1
|
Bade BC and Dela Cruz CS: Lung Cancer
2020: Epidemiology, etiology, and prevention. Clin Chest Med.
41:1–24. 2020. View Article : Google Scholar : PubMed/NCBI
|
2
|
Xu F, He L, Zhan X, Chen J, Xu H, Huang X,
Li Y, Zheng X, Lin L and Chen Y: DNA methylation-based lung
adenocarcinoma subtypes can predict prognosis, recurrence, and
immunotherapeutic implications. Aging (Albany NY). 12:25275–25293.
2020. View Article : Google Scholar : PubMed/NCBI
|
3
|
Ferlay J, Colombet M, Soerjomataram I,
Dyba T, Randi G, Bettio M, Gavin A, Visser O and Bray F: Cancer
incidence and mortality patterns in Europe: Estimates for 40
countries and 25 major cancers in 2018. Eur J Cancer. 103:356–387.
2018. View Article : Google Scholar : PubMed/NCBI
|
4
|
Cao M, Li H, Sun D and Chen W: Cancer
burden of major cancers in China: A need for sustainable actions.
Cancer Commun (Lond). 40:205–210. 2020. View Article : Google Scholar : PubMed/NCBI
|
5
|
Kerdidani D, Chouvardas P, Arjo AR,
Giopanou I, Ntaliarda G, Guo YA, Tsikitis M, Kazamias G, Potaris K,
Stathopoulos GT, et al: Wnt1 silences chemokine genes in dendritic
cells and induces adaptive immune resistance in lung
adenocarcinoma. Nat Commun. 10:14052019. View Article : Google Scholar : PubMed/NCBI
|
6
|
Xu F, Huang X, Li Y, Chen Y and Lin L:
m6A-related lncRNAs are potential biomarkers for
predicting prognoses and immune responses in patients with LUAD.
Mol Ther Nucleic Acids. 24:780–791. 2021. View Article : Google Scholar : PubMed/NCBI
|
7
|
Zhang C, Zhang J, Xu FP, Wang YG, Xie Z,
Su J, Dong S, Nie Q, Shao Y, Zhou Q, et al: Genomic landscape and
immune microenvironment features of preinvasive and early invasive
lung adenocarcinoma. J Thorac Oncol. 14:1912–1923. 2019. View Article : Google Scholar : PubMed/NCBI
|
8
|
Jurisic V, Vukovic V, Obradovic J,
Gulyaeva LF, Kushlinskii NE and Djordjević N: EGFR polymorphism and
survival of NSCLC patients treated with TKIs: A systematic review
and meta-analysis. J Oncol. 2020:19732412020. View Article : Google Scholar : PubMed/NCBI
|
9
|
Gao J, Zhang L, Peng K and Sun H:
Diagnostic value of serum tumor markers CEA, CYFRA21-1, SCCAg, NSE
and ProGRP for lung cancers of different pathological types. Nan
Fang Yi Ke Da Xue Xue Bao. 42:886–891. 2022.(In Chinese).
PubMed/NCBI
|
10
|
Li Q and Sang S: Diagnostic value and
clinical significance of combined detection of serum markers
CYFRA21-1, SCC Ag, NSE, CEA and ProGRP in Non-small cell lung
carcinoma. Clin Lab. 662020.
|
11
|
Ma L, Xie XW, Wang HY, Ma LY and Wen ZG:
Clinical evaluation of tumor markers for diagnosis in patients with
non-small cell lung cancer in China. Asian Pac J Cancer Prev.
16:4891–4894. 2015. View Article : Google Scholar : PubMed/NCBI
|
12
|
Dal Bello MG, Filiberti RA, Alama A,
Orengo AM, Mussap M, Coco S, Vanni I, Boccardo S, Rijavec E, Genova
C, et al: The role of CEA, CYFRA21-1 and NSE in monitoring tumor
response to Nivolumab in advanced non-small cell lung cancer
(NSCLC) patients. J Transl Med. 17:742019. View Article : Google Scholar : PubMed/NCBI
|
13
|
Hao C, Zhang G and Zhang L: Serum CEA
levels in 49 different types of cancer and noncancer diseases. Prog
Mol Biol Transl Sci. 162:213–227. 2019. View Article : Google Scholar : PubMed/NCBI
|
14
|
Yang Q, Zhang P, Wu R, Lu K and Zhou H:
Identifying the best marker combination in CEA, CA125, CY211, NSE,
and SCC for lung cancer screening by combining ROC curve and
logistic regression analyses: Is It Feasible? Dis Markers.
2018:20828402018. View Article : Google Scholar : PubMed/NCBI
|
15
|
Zhang Y, Luo J, Liu Z, Liu X, Ma Y, Zhang
B, Chen Y, Li X, Feng Z, Yang N, et al: Identification of hub genes
in colorectal cancer based on weighted gene co-expression network
analysis and clinical data from The Cancer Genome Atlas. Biosci
Rep. 41:BSR202112802021. View Article : Google Scholar : PubMed/NCBI
|
16
|
Kuenzi BM and Ideker T: A census of
pathway maps in cancer systems biology. Nat Rev Cancer. 20:233–246.
2020. View Article : Google Scholar : PubMed/NCBI
|
17
|
Barabási AL, Gulbahce N and Loscalzo J:
Network medicine: A network-based approach to human disease. Nat
Rev Genet. 12:56–68. 2011. View Article : Google Scholar : PubMed/NCBI
|
18
|
Tian Y, Wang SS, Zhang Z, Rodriguez OC,
Petricoin E III, Shih IeM, Chan D, Avantaggiati M, Yu G, Ye S, et
al: Integration of Network biology and imaging to study cancer
phenotypes and responses. IEEE/ACM Trans Comput Biol Bioinform.
11:1009–1019. 2014. View Article : Google Scholar : PubMed/NCBI
|
19
|
Joshi A, Rienks M, Theofilatos K and Mayr
M: Systems biology in cardiovascular disease: a multiomics
approach. Nat Rev Cardiol. 18:313–330. 2021. View Article : Google Scholar : PubMed/NCBI
|
20
|
Langfelder P and Horvath S: WGCNA: An R
package for weighted correlation network analysis. BMC
Bioinformatics. 9:5592008. View Article : Google Scholar : PubMed/NCBI
|
21
|
Fuller T, Langfelder P, Presson A and
Horvath S: Review of Weighted Gene Coexpression Network Analysis.
Handbook of Statistical Bioinformatics. Lu HH-S, Schölkopf B and
Zhao H: Springer; Berlin Heidelberg, Berlin, Heidelberg: pp.
369–388. 2011, View Article : Google Scholar
|
22
|
van Dam S, Võsa U, van der Graaf A, Franke
L and de Magalhães JP: Gene co-expression analysis for functional
classification and gene-disease predictions. Brief Bioinform.
19:575–592. 2018.PubMed/NCBI
|
23
|
Wan Q, Tang J, Han Y and Wang D:
Co-expression modules construction by WGCNA and identify potential
prognostic markers of uveal melanoma. Exp Eye Res. 166:13–20. 2018.
View Article : Google Scholar : PubMed/NCBI
|
24
|
Yin X, Wang P, Yang T, Li G, Teng X, Huang
W and Yu H: Identification of key modules and genes associated with
breast cancer prognosis using WGCNA and ceRNA network analysis.
Aging (Albany NY). 13:2519–2538. 2020. View Article : Google Scholar : PubMed/NCBI
|
25
|
Bai KH, He SY, Shu LL, Wang WD, Lin SY,
Zhang QY, Li L, Cheng L and Dai YJ: Identification of cancer stem
cell characteristics in liver hepatocellular carcinoma by WGCNA
analysis of transcriptome stemness index. Cancer Med. 9:4290–4298.
2020. View Article : Google Scholar : PubMed/NCBI
|
26
|
Zhou J, Guo H, Liu L, Hao S, Guo Z, Zhang
F, Gao Y, Wang Z and Zhang W: Construction of co-expression modules
related to survival by WGCNA and identification of potential
prognostic biomarkers in glioblastoma. J Cell Mol Med.
25:1633–1644. 2021. View Article : Google Scholar : PubMed/NCBI
|
27
|
Ritchie ME, Phipson B, Wu D, Hu Y, Law CW,
Shi W and Smyth GK: limma powers differential expression analyses
for RNA-sequencing and microarray studies. Nucleic Acids Res.
43:e472015. View Article : Google Scholar : PubMed/NCBI
|
28
|
Chen L, Yuan L, Wang Y, Wang G, Zhu Y, Cao
R, Qian G, Xie C, Liu X, Xiao Y and Wang X: Co-expression network
analysis identified FCER1G in association with progression and
prognosis in human clear cell renal cell carcinoma. Int J Biol Sci.
13:1361–1372. 2017. View Article : Google Scholar : PubMed/NCBI
|
29
|
Nakamura H, Fujii K, Gupta V, Hata H,
Koizumu H, Hoshikawa M, Naruki S, Miyata Y, Takahashi I, Miyazawa
T, et al: Identification of key modules and hub genes for
small-cell lung carcinoma and large-cell neuroendocrine lung
carcinoma by weighted gene co-expression network analysis of
clinical tissue-proteomes. PLoS One. 14:e02171052019. View Article : Google Scholar : PubMed/NCBI
|
30
|
Chen X, Hu L, Wang Y, Sun W and Yang C:
Single Cell Gene Co-expression network reveals FECH/CROT signature
as a prognostic marker. Cells. 8:6982019. View Article : Google Scholar : PubMed/NCBI
|
31
|
Di Y, Chen D, Yu W and Yan L: Bladder
cancer stage-associated hub genes revealed by WGCNA co-expression
network analysis. Hereditas. 156:72019. View Article : Google Scholar : PubMed/NCBI
|
32
|
Song Y, Pan Y and Liu J: The relevance
between the immune response-related gene module and clinical traits
in head and neck squamous cell carcinoma. Cancer Manag Res.
11:7455–7472. 2019. View Article : Google Scholar : PubMed/NCBI
|
33
|
Leon LM, Gautier M, Allan R, Ilié M,
Nottet N, Pons N, Paquet A, Lebrigand K, Truchi M, Fassy J, et al:
Correction: The nuclear hypoxia-regulated NLUCAT1 long non-coding
RNA contributes to an aggressive phenotype in lung adenocarcinoma
through regulation of oxidative stress. Oncogene. 40:26212021.
View Article : Google Scholar : PubMed/NCBI
|
34
|
Yu DH, Ruan XL, Huang JY, Liu XP, Ma HL,
Chen C, Hu WD and Li S: Analysis of the interaction network of Hub
miRNAs-Hub genes, being involved in idiopathic pulmonary fibers and
its emerging role in Non-small cell lung cancer. Front Genet.
11:3022020. View Article : Google Scholar : PubMed/NCBI
|
35
|
Wu Y, Yang L, Zhang L, Zheng X, Xu H, Wang
K and Weng X: Identification of a Four-gene signature associated
with the prognosis prediction of lung adenocarcinoma based on
integrated bioinformatics Analysis. Genes (Basel). 13:2382022.
View Article : Google Scholar : PubMed/NCBI
|
36
|
Liao Y, Wang Y, Cheng M, Huang C and Fan
X: Weighted gene coexpression network analysis of features that
control cancer stem cells reveals prognostic biomarkers in lung
adenocarcinoma. Front Genet. 11:3112020. View Article : Google Scholar : PubMed/NCBI
|
37
|
Deng L, Long F, Wang T, Dai L, Chen H,
Yang Y and Xie G: Identification of an immune classification and
prognostic genes for lung adenocarcinoma based on immune cell
signatures. Front Med (Lausanne). 9:8553872022. View Article : Google Scholar : PubMed/NCBI
|
38
|
Kar S: Unraveling cell-cycle dynamics in
cancer. Cell Syst. 2:8–10. 2016. View Article : Google Scholar : PubMed/NCBI
|
39
|
Icard P, Fournel L, Wu Z, Alifano M and
Lincet H: Interconnection between Metabolism and Cell Cycle in
Cancer. Trends Biochem Sci. 44:490–501. 2019. View Article : Google Scholar : PubMed/NCBI
|
40
|
Evan GI and Vousden KH: Proliferation,
cell cycle and apoptosis in cancer. Nature. 411:342–348. 2001.
View Article : Google Scholar : PubMed/NCBI
|
41
|
Suski JM, Braun M, Strmiska V and Sicinski
P: Targeting cell-cycle machinery in cancer. Cancer Cell.
39:759–778. 2021. View Article : Google Scholar : PubMed/NCBI
|
42
|
Colak S and Ten Dijke P: Targeting TGF-β
signaling in cancer. Trends Cancer. 3:56–71. 2017. View Article : Google Scholar : PubMed/NCBI
|
43
|
Ingham M and Schwartz GK: Cell-cycle
therapeutics come of age. J Clin Oncol. 35:2949–2959. 2017.
View Article : Google Scholar : PubMed/NCBI
|
44
|
Deng Z, Huang K, Liu D, Luo N, Liu T, Han
L, Du D, Lian D, Zhong Z and Peng J: Key candidate prognostic
biomarkers correlated with immune infiltration in hepatocellular
carcinoma. J Hepatocell Carcinoma. 8:1607–1622. 2021. View Article : Google Scholar : PubMed/NCBI
|
45
|
Huang C, He J, Dong Y, Huang L, Chen Y,
Peng A and Huang H: Identification of novel prognostic markers
associated with laryngeal squamous cell carcinoma using
comprehensive analysis. Front Oncol. 11:7791532021. View Article : Google Scholar : PubMed/NCBI
|
46
|
Liu HY and Zhang CJ: Identification of
differentially expressed genes and their upstream regulators in
colorectal cancer. Cancer Gene Ther. 24:244–250. 2017. View Article : Google Scholar : PubMed/NCBI
|
47
|
Yang H, Zhang M, Mao XY, Chang H,
Perez-Losada J and Mao JH: Distinct clinical impact and biological
function of angiopoietin and angiopoietin-like proteins in human
breast cancer. Cells. 10:25902021. View Article : Google Scholar : PubMed/NCBI
|
48
|
Beaufrère A, Caruso S, Calderaro J, Poté
N, Bijot JC, Couchy G, Cauchy F, Vilgrain V, Zucman-Rossi J and
Paradis V: Gene expression signature as a surrogate marker of
microvascular invasion on routine hepatocellular carcinoma
biopsies. J Hepatol. 76:343–352. 2022. View Article : Google Scholar : PubMed/NCBI
|
49
|
Ouyang X, Zhang G, Pan H and Huang J:
Susceptibility and severity of cancer-related fatigue in colorectal
cancer patients is associated with SLC6A4 gene single nucleotide
polymorphism rs25531 A>G genotype. Eur J Oncol Nurs. 33:97–101.
2018. View Article : Google Scholar : PubMed/NCBI
|
50
|
Wesmiller SW, Bender CM, Sereika SM,
Ahrendt G, Bonaventura M, Bovbjerg DH and Conley Y: Association
between serotonin transport polymorphisms and postdischarge nausea
and vomiting in women following breast cancer surgery. Oncol Nurs
Forum. 41:195–202. 2014. View Article : Google Scholar : PubMed/NCBI
|
51
|
Yang Q, Chu W, Yang W, Cheng Y, Chu C, Pan
X, Ye J, Cao J, Gan S and Cui X: Identification of RNA transcript
makers associated with prognosis of kidney renal clear cell
carcinoma by a competing endogenous RNA network analysis. Front
Genet. 11:5400942020. View Article : Google Scholar : PubMed/NCBI
|
52
|
Weng JY and Salazar N: DNA Methylation
analysis identifies patterns in progressive glioma grades to
predict patient survival. Int J Mol Sci. 22:10202021. View Article : Google Scholar : PubMed/NCBI
|
53
|
Zheng Y, Chen L, Li J, Yu B, Su L, Chen X,
Yu Y, Yan M, Liu B and Zhu Z: Hypermethylated DNA as potential
biomarkers for gastric cancer diagnosis. Clin Biochem.
44:1405–1411. 2011. View Article : Google Scholar : PubMed/NCBI
|
54
|
Zhang Y, Liu X, Liu L, Li J, Hu Q and Sun
R: Expression and prognostic significance of m6A-related
genes in lung adenocarcinoma. Med Sci Monit.
26:e9196442020.PubMed/NCBI
|
55
|
Yu Y and Tian X: Analysis of genes
associated with prognosis of lung adenocarcinoma based on GEO and
TCGA databases. Medicine (Baltimore). 99:e201832020. View Article : Google Scholar : PubMed/NCBI
|
56
|
Sengupta D, Zeng L, Li Y, Hausmann S,
Ghosh D, Yuan G, Nguyen TN, Lyu R, Caporicci M, Morales Benitez A,
et al: NSD2 dimethylation at H3K36 promotes lung adenocarcinoma
pathogenesis. Mol Cell. 81:4481–4492.e4489. 2021. View Article : Google Scholar : PubMed/NCBI
|