Signed weighted gene co-expression network analysis of transcriptional regulation in murine embryonic stem cells

Analysis of key lncRNA related to Parkinson's disease based on gene co-expression weight networks

OBJECTIVES

To identify a key Long chain non-coding RNAs (lncRNAs) related to PD and provide a new perspective on the role of LncRNAs in Parkinson's disease (PD) pathophysiology.

METHODS

Our study involved analyzing gene chips from the substantia nigra and white blood cells, both normal and PD-inclusive, in the Gene Expression Omnibus (GEO) database, utilizing a weighted gene co-expression network analysis (WGCNA). The technique of WGCNA facilitated the examination of differentially expressed genes (DEGs) in the substantia nigra and the white blood cells of individuals with PD. When merged with clinical data, gene modules containing crucial clinical details were chosen for network integration in GO and KEGG enrichment analysis.

RESULTS

A pair of LncRNA modules were identified. The crucial component in GSE7621 was the turquoise module. The DEGs were acquired using GSE133347. GO functions focused on phosphatidylinositol phosphate binding, inflammatory responses, and the regulation of nerves and synapses. KEGG analyses were largely enriched within the P13K-Akt, FaxO, mTOR, Oxytocin, and cGMP-PKG signaling pathways. A Venn diagram revealed that the two key LncRNA were CH17-189H20.1 and RP11-168O16.1.

CONCLUSION

Using the WGCNA method, we obtained PD-related modules, identified biologically significant gene modules, obtained core LncRNAs, and found potential target genes for enrichment analysis. The objective of our research was to advance more detailed and efficient treatment methods for lncRNAs associated with PD.

A twin analysis to estimate genetic and environmental factors contributing to variation in weighted gene co-expression network module eigengenes

Multivariate network-based analytic methods such as weighted gene co-expression network analysis are frequently applied to human and animal gene-expression data to estimate the first principal component of a module, or module eigengene (ME). MEs are interpreted as multivariate summaries of correlated gene-expression patterns and network connectivity across genes within a module. As such, they have the potential to elucidate the mechanisms by which molecular genomic variation contributes to individual differences in complex traits. Although increasingly used to test for associations between modules and complex traits, the genetic and environmental etiology of MEs has not been empirically established. It is unclear if, and to what degree, individual differences in blood-derived MEs reflect random variation versus familial aggregation arising from heritable or shared environmental influences. We used biometrical genetic analyses to estimate the contribution of genetic and environmental influences on MEs derived from blood lymphocytes collected on a sample of N = 661 older male twins from the Vietnam Era Twin Study of Aging (VETSA) whose mean age at assessment was 67.7 years (SD = 2.6 years, range = 62-74 years). Of the 26 detected MEs, 14 (56%) had statistically significant additive genetic variation with an average heritability of 44% (SD = 0.08, range = 35%-64%). Despite the relatively small sample size, this demonstration of significant family aggregation including estimates of heritability in 14 of the 26 MEs suggests that blood-based MEs are reliable and merit further exploration in terms of their associations with complex traits and diseases.

Reassessing the modularity of gene co-expression networks using the Stochastic Block Model

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.

Deconstructing Intratumoral Heterogeneity through Multiomic and Multiscale Analysis of Serial Sections

Tumors may contain billions of cells, including distinct malignant clones and nonmalignant cell types. Clarifying the evolutionary histories, prevalence, and defining molecular features of these cells is essential for improving clinical outcomes, since intratumoral heterogeneity provides fuel for acquired resistance to targeted therapies. Here we present a statistically motivated strategy for deconstructing intratumoral heterogeneity through multiomic and multiscale analysis of serial tumor sections (MOMA). By combining deep sampling of IDH-mutant astrocytomas with integrative analysis of single-nucleotide variants, copy-number variants, and gene expression, we reconstruct and validate the phylogenies, spatial distributions, and transcriptional profiles of distinct malignant clones. By genotyping nuclei analyzed by single-nucleus RNA-seq for truncal mutations, we further show that commonly used algorithms for identifying cancer cells from single-cell transcriptomes may be inaccurate. We also demonstrate that correlating gene expression with tumor purity in bulk samples can reveal optimal markers of malignant cells and use this approach to identify a core set of genes that are consistently expressed by astrocytoma truncal clones, including AKR1C3, whose expression is associated with poor outcomes in several types of cancer. In summary, MOMA provides a robust and flexible strategy for precisely deconstructing intratumoral heterogeneity and clarifying the core molecular properties of distinct cellular populations in solid tumors.

Reassessing the modularity of gene co-expression networks using the Stochastic Block Model

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 RNA-seq 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.

Transcriptome organization of white blood cells through gene co-expression network analysis in a large RNA-seq dataset

Gene co-expression network analysis enables identification of biologically meaningful clusters of co-regulated genes (modules) in an unsupervised manner. We present here the largest study conducted thus far of co-expression networks in white blood cells (WBC) based on RNA-seq data from 624 individuals. We identify 41 modules, 13 of them related to specific immune-related functions and cell types (e.g. neutrophils, B and T cells, NK cells, and plasmacytoid dendritic cells); we highlight biologically relevant lncRNAs for each annotated module of co-expressed genes. We further characterize with unprecedented resolution the modules in T cell sub-types, through the availability of 95 immune phenotypes obtained by flow cytometry in the same individuals. This study provides novel insights into the transcriptional architecture of human leukocytes, showing how network analysis can advance our understanding of coding and non-coding gene interactions in immune system cells.

Deconstructing intratumoral heterogeneity through multiomic and multiscale analysis of serial sections

Tumors may contain billions of cells including distinct malignant clones and nonmalignant cell types. Clarifying the evolutionary histories, prevalence, and defining molecular features of these cells is essential for improving clinical outcomes, since intratumoral heterogeneity provides fuel for acquired resistance to targeted therapies. Here we present a statistically motivated strategy for deconstructing intratumoral heterogeneity through multiomic and multiscale analysis of serial tumor sections (MOMA). By combining deep sampling of IDH-mutant astrocytomas with integrative analysis of single-nucleotide variants, copy-number variants, and gene expression, we reconstruct and validate the phylogenies, spatial distributions, and transcriptional profiles of distinct malignant clones. By genotyping nuclei analyzed by single-nucleus RNA-seq for truncal mutations, we further show that commonly used algorithms for identifying cancer cells from single-cell transcriptomes may be inaccurate. We also demonstrate that correlating gene expression with tumor purity in bulk samples can reveal optimal markers of malignant cells and use this approach to identify a core set of genes that is consistently expressed by astrocytoma truncal clones, including AKR1C3, whose expression is associated with poor outcomes in several types of cancer. In summary, MOMA provides a robust and flexible strategy for precisely deconstructing intratumoral heterogeneity and clarifying the core molecular properties of distinct cellular populations in solid tumors.

Identification of diagnostic biomarkers correlate with immune infiltration in extra-pulmonary tuberculosis by integrating bioinformatics and machine learning

The diagnosis of tuberculosis depends on detecting Mycobacterium tuberculosis (Mtb). Unfortunately, recognizing patients with extrapulmonary tuberculosis (EPTB) remains challenging due to the insidious clinical presentation and poor performance of diagnostic tests. To identify biomarkers for EPTB, the GSE83456 dataset was screened for differentially expressed genes (DEGs), followed by a gene enrichment analysis. One hundred and ten DEGs were obtained, mainly enriched in inflammation and immune -related pathways. Weighted gene co-expression network analysis (WGCNA) was used to identify 10 co-expression modules. The turquoise module, correlating the most highly with EPTB, contained 96 DEGs. Further screening with the least absolute shrinkage and selection operator (LASSO) and support vector machine recursive feature elimination (SVM-RFE) narrowed down the 96 DEGs to five central genes. All five key genes were validated in the GSE144127 dataset. CARD17 and GBP5 had high diagnostic capacity, with AUC values were 0.763 (95% CI: 0.717-0.805) and 0.833 (95% CI: 0.793-0.869) respectively. Using single sample gene enrichment analysis (ssGSEA), we evaluated the infiltration of 28 immune cells in EPTB and explored their relationships with key genes. The results showed 17 immune cell subtypes with significant infiltrations in EPTB. CARD17, GBP5, HOOK1, LOC730167, and HIST1H4C were significantly associated with 16, 14, 12, 6, and 4 immune cell subtypes, respectively. The RT-qPCR results confirmed that the expression levels of GBP5 and CARD17 were higher in EPTB compared to control. In conclusion, CARD17 and GBP5 have high diagnostic efficiency for EPTB and are closely related to immune cell infiltration.

(Epi)genomic adaptation driven by fine geographical scale environmental heterogeneity after recent biological invasions

Elucidating processes and mechanisms involved in rapid local adaptation to varied environments is a poorly understood but crucial component in management of invasive species. Recent studies have proposed that genetic and epigenetic variation could both contribute to ecological adaptation, yet it remains unclear on the interplay between these two components underpinning rapid adaptation in wild animal populations. To assess their respective contributions to local adaptation, we explored epigenomic and genomic responses to environmental heterogeneity in eight recently colonized ascidian (Ciona intestinalis) populations at a relatively fine geographical scale. Based on MethylRADseq data, we detected strong patterns of local environment-driven DNA methylation divergence among populations, significant epigenetic isolation by environment (IBE), and a large number of local environment-associated epigenetic loci. Meanwhile, multiple genetic analyses based on single nucleotide polymorphisms (SNPs) showed genomic footprints of divergent selection. In addition, for five genetically similar populations, we detected significant methylation divergence and local environment-driven methylation patterns, indicating the strong effects of local environments on epigenetic variation. From a functional perspective, a majority of functional genes, Gene Ontology (GO) terms, and biological pathways were largely specific to one of these two types of variation, suggesting partial independence between epigenetic and genetic adaptation. The methylation quantitative trait loci (mQTL) analysis showed that the genetic variation explained only 18.67% of methylation variation, further confirming the autonomous relationship between these two types of variation. Altogether, we highlight the complementary interplay of genetic and epigenetic variation involved in local adaptation, which may jointly promote populations' rapid adaptive capacity and successful invasions in different environments. The findings here provide valuable insights into interactions between invaders and local environments to allow invasive species to rapidly spread, thus contributing to better prediction of invasion success and development of management strategies.

Biomarker detection using corrected degree of domesticity in hybrid social network feature selection for improving classifier performance

BACKGROUND

Dimension reduction, especially feature selection, is an important step in improving classification performance for high-dimensional data. Particularly in cancer research, when reducing the number of features, i.e., genes, it is important to select the most informative features/potential biomarkers that could affect the diagnostic accuracy. Therefore, researchers continuously try to explore more efficient ways to reduce the large number of features/genes to a small but informative subset before the classification task. Hybrid methods have been extensively investigated for this purpose, and research to find the optimal approach is ongoing. Social network analysis is used as a part of a hybrid method, although there are several issues that have arisen when using social network tools, such as using a single environment for computing, constructing an adjacency matrix or computing network measures. Therefore, in our study, we apply a hybrid feature selection method consisting of several machine learning algorithms in addition to social network analysis with our proposed network metric, called the corrected degree of domesticity, in a single environment, R, to improve the support vector machine classifier's performance. In addition, we evaluate and compare the performances of several combinations used in the different steps of the method with a simulation experiment.

RESULTS

The proposed method improves the classifier's performance compared to using the whole feature set in all the cases we investigate. Additionally, in terms of the area under the receiver operating characteristic (ROC) curve, our approach improves classification performance compared to several approaches in the literature.

CONCLUSION

When using the corrected degree of domesticity as a network degree centrality measure, it is important to use our correction to compare nodes/features with no connection outside of their community since it provides a more accurate ranking among the features. Due to the nature of the hybrid method, which includes social network analysis, it is necessary to investigate possible combinations to provide an optimal solution for the microarray data used in the research.

Hub genes and their key effects on prognosis of Burkitt lymphoma

BACKGROUND

Burkitt lymphoma (BL) is an exceptionally aggressive malignant neoplasm that arises from either the germinal center or post-germinal center B cells. Patients with BL often present with rapid tumor growth and require high-intensity multi-drug therapy combined with adequate intrathecal chemotherapy prophylaxis, however, a standard treatment program for BL has not yet been established. It is important to identify biomarkers for predicting the prognosis of BLs and discriminating patients who might benefit from the therapy. Microarray data and sequencing information from public databases could offer opportunities for the discovery of new diagnostic or therapeutic targets.

AIM

To identify hub genes and perform gene ontology (GO) and survival analysis in BL.

METHODS

Gene expression profiles and clinical traits of BL patients were collected from the Gene Expression Omnibus database. Weighted gene co-expression network analysis (WGCNA) was applied to construct gene co-expression modules, and the cytoHubba tool was used to find the hub genes. Then, the hub genes were analyzed using GO and Kyoto Encyclopedia of Genes and Genomes analysis. Additionally, a Protein-Protein Interaction network and a Genetic Interaction network were constructed. Prognostic candidate genes were identified through overall survival analysis. Finally, a nomogram was established to assess the predictive value of hub genes, and drug-gene interactions were also constructed.

RESULTS

In this study, we obtained 8 modules through WGCNA analysis, and there was a significant correlation between the yellow module and age. Then we identified 10 hub genes (SRC, TLR4, CD40, STAT3, SELL, CXCL10, IL2RA, IL10RA, CCR7 and FCGR2B) by cytoHubba tool. Within these hubs, two genes were found to be associated with OS (CXCL10, P = 0.029 and IL2RA, P = 0.0066) by survival analysis. Additionally, we combined these two hub genes and age to build a nomogram. Moreover, the drugs related to IL2RA and CXCL10 might have a potential therapeutic role in relapsed and refractory BL.

CONCLUSION

From WGCNA and survival analysis, we identified CXCL10 and IL2RA that might be prognostic markers for BL.

Identification of four-gene signature to diagnose osteoarthritis through bioinformatics and machine learning methods

BACKGROUND

Although osteoarthritis (OA) is one of the most prevalent joint disorders, effective biomarkers to diagnose OA are still unavailable. This study aimed to acquire some key synovial biomarkers (hub genes) and analyze their correlation with immune infiltration in OA.

METHODS

Gene expression profiles and clinical characteristics of OA and healthy synovial samples were retrieved from the Gene Expression Omnibus (GEO) database. Hub genes for OA were mined based on a combination of weighted gene co-expression network analysis (WGCNA), the least absolute shrinkage and selection operator (LASSO), support vector machine recursive feature elimination (SVM-RFE), and random forest (RF) algorithms. A diagnostic nomogram model for OA prediction was developed based on the hub genes. Receiver operating characteristic curves (ROC) were performed to confirm the abnormal expression of hub genes in the experimemtal and validation datasets. qRT-PCR using patients' samples were conducted as well. In addition, the infiltration level of 28 immune cells in the expression profile and their relationship with hub genes were analyzed using single-sample GSEA (ssGSEA).

RESULTS

4 hub genes (ZBTB16, TNFSF11, SCRG1 and KDELR3) were obtained by WGCNA, lasso, SVM-RFE, RF algorithms as potential biomarkers for OA. The immune infiltration analyses revealed that hub genes were most correlated with regulatory T cell and natural killer cell.

CONCLUSION

A machine learning model to diagnose OA based on ZBTB16, TNFSF11, SCRG1 and KDELR3 using synovial tissue was constructed, providing theoretical foundation and guideline for diagnostic and treatment targets in OA.

Comprehensive analysis to identify a novel diagnostic marker of lung adenocarcinoma and its immune infiltration landscape

Background

Lung cancer continues to be a problem faced by all of humanity. It is the cancer with the highest morbidity and mortality in the world, and the most common histological type of lung cancer is lung adenocarcinoma (LUAD), accounting for about 40% of lung malignant tumors. This study was conducted to discuss and explore the immune-related biomarkers and pathways during the development and progression of LUAD and their relationship with immunocyte infiltration.

Methods

The cohorts of data used in this study were downloaded from the Gene Expression Complex (GEO) database and the Cancer Genome Atlas Program (TCGA) database. Through the analysis of differential expression analysis, weighted gene co-expression network analysis (WGCNA), and least absolute shrinkage and selection operator(LASSO), selecting the module with the highest correlation with LUAD progression, and then the HUB gene was further determined. The Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA) were then used to study the function of these genes. Single-sample GSEA (ssGSEA) analysis was used to investigate the penetration of 28 immunocytes and their relationship with HUB genes. Finally, the receiver operating characteristic curve (ROC) was used to evaluate these HUB genes accurately to diagnose LUAD. In addition, additional cohorts were used for external validation. Based on the TCGA database, the effect of the HUB genes on the prognosis of LUAD patients was assessed using the Kaplan-Meier curve. The mRNA levels of some HUB genes in cancer cells and normal cells were analyzed by reverse transcription-quantitative polymerase chain reaction (RT-qPCR).

Results

The turquoise module with the highest correlation with LUAD was identified among the seven modules obtained with WGCNA. Three hundred fifty-four differential genes were chosen. After LASSO analysis, 12 HUB genes were chosen as candidate biomarkers for LUAD expression. According to the immune infiltration results, CD4 + T cells, B cells, and NK cells were high in LUAD sample tissue. The ROC curve showed that all 12 HUB genes had a high diagnostic value. Finally, the functional enrichment analysis suggested that the HUB gene is mainly related to inflammatory and immune responses. According to the RT-qPCR study, we found that the expression of DPYSL2, OCIAD2, and FABP4 in A549 was higher than BEAS-2B. The expression content of DPYSL2 was lower in H1299 than in BEAS-2B. However, the expression difference of FABP4 and OCIAD2 genes in H1299 lung cancer cells was insignificant, but both showed a trend of increase.

Conclusions

The mechanism of LUAD pathogenesis and progression is closely linked to T cells, B cells, and monocytes. 12 HUB genes(ADAMTS8, CD36, DPYSL2, FABP4, FGFR4, HBA2, OCIAD2, PARP1, PLEKHH2, STX11, TCF21, TNNC1) may participate in the progression of LUAD via immune-related signaling pathways.

Identification of Hub Biomarkers and Immune and Inflammation Pathways Contributing to Kawasaki Disease Progression with RT-qPCR Verification

Background

Kawasaki disease (KD) is characterized by a disordered inflammation response of unknown etiology. Immune cells are closely associated with its onset, although the immune-related genes' expression and possibly involved immune regulatory mechanisms are little known. This study aims to identify KD-implicated significant immune- and inflammation-related biomarkers and pathways and their association with immune cell infiltration. Patients and Methods. Gene microarray data were collected from the Gene Expression Omnibus database. Differential expression analysis, weighted gene coexpression network analysis (WGCNA), least absolute shrinkage and selection operator (LASSO) regression, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and gene set enrichment analysis (GSEA) were used to find KD hub markers. GSEA was used to assess the infiltration by 28 immune cell types and their connections to essential gene markers. Receiver operating characteristic (ROC) curves were used to examine hub markers' diagnostic effectiveness. Finally, hub genes' expressions were validated in Chinese KD patients by reverse transcription-quantitative polymerase chain reaction (RT-qPCR).

Results

One hundred and fifty-one unique genes were found. Among 10 coexpression modules at WGCNA, one hub module exhibited the strongest association with KD. Thirty-six overlapping genes were identified. Six hub genes were potential biomarkers according to LASSO analysis. Immune infiltration revealed connections among activated and effector memory CD4+ T cells, neutrophils, activated dendritic cells, and macrophages. The six hub genes' diagnostic value was shown by ROC curve analysis. Hub genes were enriched in immunological and inflammatory pathways. RT-qPCR verification results of FCGR1B (P < 0.001), GPR84 (P < 0.001), KREMEN1 (P < 0.001), LRG1 (P < 0.001), and TDRD9 (P < 0.001) upregulated expression in Chinese KD patients are consistent with our database analysis.

Conclusion

Neutrophils, macrophages, and activated dendritic cells are strongly linked to KD pathophysiology. Through immune-related signaling pathways, hub genes such as FCGR1B, GPR84, KREMEN1, LRG1, and TDRD9 may be implicated in KD advancement.

Identification of Prognosis-Related Oxidative Stress Model with Immunosuppression in HCC

For hepatocellular carcinoma (HCC) patients, we attempted to establish a new oxidative stress (OS)-related prognostic model for predicting prognosis, exploring immune microenvironment, and predicting the immunotherapy response. Significantly differently expressed oxidative stress-related genes (DEOSGs) between normal and HCC samples from the Cancer Genome Atlas (TCGA) were screened, and then based on weighted gene coexpression network analysis (WGCNA), HCC-related hub genes were discovered. Based on the least absolute shrinkage and selection operator (LASSO) and cox regression analysis, a prognostic model was developed. We validated the prognostic model’s predictive power using an external validation cohort: the International Cancer Genome Consortium (ICGC).Then a nomogram was determined. Furthermore, we also examined the relationship of the risk model and clinical characteristics as well as immune microenvironment. 434 DEOSGs, comprising 62 downregulated and 372 upregulated genes (p < 0.05 and |log2FC| ≥ 1), and 257 HCC-related hub genes were recognized in HCC. Afterward, we built a five-DEOSG (LOX, CYP2C9, EIF2B4, EZH2, and SRXN1) prognostic risk model. Using the nomogram, the risk model was shown to have good prognostic value. Compared to the low risk group, HCC patients with high risk had poorer outcomes, worse pathological grades, and advanced tumor stages (p < 0.05). There were significant increases in LOX, EIF2B4, EZH2, and SRXN1 expression in HCC samples, while CYP2C9 expression was decreased. Finally, Real-time PCR (RT-qPCR) confirmed the mRNA expressions of five genes (CYP2C9, EIF2B4, EZH2, SRXN1, LOX) in HCC cell lines. Our study constructed a prognostic OS-related model with strong predictive power and potential as an immunosuppressive biomarker for HCC leading to improving prediction and providing new insights for HCC immunotherapy.

Weighted gene co-expression network analysis identifies genes related to HG Type 0 resistance and verification of hub gene GmHg1

INTRODUCTION

The soybean cyst nematode (SCN) is a major disease in soybean production thatseriously affects soybean yield. At present, there are no studies on weighted geneco-expression network analysis (WGCNA) related to SCN resistance.

METHODS

Here, transcriptome data from 36 soybean roots under SCN HG Type 0 (race 3) stresswere used in WGCNA to identify significant modules.

RESULTS AND DISCUSSION

A total of 10,000 differentially expressed genes and 21 modules were identified, of which the module most related to SCN was turquoise. In addition, the hub gene GmHg1 with high connectivity was selected, and its function was verified. GmHg1 encodes serine/threonine protein kinase (PK), and the expression of GmHg1 in SCN-resistant cultivars ('Dongnong L-204') and SCN-susceptible cultivars ('Heinong 37') increased significantly after HG Type 0 stress. Soybean plants transformed with GmHg1-OX had significantly increased SCN resistance. In contrast, the GmHg1-RNAi transgenic soybean plants significantly reduced SCN resistance. In transgenic materials, the expression patterns of 11 genes with the same expression trend as the GmHg1 gene in the 'turquoise module' were analyzed. Analysis showed that 11genes were co-expressed with GmHg1, which may be involved in the process of soybean resistance to SCN. Our work provides a new direction for studying the Molecular mechanism of soybean resistance to SCN.

Tissue specific LRRK2 interactomes reveal a distinct striatal functional unit

Mutations in LRRK2 are the most common genetic cause of Parkinson's disease. Despite substantial research efforts, the physiological and pathological role of this multidomain protein remains poorly defined. In this study, we used a systematic approach to construct the general protein-protein interactome around LRRK2, which was then evaluated taking into consideration the differential expression patterns and the co-expression behaviours of the LRRK2 interactors in 15 different healthy tissue types. The LRRK2 interactors exhibited distinct expression features in the brain as compared to the peripheral tissues analysed. Moreover, a high degree of similarity was found for the LRRK2 interactors in putamen, caudate and nucleus accumbens, thus defining a potential LRRK2 functional cluster within the striatum. The general LRRK2 interactome paired with the expression profiles of its members constitutes a powerful tool to generate tissue-specific LRRK2 interactomes. We exemplified the generation of the tissue-specific LRRK2 interactomes and explored the functions highlighted by the "core LRRK2 interactors" in the striatum in comparison with the cerebellum. Finally, we illustrated how the LRRK2 general interactome reported in this manuscript paired with the expression profiles can be used to trace the relationship between LRRK2 and specific interactors of interest, here focusing on the LRRK2 interactors belonging to the Rab protein family.

TF-Prioritizer: a Java pipeline to prioritize condition-specific transcription factors

BACKGROUND

Eukaryotic gene expression is controlled by cis-regulatory elements (CREs), including promoters and enhancers, which are bound by transcription factors (TFs). Differential expression of TFs and their binding affinity at putative CREs determine tissue- and developmental-specific transcriptional activity. Consolidating genomic datasets can offer further insights into the accessibility of CREs, TF activity, and, thus, gene regulation. However, the integration and analysis of multimodal datasets are hampered by considerable technical challenges. While methods for highlighting differential TF activity from combined chromatin state data (e.g., chromatin immunoprecipitation [ChIP], ATAC, or DNase sequencing) and RNA sequencing data exist, they do not offer convenient usability, have limited support for large-scale data processing, and provide only minimal functionality for visually interpreting results.

RESULTS

We developed TF-Prioritizer, an automated pipeline that prioritizes condition-specific TFs from multimodal data and generates an interactive web report. We demonstrated its potential by identifying known TFs along with their target genes, as well as previously unreported TFs active in lactating mouse mammary glands. Additionally, we studied a variety of ENCODE datasets for cell lines K562 and MCF-7, including 12 histone modification ChIP sequencing as well as ATAC and DNase sequencing datasets, where we observe and discuss assay-specific differences.

CONCLUSION

TF-Prioritizer accepts ATAC, DNase, or ChIP sequencing and RNA sequencing data as input and identifies TFs with differential activity, thus offering an understanding of genome-wide gene regulation, potential pathogenesis, and therapeutic targets in biomedical research.

In-depth systems biological evaluation of bovine alveolar macrophages suggests novel insights into molecular mechanisms underlying Mycobacterium bovis infection

Objective

Bovine tuberculosis (bTB) is a chronic respiratory infectious disease of domestic livestock caused by intracellular Mycobacterium bovis infection, which causes ~$3 billion in annual losses to global agriculture. Providing novel tools for bTB managements requires a comprehensive understanding of the molecular regulatory mechanisms underlying the M. bovis infection. Nevertheless, a combination of different bioinformatics and systems biology methods was used in this study in order to clearly understand the molecular regulatory mechanisms of bTB, especially the immunomodulatory mechanisms of M. bovis infection.

Methods

RNA-seq data were retrieved and processed from 78 (39 non-infected control vs. 39 M. bovis-infected samples) bovine alveolar macrophages (bAMs). Next, weighted gene co-expression network analysis (WGCNA) was performed to identify the co-expression modules in non-infected control bAMs as reference set. The WGCNA module preservation approach was then used to identify non-preserved modules between non-infected controls and M. bovis-infected samples (test set). Additionally, functional enrichment analysis was used to investigate the biological behavior of the non-preserved modules and to identify bTB-specific non-preserved modules. Co-expressed hub genes were identified based on module membership (MM) criteria of WGCNA in the non-preserved modules and then integrated with protein-protein interaction (PPI) networks to identify co-expressed hub genes/transcription factors (TFs) with the highest maximal clique centrality (MCC) score (hub-central genes).

Results

As result, WGCNA analysis led to the identification of 21 modules in the non-infected control bAMs (reference set), among which the topological properties of 14 modules were altered in the M. bovis-infected bAMs (test set). Interestingly, 7 of the 14 non-preserved modules were directly related to the molecular mechanisms underlying the host immune response, immunosuppressive mechanisms of M. bovis, and bTB development. Moreover, among the co-expressed hub genes and TFs of the bTB-specific non-preserved modules, 260 genes/TFs had double centrality in both co-expression and PPI networks and played a crucial role in bAMs-M. bovis interactions. Some of these hub-central genes/TFs, including PSMC4, SRC, BCL2L1, VPS11, MDM2, IRF1, CDKN1A, NLRP3, TLR2, MMP9, ZAP70, LCK, TNF, CCL4, MMP1, CTLA4, ITK, IL6, IL1A, IL1B, CCL20, CD3E, NFKB1, EDN1, STAT1, TIMP1, PTGS2, TNFAIP3, BIRC3, MAPK8, VEGFA, VPS18, ICAM1, TBK1, CTSS, IL10, ACAA1, VPS33B, and HIF1A, had potential targets for inducing immunomodulatory mechanisms by M. bovis to evade the host defense response.

Conclusion

The present study provides an in-depth insight into the molecular regulatory mechanisms behind M. bovis infection through biological investigation of the candidate non-preserved modules directly related to bTB development. Furthermore, several hub-central genes/TFs were identified that were significant in determining the fate of M. bovis infection and could be promising targets for developing novel anti-bTB therapies and diagnosis strategies.

SpaceX: gene co-expression network estimation for spatial transcriptomics

MOTIVATION

The analysis of spatially resolved transcriptome enables the understanding of the spatial interactions between the cellular environment and transcriptional regulation. In particular, the characterization of the gene-gene co-expression at distinct spatial locations or cell types in the tissue enables delineation of spatial co-regulatory patterns as opposed to standard differential single gene analyses. To enhance the ability and potential of spatial transcriptomics technologies to drive biological discovery, we develop a statistical framework to detect gene co-expression patterns in a spatially structured tissue consisting of different clusters in the form of cell classes or tissue domains.

RESULTS

We develop SpaceX (spatially dependent gene co-expression network), a Bayesian methodology to identify both shared and cluster-specific co-expression network across genes. SpaceX uses an over-dispersed spatial Poisson model coupled with a high-dimensional factor model which is based on a dimension reduction technique for computational efficiency. We show via simulations, accuracy gains in co-expression network estimation and structure by accounting for (increasing) spatial correlation and appropriate noise distributions. In-depth analysis of two spatial transcriptomics datasets in mouse hypothalamus and human breast cancer using SpaceX, detected multiple hub genes which are related to cognitive abilities for the hypothalamus data and multiple cancer genes (e.g. collagen family) from the tumor region for the breast cancer data.

AVAILABILITY AND IMPLEMENTATION

The SpaceX R-package is available at github.com/bayesrx/SpaceX.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Hematological and gene co-expression network analyses of high-risk beef cattle defines immunological mechanisms and biological complexes involved in bovine respiratory disease and weight gain

Bovine respiratory disease (BRD), the leading disease complex in beef cattle production systems, remains highly elusive regarding diagnostics and disease prediction. Previous research has employed cellular and molecular techniques to describe hematological and gene expression variation that coincides with BRD development. Here, we utilized weighted gene co-expression network analysis (WGCNA) to leverage total gene expression patterns from cattle at arrival and generate hematological and clinical trait associations to describe mechanisms that may predict BRD development. Gene expression counts of previously published RNA-Seq data from 23 cattle (2017; n = 11 Healthy, n = 12 BRD) were used to construct gene co-expression modules and correlation patterns with complete blood count (CBC) and clinical datasets. Modules were further evaluated for cross-populational preservation of expression with RNA-Seq data from 24 cattle in an independent population (2019; n = 12 Healthy, n = 12 BRD). Genes within well-preserved modules were subject to functional enrichment analysis for significant Gene Ontology terms and pathways. Genes which possessed high module membership and association with BRD development, regardless of module preservation (“hub genes”), were utilized for protein-protein physical interaction network and clustering analyses. Five well-preserved modules of co-expressed genes were identified. One module (“steelblue”), involved in alpha-beta T-cell complexes and Th2-type immunity, possessed significant correlation with increased erythrocytes, platelets, and BRD development. One module (“purple”), involved in mitochondrial metabolism and rRNA maturation, possessed significant correlation with increased eosinophils, fecal egg count per gram, and weight gain over time. Fifty-two interacting hub genes, stratified into 11 clusters, may possess transient function involved in BRD development not previously described in literature. This study identifies co-expressed genes and coordinated mechanisms associated with BRD, which necessitates further investigation in BRD-prediction research.

Improved biomarker discovery through a plot twist in transcriptomic data analysis

Background

Transcriptomic analysis is crucial for understanding the functional elements of the genome, with the classic method consisting of screening transcriptomics datasets for differentially expressed genes (DEGs). Additionally, since 2005, weighted gene co-expression network analysis (WGCNA) has emerged as a powerful method to explore relationships between genes. However, an approach combining both methods, i.e., filtering the transcriptome dataset by DEGs or other criteria, followed by WGCNA (DEGs + WGCNA), has become common. This is of concern because such approach can affect the resulting underlying architecture of the network under analysis and lead to wrong conclusions. Here, we explore a plot twist to transcriptome data analysis: applying WGCNA to exploit entire datasets without affecting the topology of the network, followed with the strength and relative simplicity of DEG analysis (WGCNA + DEGs). We tested WGCNA + DEGs against DEGs + WGCNA to publicly available transcriptomics data in one of the most transcriptomically complex tissues and delicate processes: vertebrate gonads undergoing sex differentiation. We further validate the general applicability of our approach through analysis of datasets from three distinct model systems: European sea bass, mouse, and human.

Results

In all cases, WGCNA + DEGs clearly outperformed DEGs + WGCNA. First, the network model fit and node connectivity measures and other network statistics improved. The gene lists filtered by each method were different, the number of modules associated with the trait of interest and key genes retained increased, and GO terms of biological processes provided a more nuanced representation of the biological question under consideration. Lastly, WGCNA + DEGs facilitated biomarker discovery.

Conclusions

We propose that building a co-expression network from an entire dataset, and only thereafter filtering by DEGs, should be the method to use in transcriptomic studies, regardless of biological system, species, or question being considered.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12915-022-01398-w.

Major transcriptomic, epigenetic and metabolic changes underlie the pluripotency continuum in rabbit preimplantation embryos

Despite the growing interest in the rabbit model for developmental and stem cell biology, the characterization of embryos at the molecular level is still poorly documented. We conducted a transcriptome analysis of rabbit preimplantation embryos from E2.7 (morula stage) to E6.6 (early primitive streak stage) using bulk and single-cell RNA-sequencing. In parallel, we studied oxidative phosphorylation and glycolysis, and analysed active and repressive epigenetic modifications during blastocyst formation and expansion. We generated a transcriptomic, epigenetic and metabolic map of the pluripotency continuum in rabbit preimplantation embryos, and identified novel markers of naive pluripotency that might be instrumental for deriving naive pluripotent stem cell lines. Although the rabbit is evolutionarily closer to mice than to primates, we found that the transcriptome of rabbit epiblast cells shares common features with those of humans and non-human primates.

NetREx: Network-based Rice Expression Analysis Server for abiotic stress conditions

Recent focus on transcriptomic studies in food crops like rice, wheat and maize provide new opportunities to address issues related to agriculture and climate change. Re-analysis of such data available in public domain supplemented with annotations across molecular hierarchy can be of immense help to the plant research community, particularly co-expression networks representing transcriptionally coordinated genes that are often part of the same biological process. With this objective, we have developed NetREx, a Network-based Rice Expression Analysis Server, that hosts ranked co-expression networks of Oryza sativa using publicly available messenger RNA sequencing data across uniform experimental conditions. It provides a range of interactable data viewers and modules for analysing user-queried genes across different stress conditions (drought, flood, cold and osmosis) and hormonal treatments (abscisic and jasmonic acid) and tissues (root and shoot). Subnetworks of user-defined genes can be queried in pre-constructed tissue-specific networks, allowing users to view the fold change, module memberships, gene annotations and analysis of their neighbourhood genes and associated pathways. The web server also allows querying of orthologous genes from Arabidopsis, wheat, maize, barley and sorghum. Here, we demonstrate that NetREx can be used to identify novel candidate genes and tissue-specific interactions under stress conditions and can aid in the analysis and understanding of complex phenotypes linked to stress response in rice. Database URL: https://bioinf.iiit.ac.in/netrex/index.html.

Identification of Three Genes Associated with Metastasis in Melanoma and Construction of a Predictive Model: A Multiracial Identification

The aim of this study was to identify hub genes associated with metastasis and prognosis in melanoma. Weighted gene coexpression network analysis (WGCNA) was performed to screen and identify hub genes. ROC and K-M analyses were used to verify the hub genes in the internal and external data sets. The risk score model and nomogram model were constructed based on the IHC result. Through WGCNA, the three hub genes, SNRPD2, SNRPD3, and EIF4A3, were identified. In the external data set, the hub genes identified were associated with the worse prognosis (TCGA, SNRPD2, $P\le 0.02$ ; SNRPD3, $P=0.12$ ; EIF4A3, $P=0.11$ ; GSE65904, SNRPD2, $P=0.04$ ; SNRPD3, $P=0.10$ ; EIF4A3, $P<0.01$ ; GSE19234, SNRPD2, $P<0.01$ ; SNRPD3, $P<0.01$ ; EIF4A3, $P<0.01$ ). In the GSE8401, we found that the hub genes were highly expressed in the metastasis compared with the nonmetastasis group (SNRPD2, $988.5\pm 47.83$ vs. $738.4\pm 35.35$ , $P<0.01$ ; SNRPD3, $502.7\pm 25.7$ vs. $416.4\pm 23.88$ , $P=0.02$ ; EIF4A3, $567.6\pm 19.56$ vs. $495.2\pm 21.1$ , $P=0.01$ ). Moreover, the hub genes were identified by the IHC in our data set. The result was similar with the external data set. The hub genes could predict the metastasis and prognosis in the Chinese MM patients. Finally, the GSEA and Pearson analysis demonstrated that the SNRPD2 was associated with the immunotherapy. The three hub genes were identified and validated in MM patients in external and internal data sets. The risk factor model was constructed and verified as a powerful model to predict metastasis and prognosis in MM patients.

High transcriptome plasticity drives phosphate starvation responses in tomato

Tomato is an important vegetable crop and fluctuating available soil phosphate (Pi) level elicits several morpho-physiological responses driven by underlying molecular responses. Therefore, understanding these molecular responses at the gene and isoform levels has become critical in the quest for developing crops with improved Pi use efficiency. A quantitative time-series RNA-seq analysis was performed to decipher the global transcriptomic changes that accompany Pi starvation in tomato. Apart from changes in the expression levels of genes, there were also alterations in the expression of alternatively-spliced transcripts. Physiological responses such as anthocyanin accumulation, reactive oxygen species generation and cell death are obvious 7 days after Pi deprivation accompanied with the maximum amount of transcriptional change in the genome making it an important stage for in-depth study while studying Pi stress responses (PSR). Our study demonstrates that transcriptomic changes under Pi deficiency are dynamic and complex in tomato. Overall, our study dwells on the dynamism of the transcriptome in eliciting a response to adapt to low Pi stress and lays it bare. Findings from this study will prove to be an invaluable resource for researchers using tomato as a model for understanding nutrient deficiency.

Identification of Hub Biomarkers and Immune-Related Pathways Participating in the Progression of Antineutrophil Cytoplasmic Antibody-Associated Glomerulonephritis

Background

Antineutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV) is a systemic autoimmune disease that generally induces the progression of rapidly progressive glomerulonephritis (GN). The purpose of this study was to identify key biomarkers and immune-related pathways involved in the progression of ANCA-associated GN (ANCA-GN) and their relationship with immune cell infiltration.

Methods

Gene microarray data were downloaded from the Gene Expression Omnibus (GEO). Hub markers for ANCA-GN were mined based on differential expression analysis, weighted gene co-expression network analysis (WGCNA) and lasso regression, followed by Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Set Enrichment Analysis (GSEA) of the differential genes. The infiltration levels of 28 immune cells in the expression profile and their relationship to hub gene markers were analysed using single-sample GSEA (ssGSEA). In addition, the accuracy of the hub markers in diagnosing ANCA-GN was subsequently evaluated using the receiver operating characteristic curve (ROC).

Results

A total of 651 differential genes were screened. Twelve co-expression modules were obtained via WGCNA; of which, one hub module (black module) had the highest correlation with ANCA-GN. A total of 66 intersecting genes were acquired by combining differential genes. Five hub genes were subsequently obtained by lasso analysis as potential biomarkers for ANCA-GN. The immune infiltration results revealed the most significant relationship among monocytes, CD4⁺ T cells and CD8⁺ T cells. ROC curve analysis demonstrated a prime diagnostic value of the five hub genes. According to the functional enrichment analysis of the differential genes, hub genes were mainly enhanced in immune- and inflammation-related pathways.

Conclusion

B cells and monocytes were closely associated with the pathogenesis of ANCA-GN. Hub genes (CYP3A5, SLC12A3, BGN, TAPBP and TMEM184B) may be involved in the progression of ANCA-GN through immune-related signal pathways.

Analyzing the Interaction of RBPJ with Mitotic Chromatin and Its Impact on Transcription Reactivation upon Mitotic Exit

The sequence-specific transcription factor RBPJ, also known as CSL (CBF1, Su(H), Lag1), is an evolutionarily conserved protein that mediates Notch signaling to guide cell fates. When cells enter mitosis, DNA is condensed and most transcription factors dissociate from chromatin; however, a few, select transcription factors, termed bookmarking factors, remain associated. These mitotic chromatin-bound factors are believed to play important roles in maintaining cell fates through cell division. RBPJ is one such factor that remains mitotic chromatin associated and therefore could function as a bookmarking factor. Here, we describe how to obtain highly purified mitotic cells from the mouse embryonal carcinoma cell line F9, perform chromatin immunoprecipitation with mitotic cells, and measure the first run of RNA synthesis upon mitotic exit. These methods serve as basis to understand the roles of mitotic bookmarking by RBPJ in propagating Notch signals through cell division.

A joint transcriptional regulatory network and protein activity inference analysis identifies clinically associated master regulators for biliary atresia

Biliary atresia (BA) is a devastating cholangiopathy in neonate. Transcription factors (TFs), a type of master regulators in biological processes and diseases, have been implicated in pathogenesis of BA. However, a global view of TFs and how they link to clinical presentations remain explored. Here, we perform a joint transcriptional regulatory network and protein activity inference analysis in order to investigate transcription factor activity in BA. By integration of three independent human BA liver transcriptome datasets, we identify 22 common master regulators, with 14 activated- and 8 repressed TFs. Gene targets of activated TFs are enriched in biological processes of SMAD, NF-kappaB and TGF-beta, while those of repressed TFs are related to lipid metabolism. Mining the clinical association of TFs, we identify inflammation-, fibrosis- and survival associated TFs. In particular, ZNF14 is predictive of poor survival and advanced live fibrosis. Supporting this observation, ZNF14 is positively correlated with T helper cells, cholangiocytes and hepatic stellate cells. In sum, our analysis reveals key clinically associated master regulators for BA.

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.

Differential Co-Expression Network Analysis Reveals Key Hub-High Traffic Genes as Potential Therapeutic Targets for COVID-19 Pandemic

Background

The recent emergence of COVID-19, rapid worldwide spread, and incomplete knowledge of molecular mechanisms underlying SARS-CoV-2 infection have limited development of therapeutic strategies. Our objective was to systematically investigate molecular regulatory mechanisms of COVID-19, using a combination of high throughput RNA-sequencing-based transcriptomics and systems biology approaches.

Methods

RNA-Seq data from peripheral blood mononuclear cells (PBMCs) of healthy persons, mild and severe 17 COVID-19 patients were analyzed to generate a gene expression matrix. Weighted gene co-expression network analysis (WGCNA) was used to identify co-expression modules in healthy samples as a reference set. For differential co-expression network analysis, module preservation and module-trait relationships approaches were used to identify key modules. Then, protein-protein interaction (PPI) networks, based on co-expressed hub genes, were constructed to identify hub genes/TFs with the highest information transfer (hub-high traffic genes) within candidate modules.

Results

Based on differential co-expression network analysis, connectivity patterns and network density, 72% (15 of 21) of modules identified in healthy samples were altered by SARS-CoV-2 infection. Therefore, SARS-CoV-2 caused systemic perturbations in host biological gene networks. In functional enrichment analysis, among 15 non-preserved modules and two significant highly-correlated modules (identified by MTRs), 9 modules were directly related to the host immune response and COVID-19 immunopathogenesis. Intriguingly, systemic investigation of SARS-CoV-2 infection identified signaling pathways and key genes/proteins associated with COVID-19's main hallmarks, e.g., cytokine storm, respiratory distress syndrome (ARDS), acute lung injury (ALI), lymphopenia, coagulation disorders, thrombosis, and pregnancy complications, as well as comorbidities associated with COVID-19, e.g., asthma, diabetic complications, cardiovascular diseases (CVDs), liver disorders and acute kidney injury (AKI). Topological analysis with betweenness centrality (BC) identified 290 hub-high traffic genes, central in both co-expression and PPI networks. We also identified several transcriptional regulatory factors, including NFKB1, HIF1A, AHR, and TP53, with important immunoregulatory roles in SARS-CoV-2 infection. Moreover, several hub-high traffic genes, including IL6, IL1B, IL10, TNF, SOCS1, SOCS3, ICAM1, PTEN, RHOA, GDI2, SUMO1, CASP1, IRAK3, HSPA5, ADRB2, PRF1, GZMB, OASL, CCL5, HSP90AA1, HSPD1, IFNG, MAPK1, RAB5A, and TNFRSF1A had the highest rates of information transfer in 9 candidate modules and central roles in COVID-19 immunopathogenesis.

Conclusion

This study provides comprehensive information on molecular mechanisms of SARS-CoV-2-host interactions and identifies several hub-high traffic genes as promising therapeutic targets for the COVID-19 pandemic.

Transcriptomic adaptation during skeletal muscle habituation to eccentric or concentric exercise training

Eccentric (ECC) and concentric (CON) contractions induce distinct muscle remodelling patterns that manifest early during exercise training, the causes of which remain unclear. We examined molecular signatures of early contraction mode-specific muscle adaptation via transcriptome-wide network and secretome analyses during 2 weeks of ECC- versus CON-specific (downhill versus uphill running) exercise training (exercise 'habituation'). Despite habituation attenuating total numbers of exercise-induced genes, functional gene-level profiles of untrained ECC or CON were largely unaltered post-habituation. Network analysis revealed 11 ECC-specific modules, including upregulated extracellular matrix and immune profiles plus downregulated mitochondrial pathways following untrained ECC. Of 3 CON-unique modules, 2 were ribosome-related and downregulated post-habituation. Across training, 376 ECC-specific and 110 CON-specific hub genes were identified, plus 45 predicted transcription factors. Secreted factors were enriched in 3 ECC- and/or CON-responsive modules, with all 3 also being under the predicted transcriptional control of SP1 and KLF4. Of 34 candidate myokine hubs, 1 was also predicted to have elevated expression in skeletal muscle versus other tissues: THBS4, of a secretome-enriched module upregulated after untrained ECC. In conclusion, distinct untrained ECC and CON transcriptional responses are dampened after habituation without substantially shifting molecular functional profiles, providing new mechanistic candidates into contraction-mode specific muscle regulation.

POU class 2 homeobox associating factor 1 (POU2AF1) participates in abdominal aortic aneurysm enlargement based on integrated bioinformatics analysis

Abdominal aortic aneurysm (AAA) is life-threatening, its natural course is progressively sac expansion and rupture. Elegant studies have been conducted to investigate the molecular markers associated with AAA growth and expansion, this topic however, still needs to be further elucidated. This study aimed to identify potential genes for AAA growth and expansion based on comprehensive bioinformatics approaches. Firstly, 29 up-regulated genes were identified through DEGs analysis between large AAA and small AAA in GSE57691. Secondly, signed WGCNA analysis was conducted based on GSE57691 and the green module was found to exhibit the topmost correlation with large AAA as well as AAA, 133 WGCNA hub genes were further identified. Merged gene set including 29 up-regulated DEGs and 858 green module genes was subjected to constructing a PPI network where 195 PPI hub genes were identified. Subsequently, 4 crucial genes including POU2AF1, FCRLA, CD79B, HLA-DOB were recognized by Venn plot. In addition, by using GSE7084 and GSE98278 for verification, POU2AF1 showed potential diagnostic value between AAA and normal groups, and exhibited a significant higher expression level in large AAA samples compared with small AAA samples. Furthermore, immunohistochemistry results indicated up-regulation of POU2AF1 in large AAA samples than small AAA samples, which implies POU2AF1 may be a key regulator in AAA enlargement and growth. In summary, this study indicates that POU2AF1 has great predictive value for the expansion of AAA, and may contribute to the further exploration of pathogenesis and progression of AAA.

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.

Gene correlation network analysis to identify regulatory factors in sciatic nerve injury

BACKGROUND

Sciatic nerve injury (SNI), which frequently occurs under the traumatic hip and hip fracture dislocation, induces serious complications such as motor and sensory loss, muscle atrophy, or even disabling. The present work aimed to determine the regulating factors and gene network related to the SNI pathology.

METHODS

Sciatic nerve injury dataset GSE18803 with 24 samples was divided into adult group and neonate group. Weighted gene co-expression network analysis (WGCNA) was carried out to identify modules associated with SNI in the two groups. Moreover, differentially expressed genes (DEGs) were determined from every group, separately. Subsequently, co-expression network and protein-protein interaction (PPI) network were overlapped to identify hub genes, while functional enrichment and Reactome analysis were used for a comprehensive analysis of potential pathways. GSE30165 was used as the test set for investigating the hub gene involvement within SNI. Gene set enrichment analysis (GSEA) was performed separately using difference between samples and gene expression level as phenotype label to further prove SNI-related signaling pathways. In addition, immune infiltration analysis was accomplished by CIBERSORT. Finally, Drug-Gene Interaction database (DGIdb) was employed for predicting the possible therapeutic agents.

RESULTS

14 SNI status modules and 97 DEGs were identified in adult group, while 15 modules and 21 DEGs in neonate group. A total of 12 hub genes was overlapping from co-expression and PPI network. After the results from both test and training sets were overlapped, we verified that the ten real hub genes showed remarkably up-regulation within SNI. According to functional enrichment of hub genes, the above genes participated in the immune effector process, inflammatory responses, the antigen processing and presentation, and the phagocytosis. GSEA also supported that gene sets with the highest significance were mostly related to the cytokine-cytokine receptor interaction. Analysis of hub genes possible related signaling pathways using gene expression level as phenotype label revealed an enrichment involved in Lysosome, Chemokine signaling pathway, and Neurotrophin signaling pathway. Immune infiltration analysis showed that Macrophages M2 and Regulatory T cells may participate in the development of SNI. At last, 25 drugs were screened from DGIdb to improve SNI treatment.

CONCLUSIONS

The gene expression network is determined in the present work based on the related regulating factors within SNI, which sheds more light on SNI pathology and offers the possible biomarkers and therapeutic targets in subsequent research.

Integrated Network Analysis to Identify Key Modules and Potential Hub Genes Involved in Bovine Respiratory Disease: A Systems Biology Approach

Background: Bovine respiratory disease (BRD) is the most common disease in the beef and dairy cattle industry. BRD is a multifactorial disease resulting from the interaction between environmental stressors and infectious agents. However, the molecular mechanisms underlying BRD are not fully understood yet. Therefore, this study aimed to use a systems biology approach to systematically evaluate this disorder to better understand the molecular mechanisms responsible for BRD. Methods: Previously published RNA-seq data from whole blood of 18 healthy and 25 BRD samples were downloaded from the Gene Expression Omnibus (GEO) and then analyzed. Next, two distinct methods of weighted gene coexpression network analysis (WGCNA), i.e., module-trait relationships (MTRs) and module preservation (MP) analysis were used to identify significant highly correlated modules with clinical traits of BRD and non-preserved modules between healthy and BRD samples, respectively. After identifying respective modules by the two mentioned methods of WGCNA, functional enrichment analysis was performed to extract the modules that are biologically related to BRD. Gene coexpression networks based on the hub genes from the candidate modules were then integrated with protein-protein interaction (PPI) networks to identify hub-hub genes and potential transcription factors (TFs). Results: Four significant highly correlated modules with clinical traits of BRD as well as 29 non-preserved modules were identified by MTRs and MP methods, respectively. Among them, two significant highly correlated modules (identified by MTRs) and six nonpreserved modules (identified by MP) were biologically associated with immune response, pulmonary inflammation, and pathogenesis of BRD. After aggregation of gene coexpression networks based on the hub genes with PPI networks, a total of 307 hub-hub genes were identified in the eight candidate modules. Interestingly, most of these hub-hub genes were reported to play an important role in the immune response and BRD pathogenesis. Among the eight candidate modules, the turquoise (identified by MTRs) and purple (identified by MP) modules were highly biologically enriched in BRD. Moreover, STAT1, STAT2, STAT3, IRF7, and IRF9 TFs were suggested to play an important role in the immune system during BRD by regulating the coexpressed genes of these modules. Additionally, a gene set containing several hub-hub genes was identified in the eight candidate modules, such as TLR2, TLR4, IL10, SOCS3, GZMB, ANXA1, ANXA5, PTEN, SGK1, IFI6, ISG15, MX1, MX2, OAS2, IFIH1, DDX58, DHX58, RSAD2, IFI44, IFI44L, EIF2AK2, ISG20, IFIT5, IFITM3, OAS1Y, HERC5, and PRF1, which are potentially critical during infection with agents of bovine respiratory disease complex (BRDC). Conclusion: This study not only helps us to better understand the molecular mechanisms responsible for BRD but also suggested eight candidate modules along with several promising hub-hub genes as diagnosis biomarkers and therapeutic targets for BRD.

Comparative analysis of hippocampal transcriptional features between major depressive disorder patients and animal models

BACKGROUND

Major depressive disorder (MDD) is a psychiatric disorder caused by various etiologies. Chronic stress models are used to simulate the heterogeneous pathogenic processes of depression. However, few studies have compared transcriptional features between stress models and MDD patients.

METHODS

We generated hippocampal transcriptional profiles of the chronic social defeat model by RNA sequencing and downloaded raw data of the same brain region from public databases of the chronic unpredictable mild stress model, the learned helplessness model, and MDD patients. Differential expression and gene co-expression analyses were integrated to compare transcriptional features between stress models and MDD patients.

RESULTS

Each stress model shared 11.4% to 16.3% of differentially expressed genes with MDD patients. Functional analysis at the gene expression level identified altered ensheathment of neurons in both stress models and MDD patients. At the gene network level, each stress model shared 20.9% to 41.6% of co-expressed genes with MDD patients. Functional analysis based on these genes found that axon guidance signaling is the most significantly enriched pathway that was shared by all stress models and MDD patients.

LIMITATIONS

This study was limited by considering only a single brain region and a single sex of stress model animals.

CONCLUSIONS

Our results show that hippocampal transcriptional features of stress models partially overlap with those of MDD patients. The canonical pathways of MDD patients, including ensheathment of neurons, PTEN signaling, and axonal guidance signaling, were shared with all stress models. Our findings provide further clues to understand the molecular mechanisms of depression.

Establishment of an Immune-Related Gene Signature for Risk Stratification for Patients with Glioma

Glioma is a frequently seen primary malignant intracranial tumor, characterized by poor prognosis. The study is aimed at constructing a prognostic model for risk stratification in patients suffering from glioma. Weighted gene coexpression network analysis (WGCNA), integrated transcriptome analysis, and combining immune-related genes (IRGs) were used to identify core differentially expressed IRGs (DE IRGs). Subsequently, univariate and multivariate Cox regression analyses were utilized to establish an immune-related risk score (IRRS) model for risk stratification for glioma patients. Furthermore, a nomogram was developed for predicting glioma patients' overall survival (OS). The turquoise module (cor = 0.67; P < 0.001) and its genes (n = 1092) were significantly pertinent to glioma progression. Ultimately, multivariate Cox regression analysis constructed an IRRS model based on VEGFA, SOCS3, SPP1, and TGFB2 core DE IRGs, with a C-index of 0.811 (95% CI: 0.786-0.836). Then, Kaplan-Meier (KM) survival curves revealed that patients presenting high risk had a dismal outcome (P < 0.0001). Also, this IRRS model was found to be an independent prognostic indicator of gliomas' survival prediction, with HR of 1.89 (95% CI: 1.252-2.85) and 2.17 (95% CI: 1.493-3.14) in the Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) datasets, respectively. We established the IRRS prognostic model, capable of effectively stratifying glioma population, convenient for decision-making in clinical practice.

Hypermethylation and Downregulation of UTP6 Are Associated With Stemness Properties, Chemoradiotherapy Resistance, and Prognosis in Rectal Cancer: A Co-expression Network Analysis

Background

To identify the hub genes associated with chemoradiotherapy resistance in rectal cancer and explore the potential mechanism.

Methods

Weighted gene co-expression network analysis (WGCNA) was performed to identify the gene modules correlated with the chemoradiotherapy resistance of rectal cancer.

Results

The mRNA expression of 31 rectal cancer patients receiving preoperative chemoradiotherapy was described in our previous study. Through WGCNA, we demonstrated that the chemoradiotherapy resistance modules were enriched for translation, DNA replication, and the androgen receptor signaling pathway. Additionally, we identified and validated UTP6 as a new effective predictor for chemoradiotherapy sensitivity and a prognostic factor for the survival of colorectal cancer patients using our data and the GSE35452 dataset. Low UTP6 expression was correlated with significantly worse disease-free survival (DFS), overall survival (OS), and event- and relapse-free survival both in our data and the R2 Platform. Moreover, we verified the UTP6 expression in 125 locally advanced rectal cancer (LARC) patients samples by immunohistochemical analysis. The results demonstrated that low UTP6 expression was associated with worse DFS and OS by Kaplan-Meier and COX regression model analyses. Gene set enrichment and co-expression analyses showed that the mechanism of the UTP6-mediated chemoradiotherapy resistance may involve the regulation of FOXK2 expression by transcription factor pathways.

Conclusion

Low expression of the UTP6 was found to be associated with chemoradiotherapy resistance and the prognosis of colorectal cancer possibly via regulating FOXK2 expression by transcription factor pathways.

Transcriptomic links to muscle mass loss and declines in cumulative muscle protein synthesis during short-term disuse in healthy younger humans

Muscle disuse leads to a rapid decline in muscle mass, with reduced muscle protein synthesis (MPS) considered the primary physiological mechanism. Here, we employed a systems biology approach to uncover molecular networks and key molecular candidates that quantitatively link to the degree of muscle atrophy and/or extent of decline in MPS during short-term disuse in humans. After consuming a bolus dose of deuterium oxide (D₂ O; 3 mL.kg^-1 ), eight healthy males (22 ± 2 years) underwent 4 days of unilateral lower-limb immobilization. Bilateral muscle biopsies were obtained post-intervention for RNA sequencing and D₂ O-derived measurement of MPS, with thigh lean mass quantified using dual-energy X-ray absorptiometry. Application of weighted gene co-expression network analysis identified 15 distinct gene clusters ("modules") with an expression profile regulated by disuse and/or quantitatively connected to disuse-induced muscle mass or MPS changes. Module scans for candidate targets established an experimentally tractable set of candidate regulatory molecules (242 hub genes, 31 transcriptional regulators) associated with disuse-induced maladaptation, many themselves potently tied to disuse-induced reductions in muscle mass and/or MPS and, therefore, strong physiologically relevant candidates. Notably, we implicate a putative role for muscle protein breakdown-related molecular networks in impairing MPS during short-term disuse, and further establish DEPTOR (a potent mTOR inhibitor) as a critical mechanistic candidate of disuse driven MPS suppression in humans. Overall, these findings offer a strong benchmark for accelerating mechanistic understanding of short-term muscle disuse atrophy that may help expedite development of therapeutic interventions.

PoLoBag: Polynomial Lasso Bagging for signed gene regulatory network inference from expression data

MOTIVATION

Inferring gene regulatory networks (GRNs) from expression data is a significant systems biology problem. A useful inference algorithm should not only unveil the global structure of the regulatory mechanisms but also the details of regulatory interactions such as edge direction (from regulator to target) and sign (activation/inhibition). Many popular GRN inference algorithms cannot infer edge signs, and those that can infer signed GRNs cannot simultaneously infer edge directions or network cycles.

RESULTS

To address these limitations of existing algorithms, we propose Polynomial Lasso Bagging (PoLoBag) for signed GRN inference with both edge directions and network cycles. PoLoBag is an ensemble regression algorithm in a bagging framework where Lasso weights estimated on bootstrap samples are averaged. These bootstrap samples incorporate polynomial features to capture higher-order interactions. Results demonstrate that PoLoBag is consistently more accurate for signed inference than state-of-the-art algorithms on simulated and real-world expression datasets.

AVAILABILITY AND IMPLEMENTATION

Algorithm and data are freely available at https://github.com/gourabghoshroy/PoLoBag.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Weighted gene co-expression network analysis identifies genes related to anthocyanin biosynthesis and functional verification of hub gene SmWRKY44

Eggplant is rich in anthocyanins, which are thought to be highly beneficial for human health. There is no study on weighted gene co-expression network analysis (WGCNA) of anthocyanin biosynthesis in eggplant. Here, transcriptome data of 33 eggplant pericarp samples treated with light were used for WGCNA to identify significant modules. Total 13000 DEGs and 12 modules were identified, and the most significant module was associated with the secondary metabolites pathways. In addition, the hub gene SmWRKY44 with high connectivity was selected and its function was verified. The expression of SmWRKY44 showed a significant correlation with anthocyanin accumulation in the eggplant peels, leaves, and flowers. SmWRKY44-OE Arabidopsis significantly increased the accumulation of anthocyanins. Yeast two-hybrid and BiFC assays showed that SmWRKY44 could interact with SmMYB1, and it was also found that they could jointly promote the biosynthesis of anthocyanins in eggplant leaves through transient expression analysis. Our work provides a new direction for studying the molecular mechanism of light-induced anthocyanin biosynthesis in eggplant.

The Value of Immune-Related Genes Signature in Osteosarcoma Based on Weighted Gene Co-expression Network Analysis

Background. Osteosarcoma (OS) is a serious malignant tumor that is more common in adolescents or children under 20 years of age. This study is aimed at obtaining immune-related genes (IRGs) associated with the progression and prognosis of OS. Method. Expression profiling data and clinical data for OS were downloaded from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database. ESTIMATE calculates immune scores and stromal scores of samples and performs the prognostic analysis. Weighted gene coexpression network analysis (WGCNA) was used to find modules correlated with immune and stromal scores. Cox regression analysis and least absolute shrinkage and selection operator (LASSO) analysis were used to explore IRGs associated with OS prognosis and construct and validate a hazard score model. Finally, we verified the expression and function of EVI2B in OS. Results. WGCNA selected twenty-eight IRGs, 10 of which were associated with OS prognosis, and LASSO further obtained three key prognostic genes. A prognostic model of EVI2B was constructed, and according to the risk score model, patients in the high-risk group had a worse prognosis than those in the low-risk group, and the prognosis was statistically significant in the high- and low-risk groups. Receiver operating characteristic (ROC) curves were used to assess the prognostic model’s accuracy and externally validate the independent GSE21257 cohort. The results of immunohistochemical staining and qPCR showed that EVI2B was a tumor suppressor gene. The differential genes in the high- and low-risk groups were analyzed by enrichment analysis of GO and KEGG, indicating that the EVI2B model is associated with immune response. Conclusion. In this study, IRG EVI2B is closely related to OS’s prognosis and can be used as a potential biomarker for prognosis and treatment of OS.

MRPL15 is a novel prognostic biomarker and therapeutic target for epithelial ovarian cancer

PURPOSE

To analyze the role of six human epididymis protein 4 (HE4)-related mitochondrial ribosomal proteins (MRPs) in ovarian cancer and selected MRPL15, which is most closely related to the tumorigenesis and prognosis of ovarian cancer, for further analyses.

METHODS

Using STRING database and MCODE plugin in Cytoscape, six MRPs were identified among genes that are upregulated in response to HE4 overexpression in epithelial ovarian cancer cells. The Cancer Genome Atlas (TCGA) ovarian cancer, GTEX, Oncomine, and TISIDB were used to analyze the expression of the six MRPs. The prognostic impact and genetic variation of these six MRPs in ovarian cancer were evaluated using Kaplan-Meier Plotter and cBioPortal, respectively. MRPL15 was selected for immunohistochemistry and GEO verification. TCGA ovarian cancer data, gene set enrichment analysis, and Enrichr were used to explore the mechanism of MRPL15 in ovarian cancer. Finally, the relationship between MRPL15 expression and immune subtype, tumor-infiltrating lymphocytes, and immune regulatory factors was analyzed using TCGA ovarian cancer data and TISIDB.

RESULTS

Six MRPs (MRPL10, MRPL15, MRPL36, MRPL39, MRPS16, and MRPS31) related to HE4 in ovarian cancer were selected. MRPL15 was highly expressed and amplified in ovarian cancer and was related to the poor prognosis of patients. Mechanism analysis indicated that MRPL15 plays a role in ovarian cancer through pathways such as the cell cycle, DNA repair, and mTOR 1 signaling. High expression of MRPL15 in ovarian cancer may be associated with its amplification and hypomethylation. Additionally, MRPL15 showed the lowest expression in C3 ovarian cancer and was correlated with proliferation of CD8⁺ T cells and dendritic cells as well as TGFβR1 and IDO1 expression.

CONCLUSION

MRPL15 may be a prognostic indicator and therapeutic target for ovarian cancer. Because of its close correlation with HE4, this study provides insights into the mechanism of HE4 in ovarian cancer.

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.

Decoding molecular markers and transcriptional circuitry of naive and primed states of human pluripotency

Pluripotent stem cells (PSCs) have been observed to occur in two distinct states - naive and primed. Both naive and primed state PSCs can give rise to tissues of all the three germ layers in vitro but differ in their potential to generate germline chimera in vivo. Understanding the molecular mechanisms that govern these two states of pluripotency in human can open a plethora of opportunities for studying early embryonic development and in biomedical applications. In this work, we use weighted gene co-expression network analysis (WGCNA) to identify the key molecular makers and their interactions that define the two distinct pluripotency states. Signed hybrid network was reconstructed from transcriptomic data (RNA-seq) of naive and primed state pluripotent samples. Our analysis revealed two sets of genes that are involved in the establishment and maintenance of naive and primed states. The naive state genes were found to be enriched for biological processes and pathways related to metabolic processes while primed state genes were associated with system development. We further filtered these lists to identify the intra-modular hubs and the hub transcription factors (TFs) for each group. Validation of the identified TFs was carried out using independent microarray datasets and we finally present a list of 52 and 33 TFs as the set of core TFs that are responsible for the induction and maintenance of naive and primed states of pluripotency in human, respectively. Among these, the TFs ZNF275, ZNF232, SP4, and MSANTD3 could be of interest as they were not reported in previous studies.

Association between SNAP25 and human glioblastoma multiform: a comprehensive bioinformatic analysis

Background: Glioblastoma multiforme (GBM) is a most common aggressive malignant brain tumor. In recent years, targeted therapy has been increasingly applied in GBM treatment.

Methods: In the present study, GSE22866 was downloaded from gene expression omnibus (GEO). The genomic and clinical data were obtained from TCGA. The differentially expressed genes (DEGs) were identified and functional analysis was performed using clusterprofiler. Then, the co-expression network for the DEGs was established using the “WGCNA” package. Next, the protein–protein interaction (PPI) was assessed using Search Tool for the Retrieval of Interacting Genes Database (STRING) and hub modules in Cytoscape were screened. The Venn diagram was plotted to showcase the overlapped hub DEGs in PPI network and TCGA. Univariate and multivariate Cox proportional hazards regression analyses were performed to predict the risk score of each patient. Validations of the hub gene were completed in other databases.

Results: Functional analysis of the DEGs verified the involvement of DEGs in growth factor binding and gated channel activity. Among the 10 GBM-related modules, the red one displayed the strongest tie with GBM. VAMP2 was filtered out as the most intimate protein. The PPI network and TCGA were comprehensively analyzed. Finally, SNAP25 was identified as a real hub gene positively correlated with GBM prognosis. The result was validated by GEPIA, ONCOMINE database and qRT-PCR.

Conclusions: SNAP25 might act as a GBM suppressor and a biomarker in GBM treatment.

Novel oxidative stress-related prognostic biomarkers for melanoma associated with tumor metastasis

ABSTRACT

Skin cutaneous melanoma (SKCM) is a prevalent skin cancer whose metastatic form is dangerous due to its high morbidity and mortality. Previous studies have systematically established the vital role of oxidative stress (OS) in melanoma progression. This study aimed to identify prognostic OS genes closely associated with SKCM and illustrate their potential mechanisms. Transcriptome data and corresponding clinical traits of patients with SKCM were retrieved from The Cancer Genome Atlas and Gene Expression Omnibus databases. A weighted gene co-expression network analysis was conducted to identify relationships between clinical features and OS genes in specific modules. Subsequently, Cox regression analysis was performed on candidate OS genes; four hub prognosis-associated OS genes (AKAP9, VPS13C, ACSL4, and HMOX2) were identified to construct a prognostic model. After a series of bioinformatics analysis, our prognostic model was identified significantly associated with the overall survival of patients with SKCM and metastatic ability of the cancer. Furthermore, our risk model demonstrated improved diagnostic accuracy in the Cancer Genome Atlas and Gene Expression Omnibus cohorts. In addition, we established 2 nomograms based on either risk score or hub genes, which displayed favorable discriminating ability for SKCM. Our results provide novel insight into the potential applications of OS-associated genes in SKCM.

Integrated analysis identifies oxidative stress genes associated with progression and prognosis in gastric cancer

Oxidative stress (OS) reactions are reported to be associated with oncogenesis and tumor progression. However, little is known about the potential diagnostic value of OS in gastric cancer (GC). This study identified hub OS genes associated with the prognosis and progression of GC and illustrated the underlying mechanisms. The transcriptome data and corresponding GC clinical information were collected from The Cancer Genome Atlas (TCGA) database. Aberrantly expressed OS genes between tumors and adjacent normal tissues were screened, and 11 prognosis-associated genes were identified with a series of bioinformatic analyses and used to construct a prognostic model. These genes were validated in the Gene Expression Omnibus (GEO) database. Furthermore, weighted gene co-expression network analysis (WGCNA) was subsequently conducted to identify the most significant hub genes for the prediction of GC progression. Analysis revealed that a good prognostic model was constructed with a better diagnostic accuracy than other clinicopathological characteristics in both TCGA and GEO cohorts. The model was also significantly associated with the overall survival of patients with GC. Meanwhile, a nomogram based on the risk score was established, which displayed a favorable discriminating ability for GC. In the WGCNA analysis, 13 progression-associated hub OS genes were identified that were also significantly associated with the progression of GC. Furthermore, functional and gene ontology (GO) analyses were performed to reveal potential pathways enriched with these genes. These results provide novel insights into the potential applications of OS-associated genes in patients with GC.