Screening genes crucial for pediatric pilocytic astrocytoma using weighted gene coexpression network analysis combined with methylation data analysis

Disclosing transcriptomics network-based signatures of glioma heterogeneity using sparse methods

Gliomas are primary malignant brain tumors with poor survival and high resistance to available treatments. Improving the molecular understanding of glioma and disclosing novel biomarkers of tumor development and progression could help to find novel targeted therapies for this type of cancer. Public databases such as The Cancer Genome Atlas (TCGA) provide an invaluable source of molecular information on cancer tissues. Machine learning tools show promise in dealing with the high dimension of omics data and extracting relevant information from it. In this work, network inference and clustering methods, namely Joint Graphical lasso and Robust Sparse K-means Clustering, were applied to RNA-sequencing data from TCGA glioma patients to identify shared and distinct gene networks among different types of glioma (glioblastoma, astrocytoma, and oligodendroglioma) and disclose new patient groups and the relevant genes behind groups' separation. The results obtained suggest that astrocytoma and oligodendroglioma have more similarities compared with glioblastoma, highlighting the molecular differences between glioblastoma and the others glioma subtypes. After a comprehensive literature search on the relevant genes pointed our from our analysis, we identified potential candidates for biomarkers of glioma. Further molecular validation of these genes is encouraged to understand their potential role in diagnosis and in the design of novel therapies.

Deciphering the role of DOCK8 in tumorigenesis by regulating immunity and the application of nanotechnology in DOCK8 deficiency therapy

The dedicator of cytokinesis 8 (DOCK8) immunodeficiency syndrome is a severe immune disorder and characterized by serum IgE levels elevation, fungal and viral infections, dermatitis and food allergies. It was well known that DOCK8 is crucial for the survival and function of multiple immune related cells. However, the critical role of DOCK8 on tumorigenesis through regulating immunity is poorly investigated. Accumulating evidences indicated that DOCK8 could affect tumorigenesis by regulating the immunity through immune cells, including NK cells, T cells, B cells and dendritic cells. Here, we summarized and discussed the critical role of DOCK8 in cytoskeleton reconstruction, CD4+ T cell differentiation, immune synaptic formation, tumor immune infiltration, tumor immune surveillance and tumorigenesis. Furthermore, the potential roles of nanotechnology in improving the hematopoietic stem cell transplantation-based therapy for DOCK8 deficiency diseases are also highlighted and discussed.

Potential biomarkers of acute myocardial infarction based on co‑expression network analysis

Acute myocardial infarction (AMI) is a common cause of death in numerous countries. Understanding the molecular mechanisms of the disease and analyzing potential biomarkers of AMI is crucial. However, specific diagnostic biomarkers have thus far not been fully established and candidate regulatory targets for AMI remain to be determined. In the present study, the AMI gene chip dataset GSE48060 comprising blood samples from control subjects with normal cardiac function (n=21) and patients with AMI (n=26) was downloaded from Gene Expression Omnibus. The differentially expressed genes (DEGs) between the AMI and control groups were identified with the online tool GEO2R. The co-expression network of DEGs was analyzed by calculating the Pearson correlation coefficient of all gene pairs, mutual rank screening and cutoff threshold screening. Subsequently, the Gene Ontology (GO) database was used to analyze the genes' functions and pathway enrichment of genes in the most important modules was performed. Kyoto Encyclopedia of Genes and Genomes (KEGG) Disease and BioCyc were used to analyze the hub genes in the module to determine important sub-pathways. In addition, the expression of hub genes was confirmed by reverse transcription-quantitative PCR in AMI and control specimens. In the present study, 52 DEGs, including 26 upregulated and 26 downregulated genes, were identified. As key hub genes, three upregulated genes (AKR1C3, RPS24 and P2RY12) and three downregulated genes (ACSL1, B3GNT5 and MGAM) were identified from the co-expression network. Furthermore, GO enrichment analysis of all AMI co-expression network genes revealed functional enrichment mainly in ‘RAGE receptor binding’ and ‘negative regulation of T cell cytokine production’. In addition, KEGG Disease and BioCyc analysis indicated functional enrichment of the genes RPS24 and P2RY12 in ‘cardiovascular diseases’, of AKR1C3 in ‘cardenolide biosynthesis’, of MGAM in ‘glycogenolysis’, of B3GNT5 in ‘glycosphingolipid biosynthesis’ and of ACSL1 in ‘icosapentaenoate biosynthesis II’. In conclusion, the hub genes AKR1C3, RPS24, P2RY12, ACSL1, B3GNT5 and MGAM are potential markers of AMI, and have potential application value in the diagnosis of AMI.

High Expression of PDE8B and DUOX2 Associated with Ability of Metastasis in Thyroid Carcinoma

BACKGROUND

Hormone is an independent factor that induces differentiation of thyroid cancer (TC) cells. The thyroid-stimulating hormone (TSH) could promote the progression and invasion in TC cells. However, few genes related to hormone changes are studied in poorly differentiated metastatic TC. This study is aimed at constructing a gene set's coexpression correlation network and verifying the changes of some hub genes involved in regulating hormone levels.

METHODS

Microarray datasets of TC samples were obtained from public Gene Expression Omnibus (GEO) databases. R software and bioinformatics packages were utilized to identify the differentially expressed genes (DEGs), important gene module eigengenes, and hub genes. Subsequently, the Gene Ontology (GO) enrichment analysis was constructed to explore important biological processes that are associated with the mechanism of poorly differentiated TC. Finally, some hub gene expressions were validated through real-time PCR and immunoblotting.

RESULTS

Gene chip with category number GSE76039 was analyzed, and 1190 DEGs were screened with criteria of P < 0.05 and ∣log2foldchange | >2. Our analysis showed that human dual oxidase 2 (DUOX2) and phosphodiesterase 8B (PDE8B) are the two important hub genes in a coexpression network. In addition, the validated experimental results showed that the expression levels of both DUOX2 and PDE8B were elevated in poorly differentiated metastatic TC tissues.

CONCLUSION

This study identified and validated that DUOX2 and PDE8B were significantly associated with the metastasis ability of thyroid carcinoma.

Identification potential epigenetic biomarkers of a human immunodeficiency virus/tuberculosis co-infection based on weighted gene co-expression network analysis

Tuberculosis (TB) is one of the most common opportunistic infections and a leading cause of death in patients infected with human immunodeficiency virus (HIV). However, conventional diagnostic tools have several limitations. The aim of this study was to screen key DNA methylated cytosine-phosphate-guanine dinucleotide (CpG) islands (CGIs) to identify potential diagnosis biomarkers in HIV mono-infected patients and HIV/TB co-infected patients based on a network analysis. The GSE50835 DNA methylation microarray data were downloaded from the Gene Expression Omnibus (GEO) database. Differentially methylated CpG islands analysis, weighted gene co-expression network analysis (WGCNA), and least absolute shrinkage and selection operator (LASSO) logistic regression were performed in 19 HIV mono-infected patients and 20 HIV/TB co-infected patients. In total, 1950 differentially methylated CpG islands were identified, and weighted co-methylation network construction and module preservation revealed one network module that can distinguish the HIV/TB co-infected patients from the HIV mono-infected patients. Based on the LASSO logistic regression, an eight-methylated CpG island diagnosis model was established that can accurately distinguish HIV/TB co-infected patients from HIV mono-infected patients with a sensitivity of 87.2%, a specificity of 88.7%, and an area under the receiver operating characteristic (ROC) curve (AUC) of 0.948. Alteration in the eight-DNA methylated CpG sites might be involved in the pathology of an HIV/TB co-infection and could be used as potential diagnosis biomarkers in HIV/TB co-infected patients.

Identification of collagen genes related to immune infiltration and epithelial-mesenchymal transition in glioma

Background

Gliomas account for the majority of fatal primary brain tumors, and there is much room for research in the underlying pathogenesis, the multistep progression of glioma, and how to improve survival. In our study, we aimed to identify potential biomarkers or therapeutic targets of glioma and study the mechanism underlying the tumor progression.

Methods

We downloaded the microarray datasets (GSE43378 and GSE7696) from the Gene Expression Omnibus (GEO) database. Then, we used weighted gene co-expression network analysis (WGCNA) to screen potential biomarkers or therapeutic targets related to the tumor progression. ESTIMATE (Estimation of STromal and Immune cells in MAlignant Tumors using Expression data) algorithm and TIMER (Tumor Immune Estimation Resource) database were used to analyze the correlation between the selected genes and the tumor microenvironment. Real-time reverse transcription polymerase chain reaction was used to measure the selected gene. Transwell and wound healing assays were used to measure the cell migration and invasion capacity. Western blotting was used to test the expression of epithelial-mesenchymal transition (EMT) related markers.

Results

We identified specific module genes that were positively correlated with the WHO grade but negatively correlated with OS of glioma. Importantly, we identified that 6 collagen genes (COL1A1, COL1A2, COL3A1, COL4A1, COL4A2, and COL5A2) could regulate the immunosuppressive microenvironment of glioma. Moreover, we found that these collagen genes were significantly involved in the EMT process of glioma. Finally, taking COL3A1 as a further research object, the results showed that knockdown of COL3A1 significantly inhibited the migration, invasion, and EMT process of SHG44 and A172 cells.

Conclusions

In summary, our study demonstrated that collagen genes play an important role in regulating the immunosuppressive microenvironment and EMT process of glioma and could serve as potential therapeutic targets for glioma management.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-021-01982-0.

LGALS4 as a Prognostic Factor in Urothelial Carcinoma of Bladder Affects Cell Functions

BACKGROUND

To identify the hub genes related to urothelial carcinoma of the bladder prognosis and to understand their underlying mechanism.

METHODS

The expression profiles of 18 pairs of urothelial carcinoma of the bladder patient tissue and paired adjacent tissue obtained from the Cancer Genome Atlas were performed. Weighted gene coexpression network analysis was employed to screen gene modules and hub genes with significant differential expressions in urothelial carcinoma of the bladder. The hub genes expression in urothelial carcinoma of the bladder tissues was validated by reverse transcription-quantitative polymerase chain reaction. The overall survival curve and disease-free survival curve of prognostic factor (LGALS4) were plotted using the Kaplan-Meier method. Furthermore, LGALS4 messenger RNA and protein expression were also assessed in 2 urothelial carcinoma of the bladder cell lines (T24 and 5637) by quantitative reverse transcription-polymerase chain reaction and Western blot. The functions of urothelial carcinoma of the bladder cells with transfected pcDNA3.1-LGALS4 were identified through MTT assay, plate clone formation assay, flow cytometry, and cell migration experiments.

RESULTS

LGALS4 was the hub gene of pink module and it was related to prognosis. Higher LGALS4 expression predicted higher probabilities of overall survival and disease-free survival. Overexpression of LGALS4 in urothelial carcinoma of the bladder cells suppressed cell viability and migration but induced apoptosis.

CONCLUSION

LGALS4 played a critical role in the progression of urothelial carcinoma of the bladder and held a promise to be the biomarker for diagnosis and treatment of urothelial carcinoma of the bladder. It predicted good prognosis of urothelial carcinoma of the bladder and restrained the growth and migration of urothelial carcinoma of the bladder cells.

Transcriptome sequencing identified hub genes for hepatocellular carcinoma by weighted-gene co-expression analysis

Hepatocellular carcinoma (HCC) is one of the most common malignancies worldwide, and it remains a challenge to understand the genetic mechanisms underlying hepatocarcinogenesis. A global gene network of differential expression profiles in HCC has yet to be fully characterized. In the present study, we performed transcriptome sequencing (mRNA and lncRNA) in liver cancer and cirrhotic tissues of nine HCC patients. We identified differentially expressed genes (DEGs) and constructed a weighted gene co-expression network for the DEGs. In total, 755 DEGs (747 mRNA and eight lncRNA) were identified, and several co-expression modules were significantly associated with HCC clinical traits, including tumor location, tumor grade, and the α-fetoprotein (AFP) level. Of note, we identified 15 hub genes in the module associated with AFP level, and three (SPX, AFP and ADGRE1) of four hub genes were validated in an independent HCC cohort (n=78). Identification of hub genes for HCC clinical traits has implications for further understanding of the molecular genetic basis of HCC.

Gene Expression Analysis Reveals Novel Shared Gene Signatures and Candidate Molecular Mechanisms between Pemphigus and Systemic Lupus Erythematosus in CD4+ T Cells

Pemphigus and systemic lupus erythematosus (SLE) are severe potentially life-threatening autoimmune diseases. They are classified as B-cell-mediated autoimmune diseases, both depending on autoreactive CD4⁺ T lymphocytes to modulate the autoimmune B-cell response. Despite the reported association of pemphigus and SLE, the molecular mechanisms underlying their comorbidity remain unknown. Weighted gene co-expression network analysis (WGCNA) of publicly available microarray datasets of CD4⁺ T cells was performed, to identify shared gene expression signatures and putative overlapping biological molecular mechanisms between pemphigus and SLE. Using WGCNA, we identified 3,280 genes co-expressed genes and 14 co-expressed gene clusters, from which one was significantly upregulated for both diseases. The pathways associated with this module include type-1 interferon gamma and defense response to viruses. Network-based meta-analysis identified RSAD2 to be the most highly ranked hub gene. By associating the modular genes with genome-wide association studies (GWASs) for pemphigus and SLE, we characterized IRF8 and STAT1 as key regulatory genes. Collectively, in this in silico study, we identify novel candidate genetic markers and pathways in CD4⁺ T cells that are shared between pemphigus and SLE, which in turn may facilitate the identification of novel therapeutic targets in these diseases.

Comprehensive analysis and experimental verification of LINC01314 as a tumor suppressor in hepatoblastoma

Hepatoblastoma (HB), as a common pediatric liver malignancy, is composed of a variety of subgroups with different clinical outcomes. Long-noncoding RNA (lncRNA) has crucial roles in cancer biology. However, the association between lncRNA and HB has not been fully investigated. In this study, we screened lncRNA expression profiles that were annotated from the GSE75271 dataset. A total of 225 differentially expressed lncRNAs (DELs) were identified based on comparison between three prognostic subgroups, and seven of them (XR_241302, XR_923061, NR_038322, XR_951687, XR_934593, NR_120317 and XR_93406) that exhibited highly predictive accuracies were selected for functional analysis. Weighted gene correlation network analysis (WGCNA) was employed to predict the biological functions of the seven DELs. The Hippo-YAP signaling pathway was predicted to be the most statistically significant predicted pathway associated with the seven DELs. Furthermore, we performed in vitro experiments to validate the biological function of one DEL, NR_120317 (LINC01314). Our results showed decreased proliferation and migration activities of HB cells overexpressing LINC01314. Moreover, mechanistic investigations revealed that LINC01314 overexpression inhibited nuclear translocation of YAP, by inducing MST1 expression and promoting phosphorylation of LATS1 and YAP, consequently downregulating the expression of cell cycle regulatory proteins (MCM7 and cyclin D1). Taken together, our findings provide evidence for LINC01314 as a potential biomarker and anti-cancer therapeutic target in patients with HB.

Identification of differentially expressed genes, lncRNAs and miRNAs which are associated with tumor malignant phenotypes in hepatoblastoma patients

Hepatoblastoma (HB) is one of the most common hepatic malignancies in the pediatric population. HB are composed of a variety of tumors, which derived from different origins and had varying clinical outcomes. However, the unclear underlying mechanisms of HB limited exploring novel biomarkers and effective therapeutic targets. We searched microarray datasets on Gene Expression Omnibus (GEO) database and selected GSE75271 and GSE75283 datasets for comprehensive analysis. Weighted gene correlation network analysis (WGCNA) was employed to identify genes which were associated with tumor malignant phenotypes, including HB subtypes, Cairo classification and tumor stage. Coexpression analysis of identified genes was also performed and lncRNA-miRNA-mRNA network was finally conducted. Our results showed that a total of 22 lncRNAs, 13 miRNAs and 66 mRNAs were identified to be associated with tumor malignant phenotypes. Mechanistically, these molecules might promote the malignant phenotypes via regulating metabolic pathways. Among of them, 6 miRNAs (hsa-miR-106b, hsa-miR-130b, hsa-miR-19a, hsa-miR-19b, hsa-miR-20a and hsa-miR-301a), 8 lncRNAs (NR_102317, XR_245338, XR_428373, XR_924945, XR_929728, XR_931611, XR_935074 and XR_946696), and 6 mRNAs (EGFR, GAREM, INSIG1, KRT81, SAR1B and SDC1) were selected to conduct a lncRNA-miRNA-mRNA network. Taken together, our findings provide evidence for exploring molecular mechanisms of HB. Those identified malignant phenotype-associated molecules might be potential biomarkers and anti-cancer therapeutic targets in future.

Network Analysis-Based Approach for Exploring the Potential Diagnostic Biomarkers of Acute Myocardial Infarction

Acute myocardial infarction (AMI) is a severe cardiovascular disease that is a serious threat to human life. However, the specific diagnostic biomarkers have not been fully clarified and candidate regulatory targets for AMI have not been identified. In order to explore the potential diagnostic biomarkers and possible regulatory targets of AMI, we used a network analysis-based approach to analyze microarray expression profiling of peripheral blood in patients with AMI. The significant differentially-expressed genes (DEGs) were screened by Limma and constructed a gene function regulatory network (GO-Tree) to obtain the inherent affiliation of significant function terms. The pathway action network was constructed, and the signal transfer relationship between pathway terms was mined in order to investigate the impact of core pathway terms in AMI. Subsequently, constructed the transcription regulatory network of DEGs. Weighted gene co-expression network analysis (WGCNA) was employed to identify significantly altered gene modules and hub genes in two groups. Subsequently, the transcription regulation network of DEGs was constructed. We found that specific gene modules may provide a better insight into the potential diagnostic biomarkers of AMI. Our findings revealed and verified that NCF4, AQP9, NFIL3, DYSF, GZMA, TBX21, PRF1 and PTGDR genes by RT-qPCR. TBX21 and PRF1 may be potential candidates for diagnostic biomarker and possible regulatory targets in AMI.

Weighted gene co-expression network analysis identifies specific modules and hub genes related to coronary artery disease

Background

The analysis of the potential molecule targets of coronary artery disease (CAD) is critical for understanding the molecular mechanisms of disease. However, studies of global microarray gene co-expression analysis of CAD still remain limited.

Methods

Microarray data of CAD (GSE23561) were downloaded from Gene Expression Omnibus, including peripheral blood samples from CAD patients (n = 6) and controls (n = 9). Limma package in R was used to identify the differentially expressed genes (DEGs) between CAD and control samples. Using weighted gene co-expression network analysis (WGCNA) package in R, WGCNA was performed to identify significant modules in the network. Then, functional and pathway enrichment analyses were conducted for genes in the most significant module using DAVID software. Moreover, hub genes in the module were analyzed by isubpathwayminer package in R and GenCLiP 2.0 tool to identify the significant sub-pathways.

Results

Total 3711 DEGs and 21 modules for them were identified in CAD samples. The most significant module was associated with the pathways of hypertrophic cardiomyopathy and membrane related functions. In addition, the top 30 hub genes with high connectivity in the module were selected, and two genes (G6PD and S100A7) were taken as key molecules via sub-pathway screening and data mining.

Conclusions

A module associated with hypertrophic cardiomyopathy pathway was detected in CAD samples. G6PD and S100A7 were the potential targets in CAD. Our finding might provide novel insight into the underlying molecular mechanism of CAD.

Weighted gene co-expression network analysis of pneumocytes under exposure to a carcinogenic dose of chloroprene

AIMS

Occupational exposure to chloroprene via inhalation may lead to acute toxicity and chronic pulmonary diseases, including lung cancer. Currently, most research is focused on epidemiological studies of chloroprene production workers. The specific molecular mechanism of carcinogenesis by chloroprene in lung tissues still remains obscure, and specific candidate therapeutic targets for lung cancer are lacking. The present study identifies specific gene modules and valuable hubs associated with lung cancer.

MAIN METHODS

We downloaded the dataset GSE40795 from the Gene Expression Omnibus (GEO) and divided the dataset into the non-carcinogenic dose chloroprene exposed mice group and the carcinogenic dose chloroprene exposed mice group. With a systemic biological view, we discovered significantly altered gene modules between the two groups and identified hub genes in the carcinogenic dose exposed group using weighted co-expression network analysis (WGCNA).

KEY FINDINGS

A total of 2434 differentially expressed genes were identified. Twelve gene modules with multiple biological activities were related to the carcinogenesis of chloroprene in lung tissue. Seven hub genes that were critical for the carcinogenesis of chloroprene in lung tissue were ultimately identified, including Cftr, Hip1, Tbl1x, Ephx1, Cbr3, Antxr2 and Ccnd2. They were implicated in inflammatory response, cell transformation, gene transcription regulation, phase II detoxification, angiogenesis, cell adhesion, motility and the cell cycle.

SIGNIFICANCE

The seven hub genes may become valuable candidates for risk assessment biomarkers and therapeutic targets in lung cancer.

A functional genomic model for predicting prognosis in idiopathic pulmonary fibrosis

Background

The course of disease for patients with idiopathic pulmonary fibrosis (IPF) is highly heterogeneous. Prognostic models rely on demographic and clinical characteristics and are not reproducible. Integrating data from genomic analyses may identify novel prognostic models and provide mechanistic insights into IPF.

Methods

Total RNA of peripheral blood mononuclear cells was subjected to microarray profiling in a training (45 IPF individuals) and two independent validation cohorts (21 IPF/10 controls, and 75 IPF individuals, respectively). To identify a gene set predictive of IPF prognosis, we incorporated genomic, clinical, and outcome data from the training cohort. Predictor genes were selected if all the following criteria were met: 1) Present in a gene co-expression module from Weighted Gene Co-expression Network Analysis (WGCNA) that correlated with pulmonary function (p < 0.05); 2) Differentially expressed between observed “good” vs. “poor” prognosis with fold change (FC) >1.5 and false discovery rate (FDR) < 2 %; and 3) Predictive of mortality (p < 0.05) in univariate Cox regression analysis. “Survival risk group prediction” was adopted to construct a functional genomic model that used the IPF prognostic predictor gene set to derive a prognostic index (PI) for each patient into either high or low risk for survival outcomes. Prediction accuracy was assessed with a repeated 10-fold cross-validation algorithm and independently assessed in two validation cohorts through multivariate Cox regression survival analysis.

Results

A set of 118 IPF prognostic predictor genes was used to derive the functional genomic model and PI. In the training cohort, high-risk IPF patients predicted by PI had significantly shorter survival compared to those labeled as low-risk patients (log rank p < 0.001). The prediction accuracy was further validated in two independent cohorts (log rank p < 0.001 and 0.002). Functional pathway analysis revealed that the canonical pathways enriched with the IPF prognostic predictor gene set were involved in T-cell biology, including iCOS, T-cell receptor, and CD28 signaling.

Conclusions

Using supervised and unsupervised analyses, we identified a set of IPF prognostic predictor genes and derived a functional genomic model that predicted high and low-risk IPF patients with high accuracy. This genomic model may complement current prognostic tools to deliver more personalized care for IPF patients.

Electronic supplementary material

The online version of this article (doi:10.1186/s12890-015-0142-8) contains supplementary material, which is available to authorized users.

Identification of candidate target genes for human peripheral arterial disease using weighted gene co‑expression network analysis

The aim of the present study was to identify the potential treatment targets of peripheral arterial disease (PAD) and provide further insights into the underlying mechanism of PAD, based on a weighted gene co‑expression network analysis (WGCNA) method. The mRNA expression profiles (accession. no. GSE27034), which included 19 samples from patients with PAD and 18 samples from normal control individuals were extracted from the Gene Expression Omnibus database. Subsequently, the differentially expressed genes (DEGs) were obtained using the Limma package and the co‑expression network modules were screened using the WGCNA approach. In addition, the protein‑protein interaction network for the DEGs in the most significant module was constructed using Cytoscape software. Functional enrichment analyses of the DEGs in the most significant module were also performed using the Database for Annotation, Visualization and Integrated Discovery and Kyoto Encyclopedia of Genes and Genomes (KEGG) Orthology‑Based Annotation System, respectively. A total of 148 DEGs were identified in PAD, which were used to construct the WGCN, in which two modules (gray module and turquoise module) were identified, with the gray module exhibiting a higher gene significance (GS) value than the turquoise module. In addition, a co‑expression network was constructed for 60 DEGs in the gray module. The functional enrichment results showed that the DEGs in the gray module were enriched in five Gene Ontology terms and four KEGG pathways. For example, cyclin‑dependent kinase inhibitor 1A (CDKN1A), FBJ murine osteosarcoma viral oncogene homolog (FOS) and prostaglandin‑endoperoxide synthase 2 (PTGS2) were enriched in response to glucocorticoid stimulus. The results of the present study suggested that DEGs in the gray module, including CDKN1A, FOS and PTGS2, may be associated with the pathogenesis of PAD, by modulating the cell cycle, and may offer potential for use as candidate treatment targets for PAD.

Identification of specific gene modules in mouse lung tissue exposed to cigarette smoke

BACKGROUND

Exposure to cigarette may affect human health and increase risk of a wide range of diseases including pulmonary diseases, such as chronic obstructive pulmonary disease (COPD), asthma, lung fibrosis and lung cancer. However, the molecular mechanisms of pathogenesis induced by cigarettes still remain obscure even with extensive studies. With systemic view, we attempted to identify the specific gene modules that might relate to injury caused by cigarette smoke and identify hub genes for potential therapeutic targets or biomarkers from specific gene modules.

MATERIALS AND METHODS

The dataset GSE18344 was downloaded from the Gene Expression Omnibus (GEO) and divided into mouse cigarette smoke exposure and control groups. Subsequently, weighted gene co-expression network analysis (WGCNA) was used to construct a gene co-expression network for each group and detected specific gene modules of cigarette smoke exposure by comparison.

RESULTS

A total of ten specific gene modules were identified only in the cigarette smoke exposure group but not in the control group. Seven hub genes were identified as well, including Fip1l1, Anp32a, Acsl4, Evl, Sdc1, Arap3 and Cd52.

CONCLUSIONS

Specific gene modules may provide better understanding of molecular mechanisms, and hub genes are potential candidates of therapeutic targets that may possible improve development of novel treatment approaches.