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Multi-Omics in Cancer Precision Medicine
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Multi-Omics in Cancer Precision Medicine

A special issue of Genes (ISSN 2073-4425). This special issue belongs to the section "Technologies and Resources for Genetics".

Deadline for manuscript submissions: closed (5 January 2022) | Viewed by 1477

Special Issue Editor

Dr. Eitan Rubin

E-Mail Website
Guest Editor
The Sharga Segal Dept. of Microbiology, Immunology and Genetics, Ben-Gurion University of the Negev, Beer-Sheva 8410501, Israel
Interests: transcriptomics; proteomics; metabolomics; immunomics; multi-omics

Special Issue Information

Dear Colleagues,

We are happy to announce a Special Issue of Genes that will be dedicated to the role that multi-omics research has/should have in Cancer Precision Medicine. We are in the middle of an information revolution in biomedical research: improvement in measurement technologies has made it possible, for the first time, to sequence thousands of exomes/full genomes, to explore the entire transcriptome, even at the single cell level, and to measure the levels of thousands of proteins and metabolites. Yet, in medical practice, mutations still reign supreme, while we know disease biology is a multi-faceted, multi-layered phenomenon.

In this issue, we hope to show that other multi-omics approaches can help to understand and treat cancer. We encourage submissions that utilize multi-omics approaches to make new discoveries about cancer and/or suggest new therapeutic approaches. We specifically look for works that demonstrate how the combination of different omics methodologies facilitates new discoveries.

Dr. Eitan Rubin
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Genes is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • genomics
  • transcriptomics
  • proteomics
  • metabolomics
  • systems biology
  • oncology
  • data sciences

Published Papers (1 paper)

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Research

10 pages, 1372 KiB  
Article
Projection of Expression Profiles to Transcription Factor Activity Space Provides Added Information
by Rut Bornshten, Michael Danilenko and Eitan Rubin
Genes 2022, 13(10), 1819; https://0-doi-org.brum.beds.ac.uk/10.3390/genes13101819 - 08 Oct 2022
Viewed by 1227
Abstract
Acute myeloid leukemia (AML) is an aggressive type of leukemia, characterized by the accumulation of highly proliferative blasts with a disrupted myeloid differentiation program. Current treatments are ineffective for most patients, partly due to the genetic heterogeneity of AML. This is driven by [...] Read more.
Acute myeloid leukemia (AML) is an aggressive type of leukemia, characterized by the accumulation of highly proliferative blasts with a disrupted myeloid differentiation program. Current treatments are ineffective for most patients, partly due to the genetic heterogeneity of AML. This is driven by genetically distinct leukemia stem cells, resulting in relapse even after most of the tumor cells are destroyed. Thus, personalized treatment approaches addressing cellular heterogeneity are urgently required. Reconstruction of Transcriptional regulatory Networks (RTN) is a tool for inferring transcriptional activity in patients with various diseases. In this study, we applied this method to transcriptome profiles of AML patients to test if it provided additional information for the interpretation of transcriptome data. We showed that when RTN results were added to RNA-seq results, superior clusters were formed, which were more homogenous and allowed the better separation of patients with low and high survival rates. We concluded that the external knowledge used for RTN analysis improved the ability of unsupervised machine learning to find meaningful patterns in the data. Full article
(This article belongs to the Special Issue Multi-Omics in Cancer Precision Medicine)
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