GTI: a novel algorithm for identifying outlier gene expression profiles from integrated microarray datasets.
BACKGROUND: Meta-analysis of gene expression microarray datasets presents significant challenges for statistical analysis. We developed and validated a new bioinformatic method for the identification of genes upregulated in subsets of samples of a given tumour type ('outlier genes'), a hal...
| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2011-01-01
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| Series: | PLoS ONE |
| Online Access: | http://europepmc.org/articles/PMC3041823?pdf=render |
| _version_ | 1811212753489100800 |
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| author | John Patrick Mpindi Henri Sara Saija Haapa-Paananen Sami Kilpinen Tommi Pisto Elmar Bucher Kalle Ojala Kristiina Iljin Paula Vainio Mari Björkman Santosh Gupta Pekka Kohonen Matthias Nees Olli Kallioniemi |
| author_facet | John Patrick Mpindi Henri Sara Saija Haapa-Paananen Sami Kilpinen Tommi Pisto Elmar Bucher Kalle Ojala Kristiina Iljin Paula Vainio Mari Björkman Santosh Gupta Pekka Kohonen Matthias Nees Olli Kallioniemi |
| author_sort | John Patrick Mpindi |
| collection | DOAJ |
| description | BACKGROUND: Meta-analysis of gene expression microarray datasets presents significant challenges for statistical analysis. We developed and validated a new bioinformatic method for the identification of genes upregulated in subsets of samples of a given tumour type ('outlier genes'), a hallmark of potential oncogenes. METHODOLOGY: A new statistical method (the gene tissue index, GTI) was developed by modifying and adapting algorithms originally developed for statistical problems in economics. We compared the potential of the GTI to detect outlier genes in meta-datasets with four previously defined statistical methods, COPA, the OS statistic, the t-test and ORT, using simulated data. We demonstrated that the GTI performed equally well to existing methods in a single study simulation. Next, we evaluated the performance of the GTI in the analysis of combined Affymetrix gene expression data from several published studies covering 392 normal samples of tissue from the central nervous system, 74 astrocytomas, and 353 glioblastomas. According to the results, the GTI was better able than most of the previous methods to identify known oncogenic outlier genes. In addition, the GTI identified 29 novel outlier genes in glioblastomas, including TYMS and CDKN2A. The over-expression of these genes was validated in vivo by immunohistochemical staining data from clinical glioblastoma samples. Immunohistochemical data were available for 65% (19 of 29) of these genes, and 17 of these 19 genes (90%) showed a typical outlier staining pattern. Furthermore, raltitrexed, a specific inhibitor of TYMS used in the therapy of tumour types other than glioblastoma, also effectively blocked cell proliferation in glioblastoma cell lines, thus highlighting this outlier gene candidate as a potential therapeutic target. CONCLUSIONS/SIGNIFICANCE: Taken together, these results support the GTI as a novel approach to identify potential oncogene outliers and drug targets. The algorithm is implemented in an R package (Text S1). |
| first_indexed | 2024-04-12T05:34:37Z |
| format | Article |
| id | doaj.art-7eec0cc5ee3445ebbe76d2ab05db70dc |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-04-12T05:34:37Z |
| publishDate | 2011-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-7eec0cc5ee3445ebbe76d2ab05db70dc2022-12-22T03:45:55ZengPublic Library of Science (PLoS)PLoS ONE1932-62032011-01-0162e1725910.1371/journal.pone.0017259GTI: a novel algorithm for identifying outlier gene expression profiles from integrated microarray datasets.John Patrick MpindiHenri SaraSaija Haapa-PaananenSami KilpinenTommi PistoElmar BucherKalle OjalaKristiina IljinPaula VainioMari BjörkmanSantosh GuptaPekka KohonenMatthias NeesOlli KallioniemiBACKGROUND: Meta-analysis of gene expression microarray datasets presents significant challenges for statistical analysis. We developed and validated a new bioinformatic method for the identification of genes upregulated in subsets of samples of a given tumour type ('outlier genes'), a hallmark of potential oncogenes. METHODOLOGY: A new statistical method (the gene tissue index, GTI) was developed by modifying and adapting algorithms originally developed for statistical problems in economics. We compared the potential of the GTI to detect outlier genes in meta-datasets with four previously defined statistical methods, COPA, the OS statistic, the t-test and ORT, using simulated data. We demonstrated that the GTI performed equally well to existing methods in a single study simulation. Next, we evaluated the performance of the GTI in the analysis of combined Affymetrix gene expression data from several published studies covering 392 normal samples of tissue from the central nervous system, 74 astrocytomas, and 353 glioblastomas. According to the results, the GTI was better able than most of the previous methods to identify known oncogenic outlier genes. In addition, the GTI identified 29 novel outlier genes in glioblastomas, including TYMS and CDKN2A. The over-expression of these genes was validated in vivo by immunohistochemical staining data from clinical glioblastoma samples. Immunohistochemical data were available for 65% (19 of 29) of these genes, and 17 of these 19 genes (90%) showed a typical outlier staining pattern. Furthermore, raltitrexed, a specific inhibitor of TYMS used in the therapy of tumour types other than glioblastoma, also effectively blocked cell proliferation in glioblastoma cell lines, thus highlighting this outlier gene candidate as a potential therapeutic target. CONCLUSIONS/SIGNIFICANCE: Taken together, these results support the GTI as a novel approach to identify potential oncogene outliers and drug targets. The algorithm is implemented in an R package (Text S1).http://europepmc.org/articles/PMC3041823?pdf=render |
| spellingShingle | John Patrick Mpindi Henri Sara Saija Haapa-Paananen Sami Kilpinen Tommi Pisto Elmar Bucher Kalle Ojala Kristiina Iljin Paula Vainio Mari Björkman Santosh Gupta Pekka Kohonen Matthias Nees Olli Kallioniemi GTI: a novel algorithm for identifying outlier gene expression profiles from integrated microarray datasets. PLoS ONE |
| title | GTI: a novel algorithm for identifying outlier gene expression profiles from integrated microarray datasets. |
| title_full | GTI: a novel algorithm for identifying outlier gene expression profiles from integrated microarray datasets. |
| title_fullStr | GTI: a novel algorithm for identifying outlier gene expression profiles from integrated microarray datasets. |
| title_full_unstemmed | GTI: a novel algorithm for identifying outlier gene expression profiles from integrated microarray datasets. |
| title_short | GTI: a novel algorithm for identifying outlier gene expression profiles from integrated microarray datasets. |
| title_sort | gti a novel algorithm for identifying outlier gene expression profiles from integrated microarray datasets |
| url | http://europepmc.org/articles/PMC3041823?pdf=render |
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