Abstract B67: Using alternative splicing microarrays to identify potential biomarkers in lung cancer
Notice bibliographique
Résumé
Abstract B67 The disruption of alternative splicing (AS) by either mutations in splicing sequences or changes in expression of splicing factors has been linked to many human diseases including cancer but the molecular changes associated with lung tumors are not well understood. Using our established quantitative AS microarray platform we have profiled matched normal and adenocarcinoma tissues from the lungs of 10 patients in order to identify changes at both the AS and transcriptional levels. These profiling experiments show a small set of exons that display pronounced and highly consistent changes between the normal and tumor tissues. This set is distinct from those genes showing changes at the transcriptional level. The results reveal how AS and transcription may be re-programmed during malignant transitions in the lung. Using a custom microarray with sets of exon body and splicing junction probes for profiling ~5000 human cassette alternative exons, we have identified 4 AS events that display pronounced inclusion level differences between normal and adenocarcinoma tissue in at least 80% of patients surveyed. These changes were confirmed by RT-PCR using the original 10 plus an additional 19 patient samples. Interestingly, all 4 of the alternative exons are located in genes that are linked to signaling pathways known to be deregulated in certain cancers. Moreover, these 4 AS events preserve frame and are conserved in mouse tissues, suggesting important functional roles. From the same dataset, a separate set of genes with transcript level changes were detected, consistent with previous findings that non-overlapping sets of genes are regulated at the AS and transcriptional levels, when comparing different tissues or corresponding tissues from different species. In addition to probe sets for profiling AS and transcript levels of the corresponding genes, our microarray contains probes to determine the transcript levels for 465 known and putative splicing factors. While significant changes in the expression levels of defined splicing factors were not detected, we observe changes in expression levels of 3 genes that contain RS domains, a feature of proteins that is predictive of a role in splicing. One of these genes is associated with the Notch pathway and the others are known tumor suppressor genes. We have confirmed that the change in AS for 1 of the target genes results in altered splicing at the protein level in addition to the RNA level changes. Work is in progress to characterize the functional role of this AS event using isoform-specific knockdown and overexpression. The increased expression of the exon-included protein isoform was evident in cell lines derived from both lung and colon cancer. We are currently determining if all 4 AS events occur in tumor tissue from breast and colon cancer patients. Also, we have expanded our profiling of human cancers by using higher density AS microarrays, and by using mRNA samples from additional normal and tumor-derived breast, colon and lung sources. Thus far, we have identified a set of conserved AS events that are consistently associated with lung adenocarcinomas. These are located in genes that function in signaling pathways that play important roles in tumorigenesis. Characterization of these AS events will advance our understanding of the role of splicing in human cancers and may also provide new targets for diagnostic applications as potential biomarkers. Citation Information: Cancer Prev Res 2008;1(7 Suppl):B67.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».