Integrated weighted gene co-expression network analysis with an application to chronic fatigue syndrome

Integrated bioinformatics and clinical data identify three novel biomarkers for osteoarthritis diagnosis and synovial immune

Osteoarthritis (OA) is a degenerative joint disease that can be aggravated by synovitis and synovial immune disorders (SID). However, the role of synovial SID-related genes in OA synovium remains poorly understood. OA synovial and peripheral blood datasets were obtained from the GEO database ( https://www.ncbi.nlm.nih.gov/ ). Immune-related genes ( https://reactome.org/ ) showing differential expression in peripheral blood were identified as immune disorder genes. Subsequently, differentially expressed immune disorder genes in OA synovium were further identified as SID genes. The Venn diagram, random forest, SVM-RFE algorithm, and multivariate analysis were employed to determine SID-related hub genes in OA synovium. Using the identified hub genes, we constructed and validated a diagnostic model for predicting OA occurrence. The correlation between hub gene expression and immune-related modules was explored using CIBERSORT and MCP-counter analyses. We identified three SID-related hub genes (ACAT1, SPHK1, and ACACB) in OA synovium. The diagnostic model incorporating these hub genes demonstrated reliable predictive accuracy (AUC = 0.939). Through qPCR analysis, we quantitated the expression levels of the hub genes and confirmed that three hub genes could serve as novel biomarkers for OA patients (AUC = 0.960). Furthermore, we observed a significant correlation between the expression of these hub genes and immune cell infiltration, as well as inflammatory cytokine levels in OA synovium. Our findings suggest that three SID-related hub genes have the potential to serve as diagnostic biomarkers for OA patients. These genes are associated with immune disorder and contribute to immune alterations within the OA synovium.

k-Clique counting on large scale-graphs: a survey

Clique counting is a crucial task in graph mining, as the count of cliques provides different insights across various domains, social and biological network analysis, community detection, recommendation systems, and fraud detection. Counting cliques is algorithmically challenging due to combinatorial explosion, especially for large datasets and larger clique sizes. There are comprehensive surveys and reviews on algorithms for counting subgraphs and triangles (three-clique), but there is a notable lack of reviews addressing k-clique counting algorithms for k > 3. This paper addresses this gap by reviewing clique counting algorithms designed to overcome this challenge. Also, a systematic analysis and comparison of exact and approximation techniques are provided by highlighting their advantages, disadvantages, and suitability for different contexts. It also presents a taxonomy of clique counting methodologies, covering approximate and exact methods and parallelization strategies. The paper aims to enhance understanding of this specific domain and guide future research of k-clique counting in large-scale graphs.

Molecular subtype identification of cerebral ischemic stroke based on ferroptosis-related genes

Cerebral ischemic stroke (CIS) has the characteristics of a high incidence, disability, and mortality rate. Here, we aimed to explore the potential pathogenic mechanisms of ferroptosis-related genes (FRGs) in CIS. Three microarray datasets from the Gene Expression Omnibus (GEO) database were utilized to analyze differentially expressed genes (DEGs) between CIS and normal controls. FRGs were obtained from a literature report and the FerrDb database. Weighted gene co-expression network analysis (WGCNA) and protein-protein interaction (PPI) network were used to screen hub genes. The receiver operating characteristic (ROC) curve was adopted to evaluate the diagnostic value of key genes in CIS, followed by analysis of immune microenvironment, transcription factor (TF) regulatory network, drug prediction, and molecular docking. In total, 128 CIS samples were divided into 2 subgroups after clustering analysis. Compared with cluster A, 1560 DEGs were identified in cluster B. After the construction of the WGCNA and PPI network, 5 hub genes, including MAPK3, WAS, DNAJC5, PRKCD, and GRB2, were identified for CIS. Interestingly, MAPK3 was a FRG that differentially expressed between cluster A and cluster B. The expression levels of 5 hub genes were all specifically highly in cluster A subtype. It is noted that neutrophils were the most positively correlated with all 5 real hub genes. PRKCD was one of the target genes of FASUDIL. In conclusion, five real hub genes were identified as potential diagnostic markers, which can distinguish the two subtypes well.

Aggregation of recount3 RNA-seq data improves inference of consensus and tissue-specific gene co-expression networks

Background

Gene co-expression networks (GCNs) describe relationships among expressed genes key to maintaining cellular identity and homeostasis. However, the small sample size of typical RNA-seq experiments which is several orders of magnitude fewer than the number of genes is too low to infer GCNs reliably. recount3, a publicly available dataset comprised of 316,443 uniformly processed human RNA-seq samples, provides an opportunity to improve power for accurate network reconstruction and obtain biological insight from the resulting networks.

Results

We compared alternate aggregation strategies to identify an optimal workflow for GCN inference by data aggregation and inferred three consensus networks: a universal network, a non-cancer network, and a cancer network in addition to 27 tissue context-specific networks. Central network genes from our consensus networks were enriched for evolutionarily constrained genes and ubiquitous biological pathways, whereas central context-specific network genes included tissue-specific transcription factors and factorization based on the hubs led to clustering of related tissue contexts. We discovered that annotations corresponding to context-specific networks inferred from aggregated data were enriched for trait heritability beyond known functional genomic annotations and were significantly more enriched when we aggregated over a larger number of samples.

Conclusion

This study outlines best practices for network GCN inference and evaluation by data aggregation. We recommend estimating and regressing confounders in each data set before aggregation and prioritizing large sample size studies for GCN reconstruction. Increased statistical power in inferring context-specific networks enabled the derivation of variant annotations that were enriched for concordant trait heritability independent of functional genomic annotations that are context-agnostic. While we observed strictly increasing held-out log-likelihood with data aggregation, we noted diminishing marginal improvements. Future directions aimed at alternate methods for estimating confounders and integrating orthogonal information from modalities such as Hi-C and ChIP-seq can further improve GCN inference.

Systems genetics approaches for understanding complex traits with relevance for human disease

Quantitative traits are often complex because of the contribution of many loci, with further complexity added by environmental factors. In medical research, systems genetics is a powerful approach for the study of complex traits, as it integrates intermediate phenotypes, such as RNA, protein, and metabolite levels, to understand molecular and physiological phenotypes linking discrete DNA sequence variation to complex clinical and physiological traits. The primary purpose of this review is to describe some of the resources and tools of systems genetics in humans and rodent models, so that researchers in many areas of biology and medicine can make use of the data.

Single-cell transcriptomics of hepatic stellate cells uncover crucial pathways and key regulators involved in non-alcoholic steatohepatitis

Background

Fibrosis is an important pathological process in the development of non-alcoholic steatohepatitis (NASH), and the activation of hepatic stellate cell (HSC) is a central event in liver fibrosis. However, the transcriptomic change of activated HSCs (aHSCs) and resting HSCs (rHSCs) in NASH patients has not been assessed. This study aimed to identify transcriptomic signature of HSCs during the development of NASH and the underlying key functional pathways.

Methods

NASH-associated transcriptomic change of HSCs was defined by single-cell RNA-sequencing (scRNA-seq) analysis, and those top upregulated genes were identified as NASH-associated transcriptomic signatures. Those functional pathways involved in the NASH-associated transcriptomic change of aHSCs were explored by weighted gene co-expression network analysis (WGCNA) and functional enrichment analyses. Key regulators were explored by upstream regulator analysis and transcription factor enrichment analysis.

Results

scRNA-seq analysis identified numerous differentially expressed genes in both rHSCs and aHSCs between NASH patients and healthy controls. Both scRNA-seq analysis and in-vivo experiments showed the existence of rHSCs (mainly expressing a-SMA) in the normal liver and the increased aHSCs (mainly expressing collagen 1) in the fibrosis liver tissues. NASH-associated transcriptomic signature of rHSC (NASHrHSCsignature) and NASH-associated transcriptomic signature of aHSC (NASHaHSCsignature) were identified. WGCNA revealed the main pathways correlated with the transcriptomic change of aHSCs. Several key upstream regulators and transcription factors for determining the functional change of aHSCs in NASH were identified.

Conclusion

This study developed a useful transcriptomic signature with the potential in assessing fibrosis severity in the development of NASH. This study also identified the main pathways in the activation of HSCs during the development of NASH.

Prognostic analysis of m6A-related genes as potential biomarkers in idiopathic pulmonary fibrosis

Idiopathic pulmonary fibrosis (IPF) is a progressive, fatal lung disease with limited treatment options. N6-methyladenosine (m6A) is a reversible RNA modification and has been implicated in various biological processes. However, there are few studies on m6A in IPF. This project mainly explores the prognostic value of m6A-related genes as potential biomarkers in IPF, in order to establish a set of accurate prognostic prediction model. In this study, we used GSE28042 dataset in GEO database to screen out 218 m6A-related candidate genes with high IPF correlation and high differential expression through differentially expressed gene analysis, WGCNA and m6A correlation analysis. The genes associated with the prognosis of IPF were screened out by univariate Cox regression analysis, LASSO analysis, and multivariate Cox regression analysis, and the multivariate Cox model of prognostic risk of related genes was constructed. We found that RBM11, RBM47, RIC3, TRAF5 and ZNF14 were key genes in our model. Finally, the prognostic prediction ability and independent prognostic characteristics of the risk model were evaluated by survival analysis and independent prognostic analysis, and verified by the GSE93606 dataset, which proved that the prognostic risk model we constructed has a strong and stable prediction efficiency.

Network module function enrichment analysis of lung squamous cell carcinoma and lung adenocarcinoma

BACKGROUND

Lung squamous cell carcinoma (LUSC) and lung adenocarcinoma (LUAD) are the two major subtypes of non-small cell lung cancer that pose a serious threat to human health. However, both subtypes currently lack effective indicators for early diagnosis.

METHODS

To identify tumor-specific indicators and predict cancer-related signaling pathways, LUSC and LUAD gene weighted co-expression networks were constructed. Combined with clinical data, core genes in LUSC and LUAD modules were then screened using protein-protein interaction networks and their functions and pathways were analyzed. Finally, the effect of core genes on survival of LUSC and LUAD patients was evaluated.

RESULTS

We identified 12 network modules in LUSC and LUAD, respectively. LUSC modules "purple" and "green" and LUAD modules "brown" and "pink" are significantly associated with overall survival and clinical traits of tumor node metastasis, respectively. Eleven genes from LUSC and eight genes from LUAD were identified as candidate core genes, respectively. Survival analysis showed that high expression of SLIT3, ABI3BP, MYOCD, PGM5, TNXB, and DNAH9 are associated with decreased survival in LUSC patients. Furthermore, high expression of BUB1, BUB1B, TTK, and UBE2C are associated with lower patient survival.

CONCLUSIONS

We found biomarker genes and biological pathways for LUSC and LUAD. These network hub genes are associated with clinical characteristics and patient outcomes and they may play important roles in LUSC and LUAD.

Shared network pattern of lung squamous carcinoma and adenocarcinoma illuminates therapeutic targets for non-small cell lung cancer

Background

Non-small cell lung cancer (NSCLC) is a malignant tumor with high mortality. Lung squamous carcinoma (LUSC) and lung adenocarcinoma (LUAD) are the common subtypes of NSCLC. However, how LUSC and LUAD are compatible remains to be elucidated.

Methods

We used a network approach to find highly interconnected genes shared with LUSC and LUAD, and we then built modules to assess the degree of preservation between them. To quantify this result, Z-scores were used to summarize the interrelationships between LUSC and LUAD. Furthermore, we correlated network hub genes with patient survival time to identify risk factors.

Results

Our findings provided a look at the regulatory pattern for LUSC and LUAD. For LUSC, several genes, such as AKR1C1, AKR1C2, and AKR1C3, play key roles in regulating network modules of cell growth pathways. In addition, CCL19, CCR7, CCL21, and LY9 are enriched in LUAD network modules of T lymphocyte-related pathways. LUSC and LUAD have similar expressed gene expression patterns. Their networks share 46 hub genes with connectivity greater than 0.9. These genes are correlated with patient survival time. Among them, the expression level of COL5A2 in LUSC and LUAD is higher than that in normal tissues, which is closely related to the poor prognosis of LUSC and LUAD patients.

Conclusion

LUSC and LUAD share a network pattern. COL5A2 may be a risk factor in poor prognosis in LUSC and LUAD. The common landscape of LUSC and LUAD will help better define the regulation of NSCLC candidate genes and achieve the goals of precision medicine.

Identification of key pathways and genes that regulate cashmere development in cashmere goats mediated by exogenous melatonin

The growth of secondary hair follicles in cashmere goats follows a seasonal cycle. Melatonin can regulate the cycle of cashmere growth. In this study, melatonin was implanted into live cashmere goats. After skin samples were collected, transcriptome sequencing and histological section observation were performed, and weighted gene co-expression network analysis (WGCNA) was used to identify key genes and establish an interaction network. A total of 14 co-expression modules were defined by WGCNA, and combined with previous analysis results, it was found that the blue module was related to the cycle of cashmere growth after melatonin implantation. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis showed that the first initiation of exogenous melatonin-mediated cashmere development was related mainly to the signaling pathway regulating stem cell pluripotency and to the Hippo, TGF-beta and MAPK signaling pathways. Via combined differential gene expression analyses, 6 hub genes were identified: PDGFRA, WNT5A, PPP2R1A, BMPR2, BMPR1A, and SMAD1. This study provides a foundation for further research on the mechanism by which melatonin regulates cashmere growth.

Construction of a lncRNA–mRNA Co-Expression Network for Nasopharyngeal Carcinoma

Long non-coding RNAs (lncRNAs) widely regulate gene expression and play important roles in the pathogenesis of human diseases, including malignant tumors. However, the functions of most lncRNAs remain to be elucidated. In order to study and screen novel lncRNAs with important functions in the carcinogenesis of nasopharyngeal carcinoma (NPC), we constructed a lncRNA expression profile of 10 NPC tissues and 6 controls through a gene microarray. We identified 1,276 lncRNAs, of which most are unknown, with different expression levels in the healthy and NPC tissues. In order to shed light on the functions of these unknown lncRNAs, we first constructed a co-expression network of lncRNAs and mRNAs using bioinformatics and systematic biological approach. Moreover, mRNAs were clustered and enriched by their biological functions, and those lncRNAs have similar expression trends with mRNAs were defined as functional molecules with potential biological significance. The module may help identify key lncRNAs in the carcinogenesis of NPC and provide clues for in-depth study of their functions and associated signaling pathways. We suggest the newly identified lncRNAs may have clinic value as biomarkers and therapeutic targets for NPC diagnosis and treatment.

miR-155-5p regulates hypoxia-induced pulmonary artery smooth muscle cell function by targeting PYGL

Pulmonary arterial hypertension (PAH) is a cardiovascular disease that has high incidence and causes massive deaths. miR-155-5p/PYGL pathway was revealed to play a crucial role in PAH by weighted gene co-expression network analysis (WGCNA). The potential mechanism of miR-155-5p in regulating hypoxia-induced pulmonary artery smooth muscle cell (PASMC) function was analyzed through in vitro experiments. Hypoxia treatment stimulated the proliferation of PASMCs and increased the expression of vascular endothelial growth factor (VEGF) and hypoxia-inducible factor-1α (HIF-1α). At the same time, revealed by qRT-PCR and western blot, the level of miR-155-5p was raised, and the level of PYGL was decreased in hypoxia-induced PASMCs. Through CCK-8 assay, transwell assay and flow cytometry, it was revealed that miR-155-5p inhibitor remarkably inhibited the cell proliferation and migration and decreased the proportion of hypoxia-stimulated PASMCs in S and G2/M phases. Dual-luciferase reporter system was subsequently applied to validate the straight regulation of miR-155-5p on PYGL based on the analysis of online database. Furthermore, siPYGL was revealed to reverse the influence of miR-155-5p inhibitor on hypoxia-induced PASMCs. These outcomes indicate that the increased level of miR-155-5p in hypoxia-stimulated PASMCs could enhance the cell proliferation, cell migration, and cell cycle progression by targeting PYGL directly. This study may supply novel treatment strategies for PAH.Abbreviations: PH, pulmonary hypertension; PAH, pulmonary arterial hypertension; WGCNA, weighted gene co-expression network analysis; PASMCs, pulmonary artery smooth muscle cells; VEGF, vascular endothelial growth factor; HIF-1α, hypoxia-inducible factor-1α; SMCs, smooth muscle cells; DEGs, differentially expressed genes; GEO, Gene Expression Omnibus; GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; FBS, fetal bovine serum; OD, optical density; BCA, bicinchoninic acid; PVDF, polyvinylidene fluoride; PBS, phosphate-buffered saline; BP, biological process; MF, molecular function; CC, cell component.

Identification of Potential Molecular Mechanism Related to Infertile Endometriosis

Objectives

In this research, we aim to explore the bioinformatic mechanism of infertile endometriosis in order to identify new treatment targets and molecular mechanism.

Methods

The Gene Expression Omnibus (GEO) database was used to download MRNA sequencing data from infertile endometriosis patients. The “limma” package in R software was used to find differentially expressed genes (DEGs). Weighted gene co-expression network analysis (WGCNA) was used to classify genes into modules, further obtained the correlation coefficient between the modules and infertility endometriosis. The intersection genes of the most disease-related modular genes and DEGs are called gene set 1. To clarify the molecular mechanisms and potential therapeutic targets for infertile endometriosis, we used Gene Ontology (GO), Kyoto Gene and Genome Encyclopedia (KEGG) enrichment, Protein-Protein Interaction (PPI) networks, and Gene Set Enrichment Analysis (GSEA) on these intersecting genes. We identified lncRNAs and miRNAs linked with infertility and created competing endogenous RNAs (ceRNA) regulation networks using the Human MicroRNA Disease Database (HMDD), mirTarBase database, and LncRNA Disease database.

Results

Firstly, WGCNA enrichment analysis was used to examine the infertile endometriosis dataset GSE120103, and we discovered that the Meorangered1 module was the most significantly related with infertile endometriosis. The intersection genes were mostly enriched in the metabolism of different amino acids, the cGMP-PKG signaling pathway, and the cAMP signaling pathway according to KEGG enrichment analysis. The Meorangered1 module genes and DEGs were then subjected to bioinformatic analysis. The hub genes in the PPI network were performed KEGG enrichment analysis, and the results were consistent with the intersection gene analysis. Finally, we used the database to identify 13 miRNAs and two lncRNAs linked to infertility in order to create the ceRNA regulatory network linked to infertile endometriosis.

Conclusion

In this study, we used a bioinformatics approach for the first time to identify amino acid metabolism as a possible major cause of infertility in patients with endometriosis and to provide potential targets for the diagnosis and treatment of these patients.

DJExpress: An Integrated Application for Differential Splicing Analysis and Visualization

RNA-seq analysis of alternative pre-mRNA splicing has facilitated an unprecedented understanding of transcriptome complexity in health and disease. However, despite the availability of countless bioinformatic pipelines for transcriptome-wide splicing analysis, the use of these tools is often limited to expert bioinformaticians. The need for high computational power, combined with computational outputs that are complicated to visualize and interpret present obstacles to the broader research community. Here we introduce DJExpress, an R package for differential expression analysis of transcriptomic features and expression-trait associations. To determine gene-level differential junction usage as well as associations between junction expression and molecular/clinical features, DJExpress uses raw splice junction counts as input data. Importantly, DJExpress runs on an average laptop computer and provides a set of interactive and intuitive visualization formats. In contrast to most existing pipelines, DJExpress can handle both annotated and de novo identified splice junctions, thereby allowing the quantification of novel splice events. Moreover, DJExpress offers a web-compatible graphical interface allowing the analysis of user-provided data as well as the visualization of splice events within our custom database of differential junction expression in cancer (DJEC DB). DJEC DB includes not only healthy and tumor tissue junction expression data from TCGA and GTEx repositories but also cancer cell line data from the DepMap project. The integration of DepMap functional genomics data sets allows association of junction expression with molecular features such as gene dependencies and drug response profiles. This facilitates identification of cancer cell models for specific splicing alterations that can then be used for functional characterization in the lab. Thus, DJExpress represents a powerful and user-friendly tool for exploration of alternative splicing alterations in RNA-seq data, including multi-level data integration of alternative splicing signatures in healthy tissue, tumors and cancer cell lines.

Gene Co-expression Analysis of the Human Substantia Nigra Identifies ZNHIT1 as an SNCA Co-expressed Gene that Protects Against α-Synuclein-Induced Impairments in Neurite Growth and Mitochondrial Dysfunction in SH-SY5Y Cells

Parkinson’s disease (PD) is neurodegenerative disorder with the pathological hallmarks of progressive degeneration of midbrain dopaminergic neurons from the substantia nigra (SN), and accumulation and spread of inclusions of aggregated α-synuclein (α-Syn). Since current PD therapies do not prevent neurodegeneration, there is a need to identify therapeutic targets that can prevent α-Syn-induced reductions in neuronal survival and neurite growth. We hypothesised that genes that are normally co-expressed with the α-Syn gene (SNCA), and whose co-expression pattern is lost in PD, may be important for protecting against α-Syn-induced dopaminergic degeneration, since broken correlations can be used as an index of functional misregulation. Gene co-expression analysis of the human SN showed that nuclear zinc finger HIT-type containing 1 (ZNHIT1) is co-expressed with SNCA and that this co-expression pattern is lost in PD. Overexpression of ZNHIT1 was found to increase deposition of the H2A.Z histone variant in SH-SY5Y cells, to promote neurite growth and to prevent α-Syn-induced reductions in neurite growth and cell viability. Analysis of ZNHIT1 co-expressed genes showed significant enrichment in genes associated with mitochondrial function. In agreement, bioenergetic state analysis of mitochondrial function revealed that ZNHIT1 increased cellular ATP synthesis. Furthermore, α-Syn-induced impairments in basal respiration, maximal respiration and spare respiratory capacity were not seen in ZNHIT1-overexpressing cells. These data show that ZNHIT1 can protect against α-Syn-induced degeneration and mitochondrial dysfunction, which rationalises further investigation of ZNHIT1 as a therapeutic target for PD.

Identification of hub genes related to immune cell infiltration in periodontitis using integrated bioinformatic analysis

BACKGROUND AND OBJECTIVE

Periodontitis is an inflammatory disease of the periodontium. However, the hub genes in periodontitis and their correlation with immune cells are not clear. This study aimed to identify hub genes and immune infiltration properties in periodontitis and to explore the correlation between hub genes and immune cells.

MATERIAL AND METHODS

Differentially expressed genes (DEGs) analysis and weighted gene co-expression network analysis (WGCNA) were performed both on GSE10334 and GSE173078 datasets. Hub genes were identified via WGCNA and DEGs. The proportions of infiltrating immune cells were calculated by CIBERSORT algorithm, and single-cell RNA-sequencing dataset GSE164241 was used to explore cell-type-specific expression profiles of hub genes.

RESULTS

Eight hub genes (DERL3, FKBP11, LAX1, CD27, SPAG4, ST6GAL1, MZB1, and SEL1L3) were selected via WGCNA and DEGs by combining GSE10334 and GSE173078 datasets. CIBERSORT analysis showed a significant difference in the proportion of B cells, dendritic cells resting, and neutrophils in the gingival tissues between healthy and periodontitis patients, and expressions of these genes were highly correlated with the infiltration of B cells in periodontitis. Furthermore, real-time quantitative PCR results further confirmed the overexpression of hub genes. Analysis of GSE164241dataset further identified that most of hub genes were mainly expressed in B cells.

CONCLUSIONS

By integrating WGCNA, DEGs, and CIBERSORT analysis, eight genes were identified to be the hub genes of periodontitis and most of them were mainly expressed in B cells encouraging further researches on B cells in periodontitis pathogenesis.

Identifying the hub genes and immune infiltration related to pyroptosis in rheumatoid arthritis

Rheumatoid arthritis (RA) is one of the most common autoimmune joint disorders globally, but its pathophysiological mechanisms have not been thoroughly investigated. Pyroptosis significantly correlates with programmed cell death. However, targeting pyroptosis has not been considered as a therapeutic strategy in RA due to a lack of systematic studies on validated biomarkers. The present study aimed to identify hub pyroptosis biomarkers and immune infiltration in RA. The gene expression profiles of synovial tissues were obtained from the Gene Expression Omnibus (GEO) database to identify differentially expressed pyroptosis genes (DEPGs). Meanwhile, the CIBERSORT algorithm was used to explore the association between immune infiltration and RA. Consequently, two hub DEPGs (EGFR and JUN) were identified as critical genes in RA. Through gene ontology and pathway enrichment analysis. EGFR and JUN were found to be primarily involved in the ErbB signaling pathway, PD-1 checkpoint pathway, GnRH signaling pathway, etc. Furthermore, for immune infiltration analysis, the pyroptosis genes EGFR and JUN were closely connected with four and one immune cell types, respectively. Overall, this study presents a novel method to identify hub DEPGs and their correlation with immune infiltration, which may provide novel perspectives into the diagnosis and treatment of patients with RA.

Cellular and molecular diversity in Sjogren's syndrome salivary glands: Towards a better definition of disease subsets

Primary Sjögren's syndrome (pSS) is a highly heterogeneous disease in terms of clinical presentation ranging from a mild disease localised to the salivary and lacrimal glands, to multiorgan complications of various degrees of severity, finishing with the evolution, in around 5% of pSS patients, to B cell lymphomas most commonly arising in the inflamed salivary glands. Currently, there are poor positive or negative predictors of disease evolution able to guide patient management and treatment at early stages of the diseases. Recent understanding of the pathogenic mechanisms driving immunopathology in pSS, particularly through histological and transcriptomic analysis of minor and parotid salivary gland (SG) biopsies, has highlighted a high degree of cellular and molecular heterogeneity of the inflammatory lesions but also allowed the identification of clusters of patients with similar underlying SG immunopathology. In particular, patients presenting with high degrees of B/T cell infiltration and the formation of ectopic lymphoid structures (ELS) in the SG have been associated, albeit with conflicting results, with higher degree of disease severity and enhanced risk of lymphoma evolution, suggesting that a dysregulated adaptive immune response plays a key role in driving disease manifestations in pSS. Recent data from randomised clinical trials with novel biological therapies in pSS have also highlighted the potential role of SG immunopathology and molecular pathology in stratifying patients for trial inclusion as well as assessing proof of mechanisms in longitudinal SG biopsies before and after treatment. Although significant progress has been made in the understanding of disease pathogenesis and heterogeneity through cellular and molecular SG pathology, further work is needed to validate their clinical utility in routine clinical settings and in randomised clinical trials.

Identification of Hub Genes Associated with Abnormal Endothelial Function in Early Coronary Atherosclerosis

Abnormal coronary endothelial function is an important step in the development of atherosclerosis. Coronary atherosclerosis is one of the main causes of death worldwide. We constructed a co-expression network to identify hub genes associated with abnormal coronary endothelial function in early coronary atherosclerosis. In brief, we used the GSE132651 dataset from the gene expression omnibus database. The top 5000 genes with greatest variances were used for weighted gene co-expression network analysis, and the module most strongly correlated with abnormal coronary endothelial function was chosen as key module. Functional enrichment analysis was performed for genes in the key module, a protein-protein interaction network was constructed to find hub genes, and gene set enrichment analysis (GSEA) was also performed. Genes were classified into 7 modules, with the midnightblue module being the one that was most related to abnormal coronary endothelial function and containing genes enriched in DNA replication, cell cycle, nucleotide excision repair, and Human T-cell leukemia virus 1 infection. We identified nine hub genes (HOXC5, PRND, PADI3, RC3H1, DAPP1, SIT1, DRICH1, GPRIN2, and RHO), which differently expressed in abnormal and normal coronary endothelial function samples. GSEA suggested that samples associated with abnormal coronary endothelial function and highly expressed hub genes were linked with immune, coagulation, hypoxia, and angiogenesis processes. These hub genes, their expression pattern, and pathways may be involved in the development of abnormal coronary endothelial function and promotion of early coronary atherosclerosis.

Identifying hub genes and immune infiltration of osteoarthritis using comprehensive bioinformatics analysis

Background

Osteoarthritis (OA) is the most common chronic degenerative joint disorder globally that is characterized by synovitis, cartilage degeneration, joint space stenosis, and sub-cartilage bone hyperplasia. However, the pathophysiologic mechanisms of OA have not been thoroughly investigated.

Methods

In this study, we conducted various bioinformatics analyses to identify hub biomarkers and immune infiltration in OA. The gene expression profiles of synovial tissues from 29 healthy controls and 36 OA samples were obtained from the gene expression omnibus database to identify differentially expressed genes (DEGs). The CIBERSORT algorithm was used to explore the association between immune infiltration and arthritis.

Results

Eighteen hub DEGs were identified as critical biomarkers for OA. Through gene ontology and pathway enrichment analyses, it was found that these DEGs were primarily involved in PI3K-Akt signaling pathway and Rap1 signaling pathway. Furthermore, immune infiltration analysis revealed differences in immune infiltration between patients with OA and healthy controls. The hub gene ZNF160 was closely related to immune cells, especially mast cell activation in OA.

Conclusion

Overall, this study presented a novel method to identify hub DEGs and their correlation with immune infiltration, which may provide novel insights into the diagnosis and treatment of patients with OA.

Molecular Biology Networks and Key Gene Regulators for Inflammatory Biomarkers Shared by Breast Cancer Development: Multi-Omics Systems Analysis

As key inflammatory biomarkers C-reactive protein (CRP) and interleukin-6 (IL6) play an important role in the pathogenesis of non-inflammatory diseases, including specific cancers, such as breast cancer (BC). Previous genome-wide association studies (GWASs) have neither explained the large proportion of genetic heritability nor provided comprehensive understanding of the underlying regulatory mechanisms. We adopted an integrative genomic network approach by incorporating our previous GWAS data for CRP and IL6 with multi-omics datasets, such as whole-blood expression quantitative loci, molecular biologic pathways, and gene regulatory networks to capture the full range of genetic functionalities associated with CRP/IL6 and tissue-specific key drivers (KDs) in gene subnetworks. We applied another systematic genomics approach for BC development to detect shared gene sets in enriched subnetworks across BC and CRP/IL6. We detected the topmost significant common pathways across CRP/IL6 (e.g., immune regulatory; chemokines and their receptors; interferon γ, JAK-STAT, and ERBB4 signaling), several of which overlapped with BC pathways. Further, in gene-gene interaction networks enriched by those topmost pathways, we identified KDs-both well-established (e.g., JAK1/2/3, STAT3) and novel (e.g., CXCR3, CD3D, CD3G, STAT6)-in a tissue-specific manner, for mechanisms shared in regulating CRP/IL6 and BC risk. Our study may provide robust, comprehensive insights into the mechanisms of CRP/IL6 regulation and highlight potential novel genetic targets as preventive and therapeutic strategies for associated disorders, such as BC.

Comprehensive characterization of pathological stage-related genes of papillary thyroid cancer along with survival prediction

It is crucial to understand the differences across papillary thyroid cancer (PTC) stages, so as to provide a basis for individualized treatments. Here, comprehensive function characterization of PTC stage‐related genes was performed and a new prognostic signature was developed for advanced patients. Two gene modules were confirmed to be closely associated with PTC stages and further six hub genes were identified that yield excellent diagnostic efficiency between tumour and normal tissues. Genetic alteration analysis indicates that they are much conservative since mutations in the DNA of them rarely occur, but changes of DNA methylation on these six genes show that 12 DNA methylation sites are significantly associated with their corresponding genes' expression. Validation data set testing also suggests that these six stage‐related hub genes would be probably potential biomarkers for marking four stages. Subsequently, a 21‐mRNA‐based prognostic risk model was constructed for PTC stage III/IV patients and it could effectively predict the survival of patients with strong prognostic ability. Functional analysis shows that differential expression genes between high‐ and low‐risk patients would promote the progress of PTC to some extent. Moreover, tumour microenvironment (TME) of high‐risk patients may be more conducive to tumour growth by ESTIMATE analysis.

Meta-Analyses of Multiple Gene Expression Profiles to Screen Hub Genes Related to Osteoarthritis

BACKGROUND

This study aimed to screen and validate the crucial genes involved in osteoarthritis (OA) and explore its potential molecular mechanisms.

METHODS

Four expression profile datasets related to OA were downloaded from the Gene Expression Omnibus (GEO). The differentially expressed genes (DEGs) from 4 microarray patterns were identified by the meta-analysis method. The weighted gene co-expression network analysis (WGCNA) method was used to investigate stable modules most related to OA. In addition, a protein-protein interaction (PPI) network was built to explore hub genes in OA. Moreover, OA-related genes and pathways were retrieved from Comparative Toxicogenomics Database (CTD).

RESULTS

A total of 1,136 DEGs were identified from 4 datasets. Based on these DEGs, WGCNA further explored 370 genes included in the 3 OA-related stable modules. A total of 10 hub genes were identified in the PPI network, including AKT1, CDC42, HLA-DQA2, TUBB, TWISTNB, GSK3B, FZD2, KLC1, GUSB, and RHOG. Besides, 5 pathways including "Lysosome," "Pathways in cancer," "Wnt signaling pathway," "ECM-receptor interaction" and "Focal adhesion" in CTD and enrichment analysis and 5 OA-related hub genes (including GSK3B, CDC42, AKT1, FZD2, and GUSB) were identified.

CONCLUSION

In this study, the meta-analysis was used to screen the central genes associated with OA in a variety of gene expression profiles. Three OA-related modules (green, turquoise, and yellow) containing 370 genes were identified through WGCNA. It was discovered through the gene-pathway network that GSK3B, CDC42, AKT1, FZD2, and GUSB may be key genes related to the progress of OA and may become promising therapeutic targets.

ARG2, MAP4K5 and TSTA3 as Diagnostic Markers of Steroid-Induced Osteonecrosis of the Femoral Head and Their Correlation With Immune Infiltration

Background

The diagnosis for steroid-induced osteonecrosis of the femoral head (SONFH) is hard to achieve at the early stage, which results in patients receiving ineffective treatment options and a poor prognosis for most cases. The present study aimed to find potential diagnostic markers of SONFH and analyze the effect exerted by infiltration of immune cells in this pathology.

Materials and Methods

R software was adopted for identifying differentially expressed genes (DEGs) and conducting functional investigation based on the microarray dataset. Then we combined SVM-RFE, WGCNA, LASSO logistic regression, and random forest (RF) algorithms for screening the diagnostic markers of SONFH and further verification by qRT-PCR. The diagnostic values were assessed through receiver operating characteristic (ROC) curves. CIBERSORT was then adopted for assessing the infiltration of immune cells and the relationship of infiltration-related immune cells and diagnostic markers.

Results

We identified 383 DEGs overall. This study found ARG2, MAP4K5, and TSTA3 (AUC = 0.980) to be diagnostic markers of SONFH. The results of qRT-PCR showed a statistically significant difference in all markers. Analysis of infiltration of immune cells indicated that neutrophils, activated dendritic cells and memory B cells were likely to show the relationship with SONFH occurrence and progress. Additionally, all diagnostic markers had different degrees of correlation with T cell follicular helper, neutrophils, memory B cells, and activated dendritic cells.

Conclusion

ARG2, MAP4K5, and TSTA3 are potential diagnostic genes for SONFH, and infiltration of immune cells may critically impact SONFH occurrence and progression.

Integrative Identification of Crucial Genes Associated With Plant Hormone-Mediated Bud Dormancy in Prunus mume

Prunus mume is an important ornamental woody plant with winter-flowering property, which is closely related to bud dormancy. Despite recent scientific headway in deciphering the mechanism of bud dormancy in P. mume, the overall picture of gene co-expression regulating P. mume bud dormancy is still unclear. Here a total of 23 modules were screened by weighted gene co-expression network analysis (WGCNA), of which 12 modules were significantly associated with heteroauxin, abscisic acid (ABA), and gibberellin (GA), including GA1, GA3, and GA4. The yellow module, which was positively correlated with the content of ABA and negatively correlated with the content of GA, was composed of 1,426 genes, among which 156 transcription factors (TFs) were annotated with transcriptional regulation function. An enrichment analysis revealed that these genes are related to the dormancy process and plant hormone signal transduction. Interestingly, the expression trends of PmABF2 and PmABF4 genes, the core members of ABA signal transduction, were positively correlated with P. mume bud dormancy. Additionally, the PmSVP gene had attracted lots of attention because of its co-expression, function enrichment, and expression level. PmABF2, PmABF4, and PmSVP were the genes with a high degree of expression in the co-expression network, which was upregulated by ABA treatment. Our results provide insights into the underlying molecular mechanism of plant hormone-regulated dormancy and screen the hub genes involved in bud dormancy in P. mume.

Identification of transcriptomic signatures and crucial pathways involved in non-alcoholic steatohepatitis

PURPOSE

Our study aimed to uncover the crucial genes and functional pathways involved in the development of non-alcoholic steatohepatitis (NASH).

METHODS

Liver transcriptome datasets were integrated with Robust rank aggregation (RRA) method, and transcriptomic signatures for NASH progression and fibrosis severity in NAFLD were developed. The functions of transcriptomic signatures were explored by multiple bioinformatic analyses, and their diagnostic role was also evaluated.

RESULTS

RRA analyses of 12 transcriptome datasets comparing NASH with non-alcoholic fatty liver (NAFL) identified 116 abnormally up-regulated genes in NASH patients. RRA analyses of five transcriptome datasets focusing fibrosis severity identified 78 abnormally up-regulated genes in NAFLD patients with advanced fibrosis. The functions of those transcriptomic signatures of NASH development or fibrosis progression were similar, and were both characterized by extracellular matrix (ECM)-related pathways (Adjusted P < 0.05). The transcriptomic signatures could effectively differentiate NASH from NAFL, and could help to identify NAFLD patients with advanced fibrosis. Gene set enrichment analysis and weighted gene co-expression network analysis further validated the key role of ECM-related pathways in NASH development. The top 10 up-regulated genes in NASH patients were SPP1, FBLN5, CHI3L1, CCL20, CD24, FABP4, GPNMB, VCAN, EFEMP1, and CXCL10, and their functions were mainly related to either ECM-related pathways or immunity-related pathways. Single cell RNA-sequencing analyses revealed that those crucial genes were expressed by distinct cells such as hepatocytes, macrophages, and hepatic stellate cells.

CONCLUSIONS

Transcriptomic signatures related to NASH development and fibrosis severity of NAFLD patients are both characterized by ECM-related pathways, and fibrosis is a main player during NASH progression. This study uncovers some novel key genes involved in NASH progression, which may be promising therapeutic targets.

Excessive daytime sleepiness is associated with altered gene expression in military personnel and veterans with posttraumatic stress disorder: an RNA sequencing study

STUDY OBJECTIVES

Posttraumatic stress disorder (PTSD) is a common condition for military personnel and veterans. PTSD has been shown to impact gene expression, however, to date no study has examined comorbid conditions which may also impact gene expression, for example, excessive daytime sleepiness (EDS). As such, this study sought to examine gene expression using RNA sequencing across three group comparisons of military personnel and veterans: (1) PTSD with EDS (PTSDwEDS) versus PTSD without EDS (PTSDw/outEDS), (2) Controls (no PTSD or EDS) versus PTSDwEDS, and (3) Controls versus PTSDw/outEDS.

METHODS

We performed experimental RNA-seq using Illumina's HiSeq 2500 Sequencing System. We also used Ingenuity Pathway Analysis (IPA), a bioinformatics application, to identify gene pathways and networks which may be disrupted.

RESULTS

There were only two genes that were significantly dysregulated between the Controls and PTSDw/outEDS, therefore IPA analysis was not conducted. However, comparisons revealed that there was significant gene dysregulation between Controls and the PTSDwEDS (251 genes), and the PTSDwEDS versus the PTSDw/outEDS (1,873 genes) groups. Four candidate networks were identified via the IPA software for analysis. Significantly dysregulated genes across the four candidate networks were associated with sleep and circadian function, metabolism, mitochondrial production and function, ubiquitination, and the glutamate system.

CONCLUSIONS

These results suggest that PTSD with concurrent EDS is associated with gene dysregulation. This dysregulation may present additional biological and health consequences for these military personnel and veterans. Further research, to track these gene changes over time and to determine the cause of the EDS reported, is vital.

Gene Expression Differences Between Young Adults Based on Trauma History and Post-traumatic Stress Disorder

Background: The purpose of this study was to identify gene expression differences associated with post-traumatic stress disorder (PTSD) and trauma exposure (TE) in a three-group study design comprised of those with and without trauma exposure and PTSD. Methods: We conducted gene expression and gene network analyses in a sample (n = 45) composed of female subjects of European Ancestry (EA) with PTSD, TE without PTSD, and controls. Results: We identified 283 genes differentially expressed between PTSD-TE groups. In an independent sample of Veterans (n = 78) a small minority of these genes were also differentially expressed. We identified 7 gene network modules significantly associated with PTSD and TE (Bonferroni corrected p ≤ 0.05), which at a false discovery rate (FDR) of q ≤ 0.2, were significantly enriched for biological pathways involved in focal adhesion, neuroactive ligand receptor interaction, and immune related processes among others. Conclusions: This study uses gene network analyses to identify significant gene modules associated with PTSD, TE, and controls. On an individual gene level, we identified a large number of differentially expressed genes between PTSD-TE groups, a minority of which were also differentially expressed in the independent sample. We also demonstrate a lack of network module preservation between PTSD and TE, suggesting that the molecular signature of PTSD and trauma are likely independent of each other. Our results provide a basis for the identification of likely disease pathways and biomarkers involved in the etiology of PTSD.

Recursive ensemble feature selection provides a robust mRNA expression signature for myalgic encephalomyelitis/chronic fatigue syndrome

Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a chronic disorder characterized by disabling fatigue. Several studies have sought to identify diagnostic biomarkers, with varying results. Here, we innovate this process by combining both mRNA expression and DNA methylation data. We performed recursive ensemble feature selection (REFS) on publicly available mRNA expression data in peripheral blood mononuclear cells (PBMCs) of 93 ME/CFS patients and 25 healthy controls, and found a signature of 23 genes capable of distinguishing cases and controls. REFS highly outperformed other methods, with an AUC of 0.92. We validated the results on a different platform (AUC of 0.95) and in DNA methylation data obtained from four public studies on ME/CFS (99 patients and 50 controls), identifying 48 gene-associated CpGs that predicted disease status as well (AUC of 0.97). Finally, ten of the 23 genes could be interpreted in the context of the derailed immune system of ME/CFS.

Transcriptome Sequencing Reveals Potential Roles of ICOS in Primary Sjögren's Syndrome

Primary Sjögren's syndrome (pSS) is a chronic systemic autoimmune disease characterized by exocrine gland damage and extraglandular involvements. To identify potential biomarkers for the early detection of pSS and to further investigate the potential roles of the biomarkers in the progression of pSS, our previous RNA sequencing data and four microarray data of salivary glands (SGs) were combined for integrative transcriptome analysis between pSS and non-pSS. Differential gene expression analysis, gene co-expression network analysis, and pathway analysis were conducted to detect hub genes, which were subsequently investigated in peripheral blood mononuclear cell (PBMC) and plasma. Correlation analysis, single-gene Gene Set Enrichment Analysis, and receiver operating characteristic (ROC) curve were applied to investigate the potential function of the hub genes and their classification capacity for pSS. A total of 51 common up-regulated genes were identified among different pSS cohorts. A key module was found to be the most closely linked to pSS, which was significantly associated with inflammation-related pathways. Seven overlapped hub genes (ICOS, SELL, CR2, BANK1, MS4A1, ZC3H12D, and CCR7) were identified, among which ICOS was demonstrated to be involved in most crucial immune pathways. ICOS was up-regulated not only in SGs but also in PBMC and plasma in pSS, and the expression of ICOS was closely associated with lymphocytic infiltration in SGs and disease activity of pSS patients. It showed a strong classification capacity with classic clinical index in SGs (ROC curve 0.9821) and significant distinct discrimination in PBMC (ROC curve 0.9107). These findings are expected to gain a further insight into the pathogenesis of pSS and provide a promising candidate for the early detection of pSS.

Weighted Gene Co-expression Network Analysis of Key Biomarkers Associated With Bronchopulmonary Dysplasia

Bronchopulmonary dysplasia (BPD) is a complex disorder resulting from interactions between genes and the environment. The accurate molecular etiology of BPD remains largely unclear. This study aimed to identify key BPD-associated genes and pathways functionally enriched using weighted gene co-expression network analysis (WGCNA). We analyzed microarray data of 62 pre-term patients with BPD and 38 pre-term patients without BPD from Gene Expression Omnibus (GEO). WGCNA was used to construct a gene expression network, and genes were classified into definite modules. In addition, the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses of BPD-related hub genes were performed. Firstly, we constructed a weighted gene co-expression network, and genes were divided into 10 modules. Among the modules, the yellow module was related to BPD progression and severity and included the following hub genes: MMP25, MMP9, SIRPA, CKAP4, SLCO4C1, and SLC2A3; and the red module included some co-expression molecules that displayed a continuous decline in expression with BPD progression and included the following hub genes: LEF1, ITK, CD6, RASGRP1, IL7R, SKAP1, CD3E, and ICOS. GO and KEGG analyses showed that high expression of inflammatory response-related genes and low expression of T cell receptor activation-related genes are significantly correlated with BPD progression. The present WGCNA-based study thus provides an overall perspective of BPD and lays the foundation for identifying potential pathways and hub genes that contribute to the development of BPD.

Weighted gene co-expression network analysis identifies RHOH and TRAF1 as key candidate genes for psoriatic arthritis

BACKGROUND

Psoriatic arthritis (PsA) is inflammatory arthritis associated with psoriasis, which involves the axial joint and the distal interphalangeal joints. Its clinical features are varied, often resulting in delayed diagnosis and treatment. Improved knowledge about disease mechanisms will catalyze the rapid development of effective targeted therapies for this disease. The perturbations in the gene co-expression network may not be detected by the differential expression analysis of the microarray. This study aims to identify key modules and hub genes in psoriatic arthritis-applied WGCNA (weighted gene co-expression network analysis) on a microarray.

METHODS

This study downloaded the array data of GSE61281 from the gene expression overview (GEO) database, which includes 20 psoriatic arthritis samples and 12 healthy controls. The analysis was performed with the WGCNA package. Gene ontology (GO) annotation and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed on these key modules. Candidate hub genes were identified using GS and MM measures, Cytoscape, and the online database STRING.

RESULTS

A total of 10 co-expression modules were constructed. The lightcyan module was identified as the key module. GO and KEGG pathway analyses were mainly enriched in dephosphorylation, regulation of small GTPase-mediated signal transduction, Ras signaling pathway, MAPK signaling pathway, and vascular smooth muscle contraction. Two hub genes, RHOH/TRAF1, were selected.

CONCLUSIONS

This finding may indicate that RHOH/TRAF1 play a critical role in the pathogenesis of PsA. This is one of the first studies in PsA using WGCNA, which may provide a new research direction for further understanding of the molecular mechanism and clinical application of PsA. Key points • The WGCNA method was applied to the expression profile microarray of psoriatic arthritis and the co-expression module was constructed. • Identify the key modules by combining the onset time of psoriasis in patients with psoriatic arthritis. • Three screening methods are used to identify and verify hub genes of key modules.

Co-Expression Network Analysis Identified LTF in Association with Metastasis Risk and Prognosis in Clear Cell Renal Cell Carcinoma

Objective

Clear cell renal cell carcinoma (ccRCC) is the most common renal cancer in adults. The 5-year survival rate of patients with advanced ccRCC is less than 30%. Lack of potential biomarkers for treatment and prognosis is a limitation for early diagnosis and treatment of ccRCC.

Methods

We collected microarray profiles of 39 ccRCC and matched normal samples to identify differential expression genes (DEGs). Then, a weighted gene co-expression network analysis (WGCNA) was constructed to identify gene modules associated with the metastasis in ccRCC. The Cancer Genome Atlas (TCGA) database and the Human Protein Atlas (HPA, https://www.proteinatlas.org/) database were used for verification set. Finally, we used biological experiments to preliminary investigate the impact of LTF on the tumor biological behavior of ccRCC, including proliferation, migration, invasion, and apoptosis.

Results

A total of 15 genetic modules were identified, and the light-green module is considered the most relevant to tumor metastasis. (P = 0.02, R² = −0.4). Protein–protein interaction (PPI) network was performed to identify the hub nodes in the light-green module. Finally, combining the results of PPI, WGCNA and DEGs, lactotransferrin (LTF) gene was regarded as “real” hub genes for cancer metastasis risk. LTF was subsequently validated using the TCGA database. Immunohistochemistry confirmed that the expression of LTF in ccRCC tumor tissue was significantly lower than that in normal tissue based on the HPA database. Intriguingly, patients with low expression of LTF had lower survival rates (HR = 0.66, 95% CI: 0.49–0.89, P = 0.0067), the expression level of the sample was negatively correlated with tumor stage (P = 0.0385), and patients with low expression of LTF gene were more likely to have distant metastasis (P = 0.038). Overexpression of LTF inhibited the proliferation, migration, invasion and promoted apoptosis of human ccRCC cells in vitro.

Conclusion

LTF might be a novel prognostic biomarker for ccRCC.

Identification of potential key pathways, genes and circulating markers in the development of intracranial aneurysm based on weighted gene co-expression network analysis

Background: Intracranial aneurysm (IA) is a disease resulted from weak brain control, characterized by local expansion or dilation of brain artery. This study aimed to construct a gene co-expression network by Weighted Gene Correlation Network Analysis (WGCNA) to explore the potential key pathways and genes for the development of IA.Method: Six IA-related gene expression data sets were downloaded from the Gene Expression Omnibus (GEO) database for identifying differentially expressed genes (DEGs). WGCNA was used to identify modules associated with IA. Functional enrichment analysis was used to explore the potential biological functions. ROC analysis was used to find markers for predicting IA.Results: Purple, greenyellow and yellow modules were significantly associated with unruptured intracranial aneurysms, while blue and turquoise modules were significantly associated with ruptured intracranial aneurysms. Functional modules significantly related to IA were enriched in Ribosome, Glutathione metabolism, cAMP signalling pathway, Lysosome, Glycosaminoglycan degradation and other pathways. CD163, FCEREG, FPR1, ITGAM, NLRC4, PDG, and TYROBP were up-regulated ruptured intracranial aneurysms and serum, these genes were potential circulating markers for predicting IA rupture.Conclusions: Potential IA-related key pathways, genes and circulating markers were identified for predicting IA rupture by WGCNA analysis.

Carboxypeptidase A6 was identified and validated as a novel potential biomarker for predicting the occurrence of active ulcerative colitis

Ulcerative colitis (UC) is a chronic, highly heterogeneous intestinal inflammation with changes in epithelial function and tissue damage. However, the pathogenesis is still unclear between active UC and inactive UC. Herein, weighted gene co‐expression network analysis was applied to explore the gene modules related to active UC. Gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA) were used to further investigate the underlying mechanism of selected genes. We found that in the blue module (r = −.72), carboxypeptidase A6 (CPA6) was chosen to validate because of its high intra‐modular connectivity and module membership. In the test sets, the expression level of CPA6 was down‐regulated in active UC compared with inactive UC and normal colon. Furthermore, CPA6 expression was decreased primarily in the descending colon and only in mucosa affected by active UC. The receiver operating characteristic curve indicated that CPA6 expression had a performed well in diagnosing active UC from inactive UC (area under the curve = 0.99). Importantly, anti‐tumour necrosis factor (TNF) treatment (infliximab and golimumab) significantly increased the CPA6 expression. Finally, GSEA and GSVA found that extracellular matrix receptor, inflammatory response and epithelial‐mesenchymal transition were highly enriched in active UC with low CPA6 expression. In conclusion, CPA6 was identified and validated as a novel potential biomarker for predicting the occurrence of active UC, probably through regulating extracellular matrix or immune response.

Identification of PIGU as the Hub Gene Associated with KRAS Mutation in Colorectal Cancer by Coexpression Analysis

Colorectal cancer (CRC) patients with KRAS mutation are refractory and usually have poor prognosis. We aimed to identify the hub gene associated with KRAS mutant CRCs. Weighted gene coexpression network analysis (WGCNA) was used to calculate the key module and the hub genes in GSE39582. Combined with the protein-protein interaction (PPI) network and survival analysis, the real hub gene was identified and further validated. With the highest module significance value and correlation coefficient, the blue module was selected as the key module, 19 genes were identified as the hub gene candidates. The above genes were significantly downregulated in KRAS mutant CRCs compared with the wild type. Four genes (AAR2, PSMA7, NELFCD, and PIGU) were further screened as the potential hub genes by the PPI network. Low expression of PIGU for KRAS mutant patients had a poor prognosis. Therefore, PIGU was identified as the hub gene. PIGU expression was also downregulated in other two CRC datasets. "MAPK SIGNALING PATHWAY" was enriched in PIGU lowly expressed samples. PIGU was identified and validated to be closely related to KRAS mutation. It could be a potential prognosis biomarker and a novel treatment target for KRAS mutant CRC patients.

SET nuclear proto-oncogene gene expression is associated with microsatellite instability in human colorectal cancer identified by co-expression analysis

BACKGROUNDS AND AIMS

Microsatellite instability (MSI) is one of the promising biomarkers in human colorectal cancers (CRCs), and it is influenced by an intricate gene interaction network. Hence, we aimed to identify and validate hub genes associated with MSI CRC and to illustrate its underlying mechanisms.

METHODS

Weighted gene co-expression network analysis (WGCNA) was used to investigate potential regulatory targets and relationships between key modules and hub genes associated with MSI CRC.

RESULTS

In the red module (r = 0.83), SET nuclear proto-oncogene (SET) was selected due to its high intra-modular connectivity and module membership. In the test sets, SET expression was downregulated in MSI CRCs compared to that in microsatellite stability (MSS) CRCs. SET expression level had a good performance in stratifying patients into MSI or MSS CRCs (area under the curve = 0.953). Moreover, the BRAF V600E mutation was highly associated with SET expression, and MSI/HLA- samples showed lower levels of SET mRNA expression than MSS/HLA- samples. Finally, gene set enrichment analysis (GSEA) indicated that patients in the SET low expression group were enriched in base excision repair.

CONCLUSION

SET was identified and validated as a novel potential biomarker in MSI CRCs, and SET probably acts through regulating the base excision repair pathway.

Weighted gene co-expression network analysis identified MYL9 and CNN1 are associated with recurrence in colorectal cancer

Colorectal cancer (CRC) is one of the most common carcinomas and the fourth leading cause of cancer-related death worldwide. One of the obstacles in the successful treatment of CRC is a high rate of recurrence. We aimed to construct weighted gene co-expression network analysis (WGCNA) to identify key modules and hub genes in association with recurrence in CRC patients. We firstly used the microarray data, GSE41258, to construct a co-expression network and identify gene modules. Furthermore, protein and protein interaction (PPI) network was also performed to screen hub genes. To validate the hub genes, an independent dataset GSE17536 was used for survival analyses. Additionally, another two databases were also performed to investigate the survival rates and expression levels of hub genes. Gene set enrichment analyses (GSEA) combined with gene ontology (GO) were performed to further explore function and mechanisms. In our study, the midnightblue module was identified to be significant, 15 hub genes were screened, four of which were identified as hub nodes in the PPI network. In the test dataset, we found higher expression of MYL9 and CNN1 were significantly associated with shorter survival time of CRC patients. GO analyses showed that MYL9 and CNN1 were enriched in “muscle system process” and “cytoskeletal protein binding”. GSEA found the two hub genes were enriched in “pathways in cancer” and “calcium signaling pathway”. In conclusion, our study demonstrated that MYL9 and CNN1 were hub genes associated with the recurrence of CRC, which may contribute to the improvement of recurrence-free survival time of CRC patients.

Comparative Transcriptome Analysis of Different Dendrobium Species Reveals Active Ingredients-Related Genes and Pathways

Dendrobium is widely used in traditional Chinese medicine, which contains many kinds of active ingredients. In recent years, many Dendrobium transcriptomes have been sequenced. Hence, weighted gene co-expression network analysis (WGCNA) was used with the gene expression profiles of active ingredients to identify the modules and genes that may associate with particular species and tissues. Three kinds of Dendrobium species and three tissues were sampled for RNA-seq to generate a high-quality, full-length transcriptome database. Based on significant changes in gene expression, we constructed co-expression networks and revealed 19 gene modules. Among them, four modules with properties correlating to active ingredients regulation and biosynthesis, and several hub genes were selected for further functional investigation. This is the first time the WGCNA method has been used to analyze Dendrobium transcriptome data. Further excavation of the gene module information will help us to further study the role and significance of key genes, key signaling pathways, and regulatory mechanisms between genes on the occurrence and development of medicinal components of Dendrobium.

Identification of gene co-expression modules and hub genes associated with lymph node metastasis of papillary thyroid cancer

Papillary thyroid cancer (PTC) is the most prevalent histological type among thyroid cancers, and some patients are at a high risk for recurrent disease or even death. Identification for the potential biomarkers of PTC may contribute to early discovery of recurrence and treatment. In The Cancer Genome Atlas (TCGA) database, we obtained the information of RNA sequence data and clinical characteristics of PTC. Weighted gene co-expression network analysis (WGCNA) was performed to construct gene co-expression networks and investigate the relationship between modules and clinical traits. Finally, we constructed 16 co-expression modules in 10,428 genes, and three key modules (darkturquoise, lightyellow, and red) associated with tumor N grade were identified. The results of functional annotation indicated that the darkturquoise module was primarily enriched in the regulation of the extracellular matrix (ECM), collagen metabolism, and cell adhesion, the lightyellow module was primarily enriched in the mitochondrial function regulation and energy synthesis, and the red module was primarily enriched in the process of cell junction, apoptosis, and inflammatory response, suggesting their significant role in the progression of PTC. In addition, the hub genes in the three modules were identified and screened for differentially expressed genes (DEGs). Relapse-free survival analyses found that 11 genes (KCNQ3, MET, FN1, ITGA3, RUNX1, ITGA2, PERP, GCSH, FAAH, NGFRAP1, and HSPA5) may play a pivotal role in PTC relapse. In general, our research revealed the key co-expression modules and identified several prognostic biomarkers, which provides some new insights into the lymph node metastasis of PTC.

Identifying genuine protein-protein interactions within communities of gene co-expression networks using a deconvolution method

Direct relationships between biological molecules connected in a gene co-expression network tend to reflect real biological activities such as gene regulation, protein-protein interactions (PPIs), and metabolisation. As correlation-based networks contain numerous indirect connections, those direct relationships are always 'hidden' in them. Compared with the global network, network communities imply more biological significance on predicting protein function, detecting protein complexes and studying network evolution. Therefore, identifying direct relationships in communities is a pervasive and important topic in the biological sciences. Unfortunately, this field has not been well studied. A major thrust of this study is to apply a deconvolution algorithm on communities stemming from different gene co-expression networks, which are constructed by fixing different thresholds for robustness analysis. Using the fifth Dialogue on Reverse Engineering Assessment and Methods challenge (DREAM5) framework, the authors demonstrate that nearly all new communities extracted from a 'deconvolution filter' contain more genuine PPIs than before deconvolution.

Identifying Key Genes and Functionally Enriched Pathways in Sjögren's Syndrome by Weighted Gene Co-Expression Network Analysis

Purpose: Sjögren’s syndrome (SS) is an autoimmune disease characterized by dry mouth and eyes. To date, the exact molecular mechanisms of its etiology are still largely unknown. The aim of this study was to identify SS related key genes and functionally enriched pathways using the weighted gene co-expression network analysis (WGCNA).

Materials and Methods: We downloaded the microarray data of 190 SS patients and 32 controls from Gene Expression Omnibus (GEO). Gene network was constructed and genes were classified into different modules using WGCNA. In addition, for the hub genes in the most related module to SS, gene ontology analysis was applied. The expression profile and diagnostic capacity (ROC curve) of interested hub genes were verified using a dataset from the GEO. Moreover, gene set enrichment analysis (GSEA) was also performed.

Results: A total of 1483 differentially expressed genes were filtered. Weighted gene coexpression network was constructed and genes were classified into 17 modules. Among them, the turquoise module was most closely associated with SS, which contained 278 genes. These genes were significantly enriched in 10 Gene Ontology terms, such as response to virus, immune response, defense response, response to cytokine stimulus, and the inflammatory response. A total of 19 hub genes (GBP1, PARP9, EPSTI1, LOC400759, STAT1, STAT2, IFIH1, EIF2AK2, TDRD7, IFI44, PARP12, FLJ20035, PARP14, ISGF3G, XAF1, RSAD2,LY6E, IFI44L, and DDX58) were identified. The expression levels of the five interested genes including EIF2AK2, GBP1, PARP12, PARP14, and TDRD7 were also confirmed. ROC curve analysis determined that the above five genes’ expression can distinguish SS from controls (the area under the curve is all greater than 0.7). GSEA suggests that the SS samples with highly expressed EIF2AK2 or TDRD7 genes are correlated with inflammatory response, interferon α response, and interferon γ response.

Conclusion: The present study applied WGCNA to generate a holistic view of SS and provide a basis for the identification of potential pathways and hub genes that may be involved in the development of SS.

Leveraging User-Friendly Network Approaches to Extract Knowledge From High-Throughput Omics Datasets

Recent technological advances for the acquisition of multi-omics data have allowed an unprecedented understanding of the complex intricacies of biological systems. In parallel, a myriad of computational analysis techniques and bioinformatics tools have been developed, with many efforts directed towards the creation and interpretation of networks from this data. In this review, we begin by examining key network concepts and terminology. Then, computational tools that allow for their construction and analysis from high-throughput omics datasets are presented. We focus on the study of functional relationships such as co-expression, protein-protein interactions, and regulatory interactions that are particularly amenable to modeling using the framework of networks. We envisage that many potential users of these analytical strategies may not be completely literate in programming languages and code adaptation, and for this reason, emphasis is given to tools' user-friendliness, including plugins for the widely adopted Cytoscape software, an open-source, cross-platform tool for network analysis, visualization, and data integration.

Population and single‑cell transcriptome analyses reveal diverse transcriptional changes associated with radioresistance in esophageal squamous cell carcinoma

Esophageal squamous cell carcinoma (ESCC) is a tumor composed of heterogeneous cells that easily become radioresistant, which leads to tumor recurrence. The most commonly used treatment for ESCC is fractionated irradiation (FIR) therapy that utilizes ionizing radiation to directly induce cytotoxic cell death. However, this treatment may not be able to eliminate all cancer cells due to high adaptive evolution. To determine whether the transcriptome dynamics during ESCC recurrence formation are associated with FIR response, an in vitro cell culture model for ESCC radioresistance that mimics the common radiotherapy process in patients with ESCC was established in the present study. High‑throughput sequencing analysis of in vitro cultured ESCC cells was performed using different cumulative irradiation doses, as well as tumor samples from FIR‑treated patients with ESCC before and after the development of radioresistance. Radioresistance‑associated genes and signaling pathways that were aberrantly expressed in radioresistant ESCC cells were identified, including autophagy‑related 9B (regulation of autophagy), DNA damage‑inducible transcript 4, myoglobin and plasminogen activator tissue type, which are associated with response to hypoxia, Bcl2‑binding component 3, tumor protein P63 and interferon γ‑inducible protein 16, which are associated with DNA damage response. The heterogeneity and dynamic gene expression of ESCC cells during acquired radioresistance were further studied in primary (41 single cells), 12 Gy FIR‑treated (87 single cells) and 30 Gy FIR‑treated (89 single cells) cancer cells using a single‑cell RNA sequencing approach. The results of the present study comprehensively characterized the transcriptome dynamics during acquired radioresistance in an in vitro model of ESCC and patient tumor samples at the population and single cell level. Single‑cell RNA sequencing revealed the heterogeneity of irradiated ESCC cells and an increase in the radioresistant ESCC cell subpopulation during acquired radioresistance. Overall, these results are of potential clinical relevance as they identify a number of signaling molecules associated with radioresistance, as well as opportunities for the development of novel therapeutic options for the treatment of ESCC.

Genome-wide Identification, Classification, Expression and Duplication Analysis of GRAS Family Genes in Juglans regia L

Fifty-two GRAS genes are identified in walnut genome. Based on the evolutionary relationship and motif analysis, the walnut GRAS gene family was divided into eight subfamilies, and the sequence features analysis of JrGRAS proteins showed that the JrGRAS protein sequences were both conserved and altered during the evolutionary process. Gene duplication analysis indicated that seven GRAS genes in walnut have orthologous genes in other species, and five of them occurred duplicated events in walnut genome. Expression pattern analysis of the GRAS family genes in walnut showed that two JrGRAS genes (JrCIGRa-b and JrSCL28a) were differentially expressed between flower bud and leaf bud (p < 0.01), and two JrGRAS genes (JrCIGRa-b and JrSCL13b-d) were differentially expressed between the different development stages of flower buds transition (p < 0.01), besides, three hub genes (JrGAIa, JrSCL3f and JrSHRc) were identified by co-expression analysis, which suggested these GRAS genes may play an important role in regulating the development of apical meristem in walnut. This study laid a foundation for further understanding of the function of GRAS family genes in walnut.

Myalgic Encephalomyelitis/Chronic Fatigue Syndrome: A Comprehensive Review

Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a debilitating chronic disease of unknown aetiology that is recognized by the World Health Organization (WHO) and the United States Center for Disease Control and Prevention (US CDC) as a disorder of the brain. The disease predominantly affects adults, with a peak age of onset of between 20 and 45 years with a female to male ratio of 3:1. Although the clinical features of the disease have been well established within diagnostic criteria, the diagnosis of ME/CFS is still of exclusion, meaning that other medical conditions must be ruled out. The pathophysiological mechanisms are unclear but the neuro-immuno-endocrinological pattern of CFS patients gleaned from various studies indicates that these three pillars may be the key point to understand the complexity of the disease. At the moment, there are no specific pharmacological therapies to treat the disease, but several studies' aims and therapeutic approaches have been described in order to benefit patients' prognosis, symptomatology relief, and the recovery of pre-existing function. This review presents a pathophysiological approach to understanding the essential concepts of ME/CFS, with an emphasis on the population, clinical, and genetic concepts associated with ME/CFS.

Co-Expression Network Analysis Revealed That the ATP5G1 Gene Is Associated With Major Depressive Disorder

Major depressive disorder (MDD) is a leading cause of disability worldwide, although its etiology and mechanism remain unknown. The aim of our study was to identify hub genes associated with MDD and to illustrate the underlying mechanisms. A weighted gene co-expression network analysis (WGCNA) was performed to identify significant gene modules and hub genes associated with MDD in peripheral blood mononuclear cells (PBMCs) (n = 45). In the blue module (R ² = 0.95), five common hub genes in both co-expression network and protein-protein interaction (PPI) network were regarded as "real" hub genes. In another independent dataset, GSE52790, four genes were still significantly down-regulated in PBMCs from MDD patients compared with the controls. Furthermore, these four genes were validated by quantitative real-time polymerase chain reaction (qRT-PCR) in PBMCs from 33 MDD patients and 41 healthy controls. The qRT-PCR analysis showed that ATP synthase membrane subunit c locus 1 (ATP5G1) was significantly down-regulated in samples from MDD patients than in control samples (t = -2.89, p-value = 0.005). Moreover, this gene was significantly differentially expressed between patients and controls in the prefrontal cortex (z = -2.83, p-value = 0.005). Highly significant differentially methylated positions were identified in the Brodmann area 25 (BA25), with probes in the ATP5G1 gene being significantly associated with MDD: cg25495775 (t = 2.82, p-value = 0.008), cg25856120 (t = -2.23, p-value = 0.033), and cg23708347 (t = -2.24, p-value = 0.032). These findings indicate that the ATP5G1 gene is associated with the pathogenesis of MDD and that it could serve as a peripheral biomarker for MDD.

DNA Methylation Module Network-Based Prognosis and Molecular Typing of Cancer

Achieving cancer prognosis and molecular typing is critical for cancer treatment. Previous studies have identified some gene signatures for the prognosis and typing of cancer based on gene expression data. Some studies have shown that DNA methylation is associated with cancer development, progression, and metastasis. In addition, DNA methylation data are more stable than gene expression data in cancer prognosis. Therefore, in this work, we focused on DNA methylation data. Some prior researches have shown that gene modules are more reliable in cancer prognosis than are gene signatures and that gene modules are not isolated. However, few studies have considered cross-talk among the gene modules, which may allow some important gene modules for cancer to be overlooked. Therefore, we constructed a gene co-methylation network based on the DNA methylation data of cancer patients, and detected the gene modules in the co-methylation network. Then, by permutation testing, cross-talk between every two modules was identified; thus, the module network was generated. Next, the core gene modules in the module network of cancer were identified using the K-shell method, and these core gene modules were used as features to study the prognosis and molecular typing of cancer. Our method was applied in three types of cancer (breast invasive carcinoma, skin cutaneous melanoma, and uterine corpus endometrial carcinoma). Based on the core gene modules identified by the constructed DNA methylation module networks, we can distinguish not only the prognosis of cancer patients but also use them for molecular typing of cancer. These results indicated that our method has important application value for the diagnosis of cancer and may reveal potential carcinogenic mechanisms.

Explorative analysis of the gene expression profile during liver regeneration of mouse: a microarray-based study

The liver is an amazing organ due to its powerful regenerative capacity. Although many studies on liver regeneration have been documented, the detailed mechanisms remain unclear. Two-third partial hepatectomy (PH) in rodents plays a crucial role in the study of liver regeneration. In this study, the time series data of gene expression during liver regeneration in mouse were analyzed using the gene set numbered GSE6998 in GEO. A variety of bioinformatics methods, including masigPro, Weighted Gene Co-expression Network Analysis (WGCNA), spatial analysis of functional enrichment (SAFE) and ingenuity canonical pathway analysis (IPA) were used to identify and compare the significantly changed pathways, potential upstream regulators and key genes during liver regeneration. Our study showed that liver regeneration in the mouse is a coordinated process, which cell-cycle-related progress are at the centre of the interaction network involved in liver regeneration. Several candidate upstream regulators including PPARA, NFE2L2, MAD1 and CNR1 and some key genes such as Cdk1, Plk1, Cdc20, Aurka, Racgap1, Cenpa, Rrm1, Rrm2 were identified. In conclusion, these findings could contribute to revealing the molecular mechanism of liver regeneration after PH, which could provide new ideas and treatment methods for regenerative medicine, oncological drug development and oncological treatment.

Stages identifying and transcriptome profiling of the floral transition in Juglans regia

Using paraffin sections, the stages of walnut female flower bud differentiation were divided into the predifferentiation period (F_1), initial differentiation period (F_2) and flower primordium differentiation period (F_3). Leaf buds collected at the same stage as F_2 were designated JRL. Transcriptomic profiling was performed, and a total of 132,154 unigenes were obtained with lengths ranging from 201 bp to 16,831 bp. The analysis of differentially expressed genes (DEGs) showed that there were 597, 784 and 532 DEGs in the three combinations F_1vsF_2, F_1vsF_3, and F_2vsF_3, respectively. The comparison F_2vsJRL showed that 374 DEGs were differentially expressed between female buds and leaf buds. Thirty-one DEGs related to flowering time were further used to construct coexpression networks, and CRY2 and NF-YA were identified as core DEGs in flowering time regulation. Eighteen DEGs related to flowering time were subjected to real-time quantitative analysis. Our work provides a foundation for further research on the walnut floral transition and provides new resources for future research on walnut biology and biotechnology.