Voting-Based Cancer Module Identification by Combining Topological and Data-Driven Properties

Recently, computational approaches integrating copy number aberrations (CNAs) and gene expression (GE) have been extensively studied to identify cancer-related genes and pathways. In this work, we integrate these two data sets with protein-protein interaction (PPI) information to find cancer-related functional modules. To integrate CNA and GE data, we first built a gene-gene relationship network from a set of seed genes by enumerating all types of pairwise correlations, e.g. GE-GE, CNA-GE, and CNA-CNA, over multiple patients. Next, we propose a voting-based cancer module identification algorithm by combining topological and data-driven properties (VToD algorithm) by using the gene-gene relationship network as a source of data-driven information, and the PPI data as topological information. We applied the VToD algorithm to 266 glioblastoma multiforme (GBM) and 96 ovarian carcinoma (OVC) samples that have both expression and copy number measurements, and identified 22 GBM modules and 23 OVC modules. Among 22 GBM modules, 15, 12, and 20 modules were significantly enriched with cancer-related KEGG, BioCarta pathways, and GO terms, respectively. Among 23 OVC modules, 19, 18, and 23 modules were significantly enriched with cancer-related KEGG, BioCarta pathways, and GO terms, respectively. Similarly, we also observed that 9 and 2 GBM modules and 15 and 18 OVC modules were enriched with cancer gene census (CGC) and specific cancer driver genes, respectively. Our proposed module-detection algorithm significantly outperformed other existing methods in terms of both functional and cancer gene set enrichments. Most of the cancer-related pathways from both cancer data sets found in our algorithm contained more than two types of gene-gene relationships, showing strong positive correlations between the number of different types of relationship and CGC enrichment -values (0.64 for GBM and 0.49 for OVC). This study suggests that identified modules containing both expression changes and CNAs can explain cancer-related activities with greater insights.     

WMAXC: A Weighted Maximum Clique Method for Identifying Condition-Specific Sub-Network

Sub-networks can expose complex patterns in an entire bio-molecular network by extracting interactions that depend on temporal or condition-specific contexts. When genes interact with each other during cellular processes, they may form differential co-expression patterns with other genes across different cell states. The identification of condition-specific sub-networks is of great importance in investigating how a living cell adapts to environmental changes. In this work, we propose the weighted MAXimum clique (WMAXC) method to identify a condition-specific sub-network. WMAXC first proposes scoring functions that jointly measure condition-specific changes to both individual genes and gene-gene co-expressions. It then employs a weaker formula of a general maximum clique problem and relates the maximum scored clique of a weighted graph to the optimization of a quadratic objective function under sparsity constraints. We combine a continuous genetic algorithm and a projection procedure to obtain a single optimal sub-network that maximizes the objective function (scoring function) over the standard simplex (sparsity constraints). We applied the WMAXC method to both simulated data and real data sets of ovarian and prostate cancer. Compared with previous methods, WMAXC selected a large fraction of cancer-related genes, which were enriched in cancer-related pathways. The results demonstrated that our method efficiently captured a subset of genes relevant under the investigated condition.     

Identifying core gene modules in glioblastoma based on multilayer factor-mediated dysfunctional regulatory networks through integrating multi-dimensional genomic data

The driver genetic aberrations collectively regulate core cellular processes underlying cancer development. However, identifying the modules of driver genetic alterations and characterizing their functional mechanisms are still major challenges for cancer studies. Here, we developed an integrative multi-omics method CMDD to identify the driver modules and their affecting dysregulated genes through characterizing genetic alteration-induced dysregulated networks. Applied to glioblastoma (GBM), the CMDD identified a core gene module of 17 genes, including seven known GBM drivers, and their dysregulated genes. The module showed significant association with shorter survival of GBM. When classifying driver genes in the module into two gene sets according to their genetic alteration patterns, we found that one gene set directly participated in the glioma pathway, while the other indirectly regulated the glioma pathway, mostly, via their dysregulated genes. Both of the two gene sets were significant contributors to survival and helpful for classifying GBM subtypes, suggesting their critical roles in GBM pathogenesis. Also, by applying the CMDD to other six cancers, we identified some novel core modules associated with overall survival of patients. Together, these results demonstrate integrative multi-omics data can identify driver modules and uncover their dysregulated genes, which is useful for interpreting cancer genome.     

癌症相关microRNA与靶基因的生物信息学分析

MicroRNAs(miRNAs)是一类长度约为22nt的内源性非编码RNA,通过与靶基因转录本互补结合调控基因的表达。近年来,研究发现miRNA与癌症发生密切相关,miRNA可以直接充当癌基因或者抑癌基因而影响肿瘤的发生和生长。为更进一步揭示癌症相关miRNA的特征及靶基因的功能,文章通过数据库搜索及文献检索,在人类基因组中发现了475个癌症相关miRNA,系统地比较了癌症相关miRNA与非癌症miRNA以及基因内和基因间区癌症相关miRNA在保守性、SNP位点分布、癌谱及转录调控等特性。研究发现,癌症相关miRNA比非癌症miRNA保守性要强,发生SNP概率比较低,同时发现miRNA所涉及癌症数目与保守性成正相关。基因组定位分析发现,癌症相关miRNA比非癌症miRNA更倾向于成簇存在。进一步对宿主基因、癌症相关miRNA及作用的靶基因与癌症发生进行关联分析,发现一些非癌症miRNA的宿主基因倾向于被癌症miRNA作用。本研究结果为深入理解miRNA与癌症之间的关系,以及进一步为miRNA作为癌症诊断指示物提供理论依据。     

Prioritizing cancer-related key miRNA-target interactions by integrative genomics

Accumulating evidence indicates that microRNAs (miRNAs) can function as oncogenes or tumor suppressor genes by controlling few key targets, which in turn contribute to the pathogenesis of cancer. The identification of cancer-related key miRNA-target interactions remains a challenge. We performed a systematic analysis of known cancer-related key interactions manually curated from published papers based on different aspects including sequence, expression and function. Known cancer-related key interactions show more miRNA binding sites (especially for 8mer binding sites), more reliable binding of miRNA to the target region, higher expression associations and broader functional coverage when compared to non-disease-related interactions. Through integrating these sequence, expression and function features, we proposed a bioinformatics approach termed PCmtI to prioritize cancer-related key interactions. Ten-fold cross-validation of our approach revealed that it can achieve an area under the receiver operating characteristic curve of 93.9%. Subsequent leave-one-miRNA-out cross-validation also demonstrated the performance of our approach. Using miR-155 as a case, we found that the top ranked interactions can account for most functions of miR-155. In addition, we further demonstrated the power of our approach by 23 recently identified cancer-related key interactions. The approach described here offers a new way for the discovery of novel cancer-related key miRNA-target interactions.     

circRNA-associated ceRNA network construction reveals the circRNAs involved in the progression and prognosis of breast cancer

Recently, increasing evidences show that circular RNAs (circRNAs) are important regulators of various diseases, especially cancer. However, the regulatory role and the potential mechanism of action of circRNAs in breast cancer remain largely unknown. In this study, weighted gene co-expression network analysis was conducted with the differentially expressed miRNAs and mRNAs in breast cancer from The Cancer Genome Atlas database to identify the key modules associated with the carcinogenesis of breast cancer. In the significant turquoise and brown modules, 22 miRNAs and 1877 mRNAs were identified, respectively. Then, We compared and predicted the target genes and performed survival analysis to identify the miRNAs and mRNAs related to the prognosis of breast cancer. A circRNA-related competitive endogenous RNA network was identified by database co-screening, and deleted in liver cancer 1 (DLC1) was identified as a key gene. Finally, to assess how genes in key modules and key genes contribute to the development of breast cancer, relevant pathway information was obtained through DAVID and Gene Set Enrichment Analysis. These data demonstrated that three circRNAs (hsa-circ-0083373, hsa-circ-0083374, and hsa-circ-0083375) that regulate DLC1 expression via hsa-mir-511 and are involved in the pathogenesis and development of breast cancer.     

Prioritizing human cancer microRNAs based on genes' functional consistency between microRNA and cancer

The identification of human cancer-related microRNAs (miRNAs) is important for cancer biology research. Although several identification methods have achieved remarkable success, they have overlooked the functional information associated with miRNAs. We present a computational framework that can be used to prioritize human cancer miRNAs by measuring the association between cancer and miRNAs based on the functional consistency score (FCS) of the miRNA target genes and the cancer-related genes. This approach proved successful in identifying the validated cancer miRNAs for 11 common human cancers with area under ROC curve (AUC) ranging from 71.15% to 96.36%. The FCS method had a significant advantage over miRNA differential expression analysis when identifying cancer-related miRNAs with a fine regulatory mechanism, such as miR-27a in colorectal cancer. Furthermore, a case study examining thyroid cancer showed that the FCS method can uncover novel cancer-related miRNAs such as miR-27a/b, which were showed significantly upregulated in thyroid cancer samples by qRT-PCR analysis. Our method can be used on a web-based server, CMP (cancer miRNA prioritization) and is freely accessible at http://bioinfo.hrbmu.edu.cn/CMP. This time- and cost-effective computational framework can be a valuable complement to experimental studies and can assist with future studies of miRNA involvement in the pathogenesis of cancers.     

A Systematic Review of MicroRNA in Glioblastoma Multiforme: Micro-modulators in the Mesenchymal Mode of Migration and Invasion

Glioblastoma multiforme (GBM) is an incurable form of brain cancer with a very poor prognosis. Because of its highly invasive nature, it is impossible to remove all tumor cells during surgical resection, making relapse inevitable. Further research into the regulatory mechanism underpinning GBM pathogenesis is therefore warranted, and over the past decade, there has been an increased focus on the functional role of microRNA (miRNA). This systematic review aims to present a comprehensive overview of all the available literature on the expression profiles and function of miRNA in GBM. Here, we have reviewed 163 papers and identified 253 upregulated, 95 downregulated, and 17 disputed miRNAs with respect to expression levels; 85 % of these miRNAs have not yet been functionally characterized. A focus in this study has been 26 interesting miRNAs involved in the mesenchymal mode of migration and invasion, demonstrating the importance of miRNAs in the context of the cellular niche. Both oncogenic and tumor-suppressive miRNAs were found to affect target genes involved in cell migration, cytoskeletal rearrangement, invasiveness, and angiogenesis. Clearly, the distinct functional properties of these miRNAs need further investigation and might hold a great potential in future molecular therapies targeting GBM.     

Prognostic value of the tumor-specific ceRNA network in epithelial ovarian cancer

Epithelial ovarian cancer is one of the leading causes of cancer-related death worldwide. Growing evidence indicates that multiple complex altered pathways play important regulatory roles in the development and progression of a variety of cancers, including epithelial ovarian cancer. However, the underlying mechanisms remain unclear. First, we identified differentially expressed messenger RNAs (mRNAs), long noncoding RNAs (lncRNAs), and microRNAs (miRNAs) in epithelial ovarian cancer by comparing the expression profiles between epithelial ovarian cancer samples and normal tissue samples in different GEO datasets. Then, GO- and KEGG-pathway-enrichment analyses were applied to investigate the primary functions of the overlapped differentially expressed mRNAs. Moreover, the primary enriched genes were used to construct the signal-network with Cytoscape software. In addition, we integrated the relationship among lncRNAs-miRNAs-mRNAs to create a competing endogenous RNA network. Finally, mRNAs that were associated with patient prognosis in epithelial ovarian cancer were selected using univariate Cox regression analysis. A total of 2,225 mRNAs, 336 lncRNAs, and 14 miRNAs were shown to be differentially expressed in epithelial ovarian cancer compared with normal tissues. The dysregulated mRNAs were primarily enriched in cell division and signal transduction, according to Gene Ontology, whereas, according to KEGG, they were primarily enriched in metabolic pathways and pathways in cancer. A total of 10 mRNAs were associated with patient prognosis in ovarian cancer. This study identifies a novel lncRNA–miRNA–mRNA network, which may suggest potential molecular mechanisms underlying the development of epithelial ovarian cancer, providing new insights for survival prediction and interventional strategies for epithelial ovarian cancer.     

Interactions of intergenic microRNAs with mRNAs of genes involved in carcinogenesis

miRNAs regulate gene expression by binding with mRNAs of many genes. Studying their effects on genes involved in oncogenesis is important in cancer diagnostics and therapeutics. The RNAHybrid 2.1 program was used to predict the strong miRNA binding sites (p < 0.0005) in target mRNAs. The program Finder 2.2 was created to verify 784 intergenic miRNAs (ig-miRNA) origin. Among 54 considered oncogenes and tumor suppressor genes, 47 genes are the best targets for ig-miRNAs. Accordingly, these genes are strongly regulated by 111 ig-miRNAs. Some miRNAs bind several mRNAs, and some mRNAs have several binding sites for miRNAs. Of the 54 mRNAs, 21.8%, 43.0%, and 35.2% of the miRNA binding sites are present in the 5'UTRs, CDSes, and 3'UTRs, respectively. The average density of the binding sites for miRNAs in the 5'UTR was 4.4 times and 4.1 times greater than in the CDS and the 3'UTR, respectively. Three types of interactions between miRNAs and mRNAs were identified, which differ according to the region of the miRNA bound to the mRNA: 1) binding occurs predominantly via the 3'-region of the miRNA; 2) binding occurs predominantly through the central region of the miRNA; and 3) binding occurs predominantly via the 5'-region of the miRNA. Several miRNAs effectively regulate only one gene, and this information could be useful in molecular medicine to modulate translation of the target mRNA. We recommend described new sites for validation by experimental investigation.     

PNME – A gene-gene parallel network module extraction method

In the domain of gene-gene network analysis, construction of co-expression networks and extraction of network modules have opened up enormous possibilities for exploring the role of genes in biological processes. Through such analysis, one can extract interesting behaviour of genes and would help in the discovery of genes participating in a common biological process. However, such network analysis methods in sequential processing mode often have been found time-consuming even for a moderately sized dataset.It is observed that most existing network construction techniques are capable of handling only positive correlations in gene-expression data whereas biologically-significant genes exhibit both positive and negative correlations. To address these problems, we propose a faster method for construction and analysis of gene-gene network and extraction of modules using a similarity measure which can identify both negatively and positively correlated co-expressed patterns. Our method utilizes General-purpose computing on graphics processing units (GPGPU) to provide fast, efficient and parallel extraction of biologically relevant network modules to support biomarker identification for breast cancer. The modules extracted are validated using p-value and q-value for both metastasis and non-metastasis stages of breast cancer. PNME has been found capable of identifying interesting biomarkers for this critical disease. We identified six genes with the interesting behaviours which have been found to cause breast cancer in homo-sapiens.