boosting drug development. By facilitating these critical aspects of drug discovery and development, our work has the potential to ultimately accelerate the identification of new therapeutic agents and improve drug safety.

Exploring potential biomarkers and lead molecules in gastric cancer by network biology, drug repurposing and virtual screening strategies.

Gastric cancer poses a significant global health challenge, necessitating innovative approaches for biomarker discovery and therapeutic intervention. This study employs a multifaceted strategy integrating network biology, drug repurposing, and virtual screening to elucidate and expand the molecular landscape of gastric cancer. We identified and prioritized key genes implicated in gastric cancer by utilizing data from diverse databases and text-mining techniques. Network analysis underscored intricate gene interactions, emphasizing potential therapeutic targets such as CTNNB1, BCL2, TP53, etc, and highlighted ACTB among the top hub genes crucial in disease progression. Drug repurposing on 626 FDA-approved drugs for digestive system-related cancers revealed Norgestimate and Nimesulide as likely top candidates for gastric cancer, validated by molecular docking and dynamics simulations. Further, combinatorial synthesis of scaffold libraries derived from known chemotypes generated 56,160 virtual compounds, of which 76 new compounds were prioritized based on promising binding affinities and interactions at critical residues. Hotspot residue analysis identified GLU 214 and others as essential for ligand binding stability, enhancing compound efficacy and specificity. These findings support the therapeutic potential of targeting beta-actin protein in gastric cancer treatment, suggesting a future for further experimental validation and clinical translation. In conclusion, this study highlights the potential of repurposable drugs and virtual screening which can be used in combination with existing anti-gastric cancer drugs for gastric cancer therapy, emphasizing the role of computational methodologies in drug discovery.

Gauging Expert Perspectives: A Survey on AI Integration in Pharmacovigilance

Abstract: The ever-growing landscape of drug safety monitoring demands innovative solutions. This paper explores how Artificial Intelligence (AI) can revolutionize pharmacovigilance by enhancing patient safety and enabling personalized medicine. A survey conducted by the author indicates a positive outlook on AI's potential in this field. AI excels at analysing vast data sources, leading to improved detection of subtle safety signals and emerging drug safety concerns. Additionally, AI-powered risk prediction allows for personalized treatment strategies, minimizing adverse drug reactions for individual patients. Furthermore, AI facilitates efficient literature mining and drug repurposing, accelerating drug discovery processes. While the potential benefits are significant, responsible implementation is crucial. Addressing potential biases in algorithms and ensuring data privacy and security remain critical considerations. Overall, AI holds immense promise for the future of pharmacovigilance, paving the way for a more efficient, accurate, and proactive system for safeguarding patient well-being.

Repurposing Valrubicin as a Potent Inhibitor of Ovarian Cancer Cell Growth.

Ovarian cancer (OC) is a leading cause of cancer-related mortality among women, and there remains a significant unmet need for new therapeutic agents to improve patient outcomes. This study aimed to explore drug repositioning by screening a library of Food and Drug Administration (FDA)-approved compounds to identify those with therapeutic potential against OC. We also aimed to elucidate the molecular mechanisms of action of such compounds to better understand how they inhibit cancer cell proliferation. Using the WST-1 assay, a library of 1710 FDA-approved drugs was screened to evaluate their effects on OC cell proliferation. The molecular mechanisms underlying the effects of selected compounds were assessed through terminal deoxynucleotidyl transferase-mediated dUTP nick end labeling (TUNEL) assay and immunoblot analysis. Screening of FDA-approved libraries revealed valrubicin as a potent inhibitor of OVCAR8 cell proliferation and SKOV3 and A2780 cell growth. Furthermore, valrubicin treatment led to increased DNA fragmentation, as evidenced by the TUNEL assay, and activated apoptosis signaling through enhancement of cleaved caspase-3 and poly(ADP-ribose) polymerase levels. Valrubicin, through drug repositioning, can be applied as a new therapeutic agent for OC.

Screening of differential gene expression patterns through survival analysis for diagnosis, prognosis and therapies of clear cell renal cell carcinoma.

Clear cell renal cell carcinoma (ccRCC) is the most prevalent subtype of kidney cancer. Although there is increasing evidence linking ccRCC to genetic alterations, the exact molecular mechanism behind this relationship is not yet completely known to the researchers. Though drug therapies are the best choice after the metastasis, unfortunately, the majority of the patients progressively develop resistance against the therapeutic drugs after receiving it for almost 2 years. In this case, multi-targeted different variants of therapeutic drugs are essential for effective treatment against ccRCC. To understand molecular mechanisms of ccRCC development and progression, and explore multi-targeted different variants of therapeutic drugs, it is essential to identify ccRCC-causing key genes (KGs). In order to obtain ccRCC-causing KGs, at first, we detected 133 common differentially expressed genes (cDEGs) between ccRCC and control samples based on nine (9) microarray gene-expression datasets with NCBI accession IDs GSE16441, GSE53757, GSE66270, GSE66272, GSE16449, GSE76351, GSE66271, GSE71963, and GSE36895. Then, we filtered these cDEGs through survival analysis with the independent TCGA and GTEx database and obtained 54 scDEGs having significant prognostic power. Next, we used protein-protein interaction (PPI) network analysis with the reduced set of 54 scDEGs to identify ccRCC-causing top-ranked eight KGs (PLG, ENO2, ALDOB, UMOD, ALDH6A1, SLC12A3, SLC12A1, SERPINA5). The pan-cancer analysis with KGs based on TCGA database showed the significant association with different subtypes of kidney cancers including ccRCC. The gene regulatory network (GRN) analysis revealed some crucial transcriptional and post-transcriptional regulators of KGs. The scDEGs-set enrichment analysis significantly identified some crucial ccRCC-causing molecular functions, biological processes, cellular components, and pathways that are linked to the KGs. The results of DNA methylation study indicated the hypomethylation and hyper-methylation of KGs, which may lead the development of ccRCC. The immune infiltrating cell types (CD8+ T and CD4+ T cell, B cell, neutrophil, dendritic cell and macrophage) analysis with KGs indicated their significant association in ccRCC, where KGs are positively correlated with CD4+ T cells, but negatively correlated with the majority of other immune cells, which is supported by the literature review also. Then we detected 10 repurposable drug molecules (Irinotecan, Imatinib, Telaglenastat, Olaparib, RG-4733, Sorafenib, Sitravatinib, Cabozantinib, Abemaciclib, and Dovitinib.) by molecular docking with KGs-mediated receptor proteins. Their ADME/T analysis and cross-validation with the independent receptors, also supported their potent against ccRCC. Therefore, these outputs might be useful inputs/resources to the wet-lab researchers and clinicians for considering an effective treatment strategy against ccRCC.

Graph Neural Network-Based Modeling with Subcategory Exploration for Drug Repositioning

Drug repositioning is a cost-effective approach to identifying new indications for existing drugs by predicting their associations with new diseases or symptoms. Recently, deep learning-based models have become the mainstream for drug repositioning. Existing methods typically regard the drug-repositioning task as a binary classification problem to find the new drug–disease associations. However, drug–disease associations may encompass some potential subcategories that can be used to enhance the classification performance. In this paper, we propose a prototype-based subcategory exploration (PSCE) model to guide the model learned with the information of a potential subcategory for drug repositioning. To achieve this, we first propose a prototype-based feature-enhancement mechanism (PFEM) that uses clustering centroids as the attention to enhance the drug–disease features by introducing subcategory information to improve the association prediction. Second, we introduce the drug–disease dual-task classification head (D3TC) of the model, which consists of a traditional binary classification head and a subcategory-classification head to learn with subcategory exploration. It leverages finer-grained pseudo-labels of subcategories to introduce additional knowledge for precise drug–disease association classification. In this study, we conducted experiments on four public datasets to compare the proposed PSCE with existing state-of-the-art approaches and our PSCE achieved a better performance than the existing ones. Finally, the effectiveness of the PFEM and D3TC was demonstrated using ablation studies.

Signaling Pathways in Clear Cell Renal Cell Carcinoma and Candidate Drugs Unveiled through Transcriptomic Network Analysis of Hub Genes

Clear cell renal cell carcinoma (ccRCC) is a type of kidney cancer. It advances quickly and often metastasizes, making the prognosis for patients challenging. This study used weighted gene co-expression network analysis (WGCNA) to study gene expression data of different stages of ccRCC obtained in the GEO database. The analysis identified three significant highly preserved gene modules across the datasets: GSE53757, GSE22541, GSE66272, and GSE73731. Functional annotation and pathway enrichment analysis using DAVID revealed inflammatory pathways (e.g., NF-kB, Hippo, and HIF-1 pathways) that may drive ccRCC development and progression. The study also introduced the involvement of viral infections associated with the disease in the metabolic reprogramming of ccRCC. A drug repurposing analysis was also conducted to identify potential drug candidates for ccRCC using the upregulated and downregulated hub genes. The top candidates are ziprasidone (dopamine and serotonin receptor antagonist) and fentiazac (cyclooxygenase inhibitor). Other drug candidates were also obtained, such as phosphodiesterase/DNA methyltransferase/ATM kinase inhibitors, acetylcholine antagonists, and NAD precursors. Overall, the study’s findings suggest that identifying several genes and signaling pathways related to ccRCC may uncover new targets, biomarkers, and even drugs that can be repurposed, which can help develop new and effective treatments for the disease.