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Multidimensional analyses identify genes of high priority for pancreatic cancer research
Zeribe C. Nwosu, … , Marina Pasca di Magliano, Costas A. Lyssiotis
Zeribe C. Nwosu, … , Marina Pasca di Magliano, Costas A. Lyssiotis
Published January 7, 2025
Citation Information: JCI Insight. 2025;10(4):e174264. https://doi.org/10.1172/jci.insight.174264.
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Research Article Gastroenterology Oncology

Multidimensional analyses identify genes of high priority for pancreatic cancer research

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Abstract

Pancreatic ductal adenocarcinoma (PDAC) is a drug-resistant and lethal cancer. Identification of the genes that consistently show altered expression across patient cohorts can expose effective therapeutic targets and strategies. To identify such genes, we separately analyzed 5 human PDAC microarray datasets. We defined genes as “consistent” if upregulated or downregulated in 4 or more datasets (adjusted P < 0.05). The genes were subsequently queried in additional datasets, including single-cell RNA-sequencing data, and we analyzed their pathway enrichment, tissue specificity, essentiality for cell viability, and association with cancer features, e.g., tumor subtype, proliferation, metastasis, and poor survival outcome. We identified 2,010 consistently upregulated and 1,928 downregulated genes, of which more than 50% to our knowledge were uncharacterized in PDAC. These genes spanned multiple processes, including cell cycle, immunity, transport, metabolism, signaling, and transcriptional/epigenetic regulation — cell cycle and glycolysis being the most altered. Several upregulated genes correlated with cancer features, and their suppression impaired PDAC cell viability in prior CRISPR/Cas9 and RNA interference screens. Furthermore, the upregulated genes predicted sensitivity to bromodomain and extraterminal (epigenetic) protein inhibition, which, in combination with gemcitabine, disrupted amino acid metabolism and in vivo tumor growth. Our results highlight genes for further studies in the quest for PDAC mechanisms, therapeutic targets, and biomarkers.

Authors

Zeribe C. Nwosu, Heather M. Giza, Maya Nassif, Verodia Charlestin, Rosa E. Menjivar, Daeho Kim, Samantha B. Kemp, Peter Sajjakulnukit, Anthony Andren, Li Zhang, William K.M. Lai, Ian Loveless, Nina Steele, Jiantao Hu, Biao Hu, Shaomeng Wang, Marina Pasca di Magliano, Costas A. Lyssiotis

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Usage data is cumulative from January 2025 through December 2025.

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