|
1
|
Golub TR, Slonim DK, Tamayo P, Huard C,
Gaasenbeek M, Mesirov JP, Coller H, Loh ML, Downing JR, Caliqiuri
MA, et al: Molecular classification of cancer: Class discovery and
class prediction by gene expression monitoring. Science.
286:531–537. 1999. View Article : Google Scholar : PubMed/NCBI
|
|
2
|
Tang Y, Zhang YQ and Huang Z: Development
of two-stage SVM-RFE gene selection strategy for microarray
expression data analysis. IEEE/ACM Trans Comput Biol Bioinform.
4:365–381. 2007. View Article : Google Scholar : PubMed/NCBI
|
|
3
|
Kuner R, Muley T, Meister M, Ruschhaupt M,
Buness A, Xu EC, Schnabel P, Warth A, Poustka A, Sültmann H and
Hoffmann H: Global gene expression analysis reveals specific
patterns of cell junctions in non-small cell lung cancer subtypes.
Lung Cancer. 63:32–38. 2009. View Article : Google Scholar : PubMed/NCBI
|
|
4
|
Sean D and Meltzer P: GEOquery: a bridge
between the gene expression omnibus (GEO) and BioConductor.
Bioinformatics. 23:18462007. View Article : Google Scholar : PubMed/NCBI
|
|
5
|
Konsak BSD, Haring V, Geier M, Hughes R,
Howarth G, Crowley T and Moore R: Identification of differential
duodenal gene expression levels and microbiota abundance correlated
with differences in energy utilization in chickens. Anim Prod Sci.
53:72013.
|
|
6
|
Ding C and Peng H: Minimum redundancy
feature selection from microarray gene expression data. J Bioinform
Comput Biol. 3:185–205. 2005. View Article : Google Scholar : PubMed/NCBI
|
|
7
|
Gao P, Wang QP, Chen L and Huang T:
Prediction of human genes' regulatory functions based on
proteinprotein interaction network. Protein Pept Lett. 19:910–916.
2012. View Article : Google Scholar : PubMed/NCBI
|
|
8
|
Gao YF, Chen L, Cai YD, Feng KY, Huang T
and Jiang Y: Predicting metabolic pathways of small molecules and
enzymes based on interaction information of chemicals and proteins.
PloS One. 7:e459442012. View Article : Google Scholar : PubMed/NCBI
|
|
9
|
Zhang J, Jiang M, Yuan F, Feng KY, Cai YD,
Xu X and Chen L: Identification of age-related macular degeneration
related genes by applying shortest path algorithm in
protein-protein interaction network. Biomed Res Int.
2013:5234152013. View Article : Google Scholar : PubMed/NCBI
|
|
10
|
Szklarczyk D, Franceschini A, Kuhn M,
Simonovic M, Roth A, Minguez P, Doerks T, Stark M, Muller J, Bork
P, et al: The STRING database in 2011: Functional interaction
networks of proteins, globally integrated and scored. Nucleic Acids
Res. 39(Database Issue): D561–D568. 2011. View Article : Google Scholar : PubMed/NCBI
|
|
11
|
Du ZP, Wu BL, Wang SH, Shen JH, Lin XH,
Zheng CP, Wu ZY, Qiu XY, Zhan XF, Xu LY and Li EM: Shortest path
analyses in the protein-protein interaction network of NGAL
(neutrophil gelatinase-associated lipocalin) overexpression in
esophageal squamous cell carcinoma. Asian Pac J Cancer Prev.
15:6899–6904. 2014. View Article : Google Scholar : PubMed/NCBI
|
|
12
|
Huang da W, Sherman BT and Lempicki RA:
Systematic and integrative analysis of large gene lists using DAVID
bioinformatics resources. Nat Protoc. 4:44–57. 2009. View Article : Google Scholar : PubMed/NCBI
|
|
13
|
Dai J, Gu J, Lu C, Lin J, Stewart D, Chang
D, Roth JA and Wu X: Genetic variations in the regulator of
G-protein signaling genes are associated with survival in
late-stage non-small cell lung cancer. PloS One. 6:e211202011.
View Article : Google Scholar : PubMed/NCBI
|
|
14
|
Jin YX, Jiang GN, Zheng H, Duan L and Ding
JA: Common genetic variants on 3q28 contribute to non-small cell
lung cancer susceptibility: Evidence from 10 case-control studies.
Mol Genet Genomics. 290:573–584. 2015. View Article : Google Scholar : PubMed/NCBI
|
|
15
|
Wang Y, McKay JD, Rafnar T, Wang Z,
Timofeeva MN, Broderick P, Zong X, Laplana M, Wei Y, Han Y, et al:
Rare variants of large effect in BRCA2 and CHEK2 affect risk of
lung cancer. Nat Genet. 46:736–741. 2014. View Article : Google Scholar : PubMed/NCBI
|
|
16
|
Choy B, Findeis-Hosey JJ, Li F, McMahon
LA, Yang Q and Xu H: High frequency of coexpression of maspin with
p63 and p53 in squamous cell carcinoma but not in adenocarcinoma of
the lung. Int J Clin Exp Pathol. 6:2542–2547. 2013.PubMed/NCBI
|
|
17
|
Liu Y, Geng Y, Li K, Wang F, Zhou H, Wang
W, Hou J and Liu W: Comparative proteomic analysis of the function
and network mechanisms of MASPIN in human lung cells. Exp Ther Med.
3:470–474. 2012. View Article : Google Scholar : PubMed/NCBI
|
|
18
|
Jadoon A, Cunningham P and McDermott LC:
Arachidonic acid metabolism in the human placenta: Identification
of a putative lipoxygenase. Placenta. 35:422–424. 2014. View Article : Google Scholar : PubMed/NCBI
|
|
19
|
Catalano A and Procopio A: New aspects on
the role of lipoxygenases in cancer progression. Histol
Histopathol. 20:969–975. 2005.PubMed/NCBI
|
|
20
|
Nie D: Cyclooxygenases and lipoxygenases
in prostate and breast cancers. Front Biosci. 12:1574–1585. 2007.
View Article : Google Scholar : PubMed/NCBI
|
|
21
|
Ma YN, Zhang X, Yu HC and Zhang JW: CTD
small phosphatase like 2 (CTDSPL2) can increase ε- and γ-globin
gene expression in K562 cells and CD34+ cells derived from
umbilical cord blood. BMC Cell Biol. 11:752010. View Article : Google Scholar : PubMed/NCBI
|
|
22
|
Zhao Y, Xiao M, Sun B, Zhang Z, Shen T,
Duan X, Yu PB, Feng XH and Lin X: C-terminal domain (CTD) small
phosphatase-like 2 modulates the canonical bone morphogenetic
protein (BMP) signaling and mesenchymal differentiation via Smad
dephosphorylation. J Biol Chem. 289:26441–26450. 2014. View Article : Google Scholar : PubMed/NCBI
|
|
23
|
Deng T, Lin D, Zhang M, Zhao Q, Li W,
Zhong B, Deng Y and Fu X: Differential expression of bone
morphogenetic protein 5 in human lung squamous cell carcinoma and
adenocarcinoma. Acta Biochim Biophys Sin (Shanghai). 47:557–563.
2015. View Article : Google Scholar : PubMed/NCBI
|
|
24
|
Hayama S, Daigo Y, Kato T, Ishikawa N,
Yamabuki T, Miyamoto M, Ito T, Tsuchiya E, Kondo S and Nakamura Y:
Activation of CDCA1-KNTC2, members of centromere protein complex,
involved in pulmonary carcinogenesis. Cancer Res. 66:10339–10348.
2006. View Article : Google Scholar : PubMed/NCBI
|
|
25
|
Kaneko N, Miura K, Gu Z, Karasawa H,
Ohnuma S, Sasaki H, Tsukamoto N, Yokoyama S, Yamamura A, Nagase H,
et al: siRNA-mediated knockdown against CDCA1 and KNTC2, both
frequently overexpressed in colorectal and gastric cancers,
suppresses cell proliferation and induces apoptosis. Biochem
Biophys Res Commun. 390:1235–1240. 2009. View Article : Google Scholar : PubMed/NCBI
|
|
26
|
Brown JR, Koretke KK, Birkeland ML,
Sanseau P and Patrick DR: Evolutionary relationships of Aurora
kinases: Implications for model organism studies and the
development of anti-cancer drugs. BMC Evol Biol. 4:392004.
View Article : Google Scholar : PubMed/NCBI
|
|
27
|
Carmena M and Earnshaw WC: The cellular
geography of aurora kinases. Nat Rev Mol Cell Biol. 4:842–854.
2003. View Article : Google Scholar : PubMed/NCBI
|
|
28
|
Lo Iacono M, Monica V, Saviozzi S, Ceppi
P, Bracco E, Papotti M and Scaqliotti GV: Aurora kinase a
expression is associated with lung cancer histological-subtypes and
with tumor de-differentiation. J Transl Med. 9:1002011. View Article : Google Scholar : PubMed/NCBI
|
|
29
|
Malumbres M and Barbacid M: Cell cycle,
CDKs and cancer: A changing paradigm. Nat Rev Cancer. 9:153–166.
2009. View Article : Google Scholar : PubMed/NCBI
|
|
30
|
Xu L, Wang C, Wen Z, Yao X, Liu Z, Li Q,
Wu Z, Xu Z, Liang Y and Ren T: Selective up-regulation of CDK2 is
critical for TLR9 signaling stimulated proliferation of human lung
cancer cell. Immunol Lett. 127:93–99. 2010. View Article : Google Scholar : PubMed/NCBI
|
