Co-expression Network Analysis Identifies Four Hub Genes Associated With Prognosis in Soft Tissue Sarcoma

Zhu, Zhifeng; Jin, Zheng; Deng, Yuyou; Wei, Lai; Yuan, Xiaowei; Zhang, Mei; Sun, Dahui

doi:10.3389/fgene.2019.00037

Cited by 33 publications

(22 citation statements)

References 35 publications

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“…Exploitation of the function of dysregulated lncRNAs will provide potential clinical applications for TN's diagnosis and treatment. WGCNA can be used to identify key lncRNAs associated with multiple cancer pathogenesis and progression [23][24][25][26]. Survival analysis verified the identified lncRNAs possessing potential indicative roles in TN prognosis.…”

Section: Introductionmentioning

confidence: 90%

Identification of Long Noncoding RNAs as Predictors of Survival in Triple-Negative Breast Cancer Based on Network Analysis

Wang

Hou

et al. 2020

BioMed Research International

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Breast cancer is the most common cancer observed in adult females, worldwide. Due to the heterogeneity and varied molecular subtypes of breast cancer, the molecular mechanisms underlying carcinogenesis in different subtypes of breast cancer are distinct. Recently, long noncoding RNAs (lncRNAs) have been shown to be oncogenic or play important roles in cancer suppression and are used as biomarkers for diagnosis and therapy. In this study, we identified 134 lncRNAs and 6,414 coding genes were differentially expressed in triple-negative (TN), human epidermal growth factor receptor 2- (HER2-) positive, luminal A-positive, and luminal B-positive breast cancer. Of these, 37 lncRNAs were found to be dysregulated in all four subtypes of breast cancers. Subtypes of breast cancer special modules and lncRNA-mRNA interaction networks were constructed through weighted gene coexpression network analysis (WGCNA). Survival analysis of another public datasets was used to verify the identified lncRNAs exhibiting potential indicative roles in TN prognosis. Results from heat map analysis of the identified lncRNAs revealed that five blocks were significantly displayed. High expressions of lncRNAs, including LINC00911, CSMD2-AS1, LINC01192, SNHG19, DSCAM-AS1, PCAT4, ACVR28-AS1, and CNTFR-AS1, and low expressions of THAP9-AS1, MALAT1, TUG1, CAHM, FAM2011, NNT-AS1, COX10-AS1, and RPARP-AS1 were associated with low survival possibility in TN breast cancers. This study provides novel lncRNAs as potential biomarkers for the therapeutic and prognostic classification of different breast cancer subtypes.

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Section: Introductionmentioning

confidence: 90%

Identification of Long Noncoding RNAs as Predictors of Survival in Triple-Negative Breast Cancer Based on Network Analysis

Wang

Hou

et al. 2020

BioMed Research International

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“…First, to further understand the relationship between hub genes and plaque vulnerability, we downloaded GSE60993 and used it as the training dataset. Then, we compared the expression differences of hub genes among different groups and showed the results in the ggplot2 package [17] in R. Subsequently, the "survival" package [18] in R was used to perform overall survival (heart failure) and disease-free survival analyses for all hub genes. Patients were divided into four groups (high vs. low) based on the hub gene expression level in comparison to the mean expression level of that hub gene.…”

Section: Methodsmentioning

confidence: 99%

Integrated weighted gene co-expression network analysis identified that TLR2 and CD40 are related to coronary artery disease

Qi¹,

Chen²,

Lin³

et al. 2020

Preprint

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Background: The current research attempted to identify possible hub genes and pathways of coronary artery disease (CAD) and to detect the possible mechanisms.Methods: Array data from GSE90074 were downloaded from the Gene Expression Omnibus (GEO) database. Integrated weighted gene co-expression network analysis (WGCNA) was performed to analyze the gene module and clinical characteristics. Gene Ontology annotation, Disease Ontology and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed by clusterProfiler and the DOSE package in R. A protein-protein interaction (PPI) network was established using Cytoscape software, and significant modules were analyzed using Molecular Complex Detection to identify hub genes. Then, further functional validation of hub genes in other microarrays and population samples was performed, and survival analysis was performed to investigate the prognosis.Results: A total of 660 genes were located in three modules and associated with CAD. GO functions identified 484 biological processes, 39 cellular components, and 22 molecular functions with an adjusted P < 0.05. Approximately 38 pathways were enriched in KEGG pathway analysis, and 147 DO items were identified with an adjusted P < 0.05 (false discovery rate, FDR set at < 0.05). There were a total of four modules with a score > 10 after PPI network analysis using the MCODE app, and two hub genes (TLR2 and CD14) were identified. Then, we validated the information from the GSE60993 dataset using the GSE59867 dataset and population samples, and we found that these two genes were associated with plaque vulnerability. These two genes varied at different time points after myocardial infarction, and both of them had the lowest prognosis of heart failure when they were expressed at low levels.Conclusion: We performed an integrated WGCNA and validated that TLR2 and CD14 were closely associated with the severity of coronary artery disease, plaque instability and the prognosis of heart failure after myocardial infarction.

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“…From that, Benjamini and Hochberg's false discovery rate (FDR) may be a better choice. Likewise, many previous works [49][50][51][52][53] related the expression levels of each identified driver gene to prognostic value (e.g., the overall survival of patients), and the genes when P-value ≤ 0.05 (Log-rank test) were considered to define significant association. Again, FDR control is crucial, so we developed the tool 'geneSA' (https ://githu b.com/huyng uyen2 50896 /geneS A) to automatically do the above task and only preserve the genes if Q-value ≤ 0.05 (Benjamini-Hochberg FDR).…”

Section: List Of Improvements Proposed In the Work Selection Of Drivmentioning

confidence: 99%

Improving existing analysis pipeline to identify and analyze cancer driver genes using multi-omics data

Nguyen¹,

2020

Sci Rep

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The cumulative of genes carrying mutations is vital for the establishment and development of cancer. However, this driver gene exploring research line has selected and used types of tools and models of analysis unsystematically and discretely. Also, the previous studies may have neglected low-frequency drivers and seldom predicted subgroup specificities of identified driver genes. In this study, we presented an improved driver gene identification and analysis pipeline that comprises the four most widely focused analyses for driver genes: enrichment analysis, clinical feature association with expression profiles of identified driver genes as well as with their functional modules, and patient stratification by existing advanced computational tools integrating multi-omics data. The improved pipeline's general usability was demonstrated straightforwardly for breast cancer, validated by some independent databases. Accordingly, 31 validated driver genes, including four novel ones, were discovered. Subsequently, we detected cancer-related significantly enriched gene ontology terms and pathways, probable drug targets, two co-expressed modules associated significantly with several clinical features, such as number of positive lymph nodes, Nottingham prognostic index, and tumor stage, and two biologically distinct groups of BRCA patients. Data and source code of the case study can be downloaded at https://github.com/hauldhut/drivergene.

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Co-expression Network Analysis Identifies Four Hub Genes Associated With Prognosis in Soft Tissue Sarcoma

Cited by 33 publications

References 35 publications

Identification of Long Noncoding RNAs as Predictors of Survival in Triple-Negative Breast Cancer Based on Network Analysis

Identification of Long Noncoding RNAs as Predictors of Survival in Triple-Negative Breast Cancer Based on Network Analysis

Integrated weighted gene co-expression network analysis identified that TLR2 and CD40 are related to coronary artery disease

Improving existing analysis pipeline to identify and analyze cancer driver genes using multi-omics data

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