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Data Science in Cancer Genomics and Precision Medicine
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Data Science in Cancer Genomics and Precision Medicine

A special issue of International Journal of Molecular Sciences (ISSN 1422-0067). This special issue belongs to the section "Molecular Genetics and Genomics".

Deadline for manuscript submissions: closed (30 December 2023) | Viewed by 12773

Special Issue Editor

Dr. Apostolos Zaravinos

E-Mail Website
Guest Editor
Cancer Genetics, Genomics and Systems Biology Laboratory, Basic and Translational Cancer Research Center (BTCRC), Nicosia 1516, 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

Special Issue Information

Dear Colleagues,

Data science in cancer genomics is a new interdisciplinary field that applies statistics and next-generation sequencing (NGS) technologies to understand alterations in the genome of cancer cells. Data generated by these technologies are often termed multi-omics data and can include information on DNA, RNA, proteins, and epigenetic modifications, among others. Data science in cancer genomics allows us to better understand the molecular basis of different cancers and exploit this information to match each patient with the most appropriate molecular targeted therapy, widely known as “Precision Medicine”. While traditional chemotherapy and radiation treatments target cellular processes common to both healthy and cancerous cells, precision medicine directs newly developed treatments specifically to cancer cells based on their underlying molecular profile.

This Special Issue of the International Journal of Molecular Sciences focuses on the research field of “Cancer Genomics and Precision Medicine” and welcomes both original research articles and review papers that deal with the molecular mechanisms underlying modification in human cancer cells.

Dr. Apostolos Zaravinos
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. International Journal of Molecular Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. There is an Article Processing Charge (APC) for publication in this open access journal. For details about the APC please see here. 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

  • cancer genomics
  • big data
  • tumor immunology
  • translational oncology
  • precision medicine
  • next generation sequencing
  • omics
  • cancer genomic datasets

Published Papers (6 papers)

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Research

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17 pages, 3420 KiB  
Article
Genetics and beyond: Precision Medicine Real-World Data for Patients with Cervical, Vaginal or Vulvar Cancer in a Tertiary Cancer Center
by Fabian B. T. Kraus, Elena Sultova, Kathrin Heinrich, Andreas Jung, C. Benedikt Westphalen, Christina V. Tauber, Jörg Kumbrink, Martina Rudelius, Frederick Klauschen, Philipp A. Greif, Alexander König, Anca Chelariu-Raicu, Bastian Czogalla, Alexander Burges, Sven Mahner, Rachel Wuerstlein and Fabian Trillsch
Int. J. Mol. Sci. 2024, 25(4), 2345; https://doi.org/10.3390/ijms25042345 - 16 Feb 2024
Viewed by 906
Abstract
Advances in molecular tumor diagnostics have transformed cancer care. However, it remains unclear whether precision oncology has the same impact and transformative nature across all malignancies. We conducted a retrospective analysis of patients with human papillomavirus (HPV)-related gynecologic malignancies who underwent comprehensive molecular [...] Read more.
Advances in molecular tumor diagnostics have transformed cancer care. However, it remains unclear whether precision oncology has the same impact and transformative nature across all malignancies. We conducted a retrospective analysis of patients with human papillomavirus (HPV)-related gynecologic malignancies who underwent comprehensive molecular profiling and subsequent discussion at the interdisciplinary Molecular Tumor Board (MTB) of the University Hospital, LMU Munich, between 11/2017 and 06/2022. We identified a total cohort of 31 patients diagnosed with cervical (CC), vaginal or vulvar cancer. Twenty-two patients (fraction: 0.71) harbored at least one mutation. Fifteen patients (0.48) had an actionable mutation and fourteen (0.45) received a recommendation for a targeted treatment within the MTB. One CC patient received a biomarker-guided treatment recommended by the MTB and achieved stable disease on the mTOR inhibitor temsirolimus for eight months. Factors leading to non-adherence to MTB recommendations in other patient cases included informed patient refusal, rapid deterioration, stable disease, or use of alternative targeted but biomarker-agnostic treatments such as antibody–drug conjugates or checkpoint inhibitors. Despite a remarkable rate of actionable mutations in HPV-related gynecologic malignancies at our institution, immediate implementation of biomarker-guided targeted treatment recommendations remained low, and access to targeted treatment options after MTB discussion remained a major challenge. Full article
(This article belongs to the Special Issue Data Science in Cancer Genomics and Precision Medicine)
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20 pages, 5628 KiB  
Article
Identification of 13 Novel Loci in a Genome-Wide Association Study on Taiwanese with Hepatocellular Carcinoma
by Ting-Yuan Liu, Chi-Chou Liao, Ya-Sian Chang, Yu-Chia Chen, Hong-Da Chen, I-Lu Lai, Cheng-Yuan Peng, Chin-Chun Chung, Yu-Pao Chou, Fuu-Jen Tsai, Long-Bin Jeng and Jan-Gowth Chang
Int. J. Mol. Sci. 2023, 24(22), 16417; https://doi.org/10.3390/ijms242216417 - 16 Nov 2023
Viewed by 1528
Abstract
Liver cancer is caused by complex interactions among genetic factors, viral infection, alcohol abuse, and metabolic diseases. We conducted a genome-wide association study and polygenic risk score (PRS) model in Taiwan, employing a nonspecific etiology approach, to identify genetic risk factors for hepatocellular [...] Read more.
Liver cancer is caused by complex interactions among genetic factors, viral infection, alcohol abuse, and metabolic diseases. We conducted a genome-wide association study and polygenic risk score (PRS) model in Taiwan, employing a nonspecific etiology approach, to identify genetic risk factors for hepatocellular carcinoma (HCC). Our analysis of 2836 HCC cases and 134,549 controls revealed 13 novel associated loci such as the FAM66C gene, noncoding genes, liver-fibrosis-related genes, metabolism-related genes, and HCC-related pathway genes. We incorporated the results from the UK Biobank and Japanese database into our study for meta-analysis to validate our findings. We also identified specific subtypes of the major histocompatibility complex that influence both viral infection and HCC progression. Using this data, we developed a PRS to predict HCC risk in the general population, patients with HCC, and HCC-affected families. The PRS demonstrated higher risk scores in families with multiple HCCs and other cancer cases. This study presents a novel approach to HCC risk analysis, identifies seven new genes associated with HCC development, and introduces a reproducible PRS model for risk assessment. Full article
(This article belongs to the Special Issue Data Science in Cancer Genomics and Precision Medicine)
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31 pages, 27085 KiB  
Article
Signatures of Co-Deregulated Genes and Their Transcriptional Regulators in Kidney Cancers
by Ioanna Ioannou, Angeliki Chatziantoniou, Constantinos Drenios, Panayiota Christodoulou, Malamati Kourti and Apostolos Zaravinos
Int. J. Mol. Sci. 2023, 24(7), 6577; https://doi.org/10.3390/ijms24076577 - 31 Mar 2023
Cited by 2 | Viewed by 2577
Abstract
There are several studies on the deregulated gene expression profiles in kidney cancer, with varying results depending on the tumor histology and other parameters. None of these, however, have identified the networks that the co-deregulated genes (co-DEGs), across different studies, create. Here, we [...] Read more.
There are several studies on the deregulated gene expression profiles in kidney cancer, with varying results depending on the tumor histology and other parameters. None of these, however, have identified the networks that the co-deregulated genes (co-DEGs), across different studies, create. Here, we reanalyzed 10 Gene Expression Omnibus (GEO) studies to detect and annotate co-deregulated signatures across different subtypes of kidney cancer or in single-gene perturbation experiments in kidney cancer cells and/or tissue. Using a systems biology approach, we aimed to decipher the networks they form along with their upstream regulators. Differential expression and upstream regulators, including transcription factors [MYC proto-oncogene (MYC), CCAAT enhancer binding protein delta (CEBPD), RELA proto-oncogene, NF-kB subunit (RELA), zinc finger MIZ-type containing 1 (ZMIZ1), negative elongation factor complex member E (NELFE) and Kruppel-like factor 4 (KLF4)] and protein kinases [Casein kinase 2 alpha 1 (CSNK2A1), mitogen-activated protein kinases 1 (MAPK1) and 14 (MAPK14), Sirtuin 1 (SIRT1), Cyclin dependent kinases 1 (CDK1) and 4 (CDK4), Homeodomain interacting protein kinase 2 (HIPK2) and Extracellular signal-regulated kinases 1 and 2 (ERK1/2)], were computed using the Characteristic Direction, as well as GEO2Enrichr and X2K, respectively, and further subjected to GO and KEGG pathways enrichment analyses. Furthermore, using CMap, DrugMatrix and the LINCS L1000 chemical perturbation databases, we highlight putative repurposing drugs, including Etoposide, Haloperidol, BW-B70C, Triamterene, Chlorphenesin, BRD-K79459005 and β-Estradiol 3-benzoate, among others, that may reverse the expression of the identified co-DEGs in kidney cancers. Of these, the cytotoxic effects of Etoposide, Catecholamine, Cyclosporin A, BW-B70C and Lasalocid sodium were validated in vitro. Overall, we identified critical co-DEGs across different subtypes in kidney cancer, and our results provide an innovative framework for their potential use in the future. Full article
(This article belongs to the Special Issue Data Science in Cancer Genomics and Precision Medicine)
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14 pages, 3533 KiB  
Article
AR Expression Correlates with Distinctive Clinicopathological and Genomic Features in Breast Cancer Regardless of ESR1 Expression Status
by Mengping Long, Chong You, Qianqian Song, Lina X. J. Hu, Zhaorong Guo, Qian Yao, Wei Hou, Wei Sun, Baosheng Liang, Xiaohua Zhou, Yiqiang Liu and Taobo Hu
Int. J. Mol. Sci. 2022, 23(19), 11468; https://doi.org/10.3390/ijms231911468 - 29 Sep 2022
Viewed by 1642
Abstract
Androgen receptor (AR) expression is frequently observed in breast cancer, but its association with estrogen receptor (ER) expression in breast cancer remains unclear. This study analyzed the clinicopathological and molecular features associated with AR negativity in both ER-positive and ER-negative breast cancer, trying [...] Read more.
Androgen receptor (AR) expression is frequently observed in breast cancer, but its association with estrogen receptor (ER) expression in breast cancer remains unclear. This study analyzed the clinicopathological and molecular features associated with AR negativity in both ER-positive and ER-negative breast cancer, trying to elucidate the molecular correlation between AR and ER. Our results showed that AR negativity was associated with different clinicopathological characteristics and molecular features in ER-positive and ER-negative breast cancer. Moreover, AR-positive breast cancer has better clinicopathological features than AR-negative breast cancer, especially in the ER-negative subtype. These results suggest that the role of AR in ER-negative breast cancer is distinctive from that in ER-positive breast cancer. Full article
(This article belongs to the Special Issue Data Science in Cancer Genomics and Precision Medicine)
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Review

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12 pages, 441 KiB  
Review
Unraveling the Molecular Puzzle: Exploring Gene Networks across Diverse EMT Status of Cell Lines
by Heewon Park
Int. J. Mol. Sci. 2023, 24(16), 12784; https://doi.org/10.3390/ijms241612784 - 14 Aug 2023
Viewed by 855
Abstract
Understanding complex disease mechanisms requires a comprehensive understanding of the gene regulatory networks, as complex diseases are often characterized by the dysregulation and dysfunction of molecular networks, rather than abnormalities in single genes. Specifically, the exploration of cell line-specific gene networks can provide [...] Read more.
Understanding complex disease mechanisms requires a comprehensive understanding of the gene regulatory networks, as complex diseases are often characterized by the dysregulation and dysfunction of molecular networks, rather than abnormalities in single genes. Specifically, the exploration of cell line-specific gene networks can provide essential clues for precision medicine, as this methodology can uncover molecular interplays specific to particular cell line statuses, such as drug sensitivity, cancer progression, etc. In this article, we provide a comprehensive review of computational strategies for cell line-specific gene network analysis: (1) cell line-specific gene regulatory network estimation and analysis of gene networks under varying epithelial–mesenchymal transition (EMT) statuses of cell lines; and (2) an explainable artificial intelligence approach for interpreting the estimated massive multiple EMT-status-specific gene networks. The objective of this review is to help readers grasp the concept of computational network biology, which holds significant implications for precision medicine by offering crucial clues. Full article
(This article belongs to the Special Issue Data Science in Cancer Genomics and Precision Medicine)
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36 pages, 9056 KiB  
Review
Big Data in Gastroenterology Research
by Madeline Alizadeh, Natalia Sampaio Moura, Alyssa Schledwitz, Seema A. Patil, Jacques Ravel and Jean-Pierre Raufman
Int. J. Mol. Sci. 2023, 24(3), 2458; https://doi.org/10.3390/ijms24032458 - 27 Jan 2023
Cited by 6 | Viewed by 3298
Abstract
Studying individual data types in isolation provides only limited and incomplete answers to complex biological questions and particularly falls short in revealing sufficient mechanistic and kinetic details. In contrast, multi-omics approaches to studying health and disease permit the generation and integration of multiple [...] Read more.
Studying individual data types in isolation provides only limited and incomplete answers to complex biological questions and particularly falls short in revealing sufficient mechanistic and kinetic details. In contrast, multi-omics approaches to studying health and disease permit the generation and integration of multiple data types on a much larger scale, offering a comprehensive picture of biological and disease processes. Gastroenterology and hepatobiliary research are particularly well-suited to such analyses, given the unique position of the luminal gastrointestinal (GI) tract at the nexus between the gut (mucosa and luminal contents), brain, immune and endocrine systems, and GI microbiome. The generation of ‘big data’ from multi-omic, multi-site studies can enhance investigations into the connections between these organ systems and organisms and more broadly and accurately appraise the effects of dietary, pharmacological, and other therapeutic interventions. In this review, we describe a variety of useful omics approaches and how they can be integrated to provide a holistic depiction of the human and microbial genetic and proteomic changes underlying physiological and pathophysiological phenomena. We highlight the potential pitfalls and alternatives to help avoid the common errors in study design, execution, and analysis. We focus on the application, integration, and analysis of big data in gastroenterology and hepatobiliary research. Full article
(This article belongs to the Special Issue Data Science in Cancer Genomics and Precision Medicine)
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