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13 pages, 1591 KiB

Open AccessArticle

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 3133

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 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

Open AccessArticle

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 52 | Viewed by 7714

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 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

Open AccessReview

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 17 | Viewed by 6448

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 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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