A 16-gene expression signature to distinguish stage I from stage II lung squamous carcinoma

Wang,Rui; Cai,Yuxing; Zhang,Baoping; Wu,Zhengxia

doi:10.3892/ijmm.2017.3332

A 16-gene expression signature to distinguish stage I from stage II lung squamous carcinoma

Authors:
- Rui Wang
- Yuxing Cai
- Baoping Zhang
- Zhengxia Wu
View Affiliations

Affiliations: Department of VIP and Geriatrics, Xi'an Gaoxin Hospital, Gaoxin Industrial Development Distinct, Xi'an, Shanxi 710075, P.R. China, Department of Respiratory Medicine, Baoji Central Hospital, Baoji, Shanxi 721008, P.R. China, Department of Thoracic Surgery, Baoji Central Hospital, Baoji, Shanxi 721008, P.R. China
Published online on: December 19, 2017 https://doi.org/10.3892/ijmm.2017.3332
Pages: 1377-1384
Copyright: © Wang 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

The present study aimed to perform screening of a gene signature for the discrimination and prognostic prediction of stage I and II lung squamous carcinoma. A microarray meta‑analysis was performed to identify differentially expressed genes (DEGs) between stage I and II lung squamous carcinoma samples in seven microarray datasets collected from the Gene Expression Omnibus database via the MetaQC and MetaDE package in R. The important DEGs were selected according to the betweenness centrality value of the protein‑protein interaction (PPI) network. Support vector machine (SVM) analysis was performed to screen the feature genes for discrimination and prognosis. One independent dataset downloaded from The Cancer Genome Atlas was used to validate the reliability. Pathway enrichment analysis was also performed for the feature genes. A total of 924 DEGs were identified to construct a PPI network consisting of 392 nodes and 686 edges. The top 100 of the 392 nodes were selected as crucial genes to construct an SVM classifier, and a 16‑gene signature (caveolin 1, eukaryotic translation elongation factor 1γ, casein kinase 2α1, tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation η, tyrosine 3‑monooxygenase/tryptophan 5‑monooxygenase activation θ, pleiotrophin, insulin receptor, insulin receptor substrate 1, 3‑phosphoinositide‑dependent protein kinase‑1, specificity protein 1, COP9 signalosome subunit 6, N‑myc downstream regulated gene 1, retinoid X receptor α, heat shock protein 90α A1, karyopherin subunit β1 and erythrocyte membrane protein band 4.1) with high discrimination accuracy was identified. This 16‑gene signature had significant prognostic value, and patients with stage II lung squamous carcinoma exhibited shorter survival rates, compared with those with stage I disease. Seven DEGs of the 16-gene signature were significantly involved in the phosphoinositide 3‑kinase‑Akt signaling pathway. The 16‑gene signature identified in the present study may be useful for stratifying the patients with stage I or II lung squamous carcinoma and predicting prognosis.

View Figures

View References

1	Siegel RL, Miller KD and Jemal A: Cancer statistics, 2017. CA Cancer J Clin. 66:7–30. 2017. View Article : Google Scholar
2	McPhail S, Johnson S, Greenberg D, Peake M and Rous B: Stage at diagnosis and early mortality from cancer in England. Br J Cancer. 112(Suppl 1): S108–S115. 2015. View Article : Google Scholar : PubMed/NCBI
3	Walters S, Maringe C, Coleman MP, Peake MD, Butler J, Young N, Bergström S, Hanna L, Jakobsen E, Kölbeck K, et al: Lung cancer survival and stage at diagnosis in Australia, Canada, Denmark, Norway, Sweden and the UK: A population-based study, 2004–2007. Thorax. 68:551–564. 2013. View Article : Google Scholar : PubMed/NCBI
4	Klarod K, Hongsprabhas P, Khampitak T, Wirasorn K, Kiertiburanakul S, Tangrassameeprasert R, Daduang J, Yongvanit P and Boonsiri P: Serum antioxidant levels and nutritional status in early and advanced stage lung cancer patients. Nutrition. 27:1156–1160. 2011. View Article : Google Scholar : PubMed/NCBI
5	Lim W, Zhang WH, Miller CR, Watters JW, Gao F, Viswanathan A, Govindan R and McLeod HL: PTEN and phosphorylated AKT expression and prognosis in early-and late-stage non-small cell lung cancer. Oncol Rep. 17:853–857. 2007.PubMed/NCBI
6	Lemon Lu Y, Liu W, Yi PY, Morrison Y, Yang C, Sun P, Szoke Z, Gerald J, Watson WLM, et al: A gene expression signature predicts survival of patients with stage I non-small cell lung cancer. PLoS Med. 3:e4672006. View Article : Google Scholar : PubMed/NCBI
7	Der SD, Sykes J, Pintilie M, Zhu CQ, Strumpf D, Liu N, Jurisica I, Shepherd FA and Tsao MS: Validation of a histology-independent prognostic gene signature for early-stage, non-small-cell lung cancer including stage IA patients. J Thorac Oncol. 9:59–64. 2014. View Article : Google Scholar
8	Huang S, Reitze NJ, Ewing AL, McCreary S, Uihlein AH, Brower SL, Wang D, Wang T, Gabrin MJ, Keating KE, et al: Analytical performance of a 15-gene prognostic assay for early-stage non-small-cell lung carcinoma using RNA-stabilized tissue. J Mol Diagn. 17:438–445. 2015. View Article : Google Scholar : PubMed/NCBI
9	Skrzypski M, Jassem E, Taron M, Sanchez JJ, Mendez P, Rzyman W, Gulida G, Raz D, Jablons D, Provencio M, et al: Three-gene expression signature predicts survival in early-stage squamous cell carcinoma of the lung. Clin Cancer Res. 14:4794–4799. 2008. View Article : Google Scholar : PubMed/NCBI
10	Krzystanek M, Moldvay J, Szüts D, Szallasi Z and Eklund AC: A robust prognostic gene expression signature for early stage lung adenocarcinoma. Biomarker Res. 4:12016. View Article : Google Scholar
11	Hwang JA, Song JS, Yu DY, Kim HR, Park HJ, Park YS, Kim WS and Choi CM: Peroxiredoxin 4 as an independent prognostic marker for survival in patients with early-stage lung squamous cell carcinoma. Int J Clin Exp Pathol. 8:6627–6635. 2015.PubMed/NCBI
12	Zhu CQ, Strumpf D, Li CY, Li Q, Liu N, Der S, Shepherd FA, Tsao MS and Jurisica I: Prognostic gene expression signature for squamous cell carcinoma of lung. Clin Cancer Res. 16:5038–5047. 2010. View Article : Google Scholar : PubMed/NCBI
13	Kim TH, Choi SJ, Lee YH, Song GG and Ji JD: Gene expression profile predicting the response to anti-TNF treatment in patients with rheumatoid arthritis; analysis of GEO datasets. Joint Bone Spine. 81:325–330. 2014. View Article : Google Scholar : PubMed/NCBI
14	Bolstad BM, Irizarry RA, Åstrand M and Speed TP: A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics. 19:185–193. 2003. View Article : Google Scholar : PubMed/NCBI
15	Smyth GK: Limma: Linear models for microarray data. Bioinformatics and computational biology solutions using R and Bioconductor. Springer; pp. 397–420. 2005, View Article : Google Scholar
16	Yang ZH, Zheng R, Gao Y and Zhang Q: Identification of suitable genes contributes to lung adenocarcinoma clustering by multiple meta-analysis methods. Clin Respir J. 10:631–646. 2016. View Article : Google Scholar
17	Kang DD, Sibille E, Kaminski N and Tseng GC: MetaQC: Objective quality control and inclusion/exclusion criteria for genomic meta-analysis. Nucleic Acids Res. 40:e152012. View Article : Google Scholar :
18	Chang LC, Lin HM, Sibille E and Tseng GC: Meta-analysis methods for combining multiple expression profiles: Comparisons, statistical characterization and an application guideline. BMC Bioinformatics. 14:3682013. View Article : Google Scholar : PubMed/NCBI
19	Kohl M, Wiese S and Warscheid B: Cytoscape: Software for visualization and analysis of biological networks. Methods Mol Biol. 696:291–303. 2011. View Article : Google Scholar
20	Peri S, Navarro JD, Kristiansen TZ, Amanchy R, Surendranath V, Muthusamy B, Gandhi TK, Chandrika KN, Deshpande N, Suresh S, et al: Human protein reference database as a discovery resource for proteomics. Nucleic Acids Res. 32:D497–D501. 2004. View Article : Google Scholar :
21	Meyer D and Wien FT: Support vector machines. The interface to libsvm in package. e10712015.
22	Therneau T: A package for survival analysis in S. R package version 2.37-4. URL http://CRANR-project.org/package=survivalBox980032. pp. 23298–20032. 2013
23	Benjamini Y and Hochberg Y: Controlling the false discovery rate: A practical and powerful approach to multiple testing. J Royal Statistical Soc Series B. 57:289–300. 1995.
24	Sunaga N, Miyajima K, Suzuki M, Sato M, White MA, Ramirez RD, Shay JW, Gazdar AF and Minna JD: Different roles for caveolin-1 in the development of non-small cell lung cancer versus small cell lung cancer. Cancer Res. 64:4277–4285. 2004. View Article : Google Scholar : PubMed/NCBI
25	Chen D, Shen C, Du H, Zhou Y and Che G: Duplex value of caveolin-1 in non-small cell lung cancer: A meta analysis. Familial Cancer. 13:449–457. 2014. View Article : Google Scholar : PubMed/NCBI
26	Ho CC, Kuo SH, Huang PH, Huang HY, Yang CH and Yang PC: Caveolin-1 expression is significantly associated with drug resistance and poor prognosis in advanced non-small cell lung cancer patients treated with gemcitabine-based chemotherapy. Lung Cancer. 59:105–110. 2008. View Article : Google Scholar
27	Matassa D, Amoroso MR, Agliarulo I, Maddalena F, Sisinni L, Paladino S, Romano S, Romano MF, Sagar V, Loreni F, et al: Translational control in the stress adaptive response of cancer cells: A novel role for the heat shock protein TRAP1. Cell Death Dis. 4:e8512013. View Article : Google Scholar : PubMed/NCBI
28	Zheng Y, McFarland BC, Drygin D, Yu H, Bellis SL, Kim H, Bredel M and Benveniste EN: Targeting protein kinase CK2 suppresses prosurvival signaling pathways and growth of glioblastoma. Clin Cancer Res. 19:6484–6494. 2013. View Article : Google Scholar : PubMed/NCBI
29	O-charoenrat P, Rusch V, Talbot SG, Sarkaria I, Viale A, Socci N, Ngai I, Rao P and Singh B: Casein kinase II alpha subunit and C1-inhibitor are independent predictors of outcome in patients with squamous cell carcinoma of the lung. Clinical Cancer Res. 10:5792–5803. 2004. View Article : Google Scholar
30	Jäger R, List B, Knabbe C, Souttou B, Raulais D, Zeiler T, Wellstein A, Aigner A, Neubauer A and Zugmaier G: Serum levels of the angiogenic factor pleiotrophin in relation to disease stage In lung cancer patients. Br J Cancer. 86:858–863. 2002. View Article : Google Scholar : PubMed/NCBI
31	Feng ZJ, Gao SB, Wu Y, Xu XF, Hua X and Jin GH: Lung cancer cell migration is regulated via repressing growth factor PTN/RPTP β/ζ signaling by menin. Oncogene. 29:5416–5426. 2010. View Article : Google Scholar : PubMed/NCBI
32	Gao S, Feng Z, Xu B, Wu Y, Yin P, Yang Y, Hua X and Jin GH: Suppression of lung adenocarcinoma through menin and poly-comb gene-mediated repression of growth factor pleiotrophin. Oncogene. 28:4095–4104. 2009. View Article : Google Scholar : PubMed/NCBI
33	Du ZY, Shi MH, Ji CH and Yu Y: Serum pleiotrophin could be an early indicator for diagnosis and prognosis of non-small cell lung cancer. Asian Pac J Cancer Prev. 16:1421–1425. 2014. View Article : Google Scholar
34	Brown KC, Perry HE, Lau JK, Jones DV, Pulliam JF, Thornhill BA, Crabtree CM, Luo H, Chen YC and Dasgupta P: Nicotine induces the up-regulation of the α7-nicotinic receptor (α7-nAChR) in human squamous cell lung cancer cells via the Sp1/GATA protein pathway. J Biol Chem. 288:33049–33059. 2013. View Article : Google Scholar : PubMed/NCBI
35	Li M, Ling B, Xiao T, Tan J, An N, Han N, Guo S, Cheng S and Zhang K: Sp1 transcriptionally regulates BRK1 expression in non-small cell lung cancer cells. Gene. 542:134–140. 2014. View Article : Google Scholar : PubMed/NCBI
36	Cha N, Lv M, Zhao YJ, Yang D, Wang EH and Wu GP: Diagnostic utility of VEGF mRNA and SP1 mRNA expression in bronchial cells of patients with lung cancer. Respirology. 19:544–548. 2014. View Article : Google Scholar : PubMed/NCBI
37	Gao S, Fang L, Phan LM, Qdaisat A, Yeung SC and Lee MH: COP9 signalosome subunit 6 (CSN6) regulates E6AP/UBE3A in cervical cancer. Oncotarget. 6:28026–28041. 2015. View Article : Google Scholar : PubMed/NCBI
38	Azuma K, Kawahara A, Hattori S, Taira T, Tsurutani J, Watari K, Shibata T, Murakami Y, Takamori S, Ono M, et al: NDRG1/Cap43/Drg-1 may predict tumor angiogenesis and poor outcome in patients with lung cancer. J Thorac Oncol. 7:779–789. 2012. View Article : Google Scholar : PubMed/NCBI
39	Wang D, Tian X and Jiang Y: NDRG1/Cap43 overexpression in tumor tissues and serum from lung cancer patients. J Cancer Res Clin Oncol. 138:1813–1820. 2012. View Article : Google Scholar : PubMed/NCBI
40	Robb VA, Li W, Gascard P, Perry A, Mohandas N and Gutmann DH: Identification of a third Protein 4.1 tumor suppressor, Protein 4.1 R, in meningioma pathogenesis. Neurobiol Dis. 13:191–202. 2003. View Article : Google Scholar : PubMed/NCBI
41	Zhou L, Luan H, Liu Q, Jiang T, Liang H, Dong X and Shang H: Activation of PI3K/Akt and ERK signaling pathways antagonized sinomenine-induced lung cancer cell apoptosis. Mol Med Rep. 5:1256–1260. 2012.PubMed/NCBI
42	Zhang C, Lan T, Hou J, Li J, Fang R, Yang Z, Zhang M, Liu J and Liu B: NOX4 promotes non-small cell lung cancer cell proliferation and metastasis through positive feedback regulation of PI3K/Akt signaling. Oncotarget. 5:4392–4405. 2014. View Article : Google Scholar : PubMed/NCBI
43	Gallegos Ruiz MI, Floor K, Roepman P, Rodriguez JA, Meijer GA, Mooi WJ, Jassem E, Niklinski J, Muley T, van Zandwijk N, et al: Integration of gene dosage and gene expression in non-small cell lung cancer, identification of HSP90 as potential target. PLoS One. 3:e00017222008. View Article : Google Scholar : PubMed/NCBI
44	Han L, Zhang G, Zhang N, Li H, Liu Y, Fu A and Zheng Y: Prognostic potential of microRNA-138 and its target mRNA PDK1 in sera for patients with non-small cell lung cancer. Med Oncol. 31:1292014. View Article : Google Scholar : PubMed/NCBI
45	Taniguchi CM, Emanuelli B and Kahn CR: Critical nodes in signalling pathways: Insights into insulin action. Nat Rev Mol Cell Biol. 7:85–96. 2006. View Article : Google Scholar : PubMed/NCBI
46	Heidegger I, Kern J, Ofer P, Klocker H and Massoner P: Oncogenic functions of IGF1R and INSR in prostate cancer include enhanced tumor growth, cell migration and angiogenesis. Oncotarget. 5:2723–2735. 2014. View Article : Google Scholar : PubMed/NCBI
47	Kim JS, Kim ES, Liu D, Lee JJ, Solis L, Behrens C, Lippman SM, Hong WK, Wistuba II and Lee HY: Prognostic impact of insulin receptor expression on survival of patients with nonsmall cell lung cancer. Cancer. 118:2454–2465. 2012. View Article : Google Scholar
48	Wang GH, Jiang FQ, Duan YH, Zeng ZP, Chen F, Dai Y, Chen JB, Liu JX, Liu J, Zhou H, et al: Targeting truncated retinoid X receptor-α by CF31 Induces TNF-α-dependent apoptosis. Cancer Res. 73:307–318. 2013. View Article : Google Scholar
49	Radhakrishnan VM and Martinez JD: 14-3-3gamma induces oncogenic transformation by stimulating MAP kinase and PI3K signaling. PLoS One. 5:e114332010. View Article : Google Scholar : PubMed/NCBI
50	Fan T, Li R, Todd NW, Qiu Q, Fang HB, Wang H, Shen J, Zhao RY, Caraway NP, Katz RL, et al: Up-regulation of 14-3-3zeta in lung cancer and its implication as prognostic and therapeutic target. Cancer Res. 67:7901–7906. 2007. View Article : Google Scholar : PubMed/NCBI