A signature based on relative gene expression orderings for lung cancer diagnosis_Journal of Biomedical Engineering

Authors：

CHEN Yanhua , ZHENG Baotong , LIN Yunqing , ZHU Huimin , ZHENG Zhijun , GUAN Qingzhou , GUO Zheng ,  YAN Haidan

Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Department of Bioinformatics, Fujian Medical University, Fuzhou 350108, P.R.China;

Corresponding author：

YAN Haidan, Email: Joyan168@126.com

Keywords：

signature; classifier; lung cancer; data normalization; batch effect

DOI：

10.7507/1001-5515.201608002

Video：

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Abstract Full text Figures/Tables Video References Cited by

Traditional classifiers, such as support vector machine and Bayesian classifier, require data normalization for removing experimental batch effects, which limit their applications at the individual level. In this paper, we aim to build a classifier to distinguish lung cancer and non-cancer lung tissues (pneumonia and normal lung tissues). We identified gene pairs as signatures to build a classifier based on the within-sample relative expression orderings of gene pairs in a particular type of tissues (cancer or non-cancer). Using multiple independent datasets as the training data, including a total of 197 lung cancer cases and 189 non-cancer cases, we identified three gene pairs. Classifying a sample by the majority voting rule, the average accuracy reached 95.34% in the training data. Using multiple independent validation datasets, including a total of 251 lung cancer samples and 141 non-cancer samples without data normalization, the average accuracy was as high as 96.78%. The rank-based signature is robust against experimental batch effects and can be used to diagnose lung cancer using samples measured by different laboratories at the individual level.

Citation： CHENYanhua, ZHENGBaotong, LINYunqing, ZHUHuimin, ZHENGZhijun, GUANQingzhou, GUOZheng, YANHaidan. A signature based on relative gene expression orderings for lung cancer diagnosis. Journal of Biomedical Engineering, 2017, 34(1): 129-133. doi: 10.7507/1001-5515.201608002 Copy

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16.	Geman D, d'avignon C, Naiman D, et al. Classifying gene expression profiles from pairwise mRNA comparisons. Stat Appl Genet Mol Biol, 2004, 3: Article19.
17.	Afsari B, Fertig E, Geman D, et al. switchBox: an R package for k-Top scoring pairs classifier development. Bioinformatics, 2015, 31(2): 273-274.
18.	Eddy J, Sung J, Geman D, et al. Relative expression analysis for molecular cancer diagnosis and prognosis. Technol Cancer Res Treat, 2010, 9(2): 149-159.
19.	Kim S, Lin C, Tseng G. MetaKTSP: a meta-analytic top scoring pair method for robust cross-study validation of omics prediction analysis. Bioinformatics, 2016, 32(13): 1966-1973.
20.	Lin X, Gao J, Zhou L, et al. A modified k-TSP algorithm and its application in LC-MS-based metabolomics study of hepatocellular carcinoma and chronic liver diseases. J Chromatogr B Analyt Technol Biomed Life Sci, 2014, 966: 100-108.
21.	Nixon A, Pang H, Starr M, et al. Prognostic and predictive blood-based biomarkers in patients with advanced pancreatic cancer: results from CALGB80303(Alliance) Clin Cancer Res, 2013, 19(24): 6957-6966.
22.	Irizarry R A, Hobbs B, Collin F, et al. Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics, 2003, 4(2): 249-264.
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25.	Hu J, Xu J. Density based pruning for identification of differentially expressed genes from microarray data. BMC Genomics, 2010, 11(Suppl 2): S3.

1. Gabere M, Hussein M, Aziz M. Filtered selection coupled with support vector machines generate a functionally relevant prediction model for colorectal cancer. Onco Targets Ther, 2016, 9: 3313-3325.
2. García-Bilbao A, Armañanzas R, Ispizua Z, et al. Identification of a biomarker panel for colorectal cancer diagnosis. BMC Cancer, 2012, 12: 43.
3. Yang Z, Zhuan B, Yan Y, et al. Identification of gene markers in the development of smoking-induced lung cancer. Gene, 2016, 576(1 Pt 3): 451-457.
4. Podolsky M, Barchuk A, Kuznetcov V, et al. Evaluation of machine learning algorithm utilization for lung cancer classification based on gene expression levels. Asian Pac J Cancer Prev, 2016, 17(2): 835-838.
5. Panebianco F, Mazzanti C, Tomei S, et al. The combination of four molecular markers improves thyroid cancer cytologic diagnosis and patient management. BMC Cancer, 2015, 15: 918.
6. Motawi T, Sadik N, Shaker O, et al. Study of microRNAs-21/221 as potential breast cancer biomarkers in Egyptian women. Gene, 2016, 590(2): 210-219.
7. Johnson W, Li C, Rabinovic A. Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics, 2007, 8(1): 118-127.
8. Leek J, Scharpf R, Bravo H, et al. Tackling the widespread and critical impact of batch effects in high-throughput data. Nat Rev Genet, 2010, 11(10): 733-739.
9. Benito M, Parker J, Du Q, et al. Adjustment of systematic microarray data biases. Bioinformatics, 2004, 20(1): 105-114.
10. Lau S, Boutros P, Pintilie M, et al. Three-gene prognostic classifier for early-stage non small-cell lung cancer. J Clin Oncol, 2007, 25(35): 5562-5569.
11. Shabalin A, Tjelmeland H, Fan C, et al. Merging two gene-expression studies via cross-platform normalization. Bioinfor-matics, 2008, 24(9): 1154-1160.
12. Wang D, Cheng L, Zhang Y, et al. Extensive up-regulation of gene expression in cancer: the normalised use of microarray data. Mol Biosyst, 2012, 8(3): 818-827.
13. Tomlins S A, Rhodes D R, Perner S, et al. Recurrent fusion of TMPRSS2 and ETS transcription factor genes in prostate cancer. Science, 2005, 310(5748): 644-648.
14. Zhou X, Shi T, Li B, et al. Genes dysregulated to different extent or oppositely in estrogen receptor-positive and estrogen receptor-negative breast cancers. PLoS One, 2013, 8(7): e70017.
15. Wang H, Sun Q, Zhao W, et al. Individual-level analysis of differential expression of genes and pathways for personalized medicine. Bioinformatics, 2015, 31(1): 62-68.
16. Geman D, d'avignon C, Naiman D, et al. Classifying gene expression profiles from pairwise mRNA comparisons. Stat Appl Genet Mol Biol, 2004, 3: Article19.
17. Afsari B, Fertig E, Geman D, et al. switchBox: an R package for k-Top scoring pairs classifier development. Bioinformatics, 2015, 31(2): 273-274.
18. Eddy J, Sung J, Geman D, et al. Relative expression analysis for molecular cancer diagnosis and prognosis. Technol Cancer Res Treat, 2010, 9(2): 149-159.
19. Kim S, Lin C, Tseng G. MetaKTSP: a meta-analytic top scoring pair method for robust cross-study validation of omics prediction analysis. Bioinformatics, 2016, 32(13): 1966-1973.
20. Lin X, Gao J, Zhou L, et al. A modified k-TSP algorithm and its application in LC-MS-based metabolomics study of hepatocellular carcinoma and chronic liver diseases. J Chromatogr B Analyt Technol Biomed Life Sci, 2014, 966: 100-108.
21. Nixon A, Pang H, Starr M, et al. Prognostic and predictive blood-based biomarkers in patients with advanced pancreatic cancer: results from CALGB80303(Alliance) Clin Cancer Res, 2013, 19(24): 6957-6966.
22. Irizarry R A, Hobbs B, Collin F, et al. Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics, 2003, 4(2): 249-264.
23. dey Sarkar S, Goswami S, Agarwal A, et al. A novel feature selection technique for text classification using naïve bayes. International scholarly research notices, 2014: 717092.
24. Li J, Wang Y, Wang L, et al. MatPred: computational identification of mature MicroRNAs within novel Pre-MicroRNAs. Biomed Res Int, 2015: 546763.
25. Hu J, Xu J. Density based pruning for identification of differentially expressed genes from microarray data. BMC Genomics, 2010, 11(Suppl 2): S3.

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