|
1
|
Bray F, Ferlay J, Soerjomataram I, Siegel
RL, Torre LA and Jemal A: Global cancer statistics 2018: GLOBOCAN
estimates of incidence and mortality worldwide for 36 cancers in
185 countries. CA Cancer J Clin. 68:394–424. 2018. View Article : Google Scholar : PubMed/NCBI
|
|
2
|
Travis WD, Brambilla E, Müller-Hermelink
HK and Harris CC: Pathology and genetics of tumours of the lung,
pleura, thymus and heart (WHO classification of tumours). IARC
Press Oxford University Press (distributor). (Lyon Oxford).
2004.
|
|
3
|
McKay JD, Hung RJ, Han Y, Zong X,
Carreras-Torres R, Christiani DC, Caporaso NE, Johansson M, Xiao X,
Li Y, et al: Large-scale association analysis identifies new lung
cancer susceptibility loci and heterogeneity in genetic
susceptibility across histological subtypes. Nat Genet.
49:1126–1132. 2017. View
Article : Google Scholar : PubMed/NCBI
|
|
4
|
Chen M, Liu X, Du J, Wang XJ and Xia L:
Differentiated regulation of immune-response related genes between
LUAD and LUSC subtypes of lung cancers. Oncotarget. 8:133–144.
2017.PubMed/NCBI
|
|
5
|
Liu B, Chen Y and Yang J: LncRNAs are
altered in lung squamous cell carcinoma and lung adenocarcinoma.
Oncotarget. 8:24275–24291. 2017.PubMed/NCBI
|
|
6
|
Relli V, Trerotola M, Guerra E and Alberti
S: Distinct lung cancer subtypes associate to distinct drivers of
tumor progression. Oncotarget. 9:35528–35540. 2018. View Article : Google Scholar : PubMed/NCBI
|
|
7
|
Mok TS, Wu YL, Thongprasert S, Yang CH,
Chu DT, Saijo N, Sunpaweravong P, Han B, Margono B, Ichinose Y, et
al: Gefitinib or carboplatin-paclitaxel in pulmonary
adenocarcinoma. N Engl J Med. 361:947–957. 2009. View Article : Google Scholar : PubMed/NCBI
|
|
8
|
Shepherd FA, Rodrigues Pereira J, Ciuleanu
T, Tan EH, Hirsh V, Thongprasert S, Campos D, Maoleekoonpiroj S,
Smylie M, Martins R, et al: Erlotinib in previously treated
non-small-cell lung cancer. N Engl J Med. 353:123–132. 2005.
View Article : Google Scholar : PubMed/NCBI
|
|
9
|
Jiang L, Zhu W, Streicher K, Morehouse C,
Brohawn P, Ge X, Dong Z, Yin X, Zhu G, Gu Y, et al: Increased
IR-A/IR-B ratio in non-small cell lung cancers associates with
lower epithelial-mesenchymal transition signature and longer
survival in squamous cell lung carcinoma. BMC Cancer. 14:1312014.
View Article : Google Scholar : PubMed/NCBI
|
|
10
|
Bosse Y and Amos CI: A decade of GWAS
results in lung cancer. Cancer Epidemiol Biomarkers Prev.
27:363–379. 2018. View Article : Google Scholar : PubMed/NCBI
|
|
11
|
Välk K, Vooder T, Kolde R, Reintam MA,
Petzold C, Vilo J and Metspalu A: Gene expression profiles of
non-small cell lung cancer: Survival prediction and new biomarkers.
Oncology. 79:283–292. 2010. View Article : Google Scholar : PubMed/NCBI
|
|
12
|
Gantenbein N, Bernhart E, Anders I,
Golob-Schwarzl N, Krassnig S, Wodlej C, Brcic L, Lindenmann J,
Fink-Neuboeck N, Gollowitsch F, et al: Influence of eukaryotic
translation initiation factor 6 on non-small cell lung cancer
development and progression. Eur J Cancer. 101:165–180. 2018.
View Article : Google Scholar : PubMed/NCBI
|
|
13
|
Qu Z, Sun F, Zhou J, Li L, Shapiro SD and
Xiao G: Interleukin-6 prevents the initiation but enhances the
progression of lung cancer. Cancer Res. 75:3209–3215. 2015.
View Article : Google Scholar : PubMed/NCBI
|
|
14
|
Huang CI, Taki T, Higashiyama M, Kohno N
and Miyake M: p16 protein expression is associated with a poor
prognosis in squamous cell carcinoma of the lung. Br J Cancer.
82:374–380. 2000. View Article : Google Scholar : PubMed/NCBI
|
|
15
|
Thomas ML and Marcato P: Epigenetic
modifications as biomarkers of tumor development, therapy response,
and recurrence across the cancer care continuum. Cancers (Basel).
10(pii): E1012018. View Article : Google Scholar : PubMed/NCBI
|
|
16
|
Burgess S, Thompson DJ, Rees JMB, Day FR,
Perry JR and Ong KK: Dissecting causal pathways using mendelian
randomization with summarized genetic data: Application to age at
menarche and risk of breast cancer. Genetics. 207:481–487.
2017.PubMed/NCBI
|
|
17
|
Wright PG: The Tariff on Animal and
Vegetable Oils. Moulton HG: The Macmillan Company; New York, NY:
1928
|
|
18
|
Davies NM, Smith GD, Windmeijer F and
Martin RM: Issues in the reporting and conduct of instrumental
variable studies: A systematic review. Epidemiology. 24:363–369.
2013. View Article : Google Scholar : PubMed/NCBI
|
|
19
|
Palmer TM, Sterne JA, Harbord RM, Lawlor
DA, Sheehan NA, Meng S, Granell R, Smith GD and Didelez V:
Instrumental variable estimation of causal risk ratios and causal
odds ratios in Mendelian randomization analyses. Am J Epidemiol.
173:1392–1403. 2011. View Article : Google Scholar : PubMed/NCBI
|
|
20
|
Chen Y and Briesacher BA: Use of
instrumental variable in prescription drug research with
observational data: A systematic review. J Clin Epidemiol.
64:687–700. 2011. View Article : Google Scholar : PubMed/NCBI
|
|
21
|
Angrist JD, Imbens GW and Rubin DB:
Identification of causal effects using instrumental variables. J Am
Stat Assoc. 91:444–455. 1996. View Article : Google Scholar
|
|
22
|
Pearl J: Causality: Models, Reasoning, and
Inference. Harvey A: Cambridge University Press; Cambridge, New
York: 2000
|
|
23
|
Baser O: Too much ado about instrumental
variable approach: Is the cure worse than the disease? Value
Health. 12:1201–1209. 2009. View Article : Google Scholar : PubMed/NCBI
|
|
24
|
Glinsky GV: Integration of HapMap-based
SNP pattern analysis and gene expression profiling reveals common
SNP profiles for cancer therapy outcome predictor genes. Cell
Cycle. 5:2613–2625. 2006. View Article : Google Scholar : PubMed/NCBI
|
|
25
|
Fabiani E, Leone G, Giachelia M, D'alo' F,
Greco M, Criscuolo M, Guidi F, Rutella S, Hohaus S and Voso MT:
Analysis of genome-wide methylation and gene expression induced by
5-aza-2′-deoxycytidine identifies BCL2L10 as a frequent methylation
target in acute myeloid leukemia. Leuk Lymphoma. 51:2275–2284.
2010. View Article : Google Scholar : PubMed/NCBI
|
|
26
|
Wang W, Baladandayuthapani V, Morris JS,
Broom BM, Manyam G and Do KA: iBAG: Integrative bayesian analysis
of high-dimensional multiplatform genomics data. Bioinformatics.
29:149–159. 2013. View Article : Google Scholar : PubMed/NCBI
|
|
27
|
de Tayrac M, Le S, Aubry M, Mosser J and
Husson F: Simultaneous analysis of distinct omics data sets with
integration of biological knowledge: Multiple Factor Analysis
approach. BMC Genomics. 10:322009. View Article : Google Scholar : PubMed/NCBI
|
|
28
|
Hou Y, Guo H, Cao C, Li X, Hu B, Zhu P, Wu
X, Wen L, Tang F, Huang Y and Peng J: Single-cell triple omics
sequencing reveals genetic, epigenetic, and transcriptomic
heterogeneity in hepatocellular carcinomas. Cell Res. 26:304–319.
2016. View Article : Google Scholar : PubMed/NCBI
|
|
29
|
Smith AA, Huang YT, Eliot M, Houseman EA,
Marsit CJ, Wiencke JK and Kelsey KT: A novel approach to the
discovery of survival biomarkers in glioblastoma using a joint
analysis of DNA methylation and gene expression. Epigenetics.
9:873–883. 2014. View Article : Google Scholar : PubMed/NCBI
|
|
30
|
Lokk K, Modhukur V, Rajashekar B, Märtens
K, Mägi R, Kolde R, Koltšina M, Nilsson TK, Vilo J, Salumets A and
Tõnisson N: DNA methylome profiling of human tissues identifies
global and tissue-specific methylation patterns. Genome Biol.
15:r542014. View Article : Google Scholar : PubMed/NCBI
|
|
31
|
Saif I, Kasmi Y, Allali K and Ennaji MM:
Prediction of DNA methylation in the promoter of gene suppressor
tumor. Gene. 651:166–173. 2018. View Article : Google Scholar : PubMed/NCBI
|
|
32
|
Liu Y, Baggerly KA, Orouji E, Manyam G,
Chen H, Lam M, Davis JS, Lee MS, Broom BM, Menter DG, et al:
Gene-specific methylation profiles for integrative
methylation-expression analysis in cancer research. bioRxiv.
2019.https://doi org/10.1101/618033April 24–2019
|
|
33
|
Denis M and Tadesse MG: Evaluation of
hierarchical models for integrative genomic analyses.
Bioinformatics. 32:738–746. 2016. View Article : Google Scholar : PubMed/NCBI
|
|
34
|
Zhao Q, Shi X, Xie Y, Huang J, Shia B and
Ma S: Combining multidimensional genomic measurements for
predicting cancer prognosis: Observations from TCGA. Brief
Bioinform. 16:291–303. 2015. View Article : Google Scholar : PubMed/NCBI
|
|
35
|
Team RC: R: A language and environment for
statistical computing. R Foundation for Statistical Computing,
Vienna, Austria, 2019. https://www.R-project.org/
|
|
36
|
Robinson MD, McCarthy DJ and Smyth GK:
edgeR: A bioconductor package for differential expression analysis
of digital gene expression data. Bioinformatics. 26:139–140. 2010.
View Article : Google Scholar : PubMed/NCBI
|
|
37
|
McCarthy DJ, Chen Y and Smyth GK:
Differential expression analysis of multifactor RNA-Seq experiments
with respect to biological variation. Nucleic Acids Res.
40:4288–4297. 2012. View Article : Google Scholar : PubMed/NCBI
|
|
38
|
Morris TJ, Butcher LM, Feber A,
Teschendorff AE, Chakravarthy AR, Wojdacz TK and Beck S: ChAMP:
450k chip analysis methylation pipeline. Bioinformatics.
30:428–430. 2014. View Article : Google Scholar : PubMed/NCBI
|
|
39
|
Zhu Y, Qiu P and Ji Y: TCGA-assembler:
Open-source software for retrieving and processing TCGA data. Nat
Methods. 11:599–600. 2014. View Article : Google Scholar : PubMed/NCBI
|
|
40
|
Colaprico A, Silva TC, Olsen C, Garofano
L, Cava C, Garolini D, Sabedot TS, Malta TM, Pagnotta SM,
Castiglioni I, et al: TCGAbiolinks: An R/bioconductor package for
integrative analysis of TCGA data. Nucleic Acids Res. 44:e712016.
View Article : Google Scholar : PubMed/NCBI
|
|
41
|
Mounir M, Lucchetta M, Silva TC, Olsen C,
Bontempi G, Chen X, Noushmehr H, Colaprico A and Papaleo E: New
functionalities in the TCGAbiolinks package for the study and
integration of cancer data from GDC and GTEx. PLoS Comput Biol.
15:e10067012019. View Article : Google Scholar : PubMed/NCBI
|
|
42
|
Janjigian YY, McDonnell K, Kris MG, Shen
R, Sima CS, Bach PB, Rizvi NA and Riely GJ: Pack-years of cigarette
smoking as a prognostic factor in patients with stage IIIB/IV
nonsmall cell lung cancer. Cancer. 116:670–675. 2010. View Article : Google Scholar : PubMed/NCBI
|
|
43
|
Peto J: That the effects of smoking should
be measured in pack-years: Misconceptions 4. Brit J Cancer.
107:406–407. 2012. View Article : Google Scholar : PubMed/NCBI
|
|
44
|
Robinson MD and Oshlack A: A scaling
normalization method for differential expression analysis of
RNA-seq data. Genome Biol. 11:R252010. View Article : Google Scholar : PubMed/NCBI
|
|
45
|
Robinson MD and Smyth GK: Small-sample
estimation of negative binomial dispersion, with applications to
SAGE data. Biostatistics. 9:321–332. 2008. View Article : Google Scholar : PubMed/NCBI
|
|
46
|
Crow M, Lim N, Ballouz S, Pavlidis P and
Gillis J: Predictability of human differential gene expression.
Proc Natl Acad Sci USA. 116:6491–6500. 2019. View Article : Google Scholar : PubMed/NCBI
|
|
47
|
Tian Y, Morris TJ, Webster AP, Yang Z,
Beck S, Feber A and Teschendorff AE: ChAMP: Updated methylation
analysis pipeline for Illumina BeadChips. Bioinformatics.
33:3982–3984. 2017. View Article : Google Scholar : PubMed/NCBI
|
|
48
|
Li B, Severson E, Pignon JC, Zhao H, Li T,
Novak J, Jiang P, Shen H, Aster JC, Rodig S, et al: Comprehensive
analyses of tumor immunity: Implications for cancer immunotherapy.
Genome Biol. 17:1742016. View Article : Google Scholar : PubMed/NCBI
|
|
49
|
Grambsch PM and Therneau TM: Proportional
Hazards Tests and Diagnostics Based on Weighted Residuals.
Biometrika. 81:515–526. 1994. View Article : Google Scholar
|
|
50
|
Staiger D and Stock JH: Instrumental
variables regression with weak instruments. Econometrica.
65:557–586. 1997. View Article : Google Scholar
|
|
51
|
Lawlor DA, Harbord RM, Sterne JA, Timpson
N and Davey Smith G: Mendelian randomization: Using genes as
instruments for making causal inferences in epidemiology. Stat Med.
27:1133–1163. 2008. View Article : Google Scholar : PubMed/NCBI
|
|
52
|
Higgins JP, Thompson SG, Deeks JJ and
Altman DG: Measuring inconsistency in meta-analyses. BMJ.
327:557–560. 2003. View Article : Google Scholar : PubMed/NCBI
|
|
53
|
Brady JJ, Chuang CH, Greenside PG, Rogers
ZN, Murray CW, Caswell DR, Hartmann U, Connolly AJ, Sweet-Cordero
EA, Kundaje A and Winslow MM: An Arntl2-driven secretome enables
lung adenocarcinoma metastatic self-sufficiency. Cancer Cell.
29:697–710. 2016. View Article : Google Scholar : PubMed/NCBI
|
|
54
|
Gao L, Guo YN, Zeng JH, Ma FC, Luo J, Zhu
HW, Xia S, Wei KL and Chen G: The expression, significance and
function of cancer susceptibility candidate 9 in lung squamous cell
carcinoma: A bioinformatics and in vitro investigation. Int J
Oncol. 54:1651–1664. 2019.PubMed/NCBI
|
|
55
|
Liu W, Ouyang S, Zhou Z, Wang M, Wang T,
Qi Y, Zhao C, Chen K and Dai L: Identification of genes associated
with cancer progression and prognosis in lung adenocarcinoma:
Analyses based on microarray from oncomine and the cancer genome
Atlas databases. Mol Genet Genomic Med. 7:e005282019.PubMed/NCBI
|
|
56
|
Liu Y, Xie D, He Z and Zheng L: Integrated
analysis reveals five potential ceRNA biomarkers in human lung
adenocarcinoma. PeerJ. 7:e66942019. View Article : Google Scholar : PubMed/NCBI
|
|
57
|
Ilie MI, Hofman V, Ortholan C, Ammadi RE,
Bonnetaud C, Havet K, Venissac N, Mouroux J, Mazure NM, Pouysségur
J and Hofman P: Overexpression of carbonic anhydrase XII in tissues
from resectable non-small cell lung cancers is a biomarker of good
prognosis. Int J Cancer. 128:1614–1623. 2011. View Article : Google Scholar : PubMed/NCBI
|
|
58
|
Ozturk S, Papageorgis P, Wong CK, Lambert
AW, Abdolmaleky HM, Thiagalingam A, Cohen HT and Thiagalingam S:
SDPR functions as a metastasis suppressor in breast cancer by
promoting apoptosis. Proc Natl Acad Sci USA. 113:638–643. 2016.
View Article : Google Scholar : PubMed/NCBI
|
|
59
|
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
|
|
60
|
Koellinger PD and de Vlaming R: Mendelian
randomization: The challenge of unobserved environmental confounds.
Int J Epidemiol. 48:665–671. 2019. View Article : Google Scholar : PubMed/NCBI
|
|
61
|
Hemani G, Zheng J, Elsworth B, Wade KH,
Haberland V, Baird D, Laurin C, Burgess S, Bowden J, Langdon R, et
al: The MR-Base platform supports systematic causal inference
across the human phenome. Elife. 7(pii): e344082018. View Article : Google Scholar : PubMed/NCBI
|
|
62
|
Haycock PC, Burgess S, Wade KH, Bowden J,
Relton C and Davey Smith G: Best (but oft-forgotten) practices: The
design, analysis, and interpretation of Mendelian randomization
studies. Am J Clin Nutr. 103:965–978. 2016. View Article : Google Scholar : PubMed/NCBI
|
