Gene Expression Profiling To Predict and Assess the Consequences of Therapy-Induced Virus Eradication in Chronic Hepatitis C Virus Infection
Notice bibliographique
Résumé
UNLABELLED: Systems biology has proven to be a powerful tool to identify reliable predictors of treatment response in chronic hepatitis C virus (HCV) infection. In the present study, we studied patients with chronic HCV infection who responded to interferon (IFN)-based therapy, as evidenced by an absence of HCV RNA at the end of treatment, and focused on two issues that have not received much attention. First, we evaluated whether specific genes or gene expression patterns in blood were able to distinguish responder patients with a viral relapse from responder patients who remained virus negative after cessation of treatment. We found that patients with chronic HCV infection who were sustained responders and relapsers after IFN-based therapy showed comparable baseline clinical parameters and immune compositions in blood. However, at baseline, the gene expression profiles of a set of 18 genes predicted treatment outcome with an accuracy of 94%. Second, we examined whether patients with successful therapy-induced clearance of HCV still exhibited gene expression patterns characteristic of HCV or whether normalization of their transcriptome was observed. We observed that the relatively high expression levels of IFN-stimulated genes (ISGs) in patients with chronic HCV infection prior to therapy were reduced after successful IFN-based antiviral therapy (at 24 weeks of follow-up). These ISGs included the CXCL10, OAS1, IFI6, DDX60, TRIM5, and STAT1 genes. In addition, 1,428 differentially expressed non-ISGs were identified in paired pre- and posttreatment samples from sustained responders, which included genes involved in transforming growth factor beta (TGF-β) signaling, apoptosis, autophagy, and nucleic acid and protein metabolism. Interestingly, 1,424 genes with altered expression levels in responder patients after viral eradication were identified, in comparison to normal expression levels in healthy individuals. Additionally, aberrant expression levels of a subset of these genes, including the interleukin-32 (IL-32), IL-16, CCND3, and RASSF1 genes, were also observed at baseline. Our findings indicate that successful antiviral therapy for patients with chronic HCV infection does not lead to normalization of their blood transcriptional signature. The altered transcriptional activity may reflect HCV-induced liver damage in previously infected individuals. IMPORTANCE: Tools to predict the efficacy of antiviral therapy for patients with HCV infection are important to select the optimal therapeutic strategy. Using a systems biology approach, we identify a set of 18 genes expressed in blood that predicts the recurrence of HCV RNA after cessation of therapy consisting of peginterferon and ribavirin. This set of genes may be applicable as a useful biomarker in clinical decision-making, since the number of genes included in the predictor is small and the correct prediction rate is high (94%). In addition, we observed that the blood transcriptional profile in patients with chronic HCV infection who were successfully treated is not normalized to the status observed in healthy individuals. Even 6 months after therapy-induced elimination of HCV RNA, gene expression profiles in blood are still altered in these patients with chronic HCV infection, strongly suggesting long-term modulation of immune parameters in previously infected patients.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 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) | 0,001 | 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 source (Gemma direct ou Codex distillé), 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 ».