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Biomedicines
Special Issues
Genomic Insights and Translational Opportunities for Human Cancers
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Genomic Insights and Translational Opportunities for Human Cancers

A special issue of Biomedicines (ISSN 2227-9059). This special issue belongs to the section "Cancer Biology and Oncology".

Deadline for manuscript submissions: 31 May 2025 | Viewed by 1714

Special Issue Editors

Dr. Apostolos Zaravinos

E-Mail Website
Guest Editor
Cancer Genetics, Genomics and Systems Biology Laboratory, Basic and Translational Cancer Research Center (BTCRC), 1516 Nicosia, Cyprus
Interests: cancer genomics; precision medicine; data science in genomics; next-generation sequencing; translational oncology; tumor immunology
Special Issues, Collections and Topics in MDPI journals
Dr. Ilias Georgakopoulos-Soares

E-Mail Website
Guest Editor
Department of Biochemistry and Molecular Biology, Penn State College of Medicine, Hershey, PA 17033-0850, USA
Interests: bioinformatics; genomics; machine learning

Special Issue Information

Dear Colleagues,

The field of cancer genomics has undergone a revolution in recent years with the advent of next-generation sequencing technologies and the development of new algorithms that can analyze large amounts of genomic information. These advances have enabled researchers to sequence cancer genomes with unprecedented accuracy, leading to new insights into the genetic basis of cancer, and opening up new avenues for cancer diagnosis, treatment, and prevention. By understanding the specific genetic mutations and transcriptomic changes that are present in a particular cancer, scientists can also develop more targeted and effective therapies. Additionally, liquid biopsies have gathered excitement for their potential utility in cancer detection, monitoring, and patient stratification. This Special Issue on genomic insights into human cancer focuses on the latest advances in cancer genomics, with an emphasis on the translational potential of these findings. The articles in this Special Issue will cover recent advances in cancer genomics, including: (i) advances in bioinformatics algorithms, such as machine learning and artificial intelligence, and their application in cancer research; (ii) novel cancer biomarkers, particularly those pertaining to liquid biopsies; and (iii) other translational research relating to cancer.

Dr. Apostolos Zaravinos
Dr. Ilias Georgakopoulos-Soares
Guest Editors

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. Biomedicines 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
  • cancer genomics
  • biomarkers
  • machine learning
  • bioinformatics
  • liquid biopsies
  • cell-free DNA
  • cell-free RNA

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Published Papers (1 paper)

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Research

19 pages, 1868 KiB  
Article
A Comparison of Tools That Identify Tumor Cells by Inferring Copy Number Variations from Single-Cell Experiments in Pancreatic Ductal Adenocarcinoma
by Daisy J. A. Oketch, Matteo Giulietti and Francesco Piva
Biomedicines 2024, 12(8), 1759; https://doi.org/10.3390/biomedicines12081759 - 5 Aug 2024
Viewed by 1089
Abstract
Single-cell RNA sequencing (scRNA-seq) technique has enabled detailed analysis of gene expression at the single cell level, enhancing the understanding of subtle mechanisms that underly pathologies and drug resistance. To derive such biological meaning from sequencing data in oncology, some critical processing must [...] Read more.
Single-cell RNA sequencing (scRNA-seq) technique has enabled detailed analysis of gene expression at the single cell level, enhancing the understanding of subtle mechanisms that underly pathologies and drug resistance. To derive such biological meaning from sequencing data in oncology, some critical processing must be performed, including identification of the tumor cells by markers and algorithms that infer copy number variations (CNVs). We compared the performance of sciCNV, InferCNV, CopyKAT and SCEVAN tools that identify tumor cells by inferring CNVs from scRNA-seq data. Sequencing data from Pancreatic Ductal Adenocarcinoma (PDAC) patients, adjacent and healthy tissues were analyzed, and the predicted tumor cells were compared to those identified by well-assessed PDAC markers. Results from InferCNV, CopyKAT and SCEVAN overlapped by less than 30% with InferCNV showing the highest sensitivity (0.72) and SCEVAN the highest specificity (0.75). We show that the predictions are highly dependent on the sample and the software used, and that they return so many false positives hence are of little use in verifying or filtering predictions made via tumor biomarkers. We highlight how critical this processing can be, warn against the blind use of these software and point out the great need for more reliable algorithms. Full article
(This article belongs to the Special Issue Genomic Insights and Translational Opportunities for Human Cancers)
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