Inhibition of Mir-21-5p Affects the Expression of LNCRNA X-Inactive Specific Transcript and Induces Apoptosis in MCF-7 Breast Cancer Cells.

Background: We aimed to investigate miR-21-5p inhibition effect on lncRNA-XIST expression and apoptosis status of MCF-7 cells. Methods: The MCF-7 cells were cultured and transfected by the anti-miR-21-5p oligonucleotide and expression of miR-21-5p, lncRNA-XIST, apoptosis-associated genes (bax and p53) and one miR-21-5p-unrelated lncRNA (BC200) was assessed by RT-qPCR. Furthermore, cell viability checked by MTT assay and apoptosis and cell cycle in transfected cells were detected by flow cytometry. Also, bioinformatics analysis on the transcriptome data confirmed that the lncRNA XIST might have a critical role in breast cancer (BC) cell apoptosis through ceRNAs mechanism and possible regulatory interactions with miR-21-5p. Results: Expression of miR-21-5p and lncRNA-XIST was significantly down- and up-regulated respectively (P<0.05). However, there was no significant change in lncRNA-BC200 expression. Also, the expression of bax and p53 upraised significantly (P<0.05). In transfected cells, MTT and flow cytometry assays reported a highly significant decrease and increase in viability and apoptosis respectively. Conclusion: Inhibition of miR-21-5p resulted in significant upregulation of lncRNA-XIST and apoptosis-associated genes bax and p53, which led to the induction of apoptosis in MCF-7 cells. Therefore, more investigations may provide a valuable target for studies on molecular therapies for BC.

CircWalk: a novel approach to predict CircRNA-disease association based on heterogeneous network representation learning.

BACKGROUND: Several types of RNA in the cell are usually involved in biological processes with multiple functions. Coding RNAs code for proteins while non-coding RNAs regulate gene expression. Some single-strand RNAs can create a circular shape via the back splicing process and convert into a new type called circular RNA (circRNA). circRNAs are among the essential non-coding RNAs in the cell that involve multiple disorders. One of the critical functions of circRNAs is to regulate the expression of other genes through sponging micro RNAs (miRNAs) in diseases. This mechanism, known as the competing endogenous RNA (ceRNA) hypothesis, and additional information obtained from biological datasets can be used by computational approaches to predict novel associations between disease and circRNAs. RESULTS: We applied multiple classifiers to validate the extracted features from the heterogeneous network and selected the most appropriate one based on some evaluation criteria. Then, the XGBoost is utilized in our pipeline to generate a novel approach, called CircWalk, to predict CircRNA-Disease associations. Our results demonstrate that CircWalk has reasonable accuracy and AUC compared with other state-of-the-art algorithms. We also use CircWalk to predict novel circRNAs associated with lung, gastric, and colorectal cancers as a case study. The results show that our approach can accurately detect novel circRNAs related to these diseases. CONCLUSIONS: Considering the ceRNA hypothesis, we integrate multiple resources to construct a heterogeneous network from circRNAs, mRNAs, miRNAs, and diseases. Next, the DeepWalk algorithm is applied to the network to extract feature vectors for circRNAs and diseases. The extracted features are used to learn a classifier and generate a model to predict novel CircRNA-Disease associations. Our approach uses the concept of the ceRNA hypothesis and the miRNA sponge effect of circRNAs to predict their associations with diseases. Our results show that this outlook could help identify CircRNA-Disease associations more accurately.

The identification of co-expressed gene modules in Streptococcus pneumonia from colonization to infection to predict novel potential virulence genes.

BACKGROUND: Streptococcus pneumonia (pneumococcus) is a human bacterial pathogen causing a range of mild to severe infections. The complicated transcriptome patterns of pneumococci during the colonization to infection process in the human body are usually determined by measuring the expression of essential virulence genes and the comparison of pathogenic with non-pathogenic bacteria through microarray analyses. As systems biology studies have demonstrated, critical co-expressing modules and genes may serve as key players in biological processes. Generally, Sample Progression Discovery (SPD) is a computational approach traditionally used to decipher biological progression trends and their corresponding gene modules (clusters) in different clinical samples underlying a microarray dataset. The present study aimed to investigate the bacterial gene expression pattern from colonization to severe infection periods (specimens isolated from the nasopharynx, lung, blood, and brain) to find new genes/gene modules associated with the infection progression. This strategy may lead to finding novel gene candidates for vaccines or drug design. RESULTS: The results included essential genes whose expression patterns varied in different bacterial conditions and have not been investigated in similar studies. CONCLUSIONS: In conclusion, the SPD algorithm, along with differentially expressed genes detection, can offer new ways of discovering new therapeutic or vaccine targeted gene products.

Detection of novel biomarkers for early detection of Non-Muscle-Invasive Bladder Cancer using Competing Endogenous RNA network analysis.

Bladder Cancer (BC) is one of the most common cancers in the world. Recent studies show that non-coding RNAs such as lncRNAs and circRNAs play critical roles in the progression of this cancer, but their regulatory relationships and functions are still largely unknown. As a new regulatory process within the cell, the coding and non-coding RNAs compete with each other to sponge their target miRNAs. This mechanism is described as "the competing endogenous RNA (ceRNA) hypothesis" which provides a new perspective to understand the regulation of gene expression in health and diseases such as cancer. In this study, to investigate the role of non-coding RNAs in BC, a new approach was used to reconstruct the ceRNA network for Non-Muscle Invasive Bladder Cancer (NMIBC) based on the expression data of coding and non-coding genes. Analysis of ceRNA networks in the early stage of BC led to the detection of an important module containing the lncRNA MEG3 as the central gene. The results show that the lncRNAs CARMN, FENDRR and ADAMTS9-AS2 may regulate MEG3 in NMIBC through sponging some important miRNAs such as miR-143-3p, miR-106a-5p and miR-34a-3p. Also, the lncRNA AC007608.2 is shown to be a potential BC related lncRNA for the first time based on ceRNA stage-specific network analysis. Furthermore, hub and altered genes in stage-specific and between stage networks led to the detection of hsa_circ_0017586 and hsa_circ_0001741 as novel potential circRNAs related to NMIBC. Finally, the hub genes in the networks were shown to be valuable candidates as biomarkers for the early stage diagnosis of BC.

Systematic analysis of genes and diseases using PheWAS-Associated networks.

Several scientific sources have reported different causes of various diseases. One of these factors is genetic variation. Natural selection, molecular evolution and susceptibility to external conditions are the main causes of genetic variations. Phenome-Wide Association Studies (PheWAS) can emphasize the associations of genetic variations and diseases. The systematic analysis of these associations can highlight various important aspects of gene correlations and disease relationships. In this study, we have investigated a systematic approach to analyze associated networks of genes and diseases to explore novel scientific information. We have constructed the Associated Gene Network (AGN, n = 1769) and the Associated Disease Network (ADN, n = 503) based on common diseases and genes, respectively. We have evaluated these networks based on topological measures and compared them with a randomized null network. The comparative modular analysis based on size and quantity is a clear indication of the significance of these networks. We have found numerous novel associations of genes involved in different diseases. We have also found different diseases related to one another, which can correlate scientific evidence. We have verified our analysis through GO and KEGG enrichment for different case studies and concluded that AGN and ADN can be used as reference biological networks for various purposes such as drug design and drug repurposing.

SOX2OT knockdown derived changes in mitotic regulatory gene network of cancer cells.

BACKGROUND: SOX2 overlapping transcript (SOX2OT) is a long non-coding RNA, over-expressed in human tumor tissues and embryonic cells. Evidences support its function in the cell cycle; however there is no clear mechanism explaining its function in cell proliferation regulation. Here we investigated cancer cell response to SOX2OT knockdown by RNA sequencing. METHODS: SOX2OT expression was inhibited by siRNA in two cancer cell lines (A549, U-87 MG), then the RNA of treated cells were used for the cDNA library synthesis and RNA sequencing. The differentially expressed genes were used for functional enrichment and the gene expression network was analyzed to find the most relevant biological process with SOX2OT function. Furthermore, the expression change of candidate genes was measured by qRT-PCR for more confirmation and the cell cycle was monitored by PI staining. RESULTS: Our findings showed that SOX2OT knockdown affects the cellular gene expression generally with enriched cell proliferation and development biological process. Particularly, the cell cycle and mitotic regulatory genes expression including: CDK2, CDK2AP2, ACTR3, and chromosome structure associated genes like SMC4, INCENP and GNL3L are changed in treated cancer cells. CONCLUSION: Our results propound SOX2OT association with cell cycle and mitosis regulation in cancer cells.

mRNA-miRNA bipartite network reconstruction to predict prognostic module biomarkers in colorectal cancer stage differentiation.

Biomarker detection is one of the most important and challenging problems in cancer studies. Recently, non-coding RNA based biomarkers such as miRNA expression levels have been used for early diagnosis of many cancer types. In this study, a systems biology approach was used to detect novel miRNA based biomarkers for CRC diagnosis in early stages. The mRNA expression data from three CRC stages (Low-grade Intraepithelial Neoplasia (LIN), High-grade Intraepithelial Neoplasia (HIN) and Adenocarcinoma) were used to reconstruct co-expression networks. The networks were clustered to extract co-expression modules and detected low preserved modules among CRC stages. Then, the experimentally validated mRNA-miRNA interaction data were applied to reconstruct three mRNA-miRNA bipartite networks. Twenty miRNAs with the highest degree (hub miRNAs) were selected in each bipartite network to reconstruct three bipartite subnetworks for further analysis. The analysis of these hub miRNAs in the bipartite subnetworks revealed 30 distinct important miRNAs as prognostic markers in CRC stages. There are two novel CRC related miRNAs (hsa-miR-190a-3p and hsa-miR-1277-5p) in these 30 hub miRNAs that have not been previously reported in CRC. Furthermore, a drug-gene interaction network was reconstructed to detect potential candidate drugs for CRC treatment. Our analysis shows that the hub miRNAs in the mRNA-miRNA bipartite network are very essential in CRC progression and should be investigated precisely in future studies. In addition, there are many important target genes in the results that may be critical in CRC progression and can be analyzed as therapeutic targets in future research.

A Type-2 fuzzy data fusion approach for building reliable weighted protein interaction networks with application in protein complex detection.

Detecting the protein complexes is an important task in analyzing the protein interaction networks. Although many algorithms predict protein complexes in different ways, surveys on the interaction networks indicate that about 50% of detected interactions are false positives. Consequently, the accuracy of existing methods needs to be improved. In this paper we propose a novel algorithm to detect the protein complexes in 'noisy' protein interaction data. First, we integrate several biological data sources to determine the reliability of each interaction and determine more accurate weights for the interactions. A data fusion component is used for this step, based on the interval type-2 fuzzy voter that provides an efficient combination of the information sources. This fusion component detects the errors and diminishes their effect on the detection protein complexes. So in the first step, the reliability scores have been assigned for every interaction in the network. In the second step, we have proposed a general protein complex detection algorithm by exploiting and adopting the strong points of other algorithms and existing hypotheses regarding real complexes. Finally, the proposed method has been applied for the yeast interaction datasets for predicting the interactions. The results show that our framework has a better performance regarding precision and F-measure than the existing approaches.

Corrigendum: MWCOACH: Protein complex prediction in weighted PPI networks [Genes Genet. Syst. (2015) 90, p. 317-324].

"J-STAGE Advance published date: 15 January 2015" on p. 317 should be changed to "J-STAGE Advance published date: 15 January 2016".

CeFunMO: A centrality based method for discovering functional motifs with application in biological networks.

Detecting functional motifs in biological networks is one of the challenging problems in systems biology. Given a multiset of colors as query and a list-colored graph (an undirected graph with a set of colors assigned to each of its vertices), the problem is reduced to finding connected subgraphs, which best cover the multiset of query. To solve this NP-complete problem, we propose a new color-based centrality measure for list-colored graphs. Based on this newly-defined measure of centrality, a novel polynomial time algorithm is developed to discover functional motifs in list-colored graphs, using a greedy strategy. This algorithm, called CeFunMO, has superior running time and acceptable accuracy in comparison with other well-known algorithms, such as RANGI and GraMoFoNe.

WCOACH: Protein complex prediction in weighted PPI networks.

Protein complexes are aggregates of protein molecules that play important roles in biological processes. Detecting protein complexes from protein-protein interaction (PPI) networks is one of the most challenging problems in computational biology, and many computational methods have been developed to solve this problem. Generally, these methods yield high false positive rates. In this article, a semantic similarity measure between proteins, based on Gene Ontology (GO) structure, is applied to weigh PPI networks. Consequently, one of the well-known methods, COACH, has been improved to be compatible with weighted PPI networks for protein complex detection. The new method, WCOACH, is compared to the COACH, ClusterOne, IPCA, CORE, OH-PIN, HC-PIN and MCODE methods on several PPI networks such as DIP, Krogan, Gavin 2002 and MIPS. WCOACH can be applied as a fast and high-performance algorithm to predict protein complexes in weighted PPI networks. All data and programs are freely available at http://bioinformatics.aut.ac.ir/wcoach.