Lung cancer is a common cancer, and expression profiling can provide an accurate indication to advance the medical intervention. However, this requires the availability of stably expressed genes as reference. Recent studies had shown that genes that are stably expressed in a tissue may not be stably expressed in other tissues suggesting the need to identify stably expressed genes in each tissue for use as reference genes. DNA microarray analysis has been used to identify those reference genes with low fluctuation. Fourteen datasets with different lung conditions were employed in our study. Coefficient of variance, followed by NormFinder, was used to identify stably expressed genes. Our results showed that classical reference genes such as GAPDH and HPRT1 were highly variable; thus, they are unsuitable as reference genes. Signal peptidase complex subunit 1 (SPCS1) and hydroxyacyl-CoA dehydrogenase beta subunit (HADHB), which are involved in fundamental biochemical processes, demonstrated high expression stability suggesting their suitability in human lung cell profiling. 1. Introduction According to American Cancer Society, lung cancer is estimated to account for 27.6% of all cancer-related deaths in America in 2010. In a report surveying cancer occurrence in Singapore from 1968 to 2007 published by National Cancer Centre Singapore, lung cancer is rated as the second and third highest occurrence of cancer in Singaporean men and women, respectively, (http://www.nccs.com.sg/pat/file/Report_1968_2007.pdf). Although lung cancer is preventable at early stage, it is usually diagnosed at advanced stage of disease, which is usually too late for current medical intervention and subsequently causes mortality [1]. Therefore, there is a need to profile gene expressions of lung epithelial cells to advance current treatment modalities. Quantification of gene expressions allows for the analysis of different genes threshold regulation [2]. Profiling of gene expression by the mean of quantitative real-time polymerase chain reaction (qRT-PCR), Northern blot, and DNA microarray analysis [3] allow the study of tumors-related biomarkers regulation and the prognosis of disease stage [4] for lung cancer patient [5]. However, a number of variables, such as selected cell types, mRNA extraction and handling techniques, and analytical quantification approaches [6] may result in different gene expression measurements and affect analysis accuracy [2]. In order to address these variations, normalization that usually involves a group of calibrating genes is employed in many gene
References
[1]
P. A. Nguewa, J. Agorreta, D. Blanco et al., “Identification of Importin 8 (IPO8) as the most accurate reference gene for the clinicopathological analysis of lung specimens,” BMC Molecular Biology, vol. 9, p. 103, 2008.
[2]
L. J. Maccoux, D. N. Clements, F. Salway, and P. J. R. Day, “Identification of new reference genes for the normalisation of canine osteoarthritic joint tissue transcripts from microarray data,” BMC Molecular Biology, vol. 8, p. 62, 2007.
[3]
S. Chey, C. Claus, and U. G. Liebert, “Validation and application of normalization factors for gene expression studies in rubella virus-infected cell lines with quantitative real-time PCR,” Journal of Cellular Biochemistry, vol. 110, no. 1, pp. 118–128, 2010.
[4]
J. A. Ludwig and J. N. Weinstein, “Biomarkers in cancer staging, prognosis and treatment selection,” Nature Reviews Cancer, vol. 5, no. 11, pp. 845–856, 2005.
[5]
D. G. Beer, S. L. R. Kardia, C. C. Huang et al., “Gene-expression profiles predict survival of patients with lung adenocarcinoma,” Nature Medicine, vol. 8, no. 8, pp. 816–824, 2002.
[6]
S. A. Bustin and T. Nolan, “Pitfalls of quantitative real-time reverse-transcription polymerase chain reaction,” Journal of Biomolecular Techniques, vol. 15, no. 3, pp. 155–166, 2004.
[7]
E. M. Airoldi, C. Huttenhower, D. Gresham et al., “Predicting cellular growth from gene expression signatures,” PLoS Computational Biology, vol. 5, no. 1, Article ID e1000257, 2009.
[8]
C. Gubern, O. Hurtado, R. Rodríguez et al., “Validation of housekeeping genes for quantitative real-time PCR in in-vivo and in-vitro models of cerebral ischaemia,” BMC Molecular Biology, vol. 10, p. 57, 2009.
[9]
J. Vandesompele, K. de Preter, F. Pattyn et al., “Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes,” Genome Biology, vol. 3, no. 7, pp. research0034.1–research0034.11, 2002.
[10]
M. W. Pfaffl, A. Tichopad, C. Prgomet, and T. P. Neuvians, “Determination of stable housekeeping genes, differentially regulated target genes and sample integrity: BestKeeper—Excel-based tool using pair-wise correlations,” Biotechnology Letters, vol. 26, no. 6, pp. 509–515, 2004.
[11]
C. L. Andersen, J. L. Jensen, and T. F. ？rntoft, “Normalization of real-time quantitative reverse transcription-PCR data: a model-based variance estimation approach to identify genes suited for normalization, applied to bladder and colon cancer data sets,” Cancer Research, vol. 64, no. 15, pp. 5245–5250, 2004.
[12]
J. Q. He, A. J. Sandford, I. M. Wang et al., “Selection of housekeeping genes for real-time PCR in atopic human bronchial epithelial cells,” European Respiratory Journal, vol. 32, no. 3, pp. 755–762, 2008.
[13]
S. Ren, F. Zhang, C. Li et al., “Selection of housekeeping genes for use in quantitative reverse transcription PCR assays on the murine cornea,” Molecular Vision, vol. 16, pp. 1076–1086, 2010.
[14]
C. R. Galiveti, T. S. Rozhdestvensky, J. Brosius, H. Lehrach, and Z. Konthur, “Application of housekeeping npcRNAs for quantitative expression analysis of human transcriptome by real-time PCR,” RNA, vol. 16, no. 2, pp. 450–461, 2010.
[15]
R. K. De and A. Ghosh, “Interval based fuzzy systems for identification of important genes from microarray gene expression data: application to carcinogenic development,” Journal of Biomedical Informatics, vol. 42, no. 6, pp. 1022–1028, 2009.
[16]
N. C. Noriega, S. G. Kohama, and H. F. Urbanski, “Microarray analysis of relative gene expression stability for selection of internal reference genes in the rhesus macaque brain,” BMC Molecular Biology, vol. 11, p. 47, 2010.
[17]
C. Y. Chia, C. W. X. Lim, W. T. Leong, and M. H. T. Ling, “High expression stability of microtubule affinity regulating kinase 3 (MARK3) makes it a reliable reference gene,” IUBMB Life, vol. 62, no. 3, pp. 200–203, 2010.
[18]
A. B. Gjuvsland, E. Plahte, and S. W. Omholt, “Threshold-dominated regulation hides genetic variation in gene expression networks,” BMC Systems Biology, vol. 1, p. 57, 2007.
[19]
D. W. Liu, S. T. Chen, and H. P. Liu, “Choice of endogenous control for gene expression in nonsmall cell lung cancer,” European Respiratory Journal, vol. 26, no. 6, pp. 1002–1008, 2005.
[20]
S. Saviozzi, F. Cordero, M. Lo Iacono, S. Novello, G. V. Scagliotti, and A. R. Calogero, “Selection of suitable reference genes for accurate normalization of gene expression profile studies in non-small cell lung cancer,” BMC Cancer, vol. 6, p. 200, 2006.
[21]
L. Benjamin, A. Evrard, C. Combescure et al., “Reference gene selection for head and neck squamous cell carcinoma gene expression studies,” BMC Molecular Biology, vol. 10, p. 78, 2009.
[22]
M. Meldgaard, C. Fenger, K. L. Lambertsen, M. D. Pedersen, R. Ladeby, and B. Finsen, “Validation of two reference genes for mRNA level studies of murine disease models in neurobiology,” Journal of Neuroscience Methods, vol. 156, no. 1-2, pp. 101–110, 2006.
[23]
B. Gur-Dedeoglu, O. Konu, B. Bozkurt, G. Ergul, S. Seckin, and I. G. Yulug, “Identification of endogenous reference genes for qRT-PCR analysis in normal matched breast tumor tissues,” Oncology Research, vol. 17, no. 8, pp. 353–365, 2009.
[24]
R. Kosir, J. Acimovic, M. Golicnik et al., “Determination of reference genes for circadian studies in different tissues and mouse strains,” BMC Molecular Biology, vol. 11, p. 60, 2010.
[25]
Y. Shen, Y. Li, F. Ye, F. Wang, W. Lu, and X. Xie, “Identification of suitable reference genes for measurement of gene expression in human cervical tissues,” Analytical Biochemistry, vol. 405, no. 2, pp. 224–229, 2010.
[26]
H. W. Rho, B. C. Lee, E. S. Choi, I. J. Choi, Y. S. Lee, and S. H. Goh, “Identification of valid reference genes for gene expression studies of human stomach cancer by reverse transcription-qPCR,” BMC Cancer, vol. 10, p. 240, 2010.
[27]
T. Brattelid, L. H. Winer, F. O. Levy, K. Liest？l, O. M. Sejersted, and K. B. Andersson, “Reference gene alternatives to Gapdh in rodent and human heart failure gene expression studies,” BMC Molecular Biology, vol. 11, p. 22, 2010.
[28]
Y. L. Li, F. Ye, Y. Hu, W. G. Lu, and X. Xie, “Identification of suitable reference genes for gene expression studies of human serous ovarian cancer by real-time polymerase chain reaction,” Analytical Biochemistry, vol. 394, no. 1, pp. 110–116, 2009.
[29]
C. Infante, M. P. Matsuoka, E. Asensio, J. P. Ca？avate, M. Reith, and M. Manchado, “Selection of housekeeping genes for gene expression studies in larvae from flatfish using real-time PCR,” BMC Molecular Biology, vol. 9, p. 28, 2008.
[30]
C. Cheadle, M. P. Vawter, W. J. Freed, and K. G. Becker, “Analysis of microarray data using Z score transformation,” Journal of Molecular Diagnostics, vol. 5, no. 2, pp. 73–81, 2003.
[31]
R. D. Barber, D. W. Harmer, R. A. Coleman, and B. J. Clark, “GAPDH as a housekeeping gene: analysis of GAPDH mRNA expression in a panel of 72 human tissues,” Physiological Genomics, vol. 21, no. 3, pp. 389–395, 2005.
[32]
D. Seol, H. Choe, H. Zheng et al., “Selection of reference genes for normalization of quantitative real-time PCR in organ culture of the rat and rabbit intervertebral disc,” BMC Research Notes, vol. 4, no. 1, p. 162, 2011.
[33]
M. A. Sirover, “New insights into an old protein: the functional diversity of mammalian glyceraldehyde-3-phosphate dehydrogenase,” Biochimica et Biophysica Acta, vol. 1432, no. 2, pp. 159–184, 1999.
[34]
W. Shi, C. M. Li, P. C. Tyler et al., “The 2.0 ？ structure of human hypoxanthine-guanine phosphoribosyltransferase in complex with a transition-state analog inhibitor,” Nature Structural Biology, vol. 6, no. 6, pp. 588–593, 1999.
[35]
D. T. Keough, D. Hocková, A. Holy et al., “Inhibition of hypoxanthine-guanine phosphoribosyltransferase by acyclic nucleoside phosphonates: a new class of antimalarial therapeutics,” Journal of Medicinal Chemistry, vol. 52, no. 14, pp. 4391–4399, 2009.
[36]
R. M. Ewing, P. Chu, F. Elisma et al., “Large-scale mapping of human protein-protein interactions by mass spectrometry,” Molecular Systems Biology, vol. 3, p. 89, 2007.
[37]
F. Ohl, M. Jung, C. Xu et al., “Gene expression studies in prostate cancer tissue: which reference gene should be selected for normalization?” Journal of Molecular Medicine, vol. 83, no. 12, pp. 1014–1024, 2005.
[38]
K. U. Kalies and E. Hartmann, “Membrane topology of the 12- and the 25-kDa subunits of the mammalian signal peptidase complex,” Journal of Biological Chemistry, vol. 271, no. 7, pp. 3925–3929, 1996.
[39]
E. Swanton and N. J. Bulleid, “Protein folding and translocation across the endoplasmic reticulum membrane,” Molecular Membrane Biology, vol. 20, no. 2, pp. 99–104, 2003.
[40]
T. Aoyama, K. Wakui, K. E. Orii, T. Hashimoto, and Y. Fukushima, “Fluorescence in situ hybridization mapping of the α and β subunits (HADHA and HADHB) of human mitochondrial fatty acid β-oxidation multienzyme complex to 2p23 and their evolution,” Cytogenetics and Cell Genetics, vol. 79, no. 3-4, pp. 221–224, 1997.
[41]
Z. Fu, N. M. Attar-Bashi, and A. J. Sinclair, “1-14C-linoleic acid distribution in various tissue lipids of guinea pigs following an oral dose,” Lipids, vol. 36, no. 3, pp. 255–260, 2001.
[42]
K. Dheda, J. F. Huggett, S. A. Bustin, M. A. Johnson, G. Rook, and A. Zumla, “Validation of housekeeping genes for normalizing RNA expression in real-time PCR,” BioTechniques, vol. 37, no. 1, pp. 112–114, 116, 118–119, 2004.
