Functional Similarity Analysis Research Articles

Integrative bioinformatics analysis to identify ferroptosis-related genes in non-obstructive azoospermia.

The objective of this study was to discern ferroptosis-related genes (FRGs) linked to non-obstructive azoospermia and investigate the associated molecular mechanisms. A dataset related to azoospermia was retrieved from the Gene Expression Omnibus database, and FRGs were sourced from GeneCards. Ferroptosis-related differentially expressed genes (FRDEGs) were discerned. Subsequently, these genes underwent analyses encompassing Gene Ontology and Kyoto Encyclopedia of Genes and Genomes, as well as protein-protein interaction (PPI) networks and assessments of functional similarity. Following the identification of hub genes, an exploration of immune infiltration, single-cell expression, diagnostic utility, and interactions involving hub genes, RNA-binding proteins (RBPs), transcription factors (TFs), microRNAs (miRNAs), and drugs was conducted. A total of 35 differentially expressed FRGs were discerned. These genes demonstrated enrichment in functions and pathways associated with ferroptosis. From the PPI network, eight hub genes were selected. Functional similarity analysis highlighted the potential pivotal roles of HMOX1 and GPX4 in azoospermia. Analysis of immune cell infiltration indicated a significant decrease in activated dendritic cells in the azoospermia group, with notable correlations between hub genes, particularly SAT1 and HMGCR, and immune cell infiltration. Unique expression patterns of hub genes across various cell types in the human testis were observed, with GPX4 prominently enriched in spermatid/sperm. Eight hub genes exhibited robust diagnostic value (AUC > 0.75). Lastly, a comprehensive hub gene-miRNA-TF-RBP-drug network was constructed. In summary, our investigation unveiled eight FRDEGs associated with azoospermia, which hold potential as biomarkers for the diagnosis and treatment of azoospermia.

Prognostic role of chemokine-related genes in acute myeloid leukemia.

Chemotactic cytokines play a crucial role in the development of acute myeloid leukemia (AML). Thus, investigating the mechanisms of chemotactic cytokine-related genes (CCRGs) in AML is of paramount importance. Using the TCGA-AML, GSE114868, and GSE12417 datasets, differential expression analysis identified differentially expressed CCRGs (DE-CCRGs). These genes were screened by overlapping differentially expressed genes (DEGs) between AML and control groups with CCRGs. Subsequently, functional enrichment analysis and the construction of a protein-protein interaction (PPI) network were conducted to explore the functions of the DE-CCRGs. Univariate Cox regression, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analyses identified relevant prognostic genes and developed a prognostic model. Survival analysis of the prognostic gene was performed, followed by functional similarity analysis, immune analysis, enrichment analysis, and drug prediction analysis. Differential expression analysis revealed 6,743 DEGs, of which 29 DE-CCRGs were selected for this study. Functional enrichment analysis indicated that DE-CCRGs were primarily involved in chemotactic cytokine-related functions and pathways. Six prognostic genes (CXCR3, CXCR2, CXCR6, CCL20, CCL4, and CCR2) were identified and incorporated into the risk model. The model's performance was validated using the GSE12417 dataset. Survival analysis showed significant differences in AML overall survival (OS) between prognostic gene high and low expression groups, indicating that prognostic gene might be significantly associated with patient survival. Additionally, nine different immune cells were identified between the two risk groups. Correlation analysis revealed that CCR2 had the most significant positive correlation with monocytes and the most significant negative correlation with resting mast cells. The tumor immune dysfunction and exclusion score was lower in the high-risk group. CXCR3, CXCR2, CXCR6, CCL20, CCL4, and CCR2 were identified as prognostic genes correlated to AML and the tumor immune microenvironment. These findings offerred novel insights into the prevention and treatment of AML.

Adaptive coding of stimulus information in human frontoparietal cortex during visual classification

The neural mechanisms of how frontal and parietal brain regions support flexible adaptation of behavior remain poorly understood. Here, we used functional magnetic resonance imaging (fMRI) and representational similarity analysis (RSA) to investigate frontoparietal representations of stimulus information during visual classification under varying task demands. Based on prior research, we predicted that increasing perceptual task difficulty should lead to adaptive changes in stimulus coding: task-relevant category information should be stronger, while task-irrelevant exemplar-level stimulus information should become weaker, reflecting a focus on the behaviorally relevant category information. Counter to our expectations, however, we found no evidence for adaptive changes in category coding. We did find weakened coding at the exemplar-level within categories however, demonstrating that task-irrelevant information is de-emphasized in frontoparietal cortex. These findings reveal adaptive coding of stimulus information at the exemplar-level, highlighting how frontoparietal regions might support behavior even under challenging conditions.

Integrative bioinformatics analysis to identify novel biomarkers associated with non-obstructive azoospermia.

This study aimed to identify autophagy-related genes (ARGs) associated with non-obstructive azoospermia and explore the underlying molecular mechanisms. Two datasets associated with azoospermia were downloaded from the Gene Expression Omnibus database, and ARGs were obtained from the Human Autophagy-dedicated Database. Autophagy-related differentially expressed genes were identified in the azoospermia and control groups. These genes were subjected to Gene Ontology and Kyoto Encyclopedia of Genes and Genomes, protein-protein interaction (PPI) network, and functional similarity analyses. After identifying the hub genes, immune infiltration and hub gene-RNA-binding protein (RBP)-transcription factor (TF)-miRNA-drug interactions were analyzed. A total 46 differentially expressed ARGs were identified between the azoospermia and control groups. These genes were enriched in autophagy-associated functions and pathways. Eight hub genes were selected from the PPI network. Functional similarity analysis revealed that HSPA5 may play a key role in azoospermia. Immune cell infiltration analysis revealed that activated dendritic cells were significantly decreased in the azoospermia group compared to those in the control groups. Hub genes, especially ATG3, KIAA0652, MAPK1, and EGFR were strongly correlated with immune cell infiltration. Finally, a hub gene-miRNA-TF-RBP-drug network was constructed. The eight hub genes, including EGFR, HSPA5, ATG3, KIAA0652, and MAPK1, may serve as biomarkers for the diagnosis and treatment of azoospermia. The study findings suggest potential targets and mechanisms for the occurrence and development of this disease.

Bioinformatics insights into the genes and pathways on severe COVID-19 pathology in patients with comorbidities.

Background: Coronavirus disease (COVID-19) infection is known for its severe clinical pathogenesis among individuals with pre-existing comorbidities. However, the molecular basis of this observation remains elusive. Thus, this study aimed to map key genes and pathway alterations in patients with COVID-19 and comorbidities using robust systems biology approaches. Methods: The publicly available genome-wide transcriptomic datasets from 120 COVID-19 patients, 281 patients suffering from different comorbidities (like cardiovascular diseases, atherosclerosis, diabetes, and obesity), and 252 patients with different infectious diseases of the lung (respiratory syncytial virus, influenza, and MERS) were studied using a range of systems biology approaches like differential gene expression, gene ontology (GO), pathway enrichment, functional similarity, mouse phenotypic analysis and drug target identification. Results: By cross-mapping the differentially expressed genes (DEGs) across different datasets, we mapped 274 shared genes to severe symptoms of COVID-19 patients or with comorbidities alone. GO terms and functional pathway analysis highlighted genes in dysregulated pathways of immune response, interleukin signaling, FCGR activation, regulation of cytokines, chemokines secretion, and leukocyte migration. Using network topology parameters, phenotype associations, and functional similarity analysis with ACE2 and TMPRSS2-two key receptors for this virus-we identified 17 genes with high connectivity (CXCL10, IDO1, LEPR, MME, PTAFR, PTGS2, MAOB, PDE4B, PLA2G2A, COL5A1, ICAM1, SERPINE1, ABCB1, IL1R1, ITGAL, NCAM1 and PRKD1) potentially contributing to the clinical severity of COVID-19 infection in patients with comorbidities. These genes are predicted to be tractable and/or with many existing approved inhibitors, modulators, and enzymes as drugs. Conclusion: By systemic implementation of computational methods, this study identified potential candidate genes and pathways likely to confer disease severity in COVID-19 patients with pre-existing comorbidities. Our findings pave the way to develop targeted repurposed therapies in COVID-19 patients.

Dynamic changes in neural representations underlie the repetition effect on false memory

Restudying word lists (e.g., dream, awake, and bed) strengthens true memory of the studied words and reduces false memory for unstudied but semantically related lures (e.g., sleep). Yet, the neural mechanisms involved in this repetition effect on false memory remain unclear. Possible mechanisms involve item-specific and semantic neural representations at encoding, and the memory strength between encoding and retrieval. This study first replicated the behavioral results (Exp. 1) and then investigated various neural mechanisms by using slow event-related functional magnetic resonance imaging (fMRI) and representational similarity analysis (Exp. 2). Behavioral results confirmed that restudy improved true memory and reduced false memory. The fMRI results showed that restudy induced item-specific neural representations at encoding in the left occipital pole, but reduced neural overlap between semantic representations at encoding in the left temporal pole. Individual differences in these two encoding neural mechanisms were correlated with the behavioral measure of false memory, with greater restudy-induced representational changes at encoding (item-specific neural representations and reduced neural overlap between semantic representations) being associated with lower false memory. Moreover, restudy enhanced the memory strength between encoding and retrieval in the visuoparietal cortex but reduced it in the frontal cortex. These findings suggest that dynamic changes in neural representations underlie the repetition effect on false memory, supporting a dual-coding neural framework.

Skeletal representations of shape in the human visual cortex

Shape perception is crucial for object recognition. However, it remains unknown exactly how shape information is represented and used by the visual system. Here, we tested the hypothesis that the visual system represents object shape via a skeletal structure. Using functional magnetic resonance imaging (fMRI) and representational similarity analysis (RSA), we found that a model of skeletal similarity explained significant unique variance in the response profiles of V3 and LO. Moreover, the skeletal model remained predictive in these regions even when controlling for other models of visual similarity that approximate low-to high-level visual features (i.e., Gabor-jet, GIST, HMAX, and AlexNet), and across different surface forms, a manipulation that altered object contours while preserving the underlying skeleton. Together, these findings shed light on shape processing in human vision, as well as the computational properties of V3 and LO. We discuss how these regions may support two putative roles of shape skeletons: namely, perceptual organization and object recognition.

New insights about the serine/threonine protein kinase substrates from Mycobacterium tuberculosis using molecular docking, quantum similarity analysis and DFT calculations

Background: The protein kinases present in the human body have received a lot of attention because of the interest in their use as therapeutic targets. However, little is known about the protein kinases associated with tuberculosis. For these reasons, this research investigates a new point of view regarding the crystallized serine/threonine protein kinases Pkn A, B and G of Mycobacterium tuberculosis. Methods: The conformational analysis shows a DFG-in motif in Pkn B and G and a DFG-out motif in Pkn A. For all the protein kinases that have been studied, the gatekeeper residue is methionine. A study of the protein kinases with their ligands was also conducted to find new insights on the binding site with a series of ligands associated to protein kinases Pkn A, B and G through molecular docking. The residues with hydrogen bonds on the hinge zone of Pkn A are GLU96 and VAL 98, of Pkn B are GLU 93 and VAL 95 and of Pkn G are GLU233 and VAL235. Results: The results show the H-bond acceptor and H-bond donor sites on the hinge zone to all ligands, establishing a structural model of the ligands on the active site with two or three interactions in this zone. This interaction model was validated using density functional theory calculations (by means of net charges and images of the electrostatic potential) and molecular quantum similarity analysis, showing a high correlation between the electronic and steric effects in each ATP complex studied. Conclusions: In this work we can see that the interactions of the hinge zone are characterized by the key factor of one or two H-bonds acceptors and one H-bond donor in the ligands of this zone. The quantum similarity analysis shows good correlation between the steric and electronic effects in each ATP complex.

Visual and Semantic Representations Predict Subsequent Memory in Perceptual and Conceptual Memory Tests.

It is generally assumed that the encoding of a single event generates multiple memory representations, which contribute differently to subsequent episodic memory. We used functional magnetic resonance imaging (fMRI) and representational similarity analysis to examine how visual and semantic representations predicted subsequent memory for single item encoding (e.g., seeing an orange). Three levels of visual representations corresponding to early, middle, and late visual processing stages were based on a deep neural network. Three levels of semantic representations were based on normative observed ("is round"), taxonomic ("is a fruit"), and encyclopedic features ("is sweet"). We identified brain regions where each representation type predicted later perceptual memory, conceptual memory, or both (general memory). Participants encoded objects during fMRI, and then completed both a word-based conceptual and picture-based perceptual memory test. Visual representations predicted subsequent perceptual memory in visual cortices, but also facilitated conceptual and general memory in more anterior regions. Semantic representations, in turn, predicted perceptual memory in visual cortex, conceptual memory in the perirhinal and inferior prefrontal cortex, and general memory in the angular gyrus. These results suggest that the contribution of visual and semantic representations to subsequent memory effects depends on a complex interaction between representation, test type, and storage location.

SemanticGO: a tool for gene functional similarity analysis in Arabidopsis thaliana and rice

Gene or pathway functional similarities are important information for researchers. However, these similarities are often described sparsely and qualitatively. The latent semantic analysis of Arabidopsis thaliana (Arabidopsis) Gene Ontology (GO) data produced a set of 200-dimension feature vectors for each gene. Pathways were represented by summing the vectors of the pathway member genes. Thus, the similarities between genes and pathways were assessed. Additionally, the gene feature vectors were correlated with external gene data, including gene expression and gene network connectivity, to elucidate the associated functions. The gene feature vectors were decoded, and their applications were demonstrated. A simple online tool, SemanticGO (http://bioinformatics.fafu.edu.cn/semanticGO/), is herein provided to enable researchers to explore the similarities between genes and pathways in both Arabidopsis and rice.

Whole exome sequencing identifies multiple novel candidate genes in familial gastroschisis

BackgroundGenetic association studies for gastroschisis have highlighted several candidate variants. However, genetic basis in gastroschisis from noninvestigated heritable factors could provide new insights into the human biology for this birth defect. We aim to identify novel gastroschisis susceptibility variants by employing whole exome sequencing (WES) in a Mexican family with recurrence of gastroschisis.MethodsWe employed WES in two affected half‐sisters with gastroschisis, mother, and father of the proband. Additionally, functional bioinformatics analysis was based on SVS–PhoRank and Ensembl–Variant Effect Predictor. The latter assessed the potentially deleterious effects (high, moderate, low, or modifier impact) from exome variants based on SIFT, PolyPhen, dbNSFP, Condel, LoFtool, MaxEntScan, and BLOSUM62 algorithms. The analysis was based on the Human Genome annotation, GRCh37/hg19. Candidate genes were prioritized and manually curated based on significant phenotypic relevance (SVS–PhoRank) and functional properties (Ensembl–Variant Effect Predictor). Functional enrichment analysis was performed using ToppGene Suite, including a manual curation of significant Gene Ontology (GO) biological processes from functional similarity analysis of candidate genes.ResultsNo single gene‐disrupting variant was identified. Instead, 428 heterozygous variations were identified for which SPATA17, PDE4DIP, CFAP65, ALPP, ZNF717, OR4C3, MAP2K3, TLR8, and UBE2NL were predicted as high impact in both cases, mother, and father of the proband. PLOD1, COL6A3, FGFRL1, HHIP, SGCD, RAPGEF1, PKD1, ZFHX3, BCAS3, EVPL, CEACAM5, and KLK14 were segregated among both cases and mother. Multiple interacting background modifiers may regulate gastroschisis susceptibility. These candidate genes highlight a role for development of blood vessel, circulatory system, muscle structure, epithelium, and epidermis, regulation of cell junction assembly, biological/cell adhesion, detection/response to endogenous stimulus, regulation of cytokine biosynthetic process, response to growth factor, postreplication repair/protein K63‐linked ubiquitination, protein‐containing complex assembly, and regulation of transcription DNA‐templated.ConclusionConsidering the likely gene‐disrupting prediction results and similar biological pattern of mechanisms, we propose a joint “multifactorial model” in gastroschisis pathogenesis.

Deciphering the functional role of spatial and temporal muscle synergies in whole-body movements

Voluntary movement is hypothesized to rely on a limited number of muscle synergies, the recruitment of which translates task goals into effective muscle activity. In this study, we investigated how to analytically characterize the functional role of different types of muscle synergies in task performance. To this end, we recorded a comprehensive dataset of muscle activity during a variety of whole-body pointing movements. We decomposed the electromyographic (EMG) signals using a space-by-time modularity model which encompasses the main types of synergies. We then used a task decoding and information theoretic analysis to probe the role of each synergy by mapping it to specific task features. We found that the temporal and spatial aspects of the movements were encoded by different temporal and spatial muscle synergies, respectively, consistent with the intuition that there should a correspondence between major attributes of movement and major features of synergies. This approach led to the development of a novel computational method for comparing muscle synergies from different participants according to their functional role. This functional similarity analysis yielded a small set of temporal and spatial synergies that describes the main features of whole-body reaching movements.

Functional similarity analysis highlights ecosystem impacts and restoration needs

AbstractAimsTo test a recently developed method of assessing functional β‐diversity – as the difference in location of the convex hull in trait space – as a means of assessing the impact of species loss and colonization over time on functional composition. This approach was tested using data from a survey and subsequent resurvey of Scottish coastal vegetation.LocationScotland, UK.ResultsFixed dunes, slacks, unimproved grassland and mires showed the highest functional overlap through time (high correspondence in convex hull position between two surveys), while salt marsh, improved grassland and heath showed the lowest. Fixed dune was the most stable in terms of retaining the highest proportion of the original convex hull, while salt marsh was least stable. Salt marsh also showed the highest new functional space occupied, whilst mires and slacks showed the least colonization by functionally distinct species. Generally sites on the west coast of Scotland were most stable, while those on the east showed the greatest functional changes. Assessing functional β‐diversity provided a different picture to assessing species β‐diversity; high species turnover might have little impact on the functional characteristics of the vegetation, whereas the invasion of one functionally different species can have a sizeable impact on ecosystem processes.ConclusionsThe method proved effective in highlighting habitats and sites where species changes had the largest impact on the functional space occupied by the plant assemblage. The method provides complementary information to that derived from other types of analysis, including about the potential needs for improved management or full‐scale restoration.

NaviGO: interactive tool for visualization and functional similarity and coherence analysis with gene ontology

BackgroundThe number of genomics and proteomics experiments is growing rapidly, producing an ever-increasing amount of data that are awaiting functional interpretation. A number of function prediction algorithms were developed and improved to enable fast and automatic function annotation. With the well-defined structure and manual curation, Gene Ontology (GO) is the most frequently used vocabulary for representing gene functions. To understand relationship and similarity between GO annotations of genes, it is important to have a convenient pipeline that quantifies and visualizes the GO function analyses in a systematic fashion.ResultsNaviGO is a web-based tool for interactive visualization, retrieval, and computation of functional similarity and associations of GO terms and genes. Similarity of GO terms and gene functions is quantified with six different scores including protein-protein interaction and context based association scores we have developed in our previous works. Interactive navigation of the GO function space provides intuitive and effective real-time visualization of functional groupings of GO terms and genes as well as statistical analysis of enriched functions.ConclusionsWe developed NaviGO, which visualizes and analyses functional similarity and associations of GO terms and genes. The NaviGO webserver is freely available at: http://kiharalab.org/web/navigo.

An improved method for functional similarity analysis of genes based on Gene Ontology.

BackgroundMeasures of gene functional similarity are essential tools for gene clustering, gene function prediction, evaluation of protein-protein interaction, disease gene prioritization and other applications. In recent years, many gene functional similarity methods have been proposed based on the semantic similarity of GO terms. However, these leading approaches may make errorprone judgments especially when they measure the specificity of GO terms as well as the IC of a term set. Therefore, how to estimate the gene functional similarity reliably is still a challenging problem.ResultsWe propose WIS, an effective method to measure the gene functional similarity. First of all, WIS computes the IC of a term by employing its depth, the number of its ancestors as well as the topology of its descendants in the GO graph. Secondly, WIS calculates the IC of a term set by means of considering the weighted inherited semantics of terms. Finally, WIS estimates the gene functional similarity based on the IC overlap ratio of term sets. WIS is superior to some other representative measures on the experiments of functional classification of genes in a biological pathway, collaborative evaluation of GO-based semantic similarity measures, protein-protein interaction prediction and correlation with gene expression. Further analysis suggests that WIS takes fully into account the specificity of terms and the weighted inherited semantics of terms between GO terms.ConclusionsThe proposed WIS method is an effective and reliable way to compare gene function. The web service of WIS is freely available at http://nclab.hit.edu.cn/WIS/.Electronic supplementary materialThe online version of this article (doi:10.1186/s12918-016-0359-z) contains supplementary material, which is available to authorized users.

SimDEF: definition-based semantic similarity measure of gene ontology terms for functional similarity analysis of genes

Measures of protein functional similarity are essential tools for function prediction, evaluation of protein-protein interactions (PPIs) and other applications. Several existing methods perform comparisons between proteins based on the semantic similarity of their GO terms; however, these measures are highly sensitive to modifications in the topological structure of GO, tend to be focused on specific analytical tasks and concentrate on the GO terms themselves rather than considering their textual definitions. We introduce simDEF, an efficient method for measuring semantic similarity of GO terms using their GO definitions, which is based on the Gloss Vector measure commonly used in natural language processing. The simDEF approach builds optimized definition vectors for all relevant GO terms, and expresses the similarity of a pair of proteins as the cosine of the angle between their definition vectors. Relative to existing similarity measures, when validated on a yeast reference database, simDEF improves correlation with sequence homology by up to 50%, shows a correlation improvement >4% with gene expression in the biological process hierarchy of GO and increases PPI predictability by > 2.5% in F1 score for molecular function hierarchy. Datasets, results and source code are available at http://kiwi.cs.dal.ca/Software/simDEF CONTACT: ahmad.pgh@dal.ca or beiko@cs.dal.ca Supplementary data are available at Bioinformatics online.

Function Based Design-by-Analogy: A Functional Vector Approach to Analogical Search

Design-by-analogy is a powerful approach to augment traditional concept generation methods by expanding the set of generated ideas using similarity relationships from solutions to analogous problems. While the concept of design-by-analogy has been known for some time, few actual methods and tools exist to assist designers in systematically seeking and identifying analogies from general data sources, databases, or repositories, such as patent databases. A new method for extracting functional analogies from data sources has been developed to provide this capability, here based on a functional basis rather than form or conflict descriptions. Building on past research, we utilize a functional vector space model (VSM) to quantify analogous similarity of an idea's functionality. We quantitatively evaluate the functional similarity between represented design problems and, in this case, patent descriptions of products. We also develop document parsing algorithms to reduce text descriptions of the data sources down to the key functions, for use in the functional similarity analysis and functional vector space modeling. To do this, we apply Zipf's law on word count order reduction to reduce the words within the documents down to the applicable functionally critical terms, thus providing a mapping process for function based search. The reduction of a document into functional analogous words enables the matching to novel ideas that are functionally similar, which can be customized various ways. This approach thereby provides relevant sources of design-by-analogy inspiration. As a verification of the approach, two original design problem case studies illustrate the distance range of analogical solutions that can be extracted. This range extends from very near-field, literal solutions to far-field cross-domain analogies.

A novel insight into Gene Ontology semantic similarity

Existing methods for computing the semantic similarity between Gene Ontology (GO) terms are often based on external datasets and, therefore are not intrinsic to GO. Furthermore, they not only fail to handle identical annotations but also show a strong bias toward well-annotated proteins when being used for measuring similarity of proteins. Inspired by the concept of cellular differentiation and dedifferentiation in developmental biology, we propose a shortest semantic differentiation distance (SSDD) based on the concept of semantic totipotency to measure the semantic similarity of GO terms and further compare the functional similarity of proteins. Using human ratings and a benchmark dataset, SSDD was found to improve upon existing methods for computing the semantic similarity of GO terms. An in-depth analysis shows that SSDD is able to distinguish identical annotations and does not depend on annotation richness, thus producing more unbiased and reliable results. Online services can be accessed at the Gene Functional Similarity Analysis Tools website (GFSAT: http://nclab.hit.edu.cn/GFSAT).

Expression profile analysis reveals putative prostate cancer-related microRNAs

Annotation of prostate cancer (PC) genomes provides a foundation for discoveries that can improve the understanding and treatment of the disease. Therefore, in the present study, we used the Student t-test to identify differentially expressed PC-related mRNAs and microRNAs (miRNAs). Then, we performed interrelated mapping of miRNA target genes between abnormally expressed mRNAs and miRNAs, and explored mRNA-target miRNA interrelated pairs to explain the biological functions of miRNA during the progression of PC, thus revealing the occurrence of miRNA-mediated PC. After Gene Set Functional Similarity analysis, we obtained 20 abnormal PC-related candidate miRNAs, including hsa-miR-26a, hsa-miR-152, hsa-miR-19a, hsa-miR-30c, hsa-miR-19b, and hsa-miR-146b-5p, among others. These results suggest that it may be possible to predict the clinical behavior of prostate cancer based on gene expression analysis.

Functional similarity analysis of human virus-encoded miRNAs

miRNAs are a class of small RNAs that regulate gene expression via RNA silencing machinery. Some viruses also encode miRNAs, contributing to the complex virus-host interactions. A better understanding of viral miRNA functions would be useful in designing new preventive strategies for treating diseases induced by viruses. To meet the challenge for how viruses module host gene expression by their encoded miRNAs, we measured the functional similarities among human viral miRNAs by using a method we reported previously. Higher order functions regulated by viral miRNAs were also identified by KEGG pathway analysis on their targets. Our study demonstrated the biological processes involved in virus-host interactions via viral miRNAs. Phylogenetic analysis suggested that viral miRNAs have distinct evolution rates compared with their corresponding genome.