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• W1983083208 abstract "Gene expression profiling could assist in revealing biomarkers of lung cancer prognosis and progression. The handling of biological samples may strongly influence global gene expression, a fact that has not been addressed in many studies. We sought to investigate the changes in gene expression that may occur as a result of sample processing time and conditions. Using Illumina Human WG-6 arrays, we quantified gene expression in lung carcinoma samples from six patients obtained at chest opening before and immediately after lung resection with storage in RNAlater [T1a(CO) and T1b(LR)], after receipt of the sample for histopathology, placed in RNAlater [T2a(HP)]; snap frozen [T2b(HP.SF)]; or snap frozen and stored for 1 week [T2c(HP.SFA)], as well as formalin-fixed, paraffin-embedded (FFPE) block samples. Sampling immediately after resection closely represented the tissue obtained in situ, with only 1% of genes differing more than twofold [T1a(CO) versus T1b(LR)]. Delaying tissue harvest for an average of 30 minutes from the operating theater had a significant impact on gene expression, with approximately 25% of genes differing between T1a(CO) and T2a(HP). Many genes previously identified as lung cancer biomarkers were altered during this period. Examination of FFPE specimens showed minimal correlation with fresh samples. This study shows that tissue collection immediately after lung resection with conservation in RNAlater is an optimal strategy for gene expression profiling. Gene expression profiling could assist in revealing biomarkers of lung cancer prognosis and progression. The handling of biological samples may strongly influence global gene expression, a fact that has not been addressed in many studies. We sought to investigate the changes in gene expression that may occur as a result of sample processing time and conditions. Using Illumina Human WG-6 arrays, we quantified gene expression in lung carcinoma samples from six patients obtained at chest opening before and immediately after lung resection with storage in RNAlater [T1a(CO) and T1b(LR)], after receipt of the sample for histopathology, placed in RNAlater [T2a(HP)]; snap frozen [T2b(HP.SF)]; or snap frozen and stored for 1 week [T2c(HP.SFA)], as well as formalin-fixed, paraffin-embedded (FFPE) block samples. Sampling immediately after resection closely represented the tissue obtained in situ, with only 1% of genes differing more than twofold [T1a(CO) versus T1b(LR)]. Delaying tissue harvest for an average of 30 minutes from the operating theater had a significant impact on gene expression, with approximately 25% of genes differing between T1a(CO) and T2a(HP). Many genes previously identified as lung cancer biomarkers were altered during this period. Examination of FFPE specimens showed minimal correlation with fresh samples. This study shows that tissue collection immediately after lung resection with conservation in RNAlater is an optimal strategy for gene expression profiling. Lung cancer (LC) is the leading cause of cancer death worldwide1Parkin D.M. Bray F. Ferlay J. Pisani P. Global cancer statistics, 2002.CA Cancer J Clin. 2005; 55: 74-108Crossref PubMed Scopus (17151) Google Scholar with non-small cell LC accounting for approximately 87% of newly diagnosed cases.2Fong T. Morgensztern D. Govindan R. EGFR inhibitors as first-line therapy in advanced non-small cell lung cancer.J Thorac Oncol. 2008; 3: 303-310Crossref PubMed Scopus (34) Google Scholar LC is the most common cancer in the UK and it is predicted that it will remain so for at least the next 20 years. Measurement of global gene expression is a powerful means of establishing the transcriptional activity of particular cells or tissues. Gene expression profiling can allow the identification of subgroups of cancer allowing early prediction of disease progression and survival.3Bhattacharjee A. Richards W.G. Staunton J. Li C. Monti S. Vasa P. Ladd C. Beheshti J. Bueno R. Gillette M. Loda M. Weber G. Mark E.J. Lander E.S. Wong W. Johnson B.E. Golub T.R. Sugarbaker D.J. Meyerson M. Classification of human lung carcinomas by mRNA expression profiling reveals distinct adenocarcinoma subclasses.Proc Natl Acad Sci USA. 2001; 98: 13790-13795Crossref PubMed Scopus (2099) Google Scholar, 4Beer D.G. Kardia S.L. Huang C.C. Giordano T.J. Levin A.M. Misek D.E. Lin L. Chen G. Gharib T.G. Thomas D.G. Lizyness M.L. Kuick R. Hayasaka S. Taylor J.M. Iannettoni M.D. Orringer M.B. Hanash S. Gene-expression profiles predict survival of patients with lung adenocarcinoma.Nat Med. 2002; 8: 816-824Crossref PubMed Scopus (1624) Google Scholar, 5Raponi M. Zhang Y. Yu J. Chen G. Lee G. Taylor J.M. Macdonald J. Thomas D. Moskaluk C. Wang Y. Beer D.G. Gene expression signatures for predicting prognosis of squamous cell and adenocarcinomas of the lung.Cancer Res. 2006; 66: 7466-7472Crossref PubMed Scopus (334) Google Scholar, 6Lonergan K.M. Chari R. Coe B.P. Wilson I.M. Tsao M.S. Ng R.T. Macaulay C. Lam S. Lam W.L. Transcriptome profiles of carcinoma-in-situ and invasive non-small cell lung cancer as revealed by SAGE.PLoS One. 2010; 5: e9162Crossref PubMed Scopus (20) Google Scholar Global gene expression studies in LC have had conflicting results and many signature-based outcome predictions have not been replicated independently (as reviewed7Petty R.D. Nicolson M.C. Kerr K.M. Collie-Duguid E. Murray G.I. Gene expression profiling in non-small cell lung cancer: from molecular mechanisms to clinical application.Clin Cancer Res. 2004; 10: 3237-3248Crossref PubMed Scopus (121) Google Scholar, 8Lacroix L. Commo F. Soria J.C. Gene expression profiling of non-small-cell lung cancer.Expert Rev Mol Diagn. 2008; 8: 167-178Crossref PubMed Scopus (27) Google Scholar). Multiple confounding factors have hampered these studies including the innate heterogeneity of cancerous tissue. Studies are often conducted with limited numbers of samples making it difficult to derive statistically stringent results from the measurement of thousands of transcripts. The quality of the biological sample used and sample handling are key factors influencing global gene expression studies. In particular RNA in ex vivo tissues degrades rapidly with the potential to influence expression patterns and bias the interpretation of results. The majority of published gene expression studies for LC have used tumor tissues that have been snap frozen or formalin-fixed paraffin embedded (FFPE), with many of the studies relying on archival or biobanked tissues.3Bhattacharjee A. Richards W.G. Staunton J. Li C. Monti S. Vasa P. Ladd C. Beheshti J. Bueno R. Gillette M. Loda M. Weber G. Mark E.J. Lander E.S. Wong W. Johnson B.E. Golub T.R. Sugarbaker D.J. Meyerson M. Classification of human lung carcinomas by mRNA expression profiling reveals distinct adenocarcinoma subclasses.Proc Natl Acad Sci USA. 2001; 98: 13790-13795Crossref PubMed Scopus (2099) Google Scholar, 4Beer D.G. Kardia S.L. Huang C.C. Giordano T.J. Levin A.M. Misek D.E. Lin L. Chen G. Gharib T.G. Thomas D.G. Lizyness M.L. Kuick R. Hayasaka S. Taylor J.M. Iannettoni M.D. Orringer M.B. Hanash S. Gene-expression profiles predict survival of patients with lung adenocarcinoma.Nat Med. 2002; 8: 816-824Crossref PubMed Scopus (1624) Google Scholar, 5Raponi M. Zhang Y. Yu J. Chen G. Lee G. Taylor J.M. Macdonald J. Thomas D. Moskaluk C. Wang Y. Beer D.G. Gene expression signatures for predicting prognosis of squamous cell and adenocarcinomas of the lung.Cancer Res. 2006; 66: 7466-7472Crossref PubMed Scopus (334) Google Scholar, 9Frank M. Döring C. Metzler D. Eckerle S. Hansmann M.L. Global gene expression profiling of formalin-fixed paraffin-embedded tumor samples: a comparison to snap-frozen material using oligonucleotide microarrays.Virchows Arch. 2007; 450: 699-711Crossref PubMed Scopus (43) Google Scholar, 10Tanney A. Oliver G.R. Farztdinov V. Kennedy R.D. Mulligan J.M. Fulton C.E. Farragher S.M. Field J.K. Johnston P.G. Harkin D.P. Proutski V. Mulligan K.A. Generation of a non-small cell lung cancer transcriptome microarray.BMC Med Genomics. 2008; 1: 20Crossref PubMed Google Scholar, 11Zhang X. Chen J. Radcliffe T. Lebrun D.P. Tron V.A. Feilotter H. An array-based analysis of microRNA expression comparing matched frozen and formalin-fixed paraffin-embedded human tissue samples.J Mol Diagn. 2008; 10: 513-519Abstract Full Text Full Text PDF PubMed Scopus (106) Google Scholar, 12Fedorowicz G. Guerrero S. Wu T.D. Modrusan Z. Microarray analysis of RNA extracted from formalin-fixed, paraffin-embedded and matched fresh-frozen ovarian adenocarcinomas.BMC Med Genomics. 2009; 2: 23Crossref PubMed Scopus (53) Google Scholar It is uncertain how this material represents biological features in vivo. To address the impact of timing of tissue sampling, processing, and storage, we have conducted a differential gene expression analysis in lung tumor tissue of patients with LC at different time points of specimen collection under various conservation conditions starting from in vivo state represented by viable tissue and ending at archival FFPE tissue. Lung carcinoma tissue samples were obtained from six patients during tumor resection surgery at the Royal Brompton Hospital in London. Demographic and clinical characteristics of the patients are detailed in Table 1. All participants gave written informed consent for research on biobanked tissue and the biobank consent was approved by the Royal Brompton and Harefield Ethics Committee (REC reference number LREC 02-261).Table 1Demographic Characteristics of Study PatientsPatient IDSexAge at surgery (years)Histological diagnosisTumor stage03Male57Squamous cell carcinomaT2N0M005Female83AdenocarcinomaT1N0M006Male62AdenocarcinomaT1N0M007Male70Broncho-alveolar cell carcinomaT2N0M008Female80Large cell undifferentiatedT2N0M009Male57Pleomorphic carcinomaT2N1M0 Open table in a new tab Figure 1 illustrates the pipeline for the tissue collection. When possible for each patient tissue samples were obtained at five collection points: viable tissue at the time of chest opening [T1a(CO)]; immediately after resection [T1b(LR)]; after transfer to histopathology [T2a(HP)]; after transfer to histopathology and snap freezing [T2b(HP.SF)]; and after transfer to histopathology and 1 week after being snap frozen and archived [T2c(HP.SFA)]. FFPE samples of each patient's tumor was obtained from histopathology after tissue had been fixed in 10% formol saline for 24 hours before being embedded in paraffin wax according to standard procedure. The FFPE blocks have been kept at room temperature for 25 to 27 months before RNA extraction for the current study. The size of tissue samples taken for the experiment was approximately 6 × 6 × 3 mm. For the FFPE samples, five or six 10-micron sections were obtained. After sampling the T1a(CO), T1b(LR), and T2a(HP) tissues were immediately placed into RNAlater (Qiagen, Germantown, MD) for 24 hours at 4°C and then frozen at −80°C until RNA extraction. No RNAlater was used for the T2b(HP.SF) and T2c(HP.SFA) samples as these were snap frozen. The average interval of samples collection after T1a(CO) was 1.9 ± 0.6 hours for T1b(LR), and 2.4 ± 0.5 hours for T2a(HP), T2b(HP.SF), and T2c(HP.SFA). For patients 08 and 09, T2a(HP), T2b(HP.SF), and T2c(HP.SFA) time point specimens were unavailable. Total RNA was extracted using the RNeasy Fibrous Tissue Mini Kit (Qiagen) for the majority of time points, with the exception of the FFPE samples processed using the RecoverAll Total Nucleic Acid Isolation Kit for FFPE (Ambion, Foster City, CA). The frozen T1 and T2 specimens were placed into the lysis buffer at room temperature and immediately homogenized using the TissueRuptor (handheld rotor-stator homogenizer) with disposable probes (Qiagen) as per the manufacturer's recommendations. After homogenization the further steps for RNA extraction were performed following the manufacturer's protocol. Using sterile scalpel blades FFPE sections were cut up into smaller fragments before RNA extraction according to the manufacturer's protocol. Yield and purity of total RNA obtained was assessed using a Nanodrop ND-1000 spectrophotometer (NanoDrop, Thermo Scientific, Wilmington, DE) with RNA integrity determined by RNA Integrity Number (RIN) using a Bioanalyzer 2100 (Agilent Technologies, Santa Clara, CA). Illumina human WG-6 v2 BeadChip microarrays (containing 48,804 probes corresponding to 43,186 genes derived from NCBI RefSeq [Build 36.2] and UniGene [Build 199] databases) (Illumina, Inc., San Diego, CA) were used to assess global gene expression for each sample. Five hundred nanograms of total RNA was amplified, converted to cRNA, fragmented, and then biotin-labeled using the Illumina TotalPrep RNA-amplification kit (Ambion, USA). Then 1.5 μg of labeled cRNA was hybridized to each array according to the Illumina whole-genome gene expression direct hybridization assay protocol 11286331, Rev. A., after which arrays were scanned using the Illumina BeadArray Reader. The images were processed and converted into signal intensities using the Illumina GenomeStudio software Version 2009.2 (Illumina, Inc.). The same software was used to perform hybridization quality control (QC). The expression data have been deposited in the EMBL-EBI Array Express database (http://www.ebi.ac.uk/arrayexpress) and are available through E-MTAB-581 accession number. The signal intensities corresponding to gene expression levels of individual arrays were background corrected and imported into text files using the Illumina GenomeStudio 2009.2 software. All subsequent analyses were performed in R language environment (R Foundation for Statistical Computing, Vienna, Austria) using the suite of programs within Bioconductor v.2.5.13Gentleman R.C. Carey V.J. Bates D.M. Bolstad B. Dettling M. Dudoit S. Ellis B. Gautier L. Ge Y. Gentry J. Hornik K. Hothorn T. Huber W. Iacus S. Irizarry R. Leisch F. Li C. Maechler M. Rossini A.J. Sawitzki G. Smith C. Smyth G. Tierney L. Yang J.Y. Zhang J. Bioconductor: open software development for computational biology and bioinformatics R.Genome Biol. 2004; 5: R80Crossref PubMed Google Scholar Text files containing gene expression values were imported into R using the lumi package.14Du P. Kibbe W.A. Lin S.M. Lumi: a pipeline for processing Illumina microarray.Bioinformatics. 2008; 24: 1547-1548Crossref PubMed Scopus (1587) Google Scholar Variance stabilizing normalization was applied to reduce between-arrays variation. The substantial differences in gene expression between FFPE and other time point samples required that normalization for FFPE was done separately. Post normalization, genes with low detection rates (P > 0.01) were removed. Two datasets were generated in anticipation of absence of gene expression signal in FFPE samples due to the high level of RNA degradation recognized to occur in this type of sample. The first comprised samples from all time points except FFPE and included 18,597 genes, each found to be expressed significantly above background in at least one of these samples. The second dataset comprised samples from all time points and included 4555 genes, each found to be expressed significantly above background in at least one FFPE sample only. Paired comparisons were performed to assess differentially expressed genes between all data points. Using the limma package robust regression was applied and individual t-statistics were calculated for each gene and each comparison followed by empirical Bayesian method application to moderate the standard deviations between genes.15Smyth G.K. Limma: linear models for microarray data.in: Bioinformatics and Computational Biology Solutions using R and Bioconductor. Springer, New York2005: 397-420Google Scholar Raw P values were adjusted for multiple comparisons using the false-discovery rate approach of Benjamini and Hochberg.16Benjamini Y. Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing.J R Stat Soc B. 1995; 57: 289-300Google Scholar Hierarchical cluster analysis was applied to datasets to evaluate the “proximity” between the time points. Using the publicly available database and research tools DAVID17Dennis Jr, G. Sherman B.T. Hosack D.A. Yang J. Gao W. Lane H.C. Lempicki R.A. DAVID: database for annotation, visualization, and integrated discovery.Genome Biol. 2003; 4: P3Crossref PubMed Google Scholar, 18Huang D.W. Sherman B.T. Lempicki R.A. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources.Nature Protoc. 2009; 4: 44-57Crossref PubMed Scopus (24210) Google Scholar and Ingenuity Pathway Analysis (IPA; Ingenuity Systems, Redwood City, CA), gene ontology, and pathway analyses were performed to consider biological meaning of differential expression of genes between the data points. In network analysis, maximum 25 networks and maximum 35 molecules per network were stipulated. High quality of RNA (RIN ≥7) is the ideal to enable robust expression microarray results to be generated. RINs <7 indicate RNA degradation. RIN scores revealed that the highest quality of RNA was obtained for T1a(CO) and T1b(LR) samples: mean 7.4 (range 6.7 to 8.5) and 7.8 (range 6.7 to 8.9), respectively;. For T2a(HP) samples mean RIN was 7.7 (range 5.4 to 8.3), whereas for snap-frozen samples [T2b(HP.SF) and T2c(HP.SFA)] the median values were 4.4 (range 3.3 to 5.6) and 5.2 (range 4.8 to 5.6), respectively. These figures highlight the beneficial use of RNAlater stabilization in preventing RNA degradation. As anticipated, the lowest RINs, range 2.2 to 2.5, were obtained for FFPE samples. It is unlikely that poor RINs in T2b(HP.SF) and T2c(HP.SFA) samples resulted from snap-freezing or long-term low-temperature storage. It is more likely that RNA degradation has occurred during the thawing of the specimens for RNA extraction.19Botling J. Edlund K. Segersten U. Tahmasebpoor S. EngströM M. SundströM M. MalmströM P.U. Micke P. Impact of thawing on RNA integrity and gene expression analysis in fresh frozen tissue.Diagn Mol Pathol. 2009; 18: 44-52Crossref PubMed Scopus (69) Google Scholar Despite differences in RNA integrity, the total yield of RNA was independent of the time points of collection and ranged between 43 and 367 μg (average 198.3 μg). For FFPE samples the yields ranged between ∼5 and 17 μg (average 8.7 μg). The Illumina Human WG-6 v2 BeadChip microarray was used to analyze whole-genome gene expression in samples from the six patients. The average number of genes significantly (P < 0.01) expressed above background was similar at all data points for unfixed samples and ranged between 12,000 and 13,000 genes. For FFPE samples the expression of approximately 3000 genes was detected above background. The highest ratio of average signal to background was found for T1a(CO) and T1b(LR) samples (average 3.2 ± 0.8), in other samples it was lower (average 2.7 ± 0.9), and in FFPE it was < 1 (average 0.2 ± 0.03). Two samples were identified as outliers based on the number of significantly expressed genes and ratio of signal to background and excluded from subsequent analyses. Following variance stabilizing normalization, 18,597 genes were significantly present at least one time point in non-fixed samples and were included in subsequent analyses. Exclusion of genes that were not significantly expressed in at least one of the FFPE samples left 4555 transcripts. As time from initial chest opening at surgery progressed there was a notable and significant increase in the number of genes that were significantly differentially expressed (Table 2). Less than 5% of genes were differentially expressed between T1a(CO) and T1b(LR) time points, and only 1% of genes differed more than twofold, indicating that the T1b(LR) point is a good representation of the in vivo state. The number of differences between T1a(CO) or T1b(LR) and subsequent points was much higher (Table 2).Table 2Number and Percentage of Significant Differentially Expressed Genes Between Different Time PointsChest openingLung resectionHisto-path RNA laterHisto-path snap frozenHisto-path snap frozen and storedT1a(CO)T1b(LR)T2a(HP)T2b(HP.SF)T2c(HP.SFA)FFPET1a(CO)–914 (4.9)4508 (24.2)5363 (28.8)6164 (33.1)4172 (91.6)T1b(LR)334/580–4378 (23.5)5160 (27.7)6324 (34.0)4110 (90.2)T2a(HP)1560/29481881/2497–79 (0.4)684 (3.7)4083 (89.6)T2b(HP.SF)1411/39521623/353713/66–32 (0.2)4030 (88.5)T2c(HP.SFA)1363/48011797/4527108/57618/14–4071 (89.4)FFPE527/3645475/3635448/3635400/3630405/3666–The total number of differentially expressed genes (FDR-adjusted, P < 0.05) is given above the central diagonal, with the percentage of total genes measured given in parentheses. Percentage was calculated using a total of 18,579 genes for all time points except for formalin-fixed and paraffin embedded tissue (FFPE). For FFPE, the denominator was the 4555 genes with measurable transcripts. Beneath the diagonal the number of genes showing increased expression is shown before “/”and the number decreased in expression is shown after “/”. Subsequent time points are compared with the one immediately prior. Open table in a new tab The total number of differentially expressed genes (FDR-adjusted, P < 0.05) is given above the central diagonal, with the percentage of total genes measured given in parentheses. Percentage was calculated using a total of 18,579 genes for all time points except for formalin-fixed and paraffin embedded tissue (FFPE). For FFPE, the denominator was the 4555 genes with measurable transcripts. Beneath the diagonal the number of genes showing increased expression is shown before “/”and the number decreased in expression is shown after “/”. Subsequent time points are compared with the one immediately prior. The T2a(HP), T2b(HP.SF), and T2c(HP.SFA) points were similar in gene expression (Table 2). The low numbers of differentially expressed genes between T2a(HP) and T2b(HP.SF), and between T2b(HP.SF) and T2c(HP.SFA), suggested that snap freezing and storage at low temperature had a low impact on gene expression. At the same time, the number of genes differentially expressed between T1a(CO) or T1b(LR) and T2a(HP) was lower than it was between T1a(CO) or T1b(LR) and T2b(HP.SF), confirming the efficiency of RNAlater conservation before freezing. As expected, FFPE showed most genes to be differentially expressed when compared with the unfixed samples (Table 2), reflecting a high level of RNA degradation. Some correlation was observed between gene expression in T1a(CO) and FFPE samples (R2 0.12 to 0.45, P < 0.0001, for the 4555 transcripts detectable in FFPE). We next considered the effects of specimen handling and processing on the expression of genes previously established by others as potential markers of LC development and prognosis of outcome. We chose 145 genes that from the literature were reported to be highly relevant for clinical application as biomarkers4Beer D.G. Kardia S.L. Huang C.C. Giordano T.J. Levin A.M. Misek D.E. Lin L. Chen G. Gharib T.G. Thomas D.G. Lizyness M.L. Kuick R. Hayasaka S. Taylor J.M. Iannettoni M.D. Orringer M.B. Hanash S. Gene-expression profiles predict survival of patients with lung adenocarcinoma.Nat Med. 2002; 8: 816-824Crossref PubMed Scopus (1624) Google Scholar, 5Raponi M. Zhang Y. Yu J. Chen G. Lee G. Taylor J.M. Macdonald J. Thomas D. Moskaluk C. Wang Y. Beer D.G. Gene expression signatures for predicting prognosis of squamous cell and adenocarcinomas of the lung.Cancer Res. 2006; 66: 7466-7472Crossref PubMed Scopus (334) Google Scholar, 6Lonergan K.M. Chari R. Coe B.P. Wilson I.M. Tsao M.S. Ng R.T. Macaulay C. Lam S. Lam W.L. Transcriptome profiles of carcinoma-in-situ and invasive non-small cell lung cancer as revealed by SAGE.PLoS One. 2010; 5: e9162Crossref PubMed Scopus (20) Google Scholar (see Supplemental Table S1 at http://jmd.amjpathol.org). Of these genes, 119 (82%) were significantly expressed in the tissues of our study and 68 of them were differentially expressed between T1a(CO)/T1b(LR) and later time points (Table 3).4Beer D.G. Kardia S.L. Huang C.C. Giordano T.J. Levin A.M. Misek D.E. Lin L. Chen G. Gharib T.G. Thomas D.G. Lizyness M.L. Kuick R. Hayasaka S. Taylor J.M. Iannettoni M.D. Orringer M.B. Hanash S. Gene-expression profiles predict survival of patients with lung adenocarcinoma.Nat Med. 2002; 8: 816-824Crossref PubMed Scopus (1624) Google Scholar, 5Raponi M. Zhang Y. Yu J. Chen G. Lee G. Taylor J.M. Macdonald J. Thomas D. Moskaluk C. Wang Y. Beer D.G. Gene expression signatures for predicting prognosis of squamous cell and adenocarcinomas of the lung.Cancer Res. 2006; 66: 7466-7472Crossref PubMed Scopus (334) Google Scholar, 6Lonergan K.M. Chari R. Coe B.P. Wilson I.M. Tsao M.S. Ng R.T. Macaulay C. Lam S. Lam W.L. Transcriptome profiles of carcinoma-in-situ and invasive non-small cell lung cancer as revealed by SAGE.PLoS One. 2010; 5: e9162Crossref PubMed Scopus (20) Google ScholarTable 3Genes Previously Considered as Potential Biomarkers of Lung Cancer Prognosis and Survival4Beer D.G. Kardia S.L. Huang C.C. Giordano T.J. Levin A.M. Misek D.E. Lin L. Chen G. Gharib T.G. Thomas D.G. Lizyness M.L. Kuick R. Hayasaka S. Taylor J.M. Iannettoni M.D. Orringer M.B. Hanash S. Gene-expression profiles predict survival of patients with lung adenocarcinoma.Nat Med. 2002; 8: 816-824Crossref PubMed Scopus (1624) Google Scholar, 5Raponi M. Zhang Y. Yu J. Chen G. Lee G. Taylor J.M. Macdonald J. Thomas D. Moskaluk C. Wang Y. Beer D.G. Gene expression signatures for predicting prognosis of squamous cell and adenocarcinomas of the lung.Cancer Res. 2006; 66: 7466-7472Crossref PubMed Scopus (334) Google Scholar, 6Lonergan K.M. Chari R. Coe B.P. Wilson I.M. Tsao M.S. Ng R.T. Macaulay C. Lam S. Lam W.L. Transcriptome profiles of carcinoma-in-situ and invasive non-small cell lung cancer as revealed by SAGE.PLoS One. 2010; 5: e9162Crossref PubMed Scopus (20) Google Scholar Found to be Significantly Differentially Expressed in Transition Between in Situ After Chest Opening or Lung Resection (T1a(CO)/T1b(LR)) and Later Time PointsGene IDGene nameFold change⁎Calculated as median value for all pair-wise comparisons, FDR-adjusted P < 0.05.AASSAminoadipate-semialdehyde synthase1.95ABCC4ATP-binding cassette, sub-family C (CFTR/MRP), member 41.54ADMAdrenomedullin1.10AKAP12A kinase (PRKA) anchor protein 121.13ALDOAAldolase A, fructose-bisphosphate1.34ALG8Asparagine-linked glycosylation 8, alpha-1,3-glucosyltransferase homolog (S. cerevisiae)1.19C6ORF15Chromosome 6 open reading frame 150.89CASKCalcium/calmodulin-dependent serine protein kinase (MAGUK family)1.49CASP4Caspase 4, apoptosis-related cysteine peptidase1.52CDS1CDP-diacylglycerol synthase (phosphatidate cytidylyltransferase) 11.48COL3A1Collagen, type III, alpha 11.40CPA3Carboxypeptidase A3 (mast cell)3.42CRKV-crk sarcoma virus CT10 oncogene homolog (avian)2.19CSTBCystatin B (stefin B)0.72CTSLCathepsin L11.63DBPD site of albumin promoter (albumin D-box)-binding protein0.81DPAGT1Dolichyl-phosphate (UDP-N-acetylglucosamine) N-acetylglucosaminephosphotransferase 1 (GlcNAc-1-P transferase)1.43EVI1MDS1 and EVI1 complex locus3.20FADDFas (TNFRSF6)-associated via death domain1.63FEZ2Fasciculation and elongation protein zeta 2 (zygin II)0.81FGFR2Fibroblast growth factor receptor 22.01FLJ20397HEAT repeat-containing 21.45FUCA1Fucosidase, alpha-L- 1, tissue0.83GAPDHGlyceraldehyde-3-phosphate dehydrogenase1.33GGA3Golgi-associated, gamma adaptin ear containing, ARF-binding protein 31.34GRB7Growth factor receptor-bound protein 71.46H2AFZH2A histone family, member Z1.03HLA-GMajor histocompatibility complex, class I, G0.56HLFHepatic leukemia factor0.68HMBSHydroxymethylbilane synthase1.50HRBArfGAP with FG repeats 11.62KIAA0746Sel-1 suppressor of lin-12-like 3 (C. elegans)1.65KLF10Kruppel-like factor 102.10KLF6Kruppel-like factor 60.78KRTDAPKeratinocyte differentiation-associated protein1.06LRIG1Leucine-rich repeats and immunoglobulin-like domains 11.52MAP4Microtubule-associated protein 41.34MAPK14Mitogen-activated protein kinase 142.19MSH3MutS homolog 3 (E. coli)1.32MT2AMetallothionein 2A0.77NME2Non-metastatic cells 2, protein (NM23B) expressed in1.52NPPurine nucleoside phosphorylase1.45NTRK2Neurotrophic tyrosine kinase, receptor, type 20.43NTSNeurotensin12.57PDE7APhosphodiesterase 7A1.13PELI2Pellino homolog 2 (Drosophila)1.29PPIFPeptidylprolyl isomerase F1.37RAB11ARAB11A, member RAS oncogene family1.11RND3Rho family GTPase 31.31RPLP0Ribosomal protein, large, P01.45RPS26Ribosomal protein S261.49RPS3Ribosomal protein S31.56SC4MOLSterol-C4-methyl oxidase-like0.78SFTPCSurfactant protein C0.50SLC2A1Solute carrier family 2 (facilitated glucose transporter), member 12.83SPRR2ESmall proline-rich protein 2E1.28STARD3StAR-related lipid transfer (START) domain-containing 30.92STC1Stanniocalcin 10.64TIA1TIA1 cytotoxic granule-associated RNA-binding protein1.78TKTL1Transketolase-like 10.29TMEM126BTransmembrane protein 126B1.37TMF1TATA element modulatory factor 12.04TPBGTrophoblast glycoprotein1.39TTRTransthyretin0.76TUBA4ATubulin, alpha 4a0.64UGP2UDP-glucose pyrophosphorylase 21.39WNT10BWingless-type MMTV integration site family, member 10B0.51ZNF552Zinc finger protein 5521.11 Calculated as median value for all pair-wise comparisons, FDR-adjusted P < 0.05. Open table in a new tab We performed a non-supervised hierarchical cluster analysis to identify genes characterizing each of the times studied. Considering the 661 genes that differed significantly across T1a(CO) to T2c(HP.SFA) (FDR-adjusted P < 0.0001), we observed that the specimens collected in the operating theater [T1a(CO) and T1b(LR)] clustered together and were distinct from a second cluster based around retrieval during routine histopathology procedures [T" @default.
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