Identification of surrogate prognostic biomarkers for allergic asthma in nasal epithelial brushing samples by WGCNA

Identification of modules and key genes associated with breast cancer subtypes through network analysis

Breast cancer is the most common malignancy in women around the world. Intratumor and intertumoral heterogeneity persist in mammary tumors. Therefore, the identification of biomarkers is essential for the treatment of this malignancy. This study analyzed 28,143 genes expressed in 49 breast cancer cell lines using a Weighted Gene Co-expression Network Analysis to determine specific target proteins for Basal A, Basal B, Luminal A, Luminal B, and HER2 ampl breast cancer subtypes. Sixty-five modules were identified, of which five were characterized as having a high correlation with breast cancer subtypes. Genes overexpressed in the tumor were found to participate in the following mechanisms: regulation of the apoptotic process, transcriptional regulation, angiogenesis, signaling, and cellular survival. In particular, we identified the following genes, considered as hubs: IFIT3, an inhibitor of viral and cellular processes; ETS1, a transcription factor involved in cell death and tumorigenesis; ENSG00000259723 lncRNA, expressed in cancers; AL033519.3, a hypothetical gene; and TMEM86A, important for regulating keratinocyte membrane properties, considered as a key in Basal A, Basal B, Luminal A, Luminal B, and HER2 ampl breast cancer subtypes, respectively. The modules and genes identified in this work can be used to identify possible biomarkers or therapeutic targets in different breast cancer subtypes.

Serum Metabolomics Reveals Metabolomic Profile and Potential Biomarkers in Asthma

PURPOSE

Asthma is a highly heterogeneous disease. Metabolomics plays a pivotal role in the pathogenesis and development of asthma. The main aims of our study were to explore the underlying mechanism of asthma and to identify novel biomarkers through metabolomics approach.

METHODS

Serum samples from 102 asthmatic patients and 18 healthy controls were collected and analyzed using liquid chromatography-mass spectrometry/mass spectrometry (LC-MS/MS) system. Multivariate analysis and weighted gene co-expression network analysis (WGCNA) were performed to explore asthma-associated metabolomics profile and metabolites. The Kyoto Encyclopedia of Genes and Genomes (KEGG) was used for pathway enrichment analysis. Subsequently, 2 selected serum hub metabolites, myristoleic acid and dodecanoylcarnitine, were replicated in a validation cohort using ultra-high performance LC-MS/MS system (UHPLC-MS/MS).

RESULTS

Distinct metabolomics profile of asthma was revealed by multivariate analysis. Then, 116 overlapped asthma-associated metabolites between multivariate analysis and WGCNA, including 12 hub metabolites, were identified. Clinical features-associated hub metabolites were also identified by WGCNA. Among 116 asthma-associated metabolites, Sphingolipid metabolism and valine, leucine and isoleucine biosynthesis were revealed by KEGG analysis. Furthermore, serum myristoleic acid and dodecanoylcarnitine were significantly higher in asthmatic patients than in healthy controls in validation cohort. Additionally, serum myristoleic acid and dodecanoylcarnitine demonstrated high sensitivities and specificities in predicting asthma.

CONCLUSIONS

Collectively, asthmatic patients showed a unique serum metabolome. Sphingolipid metabolism and valine, leucine and isoleucine biosynthesis were involved in the pathogenesis of asthma. Furthermore, our results suggest the promising values of serum myristoleic acid and dodecanoylcarnitine for asthma diagnosis in adults.

AURKA, TOP2A and MELK are the key genes identified by WGCNA for the pathogenesis of lung adenocarcinoma

The comprehensive analysis of single or multiple microarray datasets is currently available in Gene Expression Omnibus (GEO) databases, with several studies having identified genes strongly associated with the development of lung adenocarcinoma (LUAD). However, the mechanisms of LUAD development remain largely unknown and has not yet been systematically studied; thus, further studies are required in this field. In the present study, weighted gene co-expression network analysis (WGCNA) was used for the evaluation of key genes with potential high risk of LUAD, and to provide more reliable evidence concerning its pathogenesis. The GSE140797 dataset from the high-throughput GEO database was downloaded and was first analyzed using the Limma package in the R language in order to determine the differentially expressed genes. The dataset was then analyzed using the WGCNA package to analyze the co-expressed genes, and the modular genes with the highest correlation with the clinical phenotype were identified. Subsequently, the pathogenic genes shared in common between the result of the two analyses were imported into the STRING database for protein-protein interaction network analysis. The hub genes were screened out using Cytoscape, and then The Cancer Genome Atlas analysis, receiver operating characteristic analysis and survival analysis were subsequently performed. Finally, the key genes were evaluated using reverse transcription-quantitative PCR and western blot analysis. Bioinformatics analysis of the GSE140797 dataset revealed eight key genes: AURKA, BUB1, CCNB1, CDK1, MELK, NUSAP1, TOP2A and PBK. Finally, the AURKA, TOP2A and MELK genes were evaluated in samples from patients with lung cancer using WGCNA and RT-qPCR, western blot analysis experiments, providing basis for further research on the mechanisms of LUAD development and targeted therapy.

Identification of key genes in bovine muscle development by co-expression analysis

Background

Skeletal muscle is not only an important tissue involved in exercise and metabolism, but also an important part of livestock and poultry meat products. Its growth and development determines the output and quality of meat to a certain extent, and has an important impact on the economic benefits of animal husbandry. Skeletal muscle development is a complex regulatory network process, and its molecular mechanism needs to be further studied.

Method

We used a weighted co-expression network (WGCNA) and single gene set enrichment analysis (GSEA) to study the RNA-seq data set of bovine tissue differential expression analysis, and the core genes and functional enrichment pathways closely related to muscle tissue development were screened. Finally, the accuracy of the analysis results was verified by tissue expression profile detection and bovine skeletal muscle satellite cell differentiation model in vitro (BSMSCs).

Results

In this study, Atp2a1, Tmod4, Lmod3, Ryr1 and Mybpc2 were identified as marker genes in muscle tissue, which are mainly involved in glycolysis/gluconeogenesis, AMPK pathway and insulin pathway. The assay results showed that these five genes were highly expressed in muscle tissue and positively correlated with the differentiation of bovine BSMSCs.

Conclusions

In this study, several muscle tissue characteristic genes were excavated, which may play an important role in muscle development and provide new insights for bovine molecular genetic breeding.

Multidimensional endotyping using nasal proteomics predicts molecular phenotypes in the asthmatic airways

BACKGROUND

Unsupervised clustering of biomarkers derived from noninvasive samples such as nasal fluid is less evaluated as a tool for describing asthma endotypes.

OBJECTIVE

We sought to evaluate whether protein expression in nasal fluid would identify distinct clusters of patients with asthma with specific lower airway molecular phenotypes.

METHODS

Unsupervised clustering of 168 nasal inflammatory and immune proteins and Shapley values was used to stratify 43 patients with severe asthma (endotype of noneosinophilic asthma) using a 2 "modeling blocks" machine learning approach. This algorithm was also applied to nasal brushings transcriptomics from U-BIOPRED (Unbiased Biomarkers for the Prediction of Respiratory Diseases Outcomes). Feature reduction and functional gene analysis were used to compare proteomic and transcriptomic clusters. Gene set variation analysis provided enrichment scores of the endotype of noneosinophilic asthma protein signature within U-BIOPRED sputum and blood.

RESULTS

The nasal protein machine learning model identified 2 severe asthma endotypes, which were replicated in U-BIOPRED nasal transcriptomics. Cluster 1 patients had significant airway obstruction, small airways disease, air trapping, decreased diffusing capacity, and increased oxidative stress, although only 4 of 18 were current smokers. Shapley identified 20 cluster-defining proteins. Forty-one proteins were significantly higher in cluster 1. Pathways associated with proteomic and transcriptomic clusters were linked to T_H1, T_H2, neutrophil, Janus kinase-signal transducer and activator of transcription, TLR, and infection activation. Gene set variation analysis of the nasal protein and gene signatures were enriched in subjects with sputum neutrophilic/mixed granulocytic asthma and in subjects with a molecular phenotype found in sputum neutrophil-high subjects.

CONCLUSIONS

Protein or gene analysis may indicate molecular phenotypes within the asthmatic lower airway and provide a simple, noninvasive test for non-type 2 immune response asthma that is currently unavailable.

Identification of pathogenic genes associated with CKD: An integrated bioinformatics approach

Chronic kidney disease (CKD) is defined as a persistent abnormality in the structure and function of kidneys and leads to high morbidity and mortality in individuals across the world. Globally, approximately 8%–16% of the population is affected by CKD. Proper screening, staging, diagnosis, and the appropriate management of CKD by primary care clinicians are essential in preventing the adverse outcomes associated with CKD worldwide. In light of this, the identification of biomarkers for the appropriate management of CKD is urgently required. Growing evidence has suggested the role of mRNAs and microRNAs in CKD, however, the gene expression profile of CKD is presently uncertain. The present study aimed to identify diagnostic biomarkers and therapeutic targets for patients with CKD. The human microarray profile datasets, consisting of normal samples and treated samples were analyzed thoroughly to unveil the differentially expressed genes (DEGs). After selection, the interrelationship among DEGs was carried out to identify the overlapping DEGs, which were visualized using the Cytoscape program. Furthermore, the PPI network was constructed from the String database using the selected DEGs. Then, from the PPI network, significant modules and sub-networks were extracted by applying the different centralities methods (closeness, betweenness, stress, etc.) using MCODE, Cytohubba, and Centiserver. After sub-network analysis we identified six overlapped hub genes (RPS5, RPL37A, RPLP0, CXCL8, HLA-A, and ANXA1). Additionally, the enrichment analysis was undertaken on hub genes to determine their significant functions. Furthermore, these six genes were used to find their associated miRNAs and targeted drugs. Finally, two genes CXCL8 and HLA-A were common for Ribavirin drug (the gene-drug interaction), after docking studies HLA-A was selected for further investigation. To conclude our findings, we can say that the identified hub genes and their related miRNAs can serve as potential diagnostic biomarkers and therapeutic targets for CKD treatment strategies.

Identifying key genes and functionally enriched pathways in Th2-high asthma by weighted gene co-expression network analysis

Background

Asthma is a chronic lung disease characterized by reversible inflammation of the airways. The imbalance of Th1/Th2 cells plays a significant role in the mechanisms of asthma. The aim of this study was to identify asthma-related key genes and functionally enriched pathways in a Th2-high group by using weighted gene coexpression network analysis (WGCNA).

Methods

The gene expression profiles of GSE4302, which included 42 asthma patients and 28 controls, were selected from the Gene Expression Omnibus (GEO). A gene network was constructed, and genes were classified into different modules using WGCNA. Gene ontology (GO) was performed to further explore the potential function of the genes in the most related module. In addition, the expression profile and diagnostic capacity (ROC curve) of hub genes of interest were verified by dataset GSE67472.

Results

In dataset GSE4302, subjects with asthma were divided into Th2-high and Th2-low groups according to the expression of the SERPINB2, POSTN and CLCA1 genes. A weighted gene coexpression network was constructed, and genes were classified into 7 modules. Among them, the red module was most closely associated with Th2-high asthma, which contained 60 genes. These genes were significantly enriched in different biological processes and molecular functions. A total of 8 hub genes (TPSB2, CPA3, ITLN1, CST1, SERPINB10, CEACAM5, CHD26 and P2RY14) were identified, and the expression levels of these genes (except TPSB2) were confirmed in dataset GSE67472. ROC curve analysis validated that the expression of these 8 genes exhibited excellent diagnostic efficiency for Th2-high asthma and Th2-low asthma.

Conclusions

The study provides a novel perspective on Th2-high asthma by WGCNA, and the hub genes and potential pathways involved may be beneficial for the diagnosis and management of Th2-high asthma.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12920-022-01241-9.

Identification of biomarkers for early diagnosis of multiple myeloma by weighted gene co-expression network analysis and their clinical relevance

BACKGROUND

Multiple myeloma is an incurable hematologic malignancy, its early diagnosis is important. However, the biomarker for early diagnosis is limited; hence more need to be identified. The present study aimed to explore the easily tested new biomarker in multiple myeloma by weighted gene co-expression network analysis (WGCNA).

METHODS

Differentially expressed genes (DEGs) were screened using GSE47552. WGCNA was used to screen hub genes. Subsequently. Hub genes of multiple myeloma were obtained by intersection of DEGs and WGCNA. We used the T-test to screen highly expressed genes. Then, the diagnostic value of key genes was evaluated by the receiver operating characteristic (ROC) curve. Finally, expression levels of key genes were tested and proved by RT-PCR.

RESULTS

278 DEGs were screened by Limma package. Three modules were most significantly correlated with multiple myeloma. 238 key genes were screened after the intersection of WGCNA with DEGs. In addition, SNORNA is rarely studied in multiple myeloma, and ROC curve analysis in our prediction model showed that SNORA71A had a good prediction effect (p = 0.07). The expression of SNORA71A was increased in samples of multiple myeloma (P = 0.05). RT-PCR results showed that SNORA71A was upregulated in 51 patient specimens compared to the healthy group (P < 0.05). Linear correlation analysis showed that creatinine was positively correlated with SNORA71A (r = 0.49 P = 0.0002).

CONCLUSIONS

This study found that SNORA71A was up-regulated and associated with the clinical stages in multiple myeloma; it suggests that SNORA71A could be used as a novel biomarker for early diagnosis and a potential therapeutic target in multiple myeloma.

Identification of Key Genes Associated with Endothelial Cell Dysfunction in Atherosclerosis Using Multiple Bioinformatics Tools

Atherosclerosis is the most notable cardiovascular disease, the latter being the main cause of death globally. Endothelial cell dysfunction plays a major role in the pathogenesis of atherosclerosis. However, it is currently unclear which genes are involved between endothelial cell dysfunction and atherosclerosis. This study was aimed at identifying these genes. Based on the GSE83500 dataset, the quantification of endothelial cell function was conducted using single-sample gene set enrichment analysis; the coexpression modules were conducted using weighted correlation network analysis. After building module-trait relationships, tan and yellow modules were regarded as hub modules. 10 hub genes from each hub module were identified by the protein-protein interaction network analysis. The key genes (RAB5A, CTTN, ITGB1, and MMP9) were obtained by comparing the expression differences of the hub gene between atherosclerotic and normal groups from the GSE28829 and GSE43292 datasets, respectively. ROC analysis showed the diagnostic value of key genes. Moreover, the differential expression of key genes in normal and atherosclerotic aortic walls was verified. In vitro, we establish a model of ox-LDL-injured endothelial cells and transfect RAB5A overexpression and shRNA plasmids. The results showed that overexpression of RAB5A ameliorates the proliferation and migration function of ox-LDL-injured endothelial cells, including the ability of tubule formation. It was speculated that the interferon response, Notch signaling pathways, etc. were involved in this function of RAB5A by using gene set variation analysis. With the multiple bioinformatics analysis methods, we detected that yellow and tan modules are related to the abnormal proliferation and migration of endothelial cells associated with atherosclerosis. RAB5A, CTTN, ITGB1, and MMP9 can be used as potential targets for therapy and diagnostic markers. In vitro, overexpression of RAB5A can ameliorate the proliferation and migration function of ox-LDL-injured endothelial cells, and the possible molecules involved in this process were speculated.

Proteomic characteristics of beryllium sulfate-induced differentially expressed proteins in rats

Sprague Dawley rats were exposed to beryllium sulfate (BeSO₄), and proteomic and bioinformatic techniques were applied to screen for differentially expressed proteins in their lung tissue and serum. A total of 12 coexpression modules were constructed for 18 samples with 2333 proteins. Four modules were found to have significant differences in the regulation of protein coexpression modules in the serum following exposure to BeSO₄. A further three modules had significant differences in the regulation of protein coexpression modules in the lung tissues. Five modules with good correlation were obtained by calculating the gene significance and module membership values, whereas these module Hub proteins included: Hspbp1, Rps15a, Srsf2, Hadhb, Elmo3, Armt1, Rpl18, Afap1L1, Eif3d, Eif3c, and Rps3. The five proteins correlating highest with the Hub proteins in the lung tissue and serum samples were obtained using string analysis. KEGG and GO enrichment analyses showed that these proteins are mainly involved in ribosome formation, apoptosis, cell cycle regulation, and tumor necrosis factor regulation. By analyzing the biological functions of these proteins, proteins that can be used as biomarkers, such as Akt1, Prpf19, Cct2, and Rpl18, are finally obtained.

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.

Identification of key genes associated with secondary metabolites biosynthesis by system network analysis in Valeriana officinalis

Valeriana officinalis is a medicinal plant, a source of bioactive chemical compounds and secondary metabolites which are applied in pharmaceutical industries. The advent of ethnomedicine has provided alternatives for disease treatment and has increased demands for natural products and bioactive compounds. A set of preliminary steps to answers for such demands can include integrative omics for systems metabolic engineering, as an approach that contributes to the understanding of cellular metabolic status. There is a growing trend of this approach for genetically engineering metabolic pathways in plant systems, by which natural and synthetic compounds can be produced. As in the case of most medicinal plants, there are no sufficient information about molecular mechanisms involved in the regulation of metabolic pathways in V. officinalis. In this research, systems biology was performed on the RNA-seq transcriptome and metabolome data to find key genes that contribute to the synthesis of major secondary metabolites in V. officinalis. The R Package Weighted Gene Co-Expression Network Analysis (WGCNA) was employed to analyze the data. Based on the results, some major modules and hub genes were identified to be associated with the valuable secondary metabolites. In addition, some TF-encoding genes, including AP2/ERF-ERF, WRKY and NAC TF families, as well as some regulatory factors including protein kinases and transporters were identified. The results showed that several novel hub genes, such as PCMP-H24, RPS24B, ANX1 and PXL1, may play crucial roles in metabolic pathways. The current findings provide an overall insight into the metabolic pathways of V. officinalis and can expand the potential for engineering genome-scale pathways and systems metabolic engineering to increase the production of bioactive compounds by plants.

Expression profiles of genes associated with inflammatory responses and oxidative stress in lung after heat stroke

BACKGROUND

Heat stroke (HS) is a physically dysfunctional illness caused by hyperthermia. Lung, as the important place for gas-exchange and heat-dissipation organ, is often first to be injured. Lung injury caused by HS impairs the ventilation function of lung, which will subsequently cause damage to other tissues and organs. Nevertheless, the specific mechanism of lung injury in heat stroke is still unknown.

METHODS

Rat lung tissues from controls or HS models were harvested. The gene expression profile was identified by high-throughput sequencing. DEGs were calculated using R and validated by qRT-PCR. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and cell-enrichment were performed using differential expression genes (DEGs). Finally, lung histopathology was accessed by H&E staining.

RESULTS

About 471 genes were identified to be DEGs, of which 257 genes were up-regulated, and 214 genes were down-regulated. The most up-regulated and down-regulated DEGs were validated by qRT-PCR, which confirmed the tendency of expression. GO, KEGG, and protein-protein interaction (PPI)-network analyses disclosed DEGs were significantly enriched in leukocyte migration, response to lipopolysaccharide, NIK/NF-kappaB signaling, response to reactive oxygen species, response to heat, and the hub genes were Tnf, Il1b, Cxcl2, Ccl2, Mmp9, Timp1, Hmox1, Serpine1, Mmp8 and Csf1, most of which were closely related to inflammagenesis and oxidative stress. Finally, cell-enrichment analysis and histopathologic analysis showed Monocytes, Megakaryotyes, and Macrophages were enriched in response to heat stress.

CONCLUSIONS

The present study identified key genes, signal pathways and infiltrated-cell types in lung after heat stress, which will deepen our understanding of transcriptional response to heat stress, and might provide new ideas for the treatment of HS.

Weighted gene co-expression network analysis (WGCNA) to explore genes responsive to Streptococcus oralis biofilm and immune infiltration analysis in human gingival fibroblasts cells

The correlation between oral bacteria and dental implants failure has been reported. However, the effect and mechanism of bacteria during dental implants is unclear. In this study, we explored key genes and candidate gene clusters in human gingival fibroblasts (HGF) cells in response to Streptococcus oralis biofilm through weighted gene co-expression network analysis (WGCNA) and differential genes analysis using gene expression matrix, GSE134481, downloaded from the Gene Expression Omnibus (GEO) database. We obtained 325 genes in the module significantly associated with S. oralis infection and 113 differentially expressed genes (DEGs) in the S. oralis biofilm; 62 DEGs indicated significant correlation with S. oralis injury. Multiple immune pathways, such as the tumor necrosis factor (TNF) signaling pathway, were considerably enriched. We obtained a candidate genes cluster containing 12 genes – IL6, JUN, FOS, CSF2, HBEGF, EDN1, CCL2, MYC, NGF, SOCS3, CXCL1, and CXCL2; we observed 5 candidate hub genes associated with S. oralis infection – JUN, IL6, FOS, MYC, and CCL2. The fraction of macrophage M0 cells was significantly increased in biofilm treatment compared with control; expression of FOS and MYC was significantly positively correlated with macrophage M0 cells. Our findings present a fierce inflammation changes in the transcript level of HGF in response to S. oralis.

Identification and analysis of spinal cord injury subtypes using weighted gene co-expression network analysis

Background

Spinal cord injury (SCI) has an immediate and devastating impact on the control over various movements and sensations. However, no effective therapies for SCI currently exist.

Methods

To identify and analyze SCI subtypes, we obtained the expression profile data of the 1,057 genes (889 intersection genes) in GSE45550 using weighted gene co-expression network analysis (WGCNA), and 14 co-expression gene modules were identified. Next, we filtered out the network degree top 10 (degree >80) genes, considered the final key SCI genes. A multifactor regulatory network (105 interaction pairs), consisting of messenger RNAs (mRNAs), long non-coding RNAs (lncRNAs), and transcription factors (TFs) was constructed. This network was involved in the co-expression of key genes. We selected the top 10 regulatory factors (degree >4) as core regulators in the multifactor regulatory network.

Results

The results of functional enrichment analysis of the target gene expressing the core regulatory factor [1,059] showed that these target genes were enriched in pathways for human cytomegalovirus infection, chronic myeloid leukemia, and pancreatic cancer. Further, we used the key genes in the co-expression network to categorize the SCI samples in GSE45550. The expression levels of the top 6 genes (CCNB2, CCNB1, CKS2, COL5A1, KIF20A, and RACGAP1) may act as potential marker genes for different SCI subtypes. On the basis of these different subtypes, 8 SCI core gene CDK1-associated drugs were also found to provide potential therapeutic options for SCI.

Conclusions

These results may provide a novel therapeutic strategy for the treatment of SCI.

Identification of biomarkers and pathogenesis in severe asthma by coexpression network analysis

BACKGROUND

Severe asthma is a heterogeneous inflammatory disease. The increase in precise immunotherapy for severe asthmatics requires a greater understanding of molecular mechanisms and biomarkers. In this study, we aimed to identify the underlying mechanisms and hub genes that determine asthma severity.

METHODS

Differentially expressed genes (DEGs) were identified based on bronchial epithelial brushings from mild and severe asthmatics. Then, weighted gene coexpression network analysis (WGCNA) was used to identify gene networks and the module most significantly associated with asthma severity. Furthermore, hub gene screening and functional enrichment analysis were performed. Replication with another dataset was conducted to validate the hub genes.

RESULTS

DEGs from 14 mild and 11 severe asthmatics were subjected to WGCNA. Six modules associated with asthma severity were identified. Three modules were positively correlated (P < 0.001) with asthma severity and contained genes that were upregulated in severe asthmatics. Functional enrichment analysis showed that genes in the most significant module were mainly enriched in neutrophil activation and degranulation, and cytokine receptor interaction. Hub genes included CXCR1, CXCR2, CCR1, CCR7, TLR2, FPR1, FCGR3B, FCGR2A, ITGAM, and PLEK; CXCR1, CXCR2, and TLR2 were significantly related to asthma severity in the validation dataset. The combination of ten hub genes exhibited a moderate ability to distinguish between severe and mild-moderate asthmatics.

CONCLUSION

Our results identified biomarkers and characterized potential pathogenesis of severe asthma, providing insight into treatment targets and prognostic markers.

Characteristics of immune cell infiltration and associated diagnostic biomarkers in ulcerative colitis: results from bioinformatics analysis

Ulcerative colitis (UC) is a type of refractory and recurrent inflammatory disorder that occurs in colon and rectum. Immune cell infiltration plays a critical role in UC progression; therefore, this study aims to explore potential biomarkers for UC and to analyze characteristics of immune cell infiltration based on the bioinformatic analysis. In this study, 248 differentially expressed genes (DEGs) were screened, and the top 20 immune-related hub genes and pathways were assessed. Moreover, four candidate diagnostic biomarkers (DPP10, S100P, AMPD1, and ASS1) were identified and validated. Immune cell infiltration analysis identified 13 differentially infiltrated immune cells (IICs) in UC samples compared to normal samples, and the result showed that two IICs only expressed in UC samples. In addition, the present research found that DPP10 was negatively correlated with neutrophils, S100P exhibited a positive correlation with resting CD4 memory T cells, AMPD1 was positively correlated with M2 macrophages, and ASS1 was inversely associated with neutrophils and positively related to CD8 T cells. Taken together, these findings indicated that DPP10, S100P, AMPD1, and ASS1 may act as diagnostic biomarkers for UC, and that differential IICs may help to illustrate the progression of UC.

Identification of key gene modules and genes in colorectal cancer by co-expression analysis weighted gene co-expression network analysis

Colorectal cancer (CRC) has been one of the most common malignancies worldwide, which tends to get worse for the growth and aging of the population and westernized lifestyle. However, there is no effective treatment due to the complexity of its etiology. Hence, the pathogenic mechanisms remain to be clearly defined. In the present study, we adopted an advanced analytical method—Weighted Gene Co-expression Network Analysis (WGCNA) to identify the key gene modules and hub genes associated with CRC. In total, five gene co-expression modules were highly associated with CRC, of which, one gene module correlated with CRC significantly positive (R = 0.88). Functional enrichment analysis of genes in primary gene module found metabolic pathways, which might be a potentially important pathway involved in CRC. Further, we identified and verified some hub genes positively correlated with CRC by using Cytoscape software and UALCAN databases, including PAICS, ATR, AASDHPPT, DDX18, NUP107 and TOMM6. The present study discovered key gene modules and hub genes associated with CRC, which provide references to understand the pathogenesis of CRC and may be novel candidate target genes of CRC.

Identification of Biomarkers Related to Systemic Sclerosis With or Without Pulmonary Hypertension Using Co-expression Analysis

Systemic sclerosis (SSc), also known as scleroderma, is an autoimmune disease with multiple system involvement, and pulmonary complications, including pulmonary hypertension (PH), are leading causes of death. This study aimed to develop early biomarkers to distinguish SSc with or without PH from normal population using bioinformatics approaches. The gene expression profile GSE22356, which contains 10 SSc samples with PH, 10 SSc samples without PH, and 10 normal samples, was obtained from the Gene Expression Omnibus database. First, we constructed co-expression networks and identified critical gene modules using the weighted gene co-expression network analysis. Then, functional enrichment analysis of significant modules was performed. Finally, the "real" hub gene was screened out by intramodule analysis and protein-protein interaction networks, and the receiver operating characteristic analysis was conducted. A total of 5046 genes were screened out to construct co-expression networks, and 18 modules were identified. Of these modules, the turquoise module had a strong correlation with SSc only, whereas the midnightblue module showed an obvious positive correlation with SSc with PH. Functional enrichment analysis indicated that the turquoise module was mainly enriched in transcription of DNA template and its regulation and protein ubiquitination and involved in apoptosis and pyrimidine metabolism pathway. The midnightblue module was significantly associated with inflammatory and immune response and pathways in Staphylococcus aureus infection and Chagas disease. The "real" hub genes in the turquoise module were WDR36, POLR1B, and SRSF1, and those in midnightblue were TLR2 and TNFAIP6.

Expression and functional analysis of lncRNAs in the hippocampus of immature rats with status epilepticus

Long non-coding RNAs (lncRNAs) have been implicated in the regulation of gene expression at various levels. However, to date, the expression profile of lncRNAs in status epilepticus (SE) was unclear. In our study, the expression profile of lncRNAs was investigated by high-throughput sequencing based on a lithium/pilocarpine-induced SE model in immature rats. Furthermore, weighted correlation network analysis (WGCNA), gene ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were performed to construct co-expression networks and establish functions of the identified hub lncRNAs in SE. The functional role of a hub lncRNA (NONRATT010788.2) in SE was investigated in an in vitro model. Our results indicated that 7082 lncRNAs (3522 up-regulated and 3560 down-regulated), which are involved in cell proliferation, inflammatory responses, angiogenesis and autophagy, were dysregulated in the hippocampus of immature rats with SE. Additionally, WGCNA identified 667 up-regulated hub lncRNAs in turquoise module that were involved in apoptosis, inflammatory responses and angiogenesis via regulation of HIF-1, p53 and chemokine signalling pathways and via inflammatory mediator regulation of TRP channels. Knockdown of an identified hub lncRNA (NONRATT010788.2) inhibited neuronal apoptosis in vitro. Taken together, our study is the first to demonstrate the expression profile and potential function of lncRNAs in the hippocampus of immature rats with SE. The defined hub lncRNAs may participate in the pathogenesis of SE via lncRNA-miRNA-mRNA network.

Identification of key pathways and genes in different types of chronic kidney disease based on WGCNA

Chronic kidney disease (CKD) is a highly heterogeneous nephrosis that occurs when the structure and function of the kidney is damaged. Gene expression studies have been widely used to elucidate various biological processes; however, the gene expression profile of CKD is currently unclear. The present study aimed to identify diagnostic biomarkers and therapeutic targets using renal biopsy sample data from patients with CKD. Gene expression data from 30 patients with CKD and 21 living donors were analyzed by weighted gene co-expression network analysis (WGCNA), in order to identify gene networks and profiles for CKD, as well as its specific characteristics, and to potentially uncover diagnostic biomarkers and therapeutic targets for patients with CKD. In addition, functional enrichment analysis was performed on co-expressed genes to determine modules of interest. Four co-expression modules were constructed from the WGCNA. The number of genes in the constructed modules ranged from 269 genes in the Turquoise module to 60 genes in the Yellow module. All four co-expression modules were correlated with CKD clinical traits (P<0.05). For example, the Turquoise module, which mostly contained genes that were upregulated in CKD, was positively correlated with CKD clinical traits, whereas the Blue, Brown and Yellow modules were negatively correlated with clinical traits. Functional enrichment analysis revealed that the Turquoise module was mainly enriched in genes associated with the ‘defense response’, ‘mitotic cell cycle’ and ‘collagen catabolic process’ Gene Ontology (GO) terms, implying that genes involved in cell cycle arrest and fibrogenesis were upregulated in CKD. Conversely, the Yellow module was mainly enriched in genes associated with ‘glomerulus development’ and ‘kidney development’ GO terms, indicating that genes associated with renal development and damage repair were downregulated in CKD. The hub genes in the modules were acetyl-CoA carboxylase α, cyclin-dependent kinase 1, Wilm's tumour 1, NPHS2 stomatin family member, podocin, JunB proto-oncogene, AP-1 transcription factor subunit, activating transcription factor 3, forkhead box O1 and v-abl Abelson murine leukemia viral oncogene homolog 1, which were confirmed to be significantly differentially expressed in CKD biopsies. Combining the eight hub genes enabled a high capacity for discrimination between patients with CKD and healthy subjects, with an area under the receiver operating characteristic curve of 1.00. In conclusion, this study provided a framework for co-expression modules of renal biopsy samples from patients with CKD and living donors, and identified several potential diagnostic biomarkers and therapeutic targets for CKD.

High-throughput sequencing reveals circular RNA hsa_circ_0000592 as a novel player in the carcinogenesis of gastric carcinoma

Background/Aim: Gastric cancer is one of the most common malignant tumors, and its complex pathogenesis has not been fully elucidated. Circular RNAs (circRNAs) are involved in various biological processes and human diseases. However, their exact functional roles and mechanisms of action remain largely unclear. We previously discovered the differential expression of non-coding RNAs (ncRNAs) during the malignant transformation of human gastric epithelial cells. In this study, we investigated the functional roles of a significantly up-regulated circRNA (hsa_circ_0000592) in gastric cancer. Methods: N-methyl-N'-nitro-N-nitrosoguanidine (MNNG)-induced malignant-transformed gastric epithelial cells (GES-1-T) and normal gastric epithelial cells (GES-1-N) were analyzed by high-throughput circRNA sequencing. The top 15 up-regulated circRNAs in high-throughput sequencing results were further confirmed by qRT-PCR in different gastric epithelial cell lines. The function of the most significant circRNA (hsa_circ_0000592) was investigated by using RNA interference (RNAi) assays, fluorescence in situ hybridization analysis (FISH), and bioinformatics prediction methods. Results: A total of 1509 genes were up-regulated and 3142 genes were down-regulated in GES-1-T cells when compared with GES-1-N cells. When compared with GES-1-N cells, hsa_circ_0000592 was obviously up-regulated in GES-1-T cells, as well as in other gastric cancer cell lines. The silencing of hsa_circ_0000592 mRNA led to a decrease in cell proliferation, cell cycle arrest at the G0/G1 phase, an increased rate of apoptosis, and a reduction in cell migration. Furthermore, FISH showed that hsa_circ_0000592 was mainly located in the cytoplasm, and a bioinformatics analysis suggested that hsa_circ_0000592 might function by sponging multiple miRNAs, and most notably four conserved miRNAs, including miR-139-3p, miR-200, miR-367-3p, and miR-33a-3p. Conclusion: This study is the first to identify hsa_circ_0000592 as a novel circRNA with a critical role in MNNG-induced gastric cancer. Due to the essential role of hsa_circ_0000592 in gastric carcinoma cells, it may be considered as a potential biomarker for use in diagnosing gastric carcinoma. Our findings provide a new insight into the function of circRNAs in environmental carcinogen-induced gastric cancer.

Identification of four hub genes associated with adrenocortical carcinoma progression by WGCNA

BACKGROUND

Adrenocortical carcinoma (ACC) is a rare and aggressive malignant cancer in the adrenal cortex with poor prognosis. Though previous research has attempted to elucidate the progression of ACC, its molecular mechanism remains poorly understood.

METHODS

Gene transcripts per million (TPM) data were downloaded from the UCSC Xena database, which included ACC (The Cancer Genome Atlas, n = 77) and normal samples (Genotype Tissue Expression, n = 128). We used weighted gene co-expression network analysis to identify gene connections. Overall survival (OS) was determined using the univariate Cox model. A protein-protein interaction (PPI) network was constructed by the search tool for the retrieval of interacting genes.

RESULTS

To determine the critical genes involved in ACC progression, we obtained 2,953 significantly differentially expressed genes and nine modules. Among them, the blue module demonstrated significant correlation with the "Stage" of ACC. Enrichment analysis revealed that genes in the blue module were mainly enriched in cell division, cell cycle, and DNA replication. Combined with the PPI and co-expression networks, we identified four hub genes (i.e., TOP2A, TTK, CHEK1, and CENPA) that were highly expressed in ACC and negatively correlated with OS. Thus, these identified genes may play important roles in the progression of ACC and serve as potential biomarkers for future diagnosis.