Context Likelihood Of Relatedness Algorithm Research Articles

Identification of microRNA Targets in colorectal cancer through Data Sequencing

Colorectal cancer (CRC) is the third leading cause of cancer‐related mortality globally. MicroRNAs (miRNAs, miRs), a class of small non‐coding RNA molecules, have demonstrated important roles in carcinogenesis and its progression through the regulation of the epithelialmesenchymal transition (EMT), oncogenic signaling pathways, and metastasis. Despite the increase in miRNA studies and extensive analyzes of their expression, the role and function of many individual miRNAs in CRC remains poorly understood. The aim of this study was to identify miRNA targets in CRC through data sequencing, which could be a solid prognostic prediction tool and help clinical strategy.MethodsCRC gene expression data sets were collected from the public database, The Cancer Genome Atlas (TCGA). In addition, web‐based tools were used to explore TCGA data, specifically the one developed by the Broad Institute TCGA GDAC Firehose, which provides data sets, algorithms, and analysis results standardized for TCGA. This pipeline has the following steps: The CLR (Context Likelihood of Relatedness) approach is applied to infer putative miRNA regulatory connections: gene; miRNA filtering: gene pairs based on Pearson's correlation (<= ‐0.3); and miRNA filtering: gene pairs based on predicted interactions in three sequence prediction databases (Miranda, Pictar, Targetscan). The CLR algorithm was applied on 617 miRs and 18012 mRNAs across 220 samples. After 2 filtering steps, the number of 9 miR:genes pairs were detected.ResultsThe initial search with the term “Colorectal adenocarcinoma”, “COADREAD” yielded 631 cases. After the analysis of these samples, data on the significance miR:gene pairs were obtained and Table 1 shows the results of miR:gene pairs with corr < ‐0.30 and predicted interactions in three sequence prediction databases. About the miRNA connections, Table 2 shows all miRNA hubs with their associated genes in the putative direct target network. Finally, about gene connections, Table 3 shows all gene hubs with their associated miRNAs in the putative direct target network.ConclusionThe use of miRNAs as biomarkers for CRC could provide a new and less invasive technique to detect CRC and help determine prognosis. These miRNAs and their targets require further evaluation for a better understanding of their associations, ultimately, with the potential to develop new therapeutic targets. Therefore, it is proposed to develop a screening panel that should consist of multiple miRNAs that would provide a more accurate and efficient screening tool for CRC.

Coupling Genome-wide Transcriptomics and Developmental Toxicity Profiles in Zebrafish to Characterize Polycyclic Aromatic Hydrocarbon (PAH) Hazard.

Polycyclic Aromatic Hydrocarbons (PAHs) are diverse environmental pollutants associated with adverse human health effects. Many studies focus on the carcinogenic effects of a limited number of PAHs and there is an increasing need to understand mechanisms of developmental toxicity of more varied yet environmentally relevant PAHs. A previous study characterized the developmental toxicity of 123 PAHs in zebrafish. Based on phenotypic responses ranging from complete inactivity to acute mortality, we classified these PAHs into eight bins, selected 16 representative PAHs, and exposed developing zebrafish to the concentration of each PAH that induced 80% phenotypic effect. We conducted RNA sequencing at 48 h post fertilization to identify gene expression changes as a result of PAH exposure. Using the Context Likelihood of Relatedness algorithm, we inferred a network that links the PAHs based on coordinated gene responses to PAH exposure. The 16 PAHs formed two broad clusters: Cluster A was transcriptionally more similar to the controls, while Cluster B consisted of PAHs that were generally more developmentally toxic, significantly elevated cyp1a transcript levels, and induced Ahr2-dependent Cyp1a protein expression in the skin confirmed by gene-silencing studies. We found that cyp1a transcript levels were associated with transcriptomic response, but not with PAH developmental toxicity. While all cluster B PAHs predominantly activated Ahr2, they also each enriched unique pathways like ion transport signaling, which likely points to differing molecular events between the PAHs downstream of Ahr2. Thus, using a systems biology approach, we have begun to evaluate, classify, and define mechanisms of PAH toxicity.

Hub genes and gene functions associated with postmenopausal osteoporosis predicted by an integrated method.

Postmenopausal osteoporosis (PO) imposes great burden on individuals and society. This study predicted hub genes and gene functions for PO by an integration of the convergent evidence (CE) method, rank product (RP) algorithm and the combing of P-values. Using the gene expression data, genes were ranked by the CE method, RP algorithm and combing P-values, respectively. Subsequently, the top 100 genes were selected from each of the three gene lists, and then the common genes for two or three methods were denoted as informative genes of PO. A mutual information network (MIN) was constructed for the informative genes utilizing the context likelihood of relatedness algorithm. Topological centrality (degree) analysis was conducted on the MIN to investigate hub genes. Then we performed Gene Ontology (GO) enrichment analysis dependent upon the Biological Networks Gene Ontology tool (BiNGO) plugin of Cytoscape to investigate hub gene functions for PO patients. Consequently, a total of 82 informative genes were obtained by integrating the results of the three methods. There were 82 nodes and 1,741 edges in the MIN, of which 8 hub genes were identified, such as PFN1, EEF2 and S100A9. The result of GO enrichment analysis showed that 49 GO terms with P<0.001 were detected, especially the top 5 gene sets were defined as hub gene functions of PO, for instance, translational elongation, translation and cellular macromolecule biosynthetic process. In conclusion, we have predicted 8 hub genes and 5 hub gene functions associated with PO patients. The findings might help understand the molecular mechanism underlying PO.

Republished: Identifying key genes in retinoblastoma by comparing classifications of several kinds of significant genes.

[This corrects the article DOI: 10.4103/0973-1482.180678].

Identification of novel methylated targets in colorectal cancer by microarray analysis and construction of co-expression network.

The present study was conducted to investigate novel methylated targets in colorectal cancer (CRC). The mRNA expression profiles of GSE32323 in 17 cancer and non-cancerous tissues from CRC patients, as well as expression profiles of 5 CRC cell lines prior and subsequent to 5-aza-2′-deoxycytidine (5-aza-dC) treatment, were obtained from the Gene Expression Omnibus database. The differentially expressed genes (DEGs) in 5 CRC cell lines prior and subsequent to 5-aza-dC treatment were combined with the CRC-specific gene expression profiling array data. Context likelihood of relatedness algorithm was used to construct the co-expression network of CRC-specific gene expression profile. A sub-network of identified reverse-overlapped DEGs was selected and underwent Kyoto Encyclopedia of Genes and Genomes Pathway Analysis. A total of 6 reverse-overlapped DEGs were identified. This present study verified fibulin 2 (FBLN2) and protein phosphatase 1 regulatory inhibitor subunit 14A (PPP1R14A) to be downregulated in the CRC tissue sample but upregulated in CRC cell lines following 5-aza-dC treatment. The identified reverse-overlapped DEGs were enriched in tumor-associated signaling pathways, including cellular tumor antigen p53, cell cycle and NOD-like receptor (NLR) signaling pathway. A total of 2 silenced genes with abnormal methylation in CRC were identified, including FBLN2 and PPP1R14A. The reverse-overlapped DEGs were enriched in p53, cell cycle and NLR signaling pathways, indicating that reverse-overlapped DEGs, particularly FBLN2 and PPP1R14A, may be important tumor suppressors and that these reverse-overlapped DEGs are inactivated by methylation in CRC.

Combining affinity propagation clustering and mutual information network to investigate key genes in fibroid.

The aim of the present study was to investigate key genes in fibroids based on the multiple affinity propogation-Krzanowski and Lai (mAP-KL) method, which included the maxT multiple hypothesis, Krzanowski and Lai (KL) cluster quality index, affinity propagation (AP) clustering algorithm and mutual information network (MIN) constructed by the context likelihood of relatedness (CLR) algorithm. In order to achieve this goal, mAP-KL was initially implemented to investigate exemplars in fibroid, and the maxT function was employed to rank the genes of training and test sets, and the top 200 genes were obtained for further study. In addition, the KL cluster index was applied to determine the quantity of clusters and the AP clustering algorithm was conducted to identify the clusters and their exemplars. Subsequently, the support vector machine (SVM) model was selected to evaluate the classification performance of mAP-KL. Finally, topological properties (degree, closeness, betweenness and transitivity) of exemplars in MIN constructed according to the CLR algorithm were assessed to investigate key genes in fibroid. The SVM model validated that the classification between normal controls and fibroid patients by mAP-KL had a good performance. A total of 9 clusters and exemplars were identified based on mAP-KL, which were comprised of CALCOCO2, COL4A2, COPS8, SNCG, PA2G4, C17orf70, MARK3, BTNL3 and TBC1D13. By accessing the topological analysis for exemplars in MIN, SNCG and COL4A2 were identified as the two most significant genes of four types of methods, and they were denoted as key genes in the progress of fibroid. In conclusion, two key genes (SNCG and COL4A2) and 9 exemplars were successfully investigated, and these may be potential biomarkers for the detection and treatment of fibroid.

Expression, Circulation, and Excretion Profile of MicroRNA-21, -155, and -18a Following Acute Kidney Injury

MicroRNAs (miRNAs) are endogenous noncoding RNA molecules that are involved in post-transcriptional gene silencing. Using global miRNA expression profiling, we found miR-21, -155, and 18a to be highly upregulated in rat kidneys following tubular injury induced by ischemia/reperfusion (I/R) or gentamicin administration. Mir-21 and -155 also showed decreased expression patterns in blood and urinary supernatants in both models of kidney injury. Furthermore, urinary levels of miR-21 increased 1.2-fold in patients with clinical diagnosis of acute kidney injury (AKI) (n = 22) as compared with healthy volunteers (n = 25) (p < 0.05), and miR-155 decreased 1.5-fold in patients with AKI (p < 0.01). We identified 29 messenger RNA core targets of these 3 miRNAs using the context likelihood of relatedness algorithm and found these predicted gene targets to be highly enriched for genes associated with apoptosis or cell proliferation. Taken together, these results suggest that miRNA-21 and -155 could potentially serve as translational biomarkers for detection of AKI and may play a critical role in the pathogenesis of kidney injury and tissue repair process.