Weighted gene co-expression network analysis reveals key biomarkers and immune infiltration characteristics for bronchial epithelial cells from asthmatic patients

BACKGROUND

Asthma ranks among the most prevalent non-communicable diseases worldwide. Previous studies have elucidated the significant role of the immune system in its pathophysiology. Nevertheless, the immune-related mechanisms underlying asthma are complex and still inadequately understood. Thus, our objective was to investigate novel key biomarkers and immune infiltration characteristics associated with asthma by employing integrated bioinformatics tools.

METHODS

In this study, we conducted a weighted gene co-expression network analysis (WGCNA) to identify key modules and genes potentially implicated in asthma. Functional annotation of these key modules and genes was carried out through gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. Additionally, we constructed a protein-protein interaction (PPI) network using the STRING database to identify 10 hub genes. Furthermore, we evaluated the relative proportion of immune cells in bronchial epithelial cell samples from 20 healthy individuals and 88 asthmatic patients using CIBERSORT. Finally, we validated the hub genes and explored their correlation with immune infiltration.

RESULTS

Furthermore, 20 gene expression modules and 10 hub genes were identified herein. Among them, complement component 3 (C3), prostaglandin I2 receptor (PTGIR), parathyroid hormone-like hormone (PTHLH), and C-X3-C motif chemokine ligand 1 (CX3CL1) were closely correlated with the infiltration of immune cells. They may be novel candidate biomarkers or therapeutic targets for asthma. Furthermore, B cells memory, and plasma cells might play an important role in immune cell infiltration after asthma.

CONCLUSIONS

C3, PTGIR, CX3CL1, and PTHLH have important clinical diagnostic values and are correlated with infiltration of multiple immune cell types in asthma. These hub genes, B cells memory, and plasma cells may become important biological targets for therapeutic asthma drug screening and drug design.

Profiling of immune infiltration landscape of ruptured intracranial aneurysm

BACKGROUND

Previous research has indicated that the rupture of intracranial aneurysm (IA) is a significant contributor to mortality from stroke. The objective of this present study was to examine the infiltration patterns in ruptured intracranial aneurysm (RIA), with the aim of generating insights that could inform the development of effective immunotherapeutic approaches.

METHODS

To achieve this, we obtained Gene Expression Omnibus datasets pertaining to ruptured aneurysms, encompassing a total of 19 unruptured intracranial aneurysms (UIA) and 27 RIA. Subsequently, we conducted differential gene analysis and immune cell analysis specifically for the RIA.

RESULTS

According to the conducted studies, the analysis has identified 10 hub genes within key modules. Through the utilization of Kyoto Encyclopedia of Genes and Genomes pathway and gene ontology terms analyses, it has been established that genes exhibiting differential expression are associated with immune cell infiltration in the aneurysm wall. Furthermore, the implementation of the CIBERSORT algorithm has revealed that there are 22 distinct immune cells between RIA and tissues of UIA. IA samples contained a higher proportion of macrophages M1, mast cells resting, and CD4 naive T cells, while macrophages M0 and neutrophils were relatively lower in RIA compared with those in UIA.

CONCLUSION

The current study initially identified highly conservative hub genes and immune cell infiltration patterns in IA. Data presented in the current study improved understanding of immune genes that drive IA which can be exploited in development of effective immunotherapies.

Molecular characterization of allergic constitution based on network pharmacology and multi-omics analysis methods

The objective of this study was to identify critical pathways associated with allergic constitution. Shared genes among allergic rhinitis (AR), asthma (AA), and atopic dermatitis (AD) were extracted from the GWAS catalog. RNA-seq data of AR, AA, and AD from gene expression omnibus (GEO) database were preprocessed and subjected to differential gene expression analysis. The differentially expressed genes (DEGs) were merged using the Robust Rank Aggregation (RRA) algorithm. Weighted gene co-expression network analysis (WGCNA) was performed to identify modules associated with allergies. Components of Guominkang (GMK) were obtained from 6 databases and activate components were identified by SwissADME website. Utilizing the SwissTarget Prediction, PharmMapper, SymMap, and HERB, the targets of GMK were predicted and subsequently validated using gene chip data from our team previous study. Differentially expressed proteins (DEPs) related to the allergic constitution were also extracted based on a previous study. Pathway enrichment analysis was performed using KOBAS-i on the GWAS, RRA, WGCNA modules, DEPs, and GMK targets. P values from multi-omics datasets were combined by meta-analysis, and Bonferroni correction was applied. The significant pathways were further validated using Gene Set Enrichment Analysis (GSEA) with intervention data of GMK. The GWAS results yielded 172 genes. Four datasets AR1, AA1, AD1, and AD2 were acquired from GSE75011, GSE125916, and GSE184237. The RRA algorithm identified 19 upregulated and 20 downregulated genes. WGCNA identified 5 significant modules, with the blue and turquoise modules displaying a moderate correlation with allergies. By performing network pharmacology analysis, we identified 127 active ingredients of GMK and predicted 618 targets. Validation using gene chip data confirmed 107 GMK targets. Single-omics pathway analysis was conducted using KOBAS-i, and 39 significant pathways were identified across multiple omics datasets. GSEA analysis using GMK intervention data identified 11 of 39 significant pathways as the final key pathways associated with the allergic constitution. Through multi-omics integrated pathway analysis, we identified 11 critical pathways of allergic constitution, including TH1 and TH2 cell differentiation, TLR cascade, and TH17 cell differentiation. Identifying these pathways suggests that the observed alterations at the pathway level may play significant roles in the molecular characteristics of the allergic constitution.

WGCNA combined with machine learning to find potential biomarkers of liver cancer

The incidence of hepatocellular carcinoma (HCC) has been increasing in recent years. With the development of various detection technologies, machine learning is an effective method to screen disease characteristic genes. In this study, weighted gene co-expression network analysis (WGCNA) and machine learning are combined to find potential biomarkers of liver cancer, which provides a new idea for future prediction, prevention, and personalized treatment. In this study, the "limma" software package was used. P < .05 and log2 |fold-change| > 1 is the standard screening differential genes, and then the module genes obtained by WGCNA analysis are crossed to obtain the key module genes. Gene Ontology and Kyoto Gene and Genome Encyclopedia analysis was performed on key module genes, and 3 machine learning methods including lasso, support vector machine-recursive feature elimination, and RandomForest were used to screen feature genes. Finally, the validation set was used to verify the feature genes, the GeneMANIA (http://www.genemania.org) database was used to perform protein-protein interaction networks analysis on the feature genes, and the SPIED3 database was used to find potential small molecule drugs. In this study, 187 genes associated with HCC were screened by using the "limma" software package and WGCNA. After that, 6 feature genes (AADAT, APOF, GPC3, LPA, MASP1, and NAT2) were selected by RandomForest, Absolute Shrinkage and Selection Operator, and support vector machine-recursive feature elimination machine learning algorithms. These genes are also significantly different on the external dataset and follow the same trend as the training set. Finally, our findings may provide new insights into targets for diagnosis, prevention, and treatment of HCC. AADAT, APOF, GPC3, LPA, MASP1, and NAT2 may be potential genes for the prediction, prevention, and treatment of liver cancer in the future.

Identification of biomarkers associated with pediatric asthma using machine learning algorithms: A review

Pediatric asthma is a complex disease with a multifactorial etiology. The identification of biomarkers associated with pediatric asthma can provide insights into the pathogenesis of the disease and aid in the development of novel diagnostic and therapeutic strategies. This study aimed to identify potential biomarkers for pediatric asthma using Weighted Gene Co-expression Network Analysis (WGCNA) and machine learning algorithms. We obtained gene expression data from publicly available databases and performed WGCNA to identify gene co-expression modules associated with pediatric asthma. We then used machine learning algorithms, including random forest, lasso regression algorithm, and support vector machine-recursive feature elimination, to classify asthma cases and controls based on the identified gene modules. We also performed functional enrichment analyses to investigate the biological functions of the identified genes.We detected 24,544 genes exhibiting differential expression between controlled and uncontrolled genes from the GSE135192 dataset. In the combined WCGNA analysis, a total of 104 co-expression genes were screened, both controlled and uncontrolled. After screening, 11 hub genes were identified. They were AK2, PDK4, PER3, GZMH, NUMBL, NRL, SCO2, CREBZF, LARP1B, RXFP1, and VDAC3P1. The areas under their receiver operating characteristic curve were above 0.78. Our study identified potential biomarkers for pediatric asthma using WGCNA and machine learning algorithms. Our findings suggest that 11 hub genes could be used as novel diagnostic markers and treatment targets for pediatric asthma. These findings provide new insights into the pathogenesis of pediatric asthma and may aid in the development of novel diagnostic and therapeutic strategies.

Identification of LGR4 as a prognostic biomarker in KRAS-mutant lung adenocarcinoma: Evidence from integrated bioinformatics analysis

Globally, lung cancer is the leading cause of cancer-related deaths, primarily non-small cell lung cancer. Kirsten Rat Sarcoma Oncogene Homolog (KRAS) mutations are common in non-small cell lung cancer and linked to a poor prognosis. Covalent inhibitors targeting KRAS-G12C mutation have improved treatment for some patients, but most KRAS-mutant lung adenocarcinoma (KRAS-MT LUAD) cases lack targeted therapies. This gap in treatment options underscores a significant challenge in the field. Our study aimed to identify hub/key genes specifically associated with KRAS-MT LUAD. These hub genes hold the potential to serve as therapeutic targets or biomarkers, providing insights into the pathogenesis and prognosis of lung cancer. We performed a comprehensive analysis on KRAS-MT LUAD samples using diverse data sources. This included TCGA project data for RNA-seq, clinical information, and somatic mutations, along with RNA-seq data for adjacent normal tissues. DESeq2 identified differentially expressed genes (DEGs), while weighted gene co-expression network analysis revealed co-expression modules. Overlapping genes between DEGs and co-expression module with the highest significance were analyzed using gene set enrichment analysis and protein-protein interaction network analysis. Hub genes were identified with the Maximal Clique Centrality algorithm in Cytoscape. Prognostic significance was assessed through survival analysis and validated using the GSE72094 dataset from Gene Expression Omnibus (GEO) database. In KRAS-MT LUAD, 3122 DEGs were found (2131 up-regulated, 985 down-regulated). The blue module, among 25 co-expression modules from weighted gene co-expression network analysis, had the strongest correlation. 804 genes overlapped between DEGs and the blue module. Among 20 hub genes in the blue module, leucine-rich repeats containing G protein-coupled receptor 4 (LGR4) overexpression correlated with worse overall survival. The prognostic significance of LGR4 was confirmed using GSE72094, but surprisingly, the direction of the association was opposite to what was expected. LGR4 stands as a promising biomarker in KRAS-MT LUAD prognosis. Contrasting associations in TCGA and GSE72094 datasets reveal the intricate nature of KRAS-MT LUAD. Additional explorations are imperative to grasp the precise involvement of LGR4 in lung adenocarcinoma prognosis, particularly concerning KRAS mutations. These insights could potentially pave the way for targeted therapeutic interventions, addressing the existing unmet demands in this specific subgroup.

FN1 and TGFBI are key biomarkers of macrophage immune injury in diabetic kidney disease

The pathogenesis of diabetic kidney disease (DKD) is complex, and the existing treatment methods cannot control disease progression well. Macrophages play an important role in the development of DKD. This study aimed to search for biomarkers involved in immune injury induced by macrophages in DKD. The GSE96804 dataset was downloaded and analyzed by the CIBERSORT algorithm to understand the differential infiltration of macrophages between DKD and normal controls. Weighted gene co-expression network analysis was used to explore the correlation between gene expression modules and macrophages in renal tissue of DKD patients. Protein-protein interaction network and machine learning algorithm were used to screen the hub genes in the key modules. Subsequently, the GSE30528 dataset was used to further validate the expression of hub genes and analyze the diagnostic effect by the receiver operating characteristic curve. The clinical data were applied to explore the prognostic significance of hub genes. CIBERSORT analysis showed that macrophages increased significantly in DKD renal tissue samples. A total of ten modules were generated by weighted gene co-expression network analysis, of which the blue module was closely associated with macrophages. The blue module mainly played an important role in biological processes such as immune response and fibrosis. Fibronectin 1 (FN1) and transforming growth factor beta induced (TGFBI) were identified as hub genes of DKD patients. Receiver operating characteristic curve analysis was performed in the test cohort: FN1 and TGFBI had larger area under the curve values (0.99 and 0.88, respectively). Clinical validation showed that 2 hub genes were negatively correlated with the estimated glomerular filtration rate in DKD patients. In addition, FN1 and TGFBI showed a strong positive correlation with macrophage alternative activation. FN1 and TGFBI are promising biomarkers for the diagnosis and treatment of DKD patients, which may participate in immune response and fibrosis induced by macrophages.

Identification and verification of atrial fibrillation hub genes caused by primary mitral regurgitation

In the clinic, atrial fibrillation (AF) is a common arrhythmia. Despite constant innovation in treatments for AF, they remain limited by a lack of knowledge of the underlying mechanism responsible for AF. In this study, we examined the molecular mechanisms associated with primary mitral regurgitation (MR) in AF using several bioinformatics techniques. Limma was used to identify differentially expressed genes (DEGs) associated with AF using microarray data from the GSE115574 dataset. WGCNA was used to identify significant module genes. A functional enrichment analysis for overlapping genes between the DEGs and module genes was done and several AF hub genes were identified from a protein-protein interaction (PPI) network. Receiver operating characteristic (ROC) curves were generated to evaluate the validity of the hub genes. We examined 306 DEGs and 147 were upregulated and 159 were downregulated. WGCNA analysis revealed black and ivory modules that contained genes associated with AF. Functional enrichment analysis revealed various biological process terms related to AF. The AUCs for the 8 hub genes screened by the PPI network analysis were > 0.7, indicating satisfactory diagnostic accuracy. The 8 AF-related hub genes included SYT13, VSNL1, GNAO1, RGS4, RALYL, CPLX1, CHGB, and CPLX3. Our findings provide novel insight into the molecular mechanisms of AF and may lead to the development of new treatments.

Identification of two immune subtypes and four hub immune-related genes in ovarian cancer through multiple analysis

Immune classification of ovarian cancer (OV) becomes more and more influential for its immunotherapy. However, current studies had few immune subtypes of OV. It is urgent to explore the immune subtypes and deeper hub immune-related genes (IRGs) of OV for follow-up treatment. A total number of 379 OV samples were obtained from UCSC online website. Single sample gene set enrichment analysis of 29 immune gene sets was used for identifying immune subtypes of OV and gene set variation analysis were used for exploring the hallmarks and Kyoto Encyclopedia of Genes and Genomes pathways of immune types. Two immunity subtypes (Immunity_H and Immunity_L) were identified by single sample gene set enrichment analysis. The OV patients in Immunity_H group had longer overall survival compared with those in Immunity_L group. The Immunity_H had higher stromal score, immune score and estimate score and the tumor purity had the adverse tendency. Besides, the gene set variation analysis enrichment results showed positive relationship between improved immunoreaction and pathways correlated to classical signaling pathway (PI3K/AKT/MTOR, P53, TNFA/NFkB signaling pathways) and immune responses (T/B cell receptor signaling pathways and primary immunodeficiency). Furthermore, 4 hub IRGs (CCR5, IL10RA, ITGAL and PTPRC) were jointly dug by weighted gene co-expression network construction and Cytoscape. Our team also explored the mutations of 4 hub IRGs and PTPRC showed nearly 7% amplification. Besides, 8 immune-checkpoint genes had higher expression in Immuity_H group compared with Immuity_L group, except CD276. The correlation between PD-1/PD-L1 and 4 hub IRGs were explored and gene set enrichment analysis were conducted to explore the underlying mechanisms of PTPRC in OV. Finally, western-blotting showed PTPRC could regulate immune checkpoint PD-L1 expression via JAK-STAT signaling pathway. In a word, 2 immune subtypes and 4 hub IRGs of OV were identified by multiple analysis.

Mechanism of Chinese botanical drug Dizhi pill for myopia: An integrated study based on bioinformatics and network analysis

To identify the active constituents, core targets, immunomodulatory functions and potential mechanisms of Dizhi pill (DZP) in the treatment of myopia. The active constituents and drug targets of DZP were searched in the TCMSP, Herb databases and correlational studies. The targets of myopia were searched in the TTD, Genecards, OMIM and Drugbank databases. Gene expression profile data of GSE136701 were downloaded from the GEO database and subjected to WGCNA and DEG analysis to screen for significant modules and targets of myopia. Intersectional targets of myopia and DZP and core targets of myopia were analyzed through the String database. The GO and KEGG enrichment analyses of the interested targets were conducted. Cibersort algorithm was used for immune infiltration analysis to investigate the immunomodulatory functions of DZP on myopia. Autodock was used to dock the important targets and active constituents. Eight targets (STAT3, PIK3CA, PIK3R1, MAPK1, MAPK3, HSP90AA1, MIP, and LGSN) and 5 active constituents (Quercetin, Beta-sitosterol, Diincarvilone A, Ferulic acid methyl ester, and Naringenin) were identified from DZP. In pathways identified by the GO and KEGG enrichment analyses, "ATP metabolic process" and "AGE-RAGE diabetes complication signaling" pathways were closely related to the mechanisms of DZP in the treatment of myopia. Molecular docking showed that both the intersectional targets and core targets of myopia could bind stably and spontaneously with the active constituents of DZP. This study suggested that the mechanisms of DZP in the treatment of myopia were related to active constituents: Quercetin, Beta-sitosterol, Diincarvilone A, Ferulic acid methyl ester and Naringenin, intersectional targets: STAT3, PIK3CA, PIK3R1, MAPK1, MAPK3, and HSP90AA1, core targets of myopia: MIP and LGSN, AGE-RAGE signaling pathway, positive regulation of ATP metabolic process pathway and immunomodulatory functions.

Weighted gene co-expression network analysis identifies the prognosis-related models of left- and right-sided colon cancer

Left-sided colon cancer (LC) and right-sided colon cancer (RC) are 2 essentially different diseases, and the potential mechanisms regulating them remain unidentified. In this study, we applied weighted gene co-expression network analysis (WGCNA) to confirm a yellow module, mainly enriched in metabolism-related signaling pathways related to LC and RC. Based on the RNA-seq data of colon cancer in The Cancer Genome Atlas (TCGA) and GSE41258 dataset with their corresponding clinical information, a training set (TCGA: LC: n = 171; RC: n = 260) and a validation set (GSE41258: LC: n = 94; RC: n = 77) were divided. Least absolute shrinkage and selection operator (LASSO) penalized COX regression analysis identified 20 prognosis-related genes (PRGs) and helped constructed 2 risk (LC-R and RC-R) models in LC and RC, respectively. The model-based risk scores accurately performed in risk stratification for colon cancer patients. The high-risk group of the LC-R model showed associations with ECM-receptor interaction, focal adhesion, and PI3K-AKT signaling pathway. Interestingly, the low-risk group of the LC-R model showed associations with immune-related signaling pathways like antigen processing and presentation. On the other hand, the high-risk group of the RC-R model showed enrichment for cell adhesion molecules and axon guidance signaling pathways. Furthermore, we identified 20 differentially expressed PRGs between LC and RC. Our findings provide new insights into the difference between LC and RC, and uncover the potential biomarkers for the treatment of LC and RC.

Role of CCT4/ErbB signaling in nephroblastoma: Implications for a biomarker of Wilms tumor

Wilms tumor is a common abdominal malignant tumor in children. However, the molecular mechanism of Wilms tumor is unclear. GSE66405 and GSE197047 were obtained from the Gene Expression Omnibus database. To identify differentially expressed genes (DEGs) in Wilms tumor, the R package "limma" was used. Weighted gene co-expression network analysis was performed to identify the significant module. The list of DEGs was input into the Search Tool for the Retrieval of Interacting Genes database to construct a protein-protein interaction network for predicting core genes. Gene Ontology analysis and the Kyoto Encyclopedia of Genes and Genomes analysis are computational methods for assessing gene function and biological pathways. The genome was analyzed by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes and developed by gene set enrichment analysis. Comparative Toxicogenomics Database analysis was performed to find the diseases most related to the core genes. TargetScan was used to screen for miRNAs that regulate hub genes. A total of 925 DEGs were identified. The differently expressed genes were mainly enriched in the metabolic pathway, AMPK signaling pathway, ErbB signaling pathway, mRNA detection pathway, and folded protein binding. A total of 16 core genes (HNRNPK, PABPC1, HNRNPD, NCL, YBX1, EIF4G1, KHDRBS1, HNRNPAB, HSPA4, EEF2, HSP90AA1, EEF1A1, A TP5A1, SDHA, CCT4, CCT5) were obtained. chaperonin containing TCP-1 subunit 4 (CCT4) was downregulated in tumor tissue samples, which may have reverse regulatory significance for Wilms tumor. CCT4, HSP90AA1, NCL, PABPC1, and YBX1 were found to be associated with kidney disease, acute kidney injury, edema, tumor metastasis, transitional cell carcinoma, necrosis, and inflammation. The research found that the related miRNA of the CCT4 gene was hsamiR-7-5p. CCT4 might play an essential role in the occurrence and development of Wilms tumor, and they may participate in the occurrence and development of Wilms tumor through the ERBB signal pathway. CCT4 may be a promising biomarker of Wilms tumor.

Identification of hub genes related to Duchenne muscular dystrophy by weighted gene co-expression network analysis

BACKGROUND

The study was aimed to analyze the potential gene modules and hub genes of Duchenne muscular dystrophy (DMD) by weighted gene co-expression network analysis.

METHODS

Based on the muscular dystrophy tissue expression profiling microarray GSE13608 from gene expression omnibus, gene co-expression modules were analyzed using weighted gene co-expression network analysis, gene modules related to DMD were screened, gene ontology and Kyoto encyclopedia of genes and genomes enrichment analyses were performed, and signature genes in the modules were screened. The protein-protein interaction network was constructed through Cytoscape, and hub genes were identified. The expression of hub genes in DMD versus normal muscle tissue was calculated in GSE6011.

RESULTS

12 co-expressed gene modules were identified, among which black module was significantly related to DMD. The characteristic genes in the module were enriched in the regulation of immune effector processes, immune response mediated by immunoglobulin, immune response mediated by B cells, etc. SERPING1, F13A1, C1S, C1R, and HLA-DPA1 were considered as hub genes in protein-protein interaction network. Analysis of GSE6011 shows that expression of SERPING1, F13A1, C1S, C1R, and HLA-DPA1 in tissues of DMD patients were higher than normal.

CONCLUSION

SERPING1, F13A1, C1S, C1R, and HLA-DPA1 may participate in the development of DMD by regulating innate immunity and inflammation, and they are expected to be a potential biomarker and novel therapeutic targets for DMD.

IFI44L and C1QTNF5 as promising biomarkers of proliferative diabetic retinopathy

Proliferative diabetic retinopathy (PDR) is a world-wide leading cause of blindness among adults and may be associated with the influence of genetic factors. It is significant to search for genetic biomarkers of PDR. In our study, we collected genomic data about PDR from gene expression omnibus (GEO) database. Differentially expressed gene (DEG) analysis and weighted gene co-expression network analysis (WGCNA) were carried out. The gene module with the highest gene significance (GS) was defined as the key module. Hub genes were identified by Venn diagram. Then we verified the expression of hub genes in validation data sets and built a diagnostic model by least absolute shrinkage and selection operator (LASSO) regression. Enrichment analysis, including gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), gene set enrichment analysis (GSEA) and construction of a protein-protein interaction (PPI) network were conducted. In GSE60436, we identified 466 DEGs. WGCNA established 14 gene modules, and the blue module (GS = 0.64), was the key module. Interferon (IFN)-induced protein 44-like (IFI44L) and complement C1q tumor necrosis factor-related protein 5 (C1QTNF5) were identified as hub genes. The expression of hub genes in GEO datasets was verified and a diagnostic model was constructed by LASSO as follows: index = IFI44L * 0.0432 + C1QTNF5 * 0.11246. IFI44L and C1QTNF5 might affect the disease progression of PDR by regulating metabolism-related and inflammatory pathways. IFI44L and C1QTNF5 may play important roles in the disease process of PDR, and a LASSO regression model suggested that the 2 genes could serve as promising biomarkers of PDR.

Prognostic analysis and validation of diagnostic marker genes in patients with osteoporosis

Backgrounds

As a systemic skeletal dysfunction, osteoporosis (OP) is characterized by low bone mass and bone microarchitectural damage. The global incidences of OP are high.

Methods

Data were retrieved from databases like Gene Expression Omnibus (GEO), GeneCards, Search Tool for the Retrieval of Interacting Genes/Proteins (STRING), Gene Expression Profiling Interactive Analysis (GEPIA2), and other databases. R software (version 4.1.1) was used to identify differentially expressed genes (DEGs) and perform functional analysis. The Least Absolute Shrinkage and Selection Operator (LASSO) logistic regression and random forest algorithm were combined and used for screening diagnostic markers for OP. The diagnostic value was assessed by the receiver operating characteristic (ROC) curve. Molecular signature subtypes were identified using a consensus clustering approach, and prognostic analysis was performed. The level of immune cell infiltration was assessed by the Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) algorithm. The hub gene was identified using the CytoHubba algorithm. Real-time fluorescence quantitative PCR (RT-qPCR) was performed on the plasma of osteoporosis patients and control samples. The interaction network was constructed between the hub genes and miRNAs, transcription factors, RNA binding proteins, and drugs.

Results

A total of 40 DEGs, eight OP-related differential genes, six OP diagnostic marker genes, four OP key diagnostic marker genes, and ten hub genes (TNF, RARRES2, FLNA, STXBP2, EGR2, MAP4K2, NFKBIA, JUNB, SPI1, CTSD) were identified. RT-qPCR results revealed a total of eight genes had significant differential expression between osteoporosis patients and control samples. Enrichment analysis showed these genes were mainly related to MAPK signaling pathways, TNF signaling pathway, apoptosis, and Salmonella infection. RT-qPCR also revealed that the MAPK signaling pathway (p38, TRAF6) and NF-kappa B signaling pathway (c-FLIP, MIP1β) were significantly different between osteoporosis patients and control samples. The analysis of immune cell infiltration revealed that monocytes, activated CD4 memory T cells, and memory and naïve B cells may be related to the occurrence and development of OP.

Conclusions

We identified six novel OP diagnostic marker genes and ten OP-hub genes. These genes can be used to improve the prognostic of OP and to identify potential relationships between the immune microenvironment and OP. Our research will provide insights into the potential therapeutic targets and pathogenesis of osteoporosis.

Identification of key genes involved in the recurrence of glioblastoma multiforme using weighted gene co-expression network analysis and differential expression analysis

Glioblastoma multiforme (GBM) is the most fatal malignancy, and despite extensive treatment, tumors inevitably recur. This study aimed to identify recurrence-associated molecules in GBM. The gene expression profile GSE139533, containing 70 primary and 47 recurrent GBM tissues and their corresponding clinical traits, was downloaded from the Gene Expression Omnibus (GEO) database and used for weighted gene co-expression network analysis (WGCNA) and differentially expressed gene (DEG) analysis. After identifying the hub genes which differentially expressed in recurrent GBM tissues and in the gene modules correlated with recurrence, data from the Chinese Glioma Genome Atlas (CCGA) and The Cancer Genome Atlas (TCGA) databases were analyzed with GSE43378 to determine the relationship between hub genes and patient prognosis. The diagnostic value of the identified hub genes was verified using 52 GBM tissues. Three gene modules were correlated with recurrence and 2623 genes were clustered in these clinically significant modules. Among these, 13 genes - EHF, TRPM1, FXYD4, CDH15, LHX5, TP73, FBN3, TLX1, C1QL4, COL2A, SEC61G, NEUROD4 and GPR139 - were differentially expressed in recurrent GBM samples; low LHX5 and TLX1 expression predicted poor outcomes. LHX5 and TLX1 expression showed weak positive relationships with Karnofsky performance scale scores. Additionally, LHX5 and TLX1 expression was found to be decreased in our recurrent GBM samples compared with that in primary samples; these genes exhibited high diagnostic value in distinguishing recurrent samples from primary samples. Our findings indicate that LHX5 and TLX1 might be involved in GBM recurrence and act as potential biomarkers for this condition.

Construction of a 6-gene prognostic signature to assess prognosis of patients with pancreatic cancer

Pancreatic cancer (PaCa) is one of the most fatal cancers in the world. Although great efforts have made to explore the mechanisms of PaCa oncogenesis, the prognosis of PaCa patients is still unsatisfactory. Thus, it is imperative to further understand the potential carcinogenesis of PaCa and reliable prognostic models.The gene expression profile and clinical information of GSE21501 were downloaded from the Gene Expression Omnibus (GEO) database. Weighted gene co-expression network analysis (WGCNA) was applied to explore the potent genes associated with the overall survival (OS) events of PaCa patients. Cox regression model was applied to selecting prognostic genes and establish prognostic model. The prognostic values of six-gene signature were validated in TCGA-PAAD cohort.According to the WGCNA analysis, a total of 19 modules were identified and 115 hub genes in the mostly associated module were reserved for next analysis. According to the univariate and multivariate Cox regression analysis, we established a six-gene signature (FTSJ3, STAT1, STX2, CDX2, RASSF4, MACF1) which could effectively evaluate the overall survival (OS) of PaCa patients. In validated patients' cohorts, the six-gene signature exhibited excellent prognostic value in TCGA-PAAD cohort as well.We developed a six-gene signature to exactly predict OS of PaCa patients and provide a novel personalized strategy for evaluating prognosis. The findings may be contributed to medical customization and therapeutic decision in clinical practice.

Comprehensive analysis of biomarkers for prostate cancer based on weighted gene co-expression network analysis

BACKGROUND

Prostate cancer (PCa) is one of the leading causes of cancer-related death. In the present research, we adopted a comprehensive bioinformatics method to identify some biomarkers associated with the tumor progression and prognosis of PCa.

METHODS

Differentially expressed genes (DEGs) analysis and weighted gene co-expression network analysis (WGCNA) were applied for exploring gene modules correlative with tumor progression and prognosis of PCa. Clinically Significant Modules were distinguished, and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were used to Annotation, Visualization and Integrated Discovery (DAVID). Protein-protein interaction (PPI) networks were used in selecting potential hub genes. RNA-Seq data and clinical materials of prostate cancer from The Cancer Genome Atlas (TCGA) database were used for the identification and validation of hub genes. The significance of these genes was confirmed via survival analysis and immunohistochemistry.

RESULTS

2688 DEGs were filtered. Weighted gene co-expression network was constructed, and DEGs were divided into 6 modules. Two modules were selected as hub modules which were highly associated with the tumor grades. Functional enrichment analysis was performed on genes in hub modules. Thirteen hub genes in these hub modules were identified through PPT networks. Based on TCGA data, 4 of them (CCNB1, TTK, CNN1, and ACTG2) were correlated with prognosis. The protein levels of CCNB1, TTK, and ACTG2 had a degree of differences between tumor tissues and normal tissues.

CONCLUSION

Four hub genes were identified as candidate biomarkers and potential therapeutic targets for further studies of exploring molecular mechanisms and individual therapy on PCa.

Pan-cancer RNA-seq data stratifies tumours by some hallmarks of cancer

Numerous genetic and epigenetic alterations cause functional changes in cell biology underlying cancer. These hallmark functional changes constitute potentially tissue-independent anticancer therapeutic targets. We hypothesized that RNA-Seq identifies gene expression changes that underly those hallmarks, and thereby defines relevant therapeutic targets. To test this hypothesis, we analysed the publicly available TCGA-TARGET-GTEx gene expression data set from the University of California Santa CruzToil recompute project using WGCNA to delineate co-correlated 'modules' from tumour gene expression profiles and functional enrichment of these modules to hierarchically cluster tumours. This stratified tumours according to T cell activation, NK-cell activation, complement cascade, ATM, Rb, angiogenic, MAPK, ECM receptor and histone modification signalling. These correspond to the cancer hallmarks of avoiding immune destruction, tumour-promoting inflammation, evading growth suppressors, inducing angiogenesis, sustained proliferative signalling, activating invasion and metastasis, and genome instability and mutation. This approach did not detect pathways corresponding to the cancer enabling replicative immortality, resisting cell death or deregulating cellular energetics hallmarks. We conclude that RNA-Seq stratifies tumours along some, but not all, hallmarks of cancer and, therefore, could be used in conjunction with other analyses collectively to inform precision therapy.

Identification of modules and hub genes associated with platinum-based chemotherapy resistance and treatment response in ovarian cancer by weighted gene co-expression network analysis

High-grade serous ovarian carcinoma (HGSOC) is the most prevalent and malignant ovarian tumor.To identify co-expression modules and hub genes correlated with platinum-based chemotherapy resistant and sensitive HGSOC, we performed weighted gene co-expression network analysis (WGCNA) on microarray data of HGSOC with 12 resistant samples and 16 sensitive samples of GSE51373 dataset.A total of 5122 genes were included in WGCNA, and 16 modules were identified. Module-trait analysis identified that the module salmon (cor = 0.50), magenta (cor = 0.49), and black (cor = 0.45) were discovered associated with chemotherapy resistant, and the significance for these platinum-resistant modules were validated in the GSE63885 dataset. Given that the black module was validated to be the most related one, hub genes of this module, alcohol dehydrogenase 1B, cadherin 11, and vestigial like family member 3were revealed to be expressional related with platinum resistance, and could serve as prognostic markers for ovarian cancer.Our analysis might provide insight for molecular mechanisms of platinum-based chemotherapy resistance and treatment response in ovarian cancer.

Using weighted gene co-expression network analysis to identify key modules and hub genes in tongue squamous cell carcinoma

BACKGROUND

Tongue squamous cell carcinoma (TSCC) is one of the most common malignant tumors in head and neck, but its molecular mechanism is not clear.

METHODS

Weighted gene co-expression network analysis (WGCNA) combining with gene differential expression analysis, survival analysis to screen key modules and hub genes related to the progress of TSCC. Gene Set Enrichment Analysis (GSEA) was used to identify biological pathways that might be involved.

RESULTS

Weighted gene co-expression network was constructed based on dataset GSE34105. The blue module and turquoise module most related to the progress of TSCC were identified by the network. Gene Ontology (GO) enrichment analysis showed that 2 key modules were significantly enriched in apoptosis and immunity related biological processes and pathway. Network topology analysis, gene difference analysis and survival analysis were used to screen 9 hub genes (NOC2L, AIMP2, ANXA2, DIABLO, H2AFZ, MANBAL, PRDX6, SNX14, TIMM23). The expression of hub genes was significantly correlated with the prognosis of TSCC. GSEA showed that the high expression group of hub genes was mainly enriched in olfactory transduction, neuroactive ligand receptor interaction, nicotinate and nicotinamide metabolism, and the low expression group was mainly enriched in base excision repair, cysteine and methionine metabolism, oxidative phosphorylation.

CONCLUSION

Two key modules and 9 hub genes screened by WGCNA were closely related to the occurrence and prognosis of TSCC. Hub genes can be used as biomarkers and potential therapeutic targets for the accurate diagnosis and treatment of TSCC in the future.

The relevance between the immune response-related gene module and clinical traits in head and neck squamous cell carcinoma

Purpose

Head and neck squamous cell carcinoma (HNSCC) is the sixth most prevalent cancer in the world, accounting for more than 90% of head and neck malignant tumors. However, its molecular mechanism is largely unknown. To help elucidate the potential mechanism of HNSCC tumorigenesis, we investigated the gene interaction patterns associated with tumorigenesis.

Methods

Weighted gene co-expression network analysis (WGCNA) can help us to predict the intrinsic relationship or correlation between gene expression. Additionally, we further explored the combination of clinical information and module construction.

Results

Sixteen modules were constructed, among which the key module most closely associated with clinical information was identified. By analyzing the genes in this module, we found that the latter may be related to the immune response, inflammatory response and formation of the tumor microenvironment. Sixteen hub genes were identified-ARHGAP9, SASH3, CORO1A, ITGAL, PPP1R16B, TBC1D10C, IL10RA, ITK, AKNA, PRKCB, TRAF3IP3, GIMAP4, CCR7, P2RY8, GIMAP7, and SP140. We further validated these genes at the transcriptional and translation levels.

Conclusion

The innovative use of a weighted network to analyze HNSCC samples provides new insights into the molecular mechanism and prognosis of HNSCC. Additionally, the hub genes we identified can be used as biomarkers and therapeutic targets of HNSCC, laying the foundation for the accurate diagnosis and treatment of HNSCC in clinical and research in the future.

Weighted gene co-expression network analysis reveals modules and hub genes associated with the development of breast cancer

This study aimed to identify modules associated with breast cancer (BC) development by constructing a gene co-expression network, and mining hub genes that may serve as markers of invasive breast cancer (IBC).We downloaded 2 gene expression datasets from the Gene Expression Omnibus (GEO) database, and used weighted gene co-expression network analysis (WGCNA) to dynamically study the changes of co-expression genes in normal breast tissues, ductal carcinoma in situ (DCIS) tissues, and IBC tissues. Modules that highly correlated with BC development were carried out functional enrichment analysis for annotation, visualization, and integration discovery. The hub genes detected by WGCNA were also confirmed using the Oncomine dataset.We detected 17 transcriptional modules in total and 4 - namely tan, greenyellow, turquoise, and brown - were highly correlated with BC development. The functions of these 4 modules mainly concerned cell migration (tan module, P = 3.03 × 10), the cell cycle (greenyellow module, P = 3.08 × 10), cell-cell adhesion (turquoise module, P = .002), and the extracellular exosome (brown module, P = 1.38 × 10). WGCNA also mined the hub genes, which were highly correlated with the genes in the same module and with BC development. The Oncomine database confirmed that the expressions levels of 6 hub genes were significantly higher in BC tissues than in normal tissues, with fold changes larger than 2 (all P < .05). Apart from the 2 well-known genes EPCAM and MELK, during the development of BC, KRT8, KRT19, KPNA2, and ECT2 also play key roles, and may be used as new targets for the detection or treatment of BC.In summary, our study demonstrated that hub genes such as EPCAM and MELK are highly correlated with breast cancer development. However, KRT8, KRT19, KPNA2, and ECT2 may also have potential as diagnostic and prognostic biomarkers of IBC.

Identification of gene expression models for laryngeal squamous cell carcinoma using co-expression network analysis

One of the most common head and neck cancers is laryngeal squamous cell carcinoma (LSCC). LSCC exhibits high mortality rates and has a poor prognosis. The molecular mechanisms leading to the development and progression of LSCC are not entirely clear despite genetic and therapeutic advances and increased survival rates. In this study, a total of 116 differentially expressed genes (DEGs), including 11 upregulated genes and 105 downregulated genes, were screened from LSCC samples and compared with adjacent noncancerous. Statistically significant differences (log 2-fold difference > 0.5 and adjusted P-value < .05) were found in this study in the expression between tumor and nontumor larynx tissue samples. Nine cancer hub genes were found to have a high predictive power to distinguish between tumor and nontumor larynx tissue samples. Interestingly, they also appear to contribute to the progression of LSCC and malignancy via the Jak-STAT signaling pathway and focal adhesion. The model could separate patients into high-risk and low-risk groups successfully when only using the expression level of mRNA signatures. A total of 4 modules (blue, gray, turquoise, and yellow) were screened for the DEGs in the weighted co-expression network. The blue model includes cancer-specific pathways such as pancreatic cancer, bladder cancer, nonsmall cell lung cancer, colorectal cancer, glioma, Hippo signaling pathway, melanoma, chronic myeloid leukemia, prostate cancer, and proteoglycans in cancer. Endocrine resistance (CCND1, RAF1, RB1, and SMAD2) and Hippo signaling pathway (CCND1, LATS1, SMAD2, and TP53BP2) could be of importance in LSCC, because they had high connectivity degrees in the blue module. Results from this study provide a powerful biomarker discovery platform to increase understanding of the progression of LSCC and to reveal potential therapeutic targets in the treatment of LSCC. Improved monitoring of LSCC and resulting improvement of treatment of LSCC might result from this information.

ADAPTIVE TESTING OF SNP-BRAIN FUNCTIONAL CONNECTIVITY ASSOCIATION VIA A MODULAR NETWORK ANALYSIS

Due to its high dimensionality and high noise levels, analysis of a large brain functional network may not be powerful and easy to interpret; instead, decomposition of a large network into smaller subcomponents called modules may be more promising as suggested by some empirical evidence. For example, alteration of brain modularity is observed in patients suffering from various types of brain malfunctions. Although several methods exist for estimating brain functional networks, such as the sample correlation matrix or graphical lasso for a sparse precision matrix, it is still difficult to extract modules from such network estimates. Motivated by these considerations, we adapt a weighted gene co-expression network analysis (WGCNA) framework to resting-state fMRI (rs-fMRI) data to identify modular structures in brain functional networks. Modular structures are identified by using topological overlap matrix (TOM) elements in hierarchical clustering. We propose applying a new adaptive test built on the proportional odds model (POM) that can be applied to a high-dimensional setting, where the number of variables (p) can exceed the sample size (n) in addition to the usual p < n setting. We applied our proposed methods to the ADNI data to test for associations between a genetic variant and either the whole brain functional network or its various subcomponents using various connectivity measures. We uncovered several modules based on the control cohort, and some of them were marginally associated with the APOE4 variant and several other SNPs; however, due to the small sample size of the ADNI data, larger studies are needed.

Transcriptomic plasticity in brown adipose tissue contributes to an enhanced capacity for nonshivering thermogenesis in deer mice

For small mammals living at high altitude, aerobic heat generation (thermogenesis) is essential for survival during prolonged periods of cold, but is severely impaired under conditions of hypobaric hypoxia. Recent studies in deer mice (Peromyscus maniculatus) reveal adaptive enhancement of thermogenesis in high- compared to low-altitude populations under hypoxic cold stress, an enhancement that is attributable to modifications in the aerobic metabolism of muscles used in shivering. However, because small mammals rely heavily on nonshivering mechanisms for cold acclimatization, we tested for evidence of adaptive divergence in nonshivering thermogenesis (NST) under hypoxia. To do so, we measured NST and characterized transcriptional profiles of brown adipose tissue (BAT) in high- and low-altitude deer mice that were (i) wild-caught and acclimatized to their native altitude, and (ii) born and reared under common garden conditions at low elevation. We found that NST performance under hypoxia is enhanced in wild-caught, high-altitude deer mice, a difference that is associated with increased expression of coregulated genes that influence several physiological traits. These traits include vascularization and O2 supply to BAT, brown adipocyte proliferation and the uncoupling of oxidative phosphorylation from ATP synthesis in the generation of heat. Our results suggest that acclimatization to hypoxic cold stress is facilitated by enhancement of nonshivering heat production, which is driven by regulatory plasticity in a suite of genes that influence intersecting physiological pathways.

RNA-sequencing reveals early, dynamic transcriptome changes in the corollas of pollinated petunias

BACKGROUND

Pollination reduces flower longevity in many angiosperms by accelerating corolla senescence. This response requires hormone signaling between the floral organs and results in the degradation of macromolecules and organelles within the petals to allow for nutrient remobilization to developing seeds. To investigate early pollination-induced changes in petal gene expression, we utilized high-throughput sequencing to identify transcripts that were differentially expressed between corollas of pollinated Petunia × hybrida flowers and their unpollinated controls at 12, 18, and 24 hours after opening.

RESULTS

In total, close to 0.5 billion Illumina 101 bp reads were generated, de novo assembled, and annotated, resulting in an EST library of approximately 33 K genes. Over 4,700 unique, differentially expressed genes were identified using comparisons between the pollinated and unpollinated libraries followed by pairwise comparisons of pollinated libraries to unpollinated libraries from the same time point (i.e. 12-P/U, 18-P/U, and 24-P/U) in the Bioconductor R package DESeq2. Over 500 gene ontology terms were enriched. The response to auxin stimulus and response to 1-aminocyclopropane-1-carboxylic acid terms were enriched by 12 hours after pollination (hap). Using weighted gene correlation network analysis (WGCNA), three pollination-specific modules were identified. Module I had increased expression across pollinated corollas at 12, 18, and 24 h, and modules II and III had a peak of expression in pollinated corollas at 18 h. A total of 15 enriched KEGG pathways were identified. Many of the genes from these pathways were involved in metabolic processes or signaling. More than 300 differentially expressed transcription factors were identified.

CONCLUSIONS

Gene expression changes in corollas were detected within 12 hap, well before fertilization and corolla wilting or ethylene evolution. Significant changes in gene expression occurred at 18 hap, including the up-regulation of autophagy and down-regulation of ribosomal genes and genes involved in carbon fixation. This transcriptomic database will greatly expand the genetic resources available in petunia. Additionally, it will guide future research aimed at identifying the best targets for increasing flower longevity by delaying corolla senescence.

Two gene co-expression modules differentiate psychotics and controls

Schizophrenia (SCZ) and bipolar disorder (BD) are highly heritable psychiatric disorders. Associated genetic and gene expression changes have been identified, but many have not been replicated and have unknown functions. We identified groups of genes whose expressions varied together, that is co-expression modules, then tested them for association with SCZ. Using weighted gene co-expression network analysis, we show that two modules were differentially expressed in patients versus controls. One, upregulated in cerebral cortex, was enriched with neuron differentiation and neuron development genes, as well as disease genome-wide association study genetic signals; the second, altered in cerebral cortex and cerebellum, was enriched with genes involved in neuron protection functions. The findings were preserved in five expression data sets, including sets from three brain regions, from a different microarray platform, and from BD patients. From those observations, we propose neuron differentiation and development pathways may be involved in etiologies of both SCZ and BD, and neuron protection function participates in pathological process of the diseases.

OPTIMAS-DW: a comprehensive transcriptomics, metabolomics, ionomics, proteomics and phenomics data resource for maize

BACKGROUND

Maize is a major crop plant, grown for human and animal nutrition, as well as a renewable resource for bioenergy. When looking at the problems of limited fossil fuels, the growth of the world's population or the world's climate change, it is important to find ways to increase the yield and biomass of maize and to study how it reacts to specific abiotic and biotic stress situations. Within the OPTIMAS systems biology project maize plants were grown under a large set of controlled stress conditions, phenotypically characterised and plant material was harvested to analyse the effect of specific environmental conditions or developmental stages. Transcriptomic, metabolomic, ionomic and proteomic parameters were measured from the same plant material allowing the comparison of results across different omics domains. A data warehouse was developed to store experimental data as well as analysis results of the performed experiments.

DESCRIPTION

The OPTIMAS Data Warehouse (OPTIMAS-DW) is a comprehensive data collection for maize and integrates data from different data domains such as transcriptomics, metabolomics, ionomics, proteomics and phenomics. Within the OPTIMAS project, a 44K oligo chip was designed and annotated to describe the functions of the selected unigenes. Several treatment- and plant growth stage experiments were performed and measured data were filled into data templates and imported into the data warehouse by a Java based import tool. A web interface allows users to browse through all stored experiment data in OPTIMAS-DW including all data domains. Furthermore, the user can filter the data to extract information of particular interest. All data can be exported into different file formats for further data analysis and visualisation. The data analysis integrates data from different data domains and enables the user to find answers to different systems biology questions. Finally, maize specific pathway information is provided.

CONCLUSIONS

With OPTIMAS-DW a data warehouse for maize was established, which is able to handle different data domains, comprises several analysis results that will support researchers within their work and supports systems biological research in particular. The system is available at http://www.optimas-bioenergy.org/optimas_dw.