Identification of key modules and genes associated with breast cancer prognosis using WGCNA and ceRNA network analysis

Yin, Xin; Wang, Pei; Yang, Tianshu; Li, Gen; Teng, Xu; Huang, Wei; Yu, Hefen

doi:10.18632/aging.202285

Cited by 61 publications

(42 citation statements)

References 56 publications

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“…WGCNA was run as described previously (22). Briefly, a similarity matrix was constructed using expression data and converted into an adjacency matrix, a ij .…”

Section: Wgcna Of Differential Tfsmentioning

confidence: 99%

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Identification of Key Transcription Factors and Immune Infiltration Patterns Associated With Breast Cancer Prognosis Using WGCNA and Cox Regression Analysis

et al. 2021

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Breast cancer is the most frequently diagnosed cancer and the second leading cause of cancer death among women worldwide. Therefore, the need for effective breast cancer treatment is urgent. Transcription factors (TFs) directly participate in gene transcription, and their dysregulation plays a key role in breast cancer. Our study identified 459 differentially expressed TFs between tumor and normal samples from The Cancer Genome Atlas database. Based on gene expression analysis and weighted gene co-expression network analysis, the co-expression yellow module was found to be integral for breast cancer progression. A total of 121 genes in the yellow module were used for function enrichment. To further confirm prognosis-related TFs, COX regression and LASSO analyses were performed; consequently, a prognostic risk model was constructed, and its validity was verified. Ten prognosis-related TFs were identified according to their expression profile, survival probability, and target genes. COPS5, HDAC2, and NONO were recognized as hub TFs in breast cancer. These TFs were highly expressed in human breast cancer cell lines and clinical breast cancer samples; this result was consistent with the information from multiple databases. Immune infiltration analysis revealed that the proportions of resting dendritic and mast cells were greater in the low-risk group than those in the high-risk group. Thus, in this study, we identified three hub biomarkers related to breast cancer prognosis. The results provide a framework for the co-expression of TF modules and immune infiltration in breast cancer.

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“…WGCNA was run as described previously (22). Briefly, a similarity matrix was constructed using expression data and converted into an adjacency matrix, a ij .…”

Section: Wgcna Of Differential Tfsmentioning

confidence: 99%

“…In breast cancer, the novel microRNA biomarkers for each subtype of breast cancer can be detected using WGCNA (21). In addition, our previous study identified one lncRNA and five mRNA that serve as important prognostic biomarkers in breast cancer (22).…”

Section: Introductionmentioning

confidence: 99%

Identification of Key Transcription Factors and Immune Infiltration Patterns Associated With Breast Cancer Prognosis Using WGCNA and Cox Regression Analysis

et al. 2021

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“…Modular Significance (MS) and Gene Significance (GS) are used to calculate the expression patterns of modules related to disease traits. In this study, the dynamic tree cutting algorithm can be used to detect the module, and the minimum module size was 30 (20).…”

Section: The Weighted Gene Coexpression Networkmentioning

confidence: 99%

Construction of a ceRNA immunoregulatory network related to the development of vascular dementia through a weighted gene coexpression network analysis

et al. 2021

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Background: To date, vascular dementia (VaD) lacks effective treatment in clinical practice. There is also growing evidence that VaD may be closely related to the immune response. The development of highthroughput technology, and the recently discovered group of new mediators called competitive endogenous RNAs (ceRNA), provides a unique opportunity to study the immunomodulation of VaD.Methods: In this study, we used gene expression profiles in the Gene Expression Omnibus (GEO) database to obtain immune-related gene coexpression modules through a weighted gene coexpression network analysis (WGCNA) and gene enrichment analysis. We extracted and merged long non-coding RNA (lncRNA) and microRNA (miRNA) expressions from the GEO database and mapped them with related databases.Subsequently, we used Cytoscape to construct a lncRNA-miRNA-mRNA network, and then we performed an enrichment analysis on the mRNAs in the network to determine their regulatory function. Subsequently, we used an ImmuCellAI immune infiltration analysis and constructed a ceRNA sub-network of related immune cells. Finally, we conducted a gene set enrichment analysis (GSEA) to determine the potential regulatory pathways of the key factors.Results: As a result, we identified the blue module as the key module of immunity and constructed the related CeRNA network. Immune infiltration analysis showed that natural killer T (NKT) cells may be the key immune cells of VaD. Using a Pearson correlation analysis, we identified that B4GALT1, PPP1R3B, MICB, HHAT, DSC2, DNA2, SCARA3, and lncRNA NEAT1 may be the key factors of VaD. Our subsequent GSEA analysis showed that lncRNA NEAT1 may be regulated by NK cells and toll-like receptors.Conclusions: Our research provides new therapeutic targets for vascular dementia from the immunological perspective for the first time, including B4GALT1, PPP1R3B, MICB, HHAT, DSC2, DNA2, SCARA3, and lncRNA NEAT1, and our research hopes to provide new treatment options for VaD.

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“… 4 WGCNA has been widely used for detecting disease biomarkers, and elucidating biological mechanisms and drug interactions. 5–7 Although biomarkers of HF have been identified, but due to heterogeneity of HF and its complicated pathophysiological manifestations, a single gene cannot accurately predict the characteristics of HF. 8 , 9 …”

Section: Introductionmentioning

confidence: 99%

Identification of Hub Diagnostic Biomarkers and Candidate Therapeutic Drugs in Heart Failure

Guo¹,

Ning²,

Zhang³

et al. 2022

IJGM

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Purpose The objective of this study was to identify the potential regulatory mechanisms, diagnostic biomarkers, and therapeutic drugs for heart failure (HF). Methods Differentially expressed genes (DEGs) between HF and non-failing donors were screened from the GSE57345, GSE5406, and GSE3586 datasets. Database for Annotation Visualization and Integrated Discovery and Metascape were used for Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses respectively. The GSE57345 dataset was used for weighted gene co-expression network analysis (WGCNA). The intersecting hub genes from the DEGs and WGCNA were identified and verified with the GSE5406 and GSE3586 datasets. The diagnostic value of the hub genes was calculated through receiver operating characteristic analysis and net reclassification index (NRI). Gene set enrichment analysis (GSEA) was used to filter out the signaling pathways associated with the hub genes. SYBYL 2.1 was used for molecular docking of hub targets and potential HF drugs obtained from the connection map. Results Functional annotation of the DEGs showed enrichment of negative regulation of angiogenesis, endoplasmic reticulum stress response, and heart development. PTN, LUM, ISLR, and ASPN were identified as the hub genes of HF. GSEA showed that the key genes were related to the transforming growth factor-β (TGF-β) and Wnt signaling pathways. Sirolimus, LY-294002, and wortmannin have been confirmed as potential drugs for HF. Conclusion We identified new hub genes and candidate therapeutic drugs for HF, which are potential diagnostic, therapeutic and prognostic targets and warrant further investigation.

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Identification of key modules and genes associated with breast cancer prognosis using WGCNA and ceRNA network analysis

Cited by 61 publications

References 56 publications

Identification of Key Transcription Factors and Immune Infiltration Patterns Associated With Breast Cancer Prognosis Using WGCNA and Cox Regression Analysis

Identification of Key Transcription Factors and Immune Infiltration Patterns Associated With Breast Cancer Prognosis Using WGCNA and Cox Regression Analysis

Construction of a ceRNA immunoregulatory network related to the development of vascular dementia through a weighted gene coexpression network analysis

Identification of Hub Diagnostic Biomarkers and Candidate Therapeutic Drugs in Heart Failure

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