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Computational Oncogenomics
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Computational Oncogenomics

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 (31 July 2019) | Viewed by 21542

Special Issue Editor

Prof. Dr. Edwin Wang

E-Mail Website
Guest Editor
Cumming School of Medicine, University of Calgary, Calgary, AB T2N 1N4, Canada
Interests: computational systems biology; big medical data analysis; machine learning; deep learning; cancer genetics; molecular networks and cancer biomarker discovery

Special Issue Information

Dear colleagues,

Omic studies of tumors have generated a lot of data, including data from genome sequencing, transcriptomes, proteomes, ATAC-seq and genome-wide association studies (GWAS). Furthermore, single-cell genomics data have been generated in the past two-to-three years. The sampling for omics data generation have been applied to primary and metastasis tumors, with or without treatment.

We would like to collect a set of research papers, methods and reviews in a broad range of topics such as tumor genomics, transcriptome, methylome and singe-cell data. Single-cell omics and immunotherapy are currently on the state of the art, but other research fields are also on the rise. Therefore, this Special Issue is going to consider for publication:

  • Genomic studies in cancer
  • Transcriptomic studies in cancer
  • Methylome studies in cancer
  • Tumor genomics and evolution
  • Copy number variation (CNV) studies
  • Functional genetics
  • Cancer biomarkers
  • Immunotherapy
  • Network analysis for cancer outcome
  • Clinical outcomes
  • Single-cell genomics
  • Translational cancer genomics

Prof. Edwin Wang
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

  • cancer genomics
  • cancer transcriptomics
  • cancer methylome
  • tumor genetics and evolution
  • CNV
  • functional genetics
  • cancer biomarkers
  • immunotherapy
  • network analysis
  • single-cell genomics
  • translational cancer genomics
  • computational biology

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Published Papers (3 papers)

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Research

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13 pages, 1591 KiB  
Article
Prediction of Bone Metastasis in Breast Cancer Based on Minimal Driver Gene Set in Gene Dependency Network
by Jia-Nuo Li, Rui Zhong and Xiong-Hui Zhou
Genes 2019, 10(6), 466; https://doi.org/10.3390/genes10060466 - 17 Jun 2019
Cited by 10 | Viewed by 3919
Abstract
Bone is the most frequent organ for breast cancer metastasis, and thus it is essential to predict the bone metastasis of breast cancer. In our work, we constructed a gene dependency network based on the hypothesis that the relation between one gene and [...] Read more.
Bone is the most frequent organ for breast cancer metastasis, and thus it is essential to predict the bone metastasis of breast cancer. In our work, we constructed a gene dependency network based on the hypothesis that the relation between one gene and the risk of bone metastasis might be affected by another gene. Then, based on the structure controllability theory, we mined the driver gene set which can control the whole network in the gene dependency network, and the signature genes were selected from them. Survival analysis showed that the signature could distinguish the bone metastasis risks of cancer patients in the test data set and independent data set. Besides, we used the signature genes to construct a centroid classifier. The results showed that our method is effective and performed better than published methods. Full article
(This article belongs to the Special Issue Computational Oncogenomics)
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15 pages, 2101 KiB  
Article
Group Lasso Regularized Deep Learning for Cancer Prognosis from Multi-Omics and Clinical Features
by Gangcai Xie, Chengliang Dong, Yinfei Kong, Jiang F. Zhong, Mingyao Li and Kai Wang
Genes 2019, 10(3), 240; https://doi.org/10.3390/genes10030240 - 21 Mar 2019
Cited by 75 | Viewed by 9173
Abstract
Accurate prognosis of patients with cancer is important for the stratification of patients, the optimization of treatment strategies, and the design of clinical trials. Both clinical features and molecular data can be used for this purpose, for instance, to predict the survival of [...] Read more.
Accurate prognosis of patients with cancer is important for the stratification of patients, the optimization of treatment strategies, and the design of clinical trials. Both clinical features and molecular data can be used for this purpose, for instance, to predict the survival of patients censored at specific time points. Multi-omics data, including genome-wide gene expression, methylation, protein expression, copy number alteration, and somatic mutation data, are becoming increasingly common in cancer studies. To harness the rich information in multi-omics data, we developed GDP (Group lass regularized Deep learning for cancer Prognosis), a computational tool for survival prediction using both clinical and multi-omics data. GDP integrated a deep learning framework and Cox proportional hazard model (CPH) together, and applied group lasso regularization to incorporate gene-level group prior knowledge into the model training process. We evaluated its performance in both simulated and real data from The Cancer Genome Atlas (TCGA) project. In simulated data, our results supported the importance of group prior information in the regularization of the model. Compared to the standard lasso regularization, we showed that group lasso achieved higher prediction accuracy when the group prior knowledge was provided. We also found that GDP performed better than CPH for complex survival data. Furthermore, analysis on real data demonstrated that GDP performed favorably against other methods in several cancers with large-scale omics data sets, such as glioblastoma multiforme, kidney renal clear cell carcinoma, and bladder urothelial carcinoma. In summary, we demonstrated that GDP is a powerful tool for prognosis of patients with cancer, especially when large-scale molecular features are available. Full article
(This article belongs to the Special Issue Computational Oncogenomics)
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Review

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36 pages, 1363 KiB  
Review
The Many Faces of Gene Regulation in Cancer: A Computational Oncogenomics Outlook
by Enrique Hernández-Lemus, Helena Reyes-Gopar, Jesús Espinal-Enríquez and Soledad Ochoa
Genes 2019, 10(11), 865; https://doi.org/10.3390/genes10110865 - 30 Oct 2019
Cited by 25 | Viewed by 7911
Abstract
Cancer is a complex disease at many different levels. The molecular phenomenology of cancer is also quite rich. The mutational and genomic origins of cancer and their downstream effects on processes such as the reprogramming of the gene regulatory control and the molecular [...] Read more.
Cancer is a complex disease at many different levels. The molecular phenomenology of cancer is also quite rich. The mutational and genomic origins of cancer and their downstream effects on processes such as the reprogramming of the gene regulatory control and the molecular pathways depending on such control have been recognized as central to the characterization of the disease. More important though is the understanding of their causes, prognosis, and therapeutics. There is a multitude of factors associated with anomalous control of gene expression in cancer. Many of these factors are now amenable to be studied comprehensively by means of experiments based on diverse omic technologies. However, characterizing each dimension of the phenomenon individually has proven to fall short in presenting a clear picture of expression regulation as a whole. In this review article, we discuss some of the more relevant factors affecting gene expression control both, under normal conditions and in tumor settings. We describe the different omic approaches that we can use as well as the computational genomic analysis needed to track down these factors. Then we present theoretical and computational frameworks developed to integrate the amount of diverse information provided by such single-omic analyses. We contextualize this within a systems biology-based multi-omic regulation setting, aimed at better understanding the complex interplay of gene expression deregulation in cancer. Full article
(This article belongs to the Special Issue Computational Oncogenomics)
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