Monitoring methylation‑driven genes as prognostic biomarkers in patients with lung squamous cell cancer

Han,Pengkai; Liu,Qiping; Xiang,Jianhua

doi:10.3892/ol.2019.11163

Monitoring methylation‑driven genes as prognostic biomarkers in patients with lung squamous cell cancer

Authors:
- Pengkai Han
- Qiping Liu
- Jianhua Xiang
View Affiliations

Affiliations: Department of Respiratory Medicine, Chongqing Three Gorges Central Hospital, Chongqing 404100, P.R. China
Published online on: November 29, 2019 https://doi.org/10.3892/ol.2019.11163
Pages: 707-716
Copyright: © Han et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

Metrics: Total Views: 0 (Spandidos Publications: | PMC Statistics: )

Metrics: Total PDF Downloads: 0 (Spandidos Publications: | PMC Statistics: )

Cited By (CrossRef): 0 citations Loading Articles...

Abstract

Aberrant DNA methylations have been reported to be significantly associated with lung squamous cell cancer (LUSC). The aim of this study was to investigate the DNA methylation‑driven genes in LUSC by integrative bioinformatics analysis. In the present study, methylation‑driven genes in LUSC were screened out, and survival analysis related to these genes was performed to confirm their value in prognostic assessment. Gene expression and methylation data were downloaded from The Cancer Genome Atlas (TCGA), and the MethylMix algorithm was used to identify methylation‑driven genes. ConsensusPathDB was used to perform Gene Ontology and pathway enrichment analysis of methylation‑driven genes. Survival analysis was performed to investigate the correlation with prognosis. In total, 52 differentially expressed methylation‑driven genes were identified in LUSC and adjacent tissues. Survival analysis showed that DQX1, GPR75, STX12, and TRIM61 could serve as independent prognostic biomarkers. In addition, the combined methylation and gene expression survival analysis revealed that the combined expression level of the genes ALG1L, DQX1, and ZNF418 alone can be used as a prognostic marker or drug target. Methylation of four sites of gene ZNF418, four sites of ZNF701, two sites of DQX1, and four sites of DCAF4L2 was significantly associated with survival. The present study provides an important bioinformatic and relevant theoretical basis for subsequent early diagnosis and prognostic assessment of LUSC.

View Figures

View References

1	Siegel RL, Miller KD and Jemal A: Cancer statistics, 2017. CA Cancer J Clin. 67:7–30. 2017. View Article : Google Scholar : PubMed/NCBI
2	Herbst RS, Heymach JV and Lippman SM: Lung cancer. N Engl J Med. 359:1367–1380. 2008. View Article : Google Scholar : PubMed/NCBI
3	Lemjabbar-Alaoui H, Hassan OU, Yang YW and Buchanan P: Lung cancer: Biology and treatment options. Biochim Biophys Acta. 1856:189–210. 2015.PubMed/NCBI
4	Lorenzo-González M, Fernández-Villar A and Ruano-Ravina A: Disentangling tobacco-related lung cancer-genome-wide interaction study of smoking behavior and non-small cell lung cancer risk. J Thorac Dis. 11:10–13. 2019. View Article : Google Scholar : PubMed/NCBI
5	Jordan EJ, Kim HR, Arcila ME, Barron D, Chakravarty D, Gao J, Chang MT, Ni A, Kundra R, Jonsson P, et al: Prospective comprehensive molecular characterization of lung adenocarcinomas for efficient patient matching to approved and emerging therapies. Cancer Discov. 7:596–609. 2017. View Article : Google Scholar : PubMed/NCBI
6	Aoki MN, Amarante MK, de Oliveira CEC and Watanabe MAE: Biomarkers in non-small cell lung cancer: Perspectives of individualized targeted therapy. Anticancer Agents Med Chem. 18:2070–2077. 2018. View Article : Google Scholar : PubMed/NCBI
7	Shi YX, Wang Y, Li X, Zhang W, Zhou HH, Yin JY and Liu ZQ: Genome-wide DNA methylation profiling reveals novel epigenetic signatures in squamous cell lung cancer. BMC Genomics. 18:9012017. View Article : Google Scholar : PubMed/NCBI
8	Gevaert O: MethylMix: An R package for identifying DNA methylation-driven genes. Bioinformatics. 31:1839–1841. 2015. View Article : Google Scholar : PubMed/NCBI
9	Tomczak K, Czerwinska P and Wiznerowicz M: The cancer genome atlas (TCGA): An immeasurable source of knowledge. Contemp Oncol (Pozn). 19:A68–A77. 2015.PubMed/NCBI
10	Grossman RL, Heath AP, Ferretti V, Varmus HE, Lowy DR, Kibbe WA and Staudt LM: Toward a shared vision for cancer genomic data. N Engl J Med. 375:1109–1112. 2016. View Article : Google Scholar : PubMed/NCBI
11	Law CW, Alhamdoosh M, Su S, Dong X, Tian L, Smyth GK and Ritchie ME: RNA-seq analysis is easy as 1-2-3 with limma, Glimma and edgeR. Version 3. F1000Res. 5:2016. View Article : Google Scholar : PubMed/NCBI
12	Cedoz PL, Prunello M, Brennan K and Gevaert O: MethylMix 2.0: An R package for identifying DNA methylation genes. Bioinformatics. 34:3044–3046. 2018. View Article : Google Scholar : PubMed/NCBI
13	Kamburov A, Pentchev K, Galicka H, Wierling C, Lehrach H and Herwig R: ConsensusPathDB: Toward a more complete picture of cell biology. Nucleic Acids Res. 39((Database issue)): D712–D717. 2011. View Article : Google Scholar : PubMed/NCBI
14	Kamburov A, Stelzl U, Lehrach H and Herwig R: The ConsensusPathDB interaction database: 2013 update. Nucleic Acids Res. 41((Database issue)): D793–D800. 2013. View Article : Google Scholar : PubMed/NCBI
15	Herwig R, Hardt C, Lienhard M and Kamburov A: Analyzing and interpreting genome data at the network level with ConsensusPathDB. Nat Protoc. 11:1889–1907. 2016. View Article : Google Scholar : PubMed/NCBI
16	Dudley WN, Wickham R and Coombs N: An introduction to survival statistics: Kaplan-meier analysis. J Adv Pract Oncol. 7:91–100. 2016.PubMed/NCBI
17	Langer CJ, Obasaju C, Bunn P, Bonomi P, Gandara D, Hirsch FR, Kim ES, Natale RB, Novello S, Paz-Ares L, et al: Incremental innovation and progress in advanced squamous cell lung cancer: Current status and future impact of treatment. J Thorac Oncol. 11:2066–2081. 2016. View Article : Google Scholar : PubMed/NCBI
18	Paes de Araújo R, Bertoni N, Seneda AL, Felix TF, Carvalho M, Lewis KE, Hasimoto ÉN, Beckmann M, Drigo SA, Reis PP and Mur LAJ: Defining metabolic rewiring in lung squamous cell carcinoma. Metabolites. 9:E472019. View Article : Google Scholar : PubMed/NCBI
19	Kim D and Kim DH: Epigenome-based precision medicine in lung cancer. Methods Mol Biol. 1856:57–85. 2018. View Article : Google Scholar : PubMed/NCBI
20	Qazi TJ, Quan Z, Mir A and Qing H: Epigenetics in Alzheimer's disease: Perspective of DNA methylation. Mol Neurobiol. 55:1026–1044. 2018. View Article : Google Scholar : PubMed/NCBI
21	Pan Y, Liu G, Zhou F, Su B and Li Y: DNA methylation profiles in cancer diagnosis and therapeutics. Clin Exp Med. 18:1–14. 2018. View Article : Google Scholar : PubMed/NCBI
22	Schmiemann V, Böcking A, Kazimirek M, Onofre AS, Gabbert HE, Kappes R, Gerharz CD and Grote HJ: Methylation assay for the diagnosis of lung cancer on bronchial aspirates: A cohort study. Clin Cancer Res. 11:7728–7734. 2005. View Article : Google Scholar : PubMed/NCBI
23	Baylin SB and Ohm JE: Epigenetic gene silencing in cancer - a mechanism for early oncogenic pathway addiction? Nat Rev Cancer. 6:107–116. 2006. View Article : Google Scholar : PubMed/NCBI
24	Da Costa EM, McInnes G, Beaudry A and Raynal NJ: DNA methylation-targeted drugs. Cancer J. 23:270–276. 2017. View Article : Google Scholar : PubMed/NCBI
25	Manolakos A, Ochoa I, Venkat K, Goldsmith AJ and Gevaert O: CaMoDi: A new method for cancer module discovery. BMC Genomics. 15 (Suppl 10):S82014. View Article : Google Scholar : PubMed/NCBI
26	Gevaert O, Villalobos V, Sikic BI and Plevritis SK: Identification of ovarian cancer driver genes by using module network integration of multi-omics data. Interface Focus. 3:201300132013. View Article : Google Scholar : PubMed/NCBI
27	Bernstein BE, Meissner A and Lander ES: The mammalian epigenome. Cell. 128:669–681. 2007. View Article : Google Scholar : PubMed/NCBI
28	Duruisseaux M and Esteller M: Lung cancer epigenetics: From knowledge to applications. Semin Cancer Biol. 51:116–128. 2018. View Article : Google Scholar : PubMed/NCBI
29	Jones PA, Issa JP and Baylin S: Targeting the cancer epigenome for therapy. Nat Rev Genet. 17:630–641. 2016. View Article : Google Scholar : PubMed/NCBI
30	Sugimoto N, Maehara K, Yoshida K, Yasukouchi S, Osano S, Watanabe S, Aizawa M, Yugawa T, Kiyono T, Kurumizaka H, et al: Cdt1-binding protein GRWD1 is a novel histone-binding protein that facilitates MCM loading through its influence on chromatin architecture. Nucleic Acids Res. 43:5898–5911. 2015. View Article : Google Scholar : PubMed/NCBI
31	Chen R, Hong Q, Jiang J, Chen X, Jiang Z, Wang J, Liu S, Duan S and Shi S: AGTR1 promoter hypermethylation in lung squamous cell carcinoma but not in lung adenocarcinoma. Oncol Lett. 14:4989–4994. 2017. View Article : Google Scholar : PubMed/NCBI
32	Ni S, Ye M and Huang T: Short stature homeobox 2 methylation as a potential noninvasive biomarker in bronchial aspirates for lung cancer diagnosis. Oncotarget. 8:61253–61263. 2017. View Article : Google Scholar : PubMed/NCBI
33	Guo H, Zhou S, Tan L, Wu X, Wu Z and Ran R: Clinicopathological significance of WIF1 hypermethylation in NSCLC, a meta-analysis and literature review. Oncotarget. 8:2550–2557. 2017.PubMed/NCBI
34	Kim DS, Lee WK and Park JY: Promoter methylation of Wrap53α, an antisense transcript of p53, is associated with the poor prognosis of patients with non-small cell lung cancer. Oncol Lett. 16:5823–5828. 2018.PubMed/NCBI
35	Zhang X, Yang X, Wang J, Liang T, Gu Y and Yang D: Down-regulation of PAX6 by promoter methylation is associated with poor prognosis in non small cell lung cancer. Int J Clin Exp Pathol. 8:11452–11457. 2015.PubMed/NCBI
36	Dang DT, Pevsner J and Yang VW: The biology of the mammalian Krüppel-like family of transcription factors. Int J Biochem Cell Biol. 32:1103–1121. 2000. View Article : Google Scholar : PubMed/NCBI
37	Hui HX, Hu ZW, Jiang C, Wu J, Gao Y and Wang XW: ZNF418 overexpression protects against gastric carcinoma and prompts a good prognosis. Onco Targets Ther. 11:2763–2770. 2018. View Article : Google Scholar : PubMed/NCBI
38	Wang H, Chen Y, Han J, Meng Q, Xi Q, Wu G and Zhang B: DCAF4L2 promotes colorectal cancer invasion and metastasis via mediating degradation of NFκb negative regulator PPM1B. Am J Transl Res. 8:405–418. 2016.PubMed/NCBI
39	Ashktorab H, Daremipouran M, Goel A, Varma S, Leavitt R, Sun X and Brim H: DNA methylome profiling identifies novel methylated genes in African American patients with colorectal neoplasia. Epigenetics. 9:503–512. 2014. View Article : Google Scholar : PubMed/NCBI
40	Williams KC and Coppolino MG: SNARE-dependent interaction of Src, EGFR and β1 integrin regulates invadopodia formation and tumor cell invasion. J Cell Sci. 127:1712–1725. 2014. View Article : Google Scholar : PubMed/NCBI