Osteopontin Level and Promoter Polymorphism in Patients with Metastatic Breast Cancer
Notice bibliographique
Résumé
Background: Cancer initiation typically occurs when a proto-oncogene’s coding region undergoes mutation, resulting in uncontrollable cell growth and division, or when a tumour suppressor gene’s coding region is affected by a mutation that inhibits activity of the resulting gene product. The pathophysiologic result is, respectively, exaggerated cell-cycle growth or deficient programmed cell death. Osteopontin (opn) is an integrin-binding phosphoprotein that is expressed on the surface of normal cells. Osteopontin has a major role in diverse tumour components, especially those implicated in invasion and metastasis. In the present study, we aimed to illustrate the value of opn as a possible contributor in breast cancer (bca). Methods: This prospective study included 115 patients newly diagnosed with bca and distant metastasis who were recruited from the Oncology Center, Mansoura University, and the Department of Clinical Oncology and Nuclear Medicine, Mansoura University Hospital, Egypt. The patients recruited had been diagnosed with disseminated visceral metastasis (visceral crisis), with or without bone metastasis; patients with cranial metastasis were excluded from the study. All patients received first-line chemotherapy with docetaxel 75 mg/m2 plus cisplatin 75 mg/m2 or carboplatin 6 auc (area under the curve) on day 1 every 21 days for a maximum of 6 cycles or till development of toxicity. Trastuzumab (in cases of her2-positive disease) was given whenever possible (if government assistance or personal finances permitted). Serum levels of opn were assessed by enzyme-linked immunosorbent assay (elisa) before treatment was started. A group of 30 matched healthy women whose median serum opn level was 15 ng/dL were included, and that level was therefore defined as the cut-off value. In addition, opn gene mutation was determined by polymerase chain reaction (pcr). Correlations of pretreatment serum opn and opn gene mutation with various patient clinicopathologic variables, response to the treatment, progression-free survival (pfs), and overall survival (os) were assessed. Results: Mean serum opn was highest in her2-amplified bca (64.4 ± 42.3 ng/dL), and then in triple-negative bca (55.9 ± 34.7 ng/dL), followed by the luminal B and A subtypes (38.4 ± 33.1 ng/dL and 36.3 ± 32.2 ng/dL respectively, p = 0.017). Testing by pcr revealed that opn gene mutation was highest in triple-negative bca (85% opn mutant vs. 15% non-mutant), and then in her2-overexpressed bca (80% opn mutant vs. 20% non-mutant), followed by luminal B bca (61.9% opn mutant vs. 38.1% non-mutant); the least expression was detected in luminal A bca (57.9% opn mutant vs. 42.1% non-mutant). Interestingly, patients with high serum opn and opn gene mutation experienced both poor pfs (median: 12 months vs. 14 months; p = 0.001) and poor os (median: 14 months vs. 18 months; p = 0.001). Moreover, participants with opn gene mutation experienced a poor response: of those with progressive disease, 74% had opn mutation and 26% had unmutated opn (p = 0.04). Additionally, high pretreatment serum opn was correlated with poor treatment response: 49.1 ± 33.8 ng/dL in patients with progressive disease and 35.5 ± 34.3 ng/dL in those who achieved a complete response, a partial response, or stable disease (p = 0.05). Strong concordance was found between high serum opn and opn gene mutation in 69 tumours (79.3%), and strong concordance was detected between normal or low serum opn and non-mutant opn in 28 tumours (60.8%). Conclusions: The current prospective work helps to highlight opn as a valid prognostic biomarker for patients with metastatic bca and reveals that high pretreatment serum opn and opn gene mutation are both strongly linked with poor response and survival. Concordance between elisa and pcr results indicates that either method can be used for the evaluation of opn. Increased opn gene mutation in triple-negative bca could assist in tailoring the treatment response in this very aggressive tumour subtype and could be considered a targetable molecule in future studies.
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 | 0,001 |
| 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) | 0,002 | 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 ».