Author

Georgia Tsagkogeorga

University of CambridgeORCID 0000-0002-9657-453XUnclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Biochemistry· 1 Bioinformatics· 1 Cancer Biology· 1

Signature Assays, Organisms & Methods

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Research Focus:gene regulatory network inference· 3 Organism:human· 3 Assay:proteomics· 3 Assay:bulk RNA-seq· 2 Research Focus:differential expression· 2 Research Focus:cell type annotation· 1 Approach:experimental assay· 1 Approach:machine learning (classical)· 1

Frequent Co-Authors

Namshik HaniD· 3 shared Helena Santos-Rosa· 2 shared Tony KouzaridesiD· 2 shared Alexandra SapetschnigiD· 1 shared Almudena Serrano-BeníteziD· 1 shared Andrej AlendariD· 1 shared Bianca Pierattini· 1 shared Dan Leggate· 1 shared

Activity by Year

2022-2025 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 29 mutual co-author links

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3 shared papersBiochemistry

Namshik HanORCID

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3 papers

Recent·Most cited

Compensatory tRNA Modification by DUS3L Confers Resistance to METTL1 Loss in Oesophageal Cancer

Helena Santos-Rosa, Jonathan L. Price, Georgia Tsagkogeorga, Alexandra Sapetschnig, Bianca Pierattini, David Jordan, Paula Milán-Rois, Almudena Serrano-Benítez, Katherine M. Stott, Namshik Han, Tony Kouzarides, Eric Alexander Miska

The paper identifies a compensatory tRNA modification pathway mediated by DUS3L that allows oesophageal cancer cells to resist METTL1 loss, suggesting DUS3L as a biomarker and therapeutic target.

bioRxiv · Oct 13, 2025 · PreprintBiochemistry

Downregulation of dystrophin expression occurs across diverse tumors, correlates with the age of onset, staging, and reduced survival of patients.

Nancy Alnassar, Małgorzata Borczyk, Georgia Tsagkogeorga, Michał Korostyński, Namshik Han, Dariusz C. Górecki

This study shows that dystrophin expression is downregulated in many tumors, linked to advanced stages, older age of onset, and poorer survival.

Cancers · Feb 21, 2023Peer-reviewedCancer Biology

Predicting genes associated with RNA methylation pathways using machine learning

Georgia Tsagkogeorga, Helena Santos-Rosa, Andrej Alendar, Dan Leggate, Oliver Rausch, Tony Kouzarides, Hendrik Weisser, Namshik Han

This study uses machine learning to predict genes associated with RNA methylation pathways in humans using multi-omics data from the Harmonizome database.

Communications Biology · Aug 25, 2022Peer-reviewedBioinformatics