Epigenomic and transcriptomic landscaping unraveled candidate repositioned therapeutics for non-functioning pituitary neuroendocrine tumors.

Abstract

Non-functioning pituitary neuroendocrine tumors are challengingly diagnosed tumors in the clinic. Transsphenoidal surgery remains the first-line treatment. Despite the development of state-of-the-art techniques, no drug therapy is currently approved for the treatment. There are also no randomized controlled trials comparing therapeutic strategies or drug therapy for the management after surgery. Therefore, novel therapeutic interventions for the therapeutically challenging NF-PitNETs are urgently needed. We integrated epigenome and transcriptome data (both coding and non-coding) that elucidate disease-specific signatures, in addition to biological and pharmacological data, to utilize rational pathway and drug prioritization in NF-PitNETs. We constructed an epigenome- and transcriptome-based PPI network and proposed hub genes. The signature-based drug repositioning based on the integration of multi-omics data was performed. The construction of a disease-specific network based on three different biological levels revealed DCC, DLG5, ETS2, FOXO1, HBP1, HMGA2, PCGF3, PSME4, RBPMS, RREB1, SMAD1, SOCS1, SOX2, YAP1, ZFHX3 as hub proteins. Signature-based drug repositioning using hub proteins yielded repositioned drug candidates that were confirmed in silico via molecular docking. As a result of molecular docking simulations, palbociclib, linifanib, trametinib, eplerenone, niguldipine, and zuclopenthixol showed higher binding affinities with hub genes compared to their inhibitors and were proposed as potential repositioned therapeutics for the management of NF-PitNETs. The proposed systems' biomedicine-oriented multi-omics data integration for drug repurposing to provide promising results for the construction of effective clinical therapeutics. To the best of our knowledge, this is the first study reporting epigenome- and transcriptome-based drug repositioning for NF-PitNETs using in silico confirmations.