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Background: As noncoding RNAs, circular RNAs (circRNAs) can act as microRNA (miRNA) sponges due to their abundant miRNA binding sites, allowing them to regulate gene expression and influence disease development. Accurately identifying circRNA-miRNA associations (CMAs) is helpful to understand complex disease mechanisms. Given that biological experiments are time consuming and labor intensive, alternative computational methods to predict CMAs are urgently needed., Results: This study proposes a novel computational model named CMAGN, which incorporates several advanced computational methods, for predicting CMAs. First, similarity networks for circRNAs and miRNAs are constructed according to their sequences. Graph attention autoencoder is then applied to these networks to generate the first representations of circRNAs and miRNAs. The second representations of circRNAs and miRNAs are obtained from the CMA network via node2vec. The similarity networks of circRNAs and miRNAs are reconstructed on the basis of these new representations. Finally, network consistency projection is applied to the reconstructed similarity networks and the CMA network to generate a recommendation matrix., Conclusion: Five-fold cross-validation of CMAGN reveals that the area under ROC and PR curves exceed 0.96 on two widely used CMA datasets, outperforming several existing models. Additional tests elaborate the reasonability of the architecture of CMAGN and uncover its strengths and weaknesses., (© 2024. The Author(s).)

Public gene expression databases are a rapidly expanding resource of organism responses to diverse perturbations, presenting both an opportunity and a challenge for bioinformatics workflows to extract actionable knowledge of transcription regulatory network function. Here, we introduce a five-step computational pipeline, called iModulonMiner, to compile, process, curate, analyze, and characterize the totality of RNA-seq data for a given organism or cell type. This workflow is centered around the data-driven computation of co-regulated gene sets using Independent Component Analysis, called iModulons, which have been shown to have broad applications. As a demonstration, we applied this workflow to generate the iModulon structure of Bacillus subtilis using all high-quality, publicly-available RNA-seq data. Using this structure, we predicted regulatory interactions for multiple transcription factors, identified groups of co-expressed genes that are putatively regulated by undiscovered transcription factors, and predicted properties of a recently discovered single-subunit phage RNA polymerase. We also present a Python package, PyModulon, with functions to characterize, visualize, and explore computed iModulons. The pipeline, available at https://github.com/SBRG/iModulonMiner, can be readily applied to diverse organisms to gain a rapid understanding of their transcriptional regulatory network structure and condition-specific activity., Competing Interests: The authors declare that they have no competing interests., (Copyright: © 2024 Sastry et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Computational synthetic lethality (SL) method has become a promising strategy to identify SL gene pairs for targeted cancer therapy and cancer medicine development. Feature representation for integrating various biological networks is crutial to improve the identification performance. However, previous feature representation, such as matrix factorization and graph neural network, projects gene features onto latent variables by keeping a specific geometric metric. There is a lack of models of gene representational latent space with considerating multiple dimentionalities correlation and preserving latent geometric structures in both sample and feature spaces. Therefore, we propose a novel method to model gene Latent Space using matrix Tri-Factorization (LSTF) to obtain gene representation with embedding variables resulting from the potential interpretation of synthetic lethality. Meanwhile, manifold subspace regularization is applied to the tri-factorization to capture the geometrical manifold structure in the latent space with gene PPI functional and GO semantic embeddings. Then, SL gene pairs are identified by the reconstruction of the associations with gene representations in the latent space. The experimental results illustrate that LSTF is superior to other state-of-the-art methods. Case study demonstrate the effectiveness of the predicted SL associations.

Pancreatic cancer is one of the most malignant cancers with rapid progression and poor prognosis. The use of transcriptional data can be effective in finding new biomarkers for pancreatic cancer. Many network-based methods used to identify cancer biomarkers are proposed, among which the combination of network controllability appears. However, most of the existing methods do not study RNA, rely on priori and mutations information, or can only achieve classification tasks. In this study, we propose a method combined Relational Graph Convolutional Network and Deep Q-Network called RDDriver to identify pancreatic cancer biomarkers based on multi-layer heterogeneous transcriptional regulation network. Firstly, we construct a regulation network containing long non-coding RNA, microRNA, and messenger RNA. Secondly, Relational Graph Convolutional Network is used to learn the node representation. Finally, we use the idea of Deep Q-Network to build a model, which score and prioritize each RNA with the Popov-Belevitch-Hautus criterion. We train RDDriver on three small simulated networks, and calculate the average score after applying the model parameters to the regulation networks separately. To demonstrate the effectiveness of the method, we perform experiments for comparison between RDDriver and other eight methods based on the approximate benchmark of three types cancer drivers RNAs.

Recent advances in measurement technologies, particularly single-cell RNA sequencing (scRNA-seq), have revolutionized our ability to acquire large amounts of omics-level data on cellular states. As measurement techniques evolve, there has been an increasing need for data analysis methodologies, especially those focused on cell-type identification and inference of gene regulatory networks (GRNs). We have developed a new method named BootCellNet, which employs smoothing and resampling to infer GRNs. Using the inferred GRNs, BootCellNet further infers the minimum dominating set (MDS), a set of genes that determines the dynamics of the entire network. We have demonstrated that BootCellNet robustly infers GRNs and their MDSs from scRNA-seq data and facilitates unsupervised identification of cell clusters using scRNA-seq datasets of peripheral blood mononuclear cells and hematopoiesis. It has also identified COVID-19 patient-specific cells and their potential regulatory transcription factors. BootCellNet not only identifies cell types in an unsupervised and explainable way but also provides insights into the characteristics of identified cell types through the inference of GRNs and MDS., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Yutaro Kumagai. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

R package pathlinkR is designed to aid transcriptomic analyses by streamlining and simplifying the process of analyzing and interpreting differentially expressed genes derived from human RNA-Seq data. It provides an integrated approach to performing pathway enrichment and network-based analyses, while also producing publication-quality figures to summarize these results, allowing users to more efficiently interpret their findings and extract biological meaning from large amounts of data. pathlinkR is available to install from the software repository Bioconductor at https://bioconductor.org/packages/pathlinkR/, with support available through the Bioconductor forums. The code, example, and supporting data is available on the GitHub repository at https://github.com/hancockinformatics/pathlinkR, under the GPL-3.0 license, where users may report problems or make suggestions using GitHub's issue system., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Blimkie et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Background: There is increasing evidence that inflammation plays a key role in the pathophysiology of periodontitis (PT) and Alzheimer's disease (AD), but the roles of inflammation in linking PT and AD are not clear. Our aim is to analyze the potential molecular mechanisms between these two diseases using bioinformatics and systems biology approaches., Methods: To elucidate the link between PT and AD, we selected shared genes (SGs) with gene-disease-association scores of ≥ 0.1 from the Disease Gene Network (DisGeNET) database, followed by extracting the hub genes. Based on these genes, we constructed gene ontology (GO) enrichment analysis, pathway enrichment analysis, protein-protein interaction (PPI) networks, transcription factors (TFs)-gene networks, microRNAs (miRNAs)-gene regulatory networks, and gene-disease association analyses. Finally, the Drug Signatures database (DSigDB) was utilized to predict candidate molecular drugs related to hub genes., Results: A total of 21 common SGs between PT and AD were obtained. Cell cytokine activity, inflammatory response, and extracellular membrane were the most important enriched items in GO analysis. Interleukin-10 Signaling, LTF Danger Signal Response Pathway, and RAGE Pathway were identified as important shared pathways. IL6, IL10, IL1B, TNF, IFNG, CXCL8, CCL2, MMP9, TLR4 were identified as hub genes. Both shared pathways and hub genes are closely related to endoplasmic reticulum (ER) stress and mitochondrial dysfunction. Importantly, glutathione, simvastatin, and dexamethasone were identified as important candidate drugs for the treatment of PT and AD., Conclusions: There is a close link between PT and AD pathogenesis, which may involve in the inflammation, ER and mitochondrial function., (© 2024. The Author(s).)

Link prediction (LP) is a task for the identification of potential, missing and spurious links in complex networks. Protein-protein interaction (PPI) networks are important for understanding the underlying biological mechanisms of diseases. Many complex networks have been constructed using LP methods; however, there are a limited number of studies that focus on disease-related gene predictions and evaluate these genes using various evaluation criteria. The main objective of the study is to investigate the effect of a simple ensemble method in disease related gene predictions. Local similarity indices (LSIs) based disease related gene predictions were integrated by a simple ensemble decision method, simple majority voting (SMV), on the PPI network to detect accurate disease related genes. Human PPI network was utilized to discover potential disease related genes using four LSIs for the gene prediction. LSIs discovered potential links between disease related genes, which were obtained from OMIM database for gastric, colorectal, breast, prostate and lung cancers. LSIs based disease related genes were ranked due to their LSI scores in descending order for retrieving the top 10, 50 and 100 disease related genes. SMV integrated four LSIs based predictions to obtain SMV based the top 10, 50 and 100 disease related genes. The performance of LSIs based and SMV based genes were evaluated separately by employing overlap analyses, which were performed with GeneCard disease-gene relation dataset and Gene Ontology (GO) terms. The GO-terms were used for biological assessment for the inferred gene lists by LSIs and SMV on all cancer types. Adamic-Adar (AA), Resource Allocation Index (RAI), and SMV based gene lists are generally achieved good performance results on all cancers in both overlap analyses. SMV also outperformed on breast cancer data. The increment in the selection of the number of the top ranked disease related genes also enhanced the performance results of SMV., Competing Interests: The author declares that they have no competing interests., (© 2024 Cingiz.)

Intracranial aneurysms (IAs) often go undetected until rupture, leading to significant morbidity and mortality. Identifying biomarkers for early detection of IAs is crucial. The current study attempted to identify core genes linked with IAs and determine their relevance through Mendelian randomization. Limma helped identify differentially expressed genes between IAs and control superficial temporal artery samples. WGCNA was utilized to find IA-related modules and associated genes, which were further evaluated using KEGG and GO analyses to ascertain their potential roles. Five highly associated genes were screened with the CytoHubba plugin of Cytoscape software. ROC curves assessed the diagnostic efficacy of these genes. A two-sample Mendelian randomization evaluated the causal relationship between the core gene PTRPC and IAs, along with its correlation with immune infiltration. WGCNA and differential expression analysis depicted 584 related genes involved in cellular metabolism and chemokine activity. PTPRC was among the top highly associated genes identified through Cytoscape. It showed significant diagnostic value for IAs. Moreover, mendelian randomization depicted that PTPRC in CD4+ T cells is related to IA risk, with an OR of 0.63538 (95 % CI = 0.41636-0.96959, p = 0.03545). No reverse causal relationship was observed between PTPRC and IAs, with an OR of 0.99947 (95 % CI = 0.99719-1.00176, p = 0.65022). Additionally, immune cell infiltration results indicated a positive correlation between PTPRC in IAs with neutrophils and unactivated dendritic cells and a negative association with regulatory T cells (Tregs). PTPRC was identified as a core gene linked with IAs, providing evidence for IA diagnosis and studying molecular mechanisms., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

The identification of cancer driver genes (CDGs) poses challenges due to the intricate interdependencies among genes and the influence of measurement errors and noise. We propose a novel energy-constrained diffusion (ECD)-based model for identifying CDGs, termed ECD-CDGI. This model is the first to design an ECD-Attention encoder by combining the ECD technique with an attention mechanism. ECD-Attention encoder excels at generating robust gene representations that reveal the complex interdependencies among genes while reducing the impact of data noise. We concatenate topological embedding extracted from gene-gene networks through graph transformers to these gene representations. We conduct extensive experiments across three testing scenarios. Extensive experiments show that the ECD-CDGI model possesses the ability to not only be proficient in identifying known CDGs but also efficiently uncover unknown potential CDGs. Furthermore, compared to the GNN-based approach, the ECD-CDGI model exhibits fewer constraints by existing gene-gene networks, thereby enhancing its capability to identify CDGs. Additionally, ECD-CDGI is open-source and freely available. We have also launched the model as a complimentary online tool specifically crafted to expedite research efforts focused on CDGs identification., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Wang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

The identification of cancer driver genes is crucial for early detection, effective therapy, and precision medicine of cancer. Cancer is caused by the dysregulation of several genes at various levels of regulation. However, current techniques only capture a limited amount of regulatory information, which may hinder their efficacy. In this study, we present IMI-driver, a model that integrates multi-omics data into eight biological networks and applies Multi-view Collaborative Network Embedding to embed the gene regulation information from the biological networks into a low-dimensional vector space to identify cancer drivers. We apply IMI-driver to 29 cancer types from The Cancer Genome Atlas (TCGA) and compare its performance with nine other methods on nine benchmark datasets. IMI-driver outperforms the other methods, demonstrating that multi-level network integration enhances prediction accuracy. We also perform a pan-cancer analysis using the genes identified by IMI-driver, which confirms almost all our selected candidate genes as known or potential drivers. Case studies of the new positive genes suggest their roles in cancer development and progression., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Shi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

This study aimed to perform exhaustive bioinformatic analysis by using GSE29221 micro-array maps obtained from healthy controls and Type 2 Diabetes (T2DM) patients. Raw data are downloaded from the Gene Expression Omnibus database and processed by the limma package in R software to identify Differentially Expressed Genes (DEGs). Gene ontology functional analysis and Kyoto Gene Encyclopedia and Genome Pathway analysis are performed to determine the biological functions and pathways of DEGs. A protein interaction network is constructed using the STRING database and Cytoscape software to identify key genes. Finally, immune infiltration analysis is performed using the Cibersort method. This study has implications for understanding the underlying molecular mechanism of T2DM and provides potential targets for further research., Competing Interests: Declaration of competing interest The authors declare no conflicts of interest., (Copyright © 2024 HCFMUSP. Published by Elsevier España, S.L.U. All rights reserved.)

Purpose: The objective of this study was to discern ferroptosis-related genes (FRGs) linked to non-obstructive azoospermia and investigate the associated molecular mechanisms., Method: 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., Results: 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., Conclusion: In summary, our investigation unveiled eight FRDEGs associated with azoospermia, which hold potential as biomarkers for the diagnosis and treatment of azoospermia., (© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Finding communities in gene co-expression networks is a common first step toward extracting biological insight from these complex datasets. Most community detection algorithms expect genes to be organized into assortative modules, that is, groups of genes that are more associated with each other than with genes in other groups. While it is reasonable to expect that these modules exist, using methods that assume they exist a priori is risky, as it guarantees that alternative organizations of gene interactions will be ignored. Here, we ask: can we find meaningful communities without imposing a modular organization on gene co-expression networks, and how modular are these communities? For this, we use a recently developed community detection method, the weighted degree corrected stochastic block model (SBM), that does not assume that assortative modules exist. Instead, the SBM attempts to efficiently use all information contained in the co-expression network to separate the genes into hierarchically organized blocks of genes. Using RNAseq gene expression data measured in two tissues derived from an outbred population of Drosophila melanogaster, we show that (a) the SBM is able to find ten times as many groups as competing methods, that (b) several of those gene groups are not modular, and that (c) the functional enrichment for non-modular groups is as strong as for modular communities. These results show that the transcriptome is structured in more complex ways than traditionally thought and that we should revisit the long-standing assumption that modularity is the main driver of the structuring of gene co-expression networks., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Melo et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Boolean networks are largely employed to model the qualitative dynamics of cell fate processes by describing the change of binary activation states of genes and transcription factors with time. Being able to bridge such qualitative states with quantitative measurements of gene expression in cells, as scRNA-seq, is a cornerstone for data-driven model construction and validation. On one hand, scRNA-seq binarisation is a key step for inferring and validating Boolean models. On the other hand, the generation of synthetic scRNA-seq data from baseline Boolean models provides an important asset to benchmark inference methods. However, linking characteristics of scRNA-seq datasets, including dropout events, with Boolean states is a challenging task. We present scBoolSeq, a method for the bidirectional linking of scRNA-seq data and Boolean activation state of genes. Given a reference scRNA-seq dataset, scBoolSeq computes statistical criteria to classify the empirical gene pseudocount distributions as either unimodal, bimodal, or zero-inflated, and fit a probabilistic model of dropouts, with gene-dependent parameters. From these learnt distributions, scBoolSeq can perform both binarisation of scRNA-seq datasets, and generate synthetic scRNA-seq datasets from Boolean traces, as issued from Boolean networks, using biased sampling and dropout simulation. We present a case study demonstrating the application of scBoolSeq's binarisation scheme in data-driven model inference. Furthermore, we compare synthetic scRNA-seq data generated by scBoolSeq with BoolODE's, data for the same Boolean Network model. The comparison shows that our method better reproduces the statistics of real scRNA-seq datasets, such as the mean-variance and mean-dropout relationships while exhibiting clearly defined trajectories in two-dimensional projections of the data., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Magaña-López et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is a positive-stranded single-stranded RNA virus with an envelope frequently altered by unstable genetic material, making it extremely difficult for vaccines, drugs, and diagnostics to work. Understanding SARS-CoV-2 infection mechanisms requires studying gene expression changes. Deep learning methods are often considered for large-scale gene expression profiling data. Data feature-oriented analysis, however, neglects the biological process nature of gene expression, making it difficult to describe gene expression behaviors accurately. In this article, we propose a novel scheme for modeling gene expression during SARS-CoV-2 infection as networks (gene expression modes, GEM), to characterize their expression behaviors. On this basis, we investigated the relationships among GEMs to determine SARS-CoV-2's core radiation mode. Our final experiments identified key COVID-19 genes by gene function enrichment, protein interaction, and module mining. Experimental results show that ATG10, ATG14, MAP1LC3B, OPTN, WDR45, and WIPI1 genes contribute to SARS-CoV-2 virus spread by affecting autophagy.

Hypopharyngeal cancer is a disease that is associated with EGFR-mutated lung adenocarcinoma. Here we utilized a bioinformatics approach to identify genetic commonalities between these two diseases. To this end, we examined microarray datasets from GEO (Gene Expression Omnibus) to identify differentially expressed genes, common genes, and hub genes between the selected two diseases. Our analyses identified potential therapeutic molecules for the selected diseases based on 10 hub genes with the highest interactions according to the degree topology method and the maximum clique centrality (MCC). These therapeutic molecules may have the potential for simultaneous treatment of these diseases., (© 2024 The Authors. FEBS Open Bio published by John Wiley & Sons Ltd on behalf of Federation of European Biochemical Societies.)

Background: Sepsis, an infection-triggered inflammatory syndrome, poses a global clinical challenge with limited therapeutic options. Our study is designed to identify potential diagnostic biomarkers of sepsis onset in critically ill patients by bioinformatics analysis., Methods: Gene expression profiles of GSE28750 and GSE74224 were obtained from the Gene Expression Omnibus (GEO) database. These datasets were merged, normalized and de-batched. Weighted gene co-expression network analysis (WGCNA) was performed and the gene modules most associated with sepsis were identified as key modules. Functional enrichment analysis of the key module genes was then conducted. Moreover, differentially expressed gene (DEG) analysis was conducted by the "limma" R package. Protein-protein interaction (PPI) network was created using STRING and Cytoscape, and PPI hub genes were identified with the cytoHubba plugin. The PPI hub genes overlapping with the genes in key modules of WGCNA were determined to be the sepsis-related key genes. Subsequently, the key overlapping genes were validated in an external independent dataset and sepsis patients recruited in our hospital. In addition, CIBERSORT analysis evaluated immune cell infiltration and its correlation with key genes., Results: By WGCNA, the greenyellow module showed the highest positive correlation with sepsis (0.7, p  = 2 e  - 19). 293 DEGs were identified in the merged datasets. The PPI network was created, and the CytoHubba was used to calculate the top 20 genes based on four algorithms (Degree, EPC, MCC, and MNC). Ultimately, LTF, LCN2, ELANE, MPO and CEACAM8 were identified as key overlapping genes as they appeared in the PPI hub genes and the key module genes of WGCNA. These sepsis-related key genes were validated in an independent external dataset (GSE131761) and sepsis patients recruited in our hospital. Additionally, the immune infiltration profiles differed significantly between sepsis and non-sepsis critical illness groups. Correlations between immune cells and these five key genes were assessed, revealing that plasma cells, macrophages M0, monocytes, T cells regulatory, eosinophils and NK cells resting were simultaneously and significantly associated with more than two key genes., Conclusion: This study suggests a critical role of LTF, LCN2, ELANE, MPO and CEACAM8 in sepsis and may provide potential diagnostic biomarkers and therapeutic targets for the treatment of sepsis., Competing Interests: The authors declare there are no competing interests., (©2024 Lin et al.)

Background: Human cytomegalovirus (HCMV) is a herpesvirus that can infect various cell types and modulate host gene expression and immune response. It has been associated with the pathogenesis of various cancers, but its molecular mechanisms remain elusive., Methods: We comprehensively analyzed the expression of HCMV pathway genes across 26 cancer types using the Cancer Genome Atlas (TCGA) and The Genotype-Tissue Expression (GTEx) databases. We also used bioinformatics tools to study immune invasion and tumor microenvironment in pan-cancer. Cox regression and machine learning were used to analyze prognostic genes and their relationship with drug sensitivity., Results: We found that HCMV pathway genes are widely expressed in various cancers. Immune infiltration and the tumor microenvironment revealed that HCMV is involved in complex immune processes. We obtained prognostic genes for 25 cancers and significantly found 23 key genes in the HCMV pathway, which are significantly enriched in cellular chemotaxis and synaptic function and may be involved in disease progression. Notably, CaM family genes were up-regulated and AC family genes were down-regulated in most tumors. These hub genes correlate with sensitivity or resistance to various drugs, suggesting their potential as therapeutic targets., Conclusions: Our study has revealed the role of the HCMV pathway in various cancers and provided insights into its molecular mechanism and therapeutic significance. It is worth noting that the key genes of the HCMV pathway may open up new doors for cancer prevention and treatment., (© 2024. The Author(s).)

Transcriptional regulation plays a crucial role in determining cell fate and disease, yet inferring the key regulators from gene expression data remains a significant challenge. Existing methods for estimating transcription factor (TF) activity often rely on static TF-gene interaction databases and cannot adapt to changes in regulatory mechanisms across different cell types and disease conditions. Here, we present a new algorithm - Transcriptional Inference using Gene Expression and Regulatory data (TIGER) - that overcomes these limitations by flexibly modeling activation and inhibition events, up-weighting essential edges, shrinking irrelevant edges towards zero through a sparse Bayesian prior, and simultaneously estimating both TF activity levels and changes in the underlying regulatory network. When applied to yeast and cancer TF knock-out datasets, TIGER outperforms comparable methods in terms of prediction accuracy. Moreover, our application of TIGER to tissue- and cell-type-specific RNA-seq data demonstrates its ability to uncover differences in regulatory mechanisms. Collectively, our findings highlight the utility of modeling context-specific regulation when inferring transcription factor activities., (© 2024. The Author(s).)

Neuroblastoma (NB) is one of the leading causes of cancer-associated death in children. MYCN amplification is a prominent genetic marker for NB, and its targeting to halt NB progression is difficult to achieve. Therefore, an in-depth understanding of the molecular interactome of NB is needed to improve treatment outcomes. Analysis of NB multi-omics unravels valuable insight into the interplay between MYCN transcriptional and miRNA post-transcriptional modulation. Moreover, it aids in the identification of various miRNAs that participate in NB development and progression. This study proposes an integrated computational framework with three levels of high-throughput NB data (mRNA-seq, miRNA-seq, and methylation array). Similarity Network Fusion (SNF) and ranked SNF methods were utilized to identify essential genes and miRNAs. The specified genes included both miRNA-target genes and transcription factors (TFs). The interactions between TFs and miRNAs and between miRNAs and their target genes were retrieved where a regulatory network was developed. Finally, an interaction network-based analysis was performed to identify candidate biomarkers. The candidate biomarkers were further analyzed for their potential use in prognosis and diagnosis. The candidate biomarkers included three TFs and seven miRNAs. Four biomarkers have been previously studied and tested in NB, while the remaining identified biomarkers have known roles in other types of cancer. Although the specific molecular role is yet to be addressed, most identified biomarkers possess evidence of involvement in NB tumorigenesis. Analyzing cellular interactome to identify potential biomarkers is a promising approach that can contribute to optimizing efficient therapeutic regimens to target NB vulnerabilities., (© 2024. The Author(s).)

Background: Exploring the pathogenesis of osteoarthritis (OA) is important for its prevention, diagnosis, and treatment. Therefore, we aimed to construct novel signature genes (c-FRGs) combining cuproptosis-related genes (CRGs) with ferroptosis-related genes (FRGs) to explore the pathogenesis of OA and aid in its treatment., Materials and Methods: Differentially expressed c-FRGs (c-FDEGs) were obtained using R software. Enrichment analysis was performed and a protein-protein interaction (PPI) network was constructed based on these c-FDEGs. Then, seven hub genes were screened. Three machine learning methods and verification experiments were used to identify four signature biomarkers from c-FDEGs, after which gene set enrichment analysis, gene set variation analysis, single-sample gene set enrichment analysis, immune function analysis, drug prediction, and ceRNA network analysis were performed based on these signature biomarkers. Subsequently, a disease model of OA was constructed using these biomarkers and validated on the GSE82107 dataset. Finally, we analyzed the distribution of the expression of these c-FDEGs in various cell populations., Results: A total of 63 FRGs were found to be closely associated with 11 CRGs, and 40 c-FDEGs were identified. Bioenrichment analysis showed that they were mainly associated with inflammation, external cellular stimulation, and autophagy. CDKN1A, FZD7, GABARAPL2, and SLC39A14 were identified as OA signature biomarkers, and their corresponding miRNAs and lncRNAs were predicted. Finally, scRNA-seq data analysis showed that the differentially expressed c-FRGs had significantly different expression distributions across the cell populations., Conclusion: Four genes, namely CDKN1A, FZD7, GABARAPL2, and SLC39A14, are excellent biomarkers and prospective therapeutic targets for OA., (© 2024. The Author(s).)

Transcription factors (TFs) regulation is required for the vast majority of biological processes in living organisms. Some diseases may be caused by improper transcriptional regulation. Identifying the target genes of TFs is thus critical for understanding cellular processes and analyzing disease molecular mechanisms. Computational approaches can be challenging to employ when attempting to predict potential interactions between TFs and target genes. In this paper, we present a novel graph model (PPRTGI) for detecting TF-target gene interactions using DNA sequence features. Feature representations of TFs and target genes are extracted from sequence embeddings and biological associations. Then, by combining the aggregated node feature with graph structure, PPRTGI uses a graph neural network with personalized PageRank to learn interaction patterns. Finally, a bilinear decoder is applied to predict interaction scores between TF and target gene nodes. We designed experiments on six datasets from different species. The experimental results show that PPRTGI is effective in regulatory interaction inference, with our proposed model achieving an area under receiver operating characteristic score of 93.87% and an area under precision-recall curves score of 88.79% on the human dataset. This paper proposes a new method for predicting TF-target gene interactions, which provides new insights into modeling molecular networks and can thus be used to gain a better understanding of complex biological systems.

Network inference is used to model transcriptional, signaling, and metabolic interactions among genes, proteins, and metabolites that identify biological pathways influencing disease pathogenesis. Advances in machine learning (ML)-based inference models exhibit the predictive capabilities of capturing latent patterns in genomic data. Such models are emerging as an alternative to the statistical models identifying causative factors driving complex diseases. We present CoVar, an ML-based framework that builds upon the properties of existing inference models, to find the central genes driving perturbed gene expression across biological states. Unlike differentially expressed genes (DEGs) that capture changes in individual gene expression across conditions, CoVar focuses on identifying variational genes that undergo changes in their expression network interaction profiles, providing insights into changes in the regulatory dynamics, such as in disease pathogenesis. Subsequently, it finds core genes from among the nearest neighbors of these variational genes, which are central to the variational activity and influence the coordinated regulatory processes underlying the observed changes in gene expression. Through the analysis of simulated as well as yeast expression data perturbed by the deletion of the mitochondrial genome, we show that CoVar captures the intrinsic variationality and modularity in the expression data, identifying key driver genes not found through existing differential analysis methodologies., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Roy et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Background: The connection between diabetes-associated cognitive dysfunction (DACD) and Alzheimer's disease (AD) has been shown in several observational studies. However, it remains controversial as to how the two related., Objective: To explore shared genes and pathways between DACD and AD using bioinformatics analysis combined with biological experiment., Methods: We analyzed GEO microarray data to identify DEGs in AD and type 2 diabetes mellitus (T2DM) induced-DACD datasets. Weighted gene co-expression network analysis was used to find modules, while R packages identified overlapping genes. A robust protein-protein interaction network was constructed, and hub genes were identified with Gene ontology enrichment and Kyoto Encyclopedia of Genome and Genome pathway analyses. HT22 cells were cultured under high glucose and amyloid-β 25-35 (Aβ25-35) conditions to establish DACD and AD models. Quantitative polymerase chain reaction with reverse transcription verification analysis was then performed on intersection genes., Results: Three modules each in AD and T2DM induced-DACD were identified as the most relevant and 10 hub genes were screened, with analysis revealing enrichment in pathways such as synaptic vesicle cycle and GABAergic synapse. Through biological experimentation verification, 6 key genes were identified., Conclusions: This study is the first to use bioinformatics tools to uncover the genetic link between AD and DACD. GAD1, UCHL1, GAP43, CARNS1, TAGLN3, and SH3GL2 were identified as key genes connecting AD and DACD. These findings offer new insights into the diseases' pathogenesis and potential diagnostic and therapeutic targets.

Introduction: To retrieve, and classify PCa miRNAs and identify the functional relationship between miRNAs and their targets through literature collection with computational analysis., Background: MicroRNAs play a role in gene regulation, which can either repress or activate the gene. Hence, the functions of miRNAs are dependent on the target gene. This study will be the first of its kind to combine computational analysis with corpus PCa data. Effectively, our study reported the huge number of miRNAs associated with PCa along with functional information., Objective: The identification and classification of previously known full PCa miRNAs and their targets were made possible by mining the literature data. Systems Biology and curated data mining assisted in identifying optimum miRNAs and their target genes for PCa therapy., Methods: PubMed database was used to collect the PCa literature up to December 2021. Pubmed. mineR package was used to extract the microRNAs associated articles and manual curation was performed to classify the microRNAs based on the function in PCa. PPI was constructed using the STRING database. Pathway analysis was performed using PANTHER and ToppGene Suite Software. Functional analysis was performed using ShinyGO software. Cluster analysis was performed using MCODE 2.0, and Hub gene analysis was performed using cytoHubba. The genemiRNA network was reconstructed using Cytoscape., Results: Unique PCa miRNAs were retrieved and classified from mined PCa literature. Six hundred and five unique miRNAs from 250 articles were considered as oncomiRs to trigger PCa. One hundred and twenty unique miRNAs from 118 articles were considered Tumor Suppressor miRNAs to suppress the PCa. Twenty-four unique miRNAs from 22 articles were utilized as treatment miRNAs to treat PCa. miRNAs target genes and their significant pathways, functions and hub genes were identified., Conclusion: miR-27a, miR-34b, miR-495, miR-23b, miR-100, miR-218, Let-7a family, miR-27a- 5p, miR-34c, miR-34a, miR-143/-145, miR-125b, miR-124 and miR-205 with their target genes AKT1, SRC, CTNNB1, HRAS, MYC and TP53 are significant PCa targets., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

The identification of condition-specific gene sets from transcriptomic experiments is important to reveal regulatory and signaling mechanisms associated with a given cellular response. Statistical methods of differential expression analysis, designed to assess individual gene variations, have trouble highlighting modules of small varying genes whose interaction is essential to characterize phenotypic changes. To identify these highly informative gene modules, several methods have been proposed in recent years, but they have many limitations that make them of little use to biologists. Here, we propose an efficient method for identifying these active modules that operates on a data embedding combining gene expressions and interaction data. Applications carried out on real datasets show that our method can identify new groups of genes of high interest corresponding to functions not revealed by traditional approaches. Software is available at https://github.com/claudepasquier/amine., (© 2023 Pasquier et al.)

Utilizing gene expression data to infer gene regulatory networks has received great attention because gene regulation networks can reveal complex life phenomena by studying the interaction mechanism among nodes. However, the reconstruction of large-scale gene regulatory networks is often not ideal due to the curse of dimensionality and the impact of external noise. In order to solve this problem, we introduce a novel algorithms called ensemble path consistency algorithm based on conditional mutual information (EPCACMI), whose threshold of mutual information is dynamically self-adjusted. We first use principal component analysis to decompose a large-scale network into several subnetworks. Then, according to the absolute value of coefficient of each principal component, we could remove a large number of unrelated nodes in every subnetwork and infer the relationships among these selected nodes. Finally, all inferred subnetworks are integrated to form the structure of the complete network. Rather than inferring the whole network directly, the influence of a mass of redundant noise could be weakened. Compared with other related algorithms like MRNET, ARACNE, PCAPMI and PCACMI, the results show that EPCACMI is more effective and more robust when inferring gene regulatory networks with more nodes.

Background: Fetal growth restriction (FGR) is the impairment of the biological growth potential of the fetus and often leads to adverse pregnancy outcomes. The molecular mechanisms for the development of FGR, however, are still unclear. The purpose of this study is to identify critical genes associated with FGR through an integrated bioinformatics approach and explore the potential pathogenesis of FGR., Methods: We downloaded FGR-related gene microarray data, used weighted gene co-expression network analysis (WGCNA), differentially expressed genes (DEGs), and protein-protein interaction (PPI) networks to screen hub genes. The GSE24129 gene set was used for validation of critical gene expression levels and diagnostic capabilities., Results: A weighted gene co-expression network was constructed, and 5000 genes were divided into 12 modules. Of these modules, the blue module showed the closest relationship with FGR. Taking the intersection of the DEGs and genes in the blue module as pivotal genes, 277 genes were identified, and 20 crucial genes were screened from the PPI network. The GSE24129 gene set verified the expression of 20 genes, and CXCL9, CXCR3, and ITGAX genes were identified as actual pivotal genes. The expression levels of CXCL9, CXCR3, and ITGAX were increased in both the training and validation sets, and ROC curve validation revealed that these three pivotal genes had a significant diagnostic ability for FGR. Single-gene GSEA results showed that all three core genes activated "hematopoietic cell lineage" and "cell adhesion molecules" and inhibited the "cGMP-PKG signaling pathway" in the development of FGR. CXCL9, CXCR3, and ITGAX may therefore be closely associated with the development of FGR and may serve as potential biomarkers for the diagnosis and treatment of FGR., Competing Interests: The authors have no conflicts of interest to declare., (Copyright © 2023 Jingjin Yang et al.)

Objective: The present study was aimed to identify hub genes associated with recurrent spontaneous abortion (RSA) via both bioinformatics analysis and clinical verification, also to evaluate the related pathways and immune infiltration situation of RSA, for exploring its underlying mechanism., Materials and Methods: We screened candidate hub genes associated with RSA via bioinformatic analysis in the microarray datasets GSE22490 downloaded from the Gene Expression Omnibus (GEO) database. Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) and Western blotting were used to validate these hub genes. Several kinds of enrichment analysis were carried out to find out the pathways related to RSA. Additionally, CIBERSORT was used for evaluation of local immune Infiltration status of RSA., Results: There were 536 differentially expressed genes (DEGs) including 301 upregulated and 235 downregulated genes in RSA group compared with healthy control group. Four hub genes (STAT3, TLR2, TLR4 and CD86) were finally screened out according to the protein-protein interaction (PPI) network analysis, RT-qPCR and Western blotting. Enrichment analysis showed that Toll-like receptor signaling pathway, neutrophil chemotaxis, chemokine signaling pathway and Fc gamma receptor-mediated phagocytosis were strongly associated with RSA. And in immune infiltration analysis, RSA tissue was found containing a higher proportion of monocytes and eosinophils., Conclusion: This study screened out four hub genes and several important pathways changed in the trophoblastic tissue of RSA patients. We also found that monocytes and eosinophils may be involved in RSA. These findings provide theoretical basis for further studies on the mechanisms of RSA., Competing Interests: Declaration of competing interest The authors have no conflicts of interest., (Copyright © 2022. Published by Elsevier B.V.)

S-System models, non-linear differential equation models, are widely used for reconstructing gene regulatory networks from temporal gene expression data. An S-System model involves two states, generation and degeneration, and uses the kinetic parameters g ij and h ij , to represent the direction, nature, and intensity of the genetic interactions. The need for learning a large number of model parameters results in increased computational expense. Previously, we improved the performance of the algorithm using dynamic allocation of the maximum in-degree for each gene. While the method was effective for smaller networks, a large amount of computation was still needed for larger networks. This problem arose mainly due to the increased occurrence of invalid networks during optimization, primarily because the two kinetic parameters (g ij and h ij ) of the S-System model converge independently during optimization. Being independent, these two parameters can converge to values that can indicate contradictory gene interactions, specifically inhibition or activation. In this study, to address this major challenge in S-System modelling, we developed a novel method that includes two features: a penalty term that penalizes those networks with invalid kinetic orders, and a parameter, w ij , derived by combining the kinetic parameters g ij and h ij . The novel penalty term was used for candidate selection during the process of optimizing the DRNI (Dynamically Regulated Network Initialization) algorithm. Rather than remaining constant, it is dynamic, with its magnitude dependent on the number of invalid interactions in the given network. This approach encourages the generation of valid candidate solutions, and eliminates invalid networks in a systematic manner. The previous DRNI method, a two-stage approach which uses dynamic allocation of the maximum in-degree for each gene, was further improved by adding a third stage which applies the proposed w ij to handle the invalid regulations that may still exist in that candidate solutions. The method was tested on different gene expression datasets, and was able to reduce the number of iterations and produce improved network accuracies. For a 20 gene network, the number of generations required for convergence was reduced by 300, and the F-score improved by 0.05 compared to our previously reported DRNI approach. For the well-known 10 gene networks of the DREAM challenge, our method produced an improvement in the average area under the ROC curve of the DREAM4 10 gene networks., (Copyright © 2022. Published by Elsevier B.V.)

Background: Periodontitis is considered to be the leading cause of tooth loss in adults, and it interacts with some serious systemic diseases. Periodontal basic therapy is the cornerstone of periodontal disease treatment and long-term maintenance and has a positive impact on the treatment of systemic diseases., Aim: To explore the potential gene targets of periodontitis therapies by bioinformatics method., Methods: We analyzed the expression database (GSE6751) downloaded from the Gene Expression Omnibus (GEO) with weighted gene coexpression network analysis (WGCNA) to confirm the functional gene modules. Pathway enrichment network analyses the key genes in functional modules and verified the candidate genes from the samples in peripheral blood sources of GSE43525. Moreover, we confirmed the expression of target protein in the periodontal tissues of experimental periodontitis-afflicted mice using western blotting., Results: The functional gene modules were found to have biological processes, and ARRB2 , BIRC3 , CD14 , DYNLL1 , FCER1G , FCGR1A , FCGR2B , FGR , HCK , and PRKCD were screened as candidates' genes in functional modules. The 921 DEG from GSE43525 and 418 DEG is from the green module of GSE6751 and identified AMICA1 , KDELR1 , DHRS7B , LMNB1 , CTSA , S100A12 , and FCGR1A as target genes. Finally, FCGR1A (CD64) was confirmed as the key gene that affects periodontal treatment. Western blot analysis showed an increasing trend in the expression level of FCGR1A protein in the periodontal tissues of experimental periodontitis mice compared to normal mice., Conclusions: FCGR1A (CD64) may be a key gene target for periodontal therapy in patients with periodontitis and other systemic diseases., Competing Interests: All authors declared that there was no conflicts of interests., (Copyright © 2022 Ying Jin et al.)

One of the key challenges in systems biology is to derive gene regulatory networks (GRNs) from complex high-dimensional sparse data. Bayesian networks (BNs) and dynamic Bayesian networks (DBNs) have been widely applied to infer GRNs from gene expression data. GRNs are typically sparse but traditional approaches of BN structure learning to elucidate GRNs often produce many spurious (false positive) edges. We present two new BN scoring functions, which are extensions to the Bayesian Information Criterion (BIC) score, with additional penalty terms and use them in conjunction with DBN structure search methods to find a graph structure that maximises the proposed scores. Our BN scoring functions offer better solutions for inferring networks with fewer spurious edges compared to the BIC score. The proposed methods are evaluated extensively on auto regressive and DREAM4 benchmarks. We found that they significantly improve the precision of the learned graphs, relative to the BIC score. The proposed methods are also evaluated on three real time series gene expression datasets. The results demonstrate that our algorithms are able to learn sparse graphs from high-dimensional time series data. The implementation of these algorithms is open source and is available in form of an R package on GitHub at https://github.com/HamdaBinteAjmal/DBN4GRN, along with the documentation and tutorials.

The accumulated omic data provides an opportunity to exploit the mechanisms of cancers and poses a challenge for their integrative analysis. Although extensive efforts have been devoted to address this issue, the current algorithms result in undesirable performance because of the complexity of patterns and heterogeneity of data. In this study, the ultimate goal is to propose an effective and efficient algorithm (called NMF-DEC) to identify clusters by integrating the interactome and transcriptome data. By treating the expression profiles of genes as attributes of vertices in the gene interaction networks, we transform the integrative analysis of omic data into clustering of attributed networks. To circumvent the heterogeneity, we construct a similarity network for the attributes of genes and cast it into the common module detection problem in multi-layer networks. The NMF-DEC explores the relation between attributes and topological structure of networks by jointly factorizing the similarity and interaction networks with the same basis. In this optimization, the interaction network is dynamically updated and the information of attributes is dynamically incorporated, providing a better strategy to characterize the structure of modules in attributed networks. Extensive experiments indicate that compared with state-of-the-art baselines, NMF-DEC is more accurate on social network, and show better performance on cancer attributed networks, implying the superiority of the proposed methods for the integrative analysis of omic data.

Background: After encountering COVID-19 patients who test positive again after discharge, our study analyzed the pathogenesis to further assess the risk and possibility of virus reactivation., Methods: A separate microarray was acquired from the Gene Expression Omnibus (GEO), and its samples were divided into two groups: a "convalescent-RTP" group consisting of convalescent and "retesting positive" (RTP) patients (group CR) and a "healthy-RTP" group consisting of healthy control and RTP patients (group HR). The enrichment analysis was performed with R software, obtaining the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Subsequently, the protein-protein interaction (PPI) networks of each group were established, and the hub genes were discovered using the cytoHubba plugin., Results: In this study, 6622 differentially expressed genes were identified in the group CR, among which RAB11B-AS1, DISP1, MICAL3, PSMG1, and DOCK4 were up-regulated genes, and ANAPC1, IGLV1-40, SORT1, PLPPR2, and ATP1A1-AS1 were down-regulated. 7335 genes were screened in the group HR, including the top 5 up-regulated genes ALKBH6, AMBRA1, MIR1249, TRAV18, and LRRC69, and the top 5 down-regulated genes FAM241B, AC018529.3, AL031963.3, AC006946.1, and FAM149B1. The GO and KEGG analysis of the two groups revealed a significant enrichment in immune response and apoptosis. In the PPI network constructed, group CR and group HR identified 10 genes, respectively, and TP53BP1, SNRPD1, and SNRPD2 were selected as hub genes., Conclusions: Using the messenger ribonucleic acid (mRNA) expression data from GSE166253, we found TP53BP1, SNRPD1, and SNRPD2 as hub genes in RTP patients, which is vital to the management and prognostic prediction of RTP patients., (© 2022 The Authors. Journal of Clinical Laboratory Analysis published by Wiley Periodicals LLC.)

Knowledge of heterogeneous etiology and pathophysiology of schizophrenia (SZP) is reasonably inadequate and non-deterministic due to its inherent complexity and underlying vast dynamics related to genetic mechanisms. The evolution of large-scale transcriptome-wide datasets and subsequent development of relevant, robust technologies for their analyses show promises toward elucidating the genetic basis of disease pathogenesis, its early risk prediction, and predicting drug molecule targets for therapeutic intervention. In this research, we have scrutinized the genetic basis of SZP through functional annotation and network-based system biology approaches. We have determined 96 overlapping differentially expressed genes (DEGs) from 2 microarray datasets and subsequently identified their interconnecting networks to reveal transcriptome signatures like hub proteins (FYN, RAD51, SOCS3, XIAP, AKAP13, PIK3C2A, CBX5, GATA3, EIF3K, and CDKN2B), transcription factors and miRNAs. In addition, we have employed gene set enrichment to highlight significant gene ontology (e.g., positive regulation of microglial cell activation) and relevant pathways (such as axon guidance and focal adhesion) interconnected to the genes associated with SZP. Finally, we have suggested candidate drug substances like Luteolin HL60 UP as a possible therapeutic target based on these key molecular signatures., (Copyright © 2022 Elsevier B.V. All rights reserved.)

We describe a consensus approach for network construction based on fully conserved gene-gene interactions from randomly downsampled data subsets for an unbiased differential analysis of gene co-expression networks. The pipeline allows users to identify network nodes lost, conserved, and acquired in cancer as well as interpret the functional significance of these network changes. For proof of concept, the protocol is used to leverage RNA-seq data of tumor samples from TCGA and healthy tissue samples from the GTEx database. For complete details on the use and execution of this protocol, please refer to Arshad and McDonald (2021)., Competing Interests: The authors declare no competing interests., (© 2022 The Author(s).)

Objective: The purpose of this study is to explore the high-risk pathogenic driver genes for the occurrence and development of ankylosing spondylitis (AS) based on the bioinformatics method at the molecular level, to further elaborate the molecular mechanism of the pathogenesis of AS, and to provide potential biological targets for the diagnosis and treatment of clinical AS., Methods: The gene expression profile data GSE16879 were downloaded from the GEO (Gene Expression Omnibus) database, and weighted gene coexpression network analysis was performed. Highly correlated genes were divided into 14 modules, and 582 genes contained in the yellow (classic module) and 59 genes contained in grey60 (hematologic module) modules had the strongest correlation with AS. After protein-protein interaction (PPI) analysis, the top 20 genes with the highest scores were obtained from classic module and hematologic module, respectively. The DAVID (Database for Annotation, Visualization, and Integrated Discovery) database was used for Gene Ontology analysis and Kyoto Encyclopedia of Genes and Genomes analysis to analyze the biological functions of high-risk genes related to AS., Results: The results showed that the process of signal recognition particle-dependent cotranslational protein targeting to membrane, ribosome, nicotinamide adenine diphosphate hydride dehydrogenase (ubiquinone) activity, platelet activation, integrin complex, and extracellular matrix binding were enriched., Conclusions: In this study, weighted gene coexpression network analysis, an efficient system biology algorithm, was used to analyze the high-risk pathogenic driver gene of AS. We provide new targets for the diagnosis and treatment of clinical AS and new ideas for further study., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Background: Long non-coding RNAs (lncRNAs) are widely involved in gene transcription regulation and which act as epigenetic modifiers in many diseases., Objective: To determine whether lncRNAs are involved in ischemic stroke (IS), we analyzed the expression profile of lncRNAs and mRNAs in IS., Methods: RNA sequencing was performed on the blood of three pairs of IS patients and healthy controls. Differential expression analysis was used to identify differentially expressed lncRNAs (DElncRNAs) and mRNAs (DEmRNAs). Based on the co-expression relationships between lncRNA and mRNA, a series of bioinformatics analysis including GO and KEGG enrichment analysis and PPI analysis, were conducted to predict the function of lncRNA., Results: RNA sequencing produced a total of 5 DElncRNAs and 144 DEmRNAs. Influenza A pathway and Herpes simplex infection pathway were the most significant pathways. EP300 and NFKB1 were the most important target proteins, and Human leucocyte antigen (HLA) family were the key genes in IS., Conclusions: Analysis of this study revealed that dysregulated lncRNAs in IS may lead to IS by affecting the immune and inflammation system., (© 2021. The Genetics Society of Korea.)

Background: Osteoarthritis (OA) and rheumatoid arthritis (RA) are two common diseases that result in limb disability and a decrease in quality of life. The major symptoms of OA and RA are pain, swelling, stiffness, and malformation of joints, and each disease also has unique characteristics., Objective: To compare the pathological mechanisms of OA and RA via weighted correlation network analysis (WGCNA) and immune infiltration analysis and find potential diagnostic and pharmaceutical targets for the treatment of OA and RA., Methods: The gene expression profiles of ten OA and ten RA synovial tissue samples were downloaded from the Gene Expression Omnibus (GEO) database (GSE55235). After obtaining differentially expressed genes (DEGs) via GEO2R, WGCNA was conducted using an R package, and modules and genes that were highly correlated with OA and RA were identified. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment, and protein-protein interaction (PPI) network analyses were also conducted. Hub genes were identified using the Search Tool for the Retrieval of Interacting Genes (STRING) and Cytoscape software. Immune infiltration analysis was conducted using the Perl program and CIBERSORT software., Results: Two hundred ninety-nine DEGs, 24 modules, 16 GO enrichment terms, 6 KEGG pathway enrichment terms, 10 hub genes (CXCL9, CXCL10, CXCR4, CD27, CD69, CD3D, IL7R, STAT1, RGS1, and ISG20), and 8 kinds of different infiltrating immune cells (plasma cells, CD8 T cells, activated memory CD4 T cells, T helper follicular cells, M1 macrophages, Tregs, resting mast cells, and neutrophils) were found to be involved in the different pathological mechanisms of OA and RA., Conclusion: Inflammation-associated genes were the top differentially expressed hub genes between OA and RA, and their expression was downregulated in OA. Genes associated with lipid metabolism may have upregulated expression in OA. In addition, immune cells that participate in the adaptive immune response play an important role in RA. OA mainly involves immune cells that are associated with the innate immune response., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

Objective: This study aimed to identify the hub genes and pathways of genes related to oxidative stress of cartilage in osteonecrosis of femoral head (ONFH), and to predict the transcription factors of the hub genes., Methods: The GSE74089 was obtained from the Gene Expression Omnibus (GEO) database, including 4 necrotic tissues and 4 normal tissues, and the differentially expressed genes (DEGs) were identified by limma package in R language. Simultaneously, we searched for the genes related to oxidative stress in the Gene Ontology (GO) database. GO and signaling pathways analysis were performed using DAVID, Metascape, and GSEA. Protein-protein interaction (PPI) network was constructed using the STRING database, and the Degree algorithm of Cytoscape software was used to screen for hub genes. Finally, the NetworkAnalyst web tool was used to find the hub genes' transcriptional factors (TFs)., Results: In total, 440 oxidative stress-related genes were found in GSE74089 and GO database, and 88 of them were significantly differentially expressed. These genes were mainly involved in several signaling pathways, such as MAPK signaling pathway, PI3K-AKT-mTOR signaling pathway, FOXO signaling pathway. The top 10 hub genes were JUN, FOXO3, CASP3, JAK2, RELA, EZH2, ABL1, PTGS2, FBXW7, MCL1. Besides, TFAP2A, GATA2, SP1, and E2F1 may be the key regulatory factors of hub genes., Conclusions: We identified some hub genes and signaling pathways associated with oxidative stress in ONFH through a series of bioinformatics analyses.

Aims/introduction: Diabetic nephropathy (DN) is among the leading causes of end-stage renal disease worldwide. DN pathogenesis remains largely unknown. Weighted gene co-expression network analysis is a powerful bioinformatic tool for identifying key genes in diseases., Materials and Methods: The datasets GSE30122, GSE104948, GSE37463 and GSE47185 containing 23 DN and 23 normal glomeruli samples were obtained from the National Center for Biotechnology Information Gene Expression Omnibus database. After data pre-processing, weighted gene co-expression network analysis was carried out to cluster significant modules. Then, Gene Set Enrichment Analysis-based Gene Ontology analysis and visualization of network were carried out to screen the key genes in the most significant modules. The connectivity map analysis was carried out to find the significant chemical compounds. Finally, some key genes were validated in in vivo and in vitro experiments., Results: A total of 454 upregulated and 392 downregulated genes were identified. A total of 16 modules were clustered, and the most significant modules (green, red and yellow modules) were determined. The green module was associated with extracellular matrix organization, the red module was associated with immunity reaction and the yellow module was associated with kidney development. We found several key genes in these three modules separately, and part of them were validated in vivo and in vitro successfully. We found the top 15 chemical compounds that could perturb the overall expression of key genes in DN., Conclusion: Weighted gene co-expression network analysis was applied to DN expression profiling in combination with connectivity map analysis. Several novel key genes and chemical compounds were screened out, providing new molecular targets for DN., (© 2021 The Authors. Journal of Diabetes Investigation published by Asian Association for the Study of Diabetes (AASD) and John Wiley & Sons Australia, Ltd.)

The biological processes that drive cellular function can be represented by a complex network of interactions between regulators (transcription factors) and their targets (genes). A cell's epigenetic state plays an important role in mediating these interactions, primarily by influencing chromatin accessibility. However, how to effectively use epigenetic data when constructing a gene regulatory network remains an open question. Almost all existing network reconstruction approaches focus on estimating transcription factor to gene connections using transcriptomic data. In contrast, computational approaches for analyzing epigenetic data generally focus on improving transcription factor binding site predictions rather than deducing regulatory network relationships. We bridged this gap by developing SPIDER, a network reconstruction approach that incorporates epigenetic data into a message-passing framework to estimate gene regulatory networks. We validated SPIDER's predictions using ChIP-seq data from ENCODE and found that SPIDER networks are both highly accurate and include cell-line-specific regulatory interactions. Notably, SPIDER can recover ChIP-seq verified transcription factor binding events in the regulatory regions of genes that do not have a corresponding sequence motif. The networks estimated by SPIDER have the potential to identify novel hypotheses that will allow us to better characterize cell-type and phenotype specific regulatory mechanisms., (© 2021. The Author(s).)

This study used established biomarkers of death from ischemic stroke (IS) versus stroke survival to perform network, enrichment, and annotation analyses. Protein-protein interaction (PPI) network analysis revealed that the backbone of the highly connective network of IS death consisted of IL6 , ALB , TNF , SERPINE1 , VWF , VCAM1 , TGFB1 , and SELE . Cluster analysis revealed immune and hemostasis subnetworks, which were strongly interconnected through the major switches ALB and VWF . Enrichment analysis revealed that the PPI immune subnetwork of death due to IS was highly associated with TLR2/4 , TNF , JAK-STAT , NOD , IL10 , IL13 , IL4 , and TGF-β1/SMAD pathways. The top biological and molecular functions and pathways enriched in the hemostasis network of death due to IS were platelet degranulation and activation, the intrinsic pathway of fibrin clot formation, the urokinase-type plasminogen activator pathway, post-translational protein phosphorylation, integrin cell-surface interactions, and the proteoglycan-integrin extracellular matrix complex (ECM). Regulation Explorer analysis of transcriptional factors shows: (a) that NFKB1 , RELA and SP1 were the major regulating actors of the PPI network; and (b) hsa-mir-26-5p and hsa-16-5p were the major regulating microRNA actors. In conclusion, prevention of death due to IS should consider that current IS treatments may be improved by targeting VWF, the proteoglycan-integrin-ECM complex, TGF-β1/SMAD, NF-κB/RELA and SP1.

Background: The germinal center B-cell (GCB) and activated B-cell (ABC) subtypes of diffuse large B-cell lymphoma (DLBCL) have a significant difference in prognosis. This study aimed to identify potential hub genes, and key pathways involved in them., Methods: Databases including Gene Expression Omnibus (GEO), Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and STRING were accessed to obtain potential crucial genes and key pathways associated with the GCB and ABC. Then qRT-PCR and Western blot experiments were performed to verify the most clinically significant gene and pathway., Results: Three cohort datasets from the GEO database were analyzed, including 195 GCB and 169 ABC samples. We identified 1113 differentially expressed genes (DEGs) between the GCB and ABC subtypes. The DEGs were mainly enriched in biological processes (BP). The KEGG analysis showed enrichment in cell cycle and Wnt signaling pathways. We selected the top 10 genes using the STRING database and Cytoscape software. We used 5 calculation methods of the cytoHubba plugin, and found 3 central genes (IL-10, CD44, CCND2). CCND2 was significantly related to the prognosis of DLBCL patients. Besides, our experimental results demonstrated a significantly higher expression of CCND2 in the ABC-type cell line than in the GCB-type; it was proportional to the expression of key proteins in the Wnt signaling pathway., Conclusion: CCND2 overexpression and Wnt pathway activation might be the main reasons for the poor prognosis of ABC-DLBCL., (© 2021 The Authors. Journal of Clinical Laboratory Analysis published by Wiley Periodicals LLC.)

Gene Regulatory Network (GRN) is formed due to mutual transcriptional regulation within a set of protein coding genes in cellular context of an organism. Computational inference of GRN is important to understand the behavior of each gene in terms of change in its protein production rate (expression level). As Recurrent Neural Network (RNN) is efficient in GRN modeling, a bi-objective RNN formulation has been applied here. Based on Archived Multi Objective Simulated Annealing (AMOSA), four algorithms, namely, AMOSA Revised (AMOSAR), Modified Freezing based AMOSA (AMOFSA), Tabu based AMOSA (AMOTSA) and Modified Freezing and Tabu based AMOSA (AMOFTSA) have been proposed and applied to RNN (treated as GRN) for parameter learning taking four gene expression time series datasets. Comparative studies on the performance of the algorithms (based on each dataset) have been made in terms of the number of GRNs obtained in the final non-dominated front and the performance metrics, namely, recall, precision and f1 score. Two proposed variants, namely, AMOFSA and AMOTSA have been found competitive in performance. Experimental observations and statistical analysis show that, modified algorithms are better than AMOSAR and the state-of-the-art algorithms in respect of the above-mentioned metrics.

Boolean networks are a notable model of gene regulatory networks and, particularly, prominent theories discuss how they can capture cellular differentiation processes. One frequent motif in gene regulatory networks, especially in those circuits involved in cell differentiation, is autoregulation. In spite of this, the impact of autoregulation on Boolean network attractor landscape has not yet been extensively discussed in literature. In this paper we propose to model autoregulation as self-loops, and analyse how the number of attractors and their robustness may change once they are introduced in a well-known and widely used Boolean networks model, namely random Boolean networks. Results show that self-loops provide an evolutionary advantage in dynamic mechanisms of cells, by increasing both number and maximal robustness of attractors. These results provide evidence to the hypothesis that autoregulation is a straightforward functional component to consolidate cell dynamics, mainly in differentiation processes.

There has been a spate of interest in association networks in biological and medical research, for example, genetic interaction networks. In this paper, we propose a novel method, the extended joint hub graphical lasso (EDOHA), to estimate multiple related interaction networks for high dimensional omics data across multiple distinct classes. To be specific, we construct a convex penalized log likelihood optimization problem and solve it with an alternating direction method of multipliers (ADMM) algorithm. The proposed method can also be adapted to estimate interaction networks for high dimensional compositional data such as microbial interaction networks. The performance of the proposed method in the simulated studies shows that EDOHA has remarkable advantages in recognizing class-specific hubs than the existing comparable methods. We also present three applications of real datasets. Biological interpretations of our results confirm those of previous studies and offer a more comprehensive understanding of the underlying mechanism in disease., Competing Interests: The authors have declared that no competing interests exist.

Gene-expression profiling can define the cell state and gene-expression pattern of cells at the genetic level in a high-throughput manner. With the development of transcriptome techniques, processing high-dimensional genetic data has become a major challenge in expression profiling. Thanks to the recent widespread use of matrix decomposition methods in bioinformatics, a computational framework based on compressed sensing was adopted to reduce dimensionality. However, compressed sensing requires an optimization strategy to learn the modular dictionaries and activity levels from the low-dimensional random composite measurements to reconstruct the high-dimensional gene-expression data. Considering this, here we introduce and compare four compressed sensing frameworks coming from nature-inspired optimization algorithms (CSCS, ABCCS, BACS and FACS) to improve the quality of the decompression process. Several experiments establish that the three proposed methods outperform benchmark methods on nine different datasets, especially the FACS method. We illustrate therefore, the robustness and convergence of FACS in various aspects; notably, time complexity and parameter analyses highlight properties of our proposed FACS. Furthermore, differential gene-expression analysis, cell-type clustering, gene ontology enrichment and pathology analysis are conducted, which bring novel insights into cell-type identification and characterization mechanisms from different perspectives. All algorithms are written in Python and available at https://github.com/Philyzh8/Nature-inspired-CS., (© The Author(s) 2021. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

Time-course gene-expression data have been widely used to infer regulatory and signaling relationships between genes. Most of the widely used methods for such analysis were developed for bulk expression data. Single cell RNA-Seq (scRNA-Seq) data offer several advantages including the large number of expression profiles available and the ability to focus on individual cells rather than averages. However, the data also raise new computational challenges. Using a novel encoding for scRNA-Seq expression data, we develop deep learning methods for interaction prediction from time-course data. Our methods use a supervised framework which represents the data as 3D tensor and train convolutional and recurrent neural networks for predicting interactions. We tested our time-course deep learning (TDL) models on five different time-series scRNA-Seq datasets. As we show, TDL can accurately identify causal and regulatory gene-gene interactions and can also be used to assign new function to genes. TDL improves on prior methods for the above tasks and can be generally applied to new time-series scRNA-Seq data., (© The Author(s) 2021. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)