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.     

Comprehensive analysis of miRNA-gene regulatory network with clinical significance in human cancers

microRNAs(miRNAs), particularly the exosomal miRNAs have been widely used as biomarkers and promising therapeutic targets in cancer. However, a comprehensive analysis of miRNA-gene regulatory network with clinical significance remains scarce. The emergence of high-throughput multi-omics data over large, well-characterized patient cohorts provides an unprecedented opportunity to address this problem. Herein, we performed a clinic-centered analysis to identify cancer-associated miRNAs, miRNA-target axis. We first calculated the correlation among miRNA, mRNA and 75 unique clinico-pathological characteristics(CPCs) in 26 cancer types, and established an online resource(4CR). Interestingly, we found that the high expression of several DNA methylation-related enzymes was associated with adverse outcomes of cancer patients, and these genes were regulated by a cluster of miRNAs. Furthermore, by integrating exosomal miRNA and m RNA databases, we identified exosomal miRNA biomarkers for non-invasive cancer surveillance and therapy monitoring. Finally, we explored the role of CPC-related miRNAs for therapeutic effect prediction of drugs based on their shared targets. Our analysis pipeline illustrated the significance of clinic-centered analysis in miRNA-gene pair identification and provided helpful clues for future cancer studies.     

miRNA功能的研究进展

综述了miRNA功能的最新研究进展。miRNA是一类长度约20~24nt的非编码调控单链小分子RNA,其功能和作用是近年来分子生物学界关注的重点。这些微小的RNA控制着包括细胞增殖、凋亡、器官发生、发育、造血以及肿瘤发生等若干途径。最近研究发现,miRNA可能同时具有肿瘤抑制因子和源癌基因的功能,并且可能在癌症的诊断和治疗中发挥重要的作用。miRNA可以通过影响或者调控细胞增殖、分化过程中的信使RNA和关键蛋白质等参与细胞的发育。此外,miRNA对多种植物激素的调控作用对于植物体的发育也具有重要意义。     

Protein network-based Lasso regression model for the construction of disease-miRNA functional interactions

BackgroundThere is a growing body of evidence associating microRNAs (miRNAs) with human diseases. MiRNAs are new key players in the disease paradigm demonstrating roles in several human diseases. The functional association between miRNAs and diseases remains largely unclear and far from complete. With the advent of high-throughput functional genomics techniques that infer genes and biological pathways dysregulted in diseases, it is now possible to infer functional association between diseases and biological molecules by integrating disparate biological information.ResultsHere, we first used Lasso regression model to identify miRNAs associated with disease signature as a proof of concept. Then we proposed an integrated approach that uses disease-gene associations from microarray experiments and text mining, and miRNA-gene association from computational predictions and protein networks to build functional associations network between miRNAs and diseases. The findings of the proposed model were validated against gold standard datasets using ROC analysis and results were promising (AUC=0.81). Our protein network-based approach discovered 19 new functional associations between prostate cancer and miRNAs. The new 19 associations were validated using miRNA expression data and clinical profiles and showed to act as diagnostic and prognostic prostate biomarkers. The proposed integrated approach allowed us to reconstruct functional associations between miRNAs and human diseases and uncovered functional roles of newly discovered miRNAs.ConclusionsLasso regression was used to find associations between diseases and miRNAs using their gene signature. Defining miRNA gene signature by integrating the downstream effect of miRNAs demonstrated better performance than the miRNA signature alone. Integrating biological networks and multiple data to define miRNA and disease gene signature demonstrated high performance to uncover new functional associations between miRNAs and diseases.     

Discovery of microRNA-mRNA modules via population-based probabilistic learning

MOTIVATION: MicroRNAs (miRNAs) and mRNAs constitute an important part of gene regulatory networks, influencing diverse biological phenomena. Elucidating closely related miRNAs and mRNAs can be an essential first step towards the discovery of their combinatorial effects on different cellular states. Here, we propose a probabilistic learning method to identify synergistic miRNAs involving regulation of their condition-specific target genes (mRNAs) from multiple information sources, i.e. computationally predicted target genes of miRNAs and their respective expression profiles. RESULTS: We used data sets consisting of miRNA-target gene binding information and expression profiles of miRNAs and mRNAs on human cancer samples. Our method allowed us to detect functionally correlated miRNA-mRNA modules involved in specific biological processes from multiple data sources by using a balanced fitness function and efficient searching over multiple populations. The proposed algorithm found two miRNA-mRNA modules, highly correlated with respect to their expression and biological function. Moreover, the mRNAs included in the same module showed much higher correlations when the related miRNAs were highly expressed, demonstrating our method's ability for finding coherent miRNA-mRNA modules. Most members of these modules have been reported to be closely related with cancer. Consequently, our method can provide a primary source of miRNA and target sets presumed to constitute closely related parts of gene regulatory pathways.     

miR-888 is an expressed prostatic secretions-derived microRNA that promotes prostate cell growth and migration

microRNAs (miRNAs) are a growing class of small non-coding RNAs that exhibit widespread dysregulation in prostate cancer. We profiled miRNA expression in syngeneic human prostate cancer cell lines that differed in their metastatic potential in order to determine their role in aggressive prostate cancer. miR-888 was the most differentially expressed miRNA observed in human metastatic PC3-ML cells relative to non-invasive PC3-N cells, and its levels were higher in primary prostate tumors from cancer patients, particularly those with seminal vesicle invasion. We also examined a novel miRNA-based biomarker source called expressed prostatic secretions in urine (EPS urine) for miR-888 expression and found that its levels were preferentially elevated in prostate cancer patients with high-grade disease. These expression studies indicated a correlation for miR-888 in disease progression. We next tested how miR-888 regulated cancer-related pathways in vitro using human prostate cancer cell lines. Overexpression of miR-888 increased proliferation and migration, and conversely inhibition of miR-888 activity blocked these processes. miR-888 also increased colony formation in PC3-N and LNCaP cells, supporting an oncogenic role for this miRNA in the prostate. Our data indicates that miR-888 functions to promote prostate cancer progression and can suppress protein levels of the tumor suppressor genes RBL1 and SMAD4. This miRNA holds promise as a diagnostic tool using an innovative prostatic fluid source as well as a therapeutic target for aggressive prostate cancer.     

Identifying miRNA sponge modules using biclustering and regulatory scores

Background

MicroRNA (miRNA) sponges with multiple tandem miRNA binding sequences can sequester miRNAs from their endogenous target mRNAs. Therefore, miRNA sponge acting as a decoy is extremely important for long-term loss-of-function studies both in vivo and in silico. Recently, a growing number of in silico methods have been used as an effective technique to generate hypotheses for in vivo methods for studying the biological functions and regulatory mechanisms of miRNA sponges. However, most existing in silico methods only focus on studying miRNA sponge interactions or networks in cancer, the module-level properties of miRNA sponges in cancer is still largely unknown.

Results

We propose a novel in silico method, called miRSM (miRNA Sponge Module) to infer miRNA sponge modules in breast cancer. We apply miRSM to the breast invasive carcinoma (BRCA) dataset provided by The Cancer Genome Altas (TCGA), and make functional validation of the computational results. We discover that most miRNA sponge interactions are module-conserved across two modules, and a minority of miRNA sponge interactions are module-specific, existing only in a single module. Through functional annotation and differential expression analysis, we also find that the modules discovered using miRSM are functional miRNA sponge modules associated with BRCA. Moreover, the module-specific miRNA sponge interactions among miRNA sponge modules may be involved in the progression and development of BRCA. Our experimental results show that miRSM is comparable to the benchmark methods in recovering experimentally confirmed miRNA sponge interactions, and miRSM outperforms the benchmark methods in identifying interactions that are related to breast cancer.

Conclusions

Altogether, the functional validation results demonstrate that miRSM is a promising method to identify miRNA sponge modules and interactions, and may provide new insights for understanding the roles of miRNA sponges in cancer progression and development.

     

基于已知疾病基因构建共表达网络识别胃癌进展及预后相关非编码基因

本研究旨在总结整理已有胃癌研究的基础上,进一步挖掘出非编码基因在胃癌的进展及预后中起的关键作用。通过胃癌患者编码及非编码基因的表达数据,结合已知胃癌致病基因,进行编码基因与非编码基因的共表达计算,识别出由miRNA介导的并且与已知胃癌基因互作的lncRNA,挖掘出三者(mRNA-miRNA-lncRNA)相互作用的模块,进而对模块进行筛选,并对疾病相关的显著模块的基因进行生存分析。除已知的胃癌相关基因外,研究也使用了差异表达的胃癌基因,这些基因显著的富集在细胞增殖、细胞粘附、肌肉收缩、血管重塑、细胞分裂、染色体分离等生物过程,这些生物过程都与胃的基础功能及胃癌发生发展密切相关。分值最高的三元组模块内核心基因BGN在胃癌患者中显著高表达,而且和胃癌患者的预后显著相关;同时也发现该模块内的miRNA has-miRNA-153-5p和has-miRNA-5001-5p均为已证实的胃癌相关基因;模块内的mRNA和miRNA的表达异常可能是由于与他们显著相关的lncRNA的表达异常导致的。本研究为胃癌已知致病基因的表达异常研究找到了新突破口,潜在的胃癌相关的非编码基因的发现为胃癌预防与治疗提供了新的靶点,为未来的临床应用提供了依据。     

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.     

A novel algorithm based on bi-random walks to identify disease-related lncRNAs

Background

It’s a very urgent task to identify cancer genes that enables us to understand the mechanisms of biochemical processes at a biomolecular level and facilitates the development of bioinformatics. Although a large number of methods have been proposed to identify cancer genes at recent times, the biological data utilized by most of these methods is still quite less, which reflects an insufficient consideration of the relationship between genes and diseases from a variety of factors.

Results

In this paper, we propose a two-rounds random walk algorithm to identify cancer genes based on multiple biological data (TRWR-MB), including protein-protein interaction (PPI) network, pathway network, microRNA similarity network, lncRNA similarity network, cancer similarity network and protein complexes. In the first-round random walk, all cancer nodes, cancer-related genes, cancer-related microRNAs and cancer-related lncRNAs, being associated with all the cancer, are used as seed nodes, and then a random walker walks on a quadruple layer heterogeneous network constructed by multiple biological data. The first-round random walk aims to select the top score k of potential cancer genes. Then in the second-round random walk, genes, microRNAs and lncRNAs, being associated with a certain special cancer in corresponding cancer class, are regarded as seed nodes, and then the walker walks on a new quadruple layer heterogeneous network constructed by lncRNAs, microRNAs, cancer and selected potential cancer genes. After the above walks finish, we combine the results of two-rounds RWR as ranking score for experimental analysis. As a result, a higher value of area under the receiver operating characteristic curve (AUC) is obtained. Besides, cases studies for identifying new cancer genes are performed in corresponding section.

Conclusion

In summary, TRWR-MB integrates multiple biological data to identify cancer genes by analyzing the relationship between genes and cancer from a variety of biological molecular perspective.

     

Genome-wide screen for aberrantly expressed miRNAs reveals miRNA profile signature in breast cancer

Dysregulation in the expression of miRNAs contributes to the occurrence and development of many human cancers. We herein attempted to obtain the potential association between miRNA expression profile and breast cancer by applying high-throughput sequencing technology. Small RNAs from seven paired tumor and adjacent normal tissue samples were sequenced. To determine the miRNA expression profiles in tissues and sera, another five equally pooled serum samples from 20 patients and 30 normal women were sequenced. Despite a similar number in abundantly expressed miRNAs across samples, we detected varying miRNA expression profiles. Some miRNAs showed inconsistent or opposite dysregulation trends across different tumor tissues, including some abundantly expressed miRNA gene clusters and gene families. Wilcoxon sign-rank test for paired samples analysis revealed that abnormal miRNAs showed a higher level of variation across the seven tumor samples. We also completely surveyed abnormal miRNAs expressed in tumor and serum tissues in the mixed datasets based on the relative expression levels. Most of these miRNAs were significantly down-regulated in tumor samples, but nine abnormal miRNAs (miR-18a, 19a, 20a, 30a, 103b, 126, 126*, 192, 1287) were consistently expressed in tumor tissues and serum samples. Based on experimentally validated target mRNAs, functional enrichment analysis indicated that these abnormal miRNAs and miRNA groups (miRNA gene clusters and gene families) have important roles in multiple biological processes. Dynamic miRNA expression profiles, various abnormal miRNA profiles and complexity of the miRNA regulatory network reveal that the miRNA expression profile is a potential biomarker for classifying or detecting human disease.     

Lessons from miR-143/145: the importance of cell-type localization of miRNAs

miR-143 and miR-145 are co-expressed microRNAs (miRNAs) that have been extensively studied as potential tumor suppressors. These miRNAs are highly expressed in the colon and are consistently reported as being downregulated in colorectal and other cancers. Through regulation of multiple targets, they elicit potent effects on cancer cell growth and tumorigenesis. Importantly, a recent discovery demonstrates that miR-143 and miR-145 are not expressed in colonic epithelial cells; rather, these two miRNAs are highly expressed in mesenchymal cells such as fibroblasts and smooth muscle cells. The expression patterns of miR-143 and miR-145 and other miRNAs were initially determined from tissue level data without consideration that multiple different cell types, each with their own unique miRNA expression patterns, make up each tissue. Herein, we discuss the early reports on the identification of dysregulated miR-143 and miR-145 expression in colorectal cancer and how lack of consideration of cellular composition of normal tissue led to the misconception that these miRNAs are downregulated in cancer. We evaluate mechanistic data from miR-143/145 studies in context of their cell type-restricted expression pattern and the potential of these miRNAs to be considered tumor suppressors. Further, we examine other examples of miRNAs being investigated in inappropriate cell types modulating pathways in a non-biological fashion. Our review highlights the importance of determining the cellular expression pattern of each miRNA, so that downstream studies are conducted in the appropriate cell type.     

胃癌相关miRNA表达的表观遗传学调控机制

胃癌是人类最常见的肿瘤之一,其发病机制尚不完全清楚.微小RNA(microRNA,miRNA)是一组最近发现的长度为22个核苷酸左右的非编码RNA,具有负性调控基因表达的功能.本文对miRNA在胃癌发生中的作用及其表达调控机制进行综述.不断有文献显示,miRNA在多种肿瘤(包括胃癌)的发生过程中发挥着重要作用.作者和其他研究人员发现,miRNA的表达异常(如:miR-421和miR-21的上调或/和miR-31和miR-218的下调等)与胃癌的发生相关,提示miRNA是胃癌发生的重要因素.目前,miRNA表达的分子机制尚未完全明了.最近研究较清楚地显示,miRNA的表达受到DNA甲基化和组蛋白修饰等机制的调控.这说明,胃癌相关miRNA的表达水平受到表观遗传机制的调控。     

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.