SAGA: a hybrid search algorithm for Bayesian Network structure learning of transcriptional regulatory networks.

Bayesian Networks have been used for the inference of transcriptional regulatory relationships among genes, and are valuable for obtaining biological insights. However, finding optimal Bayesian Network (BN) is NP-hard. Thus, heuristic approaches have sought to effectively solve this problem. In this work, we develop a hybrid search method combining Simulated Annealing with a Greedy Algorithm (SAGA). SAGA explores most of the search space by undergoing a two-phase search: first with a Simulated Annealing search and then with a Greedy search. Three sets of background-corrected and normalized microarray datasets were used to test the algorithm. BN structure learning was also conducted using the datasets, and other established search methods as implemented in BANJO (Bayesian Network Inference with Java Objects). The Bayesian Dirichlet Equivalence (BDe) metric was used to score the networks produced with SAGA. SAGA predicted transcriptional regulatory relationships among genes in networks that evaluated to higher BDe scores with high sensitivities and specificities. Thus, the proposed method competes well with existing search algorithms for Bayesian Network structure learning of transcriptional regulatory networks.

Combined genomic expressions as a diagnostic factor for oral squamous cell carcinoma.

Trends in genetics are transforming in order to identify differential coexpressions of correlated gene expression rather than the significant individual gene. Moreover, it is known that a combined biomarker pattern improves the discrimination of a specific cancer. The identification of the combined biomarker is also necessary for the early detection of invasive oral squamous cell carcinoma (OSCC). To identify the combined biomarker that could improve the discrimination of OSCC, we explored an appropriate number of genes in a combined gene set in order to attain the highest level of accuracy. After detecting a significant gene set, including the pre-defined number of genes, a combined expression was identified using the weights of genes in a gene set. We used the Principal Component Analysis (PCA) for the weight calculation. In this process, we used three public microarray datasets. One dataset was used for identifying the combined biomarker, and the other two datasets were used for validation. The discrimination accuracy was measured by the out-of-bag (OOB) error. There was no relation between the significance and the discrimination accuracy in each individual gene. The identified gene set included both significant and insignificant genes. One of the most significant gene sets in the classification of normal and OSCC included MMP1, SOCS3 and ACOX1. Furthermore, in the case of oral dysplasia and OSCC discrimination, two combined biomarkers were identified. The combined expression revealed good performance in the validation datasets. The combined genomic expression achieved better performance in the discrimination of different conditions than a single significant gene. Therefore, it could be expected that accurate diagnosis for cancer could be possible with a combined biomarker.

Identification and preliminary validation of differently expressed genes as candidate biomarkers associated with atherosclerosis.

OBJECTIVE: Atherosclerosis (AS) is the primary cause of deadly cardio-cerebro vascular diseases globally. This study aims to explore the key differentially expressed genes (DEGs), potentially serving as predictive biomarkers for AS. METHODS: Microarray datasets were retrieved from the GEO database for DEGs and DE-miRNAs identification. Then biological function of DEGs were elucidated based on gene ontology (GO) and KEGG pathway enrichment analysis. The protein-protein interaction (PPI) network and DEGs-DE-miRNAs network were constructed, with emphasis on hub DEGs selection and their interconnections. Additionally, receiver operating characteristic (ROC) curve analysis was performed to evaluate the diagnostic precision of hub DEGs for AS. More importantly, an AS Syrian Golden hamster model was established to validate the expression levels of hub DEGs in AS. RESULTS: A total of 203 DEGs and 10 DE-miRNAs were screened, with six genes were chosen as hub DEGs. These DEGs were significantly enriched in AS-related biological processes and pathways, such as immune and inflammatory response, cellular response to IL-1 and TNF, positive regulation of angiogenesis, Type I diabetes mellitus, Cytokine-cytokine receptor interaction, TLR signaling pathway. Also, these DEGs and DE-miRNAs formed a closely-interacted DE-miRNAs - DEGs - KEGG pathway network. Besides, hub DEGs presented promising diagnostic potential for AS (AUC: 0.781 âˆ¼ 0.887). In addition, the protein expression levels of TNF-α, CXCL8, CCL4, IL-1ß, CCL3 and CCR8 were significantly increased in AS group Syrian Golden hamsters. CONCLUSION: The identified candidate genes TNF, CXCL8, CCL4, IL1B, CCL3 and CCR8 may have the potential to serve as prognostic biomarker in diagnosing AS.

Simulated annealing aided genetic algorithm for gene selection from microarray data.

In recent times, microarray gene expression datasets have gained significant popularity due to their usefulness to identify different types of cancer directly through bio-markers. These datasets possess a high gene-to-sample ratio and high dimensionality, with only a few genes functioning as bio-markers. Consequently, a significant amount of data is redundant, and it is essential to filter out important genes carefully. In this paper, we propose the Simulated Annealing aided Genetic Algorithm (SAGA), a meta-heuristic approach to identify informative genes from high-dimensional datasets. SAGA utilizes a two-way mutation-based Simulated Annealing (SA) as well as Genetic Algorithm (GA) to ensure a good trade-off between exploitation and exploration of the search space, respectively. The naive version of GA often gets stuck in a local optimum and depends on the initial population, leading to premature convergence. To address this, we have blended a clustering-based population generation with SA to distribute the initial population of GA over the entire feature space. To further enhance the performance, we reduce the initial search space by a score-based filter approach called the Mutually Informed Correlation Coefficient (MICC). The proposed method is evaluated on 6 microarray and 6 omics datasets. Comparison of SAGA with contemporary algorithms has shown that SAGA performs much better than its peers. Our code is available at https://github.com/shyammarjit/SAGA.

JNK/MAPK pathway regulation by BEX2 gene silencing in alcoholic hepatitis mice: Effects on oxidative stress.

BACKGROUND: Alcoholic hepatitis (AH) is a severe alcoholic-related liver disease that is a leading cause of morbidity and mortality, for which effective treatments are lacking. Brain-expressed X-linked gene 2 (BEX2) has been implicated in various diseases, but its association with AH has received limited attention. Thus, this study investigated BEX2's impact on the progression of AH by affecting the c-Jun NH2-terminal kinase/mitogen-activated protein kinase (JNK/MAPK) pathway. METHODS: Microarray dataset GSE28619 from the Gene Expression Omnibus database was used to identify differentially expressed genes in AH. Immunohistochemistry, terminal deoxynucleotidyl transferase-mediated dUTP-biotin nick end labeling (TUNEL), Western blot analysis, and flow cytometry were used to measure various factors in the liver tissue of AH mice. RESULTS: BEX2 expression was significantly upregulated in the model. BEX2 gene silencing increased the levels of glutathione peroxidase and superoxide dismutase while decreasing malondialdehyde content; phosphorylation of JNK, c-JUN, and p38MAPK; apoptosis rate; and the extent of JNK/MAPK pathway activation. CONCLUSIONS: These findings provide valuable insights into the mechanisms underlying AH development and highlight the potential role of BEX2 gene expression as a promising therapeutic target for AH.

Dual regularized subspace learning using adaptive graph learning and rank constraint: Unsupervised feature selection on gene expression microarray datasets.

High-dimensional problems have increasingly drawn attention in gene selection and analysis. To add insult to injury, usually the number of features is greater than number of samples in microarray gene dataset which leads to an ill-posed underdetermined equation system. Poor performance and high computational time for learning algorithms are consequences of redundant features in high-dimensional data. Feature selection is a noteworthy pre-processing method to ameliorate the curse of dimensionality with aim of maximum relevancy and minimum redundancy information preservation. Likewise, unsupervised feature selection has been important since collecting labels for data is expensive. In this paper, we develop a novel robust unsupervised feature selection to select discriminative subset of features for unlabeled data based on rank constrained and dual regularized nonnegative matrix factorization. The major focus of the proposed technique is to discard redundant features while keeping the informative features. Proposed feature selection technique consists of nonnegative matrix factorization to decompose the data into feature weight matrix and representation matrix, inner product norm as regularization for both feature weight matrix and representation matrix, adaptive structure learning to preserve local information and Schatten-p norm as rank constraint. To demonstrate the effectiveness of the proposed method, numerical studies are conducted on six benchmark microarray datasets. The results show that the proposed technique outperforms eight state-of-art unsupervised feature selection techniques in terms of clustering accuracy and normalized mutual information.

Identification of Seven Cell Cycle-Related Genes with Unfavorable Prognosis and Construction of their TF-miRNA-mRNA regulatory network in Breast Cancer.

Breast cancer (BC), with complex tumorigenesis and progression, remains the most common malignancy in women. We aimed to explore some novel and significant genes with unfavorable prognoses and potential pathways involved in BC initiation and progression via bioinformatics methods. BC tissue-specific microarray datasets of GSE42568, GSE45827 and GSE54002, which included a total of 651 BC tissues and 44 normal breast tissues, were obtained from the Gene Expression Omnibus (GEO) database, and 124 differentially expressed genes (DEGs) were identified between BC tissues and normal breast tissues via R software and an online Venn diagram tool. Database for Annotation, Visualization and Integration Discovery (DAVID) software showed that 65 upregulated DEGs were mainly enriched in the regulation of the cell cycle, and Search Tool for the Retrieval of Interacting Genes (STRING) software identified the 39 closest associated upregulated DEGs in protein-protein interactions (PPIs), which validated the high expression of genes in BC tissues by the Gene Expression Profiling Interactive Analysis (GEPIA) tool. In addition, 36 out of 39 BC patients showed significantly worse outcomes by Kaplan-Meier plotter (KM plotter), and an additional Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis revealed that seven genes (cyclin E2 (CCNE2), cyclin B1 (CCNB1), cyclin B2 (CCNB2), mitotic checkpoint serine/threonine kinase B (BUB1B), dual-specificity protein kinase (TTK), cell division cycle 20 (CDC20), and pituitary tumor transforming gene 1 (PTTG1)) were markedly enriched in the cell cycle pathway. Analysis of the clinicopathological characteristics of hub genes revealed that seven cell cycle-related genes (CCRGs) were significantly highly expressed in four BC subtypes (luminal A, luminal B, HER2-positive and triple-negative (TNBC)), and except for the CCNE2 gene, high expression levels were significantly associated with tumor pathological grade and stage and metastatic events of BC. Furthermore, genetic mutation analysis indicated that genetic alterations of CCRGs could also significantly affect BC patients' prognosis. A quantitative real-time polymerase chain reaction (qRT-PCR) assay found that the seven CCRGs were significantly differentially expressed in BC cell lines. Integration of published multilevel expression data and a bioinformatics computational approach were used to predict and construct a regulation mechanism: a transcription factor (TF)-microRNA (miRNA)-messenger RNA (mRNA) regulation network. The present work is the first to construct a regulatory network of TF-miRNA-mRNA in BC for CCRGs and provides new insights into the molecular mechanism of BC.

Screening and Identification of Potential Hub Genes in Myocardial Infarction Through Bioinformatics Analysis.

BACKGROUND: Myocardial infarction (MI) is a common cause of death worldwide. It is characterized by coronary artery occlusion that causes ischemia and hypoxia of myocardial cells, leading to irreversible myocardial damage. MATERIALS AND METHODS: To explore potential targets for treatment of MI, we reorganized and analyzed two microarray datasets (GSE4648 and GSE775). The GEO2R tool was used to screen for differentially expressed genes (DEGs) between infarcted and normal myocardium. We used the Database for Annotation, Visualization and Integrated Discovery (DAVID) to perform Gene Ontology functional annotation analysis (GO analysis) and the Kyoto Encyclopedia of Genes and Genomes for pathway enrichment analysis (KEGG analysis). We examined protein-protein interactions to characterize the relationship between differentially expressed genes, and we screened potential hub genes according to the degree of connection. PCR and Western blotting were used to identify the core genes. RESULTS: At different times of infarction, a total of 35 genes showed upregulation at all times; however, none of the genes showed downregulation at all 3 times. Similarly, 10 hub genes with high degrees of connectivity were identified. In vivo and in vitro experiments suggested that expression levels of MMP-9 increased at various times after myocardial infarction and that expression increased in a variety of cells simultaneously. CONCLUSION: Expression levels of MMP-9 increase throughout the course of acute myocardial infarction, and this expression has both positive and negative sides. Further studies are needed to explore the role of MMP-9 in MI treatment. The potential values of Il6, Spp1, Ptgs2, Serpine1, Plaur, Cxcl5, Lgals3, Serpinb2, and Cd14 are also worth exploring.

Identification of Differentially Expressed Genes under the Regulation of Transcription Factors in Osteosarcoma.

The present study was to investigate and identify the differentially expressed genes (DEGs) in the transcriptional regulatory network of osteosarcoma (OS). The gene expression dataset from Gene Expression Omnibus (GEO) datasets was downloaded. DEGs were identified and their functional annotation was also conducted. In addition, differentially expressed transcription factors (TFs) and the regulatory genes were identified. The electronic validation was used to verify the expression of selected genes. The integrated analysis led to 932 DEGs. The results of functional annotation indicated that these DEGs significantly enriched in the p53 signaling pathway, Jak-STAT signaling pathway and Wnt signaling pathway. ZNF354C, NFIC, NFATC2, SP2, FOXO3, EGR1, ZEB1, RREB1, EGR2 and SRF were covered by most TFs. The expression levels of NFIC and EGR2 in electronic validation were compatible with our bio-informatics result. In conclusion, the deregulation of these genes may provide valuable information in understanding the underlying molecular mechanism in the OS.

Integrative Analysis of Microarray Data to Reveal Regulation Patterns in the Pathogenesis of Hepatocellular Carcinoma.

BACKGROUND/AIMS: The integration of multiple profiling data and the construction of a transcriptional regulatory network may provide additional insights into the molecular mechanisms of hepatocellular carcinoma (HCC). The present study was conducted to investigate the deregulation of genes and the transcriptional regulatory network in HCC. METHODS: An integrated analysis of HCC gene expression datasets was performed in Gene Expression Omnibus. Functional annotation of the differentially expression genes (DEGs) was conducted. Furthermore, transcription factors (TFs) were identified, and a global transcriptional regulatory network was constructed. RESULTS: An integrated analysis of eight eligible gene expression profiles of HCC led to 1,835 DEGs. Consistent with the fact that the cell cycle is closely related to various tumors, the functional annotation revealed that genes involved in the cell cycle were significantly enriched. A transcriptional regulatory network was constructed using the 62 TFs, which consisted of 872 TF-target interactions between 56 TFs and 672 DEGs in the context of HCC. The top 10 TFs covering the most downstream DEGs were ZNF354C, NFATC2, ARID3A, BRCA1, ZNF263, FOXD1, GATA3, FOXO3, FOXL1, and NR4A2. This network will appeal to future investigators focusing on the development of HCC. CONCLUSIONS: The transcriptional regulatory network can provide additional information that is valuable in understanding the underlying molecular mechanism in hepatic tumorigenesis.

Cellular Signaling Pathways in Insulin Resistance-Systems Biology Analyses of Microarray Dataset Reveals New Drug Target Gene Signatures of Type 2 Diabetes Mellitus.

Purpose: Type 2 diabetes mellitus (T2DM) is a chronic and metabolic disorder affecting large set of population of the world. To widen the scope of understanding of genetic causes of this disease, we performed interactive and toxicogenomic based systems biology study to find potential T2DM related genes after cDNA differential analysis. Methods: From the list of 50-differential expressed genes (p < 0.05), we found 9-T2DM related genes using extensive data mapping. In our constructed gene-network, T2DM-related differentially expressed seeder genes (9-genes) are found to interact with functionally related gene signatures (31-genes). The genetic interaction network of both T2DM-associated seeder as well as signature genes generally relates well with the disease condition based on toxicogenomic and data curation. Results: These networks showed significant enrichment of insulin signaling, insulin secretion and other T2DM-related pathways including JAK-STAT, MAPK, TGF, Toll-like receptor, p53 and mTOR, adipocytokine, FOXO, PPAR, P13-AKT, and triglyceride metabolic pathways. We found some enriched pathways that are common in different conditions. We recognized 11-signaling pathways as a connecting link between gene signatures in insulin resistance and T2DM. Notably, in the drug-gene network, the interacting genes showed significant overlap with 13-FDA approved and few non-approved drugs. This study demonstrates the value of systems genetics for identifying 18 potential genes associated with T2DM that are probable drug targets. Conclusions: This integrative and network based approaches for finding variants in genomic data expect to accelerate identification of new drug target molecules for different diseases and can speed up drug discovery outcomes.