Inflammation-regulating factors in ascites as predictive biomarkers of drug resistance and progression-free survival in serous epithelial ovarian cancers
Notice bibliographique
Résumé
BACKGROUND: Platinum-based combination therapy is the standard first-line treatment for women with advanced serous epithelial ovarian carcinoma (EOC). However, about 20 % will not respond and are considered clinically resistant. The availability of biomarkers to predict responses to the initial therapy would provide a practical approach to identify women who would benefit from a more appropriate first-line treatment. Ascites is an attractive inflammatory fluid for biomarker discovery as it is easy and minimally invasive to obtain. The aim of this study was to evaluate whether six selected inflammation-regulating factors in ascites could serve as diagnostic or drug resistance biomarkers in patients with advanced serous EOC. METHODS: A total of 53 women with stage III/IV serous EOC and 10 women with benign conditions were enrolled in this study. Eleven of the 53 women with serous EOC were considered clinically resistant to treatment with progression-free survival<6 months. Ascites were collected at the time of the debulking surgery and the levels of cytokines were measured by ELISA. The six selected cytokines were evaluated for their ability to discriminate serous EOC from benign controls, and to discriminate platinum resistant from platinum sensitive patients. RESULTS: Median ascites levels of IL-6, IL-10 and osteoprotegerin (OPG) were significantly higher in women with advanced serous EOC than in controls (P≤0.012). There were no significant difference in the median ascites levels of leptin, soluble urokinase plasminogen activator receptor (suPAR) and CCL18 among serous EOC women and controls. In Receiver Operator curve (ROC) analysis, IL-6, IL-10 and OPG had a high area under the curve value of 0.905, 0.832 and 0.825 respectively for distinguishing EOC from benign controls. ROC analysis of individual cytokines revealed low discriminating potential to stratify patients according to their sensitivity to first-line treatment. The combination of biomarkers with the highest discriminating potential was with CA125 and leptin (AUC=0.936, 95% CI: 0.894-0.978). CONCLUSION: IL-6 was found to be strongly associated with advanced serous EOC and could be used in combination with serum CA125 to discriminate benign and EOC. Furthermore, the combination of serum CA125 and ascites leptin was a strong predictor of clinical resistance to first-line therapy.
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 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 | 0,000 | 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)
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 ».