Academic staff

HomeAcademic staffDr Chinedu Anene
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I joined LSBU as a lecturer in biomedical sciences in 2021, having been a postdoctoral bioinformatician at the CRUK Barts Cancer Institute, Queen Mary University of London. I originally studied Biomedical sciences, completing my MSc in Biomedical Sciences, followed by a PhD in Cardiovascular Genetics from the University of Bradford.

I have broad research interests in bioinformatics, cancer genomics and analytics. These research areas mainly involve applying computational and statistical approaches to big datasets to understand the cell signalling dynamics of cancer development and progression and translating this knowledge to improve the prognosis and treatment of the disease. My work combines expertise in bioinformatics, molecular biology, machine learning, and targeted wet-lab analyses in genomics and molecular pathology.

My current research has four main themes:

Transcriptome-based stratification of squamous cell carcinomas at risk for progression using big data and bioinformatics.

AI based quantification of hypoxia level in digital pathology slide of squamous cell carcinomas to aid disease management.

Development of approaches for integration of multiple sources of datasets for enhanced extraction of biological insight.

Courses taught

Biomedical Sciences - BSc (Hons)

BSc (Hons) Bioscience 2022/23

PhD

University of Bradford

2014
2017
MSc
2012
2013
BSc

Madonna University

2006
2010
AFHEA

Higher Education Academy

2017
Bioinformatician Postdoctoral Researcher
Queen Mary University of London

Computational methods for characterising biological pathways in cancer genomes and providing bioinformatics support on cancer projects, Funded by the Barts Charity.

Designed and built a deep neural network-based tool for discovering the components of signalling pathways from genomic datasets.

Implemented effective analysis workflows and data management systems for large scale genomics.

Created several data science tools for extracting biological insights from large databases, including cell deconvolution methods, microscopy tools and automated literature mining.

2017
2021
Education
Queen Mary University of London
Honorary research fellow

AI based quantification of hypoxia level in digital pathology slide of squamous cell carcinomas to aid disease management.

November 2021
Leeds Beckett University

Research collaboration on cellular dynamics of Melanoma and Breast cancer progression.

October 2017
Leeds University

Research collaboration on viral reprogramming of gene expression programs in host cells.

October 2019

Dysregulation of the miR-30c/DLL4 axis by circHIPK3 is essential for KSHV lytic replication
Harper, K.L., Mottram, T.J., Anene, C., Foster, B., Patterson, M.R., McDonnell, E., Macdonald, A., Westhead, D. and Whitehouse, A. (2022). Dysregulation of the miR-30c/DLL4 axis by circHIPK3 is essential for KSHV lytic replication. EMBO Reports. (e54117). https://doi.org/10.15252/embr.202154117

Decosus: An R Framework for Universal Integration of Cell Proportion Estimation Methods.
Anene, C.A., Taggart, E., Harwood, C., Pennington, D.J. and Wang, J. (2022). Decosus: An R Framework for Universal Integration of Cell Proportion Estimation Methods. Frontiers in genetics. 13, p. 802838. https://doi.org/10.3389/fgene.2022.802838

SFPQ promotes an oncogenic transcriptomic state in melanoma
Bi, O., Anene, C., Nsengimana, J., Roberts, W., Newton-Bishop, J. and Boyne, J.R. (2021). SFPQ promotes an oncogenic transcriptomic state in melanoma. Oncogene. 40, pp. 5192-5203. https://doi.org/10.1038/s41388-021-01912-4

ACSNI: An unsupervised machine-learning tool for prediction of tissue-specific pathway components using gene expression profiles
Anene, C., Khan F., Bewicke-Copley, F., Maniati, E. and Wang, J. (2021). ACSNI: An unsupervised machine-learning tool for prediction of tissue-specific pathway components using gene expression profiles. Patterns. 2 (6), p. 100270. https://doi.org/10.1016/j.patter.2021.100270

Systematic Evaluation of Somatic Cis-Regulatory Mutations in Follicular Lymphoma
Firat U., Bewicke-Copley, F., Anene, C., Schlesner, M., Icgc MMML-Seq Project3, Siebert, R., Okosun, J., Fitzgibbon, J. and Wang, J (2020). Systematic Evaluation of Somatic Cis-Regulatory Mutations in Follicular Lymphoma. American Society of Hematology. https://doi.org/10.1182/blood-2020-142623

The Genomic Landscape of Actinic Keratosis
Thomson, J., Bewicke-Copley, F., Anene, C., Gulati, A., Nagano, A., Purdie, K., Inman, G.J., Proby, C.M., Leigh, I.M., Harwood, C.A. and Wang, J. (2021). The Genomic Landscape of Actinic Keratosis. The Journal of Investigative Dermatology. https://doi.org/10.1016/j.jid.2020.12.024

Merkel cell polyomavirus small tumour antigen activates the p38 MAPK pathway to enhance cellular motility
Dobson, S.J., Anene, C., Boyne, J.R., Mankouri, J., Macdonald, A. and Whitehouse, A (2020). Merkel cell polyomavirus small tumour antigen activates the p38 MAPK pathway to enhance cellular motility. Biochemical Journal. 477 (14), pp. 2721-2733. https://doi.org/10.1042/BCJ20200399

The role of CAF derived exosomal microRNAs in the tumour microenvironment of melanoma
Shelton, M., Anene, C., Nsengimana, J., Roberts, W., Newton-Bishop, J. and Boyne, J.R. (2020). The role of CAF derived exosomal microRNAs in the tumour microenvironment of melanoma. Biochimica et Biophysica Acta (BBA) - Reviews on Cancer. 1875 (1), p. 188456. https://doi.org/10.1016/j.bbcan.2020.188456

Platelet induced hepatocellular carcinoma HEPG2 cell proliferation and angiogenic potential is integrin IIb3 dependent.
Rashed, Al-Hammad, Anene, C., Graham, A.M. and Roberts, W. (2015). Platelet induced hepatocellular carcinoma HEPG2 cell proliferation and angiogenic potential is integrin IIb3 dependent. Taylor & Francis. https://doi.org/10.3109/09537104.2015.1115703

Platelet microparticle delivered microRNA-Let-7a promotes the angiogenic switch
Anene, C., Graham, A.M., Boyne, J. and Roberts, W. (2018). Platelet microparticle delivered microRNA-Let-7a promotes the angiogenic switch. Biochimica et Biophysica Acta (BBA) - Molecular Basis of Disease. https://doi.org/10.1016/j.bbadis.2018.04.013