ceRNA网络分析

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表达差异

• 获得差异的mRNAs,miRNA,lncRNA
  
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• 使用火山图和热图对差异分析结果进行可视化
  
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ceRNA网络

• lncRNA可以作为miRNA的海绵去竞争性的结合miRNA,从而调节mRNA的表达

lncRNA与miRNA相互作用

• 使用miRCode预测lncRNA与miRNA之间的关系
  
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• 从miRCode数据库中预测的结果为:170个DEmiRNA中有18个可以与184个DElncRNA相互作用
  

miRNA与mRNA相互作用

• 使用miRTarBase,miRDB,TargetScan预测miRNA靶向的mRNA
  
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• 从miRTarBase/miRDB/TargetScan数据库中预测18个DEmiRNAs得到820个向mRNAs
• 将预测得到的mRNA与测序数据差异的mRNA取交集,得到49个差异表达的靶mRNA
  
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网络图

• 使用Cytoscape可视化网络,lnsRANs,miRNAs,mRNAs分别由菱形,矩形,椭圆形表示, 红色节点表示高表达, 蓝色节点表示第表达
  
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生存分析

• 为了阐明具有愈后特征的lncRNAs,miRNAs,mRNAs,使用survival R packages评估了ceRNA网络中lncRNA,miRNA和mRNA中的表达与LUSC患者总生存期(OS)之间的关系,设阈值为p<0.05,结果表明:
• 184个DElncRNA中有11个与总生存率显著相关
• 只有一个DEmiRNA(hsa-mir-183)与LUSC的总生存率相关
• 在49个DEmRNA中有5个LUSC( CITED2, CHAF1A, LIMCH1, LRRK2, SLC16A9 )与LUSC总生存率相关
  
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为进一步探索lncRNA与愈后之间的关系, 使用cox回归对ceRNA网络中的184个DElnRNAs构建临床风险模型

预后模型

富集分析

ceRNA子网络

miRNA_lnRNA <- readRDS("result/miRNA_lnRNA.rda")
miRNA_mRNA <- readRDS("result/miRNA_mRNA.rda")
lnRNA_miRNA_mRNA <- merge(miRNA_lnRNA,miRNA_mRNA,by="miRNA")

lncRNA_deg_ce <- lncRNA_deg_sig%>%
    plyr::rename(c(symbol="lnRNA"))%>%
    mutate(lnRNA_direction=ifelse(logFC>0,"up","down"))%>%
    dplyr::select(lnRNA,lnRNA_direction)
  
miRNA_deg_ce <- miRNA_deg_sig%>%
    plyr::rename(c(symbol="miRNA"))%>%
    mutate(miRNA_direction=ifelse(logFC>0,"up","down"))%>%
    dplyr::select(miRNA,miRNA_direction)

mRNA_deg_ce <- mRNA_deg_sig%>%
    plyr::rename(c(symbol="mRNA"))%>%
    mutate(mRNA_direction=ifelse(logFC>0,"up","down"))%>%
    dplyr::select(mRNA,mRNA_direction)

  lnRNA_miRNA_mRNA_ <- lnRNA_miRNA_mRNA
  lnRNA_miRNA_mRNA <- lnRNA_miRNA_mRNA_ %>%
    inner_join(lncRNA_deg_ce,by="lnRNA")%>%
    inner_join(miRNA_deg_ce,by="miRNA")%>%
    inner_join(mRNA_deg_ce,by="mRNA")%>%
    filter(lnRNA_direction!=miRNA_direction)%>%
    filter(miRNA_direction!=mRNA_direction)
length(unique(lnRNA_miRNA_mRNA$lnRNA))
saveRDS(lnRNA_miRNA_mRNA,file = "result/lnRNA_miRNA_mRNA.rda")
# readRDS("result/miRNA_lnRNA.rda")%>%
#     filter(lnRNA=="LINC00460", miRNA=="hsa-mir-143")

cytoscape <- function(lnRNA_miRNA_mRNA,filename){
  ce_lnRNA <- unique(lnRNA_miRNA_mRNA$lnRNA)
  ce_miRNA <- unique(lnRNA_miRNA_mRNA$miRNA)
  ce_mRNA <- unique(lnRNA_miRNA_mRNA$mRNA)

  cat("ceRNA网络中有: ", length(ce_lnRNA),
         " 个lnRNA对应 ",length(ce_miRNA)," 个miRNA, ",
         length(ce_miRNA)," 的miRNA对应 ",
         length(ce_mRNA), "的mRNA")
    
  miRNA_lnRNA <<- unique(lnRNA_miRNA_mRNA%>%dplyr::select(lnRNA,miRNA))
  miRNA_mRNA <<- unique(lnRNA_miRNA_mRNA%>%dplyr::select(miRNA,mRNA))
    
  lncRNA_deg_ce <- lncRNA_deg_sig%>%
    plyr::rename(c(symbol="lnRNA"))%>%
    filter(lnRNA %in%  ce_lnRNA)%>%
    mutate(direction=ifelse(logFC>0,"up","down"),type="lncRNA")%>%
    dplyr::select(name=lnRNA,direction,type)
  
  miRNA_deg_ce <- miRNA_deg_sig%>%
    plyr::rename(c(symbol="miRNA"))%>%
    filter(miRNA %in%  ce_miRNA)%>%
    mutate(direction=ifelse(logFC>0,"up","down"),type="miRNA")%>%
    dplyr::select(name=miRNA,direction,type)
  
  mRNA_deg_ce <- mRNA_deg_sig%>%
    plyr::rename(c(symbol="mRNA"))%>%
    filter(mRNA %in%  ce_mRNA)%>%
    mutate(direction=ifelse(logFC>0,"up","down"),type="mRNA")%>%
    dplyr::select(name=mRNA,direction,type)
    
  cytoscape_type <- bind_rows(mRNA_deg_ce,miRNA_deg_ce,lncRNA_deg_ce)%>%
    mutate(type = str_c(direction,type,sep="_"))%>%
    dplyr::select(-2)
    
  write.csv(cytoscape_type,file = paste0("figure/GEO/",filename,"_type.csv"),row.names = F,quote = F)
  cat("写入网络节点类型到 ",paste0("figure/GEO/",filename,"_type.csv"), " 共有 ",dim(cytoscape_type)[1]," 个")
  ceRAN_pair1 <- miRNA_lnRNA%>%
    dplyr::select(miRNA,name=lnRNA)
  ceRAN_pair2 <- miRNA_mRNA%>%
    dplyr::select(miRNA,name=mRNA)
  cytoscape_input <- bind_rows(ceRAN_pair1,ceRAN_pair2)
  write.csv(cytoscape_input,file = paste0("figure/GEO/",filename,"_input.csv"),row.names = F,quote = F)
  cat("写入网络节点关系:",paste0("figure/GEO/",filename,"_input.csv")," 共有 ",dim(cytoscape_input)[1]," 个")
}
cytoscape(lnRNA_miRNA_mRNA,filename = "geo")
unique(lnRNA_miRNA_mRNA$miRNA)

参考

Identification of lncRNA biomarkers in lung squamous cell carcinoma using comprehensive analysis of lncRNA mediated ceRNA network