Genes

Editorial

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Open AccessEditorial

An Introduction to Integrative Genomics and Systems Medicine in Cancer

by Xiaolong Cheng and Victor X. Jin

Genes 2018, 9(1), 37; https://doi.org/10.3390/genes9010037 - 12 Jan 2018

Cited by 2 | Viewed by 3625

Abstract

In this Special Issue (SI), with a theme of “Integrative Genomics and Systems Medicine in Cancer”, we have collected a total of 12 research and review articles from researchers in the field of genomics and systems medicine[...] Full article

(This article belongs to the Special Issue Integrative Genomics and Systems Medicine in Cancer)

Research

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730 KiB

Open AccessArticle

ConGEMs: Condensed Gene Co-Expression Module Discovery Through Rule-Based Clustering and Its Application to Carcinogenesis

by Saurav Mallik and Zhongming Zhao

Genes 2018, 9(1), 7; https://doi.org/10.3390/genes9010007 - 28 Dec 2017

Cited by 14 | Viewed by 4672

Abstract

For transcriptomic analysis, there are numerous microarray-based genomic data, especially those generated for cancer research. The typical analysis measures the difference between a cancer sample-group and a matched control group for each transcript or gene. Association rule mining is used to discover interesting [...] Read more.

For transcriptomic analysis, there are numerous microarray-based genomic data, especially those generated for cancer research. The typical analysis measures the difference between a cancer sample-group and a matched control group for each transcript or gene. Association rule mining is used to discover interesting item sets through rule-based methodology. Thus, it has advantages to find causal effect relationships between the transcripts. In this work, we introduce two new rule-based similarity measures—weighted rank-based Jaccard and Cosine measures—and then propose a novel computational framework to detect condensed gene co-expression modules (

C o n G E M

s) through the association rule-based learning system and the weighted similarity scores. In practice, the list of evolved condensed markers that consists of both singular and complex markers in nature depends on the corresponding condensed gene sets in either antecedent or consequent of the rules of the resultant modules. In our evaluation, these markers could be supported by literature evidence, KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway and Gene Ontology annotations. Specifically, we preliminarily identified differentially expressed genes using an empirical Bayes test. A recently developed algorithm—RANWAR—was then utilized to determine the association rules from these genes. Based on that, we computed the integrated similarity scores of these rule-based similarity measures between each rule-pair, and the resultant scores were used for clustering to identify the co-expressed rule-modules. We applied our method to a gene expression dataset for lung squamous cell carcinoma and a genome methylation dataset for uterine cervical carcinogenesis. Our proposed module discovery method produced better results than the traditional gene-module discovery measures. In summary, our proposed rule-based method is useful for exploring biomarker modules from transcriptomic data. Full article

(This article belongs to the Special Issue Integrative Genomics and Systems Medicine in Cancer)

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2777 KiB

Open AccessArticle

scRNASeqDB: A Database for RNA-Seq Based Gene Expression Profiles in Human Single Cells

by Yuan Cao, Junjie Zhu, Peilin Jia and Zhongming Zhao

Genes 2017, 8(12), 368; https://doi.org/10.3390/genes8120368 - 5 Dec 2017

Cited by 72 | Viewed by 13093

Abstract

Single-cell RNA sequencing (scRNA-Seq) is rapidly becoming a powerful tool for high-throughput transcriptomic analysis of cell states and dynamics at the single cell level. Both the number and quality of scRNA-Seq datasets have dramatically increased recently. A database that can comprehensively collect, curate, [...] Read more.

Single-cell RNA sequencing (scRNA-Seq) is rapidly becoming a powerful tool for high-throughput transcriptomic analysis of cell states and dynamics at the single cell level. Both the number and quality of scRNA-Seq datasets have dramatically increased recently. A database that can comprehensively collect, curate, and compare expression features of scRNA-Seq data in humans has not yet been built. Here, we present scRNASeqDB, a database that includes almost all the currently available human single cell transcriptome datasets (n = 38) covering 200 human cell lines or cell types and 13,440 samples. Our online web interface allows users to rank the expression profiles of the genes of interest across different cell types. It also provides tools to query and visualize data, including Gene Ontology and pathway annotations for differentially expressed genes between cell types or groups. The scRNASeqDB is a useful resource for single cell transcriptional studies. This database is publicly available at bioinfo.uth.edu/scrnaseqdb/. Full article

(This article belongs to the Special Issue Integrative Genomics and Systems Medicine in Cancer)

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Open AccessArticle

Comprehensive Profiling of lincRNAs in Lung Adenocarcinoma of Never Smokers Reveals Their Roles in Cancer Development and Prognosis

by Ying Li, Zheng Wang, Asha Nair, Wei Song, Ping Yang, Xiaoju Zhang and Zhifu Sun

Genes 2017, 8(11), 321; https://doi.org/10.3390/genes8110321 - 13 Nov 2017

Cited by 9 | Viewed by 4001

Abstract

Long intergenic non-coding RNA (lincRNA) is a family of gene transcripts, the functions of which are largely unknown. Although cigarette smoking is the main cause for lung cancer, lung cancer in non-smokers is a separate entity and its underlying cause is little known. [...] Read more.

Long intergenic non-coding RNA (lincRNA) is a family of gene transcripts, the functions of which are largely unknown. Although cigarette smoking is the main cause for lung cancer, lung cancer in non-smokers is a separate entity and its underlying cause is little known. Growing evidence suggests lincRNAs play a significant role in cancer development and progression; however, such data is lacking for lung cancer in non-smokers, or those who have never smoked. This study conducted comprehensive profiling of lincRNAs from RNA sequencing (RNA-seq) data of non-smoker patients with lung adenocarcinoma. Both known and novel lincRNAs distinctly segregated tumors from normal tissues. Approximately one third of lincRNAs were differentially expressed between tumors and normal samples and most of them were coordinated with their putative protein gene targets. More importantly, lincRNAs defined two clusters of tumors that were associated with tumor aggressiveness and patient survival. We identified a subset of lincRNAs that were differentially expressed and also associated with patient survival. Very high concordance (R² = 0.9) was observed for the differentially expressed lincRNAs in the Cancer Genome Atlas (TCGA) validation set of 85 transcriptomes and the lincRNAs associated with survival from the discovery set were similarly predictive in the validation set. These lincRNAs warrant further investigation as potential diagnostic and prognostic markers. Full article

(This article belongs to the Special Issue Integrative Genomics and Systems Medicine in Cancer)

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Open AccessArticle

Gene Regulatory Network Rewiring in the Immune Cells Associated with Cancer

by Pengyong Han, Chandrasekhar Gopalakrishnan, Haiquan Yu and Edwin Wang

Genes 2017, 8(11), 308; https://doi.org/10.3390/genes8110308 - 7 Nov 2017

Cited by 12 | Viewed by 6473

Abstract

The gene regulatory networks (GRNs) of immune cells not only indicate cell identity but also reveal the dynamic changes of immune cells when comparing their GRNs. Cancer immunotherapy has advanced in the past few years. Immune-checkpoint blockades (i.e., blocking PD-1, PD-L1, or CTLA-4) [...] Read more.

The gene regulatory networks (GRNs) of immune cells not only indicate cell identity but also reveal the dynamic changes of immune cells when comparing their GRNs. Cancer immunotherapy has advanced in the past few years. Immune-checkpoint blockades (i.e., blocking PD-1, PD-L1, or CTLA-4) have shown durable clinical effects on some patients with various advanced cancers. However, major gaps in our knowledge of immunotherapy have been recognized. To fill these gaps, we conducted a systematic analysis of the GRNs of key immune cell subsets (i.e., B cell, CD4, CD8, CD8 naïve, CD8 Effector memory, CD8 Central Memory, regulatory T, Thelper1, Thelper2, Thelp17, and NK (Nature killer) and DC (Dendritic cell) cells associated with cancer immunologic therapies. We showed that most of the GRNs of these cells in blood share key important hub regulators, but their subnetworks for controlling cell type-specific receptors are different, suggesting that transformation between these immune cell subsets could be fast so that they can rapidly respond to environmental cues. To understand how cancer cells send molecular signals to immune cells to make them more cancer-cell friendly, we compared the GRNs of the tumor-infiltrating immune T cells and their corresponding immune cells in blood. We showed that the network size of the tumor-infiltrating immune T cells’ GRNs was reduced when compared to the GRNs of their corresponding immune cells in blood. These results suggest that the shutting down certain cellular activities of the immune cells by cancer cells is one of the key molecular mechanisms for helping cancer cells to escape the defense of the host immune system. These results highlight the possibility of genetic engineering of T cells for turning on the identified subnetworks that have been shut down by cancer cells to combat tumors. Full article

(This article belongs to the Special Issue Integrative Genomics and Systems Medicine in Cancer)

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Open AccessArticle

The Pattern of microRNA Binding Site Distribution

by Fangyuan Zhang and Degeng Wang

Genes 2017, 8(11), 296; https://doi.org/10.3390/genes8110296 - 27 Oct 2017

Cited by 42 | Viewed by 5171

Abstract

Micro-RNA (miRNA or miR) regulates at least 60% of the genes in the human genome through their target sites at mRNA 3’-untranslated regions (UTR), and defects in miRNA expression regulation and target sites are frequently observed in cancers. We report here a systematic [...] Read more.

Micro-RNA (miRNA or miR) regulates at least 60% of the genes in the human genome through their target sites at mRNA 3’-untranslated regions (UTR), and defects in miRNA expression regulation and target sites are frequently observed in cancers. We report here a systematic analysis of the distribution of miRNA target sites. Using the evolutionarily conserved miRNA binding sites in the TargetScan database (release 7.1), we constructed a miRNA co-regulation network by connecting genes sharing common miRNA target sites. The network possesses characteristics of the ubiquitous small-world network. Non-hub genes in the network—those sharing miRNA target sites with small numbers of genes—tend to form small cliques with their neighboring genes, while hub genes exhibit high levels of promiscuousness in their neighboring genes. Additionally, miRNA target site distribution is extremely uneven. Among the miRNAs, the distribution concentrates on a small number of miRNAs, in that their target sites occur in an extraordinarily large number of genes, that is, they have large numbers of target genes. The distribution across the genes follows a similar pattern; the mRNAs of a small proportion of the genes contain extraordinarily large numbers of miRNA binding sites. Quantitatively, the patterns fit into the P_(K) ∝ K⁻^α relationship (P_(K): the number of miRNAs with K target genes or genes with K miRNA sites; α: a positive constant), the mathematical description of connection distribution among the nodes and a defining characteristic of the so-called scale-free networks—a subset of small-world networks. Notably, well-known tumor-suppressive miRNAs (Let-7, miR-15/16, 26, 29, 31, 34, 145, 200, 203–205, 223, and 375) collectively have more than expected target genes, and well-known cancer genes contain more than expected miRNA binding sites. In summary, miRNA target site distribution exhibits characteristics of the small-world network. The potential to use this pattern to better understand miRNA function and their oncological roles is discussed. Full article

(This article belongs to the Special Issue Integrative Genomics and Systems Medicine in Cancer)

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Open AccessArticle

Low-Grade Dysplastic Nodules Revealed as the Tipping Point during Multistep Hepatocarcinogenesis by Dynamic Network Biomarkers

by Lina Lu, Zhonglin Jiang, Yulin Dai and Luonan Chen

Genes 2017, 8(10), 268; https://doi.org/10.3390/genes8100268 - 13 Oct 2017

Cited by 10 | Viewed by 5051

Abstract

Hepatocellular carcinoma (HCC) is a complex disease with a multi-step carcinogenic process from preneoplastic lesions, including cirrhosis, low-grade dysplastic nodules (LGDNs), and high-grade dysplastic nodules (HGDNs) to HCC. There is only an elemental understanding of its molecular pathogenesis, for which a key problem [...] Read more.

Hepatocellular carcinoma (HCC) is a complex disease with a multi-step carcinogenic process from preneoplastic lesions, including cirrhosis, low-grade dysplastic nodules (LGDNs), and high-grade dysplastic nodules (HGDNs) to HCC. There is only an elemental understanding of its molecular pathogenesis, for which a key problem is to identify when and how the critical transition happens during the HCC initiation period at a molecular level. In this work, for the first time, we revealed that LGDNs is the tipping point (i.e., pre-HCC state rather than HCC state) of hepatocarcinogenesis based on a series of gene expression profiles by a new mathematical model termed dynamic network biomarkers (DNB)—a group of dominant genes or molecules for the transition. Different from the conventional biomarkers based on the differential expressions of the observed genes (or molecules) for diagnosing a disease state, the DNB model exploits collective fluctuations and correlations of the observed genes, thereby predicting the imminent disease state or diagnosing the critical state. Our results show that DNB composed of 59 genes signals the tipping point of HCC (i.e., LGDNs). On the other hand, there are a large number of differentially expressed genes between cirrhosis and HGDNs, which highlighted the stark differences or drastic changes before and after the tipping point or LGDNs, implying the 59 DNB members serving as the early-warning signals of the upcoming drastic deterioration for HCC. We further identified the biological pathways responsible for this transition, such as the type I interferon signaling pathway, Janus kinase–signal transducers and activators of transcription (JAK–STAT) signaling pathway, transforming growth factor (TGF)-β signaling pathway, retinoic acid-inducible gene I (RIG-I)-like receptor signaling pathway, cell adhesion molecules, and cell cycle. In particular, pathways related to immune system reactions and cell adhesion were downregulated, and pathways related to cell growth and death were upregulated. Furthermore, DNB was validated as an effective predictor of prognosis for HCV-induced HCC patients by survival analysis on independent data, suggesting a potential clinical application of DNB. This work provides biological insights into the dynamic regulations of the critical transitions during multistep hepatocarcinogenesis. Full article

(This article belongs to the Special Issue Integrative Genomics and Systems Medicine in Cancer)

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Open AccessArticle

Pathway Enrichment Analysis with Networks

by Lu Liu, Jinmao Wei and Jianhua Ruan

Genes 2017, 8(10), 246; https://doi.org/10.3390/genes8100246 - 28 Sep 2017

Cited by 16 | Viewed by 8216

Abstract

Detecting associations between an input gene set and annotated gene sets (e.g., pathways) is an important problem in modern molecular biology. In this paper, we propose two algorithms, termed NetPEA and NetPEA’, for conducting network-based pathway enrichment analysis. Our algorithms consider not only [...] Read more.

Detecting associations between an input gene set and annotated gene sets (e.g., pathways) is an important problem in modern molecular biology. In this paper, we propose two algorithms, termed NetPEA and NetPEA’, for conducting network-based pathway enrichment analysis. Our algorithms consider not only shared genes but also gene–gene interactions. Both algorithms utilize a protein–protein interaction network and a random walk with a restart procedure to identify hidden relationships between an input gene set and pathways, but both use different randomization strategies to evaluate statistical significance and as a result emphasize different pathway properties. Compared to an over representation-based method, our algorithms can identify more statistically significant pathways. Compared to an existing network-based algorithm, EnrichNet, our algorithms have a higher sensitivity in revealing the true causal pathways while at the same time achieving a higher specificity. A literature review of selected results indicates that some of the novel pathways reported by our algorithms are biologically relevant and important. While the evaluations are performed only with KEGG pathways, we believe the algorithms can be valuable for general functional discovery from high-throughput experiments. Full article

(This article belongs to the Special Issue Integrative Genomics and Systems Medicine in Cancer)

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Open AccessArticle

Predicting Variation of DNA Shape Preferences in Protein-DNA Interaction in Cancer Cells with a New Biophysical Model

by Kirill Batmanov and Junbai Wang

Genes 2017, 8(9), 233; https://doi.org/10.3390/genes8090233 - 18 Sep 2017

Cited by 4 | Viewed by 4934

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)

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578 KiB

Open AccessArticle

Mutation Clusters from Cancer Exome

by Zura Kakushadze and Willie Yu

Genes 2017, 8(8), 201; https://doi.org/10.3390/genes8080201 - 15 Aug 2017

Cited by 2 | Viewed by 46248

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)

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Open AccessCommunication

Evolutionary Origins of Cancer Driver Genes and Implications for Cancer Prognosis

by Xin-Yi Chu, Ling-Han Jiang, Xiong-Hui Zhou, Ze-Jia Cui and Hong-Yu Zhang

Genes 2017, 8(7), 182; https://doi.org/10.3390/genes8070182 - 14 Jul 2017

Cited by 17 | Viewed by 5566

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)

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Open AccessArticle

DNA Methylation Targets Influenced by Bisphenol A and/or Genistein Are Associated with Survival Outcomes in Breast Cancer Patients

by Rohit R. Jadhav, Julia Santucci-Pereira, Yao V. Wang, Joseph Liu, Theresa D. Nguyen, Jun Wang, Sarah Jenkins, Jose Russo, Tim H.-M. Huang, Victor X. Jin and Coral A. Lamartiniere

Genes 2017, 8(5), 144; https://doi.org/10.3390/genes8050144 - 15 May 2017

Cited by 32 | Viewed by 6803

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)

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Review

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Open AccessReview

Advances in Genomic Profiling and Analysis of 3D Chromatin Structure and Interaction

by Binhua Tang, Xiaolong Cheng, Yunlong Xi, Zixin Chen, Yufan Zhou and Victor X. Jin

Genes 2017, 8(9), 223; https://doi.org/10.3390/genes8090223 - 8 Sep 2017

Cited by 7 | Viewed by 8001

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)

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