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Abstract Background High-throughput experimental technologies can provide deeper insights into pathway perturbations in biomedical studies. Accordingly, their usage is central to the identification of molecular targets and the subsequent development of suitable treatments for various diseases. Classical interpretations of generated data, such as differential gene expression and pathway analyses, disregard interconnections between studied genes when looking for gene-disease associations. Given that these interconnections are central to cellular processes, there has been a recent interest in incorporating them in such studies. The latter allows the detection of gene modules that underlie complex phenotypes in gene interaction networks. Existing methods either impose radius-based restrictions or freely grow modules at the expense of a statistical bias towards large modules. We propose a heuristic method, inspired by Ant Colony Optimization, to apply gene-level scoring and module identification with distance-based search constraints and penalties, rather than radius-based constraints. Results We test and compare our results to other approaches using three datasets of different neurodegenerative diseases, namely Alzheimer’s, Parkinson’s, and Huntington’s, over three independent experiments. We report the outcomes of enrichment analyses and concordance of gene-level scores for each disease. Results indicate that the proposed approach generally shows superior stability in comparison to existing methods. It produces stable and meaningful enrichment results in all three datasets which have different case to control proportions and sample sizes. Conclusion The presented network-based gene expression analysis approach successfully identifies dysregulated gene modules associated with a certain disease. Using a heuristic based on Ant Colony Optimization, we perform a distance-based search with no radius constraints. Experimental results support the effectiveness and stability of our method in prioritizing modules of high relevance. Our tool is publicly available at github.com/GhadiElHasbani/ACOxGS.git.

Background: High-throughput experimental technologies can provide deeper insights into pathway perturbations in biomedical studies. Accordingly, their usage is central to the identification of molecular targets and the subsequent development of suitable treatments for various diseases. Classical interpretations of generated data, such as differential gene expression and pathway analyses, disregard interconnections between studied genes when looking for gene-disease associations. Given that these interconnections are central to cellular processes, there has been a recent interest in incorporating them in such studies. The latter allows the detection of gene modules that underlie complex phenotypes in gene interaction networks. Existing methods either impose radius-based restrictions or freely grow modules at the expense of a statistical bias towards large modules. We propose a heuristic method, inspired by Ant Colony Optimization, to apply gene-level scoring and module identification with distance-based search constraints and penalties, rather than radius-based constraints. Results: We test and compare our results to other approaches using three datasets of different neurodegenerative diseases, namely Alzheimer's, Parkinson's, and Huntington's, over three independent experiments. We report the outcomes of enrichment analyses and concordance of gene-level scores for each disease. Results indicate that the proposed approach generally shows superior stability in comparison to existing methods. It produces stable and meaningful enrichment results in all three datasets which have different case to control proportions and sample sizes. Conclusion: The presented network-based gene expression analysis approach successfully identifies dysregulated gene modules associated with a certain disease. Using a heuristic based on Ant Colony Optimization, we perform a distance-based search with no radius constraints. Experimental results support the effectiveness and stability of our method in prioritizing modules of high relevance. Our tool is publicly available at github.com/GhadiElHasbani/ACOxGS.git. [ABSTRACT FROM AUTHOR]

Numerous studies have been conducted to investigate the genomic characterization of bZIP genes and their involvement in the cellular response to endoplasmic reticulum (ER) stress. These studies have provided valuable insights into the coordinated cellular response to ER stress, which is mediated by bZIP transcription factors (TFs). However, a comprehensive and systematic investigations regarding the role of bZIP genes and their involvement in ER stress response in pak choi is currently lacking in the existing literature. To address this knowledge gap, the current study was initiated to elucidate the genomic characteristics of bZIP genes, gain insight into their expression patterns during ER stress in pak choi, and investigate the protein-to-protein interaction of bZIP genes with the ER chaperone BiP. In total, 112 members of the BcbZIP genes were identified through a comprehensive genome-wide analysis. Based on an analysis of sequence similarity, gene structure, conserved domains, and responsive motifs, the identified BcbZIP genes were categorized into 10 distinct subfamilies through phylogenetic analysis. Chromosomal location and duplication events provided insight into their genomic context and evolutionary history. Divergence analysis estimated their evolutionary history with a predicted divergence time ranging from 0.73 to 80.71 million years ago (MYA). Promoter regions of the BcbZIP genes were discovered to exhibit a wide variety of cis-elements, including light, hormone, and stress-responsive elements. GO enrichment analysis further confirmed their roles in the ER unfolded protein response (UPR), while co-expression network analysis showed a strong relationship of BcbZIP genes with ER-stress-responsive genes. Moreover, gene expression profiles and protein–protein interaction with ER chaperone BiP further confirmed their roles and capacity to respond to ER stress in pak choi. [ABSTRACT FROM AUTHOR]

Background and objectives: Antimicrobial resistance (AMR) poses a significant threat to public health in the 21st century, with bacteria such as Campylobacter jejuni (C. jejuni) exhibiting multidrug resistance due to the presence of AMR genes. Understanding the evolutionary patterns and functional relationships of these genes is crucial for addressing this issue effectively. Methods: We conducted phylogenetic analysis to examine the evolution of AMR genes in C. jejuni. Additionally, we constructed and analyzed a gene interaction network comprising 39 functional relationships. Clustering analysis was employed to identify interconnected clusters associated with AMR processes. Functional enrichment analysis was performed to explore the involvement of cellular components, molecular functions, and biological processes. Results: Our analysis revealed two interconnected clusters (C1 and C2) closely associated with AMR processes. Furthermore, genes encoding ribosomal proteins (rplE, rplV, rplG, rplK, rplA, rplJ, rpsE, rplB, rpsL, and rpmA) were identified as hub genes within the gene interaction network. These genes interact frequently with their functional counterparts, indicating their significance in AMR mechanisms. Enriched Kyoto Encyclopedia of Genes and Genomes pathway analysis highlighted the importance of the ribosome pathway in understanding antibiotic resistance mechanisms in C. jejuni. Conclusions: The findings of this study enhance our understanding of the molecular mechanisms underlying AMR in C. jejuni. By elucidating the evolutionary patterns, gene interactions, and pathway enrichment, our study provides valuable insights that may contribute to the development of novel treatments for illnesses caused by this pathogen. [ABSTRACT FROM AUTHOR]

Introduction: In recent years, highly pathogenicity avian influenza (HPAI), especially H5N1, has emerged as a major global health concern due to its potential as a zoonotic disease and its devastating impact on poultry populations. Identifying the molecular mechanisms of response to HPAI infection is critical to control, treat, and prevent the risk of a potential pandemic. Microarray technology is becoming a standard technology used in research laboratories all across the world and it is considered as one of the centers of research in cellular processes related to the level and manner of gene expression, including gene function and cell differentiation mechanisms. By using microarray technology, it is possible to obtain a detailed view of the interaction function of genes while simultaneously studying how the genome is expressed. Using microarrays provides the analysis of gene expression in response to viral infections such as influenza, etc., the study of host-pathogen interactions, and also the identification of the effectiveness of drugs and vaccines. This study aimed to analyze the microarray data of H5N1 avian influenza to compare the gene network and analyze the functional pathway in chickens and ducks. Materials and methods: Data mining and searching of microarray data related to Highly Pathogenic Avian Influenza infection was done in the GEO gene expression database (https://www.ncbi.nlm.nih.gov/geo). The microarray data set with accession number GSE33389 based on the GPL3213 platform was selected which contained lung tissue samples challenged with H5N1 virus in chickens and ducks. Normalization of selected microarray data was done using R software, and samples were grouped to compare between infected and control samples. Limma, Biobase, and GEOquery software packages in R software were used to determine the expression level of genes and to investigate the differentially expressed genes (DEGs) between healthy and H5N1 influenza virus-infected lung tissue samples in chickens and ducks. The criterion for selecting significant DEGs was considered as |logFC|>2 and P<0.05. DAVID online tool (https://david.ncifcrf.gov) was used to investigate biological pathways, structural and functional characteristics of genes with different expressions, and functional interpretation of upregulated and downregulated DEGs. It was evaluated and visualized separately based on biological processes (BP), molecular functions (MF), and cellular components (CC). KEGG tool (http://www.genome.jp/kegg) was used to evaluate and study metabolic pathway enrichment. To reveal interactions between proteins and analyze them, STRING database and Cytoscape software were used. While using the Cytohubba plugin to identify and display key genes, the main modules affecting the interaction of genes and proteins were also identified by the MCODE plugin. Results and discussion: Gene expression analysis revealed 2062 and 565 differentially expressed genes between normal and infected tissue in chickens and ducks, respectively (P<0.05 and |logFC|>2). The results of bioinformatics analysis and protein-protein interaction network analysis showed BUB1, NDC80, CDC20, PLK1, PRC1, KIF11, and AURKA genes as hub genes in chicken and also COL6A3, COL3A1, COL4A3, COL18A1,PLOD2, PLOD1, and P4HA2 as highly effective genes in duck (P< 0.05). The results of the ontology comparison of DEGs proved that most of these genes in chickens are involved in the innate immune response and inflammatory resistance of the host, and the most effective genes in ducks play a role in lipid metabolism and energy production to meet the host's resistance to disease. The results of comparative gene network analysis between chickens and ducks are promising to increase our understanding of the host response to H5N1 influenza infection and the factors affecting virus pathogenesis in different avian species. Differentially expressed genes in response to H5N1 infection in chickens and ducks play critical roles in various biological processes, including immune response, inflammation, viral replication, and host-pathogen interactions. In general, gene network analysis showed that chickens and ducks use different genetic strategies to respond to avian influenza virus infection. Conclusions: The present research was conducted to discover the response to H5N1 HPAI infection in chickens and ducks through comparative gene network analysis. It is important to note that the gene network analysis presented in this research is an initial step towards discovering the response mode of HPAI (H5N1) infection in chickens and ducks, and further functional studies, validation experiments, and integration with other omics data are needed to confirm the role of genes, pathways and hub genes in the host response to H5N1 virus. Therefore, the results of comparative gene network analysis in chickens and ducks obtained from this research can provide valuable insight into the underlying molecular mechanisms of host response to H5N1 influenza infection. Thus, by identifying differentially expressed genes, functional modules, and hub genes in this research, it can be stated that potential targets for future research have been highlighted to some extent. Undoubtedly, further studies in this field will improve our knowledge about the pathogenesis of avian influenza and will help to develop strategies for effective control and prevention of H5N1 influenza outbreaks. [ABSTRACT FROM AUTHOR]

Abstract Comprehensive analysis of multiple data sets can identify potential driver genes for various cancers. In recent years, driver gene discovery based on massive mutation data and gene interaction networks has attracted increasing attention, but there is still a need to explore combining functional and structural information of genes in protein interaction networks to identify driver genes. Therefore, we propose a network embedding framework combining functional and structural information to identify driver genes. Firstly, we combine the mutation data and gene interaction networks to construct mutation integration network using network propagation algorithm. Secondly, the struc2vec model is used for extracting gene features from the mutation integration network, which contains both gene's functional and structural information. Finally, machine learning algorithms are utilized to identify the driver genes. Compared with the previous four excellent methods, our method can find gene pairs that are distant from each other through structural similarities and has better performance in identifying driver genes for 12 cancers in the cancer genome atlas. At the same time, we also conduct a comparative analysis of three gene interaction networks, three gene standard sets, and five machine learning algorithms. Our framework provides a new perspective for feature selection to identify novel driver genes.

Cancer is a heterogeneous disease with a strong genetic component making it suitable for precision medicine approaches aimed at identifying the underlying molecular drivers within a tumour. Large scale population-level cancer sequencing consortia have identified many actionable mutations common across both cancer types and sub-types, resulting in an increasing number of successful precision medicine programs. Nonetheless, such approaches fail to consider the effects of mutations unique to an individual patient and may miss rare driver mutations, necessitating personalised approaches to driver-gene prioritisation. One approach is to quantify the functional importance of individual mutations in a single tumour based on how they affect the expression of genes in a gene interaction network (GIN). These GIN-based approaches can be broadly divided into those that utilise an existing reference GIN and those that construct de novo patient-specific GINs. These single-tumour approaches have several limitations that likely influence their results, such as use of reference cohort data, network choice, and approaches to mathematical approximation, and more research is required to evaluate the in vitro and in vivo applicability of their predictions. This review examines the current state of the art methods that identify driver genes in single tumours with a focus on GIN-based driver prioritisation.

Comprehensive analysis of multiple data sets can identify potential driver genes for various cancers. In recent years, driver gene discovery based on massive mutation data and gene interaction networks has attracted increasing attention, but there is still a need to explore combining functional and structural information of genes in protein interaction networks to identify driver genes. Therefore, we propose a network embedding framework combining functional and structural information to identify driver genes. Firstly, we combine the mutation data and gene interaction networks to construct mutation integration network using network propagation algorithm. Secondly, the struc2vec model is used for extracting gene features from the mutation integration network, which contains both gene's functional and structural information. Finally, machine learning algorithms are utilized to identify the driver genes. Compared with the previous four excellent methods, our method can find gene pairs that are distant from each other through structural similarities and has better performance in identifying driver genes for 12 cancers in the cancer genome atlas. At the same time, we also conduct a comparative analysis of three gene interaction networks, three gene standard sets, and five machine learning algorithms. Our framework provides a new perspective for feature selection to identify novel driver genes. [ABSTRACT FROM AUTHOR]

The pathogenic Enterobacter cloacae subsp. cloacae str. ATCC 13047 has contemporarily emerged as a multi-drug resistant strain. To formulate an effective treatment option, alternative therapeutic methods need to be explored. The present study focused on Gene Interaction Network study of 46 antimicrobial resistance genes to reveal the densely interconnecting and functional hub genes in E. cloacae ATCC 13047. The AMR genes were subjected to clustering, topological and functional enrichment analysis, revealing rpsE (RpsE), acrA (AcrA) and arnT (ArnT) as novel therapeutic drug targets for hindering drug resistance in the pathogenic strain. Network topology further indicated translational protein RpsE to be exploited as a promising drug-target candidate for which the structure was predicted, optimized and validated through molecular dynamics simulations (MDS). Absorption, distribution, metabolism and excretion screening recognized ZINC5441082 (N-Isopentyladenosine) (Lead_1) and ZINC1319816 (cyclopentyl-aminopurinyl-hydroxymethyl-oxolanediol) (Lead_2) as orally bioavailable compounds against RpsE. Molecular docking and MDS confirmed the binding efficacy and protein−ligand complex stability. Furthermore, binding free energy (Gbind) calculations, principal component and free energy landscape analyses affirmed the predicted nucleoside analogues against RpsE protein to be comprehensively examined as a potential treatment strategy against E. cloacae ATCC 13047. [ABSTRACT FROM AUTHOR]

Numerous studies have been conducted to investigate the genomic characterization of bZIP genes and their involvement in the cellular response to endoplasmic reticulum (ER) stress. These studies have provided valuable insights into the coordinated cellular response to ER stress, which is mediated by bZIP transcription factors (TFs). However, a comprehensive and systematic investigations regarding the role of bZIP genes and their involvement in ER stress response in pak choi is currently lacking in the existing literature. To address this knowledge gap, the current study was initiated to elucidate the genomic characteristics of bZIP genes, gain insight into their expression patterns during ER stress in pak choi, and investigate the protein-to-protein interaction of bZIP genes with the ER chaperone BiP. In total, 112 members of the BcbZIP genes were identified through a comprehensive genome-wide analysis. Based on an analysis of sequence similarity, gene structure, conserved domains, and responsive motifs, the identified BcbZIP genes were categorized into 10 distinct subfamilies through phylogenetic analysis. Chromosomal location and duplication events provided insight into their genomic context and evolutionary history. Divergence analysis estimated their evolutionary history with a predicted divergence time ranging from 0.73 to 80.71 million years ago (MYA). Promoter regions of the BcbZIP genes were discovered to exhibit a wide variety of cis-elements, including light, hormone, and stress-responsive elements. GO enrichment analysis further confirmed their roles in the ER unfolded protein response (UPR), while co-expression network analysis showed a strong relationship of BcbZIP genes with ER-stress-responsive genes. Moreover, gene expression profiles and protein–protein interaction with ER chaperone BiP further confirmed their roles and capacity to respond to ER stress in pak choi.

Abstract Background Breast cancer (BC) is one of the most prevalent cancers worldwide but its etiology remains unclear. Obesity is recognized as a risk factor for BC, and many obesity-related genes may be involved in its occurrence and development. Research assessing the complex genetic mechanisms of BC should not only consider the effect of a single gene on the disease, but also focus on the interaction between genes. This study sought to construct a gene interaction network to identify potential pathogenic BC genes. Methods The study included 953 BC patients and 963 control individuals. Chi-square analysis was used to assess the correlation between demographic characteristics and BC. The joint density-based non-parametric differential interaction network analysis and classification (JDINAC) was used to build a BC gene interaction network using single nucleotide polymorphisms (SNP). The odds ratio (OR) and 95% confidence interval (95% CI) of hub gene SNPs were evaluated using a logistic regression model. To assess reliability, the hub genes were quantified by edgeR program using BC RNA-seq data from The Cancer Genome Atlas (TCGA) and identical edges were verified by logistic regression using UK Biobank datasets. Go and KEGG enrichment analysis were used to explore the biological functions of interactive genes. Results Body mass index (BMI) and menopause are important risk factors for BC. After adjusting for potential confounding factors, the BC gene interaction network was identified using JDINAC. LEP, LEPR, XRCC6, and RETN were identified as hub genes and both hub genes and edges were verified. LEPR genetic polymorphisms (rs1137101 and rs4655555) were also significantly associated with BC. Enrichment analysis showed that the identified genes were mainly involved in energy regulation and fat-related signaling pathways. Conclusion We explored the interaction network of genes derived from SNP data in BC progression. Gene interaction networks provide new insight into the underlying mechanisms of BC.

Abstract Background As the king of all herbs, the medicinal value of ginseng is self-evident. The perennial nature of ginseng causes its quality to be influenced by various factors, one of which is the soil environment. During plant growth and development, MYB transcription factors play an important role in responding to abiotic stresses and regulating the synthesis of secondary metabolites. However, there are relatively few reports on the MYB transcription factor family in Panax ginseng. Results This study identified 420 PgMYB transcripts under 117 genes ID in the Jilin ginseng transcriptome database. Phylogenetic analysis showed that PgMYB transcripts in Jilin ginseng were classified into 19 functional subclasses. The GO annotation result indicated that the functional differentiation of PgMYB transcripts was annotated to 11 functional nodes at GO Level 2 in ginseng. Expression pattern analysis of PgMYB transcripts based on the expression data (TPM) that PgMYB transcripts were revealed spatiotemporally specific in expression patterns. We performed a weighted network co-expression network analysis on the expression of PgMYB transcripts from different samples. The co-expression network containing 51 PgMYB transcripts was formed under a soft threshold of 0.85, revealing the reciprocal relationship of PgMYB in ginseng. Treatment of adventitious roots of ginseng with different concentrations of NaCl revealed four up-regulated expression of PgMYB transcripts that can candidate genes for salt resistance studies in ginseng. Conclusions The present findings provide data resources for the subsequent study of the functions of MYB transcription factor family members in ginseng, and provide an experimental basis for the anti-salt functions of MYB transcription factors in Panax ginseng.

Abstract Background Cancer is a heterogeneous disease in which tumor genes cooperate as well as adapt and evolve to the changing conditions for individual patients. It is a meaningful task to discover the personalized cancer driver genes that can provide diagnosis and target drug for individual patients. However, most of existing methods mainly ranks potential personalized cancer driver genes by considering the patient-specific nodes information on the gene/protein interaction network. These methods ignore the personalized edge weight information in gene interaction network, leading to false positive results. Results In this work, we presented a novel algorithm (called PDGPCS) to predict the Personalized cancer Driver Genes based on the Prize-Collecting Steiner tree model by considering the personalized edge weight information. PDGPCS first constructs the personalized weighted gene interaction network by integrating the personalized gene expression data and prior known gene/protein interaction network knowledge. Then the gene mutation data and pathway data are integrated to quantify the impact of each mutant gene on every dysregulated pathway with the prize-collecting Steiner tree model. Finally, according to the mutant gene’s aggregated impact score on all dysregulated pathways, the mutant genes are ranked for prioritizing the personalized cancer driver genes. Experimental results on four TCGA cancer datasets show that PDGPCS has better performance than other personalized driver gene prediction methods. In addition, we verified that the personalized edge weight of gene interaction network can improve the prediction performance. Conclusions PDGPCS can more accurately identify the personalized driver genes and takes a step further toward personalized medicine and treatment. The source code of PDGPCS can be freely downloaded from https://github.com/NWPU-903PR/PDGPCS .

Abstract Background It is well known that seed vigor is essential for agricultural production and rice (Oryza sativa L.) is one of the most important crops in the world. Though we previously reported that miR164c regulates rice seed vigor, but whether and how other miRNAs cooperate with miR164c to regulate seed vigor is still unknown. Results Based on degradome data of six RNA samples isolated from seeds of the wild-type (WT) indica rice cultivar ‘Kasalath’ as well as two modified lines in ‘Kasalath’ background (miR164c-silenced line [MIM164c] and miR164c overexpression line [OE164c]), which were subjected to either no aging treatment or an 8-day artificial aging treatment, 1247 different target transcripts potentially cleaved by 421 miRNAs were identified. The miRNA target genes were functionally annotated via GO and KEGG enrichment analyses. By STRING database assay, a miRNA-mediated gene interaction network regulating seed vigor in rice was revealed, which comprised at least four interconnected pathways: the miR5075-mediated oxidoreductase related pathway, the plant hormone related pathway, the miR164e related pathway, and the previously reported RPS27AA related pathway. Knockout and overexpression of the target gene Os02g0817500 of miR5075 decreased and enhanced seed vigor, respectively. By Y2H assay, the proteins encoded by five seed vigor-related genes, Os08g0295100, Os07g0633100, REFA1, OsPER1 and OsGAPC3, were identified to interact with Os02g0817500. Conclusions miRNAs cooperate to regulate seed vigor in rice via an integrative gene interaction network comprising miRNA target genes and other functional genes. The result provided a basis for fully understanding the molecular mechanisms of seed vigor regulation.

Abstract Background Comorbid type 2 diabetes mellitus (T2DM) increases the risk for tuberculosis (TB) and its associated complications, although the pathological connections between T2DM and TB are unknown. The current research aims to identify shared molecular gene signatures and pathways that affirm the epidemiological association of T2DM and TB and afford clues on mechanistic basis of their association through integrative systems biology and bioinformatics approaches. Earlier research has found specific molecular markers linked to T2DM and TB, but, despite their importance, only offered a limited understanding of the genesis of this comorbidity. Our investigation used a network medicine method to find possible T2DM-TB molecular mediators. Results Functional annotation clustering, interaction networks, network cluster analysis, and network topology were part of our systematic investigation of T2DM-TB linked with 1603 differentially expressed genes (DEGs). The functional enrichment and gene interaction network analysis emphasized the importance of cytokine/chemokine signalling, T cell receptor signalling route, NF-kappa B signalling pathway and Jak-STAT signalling system. Furthermore, network analysis revealed significant DEGs such as ITGAM and STAT1, which may be necessary for T2DM-TB immune responses. Furthermore, these two genes are modulators in clusters C4 and C5, abundant in cytokine/chemokine signalling and Jak-STAT signalling pathways. Conclusions Our analyses highlight the role of ITGAM and STAT1 in T2DM-TB-associated pathways and advances our knowledge of the genetic processes driving this comorbidity.

Background and purpose: Asthma is a chronic inflammatory disorder of the airways caused by a combination of complex environmental and genetic interactions. There is an incomplete understanding of this mechanism which affect both severity of the disease and how it responds to treatment. Different gene expressions are reported in patients with asthma and healthy controls. Materials and methods: In this study, a common list of different genes expressed (DEG) was identified using bioinformatics methods. Three studies from microarray data that met the study’s inclusion criteria were selected. These studies included 268 samples, consisting of 167 patients and 101 healthy people. These studies were analyzed, and then a meta-analysis was performed. Results: Common genes that led to significant decrease or increase in gene expressions were identified and their biological, molecular, and cellular pathways were examined. In general, it was found that this set of genes can be involved in asthma and some pathways affecting the function of disease. Conclusion: According to current findings, examining the objectives of efficacy and functional analysis of new genes effective in asthma and their protein products, and also investigating the role of interactions in asthma could help in taking appropriate treatment strategies to control asthma complications and injuries.

IntroductionOsteosarcoma is a rare disorder among cancer, but the most frequently occurring among sarcomas in children and adolescents. It has been reported to possess the relapsing capability as well as accompanying collateral adverse effects which hinder the development process of an effective treatment plan. Using networks of omics data to identify cancer biomarkers could revolutionize the field in understanding the cancer. Cancer biomarkers and the molecular mechanisms behind it can both be understood by studying the biological networks underpinning the etiology of the disease.MethodsIn our study, we aimed to highlight the hub genes involved in gene-gene interaction network to understand their interaction and how they affect the various biological processes and signaling pathways involved in Osteosarcoma. Gene interaction network provides a comprehensive overview of functional gene analysis by providing insight into how genes cooperatively interact to elicit a response. Because gene interaction networks serve as a nexus to many biological problems, their employment of it to identify the hub genes that can serve as potential biomarkers remain widely unexplored. A dynamic framework provides a clear understanding of biological complexity and a pathway from the gene level to interaction networks.ResultsOur study revealed various hub genes viz. TP53, CCND1, CDK4, STAT3, and VEGFA by analyzing various topological parameters of the network, such as highest number of interactions, average shortest path length, high cluster density, etc. Their involvement in key signaling pathways, such as the FOXM1 transcription factor network, FAK-mediated signaling events, and the ATM pathway, makes them significant candidates for studying the disease. The study also highlighted significant enrichment in GO terms (Biological Processes, Molecular Function, and Cellular Processes), such as cell cycle signal transduction, cell communication, kinase binding, transcription factor activity, nucleoplasm, PML body, nuclear body, etc.ConclusionTo develop better therapeutics, a specific approach toward the disease targeting the hub genes involved in various signaling pathways must have opted to unravel the complexity of the disease. Our study has highlighted the candidate hub genes viz. TP53, CCND1 CDK4, STAT3, VEGFA. Their involvement in the major signaling pathways of Osteosarcoma makes them potential candidates to be targeted for drug development. The highly enriched signaling pathways include FOXM1 transcription pathway, ATM signal-ling pathway, FAK mediated signaling events, Arf6 signaling events, mTOR signaling pathway, and Integrin family cell surface interactions. Targeting the hub genes and their associated functional partners which we have reported in our studies may be efficacious in developing novel therapeutic targets.

Male infertility has always been one of the important factors affecting the infertility of couples of gestational age. The reasons that affect male infertility includes living habits, hereditary factors, etc. Identifying the genetic causes of male infertility can help us understand the biology of male infertility, as well as the diagnosis of genetic testing and the determination of clinical treatment options. While current research has made significant progress in the genes that cause sperm defects in men, genetic studies of sperm content defects are still lacking. This article is based on a dataset of gene expression data on the X chromosome in patients with azoospermia, mild and severe oligospermia. Due to the difference in the degree of disease between patients and the possible difference in genetic causes, common classical clustering methods such as k-means, hierarchical clustering, etc. cannot effectively identify samples (realize simultaneous clustering of samples and features). In this paper, we use machine learning and various statistical methods such as hypergeometric distribution, Gibbs sampling, Fisher test, etc. and genes the interaction network for cluster analysis of gene expression data of male infertility patients has certain advantages compared with existing methods. The cluster results were identified by differential co-expression analysis of gene expression data in male infertility patients, and the model recognition clusters were analyzed by multiple gene enrichment methods, showing different degrees of enrichment in various enzyme activities, cancer, virus-related, ATP and ADP production, and other pathways. At the same time, as this paper is an unsupervised analysis of genetic factors of male infertility patients, we constructed a simulated data set, in which the clustering results have been determined, which can be used to measure the effect of discriminant model recognition. Through comparison, it finds that the proposed model has a better identification effect. [ABSTRACT FROM AUTHOR]

Abstract Background One of the most common hormonal disorders in women of reproductive age is polycystic ovary syndrome (PCOS). In recent years, it has been found that insulin resistance is a common metabolic abnormality in women with PCOS and leads to an elevated risk of type 2 diabetes mellitus. To explore the differentially expressed genes (DEGs) that regulate these kinds of metabolic risks in PCOS women, we chose the gene expression profile of GSE8157 from the gene expression omnibus (GEO) database. Results Using the GEO2R tool, we identified a total of 339 DEGs between the case and the control sample groups. Gene ontology and Kyoto encyclopedia of gene and genome pathway enrichment analysis were subsequently conducted. High connectivity, betweenness centrality, bottleneck centrality, closeness centrality, and radiality measures were used to rank the ten hub genes. Furthermore, the overlap of these genes resulted in the development of two key genes, AR and STK11. The AMPK and adipocytokine signaling pathways are the two main pathways that these DEGs are involved. Conclusions The backbone genes, hub genes and pathways identified would assist us in further exploring the molecular basis of developing risk of type 2 diabetes mellitus in PCOS women and thus provide diagnostic or therapeutic clues.

Background: Breast cancer (BC) is one of the most prevalent cancers worldwide but its etiology remains unclear. Obesity is recognized as a risk factor for BC, and many obesity-related genes may be involved in its occurrence and development. Research assessing the complex genetic mechanisms of BC should not only consider the effect of a single gene on the disease, but also focus on the interaction between genes. This study sought to construct a gene interaction network to identify potential pathogenic BC genes.Methods: The study included 953 BC patients and 963 control individuals. Chi-square analysis was used to assess the correlation between demographic characteristics and BC. The joint density-based non-parametric differential interaction network analysis and classification (JDINAC) was used to build a BC gene interaction network using single nucleotide polymorphisms (SNP). The odds ratio (OR) and 95% confidence interval (95% CI) of hub gene SNPs were evaluated using a logistic regression model. To assess reliability, the hub genes were quantified by edgeR program using BC RNA-seq data from The Cancer Genome Atlas (TCGA) and identical edges were verified by logistic regression using UK Biobank datasets. Go and KEGG enrichment analysis were used to explore the biological functions of interactive genes.Results: Body mass index (BMI) and menopause are important risk factors for BC. After adjusting for potential confounding factors, the BC gene interaction network was identified using JDINAC. LEP, LEPR, XRCC6, and RETN were identified as hub genes and both hub genes and edges were verified. LEPR genetic polymorphisms (rs1137101 and rs4655555) were also significantly associated with BC. Enrichment analysis showed that the identified genes were mainly involved in energy regulation and fat-related signaling pathways.Conclusion: We explored the interaction network of genes derived from SNP data in BC progression. Gene interaction networks provide new insight into the underlying mechanisms of BC. [ABSTRACT FROM AUTHOR]

Background: As the king of all herbs, the medicinal value of ginseng is self-evident. The perennial nature of ginseng causes its quality to be influenced by various factors, one of which is the soil environment. During plant growth and development, MYB transcription factors play an important role in responding to abiotic stresses and regulating the synthesis of secondary metabolites. However, there are relatively few reports on the MYB transcription factor family in Panax ginseng. Results: This study identified 420 PgMYB transcripts under 117 genes ID in the Jilin ginseng transcriptome database. Phylogenetic analysis showed that PgMYB transcripts in Jilin ginseng were classified into 19 functional subclasses. The GO annotation result indicated that the functional differentiation of PgMYB transcripts was annotated to 11 functional nodes at GO Level 2 in ginseng. Expression pattern analysis of PgMYB transcripts based on the expression data (TPM) that PgMYB transcripts were revealed spatiotemporally specific in expression patterns. We performed a weighted network co-expression network analysis on the expression of PgMYB transcripts from different samples. The co-expression network containing 51 PgMYB transcripts was formed under a soft threshold of 0.85, revealing the reciprocal relationship of PgMYB in ginseng. Treatment of adventitious roots of ginseng with different concentrations of NaCl revealed four up-regulated expression of PgMYB transcripts that can candidate genes for salt resistance studies in ginseng. Conclusions: The present findings provide data resources for the subsequent study of the functions of MYB transcription factor family members in ginseng, and provide an experimental basis for the anti-salt functions of MYB transcription factors in Panax ginseng. [ABSTRACT FROM AUTHOR]

Multi-drug resistant (MDR) Shigella flexneri 2a, one of the leading bacterial agents of diarrhoeal mortality, has posed challenges in treatment strategies. The present study was conducted to identify potential therapeutic biomarkers using gene interaction network (GIN) in order to understand the cellular and molecular level interactions of both antimicrobial resistance (AMR) and virulence genes through topological and clustering metrics. Statistically significant differential gene expression (DGE), structural chemistry and dynamics were incorporated to elucidate biomarker for sustainable therapeutic regimen against MDR S. flexneri. Functional enrichments and topological metrics revealed evgS, ybjZ, tolC, gyrA, parC and their direct interactors to be associated with diverse AMR mechanisms. Histidine kinase EvgS was considered as the hub protein due to its highest prevalence in the molecular interactome profiles of both the AMR (71.6%) and virulence (45.8%) clusters interconnecting several genes concerning two-component system (TCS). DGE profiles of ΔPhoPQ (deleted regulatory PhoP and sensor PhoQ) led to the upregulation of TCS comprising EvgSA thereby validating EvgS as a promising therapeutic biomarker. Druggability and structural stability of EvgS was assessed through thermal shifts, backbone stability and coarse dynamics refinement. Structure–function relationship was established revealing the C-terminal extracellular domain as the drug-binding site which was further validated through molecular dynamics simulation. Structure elucidation of identified biomarker followed by secondary and tertiary structural validation would prove pivotal for future therapeutic interventions against subverting both AMR and virulence posed by this strain. [ABSTRACT FROM AUTHOR]

Background: Cancer is a heterogeneous disease in which tumor genes cooperate as well as adapt and evolve to the changing conditions for individual patients. It is a meaningful task to discover the personalized cancer driver genes that can provide diagnosis and target drug for individual patients. However, most of existing methods mainly ranks potential personalized cancer driver genes by considering the patient-specific nodes information on the gene/protein interaction network. These methods ignore the personalized edge weight information in gene interaction network, leading to false positive results. Results: In this work, we presented a novel algorithm (called PDGPCS) to predict the Personalized cancer Driver Genes based on the Prize-Collecting Steiner tree model by considering the personalized edge weight information. PDGPCS first constructs the personalized weighted gene interaction network by integrating the personalized gene expression data and prior known gene/protein interaction network knowledge. Then the gene mutation data and pathway data are integrated to quantify the impact of each mutant gene on every dysregulated pathway with the prize-collecting Steiner tree model. Finally, according to the mutant gene's aggregated impact score on all dysregulated pathways, the mutant genes are ranked for prioritizing the personalized cancer driver genes. Experimental results on four TCGA cancer datasets show that PDGPCS has better performance than other personalized driver gene prediction methods. In addition, we verified that the personalized edge weight of gene interaction network can improve the prediction performance. Conclusions: PDGPCS can more accurately identify the personalized driver genes and takes a step further toward personalized medicine and treatment. The source code of PDGPCS can be freely downloaded from https://github.com/NWPU-903PR/PDGPCS. [ABSTRACT FROM AUTHOR]

Hypertrophic cardiomyopathy (HCM) is a hereditary heart disease caused by mutations in the sarcomere genes, which is accompanied by myocardial fibrosis leading to progressive heart failure and arrhythmias. Recent studies suggest that the HCM development involves dysregulation of gene expression. Among the molecules involved in this process are microRNAs (miRNAs), which are short non-coding RNAs. Typically, one miRNA regulates several target genes post-transcriptionally, hence, it might be difficult to determine the role of a particular miRNA in the disease pathogenesis. In this study, using the PubMed database, we selected 15 miRNAs whose expression is associated with myocardial fibrosis, one of the critical pathological processes in HCM. We then used an earlier developed algorithm to search in silico for the signaling pathways regulated by these miRNAs and found that ten of them participate in the regulation of the TGF-β/SMAD signaling pathway. At the same time, among the SMAD signaling pathway genes, the target of the most identified miRNAs was the MYC gene, which is involved in the development of fibrosis in some tissues. In our earlier work, we found that the TGF-β/SMAD pathway is also regulated by a set of other miRNAs associated with the myocardial hypertrophy in HCM. The fact that two sets of miRNAs identified in two independent bioinformatic studies are involved in the regulation of the same signaling pathway indicates that the SMAD signaling cascade is indeed a key element in the regulation of pathological processes in HCM. The obtained data might contribute to understanding pathological processes underlying HCM development. [ABSTRACT FROM AUTHOR]

Background: It is well known that seed vigor is essential for agricultural production and rice (Oryza sativa L.) is one of the most important crops in the world. Though we previously reported that miR164c regulates rice seed vigor, but whether and how other miRNAs cooperate with miR164c to regulate seed vigor is still unknown. Results: Based on degradome data of six RNA samples isolated from seeds of the wild-type (WT) indica rice cultivar 'Kasalath' as well as two modified lines in 'Kasalath' background (miR164c-silenced line [MIM164c] and miR164c overexpression line [OE164c]), which were subjected to either no aging treatment or an 8-day artificial aging treatment, 1247 different target transcripts potentially cleaved by 421 miRNAs were identified. The miRNA target genes were functionally annotated via GO and KEGG enrichment analyses. By STRING database assay, a miRNA-mediated gene interaction network regulating seed vigor in rice was revealed, which comprised at least four interconnected pathways: the miR5075-mediated oxidoreductase related pathway, the plant hormone related pathway, the miR164e related pathway, and the previously reported RPS27AA related pathway. Knockout and overexpression of the target gene Os02g0817500 of miR5075 decreased and enhanced seed vigor, respectively. By Y2H assay, the proteins encoded by five seed vigor-related genes, Os08g0295100, Os07g0633100, REFA1, OsPER1 and OsGAPC3, were identified to interact with Os02g0817500. Conclusions: miRNAs cooperate to regulate seed vigor in rice via an integrative gene interaction network comprising miRNA target genes and other functional genes. The result provided a basis for fully understanding the molecular mechanisms of seed vigor regulation. [ABSTRACT FROM AUTHOR]

Glioblastoma multiforme (GBM) is the most malignant brain cancer with great heterogeneities in many aspects, such as prognosis, clinicopathological features, immune landscapes, and immunotherapeutic responses. Considering that gene interaction network is relatively stable in a healthy state but widely perturbed in cancers, we sought to explore the multidimensional heterogeneities of GBM through evaluating the degree of network perturbations. The gene interaction network perturbations of GBM samples (TCGA cohort) and normal samples (GTEx database) were characterized by edge perturbations, which were quantized through evaluating the change in relative gene expression value. An unsupervised consensus clustering analysis was performed to identify edge perturbation-based clusters of GBM samples. Results revealed that the edge perturbation of GBM samples was stronger than that of normal samples. Four edge perturbation-based clusters of GBM samples were identified and showed prominent heterogeneities in prognosis, clinicopathological features, somatic genomic alterations, immune landscapes, and immunotherapeutic responses. In addition, a sample-specific perturbation of gene interaction score (SPGIScore) was constructed based on the differently expressed genes (DEGs) among four clusters, and exhibited a robust ability to predict prognosis. In conclusion, the bioinformatics approach based on sample-specific edge perturbation in gene interaction network provided a new perspective to understanding the multidimensional heterogeneities of GBM.

Background and purpose: Asthma is a chronic inflammatory disorder of the airways caused by a combination of complex environmental and genetic interactions. There is an incomplete understanding of this mechanism which affect both severity of the disease and how it responds to treatment. Different gene expressions are reported in patients with asthma and healthy controls. Materials and methods: In this study, a common list of different genes expressed (DEG) was identified using bioinformatics methods. Three studies from microarray data that met the study’s inclusion criteria were selected. These studies included 268 samples, consisting of 167 patients and 101 healthy people. These studies were analyzed, and then a meta-analysis was performed. Results: Common genes that led to significant decrease or increase in gene expressions were identified and their biological, molecular, and cellular pathways were examined. In general, it was found that this set of genes can be involved in asthma and some pathways affecting the function of disease. Conclusion: According to current findings, examining the objectives of efficacy and functional analysis of new genes effective in asthma and their protein products, and also investigating the role of interactions in asthma could help in taking appropriate treatment strategies to control asthma complications and injuries. [ABSTRACT FROM AUTHOR]

A differential gene expressional network determines the prominent genes under altered phenotypes. The traditional approach requires n(n-2)/2 comparisons for n phenotypes. We present a direct method for determining a differential network under multiple phenotypes. We explore the non-discrete nature of gene expression as a pattern in a fuzzy rough set. An edge between a pair of genes represents a positive region of a fuzzy similarity relationship upon a phenotypic change. We apply a weight-ranking formula and obtain a directed ranked network; we label this as a phenotype interwoven network. Those nodes with large in-degree connectivity bubble up as significant genes under respective phenotypic changes. We tested the method on six diseases and achieved good corroboration with the results of previous studies in the two-step approach. The subgraphs of the isolated genes achieved good significance upon validation through an information theoretic approach. The top-ranking genes determined in all of our case studies are in consonance with the findings of the respective wet-lab tests. [ABSTRACT FROM AUTHOR]

Abstract Introduction: Reconstruction of gene interaction networks from experimental data provides a deep understanding of the underlying biological mechanisms. The noisy nature of the data and the large size of the network make this a very challenging task. Complex approaches handle the stochastic nature of the data but can only do this for small networks; simpler, linear models generate large networks but with less reliability. Methods: We propose a divide-and-conquer approach using probabilistic graph representations and external knowledge. We cluster the experimental data and learn an interaction network for each cluster, which are merged using the interaction network for the representative genes selected for each cluster. Results: We generated an interaction atlas for 337 human pathways yielding a network of 11,454 genes with 17,777 edges. Simulated gene expression data from this atlas formed the basis for reconstruction. Based on the area under the curve of the precision-recall curve, the proposed approach outperformed the baseline (random classifier) by ∼15-fold and conventional methods by ∼5–17-fold. The performance of the proposed workflow is significantly linked to the accuracy of the clustering step that tries to identify the modularity of the underlying biological mechanisms. Conclusions: We provide an interaction atlas generation workflow optimizing the algorithm/parameter selection. The proposed approach integrates external knowledge in the reconstruction of the interactome using probabilistic graphs. Network characterization and understanding long-range effects in interaction atlases provide means for comparative analysis with implications in biomarker discovery and therapeutic approaches. The proposed workflow is freely available at http://otulab.unl.edu/atlas.

Identifying driver genes, exactly from massive genes with mutations, promotes accurate diagnosis and treatment of cancer. In recent years, a lot of works about uncovering driver genes based on integration of mutation data and gene interaction networks is gaining more attention. However, it is in suspense if it is more effective for prioritizing driver genes when integrating various types of mutation information (frequency and functional impact) and gene networks. Hence, we build a two-stage-vote ensemble framework based on somatic mutations and mutual interactions. Specifically, we first represent and combine various kinds of mutation information, which are propagated through networks by an improved iterative framework. The first vote is conducted on iteration results by voting methods, and the second vote is performed to get ensemble results of the first poll for the final driver gene list. Compared with four excellent previous approaches, our method has better performance in identifying driver genes on |$33$| types of cancer from The Cancer Genome Atlas. Meanwhile, we also conduct a comparative analysis about two kinds of mutation information, five gene interaction networks and four voting strategies. Our framework offers a new view for data integration and promotes more latent cancer genes to be admitted. [ABSTRACT FROM AUTHOR]

Introduction: Reconstruction of gene interaction networks from experimental data provides a deep understanding of the underlying biological mechanisms. The noisy nature of the data and the large size of the network make this a very challenging task. Complex approaches handle the stochastic nature of the data but can only do this for small networks; simpler, linear models generate large networks but with less reliability. Methods: We propose a divide-and-conquer approach using probabilistic graph representations and external knowledge. We cluster the experimental data and learn an interaction network for each cluster, which are merged using the interaction network for the representative genes selected for each cluster. Results: We generated an interaction atlas for 337 human pathways yielding a network of 11,454 genes with 17,777 edges. Simulated gene expression data from this atlas formed the basis for reconstruction. Based on the area under the curve of the precision-recall curve, the proposed approach outperformed the baseline (random classifier) by ∼15-fold and conventional methods by ∼5–17-fold. The performance of the proposed workflow is significantly linked to the accuracy of the clustering step that tries to identify the modularity of the underlying biological mechanisms. Conclusions: We provide an interaction atlas generation workflow optimizing the algorithm/parameter selection. The proposed approach integrates external knowledge in the reconstruction of the interactome using probabilistic graphs. Network characterization and understanding long-range effects in interaction atlases provide means for comparative analysis with implications in biomarker discovery and therapeutic approaches. The proposed workflow is freely available at http://otulab.unl.edu/atlas. [ABSTRACT FROM AUTHOR]

Background: Since genes involved in the same biological modules usually present correlated expression profiles, lots of computational methods have been proposed to identify gene functional modules based on the expression profiles data. Recently, Sparse Singular Value Decomposition (SSVD) method has been proposed to bicluster gene expression data to identify gene modules. However, this model can only handle the gene expression data where no gene interaction information is integrated. Ignoring the prior gene interaction information may produce the identified gene modules hard to be biologically interpreted. Results: In this paper, we develop a Sparse Network-regularized SVD (SNSVD) method that integrates a prior gene interaction network from a protein protein interaction network and gene expression data to identify underlying gene functional modules. The results on a set of simulated data show that SNSVD is more effective than the traditional SVD-based methods. The further experiment results on real cancer genomic data show that most co-expressed modules are not only significantly enriched on GO/KEGG pathways, but also correspond to dense sub-networks in the prior gene interaction network. Besides, we also use our method to identify ten differentially co-expressed miRNA-gene modules by integrating matched miRNA and mRNA expression data of breast cancer from The Cancer Genome Atlas (TCGA). Several important breast cancer related miRNA-gene modules are discovered. Conclusions: All the results demonstrate that SNSVD can overcome the drawbacks of SSVD and capture more biologically relevant functional modules by incorporating a prior gene interaction network. These identified functional modules may provide a new perspective to understand the diagnostics, occurrence and progression of cancer. [ABSTRACT FROM AUTHOR]

The ovarian structure is complex and diverse, including egg cells, granulosa cells and other cell types. Its organ function depends heavily on normal development. Numerous studies have focused on certain gene function changes during ovarian development, but systematic analyses of its molecular changes are extremely rare. Here, we present a comprehensive transcriptional profile of the mouse ovary from 11 time points across multiple developmental stages, which enables us to explore the dynamics of ovarian development. By performing coexpression analysis, we identified gene modules with similar expression trends and determined 159 functional gene interaction networks based on machine learning. Most of these gene interaction networks are related to biological processes involved in the development of the ovary, which provides functional predictions for some genes with unknown functions and a reference for subsequent functional research. In general, our study provides a resource for understanding ovarian development. [Display omitted] F0B7 • Bulk RNA-seq was performed for the mouse ovary at 11 time points. F0B7 • The development of the ovary can be divided into three stages. F0B7 • 159 functional gene interaction networks were obtained using machine learning. F0B7 • Single-cell RNA-seq data verified the gene expression. [ABSTRACT FROM AUTHOR]

The number of hyperthyroidism patients is increasing these years. As a disease that can lead to cardiovascular disease, it brings great potential health risks to humans. Since hyperthyroidism can induce the occurrence of many diseases, studying its genetic factors will promote the early diagnosis and treatment of hyperthyroidism and its related diseases. Previous studies have used genome-wide association analysis (GWAS) to identify genes related to hyperthyroidism. However, these studies only identify significant sites related to the disease from a statistical point of view and ignore the complex regulation relationship between genes. In addition, mutation is not the only genetic factor of causing hyperthyroidism. Identifying hyperthyroidism-related genes from gene interactions would help researchers discover the disease mechanism. In this paper, we purposed a novel machine learning method for identifying hyperthyroidism-related genes based on gene interaction network. The method, which is called “RW-RVM,” is a combination of Random Walk (RW) and Relevance Vector Machines (RVM). RW was implemented to encode the gene interaction network. The features of genes were the regulation relationship between genes and non-coding RNAs. Finally, multiple RVMs were applied to identify hyperthyroidism-related genes. The result of 10-cross validation shows that the area under the receiver operating characteristic curve (AUC) of our method reached 0.9, and area under the precision-recall curve (AUPR) was 0.87. Seventy-eight novel genes were found to be related to hyperthyroidism. We investigated two genes of these novel genes with existing literature, which proved the accuracy of our result and method.

Drug repositioning is a new way of applying the existing therapeutics to new disease indications. Due to the exorbitant cost and high failure rate in developing new drugs, the continued use of existing drugs for treatment, especially anti-tumor drugs, has become a widespread practice. With the assistance of high-throughput sequencing techniques, many efficient methods have been proposed and applied in drug repositioning and individualized tumor treatment. Current computational methods for repositioning drugs and chemical compounds can be divided into four categories: (i) feature-based methods, (ii) matrix decomposition-based methods, (iii) network-based methods, and (iv) reverse transcriptome-based methods. In this article, we comprehensively review the widely used methods in the above four categories. Finally, we summarize the advantages and disadvantages of these methods and indicate future directions for more sensitive computational drug repositioning methods and individualized tumor treatment, which are critical for further experimental validation.

Drug repositioning is a new way of applying the existing therapeutics to new disease indications. Due to the exorbitant cost and high failure rate in developing new drugs, the continued use of existing drugs for treatment, especially anti-tumor drugs, has become a widespread practice. With the assistance of high-throughput sequencing techniques, many efficient methods have been proposed and applied in drug repositioning and individualized tumor treatment. Current computational methods for repositioning drugs and chemical compounds can be divided into four categories: (i) feature-based methods, (ii) matrix decomposition-based methods, (iii) network-based methods, and (iv) reverse transcriptome-based methods. In this article, we comprehensively review the widely used methods in the above four categories. Finally, we summarize the advantages and disadvantages of these methods and indicate future directions for more sensitive computational drug repositioning methods and individualized tumor treatment, which are critical for further experimental validation. [ABSTRACT FROM AUTHOR]

Breast cancer is a highly heterogeneous disease, and there are many forms of categorization for breast cancer based on gene expression profiles. Gene expression profiles are variables and may show differences if measured at different time points or under different conditions. In contrast, biological networks are relatively stable over time and under different conditions. In this study, we used a gene interaction network from a new point of view to explore the subtypes of breast cancer based on individual-specific edge perturbations measured by relative gene expression value. Our study reveals that there are four breast cancer subtypes based on gene interaction perturbations at the individual level. The new network-based subtypes of breast cancer show strong heterogeneity in prognosis, somatic mutations, phenotypic changes and enriched pathways. The network-based subtypes are closely related to the PAM50 subtypes and immunohistochemistry index. This work helps us to better understand the heterogeneity and mechanisms of breast cancer from a network perspective. [ABSTRACT FROM AUTHOR]

We conducted analysis to estimate genetic parameters and to identify genomic regions and candidate genes affecting direct and maternal effects of preweaning calf mortality (PWM) in Nellore cattle. Phenotypic records of 67,196 animals, and 8443 genotypes for 410,936 SNPs were used. Analysis were performed through the weighted single-step GBLUP approach and considering a threshold animal model via Bayesian Inference. Direct and maternal heritability estimates were of 0.2143 ± 0.0348 and 0.0137 ± 0.0066, respectively. The top 10 genomic regions accounted for 13.61 and 14.23% of the direct and maternal additive genetic variances and harbored a total of 63 and 91 positional candidate genes, respectively. Two overlapping regions on BTA2 were identified for both direct and maternal effects. Candidate genes are involved in biological mechanisms i.e. embryogenesis, immune response, feto-maternal communication, circadian rhythm, hormone alterations, myometrium adaptation, and milk secretion, which are critical for the successful calf growth and survival during preweaning period. • Phenotypic variation on preweaning mortality is explained by additive genetic effects • Genetic connectedness can be accurately estimated using genomic information • Preweaning traits are affected by maternal effects related to intrauterine conditions • Candidate genes are involved in embryogenesis, immune response, feto-maternal communication • Genes involved in circadian rhythm and behavior may be important for calf fitness [ABSTRACT FROM AUTHOR]

MYCT1, a target of c-Myc, inhibits laryngeal cancer cell migration, but the underlying mechanism remains unclear. In the study, we detected differentially expressed genes (DEGs) from laryngeal cancer cells transfected by MYCT1 using RNA-seq (GSE123275). DEGs from head and neck squamous cell carcinoma (HNSCC) were first screened by comparison of transcription data from the Gene Expression Omnibus (GSE6631) and the Cancer Genome Atlas (TCGA) datasets using weighted gene co-expression network analysis (WGCNA). GO and KEGG pathway analysis explained the functions of the DEGs. The DEGs overlapped between GSE6631and TCGA datasets were then compared with ours to find the key DEGs downstream of MYCT1 related to the adhesion and migration of laryngeal cancer cells. qRT-PCR and Western blot were applied to validate gene expression at mRNA and protein levels, respectively. Finally, the cell adhesion, migration, and wound healing assays were to check cell adhesion and migration abilities, respectively. As results, 39 overlapping genes were enriched in the GSE6631 and TCGA datasets, and most of them revealed adhesion function. Thirteen of 39 genes including COL6 members COL6A1, COL6A2, and COL6A3 were overlapped in GSE6631, TCGA, and GSE123275 datasets. Similar to our RNA-seq results, we confirmed that COL6 is a target of MYCT1 in laryngeal cancer cells. We also found that MYCT1 inhibited the adhesion and migration of laryngeal cancer cells via COL6. These indicate that COL6 is a potential target of MYCT1 and participates the adhesion and migration of laryngeal cancer cells, which provides an important clue for further study on how MYCT1 regulating COL6 in laryngeal cancer progression.

Maize is a cold-sensitive plant whose physiological reactions to sub-optimal temperatures are well understood, but their molecular foundations are only beginning to be deciphered. In an attempt to identify key genes involved in these reactions, we surveyed several independent transcriptomic studies addressing the response of juvenile maize to moderate or severe cold. Among the tens of thousands of genes found to change expression upon cold treatment less than 500 were reported in more than one study, indicating an astonishing variability of the expression changes, likely depending on the experimental design and plant material used. Nearly all these “common” genes were specific to either moderate or to severe cold and formed distinct interaction networks, indicating fundamentally different responses. Moreover, down-regulation of gene expression dominated strongly in moderate cold and up-regulation prevailed in severe cold. Very few of these genes have ever been mentioned in the literature as cold-stress–related, indicating that most response pathways remain poorly known at the molecular level. We posit that the genes identified by the present analysis are attractive candidates for further functional studies and their arrangement in complex interaction networks indicates that a re-interpretation of the present state of knowledge on the maize cold-response is justified.

Breast cancer is a disease with high heterogeneity. Cancer is not usually caused by a single gene, but by multiple genes and their interactions with others and surroundings. Estimating breast cancer-specific gene–gene interaction networks is critical to elucidate the mechanisms of breast cancer from a biological network perspective. In this study, sample-specific gene–gene interaction networks of breast cancer samples were established by using a sample-specific network analysis method based on gene expression profiles. Then, gene–gene interaction networks and pathways related to breast cancer and its subtypes and stages were further identified. The similarity and difference among these subtype-related (and stage-related) networks and pathways were studied, which showed highly specific for subtype Basal-like and Stages IV and V. Finally, gene pairwise interactions associated with breast cancer prognosis were identified by a Cox proportional hazards regression model, and a risk prediction model based on the gene pairs was established, which also performed very well on an independent validation data set. This work will help us to better understand the mechanism underlying the occurrence of breast cancer from the sample-specific network perspective.

Comprehensive analysis of integrated data on multi-cancers is important for understanding the biological mechanism of these cancers at the system level. Network methods give us new insight into simultaneously identifying the characteristic genes and pathways from multi-cancers. Nevertheless, when measuring the similarity quantification of genes, it is a challenge to choose suitable methods for network construction and analysis. Herein, the NIPMI method, based on Interaction Part Mutual Information (IPMI) measure for detecting characteristic genes from multi-cancers data, is proposed. First, Robust PCA was applied to select genes for network construction. Then, a network construction measure, IPMI, was proposed to effectively quantify the similarity between genes in the network, which is the highlight of NIPMI. Furthermore, we introduced a novel topological property, Topological Score, that combined the local and global properties of each node to find more candidate nodes in the network. Finally, pathway enrichment analysis was performed to validate the biological functions of multi-cancers. The experimental results demonstrated that NIPMI facilitates the identification of characteristic genes in a multicancer network; thus, it may serve as a valuable tool for detecting characteristic genes and significantly enriched pathway terms.

MYCT1, a target of c-Myc, inhibits laryngeal cancer cell migration, but the underlying mechanism remains unclear. In the study, we detected differentially expressed genes (DEGs) from laryngeal cancer cells transfected by MYCT1 using RNA-seq (GSE123275). DEGs from head and neck squamous cell carcinoma (HNSCC) were first screened by comparison of transcription data from the Gene Expression Omnibus (GSE6631) and the Cancer Genome Atlas (TCGA) datasets using weighted gene co-expression network analysis (WGCNA). GO and KEGG pathway analysis explained the functions of the DEGs. The DEGs overlapped between GSE6631and TCGA datasets were then compared with ours to find the key DEGs downstream of MYCT1 related to the adhesion and migration of laryngeal cancer cells. qRT-PCR and Western blot were applied to validate gene expression at mRNA and protein levels, respectively. Finally, the cell adhesion, migration, and wound healing assays were to check cell adhesion and migration abilities, respectively. As results, 39 overlapping genes were enriched in the GSE6631 and TCGA datasets, and most of them revealed adhesion function. Thirteen of 39 genes including COL6 members COL6A1, COL6A2, and COL6A3 were overlapped in GSE6631, TCGA, and GSE123275 datasets. Similar to our RNA-seq results, we confirmed that COL6 is a target of MYCT1 in laryngeal cancer cells. We also found that MYCT1 inhibited the adhesion and migration of laryngeal cancer cells via COL6. These indicate that COL6 is a potential target of MYCT1 and participates the adhesion and migration of laryngeal cancer cells, which provides an important clue for further study on how MYCT1 regulating COL6 in laryngeal cancer progression. [ABSTRACT FROM AUTHOR]

Maize is a cold-sensitive plant whose physiological reactions to sub-optimal temperatures are well understood, but their molecular foundations are only beginning to be deciphered. In an attempt to identify key genes involved in these reactions, we surveyed several independent transcriptomic studies addressing the response of juvenile maize to moderate or severe cold. Among the tens of thousands of genes found to change expression upon cold treatment less than 500 were reported in more than one study, indicating an astonishing variability of the expression changes, likely depending on the experimental design and plant material used. Nearly all these "common" genes were specific to either moderate or to severe cold and formed distinct interaction networks, indicating fundamentally different responses. Moreover, down-regulation of gene expression dominated strongly in moderate cold and up-regulation prevailed in severe cold. Very few of these genes have ever been mentioned in the literature as cold-stress–related, indicating that most response pathways remain poorly known at the molecular level. We posit that the genes identified by the present analysis are attractive candidates for further functional studies and their arrangement in complex interaction networks indicates that a re-interpretation of the present state of knowledge on the maize cold-response is justified. [ABSTRACT FROM AUTHOR]