|
1
|
Iwafuchi-Doi M and Zaret KS: Pioneer
transcription factors in cell reprogramming. Genes Dev.
28:2679–2692. 2014. View Article : Google Scholar : PubMed/NCBI
|
|
2
|
Filtz TM, Vogel WK and Leid M: Regulation
of transcription factor activity by interconnected
post-translational modifications. Trends Pharmacol Sci. 35:76–85.
2014. View Article : Google Scholar : PubMed/NCBI
|
|
3
|
Johnston SJ and Carroll JS: Transcription
factors and chromatin proteins as therapeutic targets in cancer.
Biochim Biophys Acta. 1855:183–192. 2015.PubMed/NCBI
|
|
4
|
Yeh JE, Toniolo PA and Frank DA: Targeting
transcription factors: Promising new strategies for cancer therapy.
Curr Opin Oncol. 25:652–658. 2013. View Article : Google Scholar : PubMed/NCBI
|
|
5
|
Chen H, Li C, Peng X, Zhou Z, Weinstein
JN; Cancer Genome Atlas Research Network, ; Liang H: A pan-cancer
analysis of enhancer expression in nearly 9,000 patient samples.
Cell. 173:386–399.e12. 2018. View Article : Google Scholar : PubMed/NCBI
|
|
6
|
Sanchez-Vega F, Mina M, Armenia J, Chatila
WK, Luna A, La KC, Dimitriadoy S, Liu DL, Kantheti HS, Saghafinia
S, et al: Oncogenic signaling pathways in The Cancer Genome Atlas.
Cell. 173:321–337.e10. 2018. View Article : Google Scholar : PubMed/NCBI
|
|
7
|
Chiu HS, Somvanshi S, Patel E, Chen TW,
Singh VP, Zorman B, Patil SL, Pan Y, Chatterjee SS; Cancer Genome
Atlas Research Network, ; Sood AK, Gunaratne PH and Sumazin P:
Pan-cancer analysis of lncRNA regulation supports their targeting
of cancer genes in each tumor context. Cell Rep. 23:297–312.e12.
2018. View Article : Google Scholar : PubMed/NCBI
|
|
8
|
Knaack SA, Siahpirani AF and Roy S: A
pan-cancer modular regulatory network analysis to identify common
and cancer-specific network components. Cancer Inform. 13 (Suppl
5):S69–S84. 2014.
|
|
9
|
Siegel RL, Miller KD and Jemal A: Cancer
statistics, 2015. CA Cancer J Clin. 65:5–29. 2015. View Article : Google Scholar : PubMed/NCBI
|
|
10
|
Binnewies M, Roberts EW, Kersten K, Chan
V, Fearon DF, Merad M, Coussens LM, Gabrilovich DI,
Ostrand-Rosenberg S, Hedrick CC, et al: Understanding the tumor
immune microenvironment (TIME) for effective therapy. Nat Med.
24:541–550. 2018. View Article : Google Scholar : PubMed/NCBI
|
|
11
|
Massari F, Santoni M, Ciccarese C, Santini
D, Alfieri S, Martignoni G, Brunelli M, Piva F, Berardi R,
Montironi R, et al: PD-1 blockade therapy in renal cell carcinoma:
Current studies and future promises. Cancer Treat Rev. 41:114–121.
2015. View Article : Google Scholar : PubMed/NCBI
|
|
12
|
An N, Yu Z and Yang X: Expression
differentiation is not helpful in identifying prognostic genes
based on TCGA datasets. Mol Ther Nucleic Acids. 11:292–299. 2018.
View Article : Google Scholar : PubMed/NCBI
|
|
13
|
Tang H, Wang S, Xiao G, Schiller J,
Papadimitrakopoulou V, Minna J, WistubaI I and Xie Y: Comprehensive
evaluation of published gene expression prognostic signatures for
biomarker-based lung cancer clinical studies. Ann Oncol.
28:733–740. 2017. View Article : Google Scholar : PubMed/NCBI
|
|
14
|
Subramanian A, Tamayo P, Mootha VK,
Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub
TR, Lander ES and Mesirov JP: Gene set enrichment analysis: A
knowledge-based approach for interpreting genome-wide expression
profiles. Proc Natl Acad Sci USA. 102:15545–15550. 2005. View Article : Google Scholar : PubMed/NCBI
|
|
15
|
Thorsson V, Gibbs DL, Brown SD, Wolf D,
Bortone DS, Ou Yang TH, Porta-Pardo E, Gao GF, Plaisier CL, Eddy
JA, et al: The immune landscape of cancer. Immunity.
48:812–830.e14. 2018. View Article : Google Scholar : PubMed/NCBI
|
|
16
|
Yoshihara K, Shahmoradgoli M, Martínez E,
Vegesna R, Kim H, Torres-Garcia W, Treviño V, Shen H, Laird PW,
Levine DA, et al: Inferring tumour purity and stromal and immune
cell admixture from expression data. Nat Commun. 4:26122013.
View Article : Google Scholar : PubMed/NCBI
|
|
17
|
Liu Z, Dai J and Shen H: Systematic
analysis reveals long noncoding RNAs regulating neighboring
transcription factors in human cancers. Biochim Biophys Acta.
1864:2785–2792. 2018. View Article : Google Scholar
|
|
18
|
Jiang P, Freedman ML, Liu JS and Liu XS:
Inference of transcriptional regulation in cancers. Proc Natl Acad
Sci USA. 112:7731–7736. 2015. View Article : Google Scholar : PubMed/NCBI
|
|
19
|
Issa AM, Chaudhari VS and Marchant GE: The
value of multigene predictors of clinical outcome in breast cancer:
An analysis of the evidence. Expert Rev Mol Diagn. 15:277–286.
2015. View Article : Google Scholar : PubMed/NCBI
|
|
20
|
Lokeshwar SD, Talukder A, Yates TJ, Hennig
MJ, Garcia-Roig M, Lahorewala SS, Mullani NN, Klaassen Z, Kava BR,
Manoharan M, et al: Molecular characterization of renal cell
carcinoma: A potential three-MicroRNA prognostic signature. Cancer
Epidemiol Biomarkers Prev. 27:464–472. 2018. View Article : Google Scholar : PubMed/NCBI
|
|
21
|
Marziali G, Buccarelli M, Giuliani A,
Ilari R, Grande S, Palma A, D'Alessandris QG, Martini M, Biffoni M,
Pallini R and Ricci-Vitiani L: A three-microRNA signature
identifies two subtypes of glioblastoma patients with different
clinical outcomes. Mol Oncol. 11:1115–1129. 2017. View Article : Google Scholar : PubMed/NCBI
|
|
22
|
Mei S, Meyer CA, Zheng R, Qin Q, Wu Q,
Jiang P, Li B, Shi X, Wang B, Fan J, et al: Cistrome cancer: A web
resource for integrative gene regulation modeling in cancer. Cancer
Res. 77:e19–e22. 2017. View Article : Google Scholar : PubMed/NCBI
|
|
23
|
Kim HD, Yu SJ, Kim HS, Kim YJ, Choe JM,
Park YG, Kim J and Sohn J: Interleukin-4 induces senescence in
human renal carcinoma cell lines through STAT6 and p38 MAPK. J Biol
Chem. 288:28743–28754. 2013. View Article : Google Scholar : PubMed/NCBI
|
|
24
|
Cassiday LA: Having it both ways:
Transcription factors that bind DNA and RNA. Nucleic Acids
Research. 30:4118–4126. 2002. View Article : Google Scholar : PubMed/NCBI
|
|
25
|
Cyriac G and Gandhi L: Emerging biomarkers
for immune checkpoint inhibition in lung cancer. Semin Cancer Biol.
52:269–277. 2018. View Article : Google Scholar : PubMed/NCBI
|
|
26
|
Schaub FX, Dhankani V, Berger AC, Trivedi
M, Richardson AB, Shaw R, Zhao W, Zhang X, Ventura A, Liu Y, et al:
Pan-cancer alterations of the MYC oncogene and its proximal network
across the Cancer Genome Atlas. Cell Syst. 6:282–300.e2. 2018.
View Article : Google Scholar : PubMed/NCBI
|
|
27
|
Rau A, Flister M, Rui H and Auer PL:
Exploring drivers of gene expression in the Cancer Genome Atlas.
Bioinformatics. 35:62–68. 2019.PubMed/NCBI
|
|
28
|
Zhu B, Tang L, Chen S, Yin C, Peng S, Li
X, Liu T, Liu W, Han C, Stawski L, et al: Targeting the upstream
transcriptional activator of PD-L1 as an alternative strategy in
melanoma therapy. Oncogene. 37:4941–4954. 2018. View Article : Google Scholar : PubMed/NCBI
|
|
29
|
Blum A, Wang P and Zenklusen JC: SnapShot:
TCGA-analyzed tumors. Cell. 173:5302018. View Article : Google Scholar : PubMed/NCBI
|
|
30
|
Kim EY, Kim A, Kim SK and Chang YS: MYC
expression correlates with PD-L1 expression in non-small cell lung
cancer. Lung Cancer. 110:63–67. 2017. View Article : Google Scholar : PubMed/NCBI
|
|
31
|
Datta S, Le-Rademacher J and Datta S:
Predicting patient survival from microarray data by accelerated
failure time modeling using partial least squares and LASSO.
Biometrics. 63:259–271. 2007. View Article : Google Scholar : PubMed/NCBI
|
