Special Issue "Integrative Genomics and Systems Medicine in Cancer"

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

Deadline for manuscript submissions: 30 September 2017

Special Issue Editors

Guest Editor
Dr. Victor Jin

The University of Texas Health Science Center at San Antonio, Department of Molecular Medicine/Institute of Biotechnology, Rm. 261 South Texas Research Facility, MC 8257, 7703 Floyd Curl Drive, San Antonio, TX, 78229-3900, USA
Website | E-Mail
Interests: Bioinformatics, Genomics, Cancer, Biological Chemistry
Co-Guest Editor
Dr. Binhua Tang

Hohai University, Jiangsu, China
E-Mail
Co-Guest Editor
Dr. Junbai Wang

Oslo University Hospital, Oslo, Norway
E-Mail

Special Issue Information

Dear Colleagues,

Cancer is one of the most complex diseases and the second deadliest disease in humans. The prevention, diagnosis and treatment of various types of cancers are the most important task and a priority in biomedical research communities. Integrative genomics and systems medicine are two interdisciplinary approaches that are synergistically merged as an emerging field. Integrative genomics conducts integrative analyses on high-throughput genomic data with novel computational algorithms and further correlates with clinical outcomes for the identification of biological pathways and molecular targets for better therapies for cancer patients, while systems medicine dissects the systems of the human body as a whole with incorporating biochemical, physiological, and environment interactions and builds predictive and actionable models that understand cancer heterogeneity and complexity.

During the past decade, we have witnessed genomics and systems medicine exponentially increased in terms of technologies, data volumes as well as publications. This Special Issue is designed to present the latest findings about regulatory genomics and systems biology in cancer. We welcome the genomics, bioinformatics, and statistical work in broad areas such as various–omics technologies, multi-dimensional data integration, systems biology approaches, precision medicine studies, single cell research, pharmacogenomics, machine learning, high performance computing, and visualization.

Dr. Victor Jin
Dr. Junbai Wang
Dr. Binhua Tang
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 papers will be 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 1200 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.

Published Papers (5 papers)

View options order results:
result details:
Displaying articles 1-5
Export citation of selected articles as:

Research

Jump to: Review

Open AccessArticle Predicting Variation of DNA Shape Preferences in Protein-DNA Interaction in Cancer Cells with a New Biophysical Model
Genes 2017, 8(9), 233; doi:10.3390/genes8090233
Received: 31 July 2017 / Revised: 13 September 2017 / Accepted: 13 September 2017 / Published: 18 September 2017
PDF Full-text (2175 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
DNA shape readout is an important mechanism of transcription factor target site recognition, in addition to the sequence readout. Several machine learning-based models of transcription factor–DNA interactions, considering DNA shape features, have been developed in recent years. Here, we present a new biophysical
[...] Read more.
DNA shape readout is an important mechanism of transcription factor target site recognition, in addition to the sequence readout. Several machine learning-based models of transcription factor–DNA interactions, considering DNA shape features, have been developed in recent years. Here, we present a new biophysical model of protein–DNA interactions by integrating the DNA shape properties. It is based on the neighbor dinucleotide dependency model BayesPI2, where new parameters are restricted to a subspace spanned by the dinucleotide form of DNA shape features. This allows a biophysical interpretation of the new parameters as a position-dependent preference towards specific DNA shape features. Using the new model, we explore the variation of DNA shape preferences in several transcription factors across various cancer cell lines and cellular conditions. The results reveal that there are DNA shape variations at FOXA1 (Forkhead Box Protein A1) binding sites in steroid-treated MCF7 cells. The new biophysical model is useful for elucidating the finer details of transcription factor–DNA interaction, as well as for predicting cancer mutation effects in the future. Full article
(This article belongs to the Special Issue Integrative Genomics and Systems Medicine in Cancer)
Figures

Figure 1

Open AccessArticle Mutation Clusters from Cancer Exome
Genes 2017, 8(8), 201; doi:10.3390/genes8080201
Received: 19 June 2017 / Revised: 26 July 2017 / Accepted: 7 August 2017 / Published: 15 August 2017
PDF Full-text (578 KB) | HTML Full-text | XML Full-text
Abstract
We apply our statistically deterministic machine learning/clustering algorithm *K-means (recently developed in https://ssrn.com/abstract=2908286) to 10,656 published exome samples for 32 cancer types. A majority of cancer types exhibit a mutation clustering structure. Our results are in-sample stable. They are also out-of-sample stable
[...] Read more.
We apply our statistically deterministic machine learning/clustering algorithm *K-means (recently developed in https://ssrn.com/abstract=2908286) to 10,656 published exome samples for 32 cancer types. A majority of cancer types exhibit a mutation clustering structure. Our results are in-sample stable. They are also out-of-sample stable when applied to 1389 published genome samples across 14 cancer types. In contrast, we find in- and out-of-sample instabilities in cancer signatures extracted from exome samples via nonnegative matrix factorization (NMF), a computationally-costly and non-deterministic method. Extracting stable mutation structures from exome data could have important implications for speed and cost, which are critical for early-stage cancer diagnostics, such as novel blood-test methods currently in development. Full article
(This article belongs to the Special Issue Integrative Genomics and Systems Medicine in Cancer)
Figures

Figure 1

Open AccessCommunication Evolutionary Origins of Cancer Driver Genes and Implications for Cancer Prognosis
Genes 2017, 8(7), 182; doi:10.3390/genes8070182
Received: 28 March 2017 / Revised: 27 June 2017 / Accepted: 10 July 2017 / Published: 14 July 2017
PDF Full-text (2958 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
The cancer atavistic theory suggests that carcinogenesis is a reverse evolution process. It is thus of great interest to explore the evolutionary origins of cancer driver genes and the relevant mechanisms underlying the carcinogenesis. Moreover, the evolutionary features of cancer driver genes could
[...] Read more.
The cancer atavistic theory suggests that carcinogenesis is a reverse evolution process. It is thus of great interest to explore the evolutionary origins of cancer driver genes and the relevant mechanisms underlying the carcinogenesis. Moreover, the evolutionary features of cancer driver genes could be helpful in selecting cancer biomarkers from high-throughput data. In this study, through analyzing the cancer endogenous molecular networks, we revealed that the subnetwork originating from eukaryota could control the unlimited proliferation of cancer cells, and the subnetwork originating from eumetazoa could recapitulate the other hallmarks of cancer. In addition, investigations based on multiple datasets revealed that cancer driver genes were enriched in genes originating from eukaryota, opisthokonta, and eumetazoa. These results have important implications for enhancing the robustness of cancer prognosis models through selecting the gene signatures by the gene age information. Full article
(This article belongs to the Special Issue Integrative Genomics and Systems Medicine in Cancer)
Figures

Figure 1

Open AccessArticle DNA Methylation Targets Influenced by Bisphenol A and/or Genistein Are Associated with Survival Outcomes in Breast Cancer Patients
Genes 2017, 8(5), 144; doi:10.3390/genes8050144
Received: 10 March 2017 / Revised: 25 April 2017 / Accepted: 9 May 2017 / Published: 15 May 2017
Cited by 1 | PDF Full-text (7108 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Early postnatal exposures to Bisphenol A (BPA) and genistein (GEN) have been reported to predispose for and against mammary cancer, respectively, in adult rats. Since the changes in cancer susceptibility occurs in the absence of the original chemical exposure, we have investigated the
[...] Read more.
Early postnatal exposures to Bisphenol A (BPA) and genistein (GEN) have been reported to predispose for and against mammary cancer, respectively, in adult rats. Since the changes in cancer susceptibility occurs in the absence of the original chemical exposure, we have investigated the potential of epigenetics to account for these changes. DNA methylation studies reveal that prepubertal BPA exposure alters signaling pathways that contribute to carcinogenesis. Prepubertal exposure to GEN and BPA + GEN revealed pathways involved in maintenance of cellular function, indicating that the presence of GEN either reduces or counters some of the alterations caused by the carcinogenic properties of BPA. We subsequently evaluated the potential of epigenetic changes in the rat mammary tissues to predict survival in breast cancer patients via the Cancer Genomic Atlas (TCGA). We identified 12 genes that showed strong predictive values for long-term survival in estrogen receptor positive patients. Importantly, two genes associated with improved long term survival, HPSE and RPS9, were identified to be hypomethylated in mammary glands of rats exposed prepuberally to GEN or to GEN + BPA respectively, reinforcing the suggested cancer suppressive properties of GEN. Full article
(This article belongs to the Special Issue Integrative Genomics and Systems Medicine in Cancer)
Figures

Figure 1

Review

Jump to: Research

Open AccessReview Advances in Genomic Profiling and Analysis of 3D Chromatin Structure and Interaction
Genes 2017, 8(9), 223; doi:10.3390/genes8090223
Received: 28 July 2017 / Revised: 25 August 2017 / Accepted: 4 September 2017 / Published: 8 September 2017
PDF Full-text (4350 KB) | HTML Full-text | XML Full-text
Abstract
Recent sequence-based profiling technologies such as high-throughput sequencing to detect fragment nucleotide sequence (Hi-C) and chromatin interaction analysis by paired-end tag sequencing (ChIA-PET) have revolutionized the field of three-dimensional (3D) chromatin architecture. It is now recognized that human genome functions as folded 3D
[...] Read more.
Recent sequence-based profiling technologies such as high-throughput sequencing to detect fragment nucleotide sequence (Hi-C) and chromatin interaction analysis by paired-end tag sequencing (ChIA-PET) have revolutionized the field of three-dimensional (3D) chromatin architecture. It is now recognized that human genome functions as folded 3D chromatin units and looping paradigm is the basic principle of gene regulation. To better interpret the 3D data dramatically accumulating in past five years and to gain deep biological insights, huge efforts have been made in developing novel quantitative analysis methods. However, the full understanding of genome regulation requires thorough knowledge in both genomic technologies and their related data analyses. We summarize the recent advances in genomic technologies in identifying the 3D chromatin structure and interaction, and illustrate the quantitative analysis methods to infer functional domains and chromatin interactions, and further elucidate the emerging single-cell Hi-C technique and its computational analysis, and finally discuss the future directions such as advances of 3D chromatin techniques in diseases. Full article
(This article belongs to the Special Issue Integrative Genomics and Systems Medicine in Cancer)
Figures

Figure 1

Journal Contact

MDPI AG
Genes Editorial Office
St. Alban-Anlage 66, 4052 Basel, Switzerland
E-Mail: 
Tel. +41 61 683 77 34
Fax: +41 61 302 89 18
Editorial Board
Contact Details Submit to Special Issue Edit a special issue Review for Genes
logo
loading...
Back to Top