Long Non-Coding RNAs as Molecular Signatures for Canine B-Cell Lymphoma Characterization.

analysis pipeline long non-coding RNA lymphoma prognostic signature

Journal

Non-coding RNA
ISSN: 2311-553X
Titre abrégé: Noncoding RNA
Pays: Switzerland
ID NLM: 101652294

Informations de publication

Date de publication:
19 Sep 2019
Historique:
received: 01 08 2019
revised: 06 09 2019
accepted: 16 09 2019
entrez: 25 9 2019
pubmed: 25 9 2019
medline: 25 9 2019
Statut: epublish

Résumé

Diffuse large B-cell lymphoma (DLBCL), marginal zone lymphoma (MZL) and follicular lymphoma (FL) are the most common B-cell lymphomas (BCL) in dogs. Recent investigations have demonstrated overlaps of these histotypes with the human counterparts, including clinical presentation, biologic behavior, tumor genetics, and treatment response. The molecular mechanisms that underlie canine BCL are still unknown and new studies to improve diagnosis, therapy, and the utilization of canine species as spontaneous animal tumor models are undeniably needed. Recent work using human DLBCL transcriptomes has suggested that long non-coding RNAs (lncRNAs) play a key role in lymphoma pathogenesis and pinpointed a restricted number of lncRNAs as potential targets for further studies. To expand the knowledge of non-coding molecules involved in canine BCL, we used transcriptomes obtained from a cohort of 62 dogs with newly-diagnosed multicentric DLBCL, MZL and FL that had undergone complete staging work-up and were treated with chemotherapy or chemo-immunotherapy. We developed a customized R pipeline performing a transcriptome assembly by multiple algorithms to uncover novel lncRNAs, and delineate genome-wide expression of unannotated and annotated lncRNAs. Our pipeline also included a new package for high performance system biology analysis, which detects high-scoring network biological neighborhoods to identify functional modules. Moreover, our customized pipeline quantified the expression of novel and annotated lncRNAs, allowing us to subtype DLBCLs into two main groups. The DLBCL subtypes showed statistically different survivals, indicating the potential use of lncRNAs as prognostic biomarkers in future studies. In this manuscript, we describe the methodology used to identify lncRNAs that differentiate B-cell lymphoma subtypes and we interpreted the biological and clinical values of the results. We inferred the potential functions of lncRNAs to obtain a comprehensive and integrative insight that highlights their impact in this neoplasm.

Sections du résumé

BACKGROUND BACKGROUND
Diffuse large B-cell lymphoma (DLBCL), marginal zone lymphoma (MZL) and follicular lymphoma (FL) are the most common B-cell lymphomas (BCL) in dogs. Recent investigations have demonstrated overlaps of these histotypes with the human counterparts, including clinical presentation, biologic behavior, tumor genetics, and treatment response. The molecular mechanisms that underlie canine BCL are still unknown and new studies to improve diagnosis, therapy, and the utilization of canine species as spontaneous animal tumor models are undeniably needed. Recent work using human DLBCL transcriptomes has suggested that long non-coding RNAs (lncRNAs) play a key role in lymphoma pathogenesis and pinpointed a restricted number of lncRNAs as potential targets for further studies.
RESULTS RESULTS
To expand the knowledge of non-coding molecules involved in canine BCL, we used transcriptomes obtained from a cohort of 62 dogs with newly-diagnosed multicentric DLBCL, MZL and FL that had undergone complete staging work-up and were treated with chemotherapy or chemo-immunotherapy. We developed a customized R pipeline performing a transcriptome assembly by multiple algorithms to uncover novel lncRNAs, and delineate genome-wide expression of unannotated and annotated lncRNAs. Our pipeline also included a new package for high performance system biology analysis, which detects high-scoring network biological neighborhoods to identify functional modules. Moreover, our customized pipeline quantified the expression of novel and annotated lncRNAs, allowing us to subtype DLBCLs into two main groups. The DLBCL subtypes showed statistically different survivals, indicating the potential use of lncRNAs as prognostic biomarkers in future studies.
CONCLUSIONS CONCLUSIONS
In this manuscript, we describe the methodology used to identify lncRNAs that differentiate B-cell lymphoma subtypes and we interpreted the biological and clinical values of the results. We inferred the potential functions of lncRNAs to obtain a comprehensive and integrative insight that highlights their impact in this neoplasm.

Identifiants

pubmed: 31546795
pii: ncrna5030047
doi: 10.3390/ncrna5030047
pmc: PMC6789837
pii:
doi:

Types de publication

Journal Article

Langues

eng

Subventions

Organisme : Ministero dell'Istruzione, dell'Università e della Ricerca
ID : RBSI14EDX9

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Auteurs

Luciano Cascione (L)

Institute of Oncology Research, Universita' della Svizzera Italiana, 6500 Bellinzona, Switzerland. luciano.cascione@ior.usi.ch.
Swiss Institute of Bioinformatics, 1000 Lausanne, Switzerland. luciano.cascione@ior.usi.ch.

Luca Giudice (L)

Department of Computer Science, University of Verona, 37100 Verona, Italy. luca.giudice@univr.it.

Serena Ferraresso (S)

Department of Comparative Biomedicine and Food Science, University of Padova, 35100 Padova, Italy. serena.ferraresso@unipd.it.

Laura Marconato (L)

Centro Oncologico Veterinario, 40037 Sasso Marconi BO, Italy. lauramarconato@yahoo.it.

Diana Giannuzzi (D)

Department of Comparative Biomedicine and Food Science, University of Padova, 35100 Padova, Italy. diana.giannuzzi@phd.unipd.it.

Sara Napoli (S)

Institute of Oncology Research, Universita' della Svizzera Italiana, 6500 Bellinzona, Switzerland. sara.napoli@ior.usi.ch.

Francesco Bertoni (F)

Institute of Oncology Research, Universita' della Svizzera Italiana, 6500 Bellinzona, Switzerland. francesco.bertoni@ior.usi.ch.

Rosalba Giugno (R)

Department of Computer Science, University of Verona, 37100 Verona, Italy. rosalba.giugno@univr.it.

Luca Aresu (L)

Department of Veterinary Sciences, University of Turin, 10095 Grugliasco, Italy. luca.aresu@unito.it.

Classifications MeSH