|
1
|
Kong C and Hansen MF: Biomarkers in
osteosarcoma. Expert Opin Med Diagn. 3:13–23. 2009.PubMed/NCBI View Article : Google Scholar
|
|
2
|
Ottaviani G and Jaffe N: The epidemiology
of osteosarcoma. Cancer Treat Res. 152:3–13. 2009.PubMed/NCBI View Article : Google Scholar
|
|
3
|
Neyssa M, Mark G, Lisa T and Richard G:
Biology and therapeutic advances for pediatric osteosarcoma.
Oncologist. 9:422–421. 2004.PubMed/NCBI View Article : Google Scholar
|
|
4
|
Machak GN, Tkachev SI, Solovyev YN,
Sinyukov PA, Ivanov SM, Kochergina NV, Ryjkov AD, Tepliakov VV,
Bokhian BY and Glebovskaya VV: Neoadjuvant chemotherapy and local
radiotherapy for high-grade osteosarcoma of the extremities. Mayo
Clin Proc. 78:147–155. 2003.PubMed/NCBI View
Article : Google Scholar
|
|
5
|
Kaya M, Wada T, Akatsuka T, Kawaguchi S,
Nagoya S, Shindoh M, Higashino F, Mezawa F, Okada F and Ishii S:
Vascular endothelial growth factor expression in untreated
osteosarcoma is predictive of pulmonary metastasis and poor
prognosis. Clin Cancer Res. 6:572–577. 2000.PubMed/NCBI
|
|
6
|
Picci P, Vanel D, Briccoli A, Talle K,
Haakenaasen U, Malaguti C, Monti C, Ferrari C, Bacci G, Saeter G
and Alvegard TA: Computed tomography of pulmonary metastases from
osteosarcoma: The less poor technique. A study of 51 patients with
histological correlation. Ann Oncol. 12:1601–1604. 2001.PubMed/NCBI View Article : Google Scholar
|
|
7
|
Li W, Xie P and Ruan WH: Overexpression of
lncRNA UCA1 promotes osteosarcoma progression and correlates with
poor prognosis. J Bone Oncol. 5:80–85. 2016.PubMed/NCBI View Article : Google Scholar
|
|
8
|
Wei L, Peng X and Wen-Hui R:
Overexpression of lncRNA UCA1 promotes osteosarcoma progression and
correlates with poor prognosis. J Bone Oncol. 5:80–85.
2016.PubMed/NCBI View Article : Google Scholar
|
|
9
|
Zhao H, Hou W, Tao J, Zhao Y, Wan G, Ma C
and Xu H: Upregulation of lncRNA HNF1A-AS1 promotes cell
proliferation and metastasis in osteosarcoma through activation of
the Wnt/β-catenin signaling pathway. Am J Transl Res. 8:3503–3512.
2016.PubMed/NCBI
|
|
10
|
Zhang CL, Zhu KP and Ma XL: Antisense
lncRNA FOXC2-AS1 promotes doxorubicin resistance in osteosarcoma by
increasing the expression of FOXC2. Cancer Lett. 396:66–75.
2017.PubMed/NCBI View Article : Google Scholar
|
|
11
|
Zhang F and Peng H: LncRNA-ANCR regulates
the cell growth of osteosarcoma by interacting with EZH2 and
affecting the expression of p21 and p27. J Orthop Surg Res.
12(103)2017.PubMed/NCBI View Article : Google Scholar
|
|
12
|
Peng ZQ, Lu RB, Xiao DM and Xiao ZM:
Increased expression of the lncRNA BANCR and its prognostic
significance in human osteosarcoma. Genetics Mol Res: doi:
10.4238/gmr.15017480, 2016.
|
|
13
|
Edgar R, Domrachev M and Lash EA: Gene
expression omnibus: NCBI gene expression and hybridization array
data repository. Nucleic Acids Res. 30:207–210. 2002.PubMed/NCBI View Article : Google Scholar
|
|
14
|
Tan Q, Thomassen M, Jochumsen KM, Zhao JH,
Christensen K and Kruse TA: Evolutionary algorithm for feature
subset selection in predicting tumor outcomes using microarray
data. In: Bioinformatics Research and Applications. ISBRA 2008.
Lecture Notes in Computer Science. Vol 4983. Măndoiu I, Sunderraman
R and Zelikovsky A (eds). Springer, Berlin, Heidelberg, 2008.
|
|
15
|
Shen R, Ghosh D, Chinnaiyan A and Meng Z:
Eigengene-based linear discriminant model for tumor classification
using gene expression microarray data. Bioinformatics.
22:2635–2642. 2006.PubMed/NCBI View Article : Google Scholar
|
|
16
|
Kannan S and Neelapu S: Abstract #1434:
Meta-analysis of multiple Follicular Lymphoma GEO datasets reveals
the significance of tumor microenvironment in disease progression.
Cancer Res. 69(1434)2009.
|
|
17
|
Li Q, Smith AJ, Schacker TW, Carlis JV,
Duan L, Reilly CS and Haase AT: Microarray analysis of lymphatic
tissue reveals stage-specific, gene expression signatures in HIV-1
infection. J Immunol. 183:1975–1982. 2015.PubMed/NCBI View Article : Google Scholar
|
|
18
|
Kampas D, Soulitzis N, Neofytou E and
Siafakas NM: Microarray cluster analysis reveals individual genes
and biological processes associated with COPD. Eur Respir J.
44(P3821)2014.
|
|
19
|
Li CY, Pang YY, Yang H, Li J, Lu HX, Wang
HL, Mo WJ, Huang LS, Feng ZB and Chen G: Identification of
miR-101-3p targets and functional features based on bioinformatics,
meta-analysis and experimental verification in hepatocellular
carcinoma. Am J Transl Res. 9:2088–2105. 2017.PubMed/NCBI
|
|
20
|
Zhou Z, Li Y, Hao H, Wang Y, Zhou Z, Wang
Z and Chu X: Screening hub genes as prognostic biomarkers of
hepatocellular carcinoma by bioinformatics analysis. Cell
Transplant. 28 (1_Suppl):76S–86S. 2019.PubMed/NCBI View Article : Google Scholar
|
|
21
|
Cuff J, Salari K, Clarke N, Esheba GE,
Forster AD, Huang S, West RB, Higgins JP, Longacre TA and Pollack
JR: Integrative bioinformatics links HNF1B with clear cell
carcinoma and tumor-associated thrombosis. PLoS One.
8(e74562)2013.PubMed/NCBI View Article : Google Scholar
|
|
22
|
Yu Z, Chen WJ, Gan TQ, Zhang XL, Xie ZC,
Ye ZH, Deng Y, Wang ZF, Cai KT, Li SK, et al: Clinical significance
and effect of lncRNA HOXA11-AS in NSCLC: A study based on
bioinformatics, in vitro and in vivo verification.
Sci Rep. 7(5567)2017.PubMed/NCBI View Article : Google Scholar
|
|
23
|
Flores RJ, Li Y, Yu A, Shen J, Rao PH, Lau
SS, Vannucci M, Lau CC and Man TK: A systems biology approach
reveals common metastatic pathways in osteosarcoma. BMC Syst Biol.
6(50)2012.PubMed/NCBI View Article : Google Scholar
|
|
24
|
Endo-Munoz L, Cumming A, Rickwood D,
Wilson D, Cueva C, Ng C, Strutton G, Cassady AI, Evdokiou A,
Sommerville S, et al: Loss of osteoclasts contributes to
development of osteosarcoma pulmonary metastases. Cancer Res.
70:7063–7072. 2010.PubMed/NCBI View Article : Google Scholar
|
|
25
|
Sun C, Yuan Q, Wu D, Meng X and Wang B:
Identification of core genes and outcome in gastric cancer using
bioinformatics analysis. Oncotarget. 8:70271–70280. 2017.PubMed/NCBI View Article : Google Scholar
|
|
26
|
Gene Ontology Consortium. The gene
ontology (GO) project in 2006. Nucleic Acids Res. 34:D322–D326.
2006.PubMed/NCBI View Article : Google Scholar
|
|
27
|
Kanehisa M and Goto S: KEGG: Kyoto
encyclopedia of genes and genomes. Nucleic Acids Res. 28:27–30.
2000.PubMed/NCBI View Article : Google Scholar
|
|
28
|
Chin CH, Chen SH, Wu HH, Ho CW, Ko MT and
Lin CY: CytoHubba: Identifying hub objects and sub-networks from
complex interactome. BMC Syst Biol 8 Suppl. 4 (Suppl
4)(S11)2014.PubMed/NCBI View Article : Google Scholar
|
|
29
|
Bader GD and Hogue CW: An automated method
for finding molecular complexes in large protein interaction
networks. BMC Bioinformatics. 4(2)2003.PubMed/NCBI View Article : Google Scholar
|
|
30
|
Livak KJ and Schmittgen TD: Analysis of
relative gene expression data using real-time quantitative PCR and
the 2(-Delta Delta C(T)) method. Methods. 25:402–408.
2001.PubMed/NCBI View Article : Google Scholar
|
|
31
|
Jiang X, Barmada MM and Visweswaran S:
Identifying genetic interactions in genome-wide data using Bayesian
networks. Genet Epidemiol. 34:575–581. 2010.PubMed/NCBI View Article : Google Scholar
|
|
32
|
De Backer MD, Ilyina T, Ma XJ, Vandoninck
S, Luyten WH and Bossche HV: Genomic profiling of the response of
candida albicans to itraconazole treatment using a DNA microarray.
Antimicrob Agents Chemother. 45:1660–1670. 2001.PubMed/NCBI View Article : Google Scholar
|
|
33
|
Mirshahidi HR and Abraham J: Genomic
profiling in clinical oncology. The predictive value of genomic
information in cancer management. Postgrad Med. 119:56–61.
2006.PubMed/NCBI View Article : Google Scholar
|
|
34
|
Wulfkuhle J, Espina V, Liotta L and
Petricoin E: Genomic and proteomic technologies for
individualisation and improvement of cancer treatment. Eur J
Cancer. 40:2623–2632. 2004.PubMed/NCBI View Article : Google Scholar
|
|
35
|
Stuart JM, Eran S, Daphne K and Kim SK: A
gene-coexpression network for global discovery of conserved genetic
modules. Science. 302:249–255. 2003.PubMed/NCBI View Article : Google Scholar
|
|
36
|
Tsuyuzaki K, Tominaga D, Kwon Y and
Miyazaki S: Two-way AIC: Detection of differentially expressed
genes from large scale microarray meta-dataset. BMC Genomics. 14
(Suppl 2)(S9)2013.PubMed/NCBI View Article : Google Scholar
|
|
37
|
Jeyachidra J and Punithavalli M (eds): A
comparative analysis of feature selection algorithms on
classification of gene microarray dataset. In: 2013 International
Conference on Information Communication and Embedded Systems
(ICICES), pp1088-1093, 2013. doi: 10.1109/ICICES.2013.6508165.
|
|
38
|
Magnan CN, Zeller M, Kayala MA, Vigil A,
Randall A, Felgner PL and Baldi P: High-throughput prediction of
protein antigenicity using protein microarray data. Bioinformatics.
26:2936–2943. 2010.PubMed/NCBI View Article : Google Scholar
|
|
39
|
Alonso-Betanzos A and Herrera F: A review
of microarray datasets and applied feature selection methods. Inf
Sci. 282:111–135. 2014.
|
|
40
|
Barrett T, Wilhite SE, Ledoux P,
Evangelista C, Kim IF, Tomashevsky M, Marshall KA, Phillippy KH,
Sherman PM, Holko M, et al: NCBI GEO: Archive for functional
genomics data sets-update. Nucleic Acids Res. 41:D991–D995.
2013.PubMed/NCBI View Article : Google Scholar
|
|
41
|
Rhodes DR, Yu J, Shanker K, Deshpande N,
Varambally R, Ghosh D, Barrette T, Pandey A and Chinnaiyan AM:
ONCOMINE: A cancer microarray database and integrated data-mining
platform. Neoplasia. 6:1–6. 2004.PubMed/NCBI View Article : Google Scholar
|
|
42
|
Tomczak K, Czerwińska P and Wiznerowicz M:
The cancer genome atlas (TCGA): An immeasurable source of
knowledge. Contemp Oncol (Pozn). 19:A68–A77. 2015.PubMed/NCBI View Article : Google Scholar
|
|
43
|
Tao Z, Shi A, Li R, Wang Y, Wang X and
Zhao J: Microarray bioinformatics in cancer- a review. J BUON.
22:838–843. 2017.PubMed/NCBI
|
|
44
|
Zhang X, Ye ZH, Liang HW, Ren FH, Li P,
Dang YW and Chen G: Down-regulation of miR-146a-5p and its
potential targets in hepatocellular carcinoma validated by a TCGA-
and GEO-based study. FEBS Open Bio. 7:504–521. 2017.PubMed/NCBI View Article : Google Scholar
|
|
45
|
Luo J, Shi K, Yin SY, Tang RX, Chen WJ,
Huang LZ, Gan TQ, Cai ZW and Chen G: Clinical value of miR-182-5p
in lung squamous cell carcinoma: A study combining data from TCGA,
GEO, and RT-qPCR validation. World J Surg Oncol.
16(76)2018.PubMed/NCBI View Article : Google Scholar
|
|
46
|
Li XJ, Pang JS, Li YM, Ahmed FA, He RQ, Ma
J, Ma FC and Chen G: Clinical value of survivin and its underlying
mechanism in ovarian cancer: A bioinformatics study based on GEO
and TCGA data mining. Pathol Res Pract. 214:385–401.
2018.PubMed/NCBI View Article : Google Scholar
|
|
47
|
Phillips KA, Liang SY and Bebber SV:
Canpers Research Group. Challenges to the translation of genomic
information into clinical practice and health policy: Utilization,
preferences, and economic value. Curr Opin Mol Ther. 10:260–266.
2008.PubMed/NCBI
|
|
48
|
Arao T, Matsumoto K, Maegawa M and Nishio
K: What can and cannot be done using a microarray analysis?
Treatment stratification and clinical applications in oncology.
Biol Pharm Bull. 34:1789–1793. 2011.PubMed/NCBI View Article : Google Scholar
|
|
49
|
Alleyne CH, Theodore N, Spetzler RF and
Coons SW: Osteosarcoma of the temporal fossa with hemorrhagic
presentation: Case report. Neurosurgery. 47:450–451.
2000.PubMed/NCBI View Article : Google Scholar
|
|
50
|
Fuchs B and Pritchard DJ: Etiology of
osteosarcoma. Clin Orthop Relat Res. 397:40–52. 2002.PubMed/NCBI View Article : Google Scholar
|
|
51
|
Heller MJ: DNA microarray technology:
Devices, systems, and applications. Annu Rev Biomed Eng. 4:129–153.
2002.PubMed/NCBI View Article : Google Scholar
|
|
52
|
Statnikov A, Aliferis CF, Tsamardinos I,
Hardin D and Levy S: A comprehensive evaluation of multicategory
classification methods for microarray gene expression cancer
diagnosis. Bioinformatics. 21:631–643. 2005.PubMed/NCBI View Article : Google Scholar
|
|
53
|
Gevaert O and Moor BD: Prediction of
cancer outcome using DNA microarray technology: Past, present and
future. Expert Opin Med Diagn. 3:157–165. 2009.PubMed/NCBI View Article : Google Scholar
|
|
54
|
Kubista B, Klinglmueller F, Bilban M,
Pfeiffer M, Lass R, Giurea A, Funovics PT, Toma C, Dominkus M, Kotz
R, et al: Microarray analysis identifies distinct gene expression
profiles associated with histological subtype in human
osteosarcoma. Int Orthop. 35:401–411. 2011.PubMed/NCBI View Article : Google Scholar
|
|
55
|
Zou C, Shen J, Tang Q, Yang Z, Yin J, Li
Z, Xie X, Huang G, Lev D and Wang J: Cancer-testis antigens
expressed in osteosarcoma identified by gene microarray correlate
with a poor patient prognosis. Cancer. 118:1845–1855.
2012.PubMed/NCBI View Article : Google Scholar
|
|
56
|
Muff R, Kumar RMR, Botter SM, Born W and
Fuchs B: Genes regulated in metastatic osteosarcoma: Evaluation by
microarray analysis in four human and two mouse cell line systems.
Sarcoma. 2012(937506)2012.PubMed/NCBI View Article : Google Scholar
|
