|
1
|
Ferlay J, Soerjomataram I, Dikshit R, Eser
S, Mathers C, Rebelo M, Parkin DM, Forman D and Bray F: Cancer
incidence and mortality worldwide: Sources, methods and major
patterns in GLOBOCAN 2012. Int J Cancer. 136:E359–E386. 2015.
View Article : Google Scholar : PubMed/NCBI
|
|
2
|
Ferlay J, Colombet M, Soerjomataram I,
Mathers C, Parkin DM, Piñeros M, Znaor A and Bray F: Estimating the
global cancer incidence and mortality in 2018: GLOBOCAN sources and
methods. Int J Cancer. 144:1941–1953. 2019. View Article : Google Scholar : PubMed/NCBI
|
|
3
|
Young E, Philpott H and Singh R:
Endoscopic diagnosis and treatment of gastric dysplasia and early
cancer: Current evidence and what the future may hold. World J
Gastroenterol. 27:5126–5151. 2021. View Article : Google Scholar : PubMed/NCBI
|
|
4
|
Huang H, Leung C, Saito E, Katanoda K, Hur
C, Kong CY, Nomura S and Shibuya K: Effect and cost-effectiveness
of national gastric cancer screening in Japan: A microsimulation
modeling study. BMC Med. 18:2572020. View Article : Google Scholar : PubMed/NCBI
|
|
5
|
Suh Y, Lee J, Woo H, Shin D, Kong SH, Lee
HJ, Shin A and Yang HK: National cancer screening program for
gastric cancer in Korea: Nationwide treatment benefit and cost.
Cancer. 126:1929–1939. 2020. View Article : Google Scholar : PubMed/NCBI
|
|
6
|
Smyth EC, Nilsson M, Grabsch HI, van
Grieken NC and Lordick F: Gastric cancer. Lancet. 396:635–648.
2020. View Article : Google Scholar : PubMed/NCBI
|
|
7
|
Liu Y, Sethi NS, Hinoue T, Schneider BG,
Cherniack AD, Sanchez-Vega F, Seoane JA, Farshidfar F, Bowlby R,
Islam M, et al: Comparative molecular analysis of gastrointestinal
adenocarcinomas. Cancer Cell. 33:721–735.e728. 2018. View Article : Google Scholar : PubMed/NCBI
|
|
8
|
Shimozaki K, Nakayama I, Takahari D,
Kamiimabeppu D, Osumi H, Wakatsuki T, Ooki A, Ogura M, Shinozaki E,
Chin K and Yamaguchi K: A novel clinical prognostic index for
patients with advanced gastric cancer: Possible contribution to the
continuum of care. ESMO Open. 6:1002342021. View Article : Google Scholar : PubMed/NCBI
|
|
9
|
Zhu C, Ren C, Han J, Ding Y, Du J, Dai N,
Dai J, Ma H, Hu Z, Shen H, et al: A five-microRNA panel in plasma
was identified as potential biomarker for early detection of
gastric cancer. Br J Cancer. 110:2291–2299. 2014. View Article : Google Scholar : PubMed/NCBI
|
|
10
|
Chen D, Ping S, Xu Y, Wang M, Jiang X,
Xiong L, Zhang L, Yu H and Xiong Z: Non-coding RNAs in gastric
cancer: From malignant hallmarks to clinical applications. Front
Cell Dev Biol. 9:7320362021. View Article : Google Scholar : PubMed/NCBI
|
|
11
|
So J, Kapoor R, Zhu F, Koh C, Zhou L, Zou
R, Tang YC, Goo PC, Rha SY, Chung HC, et al: Development and
validation of a serum microRNA biomarker panel for detecting
gastric cancer in a high-risk population. Gut. 70:829–837. 2021.
View Article : Google Scholar : PubMed/NCBI
|
|
12
|
Imaoka H, Toiyama Y, Okigami M, Yasuda H,
Saigusa S, Ohi M, Tanaka K, Inoue Y, Mohri Y and Kusunoki M:
Circulating microRNA-203 predicts metastases, early recurrence, and
poor prognosis in human gastric cancer. Gastric Cancer. 19:744–753.
2016. View Article : Google Scholar : PubMed/NCBI
|
|
13
|
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
|
|
14
|
Madar V and Batista S: FastLSU: A more
practical approach for the Benjamini-Hochberg FDR controlling
procedure for huge-scale testing problems. Bioinformatics.
32:1716–1723. 2016. View Article : Google Scholar : PubMed/NCBI
|
|
15
|
Langfelder P and Horvath S: WGCNA: An R
package for weighted correlation network analysis. BMC
Bioinformatics. 9:5592008. View Article : Google Scholar : PubMed/NCBI
|
|
16
|
Rehmsmeier M, Steffen P, Hochsmann M and
Giegerich R: Fast and effective prediction of microRNA/target
duplexes. RNA. 10:1507–1517. 2004. View Article : Google Scholar : PubMed/NCBI
|
|
17
|
Sticht C, De La Torre C, Parveen A and
Gretz N: miRWalk: An online resource for prediction of microRNA
binding sites. PLoS One. 13:e02062392018. View Article : Google Scholar : PubMed/NCBI
|
|
18
|
Shannon P, Markiel A, Ozier O, Baliga NS,
Wang JT, Ramage D, Amin N, Schwikowski B and Ideker T: Cytoscape: A
software environment for integrated models of biomolecular
interaction networks. Genome Res. 13:2498–2504. 2003. View Article : Google Scholar : PubMed/NCBI
|
|
19
|
Yu G, Wang LG, Han Y and He QY:
clusterProfiler: An R package for comparing biological themes among
gene clusters. Omics. 16:284–287. 2012. View Article : Google Scholar : PubMed/NCBI
|
|
20
|
Friedman J, Hastie T and Tibshirani R:
Regularization paths for generalized linear models via coordinate
descent. J Stat Softw. 33:1–22. 2010. View Article : Google Scholar : PubMed/NCBI
|
|
21
|
Bac J, Mirkes EM, Gorban AN, Tyukin I and
Zinovyev A: Scikit-Dimension: A python package for intrinsic
dimension estimation. Entropy (Basel). 23:13682021. View Article : Google Scholar : PubMed/NCBI
|
|
22
|
Daberdaku S and Ferrari C: Antibody
interface prediction with 3D Zernike descriptors and SVM.
Bioinformatics. 35:1870–1876. 2019. View Article : Google Scholar : PubMed/NCBI
|
|
23
|
Baldi P, Brunak S, Chauvin Y, Andersen CA
and Nielsen H: Assessing the accuracy of prediction algorithms for
classification: An overview. Bioinformatics. 16:412–424. 2000.
View Article : Google Scholar : PubMed/NCBI
|
|
24
|
Xiong Y, Wang R, Peng L, You W, Wei J,
Zhang S, Wu X, Guo J, Xu J, Lv Z and Fu Z: An integrated lncRNA,
microRNA and mRNA signature to improve prognosis prediction of
colorectal cancer. Oncotarget. 8:85463–85478. 2017. View Article : Google Scholar : PubMed/NCBI
|
|
25
|
Yokoi A, Matsuzaki J, Yamamoto Y, Yoneoka
Y, Takahashi K, Shimizu H, Uehara T, Ishikawa M, Ikeda SI, Sonoda
T, et al: Int extracellular microRNA profiling for ovarian cancer
screening. Nat Commun. 9:43192018. View Article : Google Scholar : PubMed/NCBI
|
|
26
|
Urabe F, Matsuzaki J, Yamamoto Y, Kimura
T, Hara T, Ichikawa M, Takizawa S, Aoki Y, Niida S, Sakamoto H, et
al: Large-scale circulating microRNA profiling for the liquid
biopsy of prostate cancer. Clin Cancer Res. 25:3016–3025. 2019.
View Article : Google Scholar : PubMed/NCBI
|
|
27
|
Ren S, Huang S, Ye J and Qian X: Safe
feature screening for generalized LASSO. IEEE Trans Pattern Anal
Mach Intell. 40:2992–3006. 2018. View Article : Google Scholar : PubMed/NCBI
|
|
28
|
Waldmann P, Ferenčaković M, Mészáros G,
Khayatzadeh N, Curik I and Sölkner J: AUTALASSO: An automatic
adaptive LASSO for genome-wide prediction. BMC Bioinformatics.
20:1672019. View Article : Google Scholar : PubMed/NCBI
|
|
29
|
Yan Y, Lu Y, Mao K, Zhang M, Liu H, Zhou
Q, Lin J, Zhang J, Wang J and Xiao Z: Identification and validation
of a prognostic four-genes signature for hepatocellular carcinoma:
Integrated ceRNA network analysis. Hepatol Int. 13:618–630. 2019.
View Article : Google Scholar : PubMed/NCBI
|
|
30
|
Li M, Wang H, Li W, Peng Y, Xu F, Shang J,
Dong S, Bu L, Wang H and Wei W: Identification and validation of an
immune prognostic signature in colorectal cancer. Int
Immunopharmacol. 88:1068682020. View Article : Google Scholar : PubMed/NCBI
|
|
31
|
Pan J, Dai Q, Xiang Z, Liu B and Li C:
Three biomarkers predict gastric cancer patients' susceptibility to
fluorouracil-based chemotherapy. J Cancer. 10:2953–2960. 2019.
View Article : Google Scholar : PubMed/NCBI
|
|
32
|
Kang JI, Chung HC, Jeung HC, Kim SJ, An SK
and Namkoong K: FKBP5 polymorphisms as vulnerability to anxiety and
depression in patients with advanced gastric cancer: A controlled
and prospective study. Psychoneuroendocrinology. 37:1569–1576.
2012. View Article : Google Scholar : PubMed/NCBI
|
|
33
|
Zou M, Hu X, Xu B, Tong T, Jing Y, Xi L,
Zhou W, Lu J, Wang X, Yang X and Liao F: Glutathione S-transferase
isozyme alpha 1 is predominantly involved in the cisplatin
resistance of common types of solid cancer. Oncol Rep. 41:989–998.
2019.PubMed/NCBI
|
|
34
|
Kilic M, Aynekin B, Kara A, Icen D and
Demircan K: Differentially regulated ADAMTS1, 8, and 18 in gastric
adenocarcinoma. Bratisl Lek Listy. 118:71–76. 2017.PubMed/NCBI
|
|
35
|
Liu L, Tian YC, Mao G, Zhang YG and Han L:
MiR-675 is frequently overexpressed in gastric cancer and enhances
cell proliferation and invasion via targeting a potent anti-tumor
gene PITX1. Cell Signal. 62:1093522019. View Article : Google Scholar : PubMed/NCBI
|
|
36
|
Bartel D: Metazoan microRNAs. Cell.
173:20–51. 2018. View Article : Google Scholar : PubMed/NCBI
|
|
37
|
Slack FJ and Chinnaiyan AM: The role of
non-coding RNAs in oncology. Cell. 179:1033–1055. 2019. View Article : Google Scholar : PubMed/NCBI
|
|
38
|
Dragomir M and Calin GA: Circular RNAs in
cancer-lessons learned from microRNAs. Front Oncol. 8:1792018.
View Article : Google Scholar : PubMed/NCBI
|
|
39
|
Zhong X, Wen X, Chen L, Gu N, Yu X and Sui
K: Long non-coding RNA KCNQ1OT1 promotes the progression of gastric
cancer via the miR-145-5p/ARF6 axis. J Gene Med. 23:e33302021.
View Article : Google Scholar : PubMed/NCBI
|
|
40
|
Wang J, Liu L, Sun Y, Xue Y, Qu J, Pan S,
Li H, Qu H, Wang J and Zhang J: miR-615-3p promotes proliferation
and migration and inhibits apoptosis through its potential target
CELF2 in gastric cancer. Biomed Pharmacother. 101:406–413. 2018.
View Article : Google Scholar : PubMed/NCBI
|
|
41
|
Abe S, Matsuzaki J, Sudo K, Oda I, Katai
H, Kato K, Takizawa S, Sakamoto H, Takeshita F, Niida S, et al: A
novel combination of serum microRNAs for the detection of early
gastric cancer. Gastric Cancer. 24:835–843. 2021. View Article : Google Scholar : PubMed/NCBI
|
