Identification of lncRNA–miRNA–mRNA regulatory network associated with epithelial ovarian cancer cisplatin-resistant

To construct a long noncoding RNA (lncRNA)–microRNA (miRNA)–messenger RNA (mRNA) regulatory network related to epithelial ovarian cancer (EOC) cisplatin-resistant, differentially expressed genes (DEGs), differentially expressed lncRNAs (DELs), and differentially expressed miRNAs (DEMs) between MDAH and TOV-112D cells lines were identified. Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were conducted to analyze the biological functions of DEGs. Downstream mRNAs or upstream lncRNAs for miRNAs were analyzed at miRTarBase 7.0 or DIANA-LncBase V2, respectively. A total of 485 significant DEGs, 85 DELs, and 5 DEMs were identified. Protein–protein interaction (PPI) network of DEGs contrains 81 nodes and 141 edges was constructed, and 25 hub genes related to EOC cisplatin-resistant were identified. Subsequently, a lncRNA–miRNA–mRNA regulatory network contains 4 lncRNAs, 4 miRNAs, and 35 mRNAs was established. Taken together, our study provided evidence concerning the alteration genes involved in EOC cisplatin-resistant, which will help to unravel the mechanisms underlying drug resistant.     

Comprehensive analysis of lncRNA–miRNA–mRNA during proliferative phase of rat liver regeneration

This study aims to reveal the regulatory mechanism of lncRNAs–miRNAs–mRNAs network during the proliferative phase of liver regeneration (LR). High-throughput sequencing technology was performed, and a total of 1,738 differentially expressed lncRNAs (DE lncRNAs), 167 known differentially expressed miRNAs (DE miRNAs), and 2,727 differentially expressed mRNAs were identified. Then, the target DE lncRNAs and DE mRNAs regulated by the same miRNAs were screened and a ceRNA regulatory network containing 32 miRNAs, 107 lncRNAs, and 270 mRNAs was constructed. Insulin signaling pathway, pyrimidine metabolism, axon guidance, carbohydrate digestion and absorption, and pyruvate metabolism were significantly enriched in the network. Through literature review and the regulatory relationship between lncRNAs and miRNAs, nine core lncRNAs were identified, which might play important roles during the proliferative phase of rat LR. This study analyzed lncRNA–miRNA–mRNA regulatory network for the first time during the proliferative phase of rat LR, providing clues for exploring the mechanism of LR and the treatment of liver diseases.     

Integrated microRNA–mRNA analysis of coronary artery disease

Although patients with coronary artery disease (CAD) have a high mortality rate, the pathogenesis of CAD is still poorly understood. The purpose of this study was to explore the underlying molecular mechanisms and potential target molecules for CAD. The platelet miRNA (GSE28858) and blood mRNA (GSE42148) expression profiles of patients with CAD and healthy controls were downloaded from Gene Expression Omnibus. Differentially expressed miRNAs and genes (DEGs) were identified by significant analysis of microarray algorithm after data preprocessing. Furthermore, the miRNA-target gene regulatory network was constructed based on miRecords database. The spearman correlation coefficients (ρ) between miRNAs and their target genes were calculated. Six up- (miR-340, miR-545, miR-451, miR454-5p, miR-624 and miR-585) and four down-regulated (miR-199a, miR-17-3p, miR-154 and miR-339) miRNAs were screened. Total 295 target genes of miR-545, miR-451, miR-585 and miR-154 were predicted. Among these 295 target genes, 7 genes were DEGs. Further analysis showed miR-545-TFEC and miR-585-SPOCK1 were highly positively correlated (ρ = 0.808091264; ρ = 0.874680776) in CAD samples. Therefore, differentially expressed miRNAs might participate in the pathogenesis of CAD by regulating their target genes.     

Efficiency of the miRNA–mRNA Interaction Prediction Programs

miRNAs play a key role in regulation of gene expression. Nowadays it is known more than 2500 human miRNAs, while a majority of miRNA–mRNA interactions remains unidentified. The recent development of a high-throughput CLASH (crosslinking, ligation and sequencing of hybrids) technique for discerning miRNA–mRNA interactions allowed an experimental analysis of the human miRNA–mRNA interactome. Therefore, it allowed us, for the first time, make an experimental analysis of the human miRNA–mRNA interactome as a whole and an evaluation of the quality of most commonly used miRNA prediction tools (TargetScan, PicTar, PITA, RNA22 and miRanda). To estimate efficiency of the miRNA–mRNA prediction tools, we used next parameters: sensitivity, positive predicted value, predictions in different mRNA regions (3' UTR, CDS, 5' UTR), predictions for different types of interactions (5 classes), predictions of “canonical” and “nocanonical” interactions, similarity with the random generated data. The analysis revealed low efficiency of all prediction programs in comparison with the CLASH data in terms of the all examined parameters.     

Comprehensive resequence analysis of a 97 kb region of chromosome 10q11.2 containing the MSMB gene associated with prostate cancer

Genome-wide association studies of prostate cancer have identified single nucleotide polymorphism (SNP) markers in a region of chromosome 10q11.2, harboring the microseminoprotein-β (MSMB) gene. Both the gene product of MSMB, the prostate secretory protein 94 (PSP94) and its binding protein (PSPBP), have been previously investigated as serum biomarkers for prostate cancer progression. Recent functional work has shown that different alleles of the significantly associated SNP in the promoter of MSMB found to be associated with prostate cancer risk, rs10993994, can influence its expression in tumors and in vitro studies. Since it is plausible that additional variants in this region contribute to the risk of prostate cancer, we have used next-generation sequencing technology to resequence a ~97-kb region that includes the area surrounding MSMB (chr10: 51,168,025–51,265,101) in 36 prostate cancer cases, 26 controls of European origin, and 8 unrelated CEPH individuals in order to identify additional variants to investigate in functional studies. We identified 241 novel polymorphisms within this region, including 142 in the 51-kb block of linkage disequilibrium (LD) that contains rs10993994 and the proximal promoter of MSMB. No sites were observed to be polymorphic within the exons of MSMB.     

Heparan sulphate epitope–expression is associated with the inflammatory response in metastatic malignant melanoma

Heparan sulphate (HS) represents a heterogeneous class of molecules on cell membranes and extracellular matrices. These molecules are involved in a variety of biological processes, including immune responses, through their binding and functional modulation of proteins. Recently a panel of HS-epitope–specific, human single chain antibodies have been generated by phage display, facilitating analysis of the structural heterogeneity of HS in relation to pathological conditions. In a pilot study a heterogeneous staining pattern in melanoma metastases was observed with one of the clones (EW4G1). Using a double-staining technique, the expression of this epitope was studied in 12 metastatic melanoma lesions in relation to the presence of a CD3⁺ cell infiltrate. Different staining patterns with EW4G1 were observed in the different lesions. The different staining patterns were associated with the presence and pattern of inflammation with CD3⁺ cells. A pronounced staining pattern of blood vessels with EW4G1 was associated with a more or less brisk presence of CD3⁺ cells, while a pronounced staining of tumour cells or tumour cell matrix or absence of staining with EW4G1 was associated with absence of CD3⁺ cells. These results suggest a dualistic role for HS in the recruitment and intratumoural migration of CD3⁺ cells, depending on the location of expression of its epitope recognized by EW4G1. Further characterization of the structural diversity of HS and its function in T-cell recruitment and migration is therefore warranted, since detailed understanding of this relation may provide new targets for therapeutic intervention, such that better homing and migration of T cells (in)to tumours might be achieved in immunologically based treatment strategies.     

Analyzing the pathways enriched in genes associated with nicotine dependence in the context of human protein–protein interaction network

Nicotine dependence is the primary addictive stage of cigarette smoking. Although a lot of studies have been performed to explore the molecular mechanism underlying nicotine dependence, our understanding on this disorder is still far from complete. Over the past decades, an increasing number of candidate genes involved in nicotine dependence have been identified by different technical approaches, including the genetic association analysis. In this study, we performed a comprehensive collection of candidate genes reported to be genetically associated with nicotine dependence. Then, the biochemical pathways enriched in these genes were identified by considering the gene’s propensity to be related to nicotine dependence. One of the most widely used pathway enrichment analysis approach, over-representation analysis, ignores the function non-equivalence of genes in candidate gene set and may have low discriminative power in identifying some dysfunctional pathways. To overcome such drawbacks, we constructed a comprehensive human protein–protein interaction network, and then assigned a function weighting score to each candidate gene based on their network topological features. Evaluation indicated the function weighting score scheme was consistent with available evidence. Finally, the function weighting scores of the candidate genes were incorporated into pathway analysis to identify the dysfunctional pathways involved in nicotine dependence, and the interactions between pathways was detected by pathway crosstalk analysis. Compared to conventional over-representation-based pathway analysis tool, the modified method exhibited improved discriminative power and detected some novel pathways potentially underlying nicotine dependence. In summary, we conducted a comprehensive collection of genes associated with nicotine dependence and then detected the biochemical pathways enriched in these genes using a modified pathway enrichment analysis approach with function weighting score of candidate genes integrated. Our results may provide insight into the molecular mechanism underlying nicotine dependence.     

Distinctive microRNA signature associated of neoplasms with the Wnt/β-catenin signaling pathway

As the crucial biological regulators, microRNAs that act by suppressing their target genes are involved in a variety of pathophysiological processes. It is generally accepted that microRNAs are often dysregulated in many types of neoplasm and other human diseases. In neoplasm, microRNAs may function as oncogenes or tumor suppressors. As constitutive activation of the Wnt signaling pathway is a common feature of neoplasm and contributes to its development, progression and metastasis in various cancers, numerous studies have revealed that microRNA-mediated gene regulation are interconnected with the Wnt/β-catenin signaling pathway, forming a Wnt/β-catenin–microRNA regulatory network, which is critical to successful targeting of the Wnt/β-catenin pathway for oncotherapy. In this review, we aim to accumulate recent advances on microRNAs that work in tandem with Wnt/β-catenin signaling in tumorigenesis, with particular focus on how microRNAs affect Wnt/β-catenin activity as well as how microRNAs are regulated through the Wnt/β-catenin pathway.     

Characterization and analysis of the regulatory network involved in control of lipomycin biosynthesis in Streptomyces aureofaciens Tü117

Analysis of the α-lipomycin biosynthesis gene cluster of Streptomyces aureofaciens Tü117 led to the identification of five putative regulatory genes, which are congregated into a subcluster. Analysis of the lipReg1–4 and lipX1 showed that they encode components of two-component signal transduction systems (LipReg1 and LipReg2), multiple antibiotics resistance-type regulator (LipReg3), large ATP-binding regulators of the LuxR family-type regulator (LipReg4), and small ribonuclease (LipRegX1), respectively. A combination of targeted gene disruptions, complementation experiments, lipomycin production studies, and gene expression analysis via RT-PCR suggests that all regulatory lip genes are involved in α-lipomycin production. On the basis of the obtained data, we propose that LipReg2 controls the activity of LipReg1, which in its turn govern the expression of the α-lipomycin pathway-specific regulatory gene lipReg4. The ribonuclease gene lipX1 and the transporter regulator lipReg3 appear to work independently of genes lipReg1, lipReg2, and lipReg4.     

Global analysis of a stoichiometric producer–grazer model with Holling type functional responses

Cells, the basic units of organisms, consist of multiple essential elements such as carbon, nitrogen, and phosphorus. The scarcity of any of these elements can strongly restrict cellular and organismal growth. During recent years, ecological models incorporating multiple elements have been rapidly developed in many studies, which form a new research field of mathematical and theoretical biology. Among these models, the one proposed by Loladze et al. (Bull Math Biol 62:1137–1162, 2000) is prominent and has been highly cited. However, the global analysis of this nonsmooth model has never been done. The aim of this paper is to provide the complete global analysis for the model with Holling type I functional response and perform a bifurcation analysis for the model with Holling type II functional response.     

Construction and analysis of the protein–protein interaction network for the olfactory system of the silkworm Bombyx mori

Olfaction plays an essential role in feeding and information exchange in insects. Previous studies on the olfaction of silkworms have provided a wealth of information about genes and proteins, yet, most studies have only focused on a single gene or protein related to the insect's olfaction. The aim of the current study is to determine key proteins in the olfactory system of the silkworm, and further understand protein–protein interactions (PPIs) in the olfactory system of Lepidoptera. To achieve this goal, we integrated Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and network analyses. Furthermore, we selected 585 olfactory-related proteins and constructed a (PPI) network for the olfactory system of the silkworm. Network analysis led to the identification of several key proteins, including GSTz1, LOC733095, BGIBMGA002169-TA, BGIBMGA010939-TA, GSTs2, GSTd2, Or-2, and BGIBMGA013255-TA. A comprehensive evaluation of the proteins showed that glutathione S-transferases (GSTs) had the highest ranking. GSTs also had the highest enrichment levels in GO and KEGG. In conclusion, our analysis showed that key nodes in the biological network had a significant impact on the network, and the key proteins identified via network analysis could serve as new research targets to determine their functions in olfaction.     

The mRNA–miRNA–lncRNA Regulatory Network and Factors Associated with Prognosis Prediction of Hepatocellular Carcinoma

The aim of this study was to identify novel prognostic mRNA and microRNA (miRNA) biomarkers for hepatocellular carcinoma (HCC) using methods in systems biology. Differentially expressed mRNAs, miRNAs, and long non-coding RNAs (lncRNAs) were compared between HCC tumor tissues and normal liver tissues in The Cancer Genome Atlas (TCGA) database. Subsequently, a prognosis-associated mRNA co-expression network, an mRNA–miRNA regulatory network, and an mRNA–miRNA–lncRNA regulatory network were constructed to identify prognostic biomarkers for HCC through Cox survival analysis. Seven prognosis-associated mRNA co-expression modules were obtained by analyzing these differentially expressed mRNAs. An expression module including 120 mRNAs was significantly correlated with HCC patient survival. Combined with patient survival data, several mRNAs and miRNAs, including CHST4, SLC22A8, STC2, hsa-miR-326, and hsa-miR-21 were identified from the network to predict HCC patient prognosis. Clinical significance was investigated using tissue microarray analysis of samples from 258 patients with HCC. Functional annotation of hsa-miR-326 and hsa-miR-21-5p indicated specific associations with several cancer-related pathways. The present study provides a bioinformatics method for biomarker screening, leading to the identification of an integrated mRNA–miRNA–lncRNA regulatory network and their co-expression patterns in relation to predicting HCC patient survival.     

A cross-cancer metastasis signature in the microRNA–mRNA axis of paired tissue samples

In the progression of cancer, cells acquire genetic mutations that cause uncontrolled growth. Over time, the primary tumour may undergo additional mutations that allow for the cancerous cells to spread throughout the body as metastases. Since metastatic development typically results in markedly worse patient outcomes, research into the identity and function of metastasis-associated biomarkers could eventually translate into clinical diagnostics or novel therapeutics. Although the general processes underpinning metastatic progression are understood, no clear cross-cancer biomarker profile has emerged. However, the literature suggests that some microRNAs (miRNAs) may play an important role in the metastatic progression of several cancer types. Using a subset of The Cancer Genome Atlas (TCGA) data, we performed an integrated analysis of mRNA and miRNA expression with paired metastatic and primary tumour samples to interrogate how the miRNA–mRNA regulatory axis influences metastatic progression. From this, we successfully built mRNA- and miRNA-specific classifiers that can discriminate pairs of metastatic and primary samples across 11 cancer types. In addition, we identified a number of miRNAs whose metastasis-associated dysregulation could predict mRNA metastasis-associated dysregulation. Among the most predictive miRNAs, we found several previously implicated in cancer progression, including miR-301b, miR-1296, and miR-423. Taken together, our results suggest that metastatic samples have a common cross-cancer signature when compared with their primary tumour pair, and that these miRNA biomarkers can be used to predict metastatic status as well as mRNA expression.