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

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Abstract

Purpose

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.

Methods

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.

Results

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.

Conclusion

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.

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Data availability

The datasets that were used in this study are publicly available at Gene Expression Omnibus (GEO Database) with the following links: GSE115783– https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE115783. GSE77517– https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE77517. GSE63357– https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE63357.

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Acknowledgements

The scholarships under the YOK 100/2000 Doctoral Fellowship Program and 2211-C Doctoral Fellowship Program under The Scientific and Technological Research Council of Turkey (TUBITAK), and the financial support provided to Busra Aydin under project number 3629 from the Health Institutes of Turkey (TUSEB) are greatly acknowledged.

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Authors and Affiliations

  1. Department of Bioengineering, Faculty of Engineering and Architecture, Konya Food and Agriculture University, Konya, Turkey

    B. Aydin

  2. Department of Bioengineering, Faculty of Engineering, Marmara University, RTE Basibuyuk Campus, 34720, Istanbul, Turkey

    H. Beklen, K. Y. Arga & B. Turanli

  3. Genetic and Metabolic Diseases Research and Investigation Center (GEMHAM), Marmara University, Istanbul, Turkey

    K. Y. Arga

  4. Department of Neurosurgery, Faculty of Medicine, Marmara University, Istanbul, Turkey

    F. Bayrakli

  5. Institute of Neurological Sciences, Marmara University, Istanbul, Turkey

    F. Bayrakli

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Contributions

BA designed data analysis framework. BA and HB performed the data analyses. BA, BT interpreted the results. FB and KYA conceived and directed the study. BA drafted the manuscript. BA and BT revised the manuscript. All authors read and approved the final manuscript.

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Correspondence to B. Turanli.

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The authors declare that the datasets used in this study belonged to previously published studies, therefore no ethical statement is required.

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Supplementary Information

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40618_2022_1923_MOESM1_ESM.xlsx

Supplementary file1 Supplementary Information 1: The associations of ncRNAs with target genes are represented as networks. A) The network displayed the interactions of ncRNAs in literature and their known targets. B) The network was composed of the DEncRNAs in NF-PitNET dataset and their target genes that were retrieved from literature (XLSX 11418 KB)

40618_2022_1923_MOESM2_ESM.tif

Supplementary file2 Supplementary Information 2: LncRNA and miRNA target-gene interactions retrieved from various databases (TIF 1648 KB)

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Aydin, B., Beklen, H., Arga, K.Y. et al. Epigenomic and transcriptomic landscaping unraveled candidate repositioned therapeutics for non-functioning pituitary neuroendocrine tumors. J Endocrinol Invest 46, 727–747 (2023). https://doi.org/10.1007/s40618-022-01923-2

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