Dissecting Prognosis Modules and Biomarkers in Glioblastoma Based on Weighted Gene Co-Expression Network Analysis

Cao, Fang; Fan, Yinchun; Yu, Yunhu; Yang, Guohua; Zhong, Hua

doi:10.2147/cmar.s310346

Cited by 22 publications

(16 citation statements)

References 52 publications

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“…The purpose of weighted gene co-expression network analysis (WGCNA) was to find co-expressed gene modules ( Cao et al, 2021b ). The expression of 16,394 genes in the TCGA-BLCA queue will be used as data, and the result of CIBERSORT will be used as an explanation.…”

Section: Methodsmentioning

confidence: 99%

Clinical value of M1 macrophage-related genes identification in bladder urothelial carcinoma and in vitro validation

Huang

et al. 2022

Front. Genet.

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Background: Tumor microenvironment (TME) takes a non-negligible role in the progression and metastasis of bladder urothelial carcinoma (BLCA) and tumor development could be inhibited by macrophage M1 in TME. The role of macrophage M1-related genes in BLCA adjuvant therapy has not been studied well.Methods: CIBERSOR algorithm was applied for identification tumor-infiltrating immune cells (TICs) subtypes of subjects from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) data sets. We identified potential modules of M1 macrophages by weighted gene co-expression network analysis (WGCNA). Nomogram was determined by one-way Cox regression and lasso regression analysis for M1 macrophage genes. The data from GEO are taken to verify the models externally. Kaplan-Meier and receiver operating characteristic (ROC) curves validated prognostic value of M1 macrophage genes. Finally, we divided patients into the low-risk group (LRG) and the high-risk group (HRG) based on the median risk score (RS), and the predictive value of RS in patients with BLCA immunotherapy and chemotherapy was investigated. Bladder cancer (T24, 5637, and BIU-87) and bladder uroepithelial cell line (SV-HUC-1) were used for in vitro validation. Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) was employed to validate the associated genes mRNA level.Results: 111 macrophage M1-related genes were identified using WGCNA. RS model containing three prognostically significant M1 macrophage-associated genes (FBXO6, OAS1, and TMEM229B) was formed by multiple Cox analysis, and a polygenic risk model and a comprehensive prognostic line plot was developed. The calibration curve clarified RS was a good predictor of prognosis. Patients in the LRG were more suitable for programmed cell death protein 1 (PD1) and cytotoxic T lymphocyte associate protein-4 (CTLA4) combination immunotherapy. Finally, chemotherapeutic drug models showed patients in the LRG were more sensitive to gemcitabine and mitomycin. RT-qPCR result elucidated the upregulation of FBXO6, TMEM229B, and downregulation of OAS1 in BLCA cell lines.Conclusion: A predictive model based on M1 macrophage-related genes can help guide us in the treatment of BLCA.

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Section: Methodsmentioning

confidence: 99%

Clinical value of M1 macrophage-related genes identification in bladder urothelial carcinoma and in vitro validation

Huang

et al. 2022

Front. Genet.

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“…The TCGA database ( ) and GEPIA 2.0. was used to integrate 2, 5, 9, and 10 years of survival time, survival status, gene expression data of colon cancer, and clinical information ( 20 ). The gene expression data of intersecting genes screened in Section 2.2 were used.…”

Section: Methodsmentioning

confidence: 99%

Effective prediction of potential ferroptosis critical genes in clinical colorectal cancer

et al. 2022

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BackgroundColon cancer is common worldwide, with high morbidity and poor prognosis. Ferroptosis is a novel form of cell death driven by the accumulation of iron-dependent lipid peroxides, which differs from other programmed cell death mechanisms. Programmed cell death is a cancer hallmark, and ferroptosis is known to participate in various cancers, including colon cancer. Novel ferroptosis markers and targeted colon cancer therapies are urgently needed. To this end, we performed a preliminary exploration of ferroptosis-related genes in colon cancer to enable new treatment strategies.MethodsFerroptosis-related genes in colon cancer were obtained by data mining and screening for differentially expressed genes (DEGs) using bioinformatics analysis tools. We normalized the data across four independent datasets and a ferroptosis-specific database. Identified genes were validated by immunohistochemical analysis of pathological and healthy clinical samples.ResultsWe identified DEGs in colon cancer that are involved in ferroptosis. Among these, five core genes were found: ELAVL1, GPX2, EPAS1, SLC7A5, and HMGB1. Bioinformatics analyses revealed that the expression of all five genes, except for EPAS1, was higher in tumor tissues than in healthy tissues.ConclusionsThe preliminary exploration of the five core genes revealed that they are differentially expressed in colon cancer, playing an essential role in ferroptosis. This study provides a foundation for subsequent research on ferroptosis in colon cancer.

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“…We selected direct interactions among DE-mRNAs. The R igraph package was applied to identify whether the genes were significant according to three attributes (event, betweenness, and degree) in the PPI network [ 22 ]. Genes involved in identical biological processes were grouped.…”

Section: Methodsmentioning

confidence: 99%

Identification of the circRNA–miRNA–mRNA Regulatory Network in Pterygium-Associated Conjunctival Epithelium

Luo

et al. 2022

BioMed Research International

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To investigate the regulatory mechanism of pterygium formation, we detected differentially expressed messenger RNAs (DE-mRNAs) and differentially expressed circular RNAs (DE-circRNAs) in pterygium-associated conjunctival epithelium (PCE) and normal conjunctival epithelium (NCE). Genome-wide mRNA and circRNA expression profiles of PCE and NCE were determined using high-throughput sequencing. Bioinformatics analyses, including Gene Ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, gene set enrichment analysis (GSEA), and protein–protein interaction (PPI) analysis, were conducted. The microRNAs (miRNAs) interacting with the hub DE-mRNAs and DE-circRNAs were predicted and verified using real-time quantitative PCR (RT-qPCR). The data showed that there were 536 DE-mRNAs (280 upregulated and 256 downregulated mRNAs) and 78 DE-circRNAs (20 upregulated and 58 downregulated circRNAs) in PCE. KEGG enrichment analysis indicated that the DE-mRNAs were mainly involved in the following biological processes: IL-17 signalling pathway, viral protein interaction with cytokine and cytokine receptor, cytokine–cytokine receptor interaction, ECM-receptor interaction, and focal adhesion. The GSEA results revealed that the epithelial mesenchymal transition (EMT) process was significantly enriched in upregulated mRNAs. The pterygium-associated circRNA–miRNA–mRNA network was established based on the top 10 DE-circRNAs, 4 validated miRNAs (upregulated miR-376a-5p and miR-208a-5p,downregulated miR-203a-3p and miR-200b-3p), and 31 DE-mRNAs. We found that miR-200b-3p, as a regulator of FN1, SDC2, and MEX3D, could be regulated by 5 upregulated circRNAs. In addition, we screened out EMT-related DE-mRNAs, including 6 upregulated DE-mRNAs and 6 downregulated DE-mRNAs. The EMT-related circRNA–miRNA–mRNA network was established with the top 10 circRNAs, 8 validated miRNAs (upregulated miR-17-5p, miR-181a-5p, and miR-106a-5p, downregulated miR-124-3p, miR-9-5p, miR-130b-5p, miR-1-3p, and miR-26b-5P), and 12 EMT-related DE-mRNAs. We found that hsa_circ_0002406 might upregulate FN1 and ADAM12 by sponging miR-26b-5p and miR-1-3p, respectively, thus promoting EMT in pterygium. Briefly, the study provides a novel viewpoint on the molecular pathological mechanisms in pterygium formation. CircRNA–miRNA–mRNA regulatory networks participate in the pathogenesis of pterygium and might become promising targets for pterygium prevention and treatment.

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Dissecting Prognosis Modules and Biomarkers in Glioblastoma Based on Weighted Gene Co-Expression Network Analysis

Cited by 22 publications

References 52 publications

Clinical value of M1 macrophage-related genes identification in bladder urothelial carcinoma and in vitro validation

Clinical value of M1 macrophage-related genes identification in bladder urothelial carcinoma and in vitro validation

Effective prediction of potential ferroptosis critical genes in clinical colorectal cancer

Identification of the circRNA–miRNA–mRNA Regulatory Network in Pterygium-Associated Conjunctival Epithelium

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