Reduced miR-105-1 levels are associated with poor survival of patients with non‑small cell lung cancer

Lu,Gaixia; Fu,Da; Jia,Chengyou; Chai,Li; Han,Yang; Liu,Jin; Wu,Tingmiao; Xie,Ruting; Chang,Zhengyan; Yang,Huiqiong; Luo,Pei; Lv,Zhongwei; Yu,Fei; Zhong,Xiaojun; Ma,Yushui

doi:10.3892/ol.2017.7228

Reduced miR-105-1 levels are associated with poor survival of patients with non‑small cell lung cancer

Authors:
- Gaixia Lu
- Da Fu
- Chengyou Jia
- Li Chai
- Yang Han
- Jin Liu
- Tingmiao Wu
- Ruting Xie
- Zhengyan Chang
- Huiqiong Yang
- Pei Luo
- Zhongwei Lv
- Fei Yu
- Xiaojun Zhong
- Yushui Ma
View Affiliations

Affiliations: Department of Nuclear Medicine, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai 200072, P.R. China, Department of Radiology, Translational Medicine Center and Medical Imaging Research Institute, Central Hospital of Baotou, Inner Mongolia Medical University, Baotou, Inner Mongolia Autonomous Region 014040, P.R. China, Department of Pathology, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai 200072, P.R. China, Department of Medical Oncology, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi 330006, P.R. China
Published online on: October 19, 2017 https://doi.org/10.3892/ol.2017.7228
Pages: 7842-7848
Copyright: © Lu 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

Altered expression of microRNAs (miRNAs or miRs) contributes to lung carcinogenesis. The present study performed an in silico analysis of differentially expressed miRNAs in different peripheral blood samples from patients with various diseases vs. controls using the Gene Expression Omnibus (GEO) database data, and assessed miR‑105‑1 expression in 32 normal lung and 142 non‑small cell lung cancer (NSCLC) tissue samples using reverse transcription‑quantitative polymerase chain reaction. Survival data were calculated using Kaplan‑Meier curves and a log‑rank test. The stepwise forward Cox regression model was performed for univariate and multivariate analyses of independent predictor of overall survival (OS) of patients. The data on in silico and tissue microarray analyses of miRNA expression revealed reduced miR‑105‑1 expression in different types of human cancer, particularly in NSCLC. The level of miR‑105‑1 expression was confirmed to be downregulated in NSCLC tissues compared with that in normal lung tissues. Reduced miR‑105‑1 expression was associated with larger tumor size as well as poor OS and disease‑free survival (DFS) of patients. Multivariate survival analysis demonstrated that reduced miR‑105‑1 expression and tumor size were independent predictors for OS of NSCLC patients. In conclusion, reduced miR‑105‑1 expression in NSCLC tissues is associated with poor OS and DFS of NSCLC patients.

View Figures

View References

1	Torre LA, Bray F, Siegel RL, Ferlay J, Lortet-Tieulent J and Jemal A: Global cancer statistics, 2012. CA Cancer J Clin. 65:87–108. 2015. View Article : Google Scholar : PubMed/NCBI
2	Siegel RL, Miller KD and Jemal A: Cancer statistics, 2016. CA Cancer J Clin. 66:7–30. 2016. View Article : Google Scholar : PubMed/NCBI
3	Hill A, Fisher P and Yeomanson D: Non-small cell lung cancer. BMJ. 345:e64432012. View Article : Google Scholar : PubMed/NCBI
4	Cykert S, Dilworth-Anderson P, Monroe MH, Walker P, McGuire FR, Corbie-Smith G, Edwards LJ and Bunton AJ: Factors associated with decisions to undergo surgery among patients with newly diagnosed early-stage lung cancer. JAMA. 303:2368–2376. 2010. View Article : Google Scholar : PubMed/NCBI
5	Fervers B: Chemotherapy in elderly patients with resected stage II–IIIA lung cancer. BMJ. 343:d41042011. View Article : Google Scholar : PubMed/NCBI
6	Sullivan R, Alatise OI, Anderson BO, Audisio R, Autier P, Aggarwal A, Balch C, Brennan MF, Dare A, D'Cruz A, et al: Global cancer surgery: Delivering safe, affordable, and timely cancer surgery. Lancet Oncol. 16:1193–1224. 2015. View Article : Google Scholar : PubMed/NCBI
7	Global Burden of Disease Cancer Collaboration, Fitzmaurice C, . Dicker D, Pain A, Hamavid H, Moradi-Lakeh M, MacIntyre MF, Allen C, Hansen G, Woodbrook R, et al: The Global burden of cancer 2013. JAMA Oncol. 1:505–527. 2015. View Article : Google Scholar : PubMed/NCBI
8	National Lung Screening Trial Research Team, . Church TR, Black WC, Aberle DR, Berg CD, Clingan KL, Duan F, Fagerstrom RM, Gareen IF, Gierada DS, et al: Results of initial low-dose computed tomographic screening for lung cancer. N Engl J Med. 368:1980–1991. 2013. View Article : Google Scholar : PubMed/NCBI
9	Gasparini P, Cascione L, Landi L, Carasi S, Lovat F, Tibaldi C, Alì G, D'Incecco A, Minuti G, Chella A, et al: microRNA classifiers are powerful diagnostic/prognostic tools in ALK-, EGFR-, and KRAS-driven lung cancers. Proc Natl Acad Sci USA. 112:pp. 14924–14929. 2015, View Article : Google Scholar : PubMed/NCBI
10	Cheng CJ, Bahal R, Babar IA, Pincus Z, Barrera F, Liu C, Svoronos A, Braddock DT, Glazer PM, Engelman DM, et al: MicroRNA silencing for cancer therapy targeted to the tumour microenvironment. Nature. 518:107–110. 2015. View Article : Google Scholar : PubMed/NCBI
11	Martello G, Rosato A, Ferrari F, Manfrin A, Cordenonsi M, Dupont S, Enzo E, Guzzardo V, Rondina M, Spruce T, et al: A MicroRNA targeting dicer for metastasis control. Cell. 141:1195–1207. 2010. View Article : Google Scholar : PubMed/NCBI
12	Yanaihara N, Caplen N, Bowman E, Seike M, Kumamoto K, Yi M, Stephens RM, Okamoto A, Yokota J, Tanaka T, et al: Unique microRNA molecular profiles in lung cancer diagnosis and prognosis. Cancer Cell. 9:189–198. 2006. View Article : Google Scholar : PubMed/NCBI
13	Peinado H, Alečković M, Lavotshkin S, Matei I, Costa-Silva B, Moreno-Bueno G, Hergueta-Redondo M, Williams C, García-Santos G, Ghajar C, et al: Melanoma exosomes educate bone marrow progenitor cells toward a pro-metastatic phenotype through MET. Nat Med. 18:883–891. 2012. View Article : Google Scholar : PubMed/NCBI
14	Zhou W, Fong MY, Min Y, Somlo G, Liu L, Palomares MR, Yu Y, Chow A, O'Connor ST, Chin AR, et al: Cancer-secreted miR-105 destroys vascular endothelial barriers to promote metastasis. Cancer Cell. 25:501–515. 2014. View Article : Google Scholar : PubMed/NCBI
15	Hood JL, San RS and Wickline SA: Exosomes released by melanoma cells prepare sentinel lymph nodes for tumor metastasis. Cancer Res. 71:3792–3801. 2011. View Article : Google Scholar : PubMed/NCBI
16	Guan Y, Chen L, Bao Y, Li Z, Cui R, Li G and Wang Y: Identification of low miR-105 expression as a novel poor prognostic predictor for human glioma. Int J Clin Exp Med. 8:10855–10864. 2015.PubMed/NCBI
17	NCBI Resource Coordinators: Database resources of the national center for biotechnology information. Nucleic Acids Res. 44:D7–D19. 2016. View Article : Google Scholar : PubMed/NCBI
18	Barrett T, Wilhite SE, Ledoux P, Evangelista C, Kim IF, Tomashevsky M, Marshall KA, Phillippy KH, Sherman PM, Holko M, et al: NCBI GEO: Archive for functional genomics data sets-update. Nucleic Acids Res. 41:(Database Issue). D991–D995. 2013. View Article : Google Scholar : PubMed/NCBI
19	Lantuejoul S, Rouquette I, Brambilla E and Travis WD: New WHO classification of lung adenocarcinoma and preneoplasia. Ann Pathol. 36:5–14. 2016.(In French). View Article : Google Scholar : PubMed/NCBI
20	Edge SB and Compton CC: The American Joint Committee on cancer: The 7th edition of the AJCC cancer staging manual and the future of TNM. Ann Surg Oncol. 17:1471–1474. 2010. View Article : Google Scholar : PubMed/NCBI
21	Chen C, Ridzon DA, Broomer AJ, Zhou Z, Lee DH, Nguyen JT, Barbisin M, Xu NL, Mahuvakar VR, Andersen MR, et al: Real-time quantification of microRNAs by stem-loop RT-PCR. Nucleic Acids Res. 33:e1792005. View Article : Google Scholar : PubMed/NCBI
22	Livak KJ and Schmittgen TD: Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) method. Methods. 25:402–408. 2001. View Article : Google Scholar : PubMed/NCBI
23	Schaap-Oziemlak AM, Raymakers RA, Bergevoet SM, Gilissen C, Jansen BJ, Adema GJ, Kögler G, le Sage C, Agami R, van der Reijden BA and Jansen JH: MicroRNA hsa-miR-135b regulates mineralization in osteogenic differentiation of human unrestricted somatic stem cells. Stem Cells Dev. 19:877–885. 2010. View Article : Google Scholar : PubMed/NCBI
24	Zhao B, Zhu Y, Cui K, Gao J, Yu F, Chen L and Li S: Expression and significance of PTEN and miR-92 in hepatocellular carcinoma. Mol Med Rep. 7:1413–1416. 2013. View Article : Google Scholar : PubMed/NCBI
25	Gao F, Wang T, Zhang Z, Wang R, Guo Y and Liu J: Regulation of activating protein-4-associated metastases of non-small cell lung cancer cells by miR-144. Tumour Biol. Aug 8–2015.(Epub ahead of print).
26	Qi F, He T, Jia L, Song N, Guo L, Ma X, Wang C, Xu M, Fu Y, Li L and Luo Y: The miR-30 family inhibits pulmonary vascular hyperpermeability in the premetastatic phase by direct targeting of Skp2. Clin Cancer Res. 21:3071–3080. 2015. View Article : Google Scholar : PubMed/NCBI
27	Hu Z, Chen X, Zhao Y, Tian T, Jin G, Shu Y, Chen Y, Xu L, Zen K, Zhang C and Shen H: Serum microRNA signatures identified in a genome-wide serum microRNA expression profiling predict survival of non-small-cell lung cancer. J Clin Oncol. 28:1721–1726. 2010. View Article : Google Scholar : PubMed/NCBI
28	Liu D, Hu X, Zhou H, Shi G and Wu J: Identification of aberrantly expressed miRNAs in gastric cancer. Gastroenterol Res Pract. 2014:4738172014. View Article : Google Scholar : PubMed/NCBI
29	Zen K and Zhang CY: Circulating microRNAs: A novel class of biomarkers to diagnose and monitor human cancers. Med Res Rev. 32:326–348. 2012. View Article : Google Scholar : PubMed/NCBI
30	Shen G, Rong X, Zhao J, Yang X, Li H, Jiang H, Zhou Q, Ji T, Huang S, Zhang J and Jia H: MicroRNA-105 suppresses cell proliferation and inhibits PI3K/AKT signaling in human hepatocellular carcinoma. Carcinogenesis. 35:2748–2755. 2014. View Article : Google Scholar : PubMed/NCBI
31	Han Y, Jia C, Cong X, Yu F, Cai H, Fang S, Cai L, Yang H, Sun Y, Li D, et al: Increased expression of TGFβR2 is associated with the clinical outcome of non-small cell lung cancer patients treated with chemotherapy. PLoS One. 10:e01346822015. View Article : Google Scholar : PubMed/NCBI
32	Honeywell DR, Cabrita MA, Zhao H, Dimitroulakos J and Addison CL: miR-105 inhibits prostate tumour growth by suppressing CDK6 levels. PLoS One. 8:e705152013. View Article : Google Scholar : PubMed/NCBI