• Title/Summary/Keyword: weighted gene co-expression network analysis

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Identification of Prostate Cancer LncRNAs by RNA-Seq

  • Hu, Cheng-Cheng;Gan, Ping;Zhang, Rui-Ying;Xue, Jin-Xia;Ran, Long-Ke
    • Asian Pacific Journal of Cancer Prevention
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    • v.15 no.21
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    • pp.9439-9444
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    • 2014
  • Purpose: To identify prostate cancer lncRNAs using a pipeline proposed in this study, which is applicable for the identification of lncRNAs that are differentially expressed in prostate cancer tissues but have a negligible potential to encode proteins. Materials and Methods: We used two publicly available RNA-Seq datasets from normal prostate tissue and prostate cancer. Putative lncRNAs were predicted using the biological technology, then specific lncRNAs of prostate cancer were found by differential expression analysis and co-expression network was constructed by the weighted gene co-expression network analysis. Results: A total of 1,080 lncRNA transcripts were obtained in the RNA-Seq datasets. Three genes (PCA3, C20orf166-AS1 and RP11-267A15.1) showed a significant differential expression in the prostate cancer tissues, and were thus identified as prostate cancer specific lncRNAs. Brown and black modules had significant negative and positive correlations with prostate cancer, respectively. Conclusions: The pipeline proposed in this study is useful for the prediction of prostate cancer specific lncRNAs. Three genes (PCA3, C20orf166-AS1, and RP11-267A15.1) were identified to have a significant differential expression in prostate cancer tissues. However, there have been no published studies to demonstrate the specificity of RP11-267A15.1 in prostate cancer tissues. Thus, the results of this study can provide a new theoretic insight into the identification of prostate cancer specific genes.

Identification of Specific Gene Modules in Mouse Lung Tissue Exposed to Cigarette Smoke

  • Xing, Yong-Hua;Zhang, Jun-Ling;Lu, Lu;Li, De-Guan;Wang, Yue-Ying;Huang, Song;Li, Cheng-Cheng;Zhang, Zhu-Bo;Li, Jian-Guo;Xu, Guo-Shun;Meng, Ai-Min
    • Asian Pacific Journal of Cancer Prevention
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    • v.16 no.10
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    • pp.4251-4256
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    • 2015
  • Background: Exposure to cigarette may affect human health and increase risk of a wide range of diseases including pulmonary diseases, such as chronic obstructive pulmonary disease (COPD), asthma, lung fibrosis and lung cancer. However, the molecular mechanisms of pathogenesis induced by cigarettes still remain obscure even with extensive studies. With systemic view, we attempted to identify the specific gene modules that might relate to injury caused by cigarette smoke and identify hub genes for potential therapeutic targets or biomarkers from specific gene modules. Materials and Methods: The dataset GSE18344 was downloaded from the Gene Expression Omnibus (GEO) and divided into mouse cigarette smoke exposure and control groups. Subsequently, weighted gene co-expression network analysis (WGCNA) was used to construct a gene co-expression network for each group and detected specific gene modules of cigarette smoke exposure by comparison. Results: A total of ten specific gene modules were identified only in the cigarette smoke exposure group but not in the control group. Seven hub genes were identified as well, including Fip1l1, Anp32a, Acsl4, Evl, Sdc1, Arap3 and Cd52. Conclusions: Specific gene modules may provide better understanding of molecular mechanisms, and hub genes are potential candidates of therapeutic targets that may possible improve development of novel treatment approaches.

The role of RNA epigenetic modification-related genes in the immune response of cattle to mastitis induced by Staphylococcus aureus

  • Yue Xing;Yongjie Tang;Quanzhen Chen;Siqian Chen;Wenlong Li;Siyuan Mi;Ying Yu
    • Animal Bioscience
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    • v.37 no.7
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    • pp.1141-1155
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    • 2024
  • Objective: RNA epigenetic modifications play an important role in regulating immune response of mammals. Bovine mastitis induced by Staphylococcus aureus (S. aureus) is a threat to the health of dairy cattle. There are numerous RNA modifications, and how these modification-associated enzymes systematically coordinate their immunomodulatory effects during bovine mastitis is not well reported. Therefore, the role of common RNA modification-related genes (RMRGs) in bovine S. aureus mastitis was investigated in this study. Methods: In total, 80 RMRGs were selected for this study. Four public RNA-seq data sets about bovine S. aureus mastitis were collected and one additional RNA-seq data set was generated by this study. Firstly, quantitative trait locus (QTL) database, transcriptome-wide association studies (TWAS) database and differential expression analyses were employed to characterize the potential functions of selected enzyme genes in bovine S. aureus mastitis. Correlation analysis and weighted gene co-expression network analysis (WGCNA) were used to further investigate the relationships of RMRGs from different types at the mRNA expression level. Interference experiments targeting the m6 A demethylase FTO and utilizing public MeRIP-seq dataset from bovine Mac-T cells were used to investigate the potential interaction mechanisms among various RNA modifications. Results: Bovine QTL and TWAS database in cattle revealed associations between RMRGs and immune-related complex traits. S. aureus challenged and control groups were effectively distinguished by principal component analysis based on the expression of selected RMRGs. WGCNA and correlation analysis identified modules grouping different RMRGs, with highly correlated mRNA expression. The m6 A modification gene FTO showed significant effects on the expression of m6 A and other RMRGs (such as NSUN2, CPSF2, and METTLE), indicating complex co-expression relationships among different RNA modifications in the regulation of bovine S. aureus mastitis. Conclusion: RNA epigenetic modification genes play important immunoregulatory roles in bovine S. aureus mastitis, and there are extensive interactions of mRNA expression among different RMRGs. It is necessary to investigate the interactions between RNA modification genes regulating complex traits in the future.