国家自然科学基金(61532014,61571163,61402132,61671189)
[1] Reuter J A, Spacek D V, Snyder M P. High-throughput sequencing technologies.. Mol Cell, 2015, 58: 586-597 CrossRef PubMed Google Scholar
[2] Chuang H Y, Lee E, Liu Y T, et al. Network-based classification of breast cancer metastasis. Mol Syst Biol, 2007, 3: 141--150. Google Scholar
[3] Zhang S, Liu C C, Li W. Discovery of multi-dimensional modules by integrative analysis of cancer genomic data.. Nucleic Acids Res, 2012, 40: 9379-9391 CrossRef PubMed Google Scholar
[4] Hoadley K A, Yau C, Wolf D M. Multiplatform analysis of 12 cancer types reveals molecular classification within and across tissues of origin.. Cell, 2014, 158: 929-944 CrossRef PubMed Google Scholar
[5] Yang Y, Han L, Yuan Y, et al. Gene co-expression network analysis reveals common system-level properties of prognostic genes across cancer types. Nat Commun, 2014, 5: 3231. Google Scholar
[6] Han L, Yuan Y, Zheng S Y, et al. The pan-cancer analysis of pseudogene expression reveals biologically and clinically relevant tumour subtypes. Nat Commun, 2014, 5: 3963. Google Scholar
[7] Regression Analysis of Combined Gene Expression Regulation in Acute Myeloid Leukemia. PLoS Comput Biol, 2014, 10: e1003908 CrossRef PubMed ADS Google Scholar
[8] Ping Y, Deng Y, Wang L. Identifying core gene modules in glioblastoma based on multilayer factor-mediated dysfunctional regulatory networks through integrating multi-dimensional genomic data.. Nucleic Acids Res, 2015, 43: 1997-2007 CrossRef PubMed Google Scholar
[9] Wang Y F, Liu L L, Jin H F, et al. Study on expression profile of long non-coding rna in gastric cancer cell lines under hypoxia. J Mod Oncol, 2013, 21: 225--228. Google Scholar
[10] Tang C Y, Silva-Fisher J M, Dang H X. Abstract 971: A novel long noncoding RNA, onco-lncRNA 230, induces apoptosis and invasion in lung squamous cell carcinoma. Cancer Res, 2016, 76: 971-971 CrossRef Google Scholar
[11] Wu C H, Hsu C L, Lu P C. Identification of lncRNA functions in lung cancer based on associated protein-protein interaction modules. Sci Rep, 2016, 6: 35939 CrossRef PubMed ADS Google Scholar
[12] Yuan F, Meng Z H, Yu G. An improved dbscan clustering algorithm. J Comput Res Dev, 2005, 42: 50--54. Google Scholar
[13] Wen J, Zheng B, Hu Y, et al. Comparative proteomic analysis of the esophageal squamous carcinoma cell line ec109 and its multi-drug resistant subline ec109/cddp. Int J Oncol, 2010, 36: 265--274. Google Scholar
[14] Massion P P, Zou Y, Chen H. Smoking-related genomic signatures in non-small cell lung cancer.. Am J Respir Crit Care Med, 2008, 178: 1164-1172 CrossRef PubMed Google Scholar
[15] Fuja T J, Lin F, Osann K E. Somatic mutations and altered expression of the candidate tumor suppressors csnk1 epsilon, dlg1, and edd/hhyd in mammary ductal carcinoma. Cancer Res, 2004, 64: 942-951 CrossRef Google Scholar
[16] Justilien V, Walsh M P, Ali S A. The PRKCI and SOX2 oncogenes are coamplified and cooperate to activate Hedgehog signaling in lung squamous cell carcinoma.. Cancer Cell, 2014, 25: 139-151 CrossRef PubMed Google Scholar
[17] Shinmura K, Kiyose S, Nagura K. TNK2 gene amplification is a novel predictor of a poor prognosis in patients with gastric cancer.. J Surg Oncol, 2014, 109: 189-197 CrossRef PubMed Google Scholar
[18] Fields A P, Justilien V. The guanine nucleotide exchange factor (GEF) Ect2 is an oncogene in human cancer.. Adv Enzyme Regulation, 2010, 50: 190-200 CrossRef PubMed Google Scholar
[19] Farfsing A, Engel F, Seiffert M. Gene knockdown studies revealed CCDC50 as a candidate gene in mantle cell lymphoma and chronic lymphocytic leukemia.. Leukemia, 2009, 23: 2018-2026 CrossRef PubMed Google Scholar
[20] Hagerstrand D, Tong A, Schumacher S E. Systematic interrogation of 3q26 identifies TLOC1 and SKIL as cancer drivers.. Cancer Discovery, 2013, 3: 1044-1057 CrossRef PubMed Google Scholar
[21] Arai M, Yokosuka O, Hirasawa Y. Sequential gene expression changes in cancer cell lines after treatment with the demethylation agent 5-Aza-2-deoxycytidine.. Cancer, 2006, 106: 2514-2525 CrossRef PubMed Google Scholar
[22] Uematsu K, He B, You L. Activation of the Wnt pathway in non small cell lung cancer: evidence of dishevelled overexpression.. Oncogene, 2003, 22: 7218-7221 CrossRef PubMed Google Scholar
[23] Dowling P, Clarke C, Hennessy K. Analysis of acute-phase proteins, AHSG, C3, CLI, HP and SAA, reveals distinctive expression patterns associated with breast, colorectal and lung cancer.. Int J Cancer, 2012, 131: 911-923 CrossRef PubMed Google Scholar
[24] Muzny D M, Scherer S E, Kaul R. The DNA sequence, annotation and analysis of human chromosome 3. Nature, 2006, 440: 1194-1198 CrossRef PubMed ADS Google Scholar
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
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 |
$\varepsilon$: 扫描半径参数 |
MinPts: 邻域密度阀值 |
$1$. 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. |
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 |