Identification of Important Modules and Biomarkers in Breast Cancer Based on WGCNA.
Ontology highlight
ABSTRACT:
SUBMITTER: Tian Z
PROVIDER: S-EPMC7367932 | biostudies-literature | 2020
REPOSITORIES: biostudies-literature
Publications
Identification of Important Modules and Biomarkers in Breast Cancer Based on WGCNA.
Tian Zelin Z He Weixiang W Tang Jianing J Liao Xing X Yang Qian Q Wu Yumin Y Wu Gaosong G
OncoTargets and therapy 20200712
<h4>Introduction</h4>Breast cancer (BRCA) has the highest incidence among female malignancies, and the prognosis for these patients remains poor.<h4>Materials and methods</h4>In this study, core modules and central genes related to BRCA were identified through a weighted gene co-expression network analysis (WGCNA). Gene expression profiles and clinical data of GSE25066 were obtained from the Gene Expression Omnibus (GEO) database. The result was validated with RNA-seq data from The Cancer Genome ...[more]
Similar Datasets
Project description:BackgroundTuberculosis (TB) is a significant public health concern, particularly in China. Long noncoding RNAs (lncRNAs) can provide abundant pathological information regarding etiology and could include candidate biomarkers for diagnosis of TB. However, data regarding lncRNA expression profiles and specific lncRNAs associated with TB are limited.MethodsWe performed ceRNA-microarray analysis to determine the expression profile of lncRNAs in peripheral blood mononuclear cells (PBMCs). Weighted gene co-expression network analysis (WGCNA) was then conducted to identify the critical module and genes associated with TB. Other bioinformatics analyses, including Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Ontology (GO), and co-expression networks, were conducted to explore the function of the critical module. Finally, real-time quantitative polymerase chain reaction (qPCR) was used to validate the candidate biomarkers, and receiver operating characteristic analysis was used to assess the diagnostic performance of the candidate biomarkers.ResultsBased on 8 TB patients and 9 healthy controls (HCs), a total of 1,372 differentially expressed lncRNAs were identified, including 738 upregulated lncRNAs and 634 downregulated lncRNAs. Among all lncRNAs and mRNAs in the microarray, the top 25% lncRNAs (3729) and top 25% mRNAs (2824), which exhibited higher median expression values, were incorporated into the WGCNA. The analysis generated 16 co-expression modules, among which the blue module was highly correlated with TB. GO and KEGG analyses showed that the blue module was significantly enriched in infection and immunity. Subsequently, considering module membership values (>0.85), gene significance values (>0.90) and fold-change value (>2 or < 0.5) as selection criteria, the top 10 upregulated lncRNAs and top 10 downregulated lncRNAs in the blue module were considered as potential biomarkers. The candidates were then validated in an independent validation sample set (31 TB patients and 32 HCs). The expression levels of 8 candidates differed significantly between TB patients and HCs. The lncRNAs ABHD17B (area under the curve [AUC] = 1.000) and ENST00000607464.1 (AUC = 1.000) were the best lncRNAs in distinguishing TB patients from HCs.ConclusionThis study characterized the lncRNA profiles of TB patients and identified a significant module associated with TB as well as novel potential biomarkers for TB diagnosis.
| S-EPMC10882270 | biostudies-literature
Project description:BackgroundDiabetic cardiomyopathy (DCM) lacks specific and sensitive biomarkers, and its diagnosis remains a challenge. Therefore, there is an urgent need to develop useful biomarkers to help diagnose and evaluate the prognosis of DCM. This study aims to find specific diagnostic markers for diabetic cardiomyopathy.MethodsTwo datasets (GSE106180 and GSE161827) from the GEO database were integrated to identify differentially expressed genes (DEGs) between control and type 2 diabetic cardiomyopathy. We assessed the infiltration of immune cells and used weighted coexpression network analysis (WGCNA) to construct the gene coexpression network. Then we performed a clustering analysis. Finally, a diagnostic model was built by the least absolute shrinkage and selection operator (LASSO).ResultsA total of 3066 DEGs in the GSE106180 and GSE161827 datasets. There were differences in immune cell infiltration. According to gene significance (GS) > 0.2 and module membership (MM) > 0.8, 41 yellow Module genes and 1474 turquoise Module genes were selected. Hub genes were mainly related to the "proteasomal protein catabolic process", "mitochondrial matrix" and "protein processing in endoplasmic reticulum" pathways. LASSO was used to construct a diagnostic model composed of OXCT1, CACNA2D2, BCL7B, EGLN3, GABARAP, and ACADSB and verified it in the GSE163060 and GSE175988 datasets with AUCs of 0.9333 (95% CI: 0.7801-1) and 0.96 (95% CI: 0.8861-1), respectively. H9C2 cells were verified, and the results were similar to the bioinformatics analysis.ConclusionWe constructed a diagnostic model of DCM, and OXCT1, CACNA2D2, BCL7B, EGLN3, GABARAP, and ACADSB were potential biomarkers, which may provide new insights for improving the ability of early diagnosis and treatment of diabetic cardiomyopathy.
| S-EPMC10926887 | biostudies-literature
Project description:Chronic kidney disease (CKD) is an ongoing deterioration of renal function that often progresses to end-stage renal disease. In this study, we aimed to screen and identify potential key genes for CKD using the weighted gene coexpression network (WGCNA) analysis tool. Gene expression data related to CKD were screened from GEO database, and expression datasets of GSE66494 and GSE62792 were obtained. After discrete analysis of samples, WGCNA analysis was performed to construct gene coexpression module, and the correlation between the module and disease was calculated. The modules with a significant correlation with the disease were selected for Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Then, the interaction network of related molecules was constructed, and the high score subnetwork was selected, and the candidate key molecules were identified. A total of 882 DEGs were identified in the screening datasets. A subnetwork containing 6 nodes was found with a high score of 12.08, including CEBPZ, IFI16, LYAR, BRIX1, BMS1, and DDX18. DEGs could significantly differentiate CKD and healthy individuals in principal component analysis. In addition, the MEturquiose, MEred, and MEblue in group were significantly correlated with disease in WGCNA. These 6 hub genes were found to significantly discriminate between CKD and healthy controls in the validation dataset, suggesting that they could use these molecules as candidate markers to distinguish CKD from healthy people. Overall, our study indicated that 6 hub genes may play key roles in the occurrence and development of CKD.
| S-EPMC9095404 | biostudies-literature
Project description:Breast cancer is one of the leading causes of cancer-associated mortality in women worldwide and has become a major public health problem. Although the definitive cause of breast cancer is not known, many genes sensitive to breast cancer have been detected using advanced technologies. Our study identified 3301 differentially expressed lncRNAs and mRNAs between tumor and normal samples from The Cancer Genome Atlas database. Based on the gene expression analysis and clinical traits as well as weighted gene co-expression network analysis, the co-expression Brown module was found to be key for breast cancer prognosis. A total of 453 genes in the Brown module were used for functional enrichment, protein-protein interaction analysis, lncRNA-miRNA-mRNA ceRNA network, and lncRNA-RNA binding protein-mRNA network construction. GRM4, SSTR2, PARD6B, PRR15, COX6C, and lncRNA DSCAM-AS1 were the hub genes according to protein-protein interaction, lncRNA-miRNA-mRNA and lncRNA-RNA binding protein-mRNA network. Their high expression was found to be correlated with breast cancer development, according to multiple databases. In conclusion, this study provides a framework of the co-expression gene modules of breast cancer and identifies several important biomarkers in breast cancer development and prognosis.
| S-EPMC7880379 | biostudies-literature
Project description:BackgroundOsteosarcoma (OS) is a primary malignant bone tumor. Patients with different immune characteristics respond differently to chemotherapy and have a lower chance of survival. The potential pathogenesis and therapeutic targets of OS must be investigated further.MethodsOS expression profile data and clinical information were downloaded from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) and the Gene Expression Omnibus (GEO) databases. The immune-related gene set was obtained from the ImmPort database, and the immune-related gene expression profiles were used for non-negative matrix factorization (NMF) cluster analysis of patients in the 2 databases to find the best clustering number. In the TARGET database, OS patients were classified into low-risk and high-risk groups based on the differences in their survival rates. Weighted correlation network analysis (WGCNA) was applied to the low-risk and high-risk groups to determine the module with the lowest conservatism in order to differentiate the prognosis of the 2 groups.ResultsA total of 500 key genes associated with poor prognosis were identified. Gene Ontology (GO) enrichment analysis revealed that the biological processes of these genes were primarily focused on the regulation of small guanosine triphosphatase (GTPase) mediated signal transduction, collagen-containing extracellular matrix, and Rho GTPase binding. A random survival forest identified EPHB3, TEAD1, and KRR1P1 as key genes. Their expression level was linked to overall survival. We discovered that the core genes were associated to immune cell infiltration. Simultaneously, paired survival analysis of two genes revealed differences in survival. We also reverse-predicted the main genes and developed their competitive endogenous RNA (ceRNA) network. Finally, utilizing the CellMiner database, we observed that the genes TEAD1 and EPHB3 were connected to drug sensitivity.ConclusionsIn this study, we identified the modules and key genes related to the poor prognosis of OS patients by using WGCNA, and verified their impact on the prognosis of OS patients and their role in the immune microenvironment of OS. In addition, targeted gene related antitumor drugs were screened out. The discoveries may lead to novel molecular targets and treatment methods for OS patients.KeywordsOsteosarcoma (OS); weighted gene co-expression network analysis (WGCNA); gene.
| S-EPMC9011312 | biostudies-literature
Project description:Glioblastoma (GBM) is a malignant brain tumour with poor prognosis. The potential pathogenesis and therapeutic target are still need to be explored. Herein, TCGA expression profile data and clinical information were downloaded, and the WGCNA was conducted. Hub genes which closely related to poor prognosis of GBM were obtained. Further, the relationship between the genes of interest and prognosis of GBM, and immune microenvironment were analysed. Patients from TCGA were divided into high- and low-risk group. WGCNA was applied to the high- and low-risk group and the black module with the lowest preservation was identified which could distinguish the prognosis level of these two groups. The top 10 hub genes which were closely related to poor prognosis of patients were obtained. GO analysis showed the biological process of these genes mainly enriched in: Cell cycle, Progesterone-mediated oocyte maturation and Oocyte meiosis. CDCA5 and CDCA8 were screened out as the genes of interest. We found that their expression levels were closely related to overall survival. The difference analysis resulted from the TCGA database proved both CDCA5 and CDCA8 were highly expressed in GBM. After transfection of U87-MG cells with small interfering RNA, it revealed that knockdown of the CDCA5 and CDCA8 could influence the biological behaviours of proliferation, clonogenicity and apoptosis of GBM cells. Then, single-gene analysis was performed. CDCA5 and CDCA8 both had good correlations with genes that regulate cell cycle in the p53 signalling pathway. Moreover, it revealed that high amplification of CDCA5 was correlated with CD8+ T cells while CDCA8 with CD4+ T cells in GBM. These results might provide new molecular targets and intervention strategy for GBM.
| S-EPMC7875936 | biostudies-literature
Project description:IntroductionDermatomyositis (DM) is a chronic autoimmune disease of predominantly lymphocytic infiltration mainly involving the transverse muscle. Its pathogenesis is remaining unknown. This research is designed to probe the latent pathogenesis of dermatomyositis, identify potential biomarkers, and reveal the pathogenesis of dermatomyositis through information biology analysis of gene chips.MethodsIn this study, we utilised the GSE14287 and GSE11971 datasets rooted in the Gene Expression Omnibus (GEO) databank, which included a total of 62 DM samples and 9 normal samples. The datasets were combined, and the differentially expressed gene sets were subjected to weighted gene coexpression network analysis, and the hub gene was screened using a protein interaction network from genes in modules highly correlated with dermatomyositis progression.ResultsA total of 3 key genes-myxovirus resistance-2 (MX2), oligoadenylate synthetase 1 (OAS1), and oligoadenylate synthetase 2 (OAS2)-were identified in combination with cell line samples, and the expressions of the 3 genes were verified separately. The results showed that MX2, OAS1, and OAS2 were highly expressed in LPS-treated cell lines compared to normal cell lines. The results of pathway enrichment analysis of the genes indicated that all 3 genes were enriched in the cytosolic DNA signalling and cytokine and cytokine receptor interaction signalling pathways; the results of functional enrichment analysis showed that all 3 were enriched in interferon-α response and interferon-γ response functions.ConclusionsThis is important for the study of the pathogenesis and objective treatment of dermatomyositis and provides important reference information for the targeted therapy of dermatomyositis.
| S-EPMC8736700 | biostudies-literature
Project description:Breast cancer is one of the leading causes of mortality in females. A number of prognostic markers have been identified, including single genes, multi-gene signatures and network modules; however, the robustness of these prognostic markers is insufficient. Thus, the present study proposed a more robust method to identify breast cancer prognostic modules based on weighted protein-protein interaction networks, by integrating four sets of disease-associated expression profiles. Three identified prognostic modules were closely associated with prognosis-associated functions and survival time, as determined by Cox regression and Kaplan-Meier survival analyses. The robustness of these modules was verified with an independent profile from another platform. Genes from these modules may be useful as breast cancer prognostic markers. The prognostic modules could be used to determine the prognoses of patients with breast cancer and characterize patient recovery.
| S-EPMC5434807 | biostudies-other
Project description:BackgroundColorectal cancer is the third most common cause of death among cancers in the world. Although improvements in various treatments have greatly improved the survival time of colorectal cancer patients, since colorectal cancer is often at an advanced stage when diagnosed, the prognosis of patients is still very poor. Since the ceRNA regulatory network was proposed in 2011, it has greatly promoted the study of the molecular mechanism of colorectal cancer occurrence and development.ObjectiveExploring the new molecular mechanism of colorectal cancer occurrence and development and providing new targets for the diagnosis and treatment of colorectal cancer.MethodWe analyzed the RNA-seq data of CRC from TCGA, such as differential expression analysis, weighted gene co-expression network analysis (WGCNA) and construction of ceRNA regulatory network.ResultsWe constructed a ceRNA network using RNA-seq data of CRC from TCGA. In the ceRNA regulatory network, 19 hub molecules with significant prognostic effects were ultimately identified, including 8 lncRNAs, 2 mRNAs and 9 miRNAs. These hub molecules constitute the lncRNA-miRNA, miRNA-mRNA or lncRNA-miRNA-mRNA axis.ConclusionIn this article, some new ceRNA regulatory axes have been discovered, which may potentially disclose new molecular mechanisms for the occurrence and development of colorectal cancer, thereby providing an important blueprint for the treatment and prognosis assessment of CRC patients.
| S-EPMC9482270 | biostudies-literature
Project description:Background: Ovarian cancer (OV) is a fatal gynecologic malignancy and has poor survival rate in women over the age of forty. In our study, we aimed to identify genes related to immune microenvironment regulations and explore genes associated with OV prognosis. Methods: The RNA-seq data of GDC TCGA Ovarian Cancer cohort of 376 patients was retrieved from website. Weighted gene co-expression network analysis (WGCNA) and ESTIMATE algorithm were applied to identify the key genes associated with the immune scores. The correlation between key genes and 22 immune cell types were estimated by using CIBERSORT algorithms. Results: WGCNA showed that the pink module was most correlated with the immune score. Seven of 14 key genes (FCRL3, IFNG, KCNA3, LY9, PLA2G2D, THEMIS, and TRAT1) were significantly associated with the OS of OV patients. Correlation analysis showed our key genes positively related to M1 macrophages, CD8 T cells, plasma cells, regulatory T (Treg) cells and activated memory CD4 T cells, and negatively related to naive CD4 T cells, M0 macrophages, activated dendritic cells (DCs) and memory B cells. Kaplan-Meier survival analysis showed that lower abundances of neutrophils and higher abundances of M1 macrophages, plasma cells, T cells gamma delta (γδT) cells and follicular helper T (Tfh) cells predicted better OV prognosis. Conclusion: Forteen key genes related to the immune infiltrating of OV were identified, and seven of them were significantly related to prognosis. These key genes have potential roles in tumor infiltrating immune cells differentiation and proliferation. This study provided potential prognostic markers and immunotherapy targets for OV.
| S-EPMC8634599 | biostudies-literature