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Cancer Cell International
Published in: Cancer Cell International 1/2019

Open Access 01-12-2019 | Metastasis | Primary research

A novel 4-gene signature for overall survival prediction in lung adenocarcinoma patients with lymph node metastasis

Published in: Cancer Cell International | Issue 1/2019

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Abstract

Background

Lung adenocarcinoma (LUAD) patients experiencing lymph node metastasis (LNM) always exhibit poor clinical outcomes. A biomarker or gene signature that could predict survival in these patients would have a substantial clinical impact, allowing for earlier detection of mortality risk and for individualized therapy.

Methods

With the aim to identify a novel mRNA signature associated with overall survival, we analysed LUAD patients with LNM extracted from The Cancer Genome Atlas (TCGA). LASSO Cox regression was applied to build the prediction model. An external cohort was applied to validate the prediction model.

Results

We identified a 4-gene signature that could effectively stratify a high-risk subset of these patients, and time-dependent receiver operating characteristic (tROC) analysis indicated that the signature had a powerful predictive ability. Gene Set Enrichment Analysis (GSEA) showed that the high-risk subset was mainly associated with important cancer-related hallmarks. Moreover, a predictive nomogram was established based on the signature integrated with clinicopathological features. Lastly, the signature was validated by an external cohort from Gene Expression Omnibus (GEO).

Conclusion

In summary, we developed a robust mRNA signature as an independent factor to effectively classify LUAD patients with LNM into low- and high-risk groups, which might provide a basis for personalized treatments for these patients.
Literature
1.
Chen W, Zheng R, Baade PD, Zhang S, Zeng H, Bray F, et al. Cancer statistics in China, 2015. CA Cancer J Clin. 2016;66(2):115–32.CrossRef
2.
Ettinger DS, Wood DE, Akerley W, Bazhenova LA, Borghaei H, Camidge DR, et al. Non-small cell lung cancer, Version 6. 2015. J Natl Compr Cancer Netw. 2015;13(5):515–24.CrossRef
3.
Nwogu CE, Groman A, Fahey D, Yendamuri S, Dexter E, Demmy TL, et al. Number of lymph nodes and metastatic lymph node ratio are associated with survival in lung cancer. Ann Thorac Surg. 2012;93(5):1614–9 (discussion 9–20).CrossRef
4.
Ou SH, Zell JA, Ziogas A, Anton-Culver H. Prognostic factors for survival of stage I nonsmall cell lung cancer patients: a population-based analysis of 19,702 stage I patients in the California Cancer Registry from 1989 to 2003. Cancer. 2007;110(7):1532–41.CrossRef
5.
Sakao Y, Okumura S, Mingyon M, Uehara H, Ishikawa Y, Nakagawa K. The impact of superior mediastinal lymph node metastases on prognosis in non-small cell lung cancer located in the right middle lobe. J Thorac Oncol. 2011;6(3):494–9.CrossRef
6.
Kafka A, Tomas D, Beroš V, Pećina H, Zeljko M, Pećina-Šlaus N. Brain metastases from lung cancer show increased expression of DVL1, DVL3 and beta-catenin and down-regulation of E-cadherin. Int J Mol Sci. 2014;15(6):10635–51.CrossRef
7.
Hurd PJ, Nelson CJ. Advantages of next-generation sequencing versus the microarray in epigenetic research. Brief Funct Genomic Proteomic. 2009;8(3):174–83.CrossRef
8.
Meldrum C, Doyle MA, Tothill RW. Next-generation sequencing for cancer diagnostics: a practical perspective. Clin Biochem Rev. 2011;32(4):177–95.PubMedPubMedCentral
9.
Kim S-K, Kim S-Y, Kim J-H, Roh S, Cho D-H, Kim YS, et al. A nineteen gene-based risk score classifier predicts prognosis of colorectal cancer patients. Mol Oncol. 2014;8(8):1653–66.CrossRef
10.
Dai W, Li Y, Mo S, Feng Y, Zhang L, Xu Y, et al. A robust gene signature for the prediction of early relapse in stage I–III colon cancer. Mol Oncol. 2018;12(4):463–75.CrossRef
11.
Bai F, Zhou H, Ma M, Guan C, Lyu J, Meng QH. A novel RNA sequencing-based mi RNA signature predicts with recurrence and outcome of hepatocellular carcinoma. Mol Oncol. 2018;12:1125–37.CrossRef
12.
Beer DG, Kardia SL, Huang CC, Giordano TJ, Levin AM, Misek DE, et al. Gene-expression profiles predict survival of patients with lung adenocarcinoma. Nat Med. 2002;8(8):816–24.CrossRef
13.
Director’s Challenge Consortium for the Molecular Classification of Lung A, Shedden K, Taylor JM, Enkemann SA, Tsao MS, Yeatman TJ, et al. Gene expression-based survival prediction in lung adenocarcinoma: a multi-site, blinded validation study. Nat Med. 2008;14(8):822–7.CrossRef
14.
Kratz JR, He J, Van Den Eeden SK, Zhu ZH, Gao W, Pham PT, et al. A practical molecular assay to predict survival in resected non-squamous, non-small-cell lung cancer: development and international validation studies. Lancet. 2012;379(9818):823–32.CrossRef
15.
Wistuba II, Behrens C, Lombardi F, Wagner S, Fujimoto J, Raso MG, et al. Validation of a proliferation-based expression signature as prognostic marker in early stage lung adenocarcinoma. Clin Cancer Res. 2013;19(22):6261–71.CrossRef
16.
Chen HY, Yu SL, Chen CH, Chang GC, Chen CY, Yuan A, et al. A five-gene signature and clinical outcome in non-small-cell lung cancer. N Engl J Med. 2007;356(1):11–20.CrossRef
17.
Langfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinform. 2008;9(1):559.CrossRef
18.
Horvath S, Zhang B, Carlson M, Lu K, Zhu S, Felciano R, et al. Analysis of oncogenic signaling networks in glioblastoma identifies ASPM as a molecular target. Proc Natl Acad Sci. 2006;103(46):17402–7.CrossRef
19.
Horvath S, Dong J. Geometric interpretation of gene coexpression network analysis. PLoS Comput Biol. 2008;4(8):e1000117.CrossRef
20.
Smyth GK. Limma: linear models for microarray data. Bioinformatics and computational biology solutions using R and bioconductor. Berlin: Springer; 2005. p. 397–420.CrossRef
21.
Cox DR. Regression models and life-tables. J Roy Stat Soc Ser B. 1972;34(2):187–202.
22.
Tibshirani R. The lasso method for variable selection in the Cox model. Stat Med. 1997;16(4):385–95.CrossRef
23.
Harrell FE. Ordinal logistic regression. Regression modeling strategies. Berlin: Springer; 2015. p. 311–25.
24.
Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, et al. Gene Set Enrichment Analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci. 2005;102(43):15545–50.CrossRef
25.
Kuroda H, Sakao Y, Mun M, Uehara H, Nakao M, Matsuura Y, et al. Lymph node metastases and prognosis in left upper division non-small cell lung cancers: the impact of interlobar lymph node metastasis. PLoS ONE. 2015;10(8):e0134674.CrossRef
26.
Huang X, Li X, Xie X, Ye F, Chen B, Song C, et al. High expressions of LDHA and AMPK as prognostic biomarkers for breast cancer. Breast. 2016;30:39–46.CrossRef
27.
Jin L, Chun J, Pan C, Alesi GN, Li D, Magliocca KR, et al. Phosphorylation-mediated activation of LDHA promotes cancer cell invasion and tumour metastasis. Oncogene. 2017;36(27):3797–806.CrossRef
28.
Shi M, Cui J, Du J, Wei D, Jia Z, Zhang J, et al. A novel KLF4/LDHA signaling pathway regulates aerobic glycolysis in and progression of pancreatic cancer. Clin Cancer Res. 2014;20(16):4370–80.CrossRef
29.
He Y, Chen X, Yu Y, Li J, Hu Q, Xue C, et al. LDHA is a direct target of miR-30d-5p and contributes to aggressive progression of gallbladder carcinoma. Mol Carcinog. 2018;57(6):772–83.CrossRef
30.
Zhang H, Li L, Chen Q, Li M, Feng J, Sun Y, et al. PGC 1β regulates multiple myeloma tumor growth through LDHA-mediated glycolytic metabolism. Mol Oncol. 2018;12(9):1579–95.CrossRef
31.
Ooms LM, Binge LC, Davies EM, Rahman P, Conway JR, Gurung R, et al. The inositol polyphosphate 5-phosphatase PIPP regulates AKT1-dependent breast cancer growth and metastasis. Cancer Cell. 2015;28(2):155–69.CrossRef
Metadata
Title
A novel 4-gene signature for overall survival prediction in lung adenocarcinoma patients with lymph node metastasis
Publication date
01-12-2019
Keyword
Metastasis
Published in
Cancer Cell International / Issue 1/2019
Electronic ISSN: 1475-2867
DOI
https://doi.org/10.1186/s12935-019-0822-1

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