A network-based maximum link approach towards MS identifies potentially important roles for undetected ARRB1/2 and ACTB in liver cancer progression
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Notice bibliographique
Résumé
Hepatocellular Carcinoma (HCC) ranks among the deadliest of cancers and has a complex etiology. Proteomics analysis using iTRAQ provides a direct way to analyse perturbations in protein expression during HCC progression from early- to late-stage but suffers from consistency and coverage issues. Appropriate use of network-based analytical methods can help to overcome these issues. We built an integrated and comprehensive Protein-Protein Interaction Network (PPIN) by merging several major databases. Additionally, the network was filtered for GO coherent edges. Significantly differential genes (seeds) were selected from iTRAQ data and mapped onto this network. Undetected proteins linked to seeds (linked proteins) were identified and functionally characterised. The process of network cleaning provides a list of higher quality linked proteins, which are highly enriched for similar biological process gene ontology terms. Linked proteins are also enriched for known cancer genes and are linked to many well-established cancer processes such as apoptosis and immune response. We found that there is an increased propensity for known cancer genes to be found in highly linked proteins. Three highly-linked proteins were identified that may play an important role in driving HCC progression - the G-protein coupled receptor signalling proteins, ARRB1/2 and the structural protein beta-actin, ACTB. Interestingly, both ARRB proteins evaded detection in the iTRAQ screen. ACTB was not detected in the original dataset derived from Mascot but was found to be strongly supported when we re-ran analysis using another protein detection database (Paragon). Identification of linked proteins helps to partially overcome the coverage issue in shotgun proteomics analysis. The set of linked proteins are found to be enriched for cancer-specific processes, and more likely so if they are more highly linked. Additionally, a higher quality linked set is derived if network-cleaning is performed prior. This form of network-based analysis complements the cluster-based approach, and can provide a larger list of proteins on which to perform functional analysis, as well as for biomarker identification.
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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 | 0,000 |
| 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)
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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 découle