Gene expression profiling for molecular distinction and characterization of laser captured primary lung cancers View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2008-11-07

AUTHORS

Astrid Rohrbeck, Judith Neukirchen, Michael Rosskopf, Guillermo G Pardillos, Helene Geddert, Andreas Schwalen, Helmut E Gabbert, Arndt von Haeseler, Gerald Pitschke, Matthias Schott, Ralf Kronenwett, Rainer Haas, Ulrich-Peter Rohr

ABSTRACT

MethodsWe examined gene expression profiles of tumor cells from 29 untreated patients with lung cancer (10 adenocarcinomas (AC), 10 squamous cell carcinomas (SCC), and 9 small cell lung cancer (SCLC)) in comparison to 5 samples of normal lung tissue (NT). The European and American methodological quality guidelines for microarray experiments were followed, including the stipulated use of laser capture microdissection for separation and purification of the lung cancer tumor cells from surrounding tissue.ResultsBased on differentially expressed genes, different lung cancer samples could be distinguished from each other and from normal lung tissue using hierarchical clustering. Comparing AC, SCC and SCLC with NT, we found 205, 335 and 404 genes, respectively, that were at least 2-fold differentially expressed (estimated false discovery rate: < 2.6%). Different lung cancer subtypes had distinct molecular phenotypes, which also reflected their biological characteristics. Differentially expressed genes in human lung tumors which may be of relevance in the respective lung cancer subtypes were corroborated by quantitative real-time PCR.Genetic programming (GP) was performed to construct a classifier for distinguishing between AC, SCC, SCLC, and NT. Forty genes, that could be used to correctly classify the tumor or NT samples, have been identified. In addition, all samples from an independent test set of 13 further tumors (AC or SCC) were also correctly classified.ConclusionThe data from this research identified potential candidate genes which could be used as the basis for the development of diagnostic tools and lung tumor type-specific targeted therapies. More... »

PAGES

69

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/1479-5876-6-69

DOI

http://dx.doi.org/10.1186/1479-5876-6-69

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1023385294

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/18992152


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24 schema:description MethodsWe examined gene expression profiles of tumor cells from 29 untreated patients with lung cancer (10 adenocarcinomas (AC), 10 squamous cell carcinomas (SCC), and 9 small cell lung cancer (SCLC)) in comparison to 5 samples of normal lung tissue (NT). The European and American methodological quality guidelines for microarray experiments were followed, including the stipulated use of laser capture microdissection for separation and purification of the lung cancer tumor cells from surrounding tissue.ResultsBased on differentially expressed genes, different lung cancer samples could be distinguished from each other and from normal lung tissue using hierarchical clustering. Comparing AC, SCC and SCLC with NT, we found 205, 335 and 404 genes, respectively, that were at least 2-fold differentially expressed (estimated false discovery rate: < 2.6%). Different lung cancer subtypes had distinct molecular phenotypes, which also reflected their biological characteristics. Differentially expressed genes in human lung tumors which may be of relevance in the respective lung cancer subtypes were corroborated by quantitative real-time PCR.Genetic programming (GP) was performed to construct a classifier for distinguishing between AC, SCC, SCLC, and NT. Forty genes, that could be used to correctly classify the tumor or NT samples, have been identified. In addition, all samples from an independent test set of 13 further tumors (AC or SCC) were also correctly classified.ConclusionThe data from this research identified potential candidate genes which could be used as the basis for the development of diagnostic tools and lung tumor type-specific targeted therapies.
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30 schema:keywords AC
31 Characterization of laser
32 Differentially
33 MethodsWe
34 NT samples
35 PCR
36 SCC
37 SCLC
38 addition
39 basis
40 biological characteristics
41 cancer
42 cancer samples
43 cancer subtypes
44 cancer tumor cells
45 candidate genes
46 capture microdissection
47 cells
48 characteristics
49 characterization
50 classifier
51 clustering
52 comparison
53 data
54 development
55 diagnostic tool
56 different lung cancer subtypes
57 distinct molecular phenotypes
58 distinction
59 experiments
60 expression profiles
61 expression profiling
62 further tumors
63 gene expression profiles
64 gene expression profiling
65 genes
66 genetic programming
67 guidelines
68 hierarchical clustering
69 human lung tumors
70 independent test set
71 laser
72 laser capture microdissection
73 lung cancer
74 lung cancer samples
75 lung cancer subtypes
76 lung cancer tumor cells
77 lung tissue
78 lung tumors
79 microarray experiments
80 microdissection
81 molecular distinction
82 molecular phenotypes
83 normal lung tissues
84 patients
85 phenotype
86 potential candidate genes
87 primary lung cancer
88 profile
89 profiling
90 programming
91 purification
92 quality guidelines
93 quantitative real-time PCR
94 real-time PCR
95 relevance
96 research
97 samples
98 separation
99 set
100 subtypes
101 test set
102 therapy
103 tissue
104 tool
105 tumor cells
106 tumors
107 untreated patients
108 use
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