Oncogenic Network and Hub Genes for Natural Killer/T-Cell Lymphoma Utilizing WGCNA

Liu, Huijiao; Liu, Mei; You, Hua; Li, Xiru; Li, Xiangdong

doi:10.3389/fonc.2020.00223

Cited by 19 publications

(14 citation statements)

References 34 publications

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“…This method provides unique insight into the structure and behavior of molecular interactions than individual gene. WGCNA has been used in many studies to identify disease related modules and marker genes including cancer [5][6][7][8]. In the present study, RNA sequencing profiles of breast cancer from luminal A subtype, luminal B subtype, her2 positive subtype, and basal-like subtype were downloaded from TCGA (The Cancer Genome Atlas) database [9].…”

Section: Introductionmentioning

confidence: 99%

Identifying breast cancer subtypes associated modules and biomarkers by integrated bioinformatics analysis

Wang

Liu

et al. 2021

Bioscience Reports

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Breast cancer is the most common form of cancer afflicting women worldwide. Patients with breast cancer of different molecular classifications need varied treatments. Since it is known that the development of breast cancer involves multiple genes and functions, identification of functional gene modules (clusters of the functionally related genes) is indispensable as opposed to isolated genes, in order to investigate their relationship derived from the gene co-expression analysis. In total, 6315 differentially expressed genes (DEGs) were recognized and subjected to the co-expression analysis. Seven modules were screened out. The blue and turquoise modules have been selected from the module trait association analysis since the genes in these two modules are significantly correlated with the breast cancer subtypes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment show that the blue module genes engaged in cell cycle, DNA replication, p53 signaling pathway, and pathway in cancer. According to the connectivity analysis and survival analysis, 8 out of 96 hub genes were filtered and have shown the highest expression in basal-like breast cancer. Furthermore, the hub genes were validated by the external datasets and quantitative real-time PCR (qRT-PCR). In summary, hub genes of Cyclin E1 (CCNE1), Centromere Protein N (CENPN), Checkpoint kinase 1 (CHEK1), Polo-like kinase 1 (PLK1), DNA replication and sister chromatid cohesion 1 (DSCC1), Family with sequence similarity 64, member A (FAM64A), Ubiquitin Conjugating Enzyme E2 C (UBE2C) and Ubiquitin Conjugating Enzyme E2 T (UBE2T) may serve as the prognostic markers for different subtypes of breast cancer.

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Section: Introductionmentioning

confidence: 99%

Identifying breast cancer subtypes associated modules and biomarkers by integrated bioinformatics analysis

Wang

Liu

et al. 2021

Bioscience Reports

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“…The WGCNA approach is a bioinformatics technique that allows the extraction and grouping into modules of a list of genes involved in a given biological process [19]. This technique has been used for the credible discovery of a number of genes associated with various diseases and their sub-characteristics [20][21][22][23]. WGCNA allows the correlative identification of genes with a similar expression profile to a given characteristic trait.…”

Section: Introductionmentioning

confidence: 99%

Weighted correlation network bioinformatics uncovers a key molecular biosignature driving the left-sided heart failure

et al. 2020

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Background: Left-sided heart failure (HF) is documented as a key prognostic factor in HF. However, the relative molecular mechanisms underlying left-sided HF is unknown. The purpose of this study is to unearth significant modules, pivotal genes and candidate regulatory components governing the progression of left-sided HF by bioinformatical analysis. Methods: A total of 319 samples in GSE57345 dataset were used for weighted gene correlation network analysis (WGCNA). ClusterProfiler package in R was used to conduct functional enrichment for genes uncovered from the modules of interest. Regulatory networks of genes were built using Cytoscape while Enrichr database was used for identification of transcription factors (TFs). The MCODE plugin was used for identifying hub genes in the modules of interest and their validation was performed based on GSE1869 dataset. Results: A total of six significant modules were identified. Notably, the blue module was confirmed as the most crucially associated with left-sided HF, ischemic heart disease (ISCH) and dilated cardiomyopathy (CMP). Functional enrichment conveyed that genes belonging to this module were mainly those driving the extracellular matrixassociated processes such as extracellular matrix structural constituent and collagen binding. A total of seven transcriptional factors, including Suppressor of Zeste 12 Protein Homolog (SUZ12) and nuclear factor erythroid 2 like 2 (NFE2L2), adrenergic receptor (AR), were identified as possible regulators of coexpression genes identified in the blue module. A total of three key genes (OGN, HTRA1 and MXRA5) were retained after validation of their prognostic value in left-sided HF. The results of functional enrichment confirmed that these key genes were primarily involved in response to transforming growth factor beta and extracellular matrix.

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“…We rst applied liver transcriptomics [18] to identify differentially expressed mRNAs as candidate biomarkers for the early detection of HBV-related liver injuries. STEM and WGCNA analyses were employed in the next steps, which can accurately group distinct expression patterns [19] and identify numerous intra-modular hub mRNAs [20,21] respectively. A total of 9 progressively elevated mRNAs were identi ed as the hub mRNAs by STEM and WGCNA (Fig.…”

Section: Discussionmentioning

confidence: 99%

Identification and Validation of Novel Biomarkers for HBV-Associated Hepatocellular Carcinoma, Liver Fibrosis/Cirrhosis and Chronic Hepatitis: A Case Control Study

Zhao

Zhang

Tang

et al. 2021

Preprint

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BackgroundAt present, there are no accurate diagnostic biomarkers for distinguishing among hepatitis B virus (HBV)-related liver diseases. This research aimed to identify and validate a novel biomarker panel in patients with HBV-related hepatocellular carcinoma (HCC), liver fibrosis/liver cirrhosis (LF/LC) and chronic hepatitis B (CHB).MethodsTranscriptomics sequencing was conducted on a total of 19 liver tissues of the four groups. The hub mRNAs were identified using weighted gene co-expression network analysis (WGCNA) and short time-series expression miner (STEM) method. The expression levels of selected mRNAs and proteins were assessed by quantitative real-time polymerase chain reaction (qRT-PCR) using both liver tissues and peripheral blood mononuclear cells (PBMCs) and immunohistochemical (IHC) staining in liver tissues, respectively. Then, the expression activities of the screened targets were detected by enzyme-linked immunosorbent assay (ELISA) using the plasma samples from 200 and 400 subjects in the validation and testing sets, respectively. Finally, receiver operating characteristic (ROC) curve analysis was carried out to evaluate the diagnostic performance of each mRNA biomarker.ResultsSTEM analysis revealed 25 mRNAs exhibited an increasing trend in the four groups; while 9 hub mRNAs were identified by using WGCNA. Of note, the results of qRT-PCR and immunohistochemical analyses demonstrated that SHC adaptor protein 1 (SHC1), SLAM family member 8 (SLAMF8), and interleukin-32 (IL-32) exhibited a gradually increasing trend in the four groups. Subsequent ELISA tests on the validation cohort demonstrated that the plasma levels of SHC1, SLAMF8 and IL-32 were remarkably elevated in HCC group compared to the remaining three groups. Furthermore, a diagnostic model APFSSI (age, PLT, ferritin, SHC1, SLAMF8 and IL-32) was established to distinguish HCC from LF/LC (AUC=0.904), LF/LC from CHB (AUC=0.924), and CHB from healthy controls (AUC=0.966). Finally, the results in the test set were consistent with those in the validation set. ConclusionsSHC1, SLAMF8 and IL-32 can differentiate among HCC patients, LF/LC patients, CHB patients and healthy controls. More importantly, the combination of age, PLT, ferritin, SHC1, SLAMF8, and IL-32 (APFSSI model) greater improves the diagnostic accuracy of HBV-associated liver diseases.

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Oncogenic Network and Hub Genes for Natural Killer/T-Cell Lymphoma Utilizing WGCNA

Cited by 19 publications

References 34 publications

Identifying breast cancer subtypes associated modules and biomarkers by integrated bioinformatics analysis

Identifying breast cancer subtypes associated modules and biomarkers by integrated bioinformatics analysis

Weighted correlation network bioinformatics uncovers a key molecular biosignature driving the left-sided heart failure

Identification and Validation of Novel Biomarkers for HBV-Associated Hepatocellular Carcinoma, Liver Fibrosis/Cirrhosis and Chronic Hepatitis: A Case Control Study

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