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Cancers
Special Issues
Oncogenomic and Multi-Omic Data Science and Data Engineering
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Oncogenomic and Multi-Omic Data Science and Data Engineering

A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Cancer Informatics and Big Data".

Deadline for manuscript submissions: closed (25 May 2024) | Viewed by 8558

Special Issue Editors

Prof. Dr. Lu Lu

grade E-Mail Website
Guest Editor
Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN, USA
Interests: genetics; genomics; molecular phenotype; mouse
Dr. Abhishek Kumar

E-Mail Website
Guest Editor
1. Institute of Bioinformatics, Bangalore, India
2. Manipal Academy of Higher Education (MAHE), Manipal, India
Interests: dna; copy number variation; whole genome sequencing

Special Issue Information

Dear Colleagues,

Cancer is a disease of large-scale genomic instabilities. Progress has been made in genetics and genomics as the genetic mechanisms of cancer have started to be deciphered. With advancements in genomic technologies and next-generation sequencing (NGS), oncogenomic methods have become routine practice both in cancer research and clinical diagnostics. NGS DNA-sequencing technology has transformed the genetics and genomics of cancer genomes by providing unprecedented genetic and epigenetic underpinnings of cancer onset and progression. Aided by global large-scale oncogenomic efforts, oncology has faced a transformation from “one-size-fits-all” to “precision oncology”  in terms of the patient treatment approach as it became evident that cancer is caused by several genomic perturbations, heavily dependent on individuals and their genetic and environmental factors. Yet, cancer is a complex disorder, and deciphering the pathogenesis mechanisms and translational solutions for direct applications is still a challenging task. It requires pinpointing various triggers in human cells, which cause cancer. Cancer experts and cancer genomic centers are focusing on combining the power of several omics approaches to decipher cancer-causing triggers and mechanisms using DNA-seq (WES/WGS), NGS-based expression profiling (RNA-seq, miRNA-seq and DNA methylation), single-cell profiles and cancer proteomics. Multi-omics-based oncology is a high-dimensional data-driven scientific investigation carried out at multiple levels and scales to reveal cancer cell complexities and their environment using data science and data engineering methods.

This Special Issue focuses on oncogenic progress, cancer data exploration, data science, and data engineering approaches for novel findings in oncogenomics, the computational pipeline, and tool development focusing on translational research to understand cancer biology.

We look forward to receiving your contributions.


Prof. Dr. Lu Lu
Dr. Abhishek Kumar
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 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. Cancers is an international peer-reviewed open access semimonthly 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 2900 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

  • oncogenomics
  • onco-multiomics
  • precision oncology
  • cancer genomics
  • personalized medicine
  • cancer data science
  • AI based oncological advancements
  • germline oncogenomics
  • somatic oncogenomics

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Published Papers (3 papers)

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Review

24 pages, 1573 KiB  
Review
Integrating Omics Data and AI for Cancer Diagnosis and Prognosis
by Yousaku Ozaki, Phil Broughton, Hamed Abdollahi, Homayoun Valafar and Anna V. Blenda
Cancers 2024, 16(13), 2448; https://doi.org/10.3390/cancers16132448 - 3 Jul 2024
Cited by 2 | Viewed by 3104
Abstract
Cancer is one of the leading causes of death, making timely diagnosis and prognosis very important. Utilization of AI (artificial intelligence) enables providers to organize and process patient data in a way that can lead to better overall outcomes. This review paper aims [...] Read more.
Cancer is one of the leading causes of death, making timely diagnosis and prognosis very important. Utilization of AI (artificial intelligence) enables providers to organize and process patient data in a way that can lead to better overall outcomes. This review paper aims to look at the varying uses of AI for diagnosis and prognosis and clinical utility. PubMed and EBSCO databases were utilized for finding publications from 1 January 2020 to 22 December 2023. Articles were collected using key search terms such as “artificial intelligence” and “machine learning.” Included in the collection were studies of the application of AI in determining cancer diagnosis and prognosis using multi-omics data, radiomics, pathomics, and clinical and laboratory data. The resulting 89 studies were categorized into eight sections based on the type of data utilized and then further subdivided into two subsections focusing on cancer diagnosis and prognosis, respectively. Eight studies integrated more than one form of omics, namely genomics, transcriptomics, epigenomics, and proteomics. Incorporating AI into cancer diagnosis and prognosis alongside omics and clinical data represents a significant advancement. Given the considerable potential of AI in this domain, ongoing prospective studies are essential to enhance algorithm interpretability and to ensure safe clinical integration. Full article
(This article belongs to the Special Issue Oncogenomic and Multi-Omic Data Science and Data Engineering)
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29 pages, 8811 KiB  
Review
Predictive and Prognostic Relevance of Tumor-Infiltrating Immune Cells: Tailoring Personalized Treatments against Different Cancer Types
by Tikam Chand Dakal, Nancy George, Caiming Xu, Prashanth Suravajhala and Abhishek Kumar
Cancers 2024, 16(9), 1626; https://doi.org/10.3390/cancers16091626 - 23 Apr 2024
Cited by 1 | Viewed by 2517
Abstract
TIICs are critical components of the TME and are used to estimate prognostic and treatment responses in many malignancies. TIICs in the tumor microenvironment are assessed and quantified by categorizing immune cells into three subtypes: CD66b+ tumor-associated neutrophils (TANs), FoxP3+ regulatory T cells [...] Read more.
TIICs are critical components of the TME and are used to estimate prognostic and treatment responses in many malignancies. TIICs in the tumor microenvironment are assessed and quantified by categorizing immune cells into three subtypes: CD66b+ tumor-associated neutrophils (TANs), FoxP3+ regulatory T cells (Tregs), and CD163+ tumor-associated macrophages (TAMs). In addition, many cancers have tumor-infiltrating M1 and M2 macrophages, neutrophils (Neu), CD4+ T cells (T-helper), CD8+ T cells (T-cytotoxic), eosinophils, and mast cells. A variety of clinical treatments have linked tumor immune cell infiltration (ICI) to immunotherapy receptivity and prognosis. To improve the therapeutic effectiveness of immune-modulating drugs in a wider cancer patient population, immune cells and their interactions in the TME must be better understood. This study examines the clinicopathological effects of TIICs in overcoming tumor-mediated immunosuppression to boost antitumor immune responses and improve cancer prognosis. We successfully analyzed the predictive and prognostic usefulness of TIICs alongside TMB and ICI scores to identify cancer’s varied immune landscapes. Traditionally, immune cell infiltration was quantified using flow cytometry, immunohistochemistry, gene set enrichment analysis (GSEA), CIBERSORT, ESTIMATE, and other platforms that use integrated immune gene sets from previously published studies. We have also thoroughly examined traditional limitations and newly created unsupervised clustering and deconvolution techniques (SpatialVizScore and ProTICS). These methods predict patient outcomes and treatment responses better. These models may also identify individuals who may benefit more from adjuvant or neoadjuvant treatment. Overall, we think that the significant contribution of TIICs in cancer will greatly benefit postoperative follow-up, therapy, interventions, and informed choices on customized cancer medicines. Full article
(This article belongs to the Special Issue Oncogenomic and Multi-Omic Data Science and Data Engineering)
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25 pages, 2894 KiB  
Review
NDRGs in Breast Cancer: A Review and In Silico Analysis
by Emilly S. Villodre, Anh P. N. Nguyen and Bisrat G. Debeb
Cancers 2024, 16(7), 1342; https://doi.org/10.3390/cancers16071342 - 29 Mar 2024
Viewed by 2351
Abstract
The N-myc downstream regulated gene family (NDRGs) includes four members: NDRG1, NDRG2, NDRG3, and NDRG4. These members exhibit 53–65% amino acid identity. The role of NDRGs in tumor growth and metastasis appears to be tumor- and context-dependent. While many [...] Read more.
The N-myc downstream regulated gene family (NDRGs) includes four members: NDRG1, NDRG2, NDRG3, and NDRG4. These members exhibit 53–65% amino acid identity. The role of NDRGs in tumor growth and metastasis appears to be tumor- and context-dependent. While many studies have reported that these family members have tumor suppressive roles, recent studies have demonstrated that NDRGs, particularly NDRG1 and NDRG2, function as oncogenes, promoting tumor growth and metastasis. Additionally, NDRGs are involved in regulating different signaling pathways and exhibit diverse cellular functions in breast cancers. In this review, we comprehensively outline the oncogenic and tumor suppressor roles of the NDRG family members in breast cancer, examining evidence from in vitro and in vivo breast cancer models as well as tumor tissues from breast cancer patients. We also present analyses of publicly available genomic and transcriptomic data from multiple independent cohorts of breast cancer patients. Full article
(This article belongs to the Special Issue Oncogenomic and Multi-Omic Data Science and Data Engineering)
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