Exploring public cancer gene expression signatures across bulk, single-cell and spatial transcriptomics data with signifinder Bioconductor package.

Autor:	Pirrotta S; Department of Biology, University of Padua, Padua 35121, Italy., Masatti L; Department of Biology, University of Padua, Padua 35121, Italy., Bortolato A; Department of Biology, University of Padua, Padua 35121, Italy., Corrà A; Fondazione Istituto di Ricerca Pediatrica Città della Speranza, Padua 35127, Italy., Pedrini F; Institute of Pathology, University Hospital Heidelberg, Heidelberg 69120, Germany., Aere M; Department of Biology, University of Padua, Padua 35121, Italy., Esposito G; Immunology and Molecular Oncology Diagnostic Unit of The Veneto Institute of Oncology IOV - IRCCS, Padua 35128, Italy., Martini P; Department of Molecular and Translational Medicine, University of Brescia, Brescia 25123, Italy., Risso D; Department of Statistical Sciences, University of Padua, Padua 35121, Italy., Romualdi C; Department of Biology, University of Padua, Padua 35121, Italy., Calura E; Department of Biology, University of Padua, Padua 35121, Italy.
Jazyk:	angličtina
Zdroj:	NAR genomics and bioinformatics [NAR Genom Bioinform] 2024 Oct 03; Vol. 6 (4), pp. lqae138. Date of Electronic Publication: 2024 Oct 03 (Print Publication: 2024).
DOI:	10.1093/nargab/lqae138
Abstrakt:	Understanding cancer mechanisms, defining subtypes, predicting prognosis and assessing therapy efficacy are crucial aspects of cancer research. Gene-expression signatures derived from bulk gene expression data have played a significant role in these endeavors over the past decade. However, recent advancements in high-resolution transcriptomic technologies, such as single-cell RNA sequencing and spatial transcriptomics, have revealed the complex cellular heterogeneity within tumors, necessitating the development of computational tools to characterize tumor mass heterogeneity accurately. Thus we implemented signifinder, a novel R Bioconductor package designed to streamline the collection and use of cancer transcriptional signatures across bulk, single-cell, and spatial transcriptomics data. Leveraging publicly available signatures curated by signifinder, users can assess a wide range of tumor characteristics, including hallmark processes, therapy responses, and tumor microenvironment peculiarities. Through three case studies, we demonstrate the utility of transcriptional signatures in bulk, single-cell, and spatial transcriptomic data analyses, providing insights into cell-resolution transcriptional signatures in oncology. Signifinder represents a significant advancement in cancer transcriptomic data analysis, offering a comprehensive framework for interpreting high-resolution data and addressing tumor complexity. (© The Author(s) 2024. Published by Oxford University Press on behalf of NAR Genomics and Bioinformatics.)
Databáze:	MEDLINE
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