SCIENTIA SINICA Informationis, Volume 47 , Issue 11 : 1510-1522(2017) https://doi.org/10.1360/N112017-00108

A method for mining core modules of cancer based on multi-omics biological network

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  • ReceivedMay 16, 2017
  • AcceptedJun 12, 2017
  • PublishedNov 3, 2017


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  • Figure 1

    (Color online) Progress of core module mining method

  • Figure 2

    (Color online) lncRNA experimental result. (a) Results while including lncRNA and random data; protectłinebreak (b) results while excluding lncRNA data

  • Figure 3

    Core gene module with regulating factors

  • Figure 4

    Core gene module

  • Figure 5

    Result of distinguish normal and tumor samples according to core genes

  • Figure 6

    Result of function and pathway analysis

  • Figure 7

    (Color online) TCGA survival analysis result. (a) 15-genes; (b) 12-genes

  • Figure 8

    (Color online) GEO survival analysis result. (a) 15-genes GSE8894; (b) 15-genes GSE17710; (c) 12-genes GSE8894; (d) 12-genes GSE17710

  • Table 1   Chromosome information of core genes
    Gene Chromosome Start point End point
    TP53 hs17 7668402 7687550
    CDKN2A hs9 21967752 21995043
    DROSHA hs5 31400494 31532175
    DAB2 hs5 39371674 39425233
    SMC4 hs3 160399304 160434962
    PRKCI hs3 170222432 170305982
    SKIL hs3 170357678 170396849
    ECT2 hs3 172750682 172829273
    DVL3 hs3 184155311 184173614
    AP2M1 hs3 184174846 184184091
    DNAJB11 hs3 186570676 186585800
    AHSG hs3 186612928 186621318
    CCDC50 hs3 191329082 191398670
    TNK2 hs3 195863364 195909009
    DLG1 hs3 197042560 197299272

    Algorithm 1 基于密度的关键基因模块聚类算法

    输入: Dataset: 一个包含$n$个对象的数据集
    $\varepsilon$: 扫描半径参数
    MinPts: 邻域密度阀值
    输出: 具有簇标签的基因集合
    $1$. 创建空间为$n\times~n$的二维矩阵dis用于存储关键基因间距离;
    $2$. 计算dataset中关键基因间距离并保存到dis 矩阵;
    $3$. 对于dataset中的对象,根据dis标记满足$\varepsilon$范围内密度大于Minpts 的对象为core point;
    $4$. 标记core point在$\varepsilon$范围内的非core point对象为border point;
    $5$. 标记dataset中既不是core point也不是border point的对象为noise point;
    $6$. 标记dataset中所有对象为unvisited;
    $7$. for core point中的每个对象$p~$ //深度优先连接所有core point
    $8$. 创建栈Stack;
    $9$. if $p$是visited
    $10$. Continue;
    $11$. 将$p$标记为visited压入Stack
    $12$. while(Stack不为空)
    $13$. $v$ $\leftarrow$ Stack弹出栈顶;
    $14$. for $v$邻域内的每个对象$q$
    $15$. if $q$是visited
    $16$. Continue;
    $17$. 将$q$纳入$p$所在cluster;
    $18$. 将$q$标记为visited;
    $19$. 将$q$压入Stack;
    $20$. end for
    $21$. end while
    $22$. end for
    $23$. for border point中的每个对象$b$
    $24$. 将$b$纳入$\varepsilon$范围内任意core point所属cluster;
    $25$. end for
    $26$. 输出dataset中core point 与border point 对象以及其对应的cluster.
  • Table 2   Function ID and function name
    ID Term
    GO:0035556 Intracellular signal transduction
    hsa04144 Endocytosis
    GO:0032147 Activation of protein kinase activity
    hsa05203 Viral carcinogenesis
    GO:0043065 Positive regulation of apoptotic process
    GO:0046677 Response to antibiotic
    GO:0071479 Cellular response to ionizing radiation
    GO:0090004 Positive regulation of establishment of protein localization to plasma membrane
    GO:0071158 Positive regulation of cell cycle arrest
    GO:0042326 Negative regulation of phosphorylation
    hsa04390 Hippo signaling pathway
    GO:0045197 Establishment or maintenance of epithelial cell apical/basal polarity
    GO:0090399 Replicative senescence
    GO:0045893 Positive regulation of transcription, DNA-templated
    GO:0007050 Cell cycle arrest
    GO:1903077 Negative regulation of protein localization to plasma membrane
    hsa05166 HTLV-I infection
    GO:0070830 Bicellular tight junction assembly